diff --git a/.gitignore b/.gitignore
index 7cc8b7e62..cef6188e1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -58,6 +58,7 @@ Thumbs.db
 
 # === Secrets ===
 .env
+.env.*
 *.env
 credentials.json
 token.pickle
diff --git a/agent_chat/app.py b/agent_chat/app.py
index 3c36ffa96..70dd96031 100644
--- a/agent_chat/app.py
+++ b/agent_chat/app.py
@@ -80,7 +80,13 @@ app = Flask(__name__)
 import secrets as _secrets
 app.config['SECRET_KEY'] = os.environ.get('SECRET_KEY', _secrets.token_hex(32))
 app.config['MAX_CONTENT_LENGTH'] = 50 * 1024 * 1024  # 50 MB max upload (sécurité HIGH)
-socketio = SocketIO(app, cors_allowed_origins="*")
+_ALLOWED_ORIGINS = [
+    "http://localhost:3002",
+    "http://localhost:5002",
+    "https://vwb.labs.laurinebazin.design",
+    "https://lea.labs.laurinebazin.design",
+]
+socketio = SocketIO(app, cors_allowed_origins=_ALLOWED_ORIGINS)
 
 
 # ============================================================
@@ -92,6 +98,7 @@ def set_security_headers(response):
     response.headers['X-Content-Type-Options'] = 'nosniff'
     response.headers['X-Frame-Options'] = 'SAMEORIGIN'
     response.headers['X-XSS-Protection'] = '1; mode=block'
+    response.headers['Referrer-Policy'] = 'strict-origin-when-cross-origin'
     return response
 
 
@@ -116,6 +123,16 @@ STREAMING_SERVER_URL = os.environ.get(
     "RPA_STREAMING_URL", "http://localhost:5005"
 )
 
+# Token API pour le streaming server
+_STREAMING_API_TOKEN = os.environ.get("RPA_API_TOKEN", "")
+
+def _streaming_headers() -> dict:
+    """Headers d'authentification pour les appels au streaming server."""
+    headers = {"Content-Type": "application/json"}
+    if _STREAMING_API_TOKEN:
+        headers["Authorization"] = f"Bearer {_STREAMING_API_TOKEN}"
+    return headers
+
 execution_status = {
     "running": False,
     "workflow": None,
@@ -135,6 +152,7 @@ def _fetch_connected_machines() -> List[Dict[str, Any]]:
     try:
         resp = http_requests.get(
             f"{STREAMING_SERVER_URL}/api/v1/traces/stream/machines",
+            headers=_streaming_headers(),
             timeout=3,
         )
         if resp.ok:
@@ -384,7 +402,7 @@ def api_status():
 
 @app.route('/api/workflows')
 def api_workflows():
-    """Liste unifiée des workflows (appris + VWB).
+    """Liste unifiée des workflows (appris + VWB), filtrée par OS.
 
     Sources fusionnées :
     1. Workflows appris (SemanticMatcher — data/training/workflows/)
@@ -392,10 +410,20 @@ def api_workflows():
 
     Dédupliqués par nom : si un workflow appris a été importé dans le VWB,
     seule la version VWB est retournée (c'est la version validée/corrigée).
+
+    Query params:
+        os: Filtrer par OS — 'windows' ou 'linux' (optionnel).
+            Par défaut, détecte l'OS du serveur Léa (= la machine du docteur).
     """
     if not matcher:
         return jsonify({"workflows": [], "directories": []})
 
+    # Détecter l'OS : paramètre explicite ou auto-détection depuis la plateforme
+    os_filter = request.args.get('os')
+    if not os_filter:
+        import platform
+        os_filter = 'windows' if platform.system().lower() == 'windows' else 'linux'
+
     seen_ids = set()
     workflows = []
 
@@ -433,6 +461,21 @@ def api_workflows():
             workflows.append(vwb_wf)
             seen_ids.add(vwb_id)
 
+    # Filtrer par OS : ne montrer que les workflows compatibles avec la machine du docteur
+    # Le machine_id ou source_dir contient le nom OS (ex: DESKTOP-58D5CAC_windows, dom-X870_linux)
+    if os_filter:
+        os_lower = os_filter.lower()
+        filtered_workflows = []
+        for wf in workflows:
+            mid = (wf.get("machine_id") or "").lower()
+            src = (wf.get("source") or "").lower()
+            # Un workflow VWB (sans machine_id) passe toujours le filtre
+            if wf.get("origin") == "vwb" and not mid:
+                filtered_workflows.append(wf)
+            elif os_lower in mid or os_lower in src:
+                filtered_workflows.append(wf)
+        workflows = filtered_workflows
+
     # Récupérer la liste des machines connectées depuis le streaming server
     machines = _fetch_connected_machines()
 
@@ -1128,6 +1171,7 @@ def _execute_gesture(gesture):
     try:
         resp = http_requests.post(
             f"{STREAMING_SERVER_URL}/api/v1/traces/stream/replay/raw",
+            headers=_streaming_headers(),
             json={
                 "actions": [action],
                 "session_id": "",
@@ -1654,6 +1698,7 @@ def _try_streaming_server_replay(
 
         resp = http_requests.post(
             f"{STREAMING_SERVER_URL}/api/v1/traces/stream/replay",
+            headers=_streaming_headers(),
             json=payload,
             timeout=15,
         )
@@ -1696,6 +1741,7 @@ def _poll_replay_progress(replay_id: str, workflow_name: str, total_actions: int
         try:
             resp = http_requests.get(
                 f"{STREAMING_SERVER_URL}/api/v1/traces/stream/replay/{replay_id}",
+                headers=_streaming_headers(),
                 timeout=3,
             )
             if not resp.ok:
@@ -1968,6 +2014,7 @@ def execute_workflow_copilot(match, params: Dict[str, Any]):
             try:
                 resp = http_requests.post(
                     f"{STREAMING_SERVER_URL}/api/v1/traces/stream/replay/single",
+                    headers=_streaming_headers(),
                     json={
                         "action": action,
                         "session_id": "",
diff --git a/agent_chat/autonomous_planner.py b/agent_chat/autonomous_planner.py
index bb38297cf..d8748137c 100644
--- a/agent_chat/autonomous_planner.py
+++ b/agent_chat/autonomous_planner.py
@@ -197,7 +197,8 @@ NOT_FOUND"""
                 prompt=prompt,
                 image=screenshot,
                 temperature=0.1,
-                max_tokens=100
+                max_tokens=100,
+                assistant_prefill="COORDINATES:",
             )
 
             if result.get('success'):
diff --git a/agent_rust/LISEZMOI.txt b/agent_rust/LISEZMOI.txt
new file mode 100644
index 000000000..de09ad38f
--- /dev/null
+++ b/agent_rust/LISEZMOI.txt
@@ -0,0 +1,34 @@
+╔══════════════════════════════════════════╗
+║           Léa — Assistante IA           ║
+║        Automatisation de tâches         ║
+╚══════════════════════════════════════════╝
+
+INSTALLATION
+────────────
+1. Copiez le dossier "Lea" sur votre Bureau
+2. Double-cliquez sur "Lea.exe" pour démarrer
+
+PREMIÈRE UTILISATION
+────────────────────
+• Léa s'ouvre automatiquement dans votre navigateur
+• Cliquez "Apprenez-moi une tâche" pour commencer
+• Effectuez votre tâche normalement
+• Cliquez "C'est terminé" quand vous avez fini
+• Léa a appris ! Demandez-lui de refaire la tâche
+
+ARRÊTER LÉA
+────────────
+• Fermez la fenêtre Léa dans la barre des tâches
+• Ou appuyez Ctrl+C dans le terminal
+
+BESOIN D'AIDE ?
+───────────────
+Contactez le support : [à compléter]
+
+────────────────────────────────────────────
+⚠ Cet outil utilise l'intelligence artificielle.
+Article 50 du Règlement européen sur l'IA.
+Vos données restent sur votre ordinateur et notre
+serveur sécurisé. Aucune donnée n'est partagée
+avec des tiers.
+────────────────────────────────────────────
diff --git a/agent_rust/build_demo.sh b/agent_rust/build_demo.sh
new file mode 100755
index 000000000..6a567e9e4
--- /dev/null
+++ b/agent_rust/build_demo.sh
@@ -0,0 +1,22 @@
+#!/bin/bash
+# Build du kit démo pour Windows
+set -e
+
+echo "=== Build Léa pour Windows ==="
+cargo build --release --target x86_64-pc-windows-gnu
+
+# Préparer le dossier de démo
+DEMO_DIR="demo_kit/Lea"
+rm -rf demo_kit
+mkdir -p "$DEMO_DIR"
+
+# Copier les fichiers
+cp target/x86_64-pc-windows-gnu/release/rpa-agent.exe "$DEMO_DIR/Lea.exe"
+cp config.txt "$DEMO_DIR/config.txt"
+cp LISEZMOI.txt "$DEMO_DIR/LISEZMOI.txt"
+
+echo ""
+echo "=== Kit démo prêt dans demo_kit/Lea/ ==="
+ls -lh "$DEMO_DIR/"
+echo ""
+echo "Copiez le dossier Lea/ sur le PC du docteur."
diff --git a/agent_rust/config.txt b/agent_rust/config.txt
new file mode 100644
index 000000000..37cd80424
--- /dev/null
+++ b/agent_rust/config.txt
@@ -0,0 +1,12 @@
+# === Configuration Léa ===
+# Adresse du serveur (ne pas modifier sauf instruction)
+RPA_SERVER_URL=https://lea.labs.laurinebazin.design/api/v1
+
+# Clé d'accès (ne pas modifier)
+RPA_API_TOKEN=86031addb338e449fccdb1a983f61807aec15d42d482b9c7748ad607dc23caab
+
+# Qualité des captures (1-100, défaut: 85)
+RPA_JPEG_QUALITY=85
+
+# Floutage des données sensibles (true/false)
+RPA_BLUR_SENSITIVE=true
diff --git a/agent_rust/src/capture.rs b/agent_rust/src/capture.rs
index 43a4f5589..f650a2c68 100644
--- a/agent_rust/src/capture.rs
+++ b/agent_rust/src/capture.rs
@@ -100,6 +100,10 @@ pub fn image_hash(img: &DynamicImage) -> u64 {
 }
 
 /// Retourne les dimensions du moniteur principal (largeur, hauteur).
+///
+/// xcap utilise DXGI sur Windows qui retourne toujours les pixels physiques,
+/// independamment du DPI awareness. Ceci est coherent avec les coordonnees
+/// physiques d'enigo quand le process est DPI-aware.
 pub fn screen_dimensions() -> Option<(u32, u32)> {
     let monitors = xcap::Monitor::all().ok()?;
     let primary = monitors
diff --git a/agent_rust/src/config.rs b/agent_rust/src/config.rs
index c3af97eee..5d50adc15 100644
--- a/agent_rust/src/config.rs
+++ b/agent_rust/src/config.rs
@@ -1,9 +1,13 @@
 //! Configuration de l'agent RPA.
 //!
 //! Parametres charges depuis les variables d'environnement ou valeurs par defaut.
+//! Un fichier `config.txt` (clé=valeur) peut être placé à côté de l'exécutable.
+//! Les variables d'environnement ont priorité sur le fichier.
 //! Compatible avec la configuration Python (agent_v1/config.py).
 
 use std::env;
+use std::fs;
+use std::path::PathBuf;
 
 /// Version de l'agent Rust
 pub const AGENT_VERSION: &str = "0.2.0-rust";
@@ -37,11 +41,86 @@ pub struct Config {
 
     /// Port du serveur de chat (defaut: 5004)
     pub chat_port: u16,
+
+    /// Token Bearer pour l'authentification API (defaut: vide = pas d'auth)
+    pub api_token: String,
 }
 
 impl Config {
+    /// Charge le fichier `config.txt` situé à côté de l'exécutable (ou dans le dossier courant).
+    ///
+    /// Format : une ligne par clé, `CLÉ=VALEUR`. Les lignes vides et celles commençant
+    /// par `#` sont ignorées. Seules les clés **absentes** de l'environnement sont injectées
+    /// (les variables d'environnement ont toujours priorité).
+    fn load_config_file() {
+        // 1. Chercher config.txt à côté de l'exécutable
+        let mut config_path: Option<PathBuf> = None;
+
+        if let Ok(exe) = env::current_exe() {
+            let candidate = exe.parent().map(|p| p.join("config.txt"));
+            if let Some(ref p) = candidate {
+                if p.is_file() {
+                    config_path = candidate;
+                }
+            }
+        }
+
+        // 2. Fallback : dossier courant
+        if config_path.is_none() {
+            let cwd_candidate = PathBuf::from("config.txt");
+            if cwd_candidate.is_file() {
+                config_path = Some(cwd_candidate);
+            }
+        }
+
+        let path = match config_path {
+            Some(p) => p,
+            None => return, // Pas de fichier config — ce n'est pas une erreur
+        };
+
+        let content = match fs::read_to_string(&path) {
+            Ok(c) => c,
+            Err(e) => {
+                eprintln!("[config] Impossible de lire {} : {}", path.display(), e);
+                return;
+            }
+        };
+
+        eprintln!("[config] Chargement de {}", path.display());
+
+        for line in content.lines() {
+            let trimmed = line.trim();
+
+            // Ignorer les lignes vides et les commentaires
+            if trimmed.is_empty() || trimmed.starts_with('#') {
+                continue;
+            }
+
+            // Séparer au premier '='
+            if let Some(eq_pos) = trimmed.find('=') {
+                let key = trimmed[..eq_pos].trim();
+                let value = trimmed[eq_pos + 1..].trim();
+
+                if key.is_empty() {
+                    continue;
+                }
+
+                // Ne positionner que si la variable n'existe pas déjà
+                if env::var(key).is_err() {
+                    // SAFETY: appelé une seule fois au démarrage, avant tout thread
+                    unsafe {
+                        env::set_var(key, value);
+                    }
+                }
+            }
+        }
+    }
+
     /// Charge la configuration depuis les variables d'environnement.
     ///
+    /// Le fichier `config.txt` est lu en premier (voir [`load_config_file`]) ;
+    /// les variables d'environnement déjà définies ne sont pas écrasées.
+    ///
     /// Variables supportees :
     /// - `RPA_SERVER_URL` : URL du serveur (defaut: http://localhost:5005/api/v1)
     /// - `RPA_MACHINE_ID` : Identifiant machine (defaut: hostname_os)
@@ -51,7 +130,10 @@ impl Config {
     /// - `RPA_BLUR_SENSITIVE` : Flouter les zones sensibles (defaut: true)
     /// - `RPA_LOG_RETENTION_DAYS` : Retention des logs en jours (defaut: 180)
     /// - `RPA_CHAT_PORT` : Port du serveur de chat (defaut: 5004)
+    /// - `RPA_API_TOKEN` : Token Bearer pour l'authentification (defaut: vide)
     pub fn from_env() -> Self {
+        // Charger config.txt AVANT de lire les variables d'environnement
+        Self::load_config_file();
         let machine_id = env::var("RPA_MACHINE_ID").unwrap_or_else(|_| {
             let host = hostname::get()
                 .map(|h| h.to_string_lossy().to_string())
@@ -98,6 +180,8 @@ impl Config {
             .and_then(|v| v.parse().ok())
             .unwrap_or(5004);
 
+        let api_token = env::var("RPA_API_TOKEN").unwrap_or_default();
+
         Config {
             server_url,
             machine_id,
@@ -108,6 +192,7 @@ impl Config {
             blur_sensitive,
             log_retention_days,
             chat_port,
+            api_token,
         }
     }
 
@@ -151,10 +236,11 @@ impl std::fmt::Display for Config {
     fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
         write!(
             f,
-            "Config {{ server: {}, machine: {}, capture_port: {}, heartbeat: {}s, jpeg_q: {}, blur: {}, log_retention: {}j, chat_port: {} }}",
+            "Config {{ server: {}, machine: {}, capture_port: {}, heartbeat: {}s, jpeg_q: {}, blur: {}, log_retention: {}j, chat_port: {}, auth: {} }}",
             self.server_url, self.machine_id, self.capture_port,
             self.heartbeat_interval_s, self.jpeg_quality,
             self.blur_sensitive, self.log_retention_days, self.chat_port,
+            if self.api_token.is_empty() { "none" } else { "Bearer" },
         )
     }
 }
diff --git a/agent_rust/src/main.rs b/agent_rust/src/main.rs
index 317f42375..a256cf867 100644
--- a/agent_rust/src/main.rs
+++ b/agent_rust/src/main.rs
@@ -30,6 +30,7 @@ mod replay;
 mod server;
 #[allow(dead_code)]
 mod state;
+mod sysinfo;
 mod tray;
 mod visual;
 
@@ -40,12 +41,20 @@ use std::sync::Arc;
 use std::thread;
 use std::time::Duration;
 
-/// Trouve Edge sur Windows
+/// Trouve un navigateur compatible sur Windows (Edge, Chrome, Brave, Firefox)
 #[cfg(target_os = "windows")]
-fn find_edge() -> Option<String> {
+fn find_browser() -> Option<String> {
     let paths = [
+        // Edge
         r"C:\Program Files (x86)\Microsoft\Edge\Application\msedge.exe",
         r"C:\Program Files\Microsoft\Edge\Application\msedge.exe",
+        // Chrome
+        r"C:\Program Files\Google\Chrome\Application\chrome.exe",
+        r"C:\Program Files (x86)\Google\Chrome\Application\chrome.exe",
+        // Brave
+        r"C:\Program Files\BraveSoftware\Brave-Browser\Application\brave.exe",
+        // Firefox (supporte --kiosk mais pas --app)
+        r"C:\Program Files\Mozilla Firefox\firefox.exe",
     ];
     for p in &paths {
         if std::path::Path::new(p).exists() {
@@ -56,6 +65,37 @@ fn find_edge() -> Option<String> {
 }
 
 fn main() {
+    // --- DPI awareness (DOIT etre appele avant toute operation graphique) ---
+    // Rend le process DPI-aware sur Windows pour que les API (enigo, xcap,
+    // GetSystemMetrics, etc.) travaillent en coordonnees physiques (pixels reels)
+    // au lieu de coordonnees logiques (virtualisees par le DPI scaling).
+    // Sans cet appel, un ecran 2560x1600 a 150% DPI apparait comme 1707x1067
+    // pour enigo et GetSystemMetrics, ce qui cause des erreurs de positionnement
+    // pendant le replay.
+    // PROCESS_PER_MONITOR_DPI_AWARE = 2 : le niveau le plus precis.
+    #[cfg(target_os = "windows")]
+    {
+        // SetProcessDpiAwareness (shcore.dll) et SetProcessDPIAware (user32.dll)
+        // ne sont pas toujours exposes par windows-sys selon les features.
+        // On utilise des appels FFI raw pour eviter d'ajouter des features.
+        #[link(name = "shcore")]
+        extern "system" {
+            fn SetProcessDpiAwareness(value: i32) -> i32;
+        }
+        #[link(name = "user32")]
+        extern "system" {
+            fn SetProcessDPIAware() -> i32;
+        }
+        unsafe {
+            // Tenter SetProcessDpiAwareness(2) = PROCESS_PER_MONITOR_DPI_AWARE
+            let hr = SetProcessDpiAwareness(2);
+            if hr != 0 {
+                // Fallback pour Windows < 8.1 : SetProcessDPIAware()
+                SetProcessDPIAware();
+            }
+        }
+    }
+
     // Initialiser le logging
     env_logger::Builder::from_env(
         env_logger::Env::default().default_filter_or("info"),
@@ -135,15 +175,41 @@ fn main() {
     let chat_state = state.clone();
     chat::run_chat_thread(&chat_config, chat_state);
 
+    // Synchroniser les workflows disponibles depuis le serveur
+    let sync_config = config.clone();
+    let workflows = {
+        let client = Client::new();
+        network::fetch_workflows(&client, &sync_config)
+    };
+    if workflows.is_empty() {
+        println!("[MAIN] Aucun workflow disponible pour cette machine.");
+    } else {
+        println!(
+            "[MAIN] {} workflow(s) disponible(s) :",
+            workflows.len()
+        );
+        for wf in &workflows {
+            println!(
+                "        - {} ({} noeuds, {} transitions)",
+                wf.name, wf.nodes, wf.edges
+            );
+        }
+    }
+
     println!("\n[MAIN] Agent operationnel — tous les threads demarres.\n");
 
-    // Ouvrir Léa (Edge mode app) automatiquement au démarrage
+    // Ouvrir Léa dans le navigateur disponible (mode app) au démarrage
     #[cfg(target_os = "windows")]
     {
         let chat_url = config.chat_url();
-        if let Some(edge) = find_edge() {
-            println!("[MAIN] Ouverture de Léa dans Edge...");
-            let _ = std::process::Command::new(&edge)
+        if let Some(browser) = find_browser() {
+            let browser_name = if browser.contains("chrome") { "Chrome" }
+                else if browser.contains("edge") || browser.contains("Edge") { "Edge" }
+                else if browser.contains("brave") || browser.contains("Brave") { "Brave" }
+                else if browser.contains("firefox") || browser.contains("Firefox") { "Firefox" }
+                else { "navigateur" };
+            println!("[MAIN] Ouverture de Léa dans {}...", browser_name);
+            let _ = std::process::Command::new(&browser)
                 .args(&[
                     &format!("--app={}", chat_url),
                     "--window-size=600,800",
@@ -151,6 +217,8 @@ fn main() {
                     "--no-first-run",
                 ])
                 .spawn();
+        } else {
+            println!("[MAIN] Aucun navigateur trouvé — ouvrez manuellement : {}", chat_url);
         }
     }
 
@@ -304,9 +372,8 @@ fn health_check_loop(config: &Config, state: &AgentState) {
 
     while state.is_running() {
         let url = format!("{}/stats", config.server_url);
-        let connected = client
-            .get(&url)
-            .timeout(timeout)
+        let request = client.get(&url).timeout(timeout);
+        let connected = network::with_auth(request, config)
             .send()
             .map(|r| r.status().is_success())
             .unwrap_or(false);
@@ -327,6 +394,8 @@ fn health_check_loop(config: &Config, state: &AgentState) {
 
 /// Affiche la banniere de demarrage.
 fn print_banner(config: &Config) {
+    let meta = sysinfo::get_screen_metadata();
+
     println!("======================================================");
     println!(
         "    RPA Vision Agent v{} (Rust)",
@@ -342,6 +411,17 @@ fn print_banner(config: &Config) {
     println!("  JPEG       : qualite {}", config.jpeg_quality);
     println!("  Floutage   : {}", if config.blur_sensitive { "actif" } else { "inactif" });
     println!("  Logs       : retention {} jours", config.log_retention_days);
+    println!("  Auth       : {}", if config.api_token.is_empty() { "aucune" } else { "Bearer token" });
+    println!("  Workflows  : synchronisation au demarrage");
+    println!(
+        "  Ecran      : {}x{} @ {}% DPI",
+        meta.screen_resolution[0], meta.screen_resolution[1], meta.dpi_scale
+    );
+    println!(
+        "  Moniteur   : #{} ({})",
+        meta.monitor_index,
+        if meta.monitor_index == 0 { "principal" } else { "secondaire" }
+    );
     println!("======================================================");
     println!();
     println!("  [IA] Cet agent utilise l'intelligence artificielle.");
diff --git a/agent_rust/src/network.rs b/agent_rust/src/network.rs
index 7b87bad95..15286bef9 100644
--- a/agent_rust/src/network.rs
+++ b/agent_rust/src/network.rs
@@ -5,9 +5,21 @@
 //! Compatible avec l'API de agent_v0/server_v1/api_stream.py (port 5005).
 
 use crate::config::Config;
-use reqwest::blocking::Client;
+use crate::sysinfo;
+use reqwest::blocking::{Client, RequestBuilder};
 use serde::{Deserialize, Serialize};
 
+/// Ajoute le header Authorization Bearer si un token est configure.
+///
+/// Si `config.api_token` est vide, la requete est retournee telle quelle.
+pub fn with_auth(request: RequestBuilder, config: &Config) -> RequestBuilder {
+    if config.api_token.is_empty() {
+        request
+    } else {
+        request.header("Authorization", format!("Bearer {}", config.api_token))
+    }
+}
+
 /// Action de replay reçue du serveur.
 ///
 /// Format identique à celui du Python executor (agent_v1/core/executor.py).
@@ -102,6 +114,8 @@ impl ActionResult {
 /// Envoie un heartbeat (screenshot) au serveur streaming.
 ///
 /// POST /traces/stream/image avec le screenshot en multipart.
+/// Inclut les métadonnées système (DPI, résolution, fenêtre, moniteur)
+/// dans les query params pour que le serveur puisse les exploiter.
 /// Retourne true si l'envoi a réussi.
 pub fn send_heartbeat(
     client: &Client,
@@ -112,6 +126,19 @@ pub fn send_heartbeat(
     let url = format!("{}/image", config.streaming_url());
     let shot_id = format!("heartbeat_{}", chrono::Utc::now().timestamp());
 
+    // Collecter les métadonnées système
+    let meta = sysinfo::get_screen_metadata();
+    let dpi_str = meta.dpi_scale.to_string();
+    let screen_w_str = meta.screen_resolution[0].to_string();
+    let screen_h_str = meta.screen_resolution[1].to_string();
+    let monitor_str = meta.monitor_index.to_string();
+
+    // Sérialiser window_bounds en JSON compact (ou "null")
+    let wb_str = match meta.window_bounds {
+        Some(wb) => format!("[{},{},{},{}]", wb[0], wb[1], wb[2], wb[3]),
+        None => "null".to_string(),
+    };
+
     let part = reqwest::blocking::multipart::Part::bytes(jpeg_bytes.to_vec())
         .file_name("screenshot.jpg")
         .mime_str("image/jpeg")
@@ -122,17 +149,22 @@ pub fn send_heartbeat(
 
     let form = reqwest::blocking::multipart::Form::new().part("file", part);
 
-    match client
+    let request = client
         .post(&url)
         .query(&[
             ("session_id", session_id),
             ("shot_id", &shot_id),
             ("machine_id", &config.machine_id),
+            ("dpi_scale", &dpi_str),
+            ("screen_w", &screen_w_str),
+            ("screen_h", &screen_h_str),
+            ("monitor_index", &monitor_str),
+            ("window_bounds", &wb_str),
         ])
         .multipart(form)
-        .timeout(std::time::Duration::from_secs(10))
-        .send()
-    {
+        .timeout(std::time::Duration::from_secs(10));
+
+    match with_auth(request, config).send() {
         Ok(resp) => {
             if resp.status().is_success() {
                 true
@@ -166,15 +198,15 @@ pub fn poll_next_action(client: &Client, config: &Config) -> Option<Action> {
     let url = format!("{}/replay/next", config.streaming_url());
     let session_id = config.agent_session_id();
 
-    let resp = client
+    let request = client
         .get(&url)
         .query(&[
             ("session_id", session_id.as_str()),
             ("machine_id", config.machine_id.as_str()),
         ])
-        .timeout(std::time::Duration::from_secs(5))
-        .send()
-        .ok()?;
+        .timeout(std::time::Duration::from_secs(5));
+
+    let resp = with_auth(request, config).send().ok()?;
 
     if !resp.status().is_success() {
         return None;
@@ -184,6 +216,120 @@ pub fn poll_next_action(client: &Client, config: &Config) -> Option<Action> {
     data.action
 }
 
+/// Informations résumées d'un workflow disponible.
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct WorkflowInfo {
+    /// Identifiant unique du workflow
+    pub workflow_id: String,
+
+    /// Nom lisible du workflow
+    #[serde(default)]
+    pub name: String,
+
+    /// Identifiant machine associé
+    #[serde(default)]
+    pub machine_id: String,
+
+    /// Nombre de nœuds
+    #[serde(default)]
+    pub nodes: u32,
+
+    /// Nombre de transitions
+    #[serde(default)]
+    pub edges: u32,
+}
+
+/// Réponse du serveur pour GET /traces/stream/workflows
+#[derive(Debug, Deserialize)]
+struct WorkflowsResponse {
+    #[serde(default)]
+    workflows: Vec<WorkflowInfo>,
+}
+
+/// Récupère la liste des workflows disponibles pour cette machine.
+///
+/// GET /traces/stream/workflows?machine_id=<machine_id>
+/// Sauvegarde le résultat dans workflows.json à côté de l'exécutable.
+/// Retourne la liste (éventuellement depuis le cache local si le serveur est indisponible).
+pub fn fetch_workflows(client: &Client, config: &Config) -> Vec<WorkflowInfo> {
+    let url = format!("{}/workflows", config.streaming_url());
+
+    let request = client
+        .get(&url)
+        .query(&[("machine_id", config.machine_id.as_str())])
+        .timeout(std::time::Duration::from_secs(5));
+
+    let workflows = match with_auth(request, config).send() {
+        Ok(resp) if resp.status().is_success() => {
+            match resp.json::<WorkflowsResponse>() {
+                Ok(data) => data.workflows,
+                Err(e) => {
+                    eprintln!("[WORKFLOWS] Erreur parsing reponse : {}", e);
+                    Vec::new()
+                }
+            }
+        }
+        Ok(resp) => {
+            eprintln!("[WORKFLOWS] Serveur HTTP {} — chargement cache local", resp.status());
+            return load_workflows_cache();
+        }
+        Err(e) => {
+            eprintln!("[WORKFLOWS] Serveur injoignable ({}) — chargement cache local", e);
+            return load_workflows_cache();
+        }
+    };
+
+    // Sauvegarder dans le cache local
+    save_workflows_cache(&workflows);
+
+    workflows
+}
+
+/// Chemin du fichier cache workflows.json (à côté de l'exécutable ou dans le dossier courant).
+fn workflows_cache_path() -> std::path::PathBuf {
+    if let Ok(exe) = std::env::current_exe() {
+        if let Some(dir) = exe.parent() {
+            return dir.join("workflows.json");
+        }
+    }
+    std::path::PathBuf::from("workflows.json")
+}
+
+/// Sauvegarde les workflows dans le cache local.
+fn save_workflows_cache(workflows: &[WorkflowInfo]) {
+    let path = workflows_cache_path();
+    match serde_json::to_string_pretty(workflows) {
+        Ok(json) => {
+            if let Err(e) = std::fs::write(&path, json) {
+                eprintln!("[WORKFLOWS] Erreur ecriture cache {} : {}", path.display(), e);
+            }
+        }
+        Err(e) => {
+            eprintln!("[WORKFLOWS] Erreur serialisation cache : {}", e);
+        }
+    }
+}
+
+/// Charge les workflows depuis le cache local.
+fn load_workflows_cache() -> Vec<WorkflowInfo> {
+    let path = workflows_cache_path();
+    match std::fs::read_to_string(&path) {
+        Ok(content) => {
+            match serde_json::from_str::<Vec<WorkflowInfo>>(&content) {
+                Ok(workflows) => {
+                    println!("[WORKFLOWS] {} workflow(s) charges depuis le cache local", workflows.len());
+                    workflows
+                }
+                Err(e) => {
+                    eprintln!("[WORKFLOWS] Erreur parsing cache : {}", e);
+                    Vec::new()
+                }
+            }
+        }
+        Err(_) => Vec::new(), // Pas de cache, pas d'erreur
+    }
+}
+
 /// Rapporte le résultat d'une action au serveur.
 ///
 /// POST /traces/stream/replay/result avec le résultat en JSON.
@@ -208,12 +354,12 @@ pub fn report_result(client: &Client, config: &Config, result: &ActionResult) ->
         screenshot: &result.screenshot,
     };
 
-    match client
+    let request = client
         .post(&url)
         .json(&report)
-        .timeout(std::time::Duration::from_secs(10))
-        .send()
-    {
+        .timeout(std::time::Duration::from_secs(10));
+
+    match with_auth(request, config).send() {
         Ok(resp) => {
             if resp.status().is_success() {
                 if let Ok(data) = resp.json::<serde_json::Value>() {
diff --git a/agent_rust/src/recorder.rs b/agent_rust/src/recorder.rs
index dedbe2355..010bff819 100644
--- a/agent_rust/src/recorder.rs
+++ b/agent_rust/src/recorder.rs
@@ -435,6 +435,10 @@ fn event_sender_loop(
 }
 
 /// Envoie un evenement capture au serveur streaming.
+///
+/// Inclut la resolution de l'ecran dans chaque event pour que le serveur
+/// puisse construire des ScreenStates avec la bonne resolution d'apprentissage
+/// (au lieu du fallback 1920x1080).
 fn send_event_to_server(
     client: &reqwest::blocking::Client,
     config: &Config,
@@ -443,6 +447,7 @@ fn send_event_to_server(
 ) {
     let url = format!("{}/traces/stream/event", config.server_url);
     let timestamp = chrono::Utc::now().to_rfc3339();
+    let (screen_w, screen_h) = capture::screen_dimensions().unwrap_or((1920, 1080));
 
     let payload = match event {
         CapturedEvent::Click {
@@ -460,6 +465,7 @@ fn send_event_to_server(
                 "session_name": session_name,
                 "machine_id": config.machine_id,
                 "timestamp": timestamp,
+                "screen_resolution": [screen_w, screen_h],
             })
         }
         CapturedEvent::DoubleClick {
@@ -476,6 +482,7 @@ fn send_event_to_server(
                 "session_name": session_name,
                 "machine_id": config.machine_id,
                 "timestamp": timestamp,
+                "screen_resolution": [screen_w, screen_h],
             })
         }
         CapturedEvent::Text {
@@ -491,6 +498,7 @@ fn send_event_to_server(
                 "session_name": session_name,
                 "machine_id": config.machine_id,
                 "timestamp": timestamp,
+                "screen_resolution": [screen_w, screen_h],
             })
         }
         CapturedEvent::KeyCombo { keys } => {
@@ -500,6 +508,7 @@ fn send_event_to_server(
                 "session_name": session_name,
                 "machine_id": config.machine_id,
                 "timestamp": timestamp,
+                "screen_resolution": [screen_w, screen_h],
             })
         }
         CapturedEvent::Scroll {
@@ -515,6 +524,7 @@ fn send_event_to_server(
                 "session_name": session_name,
                 "machine_id": config.machine_id,
                 "timestamp": timestamp,
+                "screen_resolution": [screen_w, screen_h],
             })
         }
     };
diff --git a/agent_rust/src/sysinfo.rs b/agent_rust/src/sysinfo.rs
new file mode 100644
index 000000000..a68815d2f
--- /dev/null
+++ b/agent_rust/src/sysinfo.rs
@@ -0,0 +1,274 @@
+//! Métadonnées système : DPI, résolution, fenêtre active, moniteur.
+//!
+//! Expose des fonctions pour capturer les informations d'affichage
+//! critiques qui seront envoyées au serveur avec chaque heartbeat.
+//! Sur Windows, utilise les API Win32 (user32.dll).
+//! Sur Linux, retourne des valeurs par défaut ou utilise xcap.
+
+use serde::Serialize;
+
+/// Métadonnées complètes de l'écran.
+#[derive(Debug, Clone, Serialize)]
+pub struct ScreenMetadata {
+    /// Facteur DPI en pourcentage (100 = normal, 150 = haute résolution)
+    pub dpi_scale: u32,
+    /// Résolution de l'écran principal [largeur, hauteur]
+    pub screen_resolution: [u32; 2],
+    /// Bounds de la fenêtre active [x, y, largeur, hauteur], None si pas de fenêtre
+    pub window_bounds: Option<[i32; 4]>,
+    /// Index du moniteur sur lequel se trouve la fenêtre active (0 = principal)
+    pub monitor_index: u32,
+}
+
+impl std::fmt::Display for ScreenMetadata {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(
+            f,
+            "{}x{} @ {}% DPI, monitor #{}",
+            self.screen_resolution[0],
+            self.screen_resolution[1],
+            self.dpi_scale,
+            self.monitor_index,
+        )?;
+        if let Some(wb) = &self.window_bounds {
+            write!(f, ", fenetre [{}x{} @ ({},{})]", wb[2], wb[3], wb[0], wb[1])?;
+        }
+        Ok(())
+    }
+}
+
+// =============================================================================
+// Windows : API Win32 via FFI
+// =============================================================================
+
+#[cfg(target_os = "windows")]
+mod win {
+    use windows_sys::Win32::Foundation::{BOOL, LPARAM, RECT};
+    use windows_sys::Win32::Graphics::Gdi::{
+        EnumDisplayMonitors, GetMonitorInfoW, MonitorFromWindow, HMONITOR, MONITORINFO,
+        MONITOR_DEFAULTTOPRIMARY,
+    };
+    use windows_sys::Win32::UI::WindowsAndMessaging::{
+        GetForegroundWindow, GetSystemMetrics, GetWindowRect, SM_CXSCREEN, SM_CYSCREEN,
+    };
+
+    // GetDpiForSystem est dans Win32_UI_HiDpi (non activée).
+    // On utilise un appel FFI raw pour éviter d'ajouter une feature.
+    extern "system" {
+        fn GetDpiForSystem() -> u32;
+    }
+
+    /// Retourne le facteur DPI en % (100 = normal, 125, 150, 200...).
+    pub fn get_dpi_scale() -> u32 {
+        unsafe {
+            let dpi = GetDpiForSystem();
+            if dpi == 0 {
+                // Fallback si l'API n'est pas disponible (Windows < 10 1607)
+                100
+            } else {
+                (dpi * 100) / 96
+            }
+        }
+    }
+
+    /// Retourne (largeur, hauteur) du moniteur principal via GetSystemMetrics.
+    ///
+    /// IMPORTANT : Retourne la resolution physique uniquement si le process est
+    /// DPI-aware (SetProcessDpiAwareness(2) appele dans main.rs). Sans cela,
+    /// retourne la resolution logique (virtualisee par le DPI scaling).
+    pub fn get_screen_resolution() -> (u32, u32) {
+        unsafe {
+            let w = GetSystemMetrics(SM_CXSCREEN);
+            let h = GetSystemMetrics(SM_CYSCREEN);
+            if w > 0 && h > 0 {
+                (w as u32, h as u32)
+            } else {
+                (0, 0)
+            }
+        }
+    }
+
+    /// Retourne (x, y, largeur, hauteur) de la fenêtre active, ou None.
+    pub fn get_window_bounds() -> Option<(i32, i32, i32, i32)> {
+        unsafe {
+            let hwnd = GetForegroundWindow();
+            if hwnd.is_null() {
+                return None;
+            }
+            let mut rect: RECT = std::mem::zeroed();
+            if GetWindowRect(hwnd, &mut rect) != 0 {
+                let w = rect.right - rect.left;
+                let h = rect.bottom - rect.top;
+                Some((rect.left, rect.top, w, h))
+            } else {
+                None
+            }
+        }
+    }
+
+    /// Flag indiquant le moniteur principal dans MONITORINFO.dwFlags.
+    const MONITORINFOF_PRIMARY: u32 = 1;
+
+    /// Retourne l'index du moniteur sur lequel se trouve la fenêtre active.
+    /// 0 = moniteur principal. Enumère tous les moniteurs pour trouver l'index.
+    pub fn get_monitor_index() -> u32 {
+        unsafe {
+            let hwnd = GetForegroundWindow();
+            if hwnd.is_null() {
+                return 0;
+            }
+
+            let target_hmon = MonitorFromWindow(hwnd, MONITOR_DEFAULTTOPRIMARY);
+            if target_hmon.is_null() {
+                return 0;
+            }
+
+            // Énumérer les moniteurs pour trouver l'index
+            struct CallbackData {
+                target: HMONITOR,
+                current_index: u32,
+                found_index: u32,
+            }
+
+            unsafe extern "system" fn enum_callback(
+                hmonitor: HMONITOR,
+                _hdc: windows_sys::Win32::Graphics::Gdi::HDC,
+                _lprect: *mut RECT,
+                lparam: LPARAM,
+            ) -> BOOL {
+                let data = &mut *(lparam as *mut CallbackData);
+
+                // Vérifier si c'est le moniteur principal — il est toujours #0
+                let mut info: MONITORINFO = std::mem::zeroed();
+                info.cbSize = std::mem::size_of::<MONITORINFO>() as u32;
+                GetMonitorInfoW(hmonitor, &mut info);
+
+                if info.dwFlags & MONITORINFOF_PRIMARY != 0 {
+                    // Moniteur principal — index 0, mais on continue pour le comptage
+                    if hmonitor == data.target {
+                        data.found_index = 0;
+                    }
+                } else if hmonitor == data.target {
+                    data.found_index = data.current_index;
+                }
+
+                data.current_index += 1;
+                1 // TRUE, continuer l'énumération
+            }
+
+            let mut data = CallbackData {
+                target: target_hmon,
+                current_index: 0,
+                found_index: 0,
+            };
+
+            EnumDisplayMonitors(
+                std::ptr::null_mut(), // HDC null = tous les moniteurs
+                std::ptr::null(),
+                Some(enum_callback),
+                &mut data as *mut CallbackData as LPARAM,
+            );
+
+            data.found_index
+        }
+    }
+}
+
+// =============================================================================
+// Linux / fallback : valeurs par défaut ou xcap
+// =============================================================================
+
+#[cfg(not(target_os = "windows"))]
+mod fallback {
+    /// Sur Linux, pas de DPI système accessible simplement. Retourne 100%.
+    pub fn get_dpi_scale() -> u32 {
+        100
+    }
+
+    /// Résolution via xcap (mêmes moniteurs que la capture).
+    pub fn get_screen_resolution() -> (u32, u32) {
+        if let Ok(monitors) = xcap::Monitor::all() {
+            if let Some(primary) = monitors.into_iter().find(|m| m.is_primary().unwrap_or(false)) {
+                let w = primary.width().unwrap_or(0);
+                let h = primary.height().unwrap_or(0);
+                return (w, h);
+            }
+        }
+        (0, 0)
+    }
+
+    /// Pas d'API window bounds sur Linux en mode headless. Retourne None.
+    pub fn get_window_bounds() -> Option<(i32, i32, i32, i32)> {
+        None
+    }
+
+    /// Moniteur principal = index 0 (fallback).
+    pub fn get_monitor_index() -> u32 {
+        0
+    }
+}
+
+// =============================================================================
+// API publique
+// =============================================================================
+
+/// Retourne le facteur DPI en % (100 = normal, 150 = haute résolution).
+pub fn get_dpi_scale() -> u32 {
+    #[cfg(target_os = "windows")]
+    {
+        win::get_dpi_scale()
+    }
+    #[cfg(not(target_os = "windows"))]
+    {
+        fallback::get_dpi_scale()
+    }
+}
+
+/// Retourne (largeur, hauteur) du moniteur principal.
+pub fn get_screen_resolution() -> (u32, u32) {
+    #[cfg(target_os = "windows")]
+    {
+        win::get_screen_resolution()
+    }
+    #[cfg(not(target_os = "windows"))]
+    {
+        fallback::get_screen_resolution()
+    }
+}
+
+/// Retourne (x, y, largeur, hauteur) de la fenêtre active, ou None.
+pub fn get_window_bounds() -> Option<(i32, i32, i32, i32)> {
+    #[cfg(target_os = "windows")]
+    {
+        win::get_window_bounds()
+    }
+    #[cfg(not(target_os = "windows"))]
+    {
+        fallback::get_window_bounds()
+    }
+}
+
+/// Retourne l'index du moniteur de la fenêtre active (0 = principal).
+pub fn get_monitor_index() -> u32 {
+    #[cfg(target_os = "windows")]
+    {
+        win::get_monitor_index()
+    }
+    #[cfg(not(target_os = "windows"))]
+    {
+        fallback::get_monitor_index()
+    }
+}
+
+/// Collecte toutes les métadonnées système en une seule structure.
+pub fn get_screen_metadata() -> ScreenMetadata {
+    let (sw, sh) = get_screen_resolution();
+    let wb = get_window_bounds().map(|(x, y, w, h)| [x, y, w, h]);
+
+    ScreenMetadata {
+        dpi_scale: get_dpi_scale(),
+        screen_resolution: [sw, sh],
+        window_bounds: wb,
+        monitor_index: get_monitor_index(),
+    }
+}
diff --git a/agent_v0/agent_v1/config.py b/agent_v0/agent_v1/config.py
index c67a8a858..bc23230eb 100644
--- a/agent_v0/agent_v1/config.py
+++ b/agent_v0/agent_v1/config.py
@@ -8,6 +8,25 @@ import platform
 import socket
 from pathlib import Path
 
+# --- DPI awareness (DOIT etre appele avant tout import de pynput/mss/tkinter) ---
+# Rend le process DPI-aware sur Windows pour que toutes les API (pynput, mss, pyautogui)
+# travaillent en coordonnees physiques (pixels reels) au lieu de coordonnees logiques
+# (virtualisees par le DPI scaling).
+# Sans cet appel, un ecran 2560x1600 a 150% DPI apparait comme 1707x1067 pour les API,
+# ce qui cause des erreurs de positionnement pendant le replay.
+# Sur Linux/Mac : no-op silencieux.
+# PROCESS_PER_MONITOR_DPI_AWARE = 2 : le niveau le plus precis.
+if platform.system() == "Windows":
+    try:
+        import ctypes
+        ctypes.windll.shcore.SetProcessDpiAwareness(2)  # PROCESS_PER_MONITOR_DPI_AWARE
+    except Exception:
+        try:
+            # Fallback pour Windows < 8.1 (API plus ancienne)
+            ctypes.windll.user32.SetProcessDPIAware()
+        except Exception:
+            pass
+
 AGENT_VERSION = "1.0.0"
 
 # Identifiant unique de la machine (utilisé pour le multi-machine)
@@ -34,7 +53,7 @@ MAX_SESSION_DURATION_S = 60 * 60  # 1 heure
 SESSIONS_ROOT = BASE_DIR / "sessions"
 
 # Paramètres Vision (Crops pour qwen3-vl)
-TARGETED_CROP_SIZE = (400, 400)
+TARGETED_CROP_SIZE = (150, 150)
 SCREENSHOT_QUALITY = 85
 
 # Floutage des données sensibles (conformité AI Act)
@@ -52,6 +71,22 @@ PERF_MONITOR_INTERVAL_S = 30
 LOGS_DIR = BASE_DIR / "logs"
 LOG_FILE = LOGS_DIR / "agent_v1.log"
 
+# --- Métadonnées système (capturées au chargement du module) ---
+# Utilisées pour la bannière de démarrage et le diagnostic.
+# Import tardif pour éviter les dépendances circulaires.
+try:
+    from .vision.system_info import get_dpi_scale, get_os_theme, get_monitor_info
+    _monitor_index, _monitors = get_monitor_info()
+    _primary = _monitors[0] if _monitors else {"width": 1920, "height": 1080}
+    SCREEN_RESOLUTION = (_primary["width"], _primary["height"])
+    DPI_SCALE = get_dpi_scale()
+    OS_THEME = get_os_theme()
+except Exception:
+    # Fallback silencieux si les métadonnées ne sont pas disponibles
+    SCREEN_RESOLUTION = (1920, 1080)
+    DPI_SCALE = 100
+    OS_THEME = "unknown"
+
 # Création des dossiers
 os.makedirs(SESSIONS_ROOT, exist_ok=True)
 os.makedirs(LOGS_DIR, exist_ok=True)
diff --git a/agent_v0/agent_v1/core/captor.py b/agent_v0/agent_v1/core/captor.py
index 3b9d93402..4dcd9392f 100644
--- a/agent_v0/agent_v1/core/captor.py
+++ b/agent_v0/agent_v1/core/captor.py
@@ -10,11 +10,20 @@ Fonctionnalités :
 - Buffer de saisie texte : accumule les frappes et émet un événement
   text_input après 500ms d'inactivité clavier
 - Surveillance du focus fenêtre
+
+NOTE DPI : Les coordonnees retournees par pynput dependent du DPI awareness
+du process. Quand SetProcessDpiAwareness(2) est appele (dans config.py),
+pynput retourne des coordonnees en pixels PHYSIQUES. Les metadonnees
+screen_metadata (resolution via mss) sont aussi en pixels physiques.
+Ceci garantit que la normalisation pos/resolution est coherente.
+Sans DPI awareness, pynput retourne des coordonnees LOGIQUES mais mss
+retourne des pixels physiques, ce qui cause une erreur de normalisation.
 """
 
 import threading
 import time
 import logging
+import platform
 from typing import Callable, Optional, List, Dict, Any, Tuple
 from pynput import mouse, keyboard
 from pynput.mouse import Button
@@ -22,10 +31,14 @@ from pynput.keyboard import Key, KeyCode
 
 # Importation relative pour rester dans le module v1
 from ..vision.capturer import VisionCapturer
+from ..vision.system_info import get_screen_metadata
 # from ..monitoring.system import SystemMonitor
 
 logger = logging.getLogger(__name__)
 
+# Détection Windows une seule fois au chargement du module
+IS_WINDOWS = platform.system() == "Windows"
+
 # Délai d'inactivité avant flush du buffer texte (en secondes)
 TEXT_FLUSH_DELAY = 0.5
 # Délai max entre deux clics pour un double-clic (en secondes)
@@ -57,6 +70,11 @@ class EventCaptorV1:
         self._text_start_pos: Optional[Tuple[int, int]] = None
         # Timer pour le flush après inactivité
         self._text_flush_timer: Optional[threading.Timer] = None
+        # Compteur de génération pour éviter qu'un timer obsolète ne flush
+        # un buffer en cours de remplissage (race condition). Incrémenté
+        # à chaque reset du timer. Le timer ne flush que si la génération
+        # n'a pas changé.
+        self._text_flush_generation: int = 0
         # Dernière position connue de la souris (pour associer le texte
         # au champ dans lequel l'utilisateur tape)
         self._last_mouse_pos: Tuple[int, int] = (0, 0)
@@ -65,6 +83,17 @@ class EventCaptorV1:
         # Dernier clic : (x, y, timestamp, button)
         self._last_click: Optional[Tuple[int, int, float, str]] = None
 
+        # --- Buffer de raw_keys (press/release bruts avec vk codes) ---
+        # Accumule chaque press/release pour le replay exact (solution AZERTY).
+        # Vidé en même temps que le text_buffer ou à l'émission d'un key_combo.
+        self._raw_key_buffer: List[Dict[str, Any]] = []
+
+        # --- Métadonnées système (DPI, résolution, moniteur, thème, langue) ---
+        # Capturées au démarrage puis rafraîchies à chaque changement de focus.
+        # Injectées dans chaque événement via le champ "screen_metadata".
+        self._screen_metadata: Dict[str, Any] = {}
+        self._screen_metadata_lock = threading.Lock()
+
     def start(self):
         self.running = True
         self.mouse_listener = mouse.Listener(
@@ -80,6 +109,9 @@ class EventCaptorV1:
         self.mouse_listener.start()
         self.keyboard_listener.start()
 
+        # Capture initiale des métadonnées système
+        self._refresh_screen_metadata()
+
         # Thread de surveillance du focus fenêtre (Proactif)
         self._focus_thread = threading.Thread(target=self._watch_window_focus, daemon=True)
         self._focus_thread.start()
@@ -131,6 +163,7 @@ class EventCaptorV1:
                     "pos": (x, y),
                     "timestamp": now,
                 }
+                self._inject_screen_metadata(event)
                 self.on_event(event)
                 # Réinitialiser pour éviter un triple-clic = 2 double-clics
                 self._last_click = None
@@ -144,6 +177,7 @@ class EventCaptorV1:
             "pos": (x, y),
             "timestamp": now,
         }
+        self._inject_screen_metadata(event)
         self.on_event(event)
 
     def _on_scroll(self, x, y, dx, dy):
@@ -168,7 +202,106 @@ class EventCaptorV1:
             return key.name
         return str(key)
 
+    # Ensemble des touches considérées comme modificateurs purs.
+    # Utilisé pour ne PAS émettre de key_combo quand seuls des
+    # modificateurs sont enfoncés (évite le bruit).
+    _MODIFIER_KEYS = {
+        Key.ctrl, Key.ctrl_l, Key.ctrl_r,
+        Key.alt, Key.alt_l, Key.alt_r,
+        Key.shift, Key.shift_l, Key.shift_r,
+        Key.cmd, Key.cmd_l, Key.cmd_r,
+    }
+    _MODIFIER_KEY_NAMES = {
+        "ctrl", "ctrl_l", "ctrl_r",
+        "alt", "alt_l", "alt_r",
+        "shift", "shift_l", "shift_r",
+        "cmd", "cmd_l", "cmd_r",
+    }
+
+    @staticmethod
+    def _vk_to_char(vk_code: int) -> Optional[str]:
+        """Convertir un virtual key code en caractère réel (AZERTY-aware).
+
+        Utilise ToUnicodeEx avec le layout clavier actif pour obtenir
+        le bon caractère même pour les touches AltGr, Shift+chiffres,
+        et autres combinaisons spécifiques au layout (AZERTY, QWERTZ, etc.).
+
+        Ne fonctionne que sur Windows. Retourne None sur Linux/Mac.
+        """
+        if not IS_WINDOWS:
+            return None
+        try:
+            import ctypes
+            import ctypes.wintypes as wt
+            user32 = ctypes.windll.user32
+
+            kbd_state = (ctypes.c_ubyte * 256)()
+            user32.GetKeyboardState(kbd_state)
+
+            buf = (ctypes.c_wchar * 8)()
+            scan = user32.MapVirtualKeyW(vk_code, 0)
+
+            # Layout du thread de la fenêtre active (gère AZERTY, QWERTZ, etc.)
+            hwnd = user32.GetForegroundWindow()
+            tid = user32.GetWindowThreadProcessId(hwnd, None)
+            hkl = user32.GetKeyboardLayout(tid)
+
+            n = user32.ToUnicodeEx(vk_code, scan, kbd_state, buf, 8, 0, hkl)
+            if n > 0:
+                return buf[0]
+        except Exception:
+            pass
+        return None
+
+    def _is_altgr_producing_char(self, key) -> Optional[str]:
+        """Détecte si la combinaison actuelle est AltGr+touche produisant un caractère.
+
+        Sur Windows AZERTY, AltGr est envoyé comme Ctrl+Alt par pynput.
+        Cette méthode vérifie si Ctrl+Alt est enfoncé et que la touche
+        produit un caractère imprimable via le layout clavier.
+        Ex: AltGr+é → ~, AltGr+( → {, AltGr+à → @
+
+        Retourne le caractère produit ou None si ce n'est pas un AltGr valide.
+        """
+        if not IS_WINDOWS:
+            return None
+        # AltGr = Ctrl+Alt (sans Win) sur Windows
+        if self.modifiers != {"ctrl", "alt"} and self.modifiers != {"ctrl", "alt", "shift"}:
+            return None
+        # Ne s'applique qu'aux touches non-modificatrices
+        if key in self._MODIFIER_KEYS:
+            return None
+        # Essayer de résoudre le caractère via ToUnicodeEx
+        # Le keyboard state inclut déjà Ctrl+Alt (= AltGr) grâce à GetKeyboardState
+        vk = getattr(key, 'vk', None)
+        if vk is not None:
+            char = self._vk_to_char(vk)
+            if char is not None and len(char) == 1 and (char.isprintable() and char != ' '):
+                return char
+        return None
+
+    @staticmethod
+    def _encode_key(key) -> Dict[str, Any]:
+        """Encode un objet pynput Key/KeyCode en dictionnaire sérialisable.
+
+        Utilisé pour constituer le buffer raw_keys (séquence press/release
+        exacte avec virtual key codes) qui permet un replay fidèle
+        indépendant du layout clavier (AZERTY, QWERTZ, etc.).
+        """
+        if isinstance(key, KeyCode):
+            return {"kind": "vk", "vk": key.vk, "char": key.char}
+        if isinstance(key, Key):
+            return {"kind": "key", "name": key.name}
+        return {"kind": "unknown", "str": str(key)}
+
     def _on_press(self, key):
+        # TOUJOURS enregistrer le press brut dans le buffer raw_keys
+        with self._text_lock:
+            self._raw_key_buffer.append({
+                "action": "press",
+                **self._encode_key(key),
+            })
+
         # Gestion des touches modificatrices
         if key in (Key.ctrl, Key.ctrl_l, Key.ctrl_r):
             self.modifiers.add("ctrl")
@@ -176,26 +309,54 @@ class EventCaptorV1:
             self.modifiers.add("alt")
         elif key in (Key.shift, Key.shift_l, Key.shift_r):
             self.modifiers.add("shift")
+        elif key in (Key.cmd, Key.cmd_l, Key.cmd_r):
+            self.modifiers.add("win")
 
         # --- Combos avec modificateur (sauf Shift seul) ---
         # Shift seul n'est pas un « vrai » modificateur pour les combos :
         # Shift+a = 'A' = saisie texte, pas un raccourci.
-        # On considère un combo seulement si Ctrl ou Alt est enfoncé.
-        has_real_modifier = self.modifiers & {"ctrl", "alt"}
+        # On considère un combo seulement si Ctrl, Alt ou Win est enfoncé.
+        has_real_modifier = self.modifiers & {"ctrl", "alt", "win"}
         if has_real_modifier:
+            # --- Détection AltGr (Windows AZERTY) ---
+            # Sur Windows, AltGr est envoyé comme Ctrl+Alt par le système.
+            # Avant de traiter comme un key_combo, vérifier si c'est
+            # AltGr qui produit un caractère imprimable (@, #, {, }, etc.)
+            altgr_char = self._is_altgr_producing_char(key)
+            if altgr_char is not None:
+                # C'est un caractère AltGr → router vers le buffer texte
+                with self._text_lock:
+                    if not self._text_buffer:
+                        self._text_start_pos = self._last_mouse_pos
+                    self._text_buffer.append(altgr_char)
+                    self._reset_flush_timer()
+                return
+
             key_name = self._get_key_name(key)
-            if key_name and key_name not in ("ctrl", "alt", "shift"):
+            # Ne PAS émettre de combo si c'est un modificateur seul
+            # (ex: appui sur Ctrl sans autre touche = pas de combo)
+            if key_name and key_name not in self._MODIFIER_KEY_NAMES:
                 # Un combo interrompt la saisie texte en cours
                 self._flush_text_buffer()
+                # Attacher les raw_keys accumulés (press des modificateurs + press de la touche)
+                with self._text_lock:
+                    raw_keys = list(self._raw_key_buffer)
+                    # NB: on ne clear pas encore — le release va suivre et sera
+                    # capturé pour le prochain buffer. On prend un snapshot.
                 event = {
                     "type": "key_combo",
                     "keys": list(self.modifiers) + [key_name],
+                    "raw_keys": raw_keys,
                     "timestamp": time.time(),
                 }
+                self._inject_screen_metadata(event)
                 self.on_event(event)
+                # Reset le buffer raw_keys après émission du combo
+                with self._text_lock:
+                    self._raw_key_buffer.clear()
             return
 
-        # --- Saisie texte (pas de Ctrl/Alt enfoncé) ---
+        # --- Saisie texte (pas de Ctrl/Alt/Win enfoncé) ---
         self._handle_text_key(key)
 
     def _handle_text_key(self, key):
@@ -217,6 +378,7 @@ class EventCaptorV1:
             if key == Key.esc:
                 # Annuler la saisie en cours
                 self._text_buffer.clear()
+                self._raw_key_buffer.clear()
                 self._text_start_pos = None
                 self._cancel_flush_timer()
                 return
@@ -234,31 +396,65 @@ class EventCaptorV1:
                 self._reset_flush_timer()
                 return
 
-            elif isinstance(key, KeyCode) and key.char is not None:
+            elif isinstance(key, KeyCode):
                 # Caractère alphanumérique / ponctuation
-                # pynput renvoie déjà le bon caractère selon le layout
-                # (AZERTY inclus) — on ne convertit rien.
-                if not self._text_buffer:
-                    self._text_start_pos = self._last_mouse_pos
-                self._text_buffer.append(key.char)
-                self._reset_flush_timer()
+                char = key.char
+
+                # AZERTY Windows : quand key.char est None (Shift+chiffres,
+                # dead keys, etc.), utiliser ToUnicodeEx avec le layout clavier
+                # actif pour obtenir le vrai caractère traduit par Windows.
+                if char is None and IS_WINDOWS:
+                    vk = getattr(key, 'vk', None)
+                    if vk is not None:
+                        char = self._vk_to_char(vk)
+
+                if char is not None and len(char) == 1:
+                    if not self._text_buffer:
+                        self._text_start_pos = self._last_mouse_pos
+                    self._text_buffer.append(char)
+                    self._reset_flush_timer()
+                    return
+
+                # key.char None et pas de vk exploitable → ignorer
                 return
             else:
                 # Touche spéciale non gérée (F1, Insert, etc.) — on ignore
                 return
 
-        # Si on arrive ici, c'est Enter ou Tab → flush immédiat
+        # Si on arrive ici, c'est Enter ou Tab → flush le buffer en cours
+        # puis émettre le caractère spécial comme text_input séparé
         self._flush_text_buffer()
 
+        # Émettre Enter comme "\n" et Tab comme "\t" pour ne pas perdre
+        # les retours à la ligne dans la saisie.
+        # Attacher les raw_keys restants (press de Enter/Tab, le release suivra)
+        with self._text_lock:
+            raw_keys = list(self._raw_key_buffer)
+            self._raw_key_buffer.clear()
+        special_char = "\n" if key == Key.enter else "\t"
+        event = {
+            "type": "text_input",
+            "text": special_char,
+            "pos": list(self._last_mouse_pos) if self._last_mouse_pos else [0, 0],
+            "timestamp": time.time(),
+        }
+        if raw_keys:
+            event["raw_keys"] = raw_keys
+        self.on_event(event)
+
     def _reset_flush_timer(self):
         """Réarme le timer de flush après chaque frappe.
 
         Doit être appelé avec self._text_lock déjà acquis.
+        Utilise un compteur de génération pour garantir que seul le
+        dernier timer programmé puisse effectivement flush le buffer.
         """
         if self._text_flush_timer is not None:
             self._text_flush_timer.cancel()
+        self._text_flush_generation += 1
+        gen = self._text_flush_generation
         self._text_flush_timer = threading.Timer(
-            TEXT_FLUSH_DELAY, self._flush_text_buffer
+            TEXT_FLUSH_DELAY, self._flush_text_buffer_if_current, args=(gen,)
         )
         self._text_flush_timer.daemon = True
         self._text_flush_timer.start()
@@ -272,18 +468,30 @@ class EventCaptorV1:
             self._text_flush_timer.cancel()
             self._text_flush_timer = None
 
+    def _flush_text_buffer_if_current(self, generation: int):
+        """Appelé par le timer. Ne flush que si la génération correspond
+        à celle du timer en cours (= pas de frappe entre-temps)."""
+        with self._text_lock:
+            if generation != self._text_flush_generation:
+                # Un timer plus récent a été programmé, celui-ci est obsolète
+                return
+        self._flush_text_buffer()
+
     def _flush_text_buffer(self):
         """Émet un événement text_input avec le contenu du buffer, puis
         le vide. Thread-safe — peut être appelé depuis le timer, le
         listener souris ou le listener clavier."""
         with self._text_lock:
             if not self._text_buffer:
-                # Rien à émettre
+                # Rien à émettre — purger aussi les raw_keys orphelins
+                self._raw_key_buffer.clear()
                 self._cancel_flush_timer()
                 return
             text = "".join(self._text_buffer)
             pos = self._text_start_pos or self._last_mouse_pos
+            raw_keys = list(self._raw_key_buffer)
             self._text_buffer.clear()
+            self._raw_key_buffer.clear()
             self._text_start_pos = None
             self._cancel_flush_timer()
 
@@ -295,32 +503,75 @@ class EventCaptorV1:
             "pos": pos,
             "timestamp": time.time(),
         }
-        logger.debug(f"text_input émis : {len(text)} caractères")
+        # Attacher les raw_keys pour le replay exact (solution AZERTY)
+        if raw_keys:
+            event["raw_keys"] = raw_keys
+        self._inject_screen_metadata(event)
+        logger.debug(f"text_input émis : {len(text)} caractères, {len(raw_keys)} raw_keys")
         self.on_event(event)
 
     def _on_release(self, key):
+        # TOUJOURS enregistrer le release brut dans le buffer raw_keys
+        with self._text_lock:
+            self._raw_key_buffer.append({
+                "action": "release",
+                **self._encode_key(key),
+            })
+
         if key in (Key.ctrl, Key.ctrl_l, Key.ctrl_r):
             self.modifiers.discard("ctrl")
         elif key in (Key.alt, Key.alt_l, Key.alt_r):
             self.modifiers.discard("alt")
         elif key in (Key.shift, Key.shift_l, Key.shift_r):
             self.modifiers.discard("shift")
+        elif key in (Key.cmd, Key.cmd_l, Key.cmd_r):
+            self.modifiers.discard("win")
+
+    # ----------------------------------------------------------------
+    # Métadonnées système
+    # ----------------------------------------------------------------
+
+    def _refresh_screen_metadata(self):
+        """Rafraîchit le cache des métadonnées système.
+
+        Appelé au démarrage et à chaque changement de focus fenêtre.
+        Thread-safe — peut être appelé depuis le thread focus.
+        """
+        try:
+            metadata = get_screen_metadata()
+            with self._screen_metadata_lock:
+                self._screen_metadata = metadata
+            logger.debug(f"Métadonnées système rafraîchies : {metadata}")
+        except Exception as e:
+            logger.error(f"Erreur refresh métadonnées système : {e}")
+
+    def _inject_screen_metadata(self, event: Dict[str, Any]) -> Dict[str, Any]:
+        """Injecte les métadonnées système cachées dans un événement."""
+        with self._screen_metadata_lock:
+            if self._screen_metadata:
+                event["screen_metadata"] = self._screen_metadata.copy()
+        return event
 
     def _watch_window_focus(self):
         """Surveille proactivement le changement de fenêtre pour le stagiaire."""
         # Importation relative simple
         from ..window_info_crossplatform import get_active_window_info
-        
+
         while self.running:
             try:
                 info = get_active_window_info()
                 if info and info != self.last_window:
+                    # Rafraîchir les métadonnées (la fenêtre a peut-être
+                    # changé de moniteur, de taille, etc.)
+                    self._refresh_screen_metadata()
+
                     event = {
                         "type": "window_focus_change",
                         "from": self.last_window,
                         "to": info,
                         "timestamp": time.time()
                     }
+                    self._inject_screen_metadata(event)
                     self.last_window = info
                     self.on_event(event)
             except Exception as e:
diff --git a/agent_v0/agent_v1/core/executor.py b/agent_v0/agent_v1/core/executor.py
index 2855c2f66..f0e54f78d 100644
--- a/agent_v0/agent_v1/core/executor.py
+++ b/agent_v0/agent_v1/core/executor.py
@@ -6,17 +6,28 @@ Opere par coordonnees normalisees (proportions) pour le rejeu en univers ferme (
 Supporte deux modes :
   - Watchdog fichier (command.json) — legacy
   - Polling serveur (GET /replay/next) — mode replay P0-5
+
+NOTE DPI : Ce module depend du DPI awareness configure dans config.py.
+L'appel a SetProcessDpiAwareness(2) DOIT avoir ete fait avant l'import de
+pynput et mss, sinon les coordonnees seront en pixels logiques (faux sur
+les ecrans haute resolution avec DPI scaling > 100%).
 """
 
 import base64
+import hashlib
 import io
 import os
 import time
 import logging
 
+# Forcer l'import de config AVANT pynput/mss pour garantir que le
+# DPI awareness est configure (SetProcessDpiAwareness(2) sur Windows).
+# Sans cela, pynput et mss utilisent des coordonnees logiques (virtualisees).
+from ..config import MACHINE_ID as _  # noqa: F401 — side-effect import
+
 import mss
 from pynput.mouse import Button, Controller as MouseController
-from pynput.keyboard import Controller as KeyboardController, Key
+from pynput.keyboard import Controller as KeyboardController, Key, KeyCode
 
 logger = logging.getLogger(__name__)
 
@@ -68,6 +79,20 @@ class ActionExecutorV1:
         self._poll_backoff_factor = 1.5  # Multiplicateur en cas d'echec
         # Token d'authentification API
         self._api_token = os.environ.get("RPA_API_TOKEN", "")
+        # Log de la resolution physique pour le diagnostic DPI
+        self._log_screen_info()
+
+    def _log_screen_info(self):
+        """Log la resolution physique de l'ecran au demarrage pour le diagnostic DPI."""
+        try:
+            monitor = self.sct.monitors[1]
+            w, h = monitor["width"], monitor["height"]
+            logger.info(
+                f"Executor initialise — resolution physique : {w}x{h} "
+                f"(mss monitors[1], DPI-aware process)"
+            )
+        except Exception as e:
+            logger.debug(f"Impossible de lire la resolution ecran : {e}")
 
     def _auth_headers(self) -> dict:
         """Headers d'authentification Bearer pour les requetes au serveur."""
@@ -180,11 +205,30 @@ class ActionExecutorV1:
                             f"-> ({x_pct:.4f}, {y_pct:.4f})"
                         )
 
+            # ---- Hash AVANT l'action (pour verification post-action) ----
+            # Seules les actions click et key_combo sont verifiees : elles
+            # provoquent un changement visible de l'ecran (ouverture de fenetre,
+            # focus, etc.). Les actions type/wait/scroll ne sont pas verifiees.
+            needs_screen_check = action_type in ("click", "key_combo")
+            hash_before = ""
+            if needs_screen_check:
+                hash_before = self._quick_screenshot_hash()
+
             if action_type == "click":
+                # Si visual_mode est activé, le resolve DOIT réussir.
+                # Pas de fallback blind — on arrête le replay si la cible
+                # n'est pas trouvée visuellement. C'est un RPA VISUEL.
+                if visual_mode and not result.get("visual_resolved"):
+                    result["success"] = False
+                    result["error"] = "Visual resolve échoué — cible non trouvée à l'écran"
+                    print(f"    [ERREUR] Visual resolve échoué — STOP (pas de clic blind)")
+                    logger.error(f"Action {action_id} : visual resolve échoué, replay stoppé")
+                    return result
+
                 real_x = int(x_pct * width)
                 real_y = int(y_pct * height)
                 button = action.get("button", "left")
-                mode = "VISUAL" if result["visual_resolved"] else "BLIND"
+                mode = "VISUAL" if result.get("visual_resolved") else "COORD"
                 print(
                     f"    [CLICK] [{mode}] ({x_pct:.3f}, {y_pct:.3f}) -> "
                     f"({real_x}, {real_y}) sur ({width}x{height}), bouton={button}"
@@ -198,7 +242,10 @@ class ActionExecutorV1:
 
             elif action_type == "type":
                 text = action.get("text", "")
+                raw_keys = action.get("raw_keys")
                 print(f"    [TYPE] Texte: '{text[:50]}' ({len(text)} chars)")
+                if raw_keys:
+                    print(f"    [TYPE] raw_keys disponibles ({len(raw_keys)} events) — replay exact")
                 # Cliquer sur le champ avant de taper (si coordonnees disponibles)
                 if x_pct > 0 and y_pct > 0:
                     real_x = int(x_pct * width)
@@ -206,16 +253,26 @@ class ActionExecutorV1:
                     print(f"    [TYPE] Clic prealable sur ({real_x}, {real_y})")
                     self._click((real_x, real_y), "left")
                     time.sleep(0.3)
-                self.keyboard.type(text)
+                if raw_keys:
+                    self._replay_raw_keys(raw_keys)
+                else:
+                    # Fallback copier-coller (anciens enregistrements sans raw_keys)
+                    self._type_text(text)
                 print(f"    [TYPE] Termine.")
-                logger.info(f"Replay type : '{text[:30]}...' ({len(text)} chars)")
+                logger.info(f"Replay type : '{text[:30]}...' ({len(text)} chars, raw_keys={'oui' if raw_keys else 'non'})")
 
             elif action_type == "key_combo":
                 keys = action.get("keys", [])
+                raw_keys = action.get("raw_keys")
                 print(f"    [KEY_COMBO] Touches: {keys}")
-                self._execute_key_combo(keys)
+                if raw_keys:
+                    print(f"    [KEY_COMBO] raw_keys disponibles ({len(raw_keys)} events) — replay exact")
+                    self._replay_raw_keys(raw_keys)
+                else:
+                    # Fallback (anciens enregistrements sans raw_keys)
+                    self._execute_key_combo(keys)
                 print(f"    [KEY_COMBO] Termine.")
-                logger.info(f"Replay key_combo : {keys}")
+                logger.info(f"Replay key_combo : {keys} (raw_keys={'oui' if raw_keys else 'non'})")
 
             elif action_type == "scroll":
                 real_x = int(x_pct * width) if x_pct > 0 else int(0.5 * width)
@@ -235,6 +292,25 @@ class ActionExecutorV1:
                 print(f"    [WAIT] Termine.")
                 logger.info(f"Replay wait : {duration_ms}ms")
 
+            elif action_type == "verify_screen":
+                # Vérification visuelle entre les groupes du replay hybride.
+                # Pour l'instant, on fait un wait de 2s pour laisser l'écran
+                # se stabiliser. La vérification réelle sera faite par le
+                # pre-check côté serveur dans GET /replay/next.
+                expected_node = action.get("expected_node", "?")
+                timeout_ms = action.get("timeout_ms", 5000)
+                wait_s = min(timeout_ms / 1000.0, 2.0)
+                print(
+                    f"    [VERIFY] Attente verification ecran "
+                    f"(node attendu: {expected_node}, wait={wait_s}s)"
+                )
+                time.sleep(wait_s)
+                print(f"    [VERIFY] Termine (verification deferred au serveur).")
+                logger.info(
+                    f"Replay verify_screen : node={expected_node}, "
+                    f"wait={wait_s}s (verification serveur)"
+                )
+
             else:
                 result["error"] = f"Type d'action inconnu : {action_type}"
                 logger.warning(result["error"])
@@ -242,8 +318,33 @@ class ActionExecutorV1:
 
             result["success"] = True
 
-            # Capturer un screenshot post-action
-            time.sleep(0.5)
+            # ---- Verification post-action : l'ecran a-t-il change ? ----
+            # Verifie UNIQUEMENT, ne tente PAS de gerer les popups
+            # (Enter/Escape perturbent l'application).
+            # Signale l'echec honnêtement — le serveur decide du retry.
+            if needs_screen_check and hash_before:
+                screen_changed = self._wait_for_screen_change(
+                    hash_before, timeout_ms=3000
+                )
+                if not screen_changed:
+                    result["success"] = False
+                    result["warning"] = "no_screen_change"
+                    result["error"] = "Ecran inchange apres l'action"
+                    print(
+                        f"    [ECHEC] Ecran inchange apres {action_type} — "
+                        f"l'action n'a pas eu d'effet visible"
+                    )
+                    logger.warning(
+                        f"Action {action_id} ({action_type}) : ecran inchange "
+                        f"— action sans effet visible"
+                    )
+                else:
+                    print(f"    [OK] Changement d'ecran detecte apres {action_type}")
+            else:
+                # Pour type/wait/scroll, petit delai pour laisser l'ecran se stabiliser
+                time.sleep(0.5)
+
+            # Capturer un screenshot post-action (apres stabilisation)
             result["screenshot"] = self._capture_screenshot_b64()
 
         except Exception as e:
@@ -257,62 +358,136 @@ class ActionExecutorV1:
         fallback_x: float, fallback_y: float,
         screen_width: int, screen_height: int,
     ) -> dict:
-        """
-        Envoyer un screenshot au serveur pour resolution visuelle de la cible.
+        """Résoudre la position d'un clic visuellement.
 
-        Capture l'ecran en haute resolution (pas de downscale pour le template
-        matching), l'encode en base64 JPEG, et POST au endpoint
-        /replay/resolve_target. Retourne les coordonnees resolues.
-        """
-        import requests
+        Stratégie VLM-DIRECT : appelle Ollama directement depuis l'agent
+        (pas via le serveur streaming) pour éviter les timeouts quand le
+        serveur est occupé par le worker.
 
+        1. VLM direct (screenshot + crop → Ollama) ~3-8s
+        2. Serveur streaming (fallback si Ollama échoue)
+        """
+        import requests as _requests
+        import json as _json
+
+        screenshot_b64 = self._capture_screenshot_b64(max_width=0, quality=75)
+        if not screenshot_b64:
+            logger.warning("Capture screenshot echouee pour visual resolve")
+            return None
+
+        # ---- VLM DIRECT (Ollama) ----
+        vlm_result = self._vlm_direct_resolve(screenshot_b64, target_spec)
+        if vlm_result and vlm_result.get("resolved"):
+            return vlm_result
+
+        # ---- FALLBACK : serveur streaming ----
+        print("    [VISUAL] VLM direct echoue, fallback serveur...")
         try:
-            # Capturer à 1280px max — assez pour le template matching
-            # et raisonnable pour le transfert réseau (~200-400Ko)
-            screenshot_b64 = self._capture_screenshot_b64(
-                max_width=1280,
-                quality=75,
-            )
-            if not screenshot_b64:
-                logger.warning("Capture screenshot echouee pour visual resolve")
-                return None
-
-            print(
-                f"    [VISUAL] Envoi screenshot ({len(screenshot_b64) // 1024} Ko) "
-                f"au serveur pour resolution..."
-            )
-
-            # Appel au serveur
             resolve_url = f"{server_url}/traces/stream/replay/resolve_target"
             payload = {
-                "session_id": "",  # Pas critique pour la resolution
+                "session_id": "",
                 "screenshot_b64": screenshot_b64,
                 "target_spec": target_spec,
                 "fallback_x_pct": fallback_x,
                 "fallback_y_pct": fallback_y,
                 "screen_width": screen_width,
                 "screen_height": screen_height,
+                "strict_mode": True,
             }
-
-            resp = requests.post(resolve_url, json=payload, headers=self._auth_headers(), timeout=60)
+            resp = _requests.post(resolve_url, json=payload, headers=self._auth_headers(), timeout=30)
             if resp.ok:
                 data = resp.json()
-                method = data.get("method", "?")
-                resolved = data.get("resolved", False)
-                print(
-                    f"    [VISUAL] Reponse serveur : resolved={resolved}, "
-                    f"method={method}, score={data.get('score', 'N/A')}"
-                )
+                print(f"    [VISUAL] Serveur : resolved={data.get('resolved')}, method={data.get('method')}")
                 return data
-            else:
-                logger.warning(f"Visual resolve HTTP {resp.status_code}: {resp.text[:200]}")
+        except Exception as e:
+            logger.warning(f"Visual resolve serveur echoue: {e}")
+        return None
+
+    def _vlm_direct_resolve(self, screenshot_b64: str, target_spec: dict) -> dict:
+        """Appeler Ollama directement pour trouver l'élément à l'écran."""
+        import requests as _requests
+        import json as _json
+        import re
+
+        anchor_b64 = target_spec.get("anchor_image_base64", "")
+        vlm_description = target_spec.get("vlm_description", "")
+        by_text = target_spec.get("by_text", "")
+        window_title = target_spec.get("window_title", "")
+
+        if not anchor_b64 and not vlm_description:
+            return None
+
+        # Prompt
+        if anchor_b64 and vlm_description:
+            prompt = f"""The first image is the current screen. The second image shows the element to find.
+{vlm_description}
+Return the CENTER coordinates as percentage of the FIRST image dimensions.
+Return ONLY JSON: {{"x_pct": 0.XX, "y_pct": 0.XX, "confidence": 0.XX}}
+If not found: {{"x_pct": null, "y_pct": null, "confidence": 0.0}}"""
+        elif vlm_description:
+            prompt = f"""{vlm_description}
+Return coordinates as percentage: {{"x_pct": 0.XX, "y_pct": 0.XX, "confidence": 0.XX}}"""
+        else:
+            prompt = f"""Find the element shown in the second image on the first image.
+Return coordinates: {{"x_pct": 0.XX, "y_pct": 0.XX, "confidence": 0.XX}}"""
+
+        images = [screenshot_b64]
+        if anchor_b64:
+            images.append(anchor_b64)
+
+        ollama_host = os.environ.get("RPA_SERVER_HOST", "localhost")
+        ollama_url = f"http://{ollama_host}:11434/api/chat"
+
+        payload = {
+            "model": os.environ.get("RPA_VLM_MODEL", "qwen3-vl:8b"),
+            "messages": [
+                {"role": "system", "content": "You are a UI element locator. Output raw JSON only."},
+                {"role": "user", "content": prompt, "images": images},
+                {"role": "assistant", "content": "{"},
+            ],
+            "stream": False,
+            "think": False,
+            "options": {"temperature": 0.1, "num_predict": 100, "num_ctx": 2048},
+        }
+
+        try:
+            print(f"    [VLM-DIRECT] Appel Ollama ({ollama_host}:11434)...")
+            start = time.time()
+            resp = _requests.post(ollama_url, json=payload, timeout=30)
+            elapsed = time.time() - start
+
+            if not resp.ok:
+                print(f"    [VLM-DIRECT] HTTP {resp.status_code} ({elapsed:.1f}s)")
                 return None
 
-        except requests.exceptions.Timeout:
-            logger.warning("Visual resolve timeout (30s)")
+            content = "{" + resp.json().get("message", {}).get("content", "")
+            print(f"    [VLM-DIRECT] Réponse en {elapsed:.1f}s : {content[:80]}")
+
+            # Parser JSON
+            match = re.search(r'\{[^}]+\}', content)
+            if not match:
+                return None
+            data = _json.loads(match.group())
+
+            x = data.get("x_pct")
+            y = data.get("y_pct")
+            conf = data.get("confidence", 0)
+
+            if x is None or y is None or conf < 0.3:
+                print(f"    [VLM-DIRECT] Non trouvé (conf={conf})")
+                return None
+            if not (0.0 <= x <= 1.0 and 0.0 <= y <= 1.0):
+                print(f"    [VLM-DIRECT] Hors limites ({x}, {y})")
+                return None
+
+            print(f"    [VLM-DIRECT] TROUVÉ ({x:.3f}, {y:.3f}) conf={conf:.2f} en {elapsed:.1f}s")
+            return {"resolved": True, "method": "vlm_direct", "x_pct": x, "y_pct": y, "score": conf}
+
+        except _requests.exceptions.Timeout:
+            print("    [VLM-DIRECT] Timeout 30s")
             return None
         except Exception as e:
-            logger.warning(f"Visual resolve echoue: {e}")
+            print(f"    [VLM-DIRECT] Erreur: {e}")
             return None
 
     def poll_and_execute(self, session_id: str, server_url: str, machine_id: str = "default") -> bool:
@@ -347,8 +522,19 @@ class ActionExecutorV1:
             )
             if not resp.ok:
                 logger.debug(f"Poll replay echoue : HTTP {resp.status_code}")
+                # Backoff sur erreur HTTP (serveur en erreur, route inconnue, etc.)
+                self._poll_backoff = min(
+                    self._poll_backoff * self._poll_backoff_factor,
+                    self._poll_backoff_max,
+                )
                 return False
 
+            # Le serveur a repondu 200 — reset le backoff immediatement,
+            # meme s'il n'y a pas d'action en attente. Cela garantit que
+            # l'agent reprend un polling rapide des que le serveur est OK.
+            self._poll_backoff = self._poll_backoff_min
+            self._last_conn_error_logged = False
+
             data = resp.json()
             action = data.get("action")
             if action is None:
@@ -360,7 +546,7 @@ class ActionExecutorV1:
                 self._poll_backoff * self._poll_backoff_factor,
                 self._poll_backoff_max,
             )
-            if not hasattr(self, '_last_conn_error_logged'):
+            if not hasattr(self, '_last_conn_error_logged') or not self._last_conn_error_logged:
                 self._last_conn_error_logged = True
                 print(f"[REPLAY] Serveur non disponible (backoff={self._poll_backoff:.1f}s) : {e}")
                 logger.warning(f"Serveur non disponible pour replay (backoff={self._poll_backoff:.1f}s): {e}")
@@ -374,10 +560,6 @@ class ActionExecutorV1:
             logger.error(f"Erreur poll GET : {e}")
             return False
 
-        # Reset du flag d'erreur connexion et du backoff (on a reussi le GET)
-        self._last_conn_error_logged = False
-        self._poll_backoff = self._poll_backoff_min
-
         # Phase 2 : Executer l'action et rapporter le resultat
         # TOUJOURS rapporter au serveur, meme en cas d'erreur d'execution
         action_type = action.get('type', '?')
@@ -412,6 +594,7 @@ class ActionExecutorV1:
             "action_id": result["action_id"],
             "success": result["success"],
             "error": result.get("error"),
+            "warning": result.get("warning"),
             "screenshot": result.get("screenshot"),
         }
         try:
@@ -438,10 +621,167 @@ class ActionExecutorV1:
 
         return True
 
+    # =========================================================================
+    # Gestion automatique des popups imprevues
+    # =========================================================================
+
+    def _handle_possible_popup(self) -> bool:
+        """Tenter de gerer une popup imprevue.
+
+        Appelee quand l'ecran n'a pas change apres une action click ou key_combo,
+        ce qui peut indiquer l'apparition d'une popup modale (dialogue de
+        confirmation "Voulez-vous remplacer ?", erreur, etc.) qui bloque
+        l'interaction attendue.
+
+        Strategie simple (non bloquante, max ~3s) :
+        1. Essayer Enter (valide le bouton par defaut de la popup)
+        2. Si ca ne marche pas, essayer Escape (ferme la popup)
+        3. Si ca ne marche pas, essayer Tab + Enter (selectionne "Oui" puis valide)
+
+        ATTENTION : ne PAS appeler pour les actions 'type' (la saisie de texte
+        ne change pas forcement l'ecran de facon detectable).
+
+        Returns:
+            True si une popup a ete geree (l'ecran a change), False sinon.
+        """
+        hash_before = self._quick_screenshot_hash()
+        if not hash_before:
+            return False
+
+        strategies = [
+            ("Enter", lambda: self._press_key(Key.enter)),
+            ("Escape", lambda: self._press_key(Key.esc)),
+            ("Tab+Enter", lambda: self._press_tab_enter()),
+        ]
+
+        for name, action_fn in strategies:
+            logger.info(f"Popup handler : tentative {name}")
+            print(f"    [POPUP] Tentative : {name}")
+            action_fn()
+            # Attendre max 1s pour voir si l'ecran change (non bloquant)
+            changed = self._wait_for_screen_change(hash_before, timeout_ms=1000)
+            if changed:
+                logger.info(f"Popup handler : {name} a fonctionne (ecran change)")
+                print(f"    [POPUP] {name} a fonctionne — popup geree")
+                return True
+
+        logger.info("Popup handler : aucune strategie n'a fonctionne")
+        print("    [POPUP] Aucune strategie n'a fonctionne")
+        return False
+
+    def _press_key(self, key):
+        """Appuyer et relacher une touche unique."""
+        self.keyboard.press(key)
+        self.keyboard.release(key)
+
+    def _press_tab_enter(self):
+        """Tab puis Enter (selectionner le bouton suivant puis valider)."""
+        self.keyboard.press(Key.tab)
+        self.keyboard.release(Key.tab)
+        time.sleep(0.1)
+        self.keyboard.press(Key.enter)
+        self.keyboard.release(Key.enter)
+
+    # =========================================================================
+    # Verification post-action (comparaison screenshots avant/apres)
+    # =========================================================================
+
+    def _quick_screenshot_hash(self) -> str:
+        """Hash rapide du screenshot actuel (MD5 de l'image redimensionnee 64x64 en niveaux de gris).
+
+        Utilise une instance mss locale pour la thread-safety.
+        Retourne une chaine vide en cas d'erreur (PIL absent, etc.).
+        """
+        try:
+            from PIL import Image
+
+            with mss.mss() as local_sct:
+                monitor = local_sct.monitors[1]
+                raw = local_sct.grab(monitor)
+            img = Image.frombytes("RGB", raw.size, raw.bgra, "raw", "BGRX")
+            # Redimensionner a 64x64 en niveaux de gris pour un hash perceptuel rapide
+            small = img.resize((64, 64)).convert("L")
+            return hashlib.md5(small.tobytes()).hexdigest()
+        except Exception as e:
+            logger.debug(f"Impossible de calculer le hash screenshot : {e}")
+            return ""
+
+    def _wait_for_screen_change(self, hash_before: str, timeout_ms: int = 5000) -> bool:
+        """Attendre que l'ecran change apres une action (max timeout_ms).
+
+        Verifie toutes les 200ms si le hash du screenshot a change.
+        Retourne True si l'ecran a change, False si timeout atteint.
+        """
+        if not hash_before:
+            return True  # Pas de reference → considerer comme change
+
+        deadline = time.time() + timeout_ms / 1000
+        check_count = 0
+
+        while time.time() < deadline:
+            time.sleep(0.2)  # 200ms entre chaque verification
+            current_hash = self._quick_screenshot_hash()
+            check_count += 1
+
+            if current_hash and current_hash != hash_before:
+                logger.info(f"Ecran change apres ~{check_count * 200}ms")
+                return True
+
+        logger.warning(
+            f"Ecran inchange apres {timeout_ms}ms ({check_count} verifications)"
+        )
+        return False
+
     # =========================================================================
     # Helpers
     # =========================================================================
 
+    def _type_text(self, text: str):
+        """Saisir du texte via copier-coller (methode principale) ou keyboard.type (fallback).
+
+        Le copier-coller via le presse-papiers est la methode principale car
+        keyboard.type() de pynput envoie les scancodes QWERTY, ce qui produit
+        des caracteres incorrects sur les claviers AZERTY (ex: "ce" -> "ci").
+        Le copier-coller est agnostique du layout clavier.
+        """
+        if not text:
+            return
+
+        clipboard_ok = False
+        try:
+            import pyperclip
+            # Sauvegarder le contenu actuel du presse-papiers
+            try:
+                old_clipboard = pyperclip.paste()
+            except Exception:
+                old_clipboard = None
+
+            pyperclip.copy(text)
+            # Ctrl+V pour coller
+            self.keyboard.press(Key.ctrl)
+            time.sleep(0.02)
+            self.keyboard.press('v')
+            self.keyboard.release('v')
+            self.keyboard.release(Key.ctrl)
+            time.sleep(0.1)
+
+            # Restaurer le presse-papiers original
+            if old_clipboard is not None:
+                try:
+                    pyperclip.copy(old_clipboard)
+                except Exception:
+                    pass
+
+            clipboard_ok = True
+            logger.debug(f"Texte saisi via presse-papiers ({len(text)} chars)")
+        except ImportError:
+            logger.debug("pyperclip non disponible, fallback sur keyboard.type()")
+        except Exception as e:
+            logger.warning(f"Copier-coller echoue ({e}), fallback sur keyboard.type()")
+
+        if not clipboard_ok:
+            self.keyboard.type(text)
+
     def _click(self, pos, button_name):
         """Deplacer la souris et cliquer.
 
@@ -500,6 +840,50 @@ class ActionExecutorV1:
         for mod in reversed(modifiers):
             self.keyboard.release(mod)
 
+    def _replay_raw_keys(self, raw_keys: list):
+        """Rejouer une séquence press/release exacte via virtual key codes.
+
+        Utilise KeyCode.from_vk() pour reconstituer les touches à partir
+        de leur vk code, ce qui garantit un replay fidèle indépendant du
+        layout clavier (AZERTY, QWERTZ, etc.).
+
+        Chaque événement raw_key est un dict avec :
+        - "action": "press" ou "release"
+        - "kind": "vk" (touche avec virtual key code) ou "key" (touche spéciale pynput)
+        - "vk": int (si kind == "vk")
+        - "name": str (si kind == "key", ex: "ctrl_l", "enter")
+        - "char": str ou None (si kind == "vk", informatif)
+        """
+        for event in raw_keys:
+            key = self._decode_raw_key(event)
+            if key is None:
+                continue
+            action = event.get("action", "")
+            if action == "press":
+                self.keyboard.press(key)
+            elif action == "release":
+                self.keyboard.release(key)
+            else:
+                logger.warning(f"Action raw_key inconnue : {action}")
+                continue
+            time.sleep(0.01)  # Petit délai entre chaque événement
+
+    @staticmethod
+    def _decode_raw_key(data: dict):
+        """Décoder un événement raw_key en objet pynput (Key ou KeyCode).
+
+        Retourne None si le décodage échoue (touche inconnue).
+        """
+        kind = data.get("kind", "")
+        if kind == "key":
+            name = data.get("name", "")
+            return getattr(Key, name, None)
+        if kind == "vk":
+            vk = data.get("vk")
+            if vk is not None:
+                return KeyCode.from_vk(vk)
+        return None
+
     def _capture_screenshot_b64(self, max_width: int = 800, quality: int = 60) -> str:
         """
         Capturer l'ecran et retourner le screenshot en base64.
@@ -512,8 +896,12 @@ class ActionExecutorV1:
         try:
             from PIL import Image
 
-            monitor = self.sct.monitors[1]
-            raw = self.sct.grab(monitor)
+            # Créer une instance mss locale (thread-safe)
+            # mss utilise des handles Windows thread-local (srcdc, memdc)
+            # qui ne peuvent pas être partagés entre threads
+            with mss.mss() as local_sct:
+                monitor = local_sct.monitors[1]
+                raw = local_sct.grab(monitor)
             img = Image.frombytes("RGB", raw.size, raw.bgra, "raw", "BGRX")
 
             # Redimensionner si max_width > 0
@@ -530,5 +918,7 @@ class ActionExecutorV1:
             logger.debug("PIL non disponible, pas de screenshot base64")
             return ""
         except Exception as e:
-            logger.debug(f"Capture screenshot base64 echouee : {e}")
+            logger.warning(f"Capture screenshot base64 echouee : {e}")
+            import traceback
+            traceback.print_exc()
             return ""
diff --git a/agent_v0/agent_v1/main.py b/agent_v0/agent_v1/main.py
index eded86e36..2005731ae 100644
--- a/agent_v0/agent_v1/main.py
+++ b/agent_v0/agent_v1/main.py
@@ -14,7 +14,10 @@ import uuid
 import time
 import logging
 import threading
-from .config import SESSIONS_ROOT, AGENT_VERSION, SERVER_URL, MACHINE_ID, LOG_RETENTION_DAYS
+from .config import (
+    SESSIONS_ROOT, AGENT_VERSION, SERVER_URL, MACHINE_ID, LOG_RETENTION_DAYS,
+    SCREEN_RESOLUTION, DPI_SCALE, OS_THEME,
+)
 from .core.captor import EventCaptorV1
 from .core.executor import ActionExecutorV1
 from .network.streamer import TraceStreamer
@@ -103,6 +106,14 @@ class AgentV1:
         self._capture_server = CaptureServer()
         self._capture_server.start()
 
+        # Bannière de démarrage avec métadonnées système
+        logger.info(
+            f"Agent V1 v{AGENT_VERSION} | Machine={self.machine_id} | "
+            f"Ecran={SCREEN_RESOLUTION[0]}x{SCREEN_RESOLUTION[1]} | "
+            f"DPI={DPI_SCALE}% | Theme={OS_THEME} | "
+            f"Serveur={SERVER_URL}"
+        )
+
         # UI Tray intelligent (remplace TrayAppV1, plus de PyQt5)
         self.ui = SmartTrayV1(
             self.start_session,
@@ -142,8 +153,9 @@ class AgentV1:
         # Watchdog de Commandes (GHOST Replay — legacy fichier)
         threading.Thread(target=self._command_watchdog_loop, daemon=True).start()
 
-        # Boucle de polling replay (P0-5 — pull depuis le serveur)
-        threading.Thread(target=self._replay_poll_loop, daemon=True).start()
+        # Note: la boucle de polling replay est déjà lancée dans __init__ (ligne 102)
+        # Ne PAS en relancer une ici — deux threads poll simultanés causent
+        # une race condition où les actions sont consommées mais pas exécutées.
 
         logger.info(f"Session {self.session_id} ({workflow_name}) sur machine {self.machine_id} en cours...")
 
@@ -159,7 +171,7 @@ class AgentV1:
         else:
             cmd_path = str(BASE_DIR / "command.json")
 
-        while self.running:
+        while self.running and self.session_id:
             # Ne pas traiter les commandes fichier pendant un replay serveur
             if self._replay_active:
                 time.sleep(1)
@@ -197,8 +209,11 @@ class AgentV1:
                 time.sleep(REPLAY_POLL_INTERVAL)
                 continue
 
-            # Utiliser la session active ou un ID par défaut pour le replay
-            poll_session = self.session_id or f"agent_{self.user_id}"
+            # TOUJOURS utiliser un session_id stable pour le replay.
+            # L'enregistrement et le replay sont indépendants : le serveur
+            # envoie les actions sur agent_{user_id}, pas sur la session
+            # d'enregistrement (sess_xxx).
+            poll_session = f"agent_{self.user_id}"
 
             # Log periodique pour confirmer que la boucle tourne (toutes les 60s)
             poll_count += 1
@@ -290,18 +305,40 @@ class AgentV1:
             time.sleep(5)
 
     def stop_session(self):
-        self.running = False
+        # Arrêter la capture et le streaming de la session d'enregistrement
         if self.captor: self.captor.stop()
         if self.streamer: self.streamer.stop()
         logger.info(f"Session {self.session_id} terminée.")
 
+        # Reset le session_id pour que le poll replay utilise l'ID stable
+        self.session_id = None
+
+        # Reset le backoff de l'executor pour reprendre le polling immédiatement
+        if self._executor:
+            self._executor._poll_backoff = self._executor._poll_backoff_min
+            self._executor._server_available = True
+            if hasattr(self._executor, '_last_conn_error_logged'):
+                self._executor._last_conn_error_logged = False
+
+        # NE PAS mettre self.running = False ici !
+        # self.running contrôle la boucle _replay_poll_loop (permanente).
+        # Seule la sortie du programme doit le mettre à False.
+        # Les boucles _heartbeat_loop et _command_watchdog_loop vérifieront
+        # self.session_id pour savoir si elles doivent fonctionner.
+
+        logger.info(
+            f"Session arrêtée — replay poll actif avec session="
+            f"agent_{self.user_id}"
+        )
+
     _last_heartbeat_hash: str = ""
 
     def _heartbeat_loop(self):
         """Capture périodique pour donner du contexte au stagiaire.
         Déduplication : n'envoie que si l'écran a changé.
+        Tourne tant que session_id est défini (= enregistrement actif).
         """
-        while self.running:
+        while self.running and self.session_id:
             try:
                 full_path = self.vision.capture_full_context("heartbeat")
                 if full_path:
diff --git a/agent_v0/agent_v1/ui/chat_window.py b/agent_v0/agent_v1/ui/chat_window.py
index 53fd2bcea..12f97c0b2 100644
--- a/agent_v0/agent_v1/ui/chat_window.py
+++ b/agent_v0/agent_v1/ui/chat_window.py
@@ -413,10 +413,8 @@ class ChatWindow:
 
         buttons = [
             ("\U0001f393 Apprenez-moi", self._on_quick_record),
-            ("\u25b6\ufe0f Lancer", self._on_quick_tasks),
-            ("\U0001f4ca Donn\u00e9es", self._on_quick_import),
+            ("\u25b6\ufe0f Lancer une t\u00e2che", self._on_quick_tasks),
             ("\u23f9\ufe0f Arr\u00eater", self._on_quick_stop),
-            ("\u2753 Aide", self._on_quick_help),
         ]
 
         for text, cmd in buttons:
diff --git a/agent_v0/agent_v1/vision/system_info.py b/agent_v0/agent_v1/vision/system_info.py
new file mode 100644
index 000000000..5dbe37846
--- /dev/null
+++ b/agent_v0/agent_v1/vision/system_info.py
@@ -0,0 +1,195 @@
+# agent_v1/vision/system_info.py
+"""
+Capture des metadonnees systeme pour enrichir les evenements.
+
+Collecte DPI, resolution, fenetre active, moniteur, theme OS et langue.
+Les fonctions Windows (ctypes.windll, winreg) ont des fallbacks gracieux
+pour Linux/Mac.
+"""
+
+import platform
+import locale
+import logging
+from typing import Any, Dict, List, Optional, Tuple
+
+logger = logging.getLogger(__name__)
+
+# Cache du systeme d'exploitation pour eviter les appels repetes
+_SYSTEM = platform.system()
+
+
+def get_dpi_scale() -> int:
+    """Retourne le facteur DPI en % (100 = normal, 150 = haute resolution).
+
+    Windows : ctypes.windll.user32.GetDpiForSystem()
+    Linux/Mac : fallback 100
+
+    NOTE : Le process DOIT deja etre DPI-aware (via SetProcessDpiAwareness(2)
+    appele dans config.py) pour que GetDpiForSystem retourne le vrai DPI.
+    """
+    if _SYSTEM == "Windows":
+        try:
+            import ctypes
+            dpi = ctypes.windll.user32.GetDpiForSystem()
+            return round(dpi * 100 / 96)  # 96 DPI = 100%
+        except Exception as e:
+            logger.debug(f"Impossible de lire le DPI Windows : {e}")
+            return 100
+    return 100  # Linux/Mac fallback
+
+
+def get_window_bounds() -> Optional[List[int]]:
+    """Retourne [x, y, width, height] de la fenetre active.
+
+    Windows : ctypes GetWindowRect(GetForegroundWindow())
+    Linux/Mac : fallback None
+    """
+    if _SYSTEM == "Windows":
+        try:
+            import ctypes
+            import ctypes.wintypes
+
+            hwnd = ctypes.windll.user32.GetForegroundWindow()
+            if not hwnd:
+                return None
+            rect = ctypes.wintypes.RECT()
+            ctypes.windll.user32.GetWindowRect(hwnd, ctypes.byref(rect))
+            return [
+                rect.left,
+                rect.top,
+                rect.right - rect.left,
+                rect.bottom - rect.top,
+            ]
+        except Exception as e:
+            logger.debug(f"Impossible de lire les bounds fenetre : {e}")
+            return None
+
+    # Linux : tentative via xdotool
+    if _SYSTEM == "Linux":
+        try:
+            import subprocess
+
+            wid = subprocess.check_output(
+                ["xdotool", "getactivewindow"],
+                stderr=subprocess.DEVNULL,
+            ).decode().strip()
+            geom = subprocess.check_output(
+                ["xdotool", "getwindowgeometry", "--shell", wid],
+                stderr=subprocess.DEVNULL,
+            ).decode()
+            # Parse "X=...\nY=...\nWIDTH=...\nHEIGHT=..."
+            vals: Dict[str, int] = {}
+            for line in geom.strip().splitlines():
+                if "=" in line:
+                    k, v = line.split("=", 1)
+                    vals[k.strip()] = int(v.strip())
+            if {"X", "Y", "WIDTH", "HEIGHT"} <= vals.keys():
+                return [vals["X"], vals["Y"], vals["WIDTH"], vals["HEIGHT"]]
+        except Exception:
+            pass
+
+    return None
+
+
+def get_monitor_info() -> Tuple[int, List[Dict[str, int]]]:
+    """Retourne (monitor_index, liste_moniteurs).
+
+    Chaque moniteur : {width, height, x, y}
+    monitor_index : index du moniteur contenant la fenetre active
+    """
+    monitors: List[Dict[str, int]] = []
+    active_index = 0
+
+    try:
+        import mss
+
+        with mss.mss() as sct:
+            for mon in sct.monitors[1:]:  # Skip le moniteur virtuel (index 0)
+                monitors.append({
+                    "width": mon["width"],
+                    "height": mon["height"],
+                    "x": mon["left"],
+                    "y": mon["top"],
+                })
+    except Exception as e:
+        logger.debug(f"mss indisponible, resolution par defaut : {e}")
+        monitors = [{"width": 1920, "height": 1080, "x": 0, "y": 0}]
+
+    # Determiner quel moniteur contient la fenetre active
+    bounds = get_window_bounds()
+    if bounds and len(monitors) > 1:
+        wx, wy = bounds[0], bounds[1]
+        for i, mon in enumerate(monitors):
+            if (mon["x"] <= wx < mon["x"] + mon["width"]
+                    and mon["y"] <= wy < mon["y"] + mon["height"]):
+                active_index = i
+                break
+
+    return active_index, monitors
+
+
+def get_os_theme() -> str:
+    """Retourne 'light', 'dark' ou 'unknown'."""
+    if _SYSTEM == "Windows":
+        try:
+            import winreg
+
+            key = winreg.OpenKey(
+                winreg.HKEY_CURRENT_USER,
+                r"Software\Microsoft\Windows\CurrentVersion\Themes\Personalize",
+            )
+            value, _ = winreg.QueryValueEx(key, "AppsUseLightTheme")
+            winreg.CloseKey(key)
+            return "light" if value == 1 else "dark"
+        except Exception as e:
+            logger.debug(f"Impossible de lire le theme Windows : {e}")
+            return "unknown"
+
+    # Linux : tentative via gsettings (GNOME)
+    if _SYSTEM == "Linux":
+        try:
+            import subprocess
+
+            result = subprocess.check_output(
+                ["gsettings", "get", "org.gnome.desktop.interface", "color-scheme"],
+                stderr=subprocess.DEVNULL,
+            ).decode().strip().strip("'\"")
+            if "dark" in result.lower():
+                return "dark"
+            elif "light" in result.lower() or "default" in result.lower():
+                return "light"
+        except Exception:
+            pass
+
+    return "unknown"
+
+
+def get_os_language() -> str:
+    """Retourne le code langue (fr, en, de, etc.)."""
+    try:
+        lang = locale.getdefaultlocale()[0]  # ex: 'fr_FR'
+        if lang:
+            return lang[:2]  # ex: 'fr'
+    except Exception:
+        pass
+    return "unknown"
+
+
+def get_screen_metadata() -> Dict[str, Any]:
+    """Capture toutes les metadonnees systeme en une fois.
+
+    Appelee une fois au demarrage + a chaque changement de focus.
+    Resultat injecte dans les evenements envoyes au serveur.
+    """
+    monitor_index, monitors = get_monitor_info()
+    primary = monitors[0] if monitors else {"width": 1920, "height": 1080}
+
+    return {
+        "dpi_scale": get_dpi_scale(),
+        "monitor_index": monitor_index,
+        "monitors": monitors,
+        "screen_resolution": [primary["width"], primary["height"]],
+        "window_bounds": get_window_bounds(),
+        "os_theme": get_os_theme(),
+        "os_language": get_os_language(),
+    }
diff --git a/agent_v0/deploy/windows_client/agent_v1/config.py b/agent_v0/deploy/windows_client/agent_v1/config.py
index 554896721..0bbb3838c 100644
--- a/agent_v0/deploy/windows_client/agent_v1/config.py
+++ b/agent_v0/deploy/windows_client/agent_v1/config.py
@@ -8,6 +8,23 @@ import platform
 import socket
 from pathlib import Path
 
+# --- DPI awareness (DOIT etre appele avant tout import de pynput/mss/tkinter) ---
+# Rend le process DPI-aware sur Windows pour que toutes les API (pynput, mss, pyautogui)
+# travaillent en coordonnees physiques (pixels reels) au lieu de coordonnees logiques
+# (virtualisees par le DPI scaling).
+# Sans cet appel, un ecran 2560x1600 a 150% DPI apparait comme 1707x1067 pour les API,
+# ce qui cause des erreurs de positionnement pendant le replay.
+# PROCESS_PER_MONITOR_DPI_AWARE = 2 : le niveau le plus precis.
+if platform.system() == "Windows":
+    try:
+        import ctypes
+        ctypes.windll.shcore.SetProcessDpiAwareness(2)  # PROCESS_PER_MONITOR_DPI_AWARE
+    except Exception:
+        try:
+            ctypes.windll.user32.SetProcessDPIAware()
+        except Exception:
+            pass
+
 AGENT_VERSION = "1.0.0"
 
 # Identifiant unique de la machine (utilisé pour le multi-machine)
diff --git a/agent_v0/deploy/windows_client/agent_v1/core/executor.py b/agent_v0/deploy/windows_client/agent_v1/core/executor.py
index 0645a6729..22b0c6e4c 100644
--- a/agent_v0/deploy/windows_client/agent_v1/core/executor.py
+++ b/agent_v0/deploy/windows_client/agent_v1/core/executor.py
@@ -6,13 +6,25 @@ Opere par coordonnees normalisees (proportions) pour le rejeu en univers ferme (
 Supporte deux modes :
   - Watchdog fichier (command.json) — legacy
   - Polling serveur (GET /replay/next) — mode replay P0-5
+
+NOTE DPI : Ce module depend du DPI awareness configure dans config.py.
+L'appel a SetProcessDpiAwareness(2) DOIT avoir ete fait avant l'import de
+pynput et mss, sinon les coordonnees seront en pixels logiques (faux sur
+les ecrans haute resolution avec DPI scaling > 100%).
 """
 
 import base64
+import hashlib
 import io
+import os
 import time
 import logging
 
+# Forcer l'import de config AVANT pynput/mss pour garantir que le
+# DPI awareness est configure (SetProcessDpiAwareness(2) sur Windows).
+# Sans cela, pynput et mss utilisent des coordonnees logiques (virtualisees).
+from ..config import MACHINE_ID as _  # noqa: F401 — side-effect import
+
 import mss
 from pynput.mouse import Button, Controller as MouseController
 from pynput.keyboard import Controller as KeyboardController, Key
@@ -65,6 +77,28 @@ class ActionExecutorV1:
         self._poll_backoff_min = 1.0   # Delai minimal (reset apres succes)
         self._poll_backoff_max = 30.0  # Delai maximal
         self._poll_backoff_factor = 1.5  # Multiplicateur en cas d'echec
+        # Token d'authentification API
+        self._api_token = os.environ.get("RPA_API_TOKEN", "")
+        # Log de la resolution physique pour le diagnostic DPI
+        self._log_screen_info()
+
+    def _log_screen_info(self):
+        """Log la resolution physique de l'ecran au demarrage pour le diagnostic DPI."""
+        try:
+            monitor = self.sct.monitors[1]
+            w, h = monitor["width"], monitor["height"]
+            logger.info(
+                f"Executor initialise — resolution physique : {w}x{h} "
+                f"(mss monitors[1], DPI-aware process)"
+            )
+        except Exception as e:
+            logger.debug(f"Impossible de lire la resolution ecran : {e}")
+
+    def _auth_headers(self) -> dict:
+        """Headers d'authentification Bearer pour les requetes au serveur."""
+        if self._api_token:
+            return {"Authorization": f"Bearer {self._api_token}"}
+        return {}
 
     @property
     def sct(self):
@@ -171,6 +205,15 @@ class ActionExecutorV1:
                             f"-> ({x_pct:.4f}, {y_pct:.4f})"
                         )
 
+            # ---- Hash AVANT l'action (pour verification post-action) ----
+            # Seules les actions click et key_combo sont verifiees : elles
+            # provoquent un changement visible de l'ecran (ouverture de fenetre,
+            # focus, etc.). Les actions type/wait/scroll ne sont pas verifiees.
+            needs_screen_check = action_type in ("click", "key_combo")
+            hash_before = ""
+            if needs_screen_check:
+                hash_before = self._quick_screenshot_hash()
+
             if action_type == "click":
                 real_x = int(x_pct * width)
                 real_y = int(y_pct * height)
@@ -197,7 +240,7 @@ class ActionExecutorV1:
                     print(f"    [TYPE] Clic prealable sur ({real_x}, {real_y})")
                     self._click((real_x, real_y), "left")
                     time.sleep(0.3)
-                self.keyboard.type(text)
+                self._type_text(text)
                 print(f"    [TYPE] Termine.")
                 logger.info(f"Replay type : '{text[:30]}...' ({len(text)} chars)")
 
@@ -226,6 +269,25 @@ class ActionExecutorV1:
                 print(f"    [WAIT] Termine.")
                 logger.info(f"Replay wait : {duration_ms}ms")
 
+            elif action_type == "verify_screen":
+                # Vérification visuelle entre les groupes du replay hybride.
+                # Pour l'instant, on fait un wait de 2s pour laisser l'écran
+                # se stabiliser. La vérification réelle sera faite par le
+                # pre-check côté serveur dans GET /replay/next.
+                expected_node = action.get("expected_node", "?")
+                timeout_ms = action.get("timeout_ms", 5000)
+                wait_s = min(timeout_ms / 1000.0, 2.0)
+                print(
+                    f"    [VERIFY] Attente verification ecran "
+                    f"(node attendu: {expected_node}, wait={wait_s}s)"
+                )
+                time.sleep(wait_s)
+                print(f"    [VERIFY] Termine (verification deferred au serveur).")
+                logger.info(
+                    f"Replay verify_screen : node={expected_node}, "
+                    f"wait={wait_s}s (verification serveur)"
+                )
+
             else:
                 result["error"] = f"Type d'action inconnu : {action_type}"
                 logger.warning(result["error"])
@@ -233,8 +295,41 @@ class ActionExecutorV1:
 
             result["success"] = True
 
-            # Capturer un screenshot post-action
-            time.sleep(0.5)
+            # ---- Verification post-action : l'ecran a-t-il change ? ----
+            if needs_screen_check and hash_before:
+                screen_changed = self._wait_for_screen_change(
+                    hash_before, timeout_ms=5000
+                )
+                if not screen_changed:
+                    # Ecran inchange — tenter de gerer une popup imprevue
+                    # (dialogue de confirmation, erreur, etc.)
+                    popup_handled = self._handle_possible_popup()
+                    if popup_handled:
+                        result["warning"] = "popup_handled"
+                        print(
+                            f"    [OK] Popup geree automatiquement apres {action_type}"
+                        )
+                        logger.info(
+                            f"Action {action_id} ({action_type}) : popup geree "
+                            f"automatiquement"
+                        )
+                    else:
+                        result["warning"] = "no_screen_change"
+                        print(
+                            f"    [WARN] Ecran inchange apres {action_type} — "
+                            f"l'action n'a peut-etre pas eu d'effet"
+                        )
+                        logger.warning(
+                            f"Action {action_id} ({action_type}) : ecran inchange "
+                            f"apres 5s — possible echec silencieux"
+                        )
+                else:
+                    print(f"    [OK] Changement d'ecran detecte apres {action_type}")
+            else:
+                # Pour type/wait/scroll, petit delai pour laisser l'ecran se stabiliser
+                time.sleep(0.5)
+
+            # Capturer un screenshot post-action (apres stabilisation)
             result["screenshot"] = self._capture_screenshot_b64()
 
         except Exception as e:
@@ -251,17 +346,18 @@ class ActionExecutorV1:
         """
         Envoyer un screenshot au serveur pour resolution visuelle de la cible.
 
-        Capture l'ecran en haute resolution (pas de downscale pour le template
-        matching), l'encode en base64 JPEG, et POST au endpoint
-        /replay/resolve_target. Retourne les coordonnees resolues.
+        Capture l'ecran en resolution native (pas de downscale, necessaire pour
+        le template matching precis cross-resolution), l'encode en base64 JPEG,
+        et POST au endpoint /replay/resolve_target. Retourne les coordonnees resolues.
         """
         import requests
 
         try:
-            # Capturer à 1280px max — assez pour le template matching
-            # et raisonnable pour le transfert réseau (~200-400Ko)
+            # Capturer à résolution native pour le template matching
+            # (le downscale nuit à la précision du matching quand les
+            # résolutions d'apprentissage et de replay diffèrent)
             screenshot_b64 = self._capture_screenshot_b64(
-                max_width=1280,
+                max_width=0,
                 quality=75,
             )
             if not screenshot_b64:
@@ -283,9 +379,10 @@ class ActionExecutorV1:
                 "fallback_y_pct": fallback_y,
                 "screen_width": screen_width,
                 "screen_height": screen_height,
+                "strict_mode": True,  # Replay = seuil strict 0.90 + YOLO
             }
 
-            resp = requests.post(resolve_url, json=payload, timeout=60)
+            resp = requests.post(resolve_url, json=payload, headers=self._auth_headers(), timeout=60)
             if resp.ok:
                 data = resp.json()
                 method = data.get("method", "?")
@@ -333,12 +430,24 @@ class ActionExecutorV1:
             resp = requests.get(
                 replay_next_url,
                 params={"session_id": session_id, "machine_id": machine_id},
+                headers=self._auth_headers(),
                 timeout=5,
             )
             if not resp.ok:
                 logger.debug(f"Poll replay echoue : HTTP {resp.status_code}")
+                # Backoff sur erreur HTTP (serveur en erreur, route inconnue, etc.)
+                self._poll_backoff = min(
+                    self._poll_backoff * self._poll_backoff_factor,
+                    self._poll_backoff_max,
+                )
                 return False
 
+            # Le serveur a repondu 200 — reset le backoff immediatement,
+            # meme s'il n'y a pas d'action en attente. Cela garantit que
+            # l'agent reprend un polling rapide des que le serveur est OK.
+            self._poll_backoff = self._poll_backoff_min
+            self._last_conn_error_logged = False
+
             data = resp.json()
             action = data.get("action")
             if action is None:
@@ -350,7 +459,7 @@ class ActionExecutorV1:
                 self._poll_backoff * self._poll_backoff_factor,
                 self._poll_backoff_max,
             )
-            if not hasattr(self, '_last_conn_error_logged'):
+            if not hasattr(self, '_last_conn_error_logged') or not self._last_conn_error_logged:
                 self._last_conn_error_logged = True
                 print(f"[REPLAY] Serveur non disponible (backoff={self._poll_backoff:.1f}s) : {e}")
                 logger.warning(f"Serveur non disponible pour replay (backoff={self._poll_backoff:.1f}s): {e}")
@@ -364,10 +473,6 @@ class ActionExecutorV1:
             logger.error(f"Erreur poll GET : {e}")
             return False
 
-        # Reset du flag d'erreur connexion et du backoff (on a reussi le GET)
-        self._last_conn_error_logged = False
-        self._poll_backoff = self._poll_backoff_min
-
         # Phase 2 : Executer l'action et rapporter le resultat
         # TOUJOURS rapporter au serveur, meme en cas d'erreur d'execution
         action_type = action.get('type', '?')
@@ -402,12 +507,14 @@ class ActionExecutorV1:
             "action_id": result["action_id"],
             "success": result["success"],
             "error": result.get("error"),
+            "warning": result.get("warning"),
             "screenshot": result.get("screenshot"),
         }
         try:
             resp2 = requests.post(
                 replay_result_url,
                 json=report,
+                headers=self._auth_headers(),
                 timeout=10,
             )
             if resp2.ok:
@@ -427,10 +534,167 @@ class ActionExecutorV1:
 
         return True
 
+    # =========================================================================
+    # Gestion automatique des popups imprevues
+    # =========================================================================
+
+    def _handle_possible_popup(self) -> bool:
+        """Tenter de gerer une popup imprevue.
+
+        Appelee quand l'ecran n'a pas change apres une action click ou key_combo,
+        ce qui peut indiquer l'apparition d'une popup modale (dialogue de
+        confirmation "Voulez-vous remplacer ?", erreur, etc.) qui bloque
+        l'interaction attendue.
+
+        Strategie simple (non bloquante, max ~3s) :
+        1. Essayer Enter (valide le bouton par defaut de la popup)
+        2. Si ca ne marche pas, essayer Escape (ferme la popup)
+        3. Si ca ne marche pas, essayer Tab + Enter (selectionne "Oui" puis valide)
+
+        ATTENTION : ne PAS appeler pour les actions 'type' (la saisie de texte
+        ne change pas forcement l'ecran de facon detectable).
+
+        Returns:
+            True si une popup a ete geree (l'ecran a change), False sinon.
+        """
+        hash_before = self._quick_screenshot_hash()
+        if not hash_before:
+            return False
+
+        strategies = [
+            ("Enter", lambda: self._press_key(Key.enter)),
+            ("Escape", lambda: self._press_key(Key.esc)),
+            ("Tab+Enter", lambda: self._press_tab_enter()),
+        ]
+
+        for name, action_fn in strategies:
+            logger.info(f"Popup handler : tentative {name}")
+            print(f"    [POPUP] Tentative : {name}")
+            action_fn()
+            # Attendre max 1s pour voir si l'ecran change (non bloquant)
+            changed = self._wait_for_screen_change(hash_before, timeout_ms=1000)
+            if changed:
+                logger.info(f"Popup handler : {name} a fonctionne (ecran change)")
+                print(f"    [POPUP] {name} a fonctionne — popup geree")
+                return True
+
+        logger.info("Popup handler : aucune strategie n'a fonctionne")
+        print("    [POPUP] Aucune strategie n'a fonctionne")
+        return False
+
+    def _press_key(self, key):
+        """Appuyer et relacher une touche unique."""
+        self.keyboard.press(key)
+        self.keyboard.release(key)
+
+    def _press_tab_enter(self):
+        """Tab puis Enter (selectionner le bouton suivant puis valider)."""
+        self.keyboard.press(Key.tab)
+        self.keyboard.release(Key.tab)
+        time.sleep(0.1)
+        self.keyboard.press(Key.enter)
+        self.keyboard.release(Key.enter)
+
+    # =========================================================================
+    # Verification post-action (comparaison screenshots avant/apres)
+    # =========================================================================
+
+    def _quick_screenshot_hash(self) -> str:
+        """Hash rapide du screenshot actuel (MD5 de l'image redimensionnee 64x64 en niveaux de gris).
+
+        Utilise une instance mss locale pour la thread-safety.
+        Retourne une chaine vide en cas d'erreur (PIL absent, etc.).
+        """
+        try:
+            from PIL import Image
+
+            with mss.mss() as local_sct:
+                monitor = local_sct.monitors[1]
+                raw = local_sct.grab(monitor)
+            img = Image.frombytes("RGB", raw.size, raw.bgra, "raw", "BGRX")
+            # Redimensionner a 64x64 en niveaux de gris pour un hash perceptuel rapide
+            small = img.resize((64, 64)).convert("L")
+            return hashlib.md5(small.tobytes()).hexdigest()
+        except Exception as e:
+            logger.debug(f"Impossible de calculer le hash screenshot : {e}")
+            return ""
+
+    def _wait_for_screen_change(self, hash_before: str, timeout_ms: int = 5000) -> bool:
+        """Attendre que l'ecran change apres une action (max timeout_ms).
+
+        Verifie toutes les 200ms si le hash du screenshot a change.
+        Retourne True si l'ecran a change, False si timeout atteint.
+        """
+        if not hash_before:
+            return True  # Pas de reference → considerer comme change
+
+        deadline = time.time() + timeout_ms / 1000
+        check_count = 0
+
+        while time.time() < deadline:
+            time.sleep(0.2)  # 200ms entre chaque verification
+            current_hash = self._quick_screenshot_hash()
+            check_count += 1
+
+            if current_hash and current_hash != hash_before:
+                logger.info(f"Ecran change apres ~{check_count * 200}ms")
+                return True
+
+        logger.warning(
+            f"Ecran inchange apres {timeout_ms}ms ({check_count} verifications)"
+        )
+        return False
+
     # =========================================================================
     # Helpers
     # =========================================================================
 
+    def _type_text(self, text: str):
+        """Saisir du texte via copier-coller (methode principale) ou keyboard.type (fallback).
+
+        Le copier-coller via le presse-papiers est la methode principale car
+        keyboard.type() de pynput envoie les scancodes QWERTY, ce qui produit
+        des caracteres incorrects sur les claviers AZERTY (ex: "ce" -> "ci").
+        Le copier-coller est agnostique du layout clavier.
+        """
+        if not text:
+            return
+
+        clipboard_ok = False
+        try:
+            import pyperclip
+            # Sauvegarder le contenu actuel du presse-papiers
+            try:
+                old_clipboard = pyperclip.paste()
+            except Exception:
+                old_clipboard = None
+
+            pyperclip.copy(text)
+            # Ctrl+V pour coller
+            self.keyboard.press(Key.ctrl)
+            time.sleep(0.02)
+            self.keyboard.press('v')
+            self.keyboard.release('v')
+            self.keyboard.release(Key.ctrl)
+            time.sleep(0.1)
+
+            # Restaurer le presse-papiers original
+            if old_clipboard is not None:
+                try:
+                    pyperclip.copy(old_clipboard)
+                except Exception:
+                    pass
+
+            clipboard_ok = True
+            logger.debug(f"Texte saisi via presse-papiers ({len(text)} chars)")
+        except ImportError:
+            logger.debug("pyperclip non disponible, fallback sur keyboard.type()")
+        except Exception as e:
+            logger.warning(f"Copier-coller echoue ({e}), fallback sur keyboard.type()")
+
+        if not clipboard_ok:
+            self.keyboard.type(text)
+
     def _click(self, pos, button_name):
         """Deplacer la souris et cliquer.
 
@@ -501,8 +765,12 @@ class ActionExecutorV1:
         try:
             from PIL import Image
 
-            monitor = self.sct.monitors[1]
-            raw = self.sct.grab(monitor)
+            # Créer une instance mss locale (thread-safe)
+            # mss utilise des handles Windows thread-local (srcdc, memdc)
+            # qui ne peuvent pas être partagés entre threads
+            with mss.mss() as local_sct:
+                monitor = local_sct.monitors[1]
+                raw = local_sct.grab(monitor)
             img = Image.frombytes("RGB", raw.size, raw.bgra, "raw", "BGRX")
 
             # Redimensionner si max_width > 0
@@ -519,5 +787,7 @@ class ActionExecutorV1:
             logger.debug("PIL non disponible, pas de screenshot base64")
             return ""
         except Exception as e:
-            logger.debug(f"Capture screenshot base64 echouee : {e}")
+            logger.warning(f"Capture screenshot base64 echouee : {e}")
+            import traceback
+            traceback.print_exc()
             return ""
diff --git a/agent_v0/server_v1/api_stream.py b/agent_v0/server_v1/api_stream.py
index f41ea6b7d..f295c4085 100644
--- a/agent_v0/server_v1/api_stream.py
+++ b/agent_v0/server_v1/api_stream.py
@@ -25,8 +25,7 @@ from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
 
 from .replay_verifier import ReplayVerifier, VerificationResult
-from .session_worker import SessionWorker
-from .stream_processor import StreamProcessor
+from .stream_processor import StreamProcessor, build_replay_from_raw_events
 from .worker_stream import StreamWorker
 
 # Instance globale du vérificateur de replay (comparaison screenshots avant/après)
@@ -53,6 +52,20 @@ try:
 except ImportError:
     _gesture_catalog = None
 
+# Authentification automatique (optionnel) — détection des écrans d'auth pendant le replay
+# Nécessite un vault configuré via la variable d'env RPA_AUTH_VAULT_PATH + RPA_AUTH_VAULT_PASSWORD
+_auth_handler = None
+try:
+    _vault_path = os.environ.get("RPA_AUTH_VAULT_PATH")
+    _vault_password = os.environ.get("RPA_AUTH_VAULT_PASSWORD")
+    if _vault_path and _vault_password:
+        from core.auth.credential_vault import CredentialVault
+        from core.auth.auth_handler import AuthHandler
+        _auth_vault = CredentialVault(_vault_path, _vault_password)
+        _auth_handler = AuthHandler(_auth_vault)
+except Exception:
+    _auth_handler = None
+
 logger = logging.getLogger("api_stream")
 
 # =========================================================================
@@ -64,6 +77,7 @@ logger = logging.getLogger("api_stream")
 API_TOKEN = os.environ.get("RPA_API_TOKEN", secrets.token_hex(32))
 
 # Endpoints publics (pas besoin de token)
+# En production, /docs et /redoc sont désactivés (voir ci-dessous)
 _PUBLIC_PATHS = {"/health", "/docs", "/openapi.json", "/redoc"}
 
 
@@ -84,6 +98,7 @@ _RATE_LIMIT_WINDOW = 60  # secondes
 _RATE_LIMITS = {
     "/api/v1/traces/stream/replay": 10,         # 10 replays par minute
     "/api/v1/traces/stream/replay/raw": 10,
+    "/api/v1/traces/stream/replay-session": 10, # 10 replays session par minute
     "/api/v1/traces/stream/replay/single": 30,  # 30 actions Copilot par minute
     "/api/v1/traces/stream/finalize": 5,
     "/api/v1/traces/stream/image": 200,         # 200 images par minute (heartbeats)
@@ -110,6 +125,7 @@ _ALLOWED_ACTION_TYPES = {
     "click", "type", "key_combo", "scroll", "wait",
     "file_open", "file_save", "file_close", "file_new", "file_dialog",
     "double_click", "right_click", "drag",
+    "verify_screen",  # Replay hybride : vérification visuelle entre groupes
 }
 _MAX_ACTION_TEXT_LENGTH = 10000
 _MAX_KEYS_PER_COMBO = 10
@@ -164,20 +180,29 @@ def _validate_replay_action(action: dict) -> Optional[str]:
     return None  # Valide
 
 
+# En production (ENVIRONMENT != development), désactiver la doc Swagger
+_is_production = os.environ.get("ENVIRONMENT", "development") != "development"
+
 app = FastAPI(
     title="RPA Vision V3 - Streaming API v1",
     dependencies=[Depends(_verify_token)],
+    docs_url=None if _is_production else "/docs",
+    redoc_url=None if _is_production else "/redoc",
+    openapi_url=None if _is_production else "/openapi.json",
 )
 
 # CORS — origines autorisées (VWB frontend, Agent Chat, Dashboard)
 # Configurable via variable d'environnement CORS_ORIGINS (séparées par des virgules)
+# Inclut le domaine public pour l'accès internet via NPM reverse proxy
 _DEFAULT_CORS_ORIGINS = (
     "http://localhost:3002,"       # VWB Frontend (Vite/React)
     "http://localhost:5002,"       # VWB Backend (Flask)
     "http://localhost:5004,"       # Agent Chat
     "http://localhost:5001,"       # Web Dashboard
     "http://192.168.1.40:3002,"   # VWB Frontend depuis le réseau local
-    "http://192.168.1.40:5004"    # Agent Chat depuis le réseau local
+    "http://192.168.1.40:5004,"   # Agent Chat depuis le réseau local
+    "https://lea.labs.laurinebazin.design,"  # Domaine public HTTPS
+    "https://vwb.labs.laurinebazin.design"   # VWB public HTTPS
 )
 CORS_ORIGINS = os.environ.get("CORS_ORIGINS", _DEFAULT_CORS_ORIGINS).split(",")
 CORS_ORIGINS = [o.strip() for o in CORS_ORIGINS if o.strip()]
@@ -185,11 +210,25 @@ CORS_ORIGINS = [o.strip() for o in CORS_ORIGINS if o.strip()]
 app.add_middleware(
     CORSMiddleware,
     allow_origins=CORS_ORIGINS,
+    allow_credentials=True,
     allow_methods=["GET", "POST"],
     allow_headers=["Content-Type", "Authorization"],
 )
 
 
+@app.middleware("http")
+async def security_headers_middleware(request: Request, call_next):
+    """Ajouter les headers de sécurité sur toutes les réponses."""
+    response = await call_next(request)
+    response.headers["X-Content-Type-Options"] = "nosniff"
+    response.headers["X-Frame-Options"] = "DENY"
+    response.headers["X-XSS-Protection"] = "1; mode=block"
+    response.headers["Referrer-Policy"] = "strict-origin-when-cross-origin"
+    if request.url.scheme == "https" or request.headers.get("X-Forwarded-Proto") == "https":
+        response.headers["Strict-Transport-Security"] = "max-age=31536000; includeSubDomains"
+    return response
+
+
 @app.middleware("http")
 async def rate_limit_middleware(request: Request, call_next):
     """Middleware de rate limiting sur les endpoints sensibles."""
@@ -211,13 +250,97 @@ ROOT_DIR = Path(__file__).parent.parent.parent
 LIVE_SESSIONS_DIR = ROOT_DIR / "data" / "training" / "live_sessions"
 LIVE_SESSIONS_DIR.mkdir(parents=True, exist_ok=True)
 
-# Instance globale partagée
+# =========================================================================
+# Communication avec le worker VLM (process séparé)
+# Le serveur HTTP ne fait JAMAIS de VLM — il écrit dans des fichiers
+# que le worker VLM (run_worker.py) lit dans son propre process.
+# =========================================================================
+_DATA_DIR = ROOT_DIR / "data" / "training"
+WORKER_QUEUE_FILE = _DATA_DIR / "_worker_queue.txt"
+REPLAY_LOCK_FILE = _DATA_DIR / "_replay_active.lock"
+
+# Instance globale partagée (le StreamProcessor reste dans le serveur HTTP
+# pour le CLIP, l'indexation FAISS, la gestion des sessions, le replay —
+# mais ne fait PAS de VLM/reprocess_session, c'est le worker séparé qui s'en charge)
 processor = StreamProcessor(data_dir=str(LIVE_SESSIONS_DIR))
 worker = StreamWorker(live_dir=str(LIVE_SESSIONS_DIR), processor=processor)
 
-# Worker asynchrone pour le traitement des sessions finalisées
-# (analyse VLM + construction workflow en arrière-plan)
-session_worker = SessionWorker(processor=processor, poll_interval=10)
+
+def _enqueue_to_worker(session_id: str):
+    """Ajoute un session_id à la queue du worker VLM (fichier sur disque).
+
+    Le worker VLM (process séparé) lit ce fichier et traite les sessions.
+    Évite les doublons : vérifie si le session_id est déjà dans la queue.
+    """
+    try:
+        WORKER_QUEUE_FILE.parent.mkdir(parents=True, exist_ok=True)
+
+        # Lire la queue existante pour éviter les doublons
+        existing = set()
+        if WORKER_QUEUE_FILE.exists():
+            existing = {
+                line.strip()
+                for line in WORKER_QUEUE_FILE.read_text(encoding="utf-8").splitlines()
+                if line.strip()
+            }
+
+        if session_id in existing:
+            logger.info(f"Session {session_id} déjà dans la queue worker, skip")
+            return
+
+        # Ajouter à la fin du fichier
+        with open(WORKER_QUEUE_FILE, "a", encoding="utf-8") as f:
+            f.write(session_id + "\n")
+
+        logger.info(f"Session {session_id} ajoutée à la queue worker ({WORKER_QUEUE_FILE})")
+    except Exception as e:
+        logger.error(f"Erreur écriture queue worker : {e}")
+
+
+def _set_replay_lock(replay_id: str = ""):
+    """Crée le fichier lock de replay (signale au worker VLM de se suspendre)."""
+    try:
+        REPLAY_LOCK_FILE.parent.mkdir(parents=True, exist_ok=True)
+        REPLAY_LOCK_FILE.write_text(
+            f"replay_id={replay_id}\ntimestamp={time.time()}\n",
+            encoding="utf-8",
+        )
+        logger.info(f"Replay lock créé : {REPLAY_LOCK_FILE} (replay={replay_id})")
+    except Exception as e:
+        logger.error(f"Erreur création replay lock : {e}")
+
+
+def _clear_replay_lock():
+    """Supprime le fichier lock de replay (le worker VLM peut reprendre)."""
+    try:
+        REPLAY_LOCK_FILE.unlink(missing_ok=True)
+        logger.info("Replay lock supprimé, worker VLM autorisé à reprendre")
+    except Exception as e:
+        logger.error(f"Erreur suppression replay lock : {e}")
+
+
+def _get_worker_queue_status() -> Dict[str, Any]:
+    """Retourne l'état de la queue du worker VLM (pour le monitoring)."""
+    queue = []
+    if WORKER_QUEUE_FILE.exists():
+        try:
+            queue = [
+                line.strip()
+                for line in WORKER_QUEUE_FILE.read_text(encoding="utf-8").splitlines()
+                if line.strip()
+            ]
+        except Exception:
+            pass
+
+    return {
+        "running": True,  # On ne sait pas si le worker process tourne, mais la queue existe
+        "queue_length": len(queue),
+        "queue": queue,
+        "replay_lock_active": REPLAY_LOCK_FILE.exists(),
+        "queue_file": str(WORKER_QUEUE_FILE),
+        "note": "Le worker VLM tourne dans un process séparé (run_worker.py)",
+    }
+
 
 # =========================================================================
 # Compteur d'analyses en cours par session (pour attendre avant finalize)
@@ -274,6 +397,7 @@ class ReplayResultReport(BaseModel):
     action_id: str
     success: bool
     error: Optional[str] = None
+    warning: Optional[str] = None  # "no_screen_change", "popup_handled", etc.
     screenshot: Optional[str] = None  # Chemin ou base64 du screenshot post-action
     screenshot_after: Optional[str] = None  # Chemin ou base64 du screenshot APRES l'action
     actual_position: Optional[Dict[str, float]] = None  # {"x": px, "y": py} position réelle du clic
@@ -359,11 +483,54 @@ async def health_check():
     return {"status": "healthy", "version": "1.0.0"}
 
 
+def _check_gpu_ready():
+    """Vérifier que le GPU a assez de VRAM pour le pipeline.
+
+    Minimum 6 GB requis pour qwen3-vl:8b et les modèles CLIP/FAISS.
+    Loggue un avertissement si insuffisante, info sinon.
+    """
+    try:
+        import subprocess
+        result = subprocess.run(
+            ["nvidia-smi", "--query-gpu=memory.free", "--format=csv,noheader,nounits"],
+            capture_output=True, text=True, timeout=5
+        )
+        if result.returncode != 0:
+            logger.debug(f"nvidia-smi retour non-zéro : {result.stderr.strip()}")
+            return
+        # nvidia-smi peut retourner plusieurs lignes (multi-GPU) — prendre la première
+        free_mb_str = result.stdout.strip().split("\n")[0].strip()
+        free_mb = int(free_mb_str)
+        if free_mb < 6000:  # 6 GB minimum pour qwen3-vl:8b
+            logger.warning(
+                f"VRAM insuffisante : {free_mb} MB libres (minimum 6000 MB). "
+                f"Vérifier les process GPU avec nvidia-smi."
+            )
+            print(
+                f"\n  [GPU WARNING] VRAM insuffisante : {free_mb} MB libres "
+                f"(minimum 6000 MB)\n"
+            )
+        else:
+            logger.info(f"GPU OK : {free_mb} MB VRAM libres")
+    except FileNotFoundError:
+        logger.debug("nvidia-smi non trouvé — pas de GPU NVIDIA détecté")
+    except Exception as e:
+        logger.debug(f"GPU check échoué : {e}")
+
+
 @app.on_event("startup")
 async def startup():
-    """Démarrer le worker, le session_worker et charger les workflows existants."""
+    """Démarrer le worker de streaming et charger les workflows existants.
+
+    NOTE: Le VLM (SessionWorker) tourne maintenant dans un process séparé
+    (run_worker.py). Ce serveur HTTP ne fait PLUS de VLM — il reste toujours
+    réactif pour les replays, events, images.
+    """
     global _cleanup_running, _cleanup_thread
 
+    # Vérifier la VRAM GPU disponible au démarrage
+    _check_gpu_ready()
+
     # Afficher le token API au démarrage pour que l'utilisateur puisse configurer l'agent
     _token_source = "env RPA_API_TOKEN" if os.environ.get("RPA_API_TOKEN") else "auto-généré"
     logger.info(f"API Token ({_token_source}): {API_TOKEN}")
@@ -378,12 +545,8 @@ async def startup():
     # Charger les workflows existants depuis le disque
     _load_existing_workflows()
 
-    # Démarrer le worker de traitement asynchrone des sessions finalisées
-    session_worker.start()
-
-    # Scanner les sessions finalisées mais jamais traitées (0 states, workflow non construit)
-    # et les ajouter à la queue de traitement automatiquement
-    _enqueue_pending_sessions()
+    # S'assurer que le replay lock est nettoyé au démarrage (crash précédent)
+    _clear_replay_lock()
 
     # Démarrer le thread de nettoyage périodique
     _cleanup_running = True
@@ -391,31 +554,11 @@ async def startup():
     _cleanup_thread.start()
 
     logger.info(
-        "API Streaming démarrée — StreamProcessor, Worker, SessionWorker et Cleanup prêts."
+        "API Streaming démarrée — StreamProcessor, Worker et Cleanup prêts. "
+        "VLM Worker dans un process séparé (run_worker.py)."
     )
 
 
-def _enqueue_pending_sessions():
-    """Scanner et ajouter les sessions finalisées en attente à la queue du SessionWorker.
-
-    Appelé au démarrage du serveur pour rattraper les sessions finalisées
-    dont l'analyse VLM n'a jamais été complétée (crash, redémarrage, etc.).
-    """
-    try:
-        pending = processor.find_pending_sessions()
-        if pending:
-            logger.info(
-                f"Sessions en attente trouvées au démarrage : {len(pending)} "
-                f"({', '.join(pending[:5])}{'...' if len(pending) > 5 else ''})"
-            )
-            for sid in pending:
-                session_worker.enqueue(sid)
-        else:
-            logger.info("Aucune session en attente au démarrage")
-    except Exception as e:
-        logger.error(f"Erreur scan sessions en attente : {e}")
-
-
 def _load_existing_workflows():
     """Charger les workflows JSON existants dans processor._workflows.
 
@@ -464,8 +607,9 @@ def _load_existing_workflows():
 async def shutdown():
     global _cleanup_running
     _cleanup_running = False
-    session_worker.stop()
     worker.stop()
+    # Nettoyer le replay lock au shutdown (sinon le worker VLM resterait bloqué)
+    _clear_replay_lock()
     processor.session_manager.flush()
     logger.info("API Streaming arrêtée.")
 
@@ -552,6 +696,14 @@ _analyzed_shots: Dict[str, set] = defaultdict(set)
 # Hash du dernier screenshot analysé par session (déduplication par similarité)
 _last_screenshot_hash: Dict[str, str] = {}
 
+# Dernier heartbeat reçu par session : {session_id: {"path": str, "timestamp": float}}
+# Utilisé par le pre-check de replay pour vérifier l'état de l'écran avant action
+_last_heartbeat: Dict[str, Dict[str, Any]] = {}
+# Seuil max d'ancienneté du heartbeat (secondes) — au-delà, skip le pre-check
+_HEARTBEAT_MAX_AGE_SECONDS = 10.0
+# Seuil de similarité cosine pour valider le pre-check
+_PRECHECK_SIMILARITY_THRESHOLD = 0.85
+
 # ThreadPool pour l'analyse GPU (évite de bloquer le event loop async)
 _gpu_executor = ThreadPoolExecutor(max_workers=2, thread_name_prefix="gpu_analysis")
 
@@ -612,6 +764,11 @@ async def stream_image(
     # mais pas envoyés au GPU — sinon le ThreadPool (1 worker, ~10-30s/analyse)
     # est submergé et la finalisation timeout avec 0 states.
     if shot_id.startswith("heartbeat_"):
+        # Mémoriser le dernier heartbeat pour le pre-check de replay
+        _last_heartbeat[session_id] = {
+            "path": file_path_str,
+            "timestamp": time.time(),
+        }
         return {"status": "heartbeat_stored", "shot_id": shot_id}
     if shot_id.startswith("focus_"):
         return {"status": "focus_stored", "shot_id": shot_id}
@@ -677,7 +834,7 @@ async def finalize(session_id: str, machine_id: str = "default"):
     """Clôture la session et place le traitement en file d'attente.
 
     Ne bloque plus : marque la session comme finalisée et l'ajoute à la queue
-    du SessionWorker pour analyse VLM + construction workflow en arrière-plan.
+    du worker VLM (process séparé) pour analyse + construction workflow.
 
     Le client peut suivre la progression via GET /api/v1/traces/stream/processing/status.
 
@@ -695,10 +852,10 @@ async def finalize(session_id: str, machine_id: str = "default"):
 
     # Marquer la session comme finalisée (persistée sur disque)
     processor.session_manager.finalize(session_id)
-    logger.info(f"Session {session_id} finalisée, ajout à la queue de traitement")
+    logger.info(f"Session {session_id} finalisée, ajout à la queue du worker VLM")
 
-    # Ajouter à la queue du SessionWorker pour traitement asynchrone
-    session_worker.enqueue(session_id)
+    # Écrire dans le fichier queue pour le worker VLM (process séparé)
+    _enqueue_to_worker(session_id)
 
     # Compter les screenshots full disponibles pour donner une estimation
     session_dir = processor._find_session_dir(session_id)
@@ -727,17 +884,14 @@ async def finalize(session_id: str, machine_id: str = "default"):
 
 @app.get("/api/v1/traces/stream/processing/status")
 async def get_processing_status():
-    """État du worker de traitement asynchrone des sessions finalisées.
+    """État de la queue de traitement VLM (worker process séparé).
 
     Retourne :
-    - queue_length : nombre de sessions en attente
+    - queue_length : nombre de sessions en attente dans le fichier queue
     - queue : liste des session_ids en attente
-    - current_session : session en cours de traitement (ou null)
-    - current_progress : progression de la session en cours
-    - completed : historique des sessions traitées avec succès
-    - failed : historique des sessions échouées
+    - replay_lock_active : si un replay est en cours (worker suspendu)
     """
-    return session_worker.get_status()
+    return _get_worker_queue_status()
 
 
 @app.post("/api/v1/traces/stream/processing/requeue")
@@ -755,12 +909,12 @@ async def requeue_session(session_id: str):
             detail=f"Session {session_id} non trouvée",
         )
 
-    session_worker.enqueue(session_id)
+    _enqueue_to_worker(session_id)
 
     return {
         "status": "requeued",
         "session_id": session_id,
-        "queue_status": session_worker.get_status(),
+        "queue_status": _get_worker_queue_status(),
     }
 
 
@@ -923,10 +1077,54 @@ def _workflow_to_actions(workflow, params: Optional[Dict[str, Any]] = None) -> L
 
     Parcourt le graphe depuis les entry_nodes en suivant les edges.
     Chaque edge produit une action normalisée avec coordonnées en pourcentage.
+
+    Mode intelligent (workflows appris par Léa) :
+        Si le workflow a des nodes avec des prototype_vectors, utilise le
+        StreamProcessor.extract_enriched_actions() qui enrichit les actions
+        avec les données de la session originale, le ciblage visuel et le
+        pre-check/post-check par embedding CLIP.
+
+    Mode classique (workflows VWB/manuels) :
+        Parcours BFS classique avec _edge_to_normalized_actions().
     """
-    actions = []
     params = params or {}
 
+    # Détection d'un workflow appris (a des nodes avec prototype_vectors)
+    # et qui a des edges structurés
+    if _is_learned_workflow(workflow):
+        # Priorité 1 : replay hybride (événements bruts + structure workflow)
+        # Beaucoup plus fiable car utilise les actions utilisateur réelles
+        # au lieu des compound actions du GraphBuilder qui perdent les détails
+        hybrid = processor.build_hybrid_replay(workflow)
+        if hybrid:
+            logger.info(
+                "Replay hybride : %d actions depuis events bruts + structure workflow",
+                len(hybrid),
+            )
+            # Optimisation par gestes clavier si disponible
+            if _gesture_catalog and hybrid:
+                hybrid = _gesture_catalog.optimize_replay_actions(hybrid)
+            return hybrid
+
+        # Priorité 2 : enrichissement classique (fallback si hybride échoue)
+        enriched = processor.extract_enriched_actions(workflow, params)
+        if enriched:
+            logger.info(
+                "Replay intelligent : %d actions enrichies depuis le workflow appris",
+                len(enriched),
+            )
+            # Optimisation par gestes clavier si disponible
+            if _gesture_catalog and enriched:
+                enriched = _gesture_catalog.optimize_replay_actions(enriched)
+            return enriched
+        # Si l'enrichissement échoue aussi, fallback sur le mode classique
+        logger.warning(
+            "Enrichissement échoué pour le workflow appris, fallback mode classique"
+        )
+
+    # Mode classique (VWB/manuels ou fallback)
+    actions = []
+
     # Construire un index des edges sortants par node
     outgoing: Dict[str, list] = defaultdict(list)
     for edge in workflow.edges:
@@ -961,6 +1159,42 @@ def _workflow_to_actions(workflow, params: Optional[Dict[str, Any]] = None) -> L
     return actions
 
 
+def _is_learned_workflow(workflow) -> bool:
+    """Détecter si un workflow est un workflow appris (vs VWB/manuel).
+
+    Un workflow appris a :
+    - Des nodes avec _prototype_vector dans metadata
+    - Des edges avec from_node/to_node
+    - Un learning_state indicatif (OBSERVATION, COACHING, AUTO_CANDIDATE, etc.)
+
+    Un workflow VWB/manuel a généralement :
+    - Des edges avec des target_spec complets (by_text, by_role remplis)
+    - Pas de prototype_vectors
+    """
+    # Accéder aux données (objet ou dict)
+    if hasattr(workflow, 'nodes'):
+        nodes = workflow.nodes
+        edges = workflow.edges
+    elif isinstance(workflow, dict):
+        nodes = workflow.get('nodes', [])
+        edges = workflow.get('edges', [])
+    else:
+        return False
+
+    if not nodes or not edges:
+        return False
+
+    # Vérifier si au moins un node a un prototype_vector
+    has_prototype = False
+    for node in nodes:
+        metadata = node.metadata if hasattr(node, 'metadata') else node.get('metadata', {})
+        if isinstance(metadata, dict) and '_prototype_vector' in metadata:
+            has_prototype = True
+            break
+
+    return has_prototype
+
+
 def _edge_to_normalized_actions(edge, params: Dict[str, Any]) -> List[Dict[str, Any]]:
     """
     Convertir un WorkflowEdge en liste d'actions normalisées pour l'Agent V1.
@@ -1183,10 +1417,13 @@ async def start_replay(request: ReplayRequest):
         if resolved_machine_id and resolved_machine_id != "default":
             _machine_replay_target[resolved_machine_id] = session_id
 
+    # Signaler au worker VLM (process séparé) qu'un replay est actif → se suspendre
+    _set_replay_lock(replay_id)
+
     logger.info(
         f"Replay démarré : {replay_id} | workflow={workflow_id} | "
         f"session={session_id} | machine={resolved_machine_id} | "
-        f"{len(actions)} actions à exécuter"
+        f"{len(actions)} actions à exécuter (worker suspendu)"
     )
 
     return {
@@ -1271,9 +1508,12 @@ async def start_raw_replay(request: RawReplayRequest):
         if resolved_machine_id and resolved_machine_id != "default":
             _machine_replay_target[resolved_machine_id] = session_id
 
+    # Signaler au worker VLM (process séparé) qu'un replay est actif → se suspendre
+    _set_replay_lock(replay_id)
+
     logger.info(
         f"Replay libre démarré : {replay_id} | task='{task}' | "
-        f"session={session_id} | machine={resolved_machine_id} | {len(actions)} actions"
+        f"session={session_id} | machine={resolved_machine_id} | {len(actions)} actions (worker suspendu)"
     )
 
     return {
@@ -1286,6 +1526,195 @@ async def start_raw_replay(request: RawReplayRequest):
     }
 
 
+@app.post("/api/v1/traces/stream/replay-session")
+async def replay_from_session(
+    session_id: str,
+    machine_id: str = "default",
+):
+    """Rejouer une session directement depuis ses événements bruts.
+
+    Pas besoin d'attendre le traitement VLM/GraphBuilder.
+    Construit le replay propre automatiquement depuis live_events.jsonl.
+
+    Pipeline :
+    1. Charge les events bruts de la session
+    2. Filtre les parasites (heartbeat, focus_change, action_result)
+    3. Fusionne les text_input consécutifs
+    4. Normalise les coordonnées en pourcentage
+    5. Ajoute des waits contextuels (après Win+R, Ctrl+S, Alt+F4, Enter)
+    6. Coupe après Alt+F4
+    7. Injecte dans la queue de replay
+
+    Résultat typique : ~15-20 actions propres, prêtes à exécuter immédiatement.
+    """
+    if not session_id:
+        raise HTTPException(status_code=400, detail="session_id requis")
+
+    # ── 1. Trouver le fichier live_events.jsonl de la session ──
+    events_file = None
+
+    # Chercher dans le sous-dossier machine_id (format standard)
+    if machine_id and machine_id != "default":
+        candidate = LIVE_SESSIONS_DIR / machine_id / session_id / "live_events.jsonl"
+        if candidate.exists():
+            events_file = candidate
+
+    # Fallback : chercher dans tous les sous-dossiers machine
+    if not events_file:
+        for machine_dir in LIVE_SESSIONS_DIR.iterdir():
+            if not machine_dir.is_dir():
+                continue
+            candidate = machine_dir / session_id / "live_events.jsonl"
+            if candidate.exists():
+                events_file = candidate
+                # Résoudre le machine_id depuis le dossier
+                if machine_id == "default":
+                    machine_id = machine_dir.name
+                break
+
+    # Dernier fallback : dossier session directement sous LIVE_SESSIONS_DIR
+    if not events_file:
+        candidate = LIVE_SESSIONS_DIR / session_id / "live_events.jsonl"
+        if candidate.exists():
+            events_file = candidate
+
+    if not events_file:
+        raise HTTPException(
+            status_code=404,
+            detail=f"Session '{session_id}' introuvable. "
+                   f"Fichier live_events.jsonl non trouvé dans "
+                   f"{LIVE_SESSIONS_DIR}/{machine_id}/{session_id}/"
+        )
+
+    # ── 2. Charger les événements bruts ──
+    raw_events = []
+    try:
+        for line in events_file.read_text(encoding="utf-8").splitlines():
+            line = line.strip()
+            if not line:
+                continue
+            try:
+                raw_events.append(json.loads(line))
+            except json.JSONDecodeError:
+                continue
+    except Exception as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Erreur lecture events de la session : {e}"
+        )
+
+    if not raw_events:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Session '{session_id}' : aucun événement trouvé dans live_events.jsonl"
+        )
+
+    # ── 3. Construire le replay propre depuis les events bruts ──
+    # Passer le répertoire de session pour activer le visual replay (crops de référence)
+    session_dir = str(events_file.parent)
+    actions = build_replay_from_raw_events(
+        raw_events, session_id=session_id, session_dir=session_dir,
+    )
+
+    if not actions:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Session '{session_id}' : aucune action exploitable après nettoyage "
+                   f"({len(raw_events)} événements bruts)"
+        )
+
+    # Limite de sécurité
+    if len(actions) > MAX_ACTIONS_PER_REPLAY:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Trop d'actions ({len(actions)} > {MAX_ACTIONS_PER_REPLAY}). "
+                   "La session est trop longue pour un replay direct."
+        )
+
+    # Validation de chaque action (sécurité HIGH)
+    for i, action in enumerate(actions):
+        error = _validate_replay_action(action)
+        if error:
+            logger.warning(
+                "replay-session : action #%d invalide (%s), suppression", i, error
+            )
+            # Supprimer les actions invalides plutôt que rejeter tout le replay
+            actions[i] = None
+    actions = [a for a in actions if a is not None]
+
+    if not actions:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Session '{session_id}' : toutes les actions ont été rejetées par la validation"
+        )
+
+    # Optimisation par gestes clavier si disponible
+    if _gesture_catalog and actions:
+        actions = _gesture_catalog.optimize_replay_actions(actions)
+
+    # ── 4. Trouver la session de replay cible (Agent V1 actif) ──
+    # L'agent actif peut avoir une session différente de la session source
+    target_session_id = _find_active_agent_session(machine_id=machine_id)
+    if not target_session_id:
+        # Fallback : utiliser la session source si c'est une session Agent V1
+        if session_id.startswith("sess_"):
+            target_session_id = session_id
+        else:
+            raise HTTPException(
+                status_code=404,
+                detail=f"Aucune session Agent V1 active sur la machine '{machine_id}'. "
+                       "Lancez l'Agent V1 sur le PC cible."
+            )
+
+    # ── 5. Injecter dans la queue de replay ──
+    replay_id = f"replay_sess_{uuid.uuid4().hex[:8]}"
+
+    with _replay_lock:
+        _replay_queues[target_session_id] = list(actions)
+        _replay_states[replay_id] = _create_replay_state(
+            replay_id=replay_id,
+            workflow_id=f"session_replay:{session_id}",
+            session_id=target_session_id,
+            total_actions=len(actions),
+            params={},
+            machine_id=machine_id,
+        )
+        # Enregistrer le mapping machine -> session pour le replay ciblé
+        if machine_id and machine_id != "default":
+            _machine_replay_target[machine_id] = target_session_id
+
+    # Signaler au worker VLM (process séparé) qu'un replay est actif → se suspendre
+    _set_replay_lock(replay_id)
+
+    logger.info(
+        "Replay session démarré : %s | source=%s | target=%s | machine=%s | %d actions (worker suspendu)",
+        replay_id, session_id, target_session_id, machine_id, len(actions),
+    )
+
+    return {
+        "replay_id": replay_id,
+        "status": "running",
+        "source_session_id": session_id,
+        "target_session_id": target_session_id,
+        "machine_id": machine_id,
+        "total_actions": len(actions),
+        "total_raw_events": len(raw_events),
+        "actions_preview": [
+            {
+                k: (
+                    # Ne pas sérialiser l'image base64 dans le preview
+                    {kk: ("..." if kk == "anchor_image_base64" else vv) for kk, vv in v.items()}
+                    if k == "target_spec" and isinstance(v, dict)
+                    else v
+                )
+                for k, v in a.items()
+                if k != "action_id"
+            }
+            for a in actions[:5]
+        ],
+    }
+
+
 @app.post("/api/v1/traces/stream/replay/single")
 async def enqueue_single_action(request: SingleActionRequest):
     """
@@ -1341,6 +1770,202 @@ async def enqueue_single_action(request: SingleActionRequest):
     }
 
 
+# =========================================================================
+# Pre-check écran — Vérification pré-action par embedding CLIP
+# =========================================================================
+
+
+def _pre_check_screen_state(
+    session_id: str,
+    expected_node_id: str,
+    current_screenshot_path: str,
+    active_processor: StreamProcessor,
+) -> Dict[str, Any]:
+    """Vérifier que l'écran actuel correspond à l'état attendu du node.
+
+    Compare le screenshot actuel avec le prototype du node attendu
+    via similarité d'embedding CLIP (rapide, ~200ms).
+
+    Args:
+        session_id: ID de la session de replay
+        expected_node_id: ID du node source de l'action (from_node)
+        current_screenshot_path: Chemin du screenshot heartbeat récent
+        active_processor: Instance StreamProcessor avec le CLIPEmbedder chargé
+
+    Returns:
+        {"match": True/False, "similarity": float, "expected_node": str,
+         "reason": str (si mismatch), "popup_detected": bool}
+    """
+    result: Dict[str, Any] = {
+        "match": True,
+        "similarity": 1.0,
+        "expected_node": expected_node_id,
+        "popup_detected": False,
+    }
+
+    try:
+        # 1. Trouver le workflow actif pour cette session
+        replay_state = None
+        workflow = None
+        with _replay_lock:
+            for state in _replay_states.values():
+                if state["session_id"] == session_id and state["status"] == "running":
+                    replay_state = state
+                    break
+
+        if not replay_state:
+            result["reason"] = "no_active_replay"
+            return result
+
+        workflow_id = replay_state.get("workflow_id", "")
+        with active_processor._data_lock:
+            workflow = active_processor._workflows.get(workflow_id)
+
+        if workflow is None:
+            result["reason"] = "workflow_not_found"
+            return result
+
+        # 2. Récupérer le prototype du node attendu
+        # Supporter à la fois les objets Workflow et les dicts bruts
+        node = None
+        if hasattr(workflow, "get_node"):
+            node = workflow.get_node(expected_node_id)
+        elif isinstance(workflow, dict):
+            # Format dict brut (workflows VWB/manuels)
+            for n in workflow.get("nodes", []):
+                if n.get("node_id") == expected_node_id:
+                    node = n
+                    break
+
+        if node is None:
+            result["reason"] = "node_not_found"
+            return result
+
+        # Extraire le prototype vector
+        metadata = node.metadata if hasattr(node, "metadata") else node.get("metadata", {})
+        proto_list = metadata.get("_prototype_vector")
+        if not proto_list or not isinstance(proto_list, (list, tuple)):
+            result["reason"] = "no_prototype_vector"
+            return result
+
+        import numpy as np
+        prototype_vector = np.array(proto_list, dtype=np.float32)
+
+        # 3. Calculer l'embedding CLIP du screenshot actuel
+        active_processor._ensure_initialized()
+        if active_processor._clip_embedder is None:
+            result["reason"] = "clip_embedder_unavailable"
+            return result
+
+        from PIL import Image
+        pil_image = Image.open(current_screenshot_path)
+        current_vector = active_processor._clip_embedder.embed_image(pil_image)
+
+        if current_vector is None or len(current_vector) == 0:
+            result["reason"] = "embedding_failed"
+            return result
+
+        # 4. Similarité cosine
+        current_vector = current_vector.flatten().astype(np.float32)
+        prototype_vector = prototype_vector.flatten().astype(np.float32)
+
+        norm_current = np.linalg.norm(current_vector)
+        norm_proto = np.linalg.norm(prototype_vector)
+        if norm_current < 1e-8 or norm_proto < 1e-8:
+            result["reason"] = "zero_norm_vector"
+            result["match"] = False
+            result["similarity"] = 0.0
+            return result
+
+        similarity = float(
+            np.dot(current_vector, prototype_vector) / (norm_current * norm_proto)
+        )
+        result["similarity"] = round(similarity, 4)
+        result["match"] = similarity >= _PRECHECK_SIMILARITY_THRESHOLD
+
+        if not result["match"]:
+            result["reason"] = "screen_mismatch"
+            logger.warning(
+                f"Pre-check MISMATCH pour session={session_id} "
+                f"node={expected_node_id}: similarity={similarity:.4f} "
+                f"< seuil={_PRECHECK_SIMILARITY_THRESHOLD}"
+            )
+
+            # 5. Détection de popup par changement de titre de fenêtre
+            result["popup_detected"] = _detect_popup_hint(
+                session_id, workflow, expected_node_id
+            )
+
+    except Exception as e:
+        # Ne jamais bloquer le replay en cas d'erreur du pre-check
+        logger.error(f"Pre-check échoué (non bloquant): {e}")
+        result["match"] = True  # Fallback permissif
+        result["reason"] = f"precheck_error: {e}"
+
+    return result
+
+
+def _detect_popup_hint(
+    session_id: str,
+    workflow: Any,
+    expected_node_id: str,
+) -> bool:
+    """Détecter si une popup ou un dialogue modal est probable.
+
+    Compare le titre de fenêtre actuel (via last_window_info de la session)
+    avec le titre attendu du node dans le workflow. Un changement de titre
+    suggère une popup/dialogue inattendu.
+
+    Args:
+        session_id: ID de la session
+        workflow: Workflow object ou dict
+        expected_node_id: ID du node attendu
+
+    Returns:
+        True si un changement de titre suggère une popup
+    """
+    try:
+        # Titre actuel depuis la session
+        session = processor.session_manager.get_session(session_id)
+        if not session:
+            return False
+        current_title = session.last_window_info.get("title", "").strip().lower()
+        if not current_title or current_title == "unknown":
+            return False
+
+        # Titre attendu depuis le node du workflow
+        expected_title = ""
+        if hasattr(workflow, "get_node"):
+            node = workflow.get_node(expected_node_id)
+            if node and hasattr(node, "template") and hasattr(node.template, "window"):
+                window_spec = node.template.window
+                if hasattr(window_spec, "title_contains") and window_spec.title_contains:
+                    expected_title = window_spec.title_contains.strip().lower()
+        elif isinstance(workflow, dict):
+            for n in workflow.get("nodes", []):
+                if n.get("node_id") == expected_node_id:
+                    template = n.get("template", {})
+                    window = template.get("window", {})
+                    expected_title = (window.get("title_contains") or "").strip().lower()
+                    break
+
+        if not expected_title:
+            return False
+
+        # Si le titre actuel ne contient plus le titre attendu, popup probable
+        if expected_title not in current_title:
+            logger.info(
+                f"Popup détectée: titre actuel='{current_title}' "
+                f"ne contient pas '{expected_title}'"
+            )
+            return True
+
+    except Exception as e:
+        logger.debug(f"Détection popup échouée: {e}")
+
+    return False
+
+
 @app.get("/api/v1/traces/stream/replay/next")
 async def get_next_action(session_id: str, machine_id: str = "default"):
     """
@@ -1349,6 +1974,11 @@ async def get_next_action(session_id: str, machine_id: str = "default"):
     Retourne la prochaine action de la queue ou {"action": null} si rien.
     Modèle pull : l'agent demande, pas de WebSocket nécessaire.
 
+    Inclut un pre-check optionnel : si un heartbeat récent est disponible,
+    compare l'écran actuel avec le node attendu via similarité CLIP.
+    En cas de mismatch, retourne une action "wait" au lieu de l'action réelle,
+    laissant le client le temps de retrouver le bon état.
+
     Multi-machine : si machine_id est fourni, ne retourne que les actions
     destinées à cette machine (évite les fuites cross-machine).
 
@@ -1357,6 +1987,9 @@ async def get_next_action(session_id: str, machine_id: str = "default"):
     """
     with _replay_lock:
         queue = _replay_queues.get(session_id, [])
+        # Log seulement quand il y a des actions à distribuer
+        if queue:
+            logger.info(f"[REPLAY-QUEUE] session={session_id}, actions_en_attente={len(queue)}")
 
         if not queue and machine_id != "default":
             # Lookup 1 : machine_replay_target (mapping explicite POST /replay)
@@ -1393,15 +2026,157 @@ async def get_next_action(session_id: str, machine_id: str = "default"):
         if not queue:
             return {"action": None, "session_id": session_id, "machine_id": machine_id}
 
-        # Retirer la première action de la queue (FIFO)
-        action = queue.pop(0)
+        # Peek à la prochaine action SANS la retirer (pour le pre-check)
+        action = queue[0]
+
+    # ---- Pre-check écran (optionnel, non bloquant) ----
+    # Ne s'applique qu'aux actions qui ont un from_node (actions de workflow,
+    # pas les wait/retry auto-injectés ni les actions Copilot/Agent Libre)
+    from_node = action.get("from_node")
+    precheck_result = None
+    if from_node and action.get("type") not in ("wait",):
+        heartbeat = _last_heartbeat.get(session_id)
+        if heartbeat:
+            age = time.time() - heartbeat["timestamp"]
+            if age <= _HEARTBEAT_MAX_AGE_SECONDS:
+                try:
+                    import asyncio
+                    loop = asyncio.get_event_loop()
+                    # Exécuter le pre-check dans un thread séparé pour ne pas
+                    # bloquer l'event loop async (CLIP embed ~200ms)
+                    precheck_result = await asyncio.wait_for(
+                        loop.run_in_executor(
+                            None,  # ThreadPool par défaut
+                            _pre_check_screen_state,
+                            session_id,
+                            from_node,
+                            heartbeat["path"],
+                            processor,
+                        ),
+                        timeout=0.5,  # Max 500ms pour le pre-check
+                    )
+                except asyncio.TimeoutError:
+                    logger.warning(
+                        f"Pre-check timeout (>500ms) pour session={session_id} "
+                        f"node={from_node}, skip"
+                    )
+                    precheck_result = None
+                except Exception as e:
+                    logger.error(f"Pre-check exception (non bloquant): {e}")
+                    precheck_result = None
+            else:
+                logger.debug(
+                    f"Pre-check skip: heartbeat trop ancien ({age:.1f}s "
+                    f"> {_HEARTBEAT_MAX_AGE_SECONDS}s)"
+                )
+
+    # Si le pre-check détecte un mismatch, ne pas retirer l'action de la queue
+    # et retourner une action "wait" pour que le client attende et ré-essaie
+    if precheck_result and not precheck_result["match"]:
+        # ---- Auth auto : détecter un écran d'authentification (optionnel) ----
+        # Si le mismatch est dû à un écran d'auth, injecter les actions d'auth
+        # en tête de queue pour que l'agent s'authentifie automatiquement.
+        if _auth_handler and not precheck_result.get("popup_detected"):
+            try:
+                # Construire un ScreenState minimal depuis le heartbeat
+                heartbeat = _last_heartbeat.get(session_id, {})
+                _auth_screen_state = {
+                    "perception": {"detected_text": heartbeat.get("detected_text", [])},
+                    "ui_elements": heartbeat.get("ui_elements", []),
+                    "window": heartbeat.get("window_info", {}),
+                    "ocr_text": heartbeat.get("ocr_text", ""),
+                }
+                auth_request = _auth_handler.detect_auth_screen(_auth_screen_state)
+                if auth_request and auth_request.confidence >= 0.5:
+                    auth_actions = _auth_handler.get_auth_actions(auth_request)
+                    if auth_actions:
+                        # Injecter les actions d'auth en tête de queue (avant l'action bloquée)
+                        with _replay_lock:
+                            current_q = _replay_queues.get(session_id, [])
+                            _replay_queues[session_id] = auth_actions + current_q
+                        logger.info(
+                            f"Auth auto : {len(auth_actions)} actions injectées pour "
+                            f"session={session_id} app={auth_request.app_name} "
+                            f"type={auth_request.auth_type} (confiance={auth_request.confidence:.2f})"
+                        )
+                        # Retourner la première action d'auth immédiatement
+                        with _replay_lock:
+                            first_auth = _replay_queues[session_id].pop(0)
+                        return {
+                            "action": first_auth,
+                            "session_id": session_id,
+                            "machine_id": machine_id,
+                            "precheck": precheck_result,
+                            "auth_detected": True,
+                        }
+            except Exception as e:
+                logger.warning(f"Auth auto : détection échouée (non bloquant) : {e}")
+
+        if precheck_result.get("popup_detected"):
+            wait_action = {
+                "action_id": f"precheck_wait_{uuid.uuid4().hex[:6]}",
+                "type": "wait",
+                "reason": "popup_detected",
+                "suggestion": "press_escape_or_click_close",
+                "expected_node": from_node,
+                "similarity": precheck_result["similarity"],
+                "duration_ms": 2000,
+            }
+            logger.warning(
+                f"Pre-check: popup détectée pour session={session_id} "
+                f"node={from_node}, envoi wait+suggestion"
+            )
+        else:
+            wait_action = {
+                "action_id": f"precheck_wait_{uuid.uuid4().hex[:6]}",
+                "type": "wait",
+                "reason": "screen_mismatch",
+                "expected_node": from_node,
+                "similarity": precheck_result["similarity"],
+                "threshold": _PRECHECK_SIMILARITY_THRESHOLD,
+                "duration_ms": 1500,
+            }
+            logger.warning(
+                f"Pre-check: mismatch écran pour session={session_id} "
+                f"node={from_node} (sim={precheck_result['similarity']:.4f}), envoi wait"
+            )
+        return {
+            "action": wait_action,
+            "session_id": session_id,
+            "machine_id": machine_id,
+            "precheck": precheck_result,
+        }
+
+    # Pre-check OK (ou skip) : retirer l'action de la queue et l'envoyer
+    with _replay_lock:
+        current_queue = _replay_queues.get(session_id, [])
+        if current_queue and current_queue[0].get("action_id") == action.get("action_id"):
+            current_queue.pop(0)
+        # Else: queue a changé entre temps (race condition bénigne), on envoie quand même
+
+        # Sauvegarder l'action envoyée pour le retry (si la vérification échoue)
+        action_id_sent = action.get("action_id", "")
+        if action_id_sent:
+            _retry_pending[action_id_sent] = {
+                "action": dict(action),
+                "retry_count": 0,
+                "replay_id": "",
+            }
 
     logger.info(
         f"Action envoyée à {session_id} (machine={machine_id}) : "
         f"{action.get('type')} (id={action.get('action_id')})"
+        f"{' [precheck OK sim=' + str(precheck_result['similarity']) + ']' if precheck_result else ''}"
     )
 
-    return {"action": action, "session_id": session_id, "machine_id": machine_id}
+    response: Dict[str, Any] = {
+        "action": action,
+        "session_id": session_id,
+        "machine_id": machine_id,
+    }
+    if precheck_result:
+        response["precheck"] = precheck_result
+    return response
 
 
 @app.post("/api/v1/traces/stream/replay/result")
@@ -1447,8 +2222,27 @@ async def report_action_result(report: ReplayResultReport):
             replay_state["last_screenshot"] = screenshot_after
 
     # === Vérification post-action ===
+    # Ne vérifier que les actions "click" — les "type" et "key_combo" sont
+    # toujours considérées réussies si l'agent dit success (pas de position à vérifier,
+    # et le screenshot change peu pour une frappe clavier)
+    #
+    # Si l'agent a envoyé un warning "no_screen_change" ou "popup_handled",
+    # il a déjà tenté de gérer la situation (popup handler). Ne PAS relancer
+    # de retry côté serveur — continuer vers l'action suivante.
+    agent_warning = report.warning or ""
+    agent_handled_popup = agent_warning in ("no_screen_change", "popup_handled")
+    if agent_handled_popup:
+        logger.info(
+            f"Action {action_id} : agent warning='{agent_warning}' — "
+            f"popup déjà gérée côté agent, pas de retry serveur"
+        )
+
+    action_type_for_verify = (original_action or {}).get("type", "unknown")
+    skip_verify = action_type_for_verify in ("type", "key_combo", "wait")
+    # Skip aussi la vérification serveur si l'agent a déjà géré la popup
+    skip_verify = skip_verify or agent_handled_popup
     verification = None
-    if report.success and screenshot_after:
+    if report.success and screenshot_after and not skip_verify:
         # Chercher le screenshot avant (dernier connu de la session)
         screenshot_before = replay_state.get("_last_screenshot_before")
         if screenshot_before:
@@ -1478,6 +2272,7 @@ async def report_action_result(report: ReplayResultReport):
             "action_id": action_id,
             "success": report.success,
             "error": report.error,
+            "warning": report.warning,
             "has_screenshot": bool(screenshot_after),
             "actual_position": report.actual_position,
             "retry_count": retry_count,
@@ -1509,8 +2304,39 @@ async def report_action_result(report: ReplayResultReport):
                 replay_state["completed_actions"] += 1
                 replay_state["current_action_index"] += 1
 
+        elif not report.success and agent_warning == "no_screen_change":
+            # L'action a été exécutée mais l'écran n'a pas changé.
+            # PAS de retry — loguer l'échec et continuer vers l'action suivante.
+            # C'est plus honnête que "success" et évite les retries en boucle.
+            replay_state["unverified_actions"] += 1
+            replay_state["completed_actions"] += 1
+            replay_state["current_action_index"] += 1
+            logger.warning(
+                f"Action {action_id} : écran inchangé (no_screen_change) — "
+                f"action sans effet visible, on continue"
+            )
+
+        elif not report.success and "visual resolve" in (report.error or "").lower():
+            # Visual resolve échoué — STOP immédiat, pas de retry.
+            # L'élément n'est pas à l'écran, retrier ne changera rien.
+            replay_state["failed_actions"] += 1
+            replay_state["status"] = "error"
+            error_entry = {
+                "action_id": action_id,
+                "error": report.error or "Visual resolve échoué",
+                "retry_count": 0,
+                "timestamp": time.time(),
+            }
+            replay_state["error_log"].append(error_entry)
+            with _replay_lock:
+                _replay_queues[session_id] = []
+            logger.error(
+                f"Replay STOPPÉ : visual resolve échoué pour {action_id} — "
+                f"{report.error}"
+            )
+
         elif not report.success and retry_count < MAX_RETRIES_PER_ACTION:
-            # Échec mais on a encore des retries
+            # Échec réel (pas juste screen inchangé ou visual) — retry
             action_to_retry = original_action or {"action_id": action_id, "type": "unknown"}
             _schedule_retry(
                 session_id, replay_state, action_to_retry,
@@ -1550,6 +2376,14 @@ async def report_action_result(report: ReplayResultReport):
                 f"{replay_state['unverified_actions']} non vérifiées)"
             )
 
+        # Libérer le GPU pour le worker VLM si le replay est terminé ou en erreur
+        if replay_state["status"] in ("completed", "error"):
+            _clear_replay_lock()
+            logger.info(
+                f"Replay {replay_state['replay_id']} terminé (status={replay_state['status']}) "
+                f"— worker VLM autorisé à reprendre"
+            )
+
     return {
         "status": "recorded",
         "action_id": action_id,
@@ -1769,6 +2603,7 @@ class ResolveTargetRequest(BaseModel):
     fallback_y_pct: float = 0.0
     screen_width: int = 1920
     screen_height: int = 1080
+    strict_mode: bool = False  # True pour replay sessions (seuil template 0.90 + YOLO)
 
 
 @app.post("/api/v1/traces/stream/replay/resolve_target")
@@ -1781,10 +2616,10 @@ async def resolve_target(request: ResolveTargetRequest):
     de l'élément trouvé.
 
     Stratégie de matching (par priorité) :
-    1. by_text — chercher un élément dont le label contient le texte
-    2. by_role — chercher un élément avec le bon rôle sémantique
-    3. by_text + by_role — intersection des deux
-    4. fallback — utiliser les coordonnées statiques
+    1. Template matching OpenCV (~100ms) — si anchor_image_base64 fourni
+    2. VLM Quick Find (~5-10s) — 1 appel VLM pour localiser l'élément
+    3. Matching sémantique complet (~15-20s) — ScreenAnalyzer + OCR + UI detection
+    4. Fallback — coordonnées statiques
     """
     import base64
     import io
@@ -1806,8 +2641,8 @@ async def resolve_target(request: ResolveTargetRequest):
         tmp_path = tmp.name
 
     try:
-        # Lancer la résolution visuelle dans un thread SÉPARÉ (pas le GPU executor
-        # qui peut être saturé par le SessionWorker). Le template matching est CPU-only.
+        # Lancer la résolution visuelle dans un thread SÉPARÉ (pas le GPU executor).
+        # Le template matching est CPU-only.
         import asyncio
         loop = asyncio.get_event_loop()
         result = await loop.run_in_executor(
@@ -1819,6 +2654,7 @@ async def resolve_target(request: ResolveTargetRequest):
             request.screen_height,
             request.fallback_x_pct,
             request.fallback_y_pct,
+            request.strict_mode,
         )
         return result
     except Exception as e:
@@ -1894,13 +2730,15 @@ def _resolve_by_template_matching(
             return None
 
         # Template matching multi-échelle : essayer l'échelle 1.0 d'abord,
-        # puis quelques variations si la résolution a changé
+        # puis quelques variations si la résolution a changé.
+        # Plage étendue 0.5x-2.0x pour couvrir les écarts importants
+        # (ex: apprentissage 2560x1600 → replay 1280x720 = ratio ~0.5x)
         best_val = -1.0
         best_loc = None
         best_scale = 1.0
         best_anchor_size = (aw, ah)
 
-        for scale in [1.0, 0.9, 1.1, 0.8, 1.2, 0.75, 1.25]:
+        for scale in [1.0, 0.9, 1.1, 0.8, 1.2, 0.75, 1.25, 0.6, 1.5, 0.5, 1.75, 2.0]:
             if scale != 1.0:
                 new_w = int(aw * scale)
                 new_h = int(ah * scale)
@@ -1973,6 +2811,508 @@ def _resolve_by_template_matching(
         return None
 
 
+def _validate_match_context(
+    result: Dict[str, Any],
+    original_x_pct: float,
+    original_y_pct: float,
+    target_spec: Dict[str, Any],
+    max_distance: float = 0.35,
+) -> bool:
+    """Vérifier que la position trouvée est dans la même zone que l'originale.
+
+    Évite les faux positifs du template matching : un bouton similaire visuellement
+    mais situé dans une zone très différente de l'écran.
+
+    Args:
+        result: Résultat du template matching (contient x_pct, y_pct).
+        original_x_pct: Position X originale (pourcentage, 0.0-1.0).
+        original_y_pct: Position Y originale (pourcentage, 0.0-1.0).
+        target_spec: Spécification de la cible (non utilisé pour l'instant,
+            mais disponible pour des règles contextuelles futures).
+        max_distance: Distance euclidienne maximum acceptée (en pourcentage de l'écran).
+            Défaut 0.35 = ~35% de la diagonale, assez permissif pour les UI dynamiques.
+
+    Returns:
+        True si la position est valide (même zone), False sinon.
+    """
+    found_x = result.get("x_pct", 0.0)
+    found_y = result.get("y_pct", 0.0)
+
+    # Distance euclidienne en pourcentage de l'écran
+    dx = found_x - original_x_pct
+    dy = found_y - original_y_pct
+    distance = (dx ** 2 + dy ** 2) ** 0.5
+
+    if distance > max_distance:
+        logger.debug(
+            "Context validation : distance=%.3f > max=%.3f "
+            "(found=(%.3f, %.3f), original=(%.3f, %.3f))",
+            distance, max_distance, found_x, found_y, original_x_pct, original_y_pct,
+        )
+        return False
+
+    return True
+
+
+# =========================================================================
+# YOLO/OmniParser — Résolution par détection d'éléments UI
+# =========================================================================
+
+# Chargement paresseux d'OmniParser (singleton, GPU)
+_omniparser_available: Optional[bool] = None  # None = pas encore vérifié
+_omniparser_instance = None
+_omniparser_lock = threading.Lock()
+
+
+def _get_omniparser():
+    """Obtenir l'instance OmniParser (lazy loading, thread-safe).
+
+    Returns:
+        OmniParserAdapter ou None si non disponible.
+    """
+    global _omniparser_available, _omniparser_instance
+    if _omniparser_available is False:
+        return None
+    if _omniparser_instance is not None:
+        return _omniparser_instance
+
+    with _omniparser_lock:
+        if _omniparser_available is False:
+            return None
+        if _omniparser_instance is not None:
+            return _omniparser_instance
+        try:
+            from core.detection.omniparser_adapter import OmniParserAdapter
+            adapter = OmniParserAdapter()
+            if adapter.available:
+                _omniparser_instance = adapter
+                _omniparser_available = True
+                logger.info("OmniParser disponible pour la résolution YOLO")
+                return adapter
+            else:
+                _omniparser_available = False
+                logger.info("OmniParser : modèles non trouvés, YOLO désactivé")
+                return None
+        except ImportError:
+            _omniparser_available = False
+            logger.info("OmniParser non installé, YOLO désactivé")
+            return None
+        except Exception as e:
+            _omniparser_available = False
+            logger.warning("OmniParser init échouée : %s", e)
+            return None
+
+
+def _resolve_by_yolo(
+    screenshot_path: str,
+    anchor_image_b64: str,
+    screen_width: int,
+    screen_height: int,
+    target_spec: Dict[str, Any],
+) -> Optional[Dict[str, Any]]:
+    """Résolution via YOLO/OmniParser : détecte tous les éléments UI
+    puis matche le crop de référence contre les éléments détectés.
+
+    Stratégie :
+    1. OmniParser détecte tous les éléments UI du screenshot (~0.6-0.8s)
+    2. Pour chaque élément détecté, template matching local contre l'anchor
+    3. Si 1 seul bon match (score >= 0.50) → accepter
+    4. Si 2+ matchs ambigus → retourner None (le VLM tranchera)
+
+    Args:
+        screenshot_path: Chemin vers le screenshot JPEG
+        anchor_image_b64: Image de l'anchor encodée en base64
+        screen_width: Largeur de l'écran
+        screen_height: Hauteur de l'écran
+        target_spec: Spécification de la cible
+
+    Returns:
+        Dict avec resolved=True/False, x_pct, y_pct, score
+        ou None si OmniParser pas disponible ou aucun match
+    """
+    import base64
+
+    try:
+        import cv2
+        import numpy as np
+    except ImportError:
+        return None
+
+    omniparser = _get_omniparser()
+    if omniparser is None:
+        return None
+
+    t0 = time.time()
+
+    try:
+        from PIL import Image as PILImage
+
+        # Charger le screenshot en PIL
+        screenshot_pil = PILImage.open(screenshot_path)
+        sw, sh = screenshot_pil.size
+
+        # Charger le screenshot en numpy/OpenCV pour le template matching
+        screenshot_np = np.array(screenshot_pil)
+        if len(screenshot_np.shape) == 3 and screenshot_np.shape[2] == 3:
+            # PIL est RGB, convertir en BGR pour OpenCV
+            screenshot_bgr = cv2.cvtColor(screenshot_np, cv2.COLOR_RGB2BGR)
+        else:
+            screenshot_bgr = screenshot_np
+        screenshot_gray = cv2.cvtColor(screenshot_bgr, cv2.COLOR_BGR2GRAY)
+
+        # Décoder l'anchor depuis base64
+        anchor_bytes = base64.b64decode(anchor_image_b64)
+        anchor_array = np.frombuffer(anchor_bytes, dtype=np.uint8)
+        anchor_img = cv2.imdecode(anchor_array, cv2.IMREAD_COLOR)
+        if anchor_img is None:
+            logger.warning("YOLO resolve : impossible de décoder l'anchor")
+            return None
+        anchor_gray = cv2.cvtColor(anchor_img, cv2.COLOR_BGR2GRAY)
+        anchor_h, anchor_w = anchor_gray.shape[:2]
+
+        # Détecter tous les éléments UI avec OmniParser
+        elements = omniparser.detect(screenshot_pil)
+        if not elements:
+            elapsed = time.time() - t0
+            logger.info("YOLO resolve : 0 éléments détectés (%.1fs)", elapsed)
+            return None
+
+        logger.info(
+            "YOLO resolve : %d éléments détectés, matching anchor %dx%d...",
+            len(elements), anchor_w, anchor_h,
+        )
+
+        # Matcher l'anchor contre chaque élément détecté
+        YOLO_MATCH_THRESHOLD = 0.50
+        matches = []
+
+        for elem in elements:
+            x1, y1, x2, y2 = elem.bbox
+            elem_w = x2 - x1
+            elem_h = y2 - y1
+
+            # Ignorer les éléments trop petits
+            if elem_w < 5 or elem_h < 5:
+                continue
+
+            # Extraire le crop de l'élément depuis le screenshot
+            elem_crop = screenshot_gray[y1:y2, x1:x2]
+            if elem_crop.size == 0:
+                continue
+
+            # Template matching local : resize anchor pour matcher la taille de l'élément
+            # ou inversement, selon les dimensions relatives
+            try:
+                # Approche : resize l'anchor à la taille du crop et comparer
+                if elem_w > 0 and elem_h > 0:
+                    anchor_resized = cv2.resize(anchor_gray, (elem_w, elem_h))
+                    result = cv2.matchTemplate(
+                        elem_crop, anchor_resized, cv2.TM_CCOEFF_NORMED
+                    )
+                    _, max_val, _, _ = cv2.minMaxLoc(result)
+                else:
+                    continue
+
+                # Aussi essayer le crop à la taille de l'anchor si c'est plus grand
+                if elem_w >= anchor_w and elem_h >= anchor_h:
+                    result2 = cv2.matchTemplate(
+                        elem_crop, anchor_gray, cv2.TM_CCOEFF_NORMED
+                    )
+                    _, max_val2, _, _ = cv2.minMaxLoc(result2)
+                    max_val = max(max_val, max_val2)
+
+                if max_val >= YOLO_MATCH_THRESHOLD:
+                    matches.append((elem, max_val))
+
+            except cv2.error:
+                continue
+
+        elapsed = time.time() - t0
+
+        if not matches:
+            logger.info(
+                "YOLO resolve : aucun match >= %.2f parmi %d éléments (%.1fs)",
+                YOLO_MATCH_THRESHOLD, len(elements), elapsed,
+            )
+            return None
+
+        # Trier par score décroissant
+        matches.sort(key=lambda m: m[1], reverse=True)
+        best_elem, best_score = matches[0]
+
+        # Si 2+ matchs avec des scores proches (< 0.10 d'écart), c'est ambigu
+        # → laisser le VLM trancher
+        if len(matches) >= 2:
+            second_score = matches[1][1]
+            if best_score - second_score < 0.10:
+                logger.info(
+                    "YOLO resolve : %d matchs ambigus (best=%.3f, second=%.3f, "
+                    "écart=%.3f < 0.10), VLM requis (%.1fs)",
+                    len(matches), best_score, second_score,
+                    best_score - second_score, elapsed,
+                )
+                return None
+
+        # 1 seul match clair → accepter
+        cx, cy = best_elem.center
+        x_pct = round(cx / sw, 6) if sw > 0 else 0.0
+        y_pct = round(cy / sh, 6) if sh > 0 else 0.0
+
+        logger.info(
+            "YOLO resolve OK : '%s' (%s) score=%.3f → (%.4f, %.4f) "
+            "parmi %d éléments, %d matchs (%.1fs)",
+            best_elem.label, best_elem.element_type, best_score,
+            x_pct, y_pct, len(elements), len(matches), elapsed,
+        )
+
+        return {
+            "resolved": True,
+            "method": "yolo_omniparser",
+            "x_pct": x_pct,
+            "y_pct": y_pct,
+            "matched_element": {
+                "label": best_elem.label,
+                "type": best_elem.element_type,
+                "role": "yolo_detected",
+                "center": [cx, cy],
+                "confidence": best_score,
+            },
+            "score": best_score,
+            "yolo_elements_count": len(elements),
+            "yolo_matches_count": len(matches),
+        }
+
+    except Exception as e:
+        elapsed = time.time() - t0
+        logger.warning("YOLO resolve : exception (%.1fs) — %s", elapsed, e)
+        return None
+
+
+# =========================================================================
+# VLM Quick Find — Fallback léger quand le template matching échoue
+# =========================================================================
+
+# Client Ollama singleton (initialisé au premier appel, pas au démarrage)
+_vlm_client = None
+_vlm_client_lock = threading.Lock()
+
+# Timeout dédié pour le VLM Quick Find (plus court que le timeout par défaut)
+_VLM_QUICK_FIND_TIMEOUT = 30  # secondes
+
+
+def _get_vlm_client():
+    """Obtenir ou créer le client Ollama singleton pour le VLM Quick Find.
+
+    Initialisation paresseuse : le client n'est créé qu'au premier appel,
+    pas au démarrage du serveur (évite de bloquer si Ollama est down).
+    """
+    global _vlm_client
+    if _vlm_client is not None:
+        return _vlm_client
+    with _vlm_client_lock:
+        if _vlm_client is not None:
+            return _vlm_client
+        try:
+            from core.detection.ollama_client import OllamaClient
+            _vlm_client = OllamaClient(
+                endpoint="http://localhost:11434",
+                model="qwen3-vl:8b",
+                timeout=_VLM_QUICK_FIND_TIMEOUT,
+            )
+            logger.info("VLM Quick Find : client Ollama initialisé (qwen3-vl:8b)")
+        except Exception as e:
+            logger.warning(f"VLM Quick Find : impossible d'initialiser le client Ollama : {e}")
+            return None
+    return _vlm_client
+
+
+def _build_target_description(target_spec: Dict[str, Any]) -> str:
+    """Construire une description textuelle de l'élément à trouver.
+
+    Utilisé par le VLM Quick Find pour savoir quoi chercher sur le screenshot.
+
+    Args:
+        target_spec: Spécification de la cible (by_text, by_role, etc.)
+
+    Returns:
+        Description en langage naturel, ex: "un bouton contenant 'Valider'"
+    """
+    by_text = target_spec.get("by_text", "").strip()
+    by_role = target_spec.get("by_role", "").strip()
+
+    if by_text and by_role:
+        return f"un {by_role} contenant '{by_text}'"
+    elif by_text:
+        return f"élément contenant le texte '{by_text}'"
+    elif by_role:
+        return f"un {by_role}"
+    else:
+        return "l'élément interactif principal"
+
+
+def _vlm_quick_find(
+    screenshot_path: str,
+    target_description: str,
+    anchor_image_b64: Optional[str] = None,
+) -> Optional[Dict[str, Any]]:
+    """Demander au VLM de localiser un élément sur le screenshot.
+
+    Stratégie VLM-first pour le replay : le VLM comprend le contexte
+    de l'écran et peut trouver un élément même si l'apparence a changé.
+
+    Modes de fonctionnement :
+    - Avec anchor_image_b64 + description : multi-image (screenshot + crop de référence).
+      Le VLM voit le screenshot ET le crop, ce qui est beaucoup plus précis.
+    - Avec description seule : single-image, le VLM cherche par la description textuelle.
+    - Avec anchor_image_b64 seule (pas de description) : multi-image avec prompt visuel pur.
+
+    Args:
+        screenshot_path: Chemin du screenshot actuel
+        target_description: Description riche de l'élément à trouver.
+            Ex: "Dans la fenêtre 'Exécuter', l'élément cliqué en bas au centre"
+        anchor_image_b64: Image de référence (crop) en base64 (optionnel).
+            Si fourni, envoyé comme seconde image au VLM pour comparaison visuelle.
+
+    Returns:
+        {"x_pct": float, "y_pct": float, "confidence": float, "method": "vlm_quick_find"}
+        ou None si l'élément n'est pas trouvé ou en cas d'erreur
+    """
+    client = _get_vlm_client()
+    if client is None:
+        logger.debug("VLM Quick Find : client Ollama non disponible, skip")
+        return None
+
+    t0 = time.time()
+
+    # Construire le prompt adapté selon les informations disponibles
+    has_anchor = bool(anchor_image_b64)
+    has_description = bool(target_description and target_description.strip())
+
+    if has_anchor and has_description:
+        # Mode optimal : screenshot + crop de référence + description textuelle
+        prompt = (
+            "The first image is the current screen. "
+            "The second image shows the element I want to click.\n\n"
+            f"Context: {target_description}\n\n"
+            "Find this exact element on the screen and return its CENTER coordinates "
+            "as percentage of the screen dimensions.\n"
+            'Return ONLY a JSON object: {"x_pct": 0.XX, "y_pct": 0.XX, "confidence": 0.XX}\n'
+            'If the element is not visible, return: {"x_pct": null, "y_pct": null, "confidence": 0.0}'
+        )
+    elif has_anchor:
+        # Mode visuel pur : screenshot + crop, pas de description
+        prompt = (
+            "The first image is the current screen. "
+            "The second image shows the element I want to click.\n\n"
+            "Find this exact element on the screen and return its CENTER coordinates "
+            "as percentage of the screen dimensions.\n"
+            'Return ONLY a JSON object: {"x_pct": 0.XX, "y_pct": 0.XX, "confidence": 0.XX}\n'
+            'If the element is not visible, return: {"x_pct": null, "y_pct": null, "confidence": 0.0}'
+        )
+    else:
+        # Mode description seule
+        prompt = (
+            "Look at this screenshot carefully.\n\n"
+            f"{target_description}\n\n"
+            "Find this element and return its CENTER coordinates "
+            "as percentage of the image dimensions.\n"
+            'Return ONLY a JSON object: {"x_pct": 0.XX, "y_pct": 0.XX, "confidence": 0.XX}\n'
+            'If the element is not visible, return: {"x_pct": null, "y_pct": null, "confidence": 0.0}'
+        )
+
+    system_prompt = "You are a UI element locator. Output raw JSON only. No explanation."
+
+    try:
+        # Préparer les images supplémentaires (anchor crop)
+        extra_images = [anchor_image_b64] if has_anchor else None
+
+        result = client.generate(
+            prompt=prompt,
+            image_path=screenshot_path,
+            system_prompt=system_prompt,
+            temperature=0.1,
+            max_tokens=200,
+            force_json=False,
+            extra_images_b64=extra_images,
+        )
+
+        elapsed = time.time() - t0
+
+        if not result.get("success"):
+            logger.info(
+                "VLM Quick Find : échec appel VLM (%.1fs) — %s",
+                elapsed, result.get("error", "?"),
+            )
+            return None
+
+        response_text = result.get("response", "").strip()
+        if not response_text:
+            logger.info("VLM Quick Find : réponse vide du VLM (%.1fs)", elapsed)
+            return None
+
+        # Parser la réponse JSON (réutiliser le parser robuste d'OllamaClient)
+        parsed = client._extract_json_from_response(response_text)
+        if parsed is None:
+            logger.info(
+                "VLM Quick Find : réponse non-JSON (%.1fs) — %.80s",
+                elapsed, response_text,
+            )
+            return None
+
+        # Valider les coordonnées
+        x_pct = parsed.get("x_pct")
+        y_pct = parsed.get("y_pct")
+        confidence = float(parsed.get("confidence", 0.0))
+
+        if x_pct is None or y_pct is None or confidence < 0.3:
+            logger.info(
+                "VLM Quick Find : élément non trouvé ou confiance trop basse "
+                "(%.1fs, confidence=%.2f) pour '%s'",
+                elapsed, confidence,
+                target_description[:80] if target_description else "(anchor only)",
+            )
+            return None
+
+        x_pct = float(x_pct)
+        y_pct = float(y_pct)
+
+        # Vérifier que les coordonnées sont dans les bornes [0, 1]
+        if not (0.0 <= x_pct <= 1.0 and 0.0 <= y_pct <= 1.0):
+            logger.info(
+                "VLM Quick Find : coordonnées hors bornes (%.4f, %.4f), ignoré",
+                x_pct, y_pct,
+            )
+            return None
+
+        mode_str = "multi-image" if has_anchor else "description"
+        desc_short = (target_description[:60] + "...") if target_description and len(target_description) > 60 else (target_description or "(anchor)")
+        logger.info(
+            "VLM Quick Find OK [%s] : '%s' → (%.4f, %.4f) confidence=%.2f en %.1fs",
+            mode_str, desc_short, x_pct, y_pct, confidence, elapsed,
+        )
+
+        return {
+            "resolved": True,
+            "method": "vlm_quick_find",
+            "x_pct": round(x_pct, 6),
+            "y_pct": round(y_pct, 6),
+            "matched_element": {
+                "label": target_description or "anchor_visual",
+                "type": "vlm_located",
+                "role": "vlm_quick_find",
+                "confidence": confidence,
+            },
+            "score": confidence,
+        }
+
+    except Exception as e:
+        elapsed = time.time() - t0
+        logger.warning(
+            "VLM Quick Find : exception (%.1fs) — %s", elapsed, e,
+        )
+        return None
+
+
 def _resolve_target_sync(
     screenshot_path: str,
     target_spec: Dict[str, Any],
@@ -1980,18 +3320,122 @@ def _resolve_target_sync(
     screen_height: int,
     fallback_x_pct: float,
     fallback_y_pct: float,
+    strict_mode: bool = False,
 ) -> Dict[str, Any]:
-    """Résoudre la cible visuellement (exécuté dans le thread GPU).
+    """Résoudre la cible visuellement (exécuté dans un thread séparé).
 
-    Stratégies de matching (par priorité) :
-    1. anchor_image_base64 — template matching OpenCV (pour ancres VWB)
-    2. by_text / by_role — matching sémantique via ScreenAnalyzer
-    3. fallback — coordonnées statiques
+    Hiérarchie de résolution (strict_mode=True, replay sessions) — VLM-FIRST :
+    1. VLM Quick Find (~3-8s) — compréhension sémantique de l'écran, multi-image
+       (screenshot + crop de référence + description riche)
+    2. Template matching OpenCV (~100ms) — fallback pixel, seuil STRICT 0.90
+    3. resolved=False → STOP le replay
+
+    Le VLM comprend le contexte (titre de fenêtre, type d'élément, position)
+    et peut trouver un élément même si l'écran est différent de l'enregistrement.
+    Le template matching ne compare que des pixels et produit des faux positifs.
+
+    Hiérarchie classique (strict_mode=False, VWB et autres) — INCHANGÉE :
+    1. Template matching OpenCV (~100ms) — seuil 0.70
+    1.5. VLM Quick Find si template échoue et by_text/by_role dispo
+    2. by_text/by_role → VLM Quick Find puis ScreenAnalyzer
+    3. fallback coordonnées statiques
     """
-    # ---------------------------------------------------------------
-    # Stratégie 1 : Template matching par image d'ancre
-    # ---------------------------------------------------------------
     anchor_image_b64 = target_spec.get("anchor_image_base64", "")
+
+    # ===================================================================
+    # MODE STRICT (replay sessions) — Stratégie VLM-FIRST
+    # ===================================================================
+    if strict_mode and anchor_image_b64:
+        # ---------------------------------------------------------------
+        # Étape 1 : VLM Quick Find (compréhension sémantique)
+        # Le VLM reçoit le screenshot + le crop de référence + la description
+        # riche (titre fenêtre, position relative, texte visible, type).
+        # ---------------------------------------------------------------
+        vlm_description = target_spec.get("vlm_description", "")
+        # Fallback : construire la description depuis by_text/by_role
+        if not vlm_description:
+            by_text = target_spec.get("by_text", "").strip()
+            by_role = target_spec.get("by_role", "").strip()
+            if by_text or by_role:
+                vlm_description = _build_target_description(target_spec)
+
+        # Toujours tenter le VLM si on a un anchor (multi-image) ou une description
+        if vlm_description or anchor_image_b64:
+            vlm_result = _vlm_quick_find(
+                screenshot_path=screenshot_path,
+                target_description=vlm_description,
+                anchor_image_b64=anchor_image_b64,
+            )
+            if vlm_result and vlm_result.get("resolved"):
+                if vlm_result.get("score", 0) >= 0.3:
+                    logger.info(
+                        "Strict resolve VLM-first : VLM OK (score=%.2f) pour '%s'",
+                        vlm_result.get("score", 0),
+                        vlm_description[:60] if vlm_description else "(anchor)",
+                    )
+                    return vlm_result
+                else:
+                    logger.info(
+                        "Strict resolve VLM-first : VLM score=%.2f trop bas, passage template",
+                        vlm_result.get("score", 0),
+                    )
+            else:
+                logger.info(
+                    "Strict resolve VLM-first : VLM échoué pour '%s', passage template matching",
+                    vlm_description[:60] if vlm_description else "(anchor)",
+                )
+
+        # ---------------------------------------------------------------
+        # Étape 2 : Template matching (fallback pixel) — seuil STRICT 0.90
+        # ---------------------------------------------------------------
+        result = _resolve_by_template_matching(
+            screenshot_path=screenshot_path,
+            anchor_image_b64=anchor_image_b64,
+            screen_width=screen_width,
+            screen_height=screen_height,
+            confidence_threshold=0.90,
+        )
+        if result:
+            score = result.get("score", 0)
+            # Score >= 0.95 : match quasi-parfait, pas besoin de valider le contexte
+            if score >= 0.95:
+                logger.info(
+                    "Strict resolve VLM-first : template matching fallback OK "
+                    "(score=%.3f >= 0.95, contexte skip — match quasi-parfait)",
+                    score,
+                )
+                return result
+            elif _validate_match_context(result, fallback_x_pct, fallback_y_pct, target_spec):
+                logger.info(
+                    "Strict resolve VLM-first : template matching fallback OK "
+                    "(score=%.3f >= 0.90, context OK)",
+                    score,
+                )
+                return result
+            else:
+                logger.warning(
+                    "Strict resolve VLM-first : template score=%.3f MAIS contexte invalide, rejeté",
+                    score,
+                )
+
+        # ---------------------------------------------------------------
+        # Étape 3 : RIEN ne fonctionne → resolved=False → STOP replay
+        # ---------------------------------------------------------------
+        return {
+            "resolved": False,
+            "method": "strict_vlm_template_failed",
+            "reason": "vlm_and_template_all_failed",
+            "x_pct": fallback_x_pct,
+            "y_pct": fallback_y_pct,
+        }
+
+    # ===================================================================
+    # MODE CLASSIQUE (VWB et autres) — Comportement existant
+    # ===================================================================
+
+    # ---------------------------------------------------------------
+    # Stratégie 1 : Template matching par image d'ancre (seuil 0.70)
+    # ---------------------------------------------------------------
     if anchor_image_b64:
         result = _resolve_by_template_matching(
             screenshot_path=screenshot_path,
@@ -2003,10 +3447,29 @@ def _resolve_target_sync(
         if result:
             return result
         logger.info(
-            "Template matching échoué pour ancre '%s', fallback",
+            "Template matching échoué pour ancre '%s', tentative VLM Quick Find",
             target_spec.get("anchor_id", "?"),
         )
-        # Pas de ScreenAnalyzer fallback — trop lent pour le replay interactif
+
+        # ---------------------------------------------------------------
+        # Stratégie 1.5 : VLM Quick Find (fallback léger après template matching)
+        # ---------------------------------------------------------------
+        by_text = target_spec.get("by_text", "").strip()
+        by_role = target_spec.get("by_role", "").strip()
+        if by_text or by_role:
+            vlm_desc = _build_target_description(target_spec)
+            vlm_result = _vlm_quick_find(
+                screenshot_path=screenshot_path,
+                target_description=vlm_desc,
+                anchor_image_b64=anchor_image_b64,
+            )
+            if vlm_result:
+                return vlm_result
+            logger.info(
+                "VLM Quick Find échoué pour ancre '%s', fallback coordonnées",
+                target_spec.get("anchor_id", "?"),
+            )
+
         return {
             "resolved": False,
             "method": "fallback",
@@ -2016,7 +3479,7 @@ def _resolve_target_sync(
         }
 
     # ---------------------------------------------------------------
-    # Stratégie 2 : Matching sémantique via ScreenAnalyzer
+    # Stratégie 2 : VLM Quick Find (léger, ~5-10s)
     # ---------------------------------------------------------------
     by_text = target_spec.get("by_text", "")
     by_role = target_spec.get("by_role", "")
@@ -2031,6 +3494,23 @@ def _resolve_target_sync(
             "y_pct": fallback_y_pct,
         }
 
+    # Tenter le VLM Quick Find AVANT ScreenAnalyzer (beaucoup plus rapide)
+    if by_text or by_role:
+        vlm_desc = _build_target_description(target_spec)
+        vlm_result = _vlm_quick_find(
+            screenshot_path=screenshot_path,
+            target_description=vlm_desc,
+        )
+        if vlm_result:
+            return vlm_result
+        logger.info(
+            "VLM Quick Find échoué pour '%s', fallback ScreenAnalyzer",
+            vlm_desc,
+        )
+
+    # ---------------------------------------------------------------
+    # Stratégie 3 : Matching sémantique via ScreenAnalyzer (~15-20s)
+    # ---------------------------------------------------------------
     processor._ensure_initialized()
 
     if processor._screen_analyzer is None:
@@ -2158,6 +3638,114 @@ def _fallback_response(request: ResolveTargetRequest, reason: str, detail: str)
     }
 
 
+# =========================================================================
+# Learning Pack — Export / Import pour la fédération des apprentissages
+# =========================================================================
+
+class LearningPackImportRequest(BaseModel):
+    """Corps de la requête d'import d'un Learning Pack."""
+    # Le pack complet au format JSON (structure LearningPack.to_dict())
+    pack: Dict[str, Any]
+
+
+@app.get("/api/v1/traces/stream/learning-pack/export")
+async def export_learning_pack(client_id: str, request: Request):
+    """
+    Exporter les apprentissages d'un client en Learning Pack anonymisé.
+
+    Le client_id est haché (SHA-256) dans le pack exporté —
+    aucune donnée d'identification ne sort du serveur.
+
+    Query params:
+        client_id: identifiant du client (obligatoire).
+
+    Returns:
+        JSON du LearningPack anonymisé.
+    """
+    try:
+        from core.federation.learning_pack import LearningPackExporter
+        from core.models.workflow_graph import Workflow
+    except ImportError as exc:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Module federation non disponible : {exc}",
+        )
+
+    if not client_id or not client_id.strip():
+        raise HTTPException(status_code=400, detail="client_id requis")
+
+    # Récupérer tous les workflows chargés par le StreamProcessor
+    workflows = list(processor._workflows.values())
+    if not workflows:
+        raise HTTPException(
+            status_code=404,
+            detail="Aucun workflow trouvé pour l'export",
+        )
+
+    exporter = LearningPackExporter()
+    pack = exporter.export(workflows, client_id=client_id.strip())
+
+    logger.info(
+        "Learning pack exporté pour client_id=%s (hash=%s) : %d workflows, %d prototypes",
+        client_id[:8] + "...", pack.source_hash[:16] + "...",
+        len(workflows), len(pack.screen_prototypes),
+    )
+    return pack.to_dict()
+
+
+@app.post("/api/v1/traces/stream/learning-pack/import")
+async def import_learning_pack(body: LearningPackImportRequest, request: Request):
+    """
+    Importer un Learning Pack dans l'index FAISS global.
+
+    Body JSON:
+        { "pack": { ... } }   — structure LearningPack complète
+
+    Returns:
+        Statistiques de l'import (vecteurs ajoutés, total index, etc.).
+    """
+    try:
+        from core.federation.learning_pack import LearningPack
+        from core.federation.faiss_global import GlobalFAISSIndex
+    except ImportError as exc:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Module federation non disponible : {exc}",
+        )
+
+    try:
+        pack = LearningPack.from_dict(body.pack)
+    except Exception as exc:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Format de Learning Pack invalide : {exc}",
+        )
+
+    # Utiliser ou créer l'index global (singleton au niveau du module)
+    global _global_faiss_index
+    if _global_faiss_index is None:
+        _global_faiss_index = GlobalFAISSIndex()
+
+    added = _global_faiss_index.add_pack(pack)
+    stats = _global_faiss_index.get_stats()
+
+    logger.info(
+        "Learning pack importé : pack_id=%s, +%d vecteurs (total=%d)",
+        pack.pack_id, added, stats["total_vectors"],
+    )
+    return {
+        "status": "ok",
+        "pack_id": pack.pack_id,
+        "source_hash": pack.source_hash,
+        "vectors_added": added,
+        "index_stats": stats,
+    }
+
+
+# Index FAISS global (singleton, initialisé au premier import)
+_global_faiss_index = None
+
+
 if __name__ == "__main__":
     import uvicorn
 
diff --git a/agent_v0/server_v1/live_session_manager.py b/agent_v0/server_v1/live_session_manager.py
index d66964cf7..181a6b214 100644
--- a/agent_v0/server_v1/live_session_manager.py
+++ b/agent_v0/server_v1/live_session_manager.py
@@ -158,16 +158,35 @@ class LiveSessionManager:
             session.events.append(event_data)
             session.last_activity = datetime.now()
             # Extraire le contexte fenêtre si présent
+            # Format 1 : {"window": {"title": ..., "app_name": ...}} (Python agent)
+            # Format 2 : {"window_title": "...", "screen_resolution": [w, h]} (Rust agent)
             window = event_data.get("window")
             if window and isinstance(window, dict):
                 session.last_window_info = window
-                # Accumuler les titres/apps pour le nommage automatique
-                title = window.get("title", "").strip()
-                app_name = window.get("app_name", "").strip()
-                if title and title != "Unknown":
-                    session.window_titles_seen[title] = session.window_titles_seen.get(title, 0) + 1
-                if app_name and app_name != "unknown":
-                    session.app_names_seen[app_name] = session.app_names_seen.get(app_name, 0) + 1
+            elif event_data.get("window_title"):
+                # Format Rust agent : extraire le titre et la résolution
+                info = {
+                    "title": event_data["window_title"],
+                    "app_name": session.last_window_info.get("app_name", "unknown"),
+                }
+                # Propager la résolution si fournie par l'agent
+                screen_res = event_data.get("screen_resolution")
+                if screen_res and isinstance(screen_res, list) and len(screen_res) == 2:
+                    info["screen_resolution"] = screen_res
+                # Propager les métadonnées d'environnement graphique
+                for meta_key in ("dpi_scale", "monitor_index", "window_bounds",
+                                 "monitors", "os_theme", "os_language"):
+                    meta_val = event_data.get(meta_key)
+                    if meta_val is not None:
+                        info[meta_key] = meta_val
+                session.last_window_info = info
+            # Accumuler les titres/apps pour le nommage automatique
+            title = session.last_window_info.get("title", "").strip()
+            app_name = session.last_window_info.get("app_name", "").strip()
+            if title and title != "Unknown":
+                session.window_titles_seen[title] = session.window_titles_seen.get(title, 0) + 1
+            if app_name and app_name != "unknown":
+                session.app_names_seen[app_name] = session.app_names_seen.get(app_name, 0) + 1
             self._maybe_persist(session_id)
 
     def add_screenshot(self, session_id: str, shot_id: str, file_path: str) -> None:
@@ -227,16 +246,41 @@ class LiveSessionManager:
                 "captured_at": datetime.now().isoformat(),
             })
 
+        # Résolution réelle depuis les events (envoyée par l'agent Rust/Python),
+        # fallback sur 1920x1080 si non disponible
+        screen_res = session.last_window_info.get("screen_resolution", [1920, 1080])
+
+        # Métadonnées d'environnement graphique dynamiques
+        screen_info: Dict[str, Any] = {"primary_resolution": screen_res}
+        dpi_scale = session.last_window_info.get("dpi_scale")
+        if dpi_scale is not None:
+            screen_info["dpi_scale"] = dpi_scale
+        monitors = session.last_window_info.get("monitors")
+        if monitors is not None:
+            screen_info["monitors"] = monitors
+        monitor_index = session.last_window_info.get("monitor_index")
+        if monitor_index is not None:
+            screen_info["monitor_index"] = monitor_index
+
+        env_info: Dict[str, Any] = {
+            "os": platform.system().lower(),
+            "hostname": socket.gethostname(),
+            "machine_id": session.machine_id,
+            "screen": screen_info,
+        }
+        # Propager os_theme / os_language si disponibles
+        os_theme = session.last_window_info.get("os_theme")
+        if os_theme is not None:
+            env_info["os_theme"] = os_theme
+        os_language = session.last_window_info.get("os_language")
+        if os_language is not None:
+            env_info["os_language"] = os_language
+
         return {
             "schema_version": "rawsession_v1",
             "session_id": session.session_id,
             "agent_version": "agent_v1_stream",
-            "environment": {
-                "os": platform.system().lower(),
-                "hostname": socket.gethostname(),
-                "machine_id": session.machine_id,
-                "screen": {"primary_resolution": [1920, 1080]},
-            },
+            "environment": env_info,
             "user": {"id": "remote_agent"},
             "context": {
                 "workflow": session.last_window_info.get("title", ""),
diff --git a/agent_v0/server_v1/run_worker.py b/agent_v0/server_v1/run_worker.py
new file mode 100644
index 000000000..ac373dff3
--- /dev/null
+++ b/agent_v0/server_v1/run_worker.py
@@ -0,0 +1,397 @@
+# agent_v0/server_v1/run_worker.py
+"""
+Worker VLM autonome — tourne dans un process Python SEPARE du serveur HTTP.
+
+Résout le problème du GIL : le serveur HTTP (FastAPI) reste réactif car le
+VLM (ScreenAnalyzer, CLIP, FAISS, GraphBuilder) tourne dans ce process dédié.
+
+Usage:
+    python -m agent_v0.server_v1.run_worker
+
+Architecture :
+    Process 1 : Serveur HTTP (FastAPI, port 5005) — distribue les replays, reçoit events/images
+    Process 2 : Ce worker — analyse VLM des sessions finalisées
+    Process 3 : Ollama (port 11434) — LLM local
+
+Communication inter-process par fichiers (pas de Redis) :
+    - _worker_queue.txt  : liste des session_ids à traiter (ajoutés par le serveur HTTP)
+    - _replay_active.lock : quand présent, le worker se suspend (le GPU est utilisé par le replay)
+
+Le worker :
+1. Scanne _worker_queue.txt pour trouver les sessions à traiter
+2. Vérifie _replay_active.lock avant chaque screenshot (priorité au replay)
+3. Traite les sessions une par une (VLM + CLIP + GraphBuilder)
+4. Sauvegarde les workflows JSON sur disque
+5. Se suspend quand un replay est actif (libère le GPU)
+"""
+
+import logging
+import os
+import signal
+import sys
+import time
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, List, Optional
+
+logger = logging.getLogger("vlm_worker")
+
+# Chemins de base (relatifs au working directory = racine du projet)
+ROOT_DIR = Path(__file__).parent.parent.parent
+DATA_DIR = ROOT_DIR / "data" / "training"
+LIVE_SESSIONS_DIR = DATA_DIR / "live_sessions"
+QUEUE_FILE = DATA_DIR / "_worker_queue.txt"
+REPLAY_LOCK = DATA_DIR / "_replay_active.lock"
+
+# Intervalle de polling quand la queue est vide (secondes)
+POLL_INTERVAL = 10
+
+# Intervalle de vérification du replay lock (secondes)
+REPLAY_CHECK_INTERVAL = 2
+
+# Timeout max d'attente du replay lock avant reprise forcée (secondes)
+REPLAY_WAIT_TIMEOUT = 120
+
+
+class VLMWorker:
+    """Worker VLM autonome qui traite les sessions finalisées.
+
+    Tourne en boucle infinie dans un process séparé du serveur HTTP.
+    Communique via le filesystem :
+    - Lit les session_ids depuis _worker_queue.txt
+    - Vérifie _replay_active.lock pour se suspendre
+    - Écrit les workflows dans data/training/workflows/
+    """
+
+    def __init__(self):
+        self._running = False
+        self._processor = None  # Initialisé au premier besoin (lazy loading GPU)
+        self._current_session: Optional[str] = None
+
+        # Stats
+        self._stats: Dict[str, int] = {
+            "sessions_processed": 0,
+            "sessions_failed": 0,
+            "sessions_skipped": 0,
+            "total_screenshots_analyzed": 0,
+        }
+        self._completed: List[Dict] = []
+        self._failed: List[Dict] = []
+
+    def _get_processor(self):
+        """Lazy init du StreamProcessor (charge les modèles GPU au premier appel)."""
+        if self._processor is None:
+            logger.info("Initialisation du StreamProcessor (chargement GPU)...")
+            from .stream_processor import StreamProcessor
+            self._processor = StreamProcessor(data_dir=str(LIVE_SESSIONS_DIR))
+            logger.info("StreamProcessor initialisé.")
+        return self._processor
+
+    def start(self):
+        """Boucle principale du worker."""
+        self._running = True
+        logger.info(
+            "VLM Worker démarré — surveillance de %s",
+            QUEUE_FILE,
+        )
+        logger.info("  Replay lock : %s", REPLAY_LOCK)
+        logger.info("  Sessions dir : %s", LIVE_SESSIONS_DIR)
+        logger.info("  Poll interval : %ds", POLL_INTERVAL)
+
+        while self._running:
+            try:
+                # Vérifier si un replay est actif
+                if self._is_replay_active():
+                    self._wait_for_replay_end()
+                    continue
+
+                # Lire la prochaine session de la queue
+                session_id = self._read_next_session()
+                if session_id:
+                    self._process_session(session_id)
+                else:
+                    time.sleep(POLL_INTERVAL)
+
+            except KeyboardInterrupt:
+                logger.info("Interruption clavier, arrêt du worker.")
+                self._running = False
+            except Exception as e:
+                logger.error("Erreur dans la boucle principale : %s", e, exc_info=True)
+                time.sleep(5)  # Éviter une boucle d'erreurs rapide
+
+        logger.info("VLM Worker arrêté.")
+
+    def stop(self):
+        """Arrêt propre du worker."""
+        self._running = False
+        logger.info("Arrêt demandé.")
+
+    # =========================================================================
+    # Queue management (fichier _worker_queue.txt)
+    # =========================================================================
+
+    def _read_next_session(self) -> Optional[str]:
+        """Lit et retire le premier session_id de la queue.
+
+        Format du fichier : une ligne par session_id.
+        Retire la ligne traitée de façon atomique (réécriture complète).
+        """
+        if not QUEUE_FILE.exists():
+            return None
+
+        try:
+            lines = QUEUE_FILE.read_text(encoding="utf-8").strip().splitlines()
+            if not lines:
+                return None
+
+            # Prendre le premier session_id non vide
+            session_id = None
+            remaining = []
+            for line in lines:
+                line = line.strip()
+                if not line:
+                    continue
+                if session_id is None:
+                    session_id = line
+                else:
+                    remaining.append(line)
+
+            # Réécrire le fichier sans la première ligne (atomique via rename)
+            tmp_file = QUEUE_FILE.with_suffix(".tmp")
+            if remaining:
+                tmp_file.write_text(
+                    "\n".join(remaining) + "\n",
+                    encoding="utf-8",
+                )
+            else:
+                tmp_file.write_text("", encoding="utf-8")
+            tmp_file.rename(QUEUE_FILE)
+
+            if session_id:
+                logger.info(
+                    "Session déqueuée : %s (%d restantes dans la queue)",
+                    session_id,
+                    len(remaining),
+                )
+            return session_id
+
+        except Exception as e:
+            logger.error("Erreur lecture queue %s : %s", QUEUE_FILE, e)
+            return None
+
+    # =========================================================================
+    # Replay lock (_replay_active.lock)
+    # =========================================================================
+
+    def _is_replay_active(self) -> bool:
+        """Vérifie si un replay est en cours (fichier lock présent)."""
+        return REPLAY_LOCK.exists()
+
+    def _wait_for_replay_end(self):
+        """Attend que le replay se termine (suppression du fichier lock).
+
+        Timeout de sécurité : REPLAY_WAIT_TIMEOUT secondes max.
+        """
+        start = time.time()
+        logger.info(
+            "Replay actif détecté (%s), worker en pause...",
+            REPLAY_LOCK,
+        )
+
+        while self._running and REPLAY_LOCK.exists():
+            elapsed = time.time() - start
+            if elapsed > REPLAY_WAIT_TIMEOUT:
+                logger.warning(
+                    "Timeout d'attente du replay (%ds), reprise forcée.",
+                    REPLAY_WAIT_TIMEOUT,
+                )
+                break
+            time.sleep(REPLAY_CHECK_INTERVAL)
+
+        elapsed = time.time() - start
+        if elapsed > 0.5:
+            logger.info("Replay terminé, worker reprend après %.1fs de pause.", elapsed)
+
+    # =========================================================================
+    # Traitement d'une session
+    # =========================================================================
+
+    def _process_session(self, session_id: str):
+        """Traite une session complète (analyse VLM + construction workflow)."""
+        self._current_session = session_id
+        logger.info("=== Début traitement session %s ===", session_id)
+        start_time = time.time()
+
+        try:
+            proc = self._get_processor()
+
+            # Vérifier que le dossier session existe
+            session_dir = proc._find_session_dir(session_id)
+            if not session_dir:
+                logger.error(
+                    "Dossier session %s introuvable, skip.",
+                    session_id,
+                )
+                self._stats["sessions_skipped"] += 1
+                return
+
+            shots_dir = session_dir / "shots"
+            full_shots = sorted(shots_dir.glob("shot_*_full.png")) if shots_dir.exists() else []
+            if not full_shots:
+                logger.warning(
+                    "Aucun screenshot full dans %s, skip.",
+                    shots_dir,
+                )
+                self._stats["sessions_skipped"] += 1
+                return
+
+            logger.info(
+                "Session %s : %d screenshots full à analyser dans %s",
+                session_id,
+                len(full_shots),
+                shots_dir,
+            )
+
+            # Utiliser reprocess_session du StreamProcessor
+            # qui fait : ScreenAnalyzer + CLIP + FAISS + GraphBuilder
+            result = proc.reprocess_session(
+                session_id,
+                progress_callback=self._progress_callback,
+            )
+
+            elapsed = time.time() - start_time
+
+            if result.get("error"):
+                logger.error(
+                    "Échec session %s après %.1fs : %s",
+                    session_id,
+                    elapsed,
+                    result["error"],
+                )
+                self._stats["sessions_failed"] += 1
+                self._failed.append({
+                    "session_id": session_id,
+                    "error": result["error"],
+                    "elapsed_seconds": round(elapsed, 1),
+                    "timestamp": datetime.now().isoformat(),
+                })
+            elif result.get("status") == "insufficient_data":
+                logger.warning(
+                    "Session %s : données insuffisantes (%d states) après %.1fs",
+                    session_id,
+                    result.get("states_count", 0),
+                    elapsed,
+                )
+                self._stats["sessions_failed"] += 1
+                self._failed.append({
+                    "session_id": session_id,
+                    "error": "insufficient_data",
+                    "states_count": result.get("states_count", 0),
+                    "elapsed_seconds": round(elapsed, 1),
+                    "timestamp": datetime.now().isoformat(),
+                })
+            else:
+                logger.info(
+                    "Session %s traitée en %.1fs | workflow=%s | %d nodes, %d edges",
+                    session_id,
+                    elapsed,
+                    result.get("workflow_id", "?"),
+                    result.get("nodes", 0),
+                    result.get("edges", 0),
+                )
+                self._stats["sessions_processed"] += 1
+                self._stats["total_screenshots_analyzed"] += result.get("states_analyzed", 0)
+                self._completed.append({
+                    "session_id": session_id,
+                    "workflow_id": result.get("workflow_id"),
+                    "workflow_name": result.get("workflow_name"),
+                    "nodes": result.get("nodes", 0),
+                    "edges": result.get("edges", 0),
+                    "states_analyzed": result.get("states_analyzed", 0),
+                    "elapsed_seconds": round(elapsed, 1),
+                    "timestamp": datetime.now().isoformat(),
+                })
+
+        except Exception as e:
+            elapsed = time.time() - start_time
+            logger.error(
+                "Exception inattendue pour session %s après %.1fs : %s",
+                session_id,
+                elapsed,
+                e,
+                exc_info=True,
+            )
+            self._stats["sessions_failed"] += 1
+            self._failed.append({
+                "session_id": session_id,
+                "error": f"exception: {e}",
+                "elapsed_seconds": round(elapsed, 1),
+                "timestamp": datetime.now().isoformat(),
+            })
+
+        finally:
+            self._current_session = None
+
+        logger.info("=== Fin traitement session %s ===", session_id)
+
+    def _progress_callback(self, session_id: str, current: int, total: int, shot_id: str = ""):
+        """Callback de progression appelé par reprocess_session.
+
+        Vérifie aussi le replay lock entre chaque screenshot.
+        """
+        logger.info(
+            "Session %s : screenshot %d/%d%s",
+            session_id,
+            current,
+            total,
+            f" ({shot_id})" if shot_id else "",
+        )
+
+        # Vérifier si un replay est devenu actif pendant le traitement
+        if self._is_replay_active():
+            logger.info(
+                "Replay détecté pendant l'analyse de %s, pause...",
+                session_id,
+            )
+            self._wait_for_replay_end()
+
+
+def main():
+    """Point d'entrée du worker VLM autonome."""
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s [VLM-WORKER] %(levelname)s %(message)s",
+    )
+
+    # Réduire le bruit des loggers tiers
+    logging.getLogger("httpx").setLevel(logging.WARNING)
+    logging.getLogger("httpcore").setLevel(logging.WARNING)
+    logging.getLogger("urllib3").setLevel(logging.WARNING)
+
+    # Créer les dossiers nécessaires
+    DATA_DIR.mkdir(parents=True, exist_ok=True)
+    LIVE_SESSIONS_DIR.mkdir(parents=True, exist_ok=True)
+
+    worker = VLMWorker()
+
+    # Gestion propre des signaux
+    def _handle_signal(signum, frame):
+        logger.info("Signal %s reçu, arrêt en cours...", signal.Signals(signum).name)
+        worker.stop()
+
+    signal.signal(signal.SIGTERM, _handle_signal)
+    signal.signal(signal.SIGINT, _handle_signal)
+
+    # Afficher l'état au démarrage
+    print(f"\n{'='*60}")
+    print(f"  VLM Worker — Process séparé du serveur HTTP")
+    print(f"  Queue : {QUEUE_FILE}")
+    print(f"  Lock  : {REPLAY_LOCK}")
+    print(f"  PID   : {os.getpid()}")
+    print(f"{'='*60}\n")
+
+    worker.start()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/agent_v0/server_v1/stream_processor.py b/agent_v0/server_v1/stream_processor.py
index 5c3ca23d5..9e264bb49 100644
--- a/agent_v0/server_v1/stream_processor.py
+++ b/agent_v0/server_v1/stream_processor.py
@@ -7,10 +7,12 @@ pour traiter en temps réel les screenshots et événements reçus via fibre.
 Tous les calculs GPU tournent ici (serveur RTX 5070).
 """
 
+import base64
 import hashlib
 import logging
 import os
 import threading
+from concurrent.futures import ThreadPoolExecutor, as_completed
 from datetime import datetime
 from pathlib import Path
 from typing import Any, Dict, List, Optional
@@ -21,6 +23,797 @@ from .live_session_manager import LiveSessionManager
 
 logger = logging.getLogger(__name__)
 
+# ---------------------------------------------------------------------------
+# Filtrage des événements parasites (modificateurs seuls, text_input vide, etc.)
+# Utilisé à 3 niveaux : réception (process_event), expansion (compound), et
+# en amont dans GraphBuilder._find_transition_events / _build_compound_action.
+# ---------------------------------------------------------------------------
+
+_MODIFIER_ONLY_KEYS = {
+    "ctrl", "ctrl_l", "ctrl_r", "control", "control_l", "control_r",
+    "alt", "alt_l", "alt_r",
+    "shift", "shift_l", "shift_r",
+    "win", "win_l", "win_r", "cmd", "cmd_l", "cmd_r",
+    "meta", "meta_l", "meta_r", "super", "super_l", "super_r",
+}
+
+# Table de conversion des caractères de contrôle vers les touches lisibles
+# (produits par certains agents qui capturent les raw keycodes)
+_CONTROL_CHAR_MAP = {
+    '\x01': 'a', '\x02': 'b', '\x03': 'c', '\x04': 'd', '\x05': 'e',
+    '\x06': 'f', '\x07': 'g', '\x08': 'h', '\x09': 'i', '\x0a': 'j',
+    '\x0b': 'k', '\x0c': 'l', '\x0d': 'm', '\x0e': 'n', '\x0f': 'o',
+    '\x10': 'p', '\x11': 'q', '\x12': 'r', '\x13': 's', '\x14': 't',
+    '\x15': 'u', '\x16': 'v', '\x17': 'w', '\x18': 'x', '\x19': 'y',
+    '\x1a': 'z',
+}
+
+# Types d'événements parasites à ignorer dans les actions enrichies
+_PARASITIC_ACTION_TYPES = frozenset({
+    'heartbeat', 'focus_change', 'window_focus_change',
+    'screenshot', 'status', 'ping', 'pong',
+})
+
+
+def _is_modifier_only(keys: list) -> bool:
+    """Retourne True si la liste de touches ne contient que des modificateurs."""
+    if not keys:
+        return True
+    return all(k.lower() in _MODIFIER_ONLY_KEYS for k in keys)
+
+
+def _sanitize_keys(keys: list) -> list:
+    """Nettoyer une liste de touches : convertir les caractères de contrôle."""
+    cleaned = []
+    for k in keys:
+        if not k:
+            continue
+        if k in _CONTROL_CHAR_MAP:
+            cleaned.append(_CONTROL_CHAR_MAP[k])
+        else:
+            cleaned.append(k)
+    return cleaned
+
+
+def _is_parasitic_event(event_data: Dict[str, Any]) -> bool:
+    """Retourne True si l'événement est parasite et doit être filtré.
+
+    Événements rejetés :
+    - key_press / key_combo avec uniquement des modificateurs seuls
+    - key_press / key_combo avec liste de touches vide
+    - text_input avec texte vide
+    """
+    event_type = event_data.get("type", "")
+
+    if event_type in ("key_press", "key_combo"):
+        keys = event_data.get("keys", event_data.get("data", {}).get("keys", []))
+        if not keys or _is_modifier_only(keys):
+            return True
+
+    elif event_type == "text_input":
+        text = event_data.get("text", event_data.get("data", {}).get("text", ""))
+        if not text:
+            return True
+
+    return False
+
+
+def _merge_consecutive_text_inputs(steps: list) -> list:
+    """Fusionne les text_input consécutifs en un seul."""
+    merged = []
+    for step in steps:
+        if (step.get("type") in ("text_input", "type")
+                and merged
+                and merged[-1].get("type") in ("text_input", "type")):
+            merged[-1]["text"] = merged[-1].get("text", "") + step.get("text", "")
+        else:
+            merged.append(dict(step))  # copie pour ne pas muter l'original
+    return merged
+
+
+def _dedup_consecutive_combos(steps: list) -> list:
+    """Supprime les key_combo dupliqués consécutifs."""
+    deduped = []
+    for step in steps:
+        if (step.get("type") in ("key_combo", "key_press")
+                and deduped
+                and deduped[-1].get("type") in ("key_combo", "key_press")
+                and deduped[-1].get("keys") == step.get("keys")):
+            continue  # Doublon → skip
+        deduped.append(step)
+    return deduped
+
+
+def _filter_parasitic_steps(steps: list) -> list:
+    """Supprime les steps key_combo/key_press avec uniquement des modificateurs seuls."""
+    return [
+        s for s in steps
+        if not (
+            s.get("type") in ("key_combo", "key_press")
+            and _is_modifier_only(s.get("keys", []))
+        )
+    ]
+
+
+def _ensure_min_waits(steps: list, min_wait_ms: int = 300) -> list:
+    """Ajoute un wait de min_wait_ms entre les steps si aucun wait n'existe."""
+    if not steps:
+        return steps
+    result = [steps[0]]
+    for step in steps[1:]:
+        if result[-1].get("type") != "wait" and step.get("type") != "wait":
+            result.append({"type": "wait", "duration_ms": min_wait_ms})
+        result.append(step)
+    return result
+
+
+def clean_compound_steps(steps: list) -> list:
+    """Pipeline complet de nettoyage des steps d'une compound action.
+
+    Applique dans l'ordre :
+    1. Suppression des steps modificateurs seuls
+    2. Fusion des text_input consécutifs
+    3. Déduplication des key_combo consécutifs identiques
+    4. Ajout de waits minimum entre steps si absents
+    """
+    cleaned = _filter_parasitic_steps(steps)
+    cleaned = _merge_consecutive_text_inputs(cleaned)
+    cleaned = _dedup_consecutive_combos(cleaned)
+    cleaned = _ensure_min_waits(cleaned)
+    return cleaned
+
+
+def clean_enriched_actions(actions: list) -> list:
+    """Nettoyer une liste d'actions enrichies pour éliminer le bruit de replay.
+
+    Appliqué après construction de toutes les actions enrichies (post-BFS),
+    travaille sur les actions au format replay (type, keys, text, etc.).
+
+    Filtres appliqués dans l'ordre :
+    1. Supprimer les types parasites (heartbeat, focus_change, screenshot, etc.)
+    2. Sanitiser les touches (caractères de contrôle → lettres)
+    3. Supprimer les key_combo avec uniquement des modificateurs seuls
+    4. Supprimer les actions type/text_input avec texte vide
+    5. Dédupliquer les key_combo consécutifs identiques
+    6. Fusionner les text_input (type) consécutifs dans la même fenêtre
+    7. Supprimer les waits consécutifs (garder le plus long)
+    """
+    if not actions:
+        return actions
+
+    # ── Étape 1-4 : filtrer les actions parasites ──
+    filtered = []
+    for a in actions:
+        atype = a.get('type', '')
+
+        # Types parasites issus du streaming brut
+        if atype in _PARASITIC_ACTION_TYPES:
+            continue
+
+        # key_combo : sanitiser les touches, puis filtrer les modificateurs seuls
+        if atype == 'key_combo':
+            keys = _sanitize_keys(a.get('keys', []))
+            if _is_modifier_only(keys):
+                continue
+            if not keys:
+                continue
+            a = dict(a, keys=keys)
+
+        # type/text_input : supprimer si texte vide
+        if atype == 'type' and not a.get('text', '').strip():
+            continue
+
+        filtered.append(a)
+
+    # ── Étape 5 : dédupliquer les key_combo consécutifs identiques ──
+    deduped = []
+    for a in filtered:
+        if (deduped
+                and a.get('type') == 'key_combo'
+                and deduped[-1].get('type') == 'key_combo'
+                and a.get('keys') == deduped[-1].get('keys')):
+            continue
+        deduped.append(a)
+
+    # ── Étape 6 : fusionner les text_input (type) consécutifs ──
+    merged = []
+    for a in deduped:
+        if (merged
+                and a.get('type') == 'type'
+                and merged[-1].get('type') == 'type'
+                # Même fenêtre cible (ou pas de fenêtre)
+                and a.get('window_title', '') == merged[-1].get('window_title', '')):
+            merged[-1] = dict(merged[-1], text=merged[-1].get('text', '') + a.get('text', ''))
+            continue
+        merged.append(a)
+
+    # ── Étape 7 : supprimer les waits consécutifs (garder le plus long) ──
+    cleaned = []
+    for a in merged:
+        if (cleaned
+                and a.get('type') == 'wait'
+                and cleaned[-1].get('type') == 'wait'):
+            if a.get('duration_ms', 0) > cleaned[-1].get('duration_ms', 0):
+                cleaned[-1] = a
+            continue
+        cleaned.append(a)
+
+    return cleaned
+
+
+# ---------------------------------------------------------------------------
+# Replay direct depuis événements bruts (sans VLM/GraphBuilder)
+# ---------------------------------------------------------------------------
+
+# Types d'événements ignorés lors de la construction du replay brut
+_IGNORED_EVENT_TYPES = frozenset({
+    'heartbeat', 'focus_change', 'window_focus_change',
+    'screenshot', 'action_result', 'status', 'ping', 'pong',
+})
+
+# Combos de raccourcis spéciaux qui nécessitent un wait post-action
+_POST_COMBO_WAITS = {
+    # (tuple de touches normalisées, triées en minuscule) -> wait_ms
+    # NB : les tuples sont sorted() alphabétiquement
+    ('r', 'win'): 3000,          # Win+R → Exécuter
+    ('r', 'super'): 3000,
+    ('meta', 'r'): 3000,
+    ('enter',): 2000,            # Enter (confirmation)
+    ('return',): 2000,
+    ('ctrl', 's'): 3000,         # Ctrl+S
+    ('ctrl', 's', 'shift'): 3000,  # Ctrl+Shift+S
+    ('alt', 'f4'): 2000,         # Alt+F4
+}
+
+
+def _extract_screen_resolution(events: list) -> tuple:
+    """Extraire la résolution d'écran depuis les métadonnées des événements.
+
+    Cherche d'abord le champ `screen_resolution` dans `screen_metadata`,
+    puis infère depuis les positions maximales des clics.
+
+    Returns:
+        Tuple (width, height).
+    """
+    # Priorité 1 : screen_metadata.screen_resolution explicite
+    for evt in events:
+        event_data = evt.get("event", evt)
+        sm = event_data.get("screen_metadata", {})
+        sr = sm.get("screen_resolution")
+        if sr and isinstance(sr, (list, tuple)) and len(sr) == 2:
+            w, h = int(sr[0]), int(sr[1])
+            if w > 0 and h > 0:
+                return (w, h)
+
+    # Priorité 2 : inférer depuis les positions max des clics
+    return StreamProcessor._infer_screen_resolution(events)
+
+
+def _should_cut_after_event(
+    event_data: dict,
+    saw_save_combo: bool = False,
+) -> bool:
+    """Retourne True si on doit couper le replay après cet événement.
+
+    Coupe quand :
+    - Alt+F4 (fermeture par raccourci)
+    - Clic dans le systray (fenêtres "unknown_window", "dépassement de capacité",
+      "Fenêtre de dépassement") — c'est le "C'est terminé" de Léa
+    - Après un Ctrl+S/Ctrl+Shift+S suivi d'un clic dans une fenêtre non-applicative
+      (terminal, systray, agent python, etc.)
+    """
+    evt_type = event_data.get("type", "")
+
+    # Alt+F4
+    if evt_type in ("key_combo", "key_press"):
+        keys = event_data.get("keys", [])
+        keys_lower = {k.lower() for k in keys if k}
+        if "f4" in keys_lower and ("alt" in keys_lower or "alt_l" in keys_lower
+                                    or "alt_r" in keys_lower):
+            return True
+
+    # Clic dans le systray (fenêtres de fin de session)
+    if evt_type == "mouse_click":
+        window = event_data.get("window", {})
+        title = (window.get("title", "") if isinstance(window, dict) else "").lower()
+        if any(t in title for t in [
+            "unknown_window", "dépassement de capacité",
+            "fenêtre de dépassement", "overflow",
+        ]):
+            return True
+
+        # Après un Ctrl+S/Ctrl+Shift+S : couper si clic dans une fenêtre
+        # non-applicative (terminal, agent, systray)
+        if saw_save_combo:
+            _CUT_WINDOW_PATTERNS = [
+                "cmd.exe", "system32", "dépassement", "unknown",
+                "powershell", "windowsterminal", "python.exe",
+                "terminal", "systray",
+            ]
+            if any(t in title for t in _CUT_WINDOW_PATTERNS):
+                return True
+            # Couper aussi par app_name (plus robuste que le titre)
+            app_name = (window.get("app_name", "") if isinstance(window, dict) else "").lower()
+            if any(t in app_name for t in [
+                "windowsterminal", "cmd.exe", "powershell",
+                "python.exe", "explorer.exe",
+            ]):
+                return True
+
+    return False
+
+
+def _needs_post_wait(action: dict) -> int:
+    """Retourne le wait en ms à insérer après cette action, ou 0."""
+    if action.get("type") == "key_combo":
+        keys = action.get("keys", [])
+        key_tuple = tuple(sorted(k.lower() for k in keys if k))
+        wait_ms = _POST_COMBO_WAITS.get(key_tuple, 0)
+        if wait_ms:
+            return wait_ms
+    return 0
+
+
+def _load_crop_for_event(
+    event_data: dict,
+    session_dir: Optional[Path],
+    screen_w: int = 0,
+    screen_h: int = 0,
+) -> Optional[str]:
+    """Charger le crop de référence (anchor) associé à un événement mouse_click.
+
+    Stratégie de recherche (par priorité) :
+    1. vision_info.crop → extraire le nom de fichier, chercher dans session_dir/shots/
+    2. screenshot_id → chercher {screenshot_id}_crop.png dans session_dir/shots/
+    3. Timestamp → chercher le focus_XXXX.png le plus proche dans session_dir/shots/
+    4. Fallback → cropper le full screenshot autour de la position du clic (400x400)
+
+    Args:
+        event_data: Événement brut (type mouse_click).
+        session_dir: Répertoire de la session (contient shots/).
+        screen_w: Largeur écran (pour le fallback crop).
+        screen_h: Hauteur écran (pour le fallback crop).
+
+    Returns:
+        Image crop encodée en base64, ou None si rien trouvé.
+    """
+    if not session_dir:
+        return None
+
+    shots_dir = Path(session_dir) / "shots"
+    if not shots_dir.is_dir():
+        return None
+
+    def _read_png_b64(path: Path) -> Optional[str]:
+        """Lire un fichier PNG et retourner son contenu en base64."""
+        try:
+            if path.exists() and path.stat().st_size > 0:
+                return base64.b64encode(path.read_bytes()).decode("utf-8")
+        except Exception as e:
+            logger.debug("Impossible de lire le crop %s : %s", path, e)
+        return None
+
+    # ── Stratégie 1 : vision_info.crop (nom de fichier Windows → chercher localement) ──
+    vision_info = event_data.get("vision_info", {})
+    if isinstance(vision_info, dict):
+        crop_path_str = vision_info.get("crop", "")
+        if crop_path_str:
+            # Extraire le nom de fichier depuis le chemin Windows
+            # Ex: "C:\\rpa_vision\\...\\shots\\shot_0002_crop.png" → "shot_0002_crop.png"
+            crop_filename = crop_path_str.replace("\\", "/").split("/")[-1]
+            result = _read_png_b64(shots_dir / crop_filename)
+            if result:
+                logger.debug("Crop trouvé via vision_info : %s", crop_filename)
+                return result
+
+    # ── Stratégie 2 : screenshot_id → {screenshot_id}_crop.png ──
+    screenshot_id = event_data.get("screenshot_id", "")
+    if screenshot_id:
+        crop_filename = f"{screenshot_id}_crop.png"
+        result = _read_png_b64(shots_dir / crop_filename)
+        if result:
+            logger.debug("Crop trouvé via screenshot_id : %s", crop_filename)
+            return result
+
+    # ── Stratégie 3 : Timestamp → focus_XXXX.png le plus proche ──
+    evt_ts = float(event_data.get("timestamp", 0))
+    if evt_ts > 0:
+        try:
+            focus_files = sorted(shots_dir.glob("focus_*.png"))
+            if focus_files:
+                best_file = None
+                best_delta = float("inf")
+                for f in focus_files:
+                    # Extraire le timestamp du nom : focus_1774437474.png
+                    try:
+                        ts_str = f.stem.split("_", 1)[1]
+                        file_ts = float(ts_str)
+                        delta = abs(file_ts - evt_ts)
+                        if delta < best_delta:
+                            best_delta = delta
+                            best_file = f
+                    except (ValueError, IndexError):
+                        continue
+                # Accepter si le focus est à moins de 5 secondes du clic
+                if best_file and best_delta < 5.0:
+                    result = _read_png_b64(best_file)
+                    if result:
+                        logger.debug(
+                            "Crop trouvé via timestamp (focus, delta=%.1fs) : %s",
+                            best_delta, best_file.name,
+                        )
+                        return result
+        except Exception as e:
+            logger.debug("Erreur recherche focus par timestamp : %s", e)
+
+    # ── Stratégie 4 : Fallback → cropper le full screenshot autour du clic ──
+    if screenshot_id and screen_w > 0 and screen_h > 0:
+        full_path = shots_dir / f"{screenshot_id}_full.png"
+        if full_path.exists():
+            try:
+                from PIL import Image
+                import io
+
+                img = Image.open(full_path)
+                pos = event_data.get("pos", [])
+                if pos and len(pos) == 2:
+                    cx, cy = int(pos[0]), int(pos[1])
+                    # Crop 150x150 centré sur le clic (plus discriminant, moins de bruit)
+                    crop_size = 75
+                    x1 = max(0, cx - crop_size)
+                    y1 = max(0, cy - crop_size)
+                    x2 = min(img.width, cx + crop_size)
+                    y2 = min(img.height, cy + crop_size)
+                    cropped = img.crop((x1, y1, x2, y2))
+                    buf = io.BytesIO()
+                    cropped.save(buf, format="PNG")
+                    result = base64.b64encode(buf.getvalue()).decode("utf-8")
+                    logger.debug(
+                        "Crop fallback (full screenshot cropped) : %s, zone=%dx%d",
+                        full_path.name, x2 - x1, y2 - y1,
+                    )
+                    return result
+            except ImportError:
+                logger.debug("PIL non disponible pour le crop fallback")
+            except Exception as e:
+                logger.debug("Erreur crop fallback depuis full screenshot : %s", e)
+
+    return None
+
+
+def build_replay_from_raw_events(
+    events: list,
+    session_id: str = "",
+    session_dir: Optional[str] = None,
+) -> list:
+    """Construire un replay propre directement depuis les événements bruts d'une session.
+
+    Pas de dépendance au VLM, au GraphBuilder ou aux workflows.
+    Fonctionne immédiatement après la capture.
+
+    Pipeline de traitement :
+    1. Filtrer les événements parasites (heartbeat, focus_change, action_result)
+    2. Extraire la résolution d'écran depuis les métadonnées
+    3. Couper après Alt+F4 ou après des clics systray post-sauvegarde
+    4. Fusionner les text_input consécutifs (même séparés par <500ms)
+    5. Convertir en actions normalisées (coordonnées en %, waits adaptés)
+    6. Pour les clics : activer visual_mode et attacher le crop de référence (anchor)
+    7. Appliquer clean_enriched_actions() (dédup combos, sanitize, merge texte)
+    8. Insérer des waits contextuels après raccourcis critiques
+
+    Args:
+        events: Événements bruts chargés depuis live_events.jsonl.
+            Format : [{"session_id": ..., "event": {...}}, ...]
+        session_id: Identifiant de session (pour le logging).
+        session_dir: Répertoire de la session (contient shots/). Si fourni,
+            les crops de référence sont attachés aux clics pour le visual replay.
+
+    Returns:
+        Liste d'actions prêtes pour la queue de replay.
+    """
+    import uuid
+
+    if not events:
+        return []
+
+    # Résoudre le répertoire de session pour les crops visuels
+    session_dir_path = Path(session_dir) if session_dir else None
+    if session_dir_path and not session_dir_path.is_dir():
+        logger.warning(
+            "session_dir '%s' n'existe pas — visual replay désactivé", session_dir,
+        )
+        session_dir_path = None
+
+    # ── 1. Extraire la résolution d'écran ──
+    screen_w, screen_h = _extract_screen_resolution(events)
+    logger.info(
+        "build_replay_from_raw_events(%s) : %d événements bruts, résolution=%dx%d, visual=%s",
+        session_id, len(events), screen_w, screen_h,
+        bool(session_dir_path),
+    )
+
+    # ── 2. Filtrer et normaliser les événements ──
+    actionable_events = []
+    saw_save_combo = False  # Tracker Ctrl+S / Ctrl+Shift+S pour la coupure systray
+    for raw_evt in events:
+        event_data = raw_evt.get("event", raw_evt)
+        evt_type = event_data.get("type", "")
+
+        # Ignorer les types parasites
+        if evt_type in _IGNORED_EVENT_TYPES:
+            continue
+
+        # Tracker les raccourcis de sauvegarde (Ctrl+S, Ctrl+Shift+S)
+        if evt_type in ("key_combo", "key_press"):
+            keys = _sanitize_keys(event_data.get("keys", []))
+            keys_lower = {k.lower() for k in keys if k}
+            if "s" in keys_lower and ("ctrl" in keys_lower or "ctrl_l" in keys_lower
+                                       or "ctrl_r" in keys_lower):
+                saw_save_combo = True
+
+        # Vérifier la coupure AVANT d'ajouter l'événement.
+        # Pour les clics post-sauvegarde sur des fenêtres non-applicatives,
+        # on ne veut PAS inclure le clic qui déclenche la coupure.
+        if _should_cut_after_event(event_data, saw_save_combo=saw_save_combo):
+            # Alt+F4 est une action applicative → l'inclure
+            is_alt_f4 = False
+            if evt_type in ("key_combo", "key_press"):
+                _keys_lower = {k.lower() for k in event_data.get("keys", []) if k}
+                is_alt_f4 = "f4" in _keys_lower and (
+                    "alt" in _keys_lower or "alt_l" in _keys_lower or "alt_r" in _keys_lower
+                )
+            if is_alt_f4:
+                actionable_events.append(event_data)
+            # Sinon c'est un clic parasite → ne PAS l'inclure
+            logger.debug(
+                "Coupure du replay (saw_save=%s, type=%s, included=%s)",
+                saw_save_combo, evt_type, is_alt_f4,
+            )
+            break
+
+        actionable_events.append(event_data)
+
+    # ── 3. Fusionner les text_input consécutifs ──
+    # Tous les text_input consécutifs sont fusionnés en un seul, indépendamment
+    # du gap temporel. L'utilisateur tape lettre par lettre mais on veut un
+    # seul "type" avec tout le texte dans le replay.
+    # Seul un changement de fenêtre (window_title différent) coupe la fusion.
+    merged_events = []
+    for evt in actionable_events:
+        evt_type = evt.get("type", "")
+        evt_ts = float(evt.get("timestamp", 0))
+
+        if evt_type == "text_input":
+            text = evt.get("text", "")
+            if not text:
+                continue
+
+            # Les \n et \t ne sont PAS du texte — ce sont des touches Enter/Tab
+            # qui doivent devenir des key_combo pour le replay
+            if text == "\n":
+                merged_events.append({
+                    "type": "key_combo",
+                    "keys": ["enter"],
+                    "timestamp": evt_ts,
+                })
+                continue
+            if text == "\t":
+                merged_events.append({
+                    "type": "key_combo",
+                    "keys": ["tab"],
+                    "timestamp": evt_ts,
+                })
+                continue
+
+            # Fusionner avec le précédent text_input si même application
+            # On compare par app_name (pas title, car le titre change pendant la frappe)
+            if merged_events and merged_events[-1].get("type") == "text_input":
+                prev_app = merged_events[-1].get("window", {}).get("app_name", "")
+                curr_app = evt.get("window", {}).get("app_name", "")
+                # Même application (ou application inconnue) → fusionner
+                if not prev_app or not curr_app or prev_app == curr_app:
+                    merged_events[-1]["text"] = merged_events[-1].get("text", "") + text
+                    merged_events[-1]["_end_ts"] = evt_ts
+                    # Fusionner aussi les raw_keys (replay exact)
+                    if evt.get("raw_keys"):
+                        prev_raw = merged_events[-1].get("raw_keys", [])
+                        merged_events[-1]["raw_keys"] = prev_raw + evt["raw_keys"]
+                    continue
+
+            merged_events.append(dict(evt, _end_ts=evt_ts))
+        else:
+            merged_events.append(dict(evt))
+
+    # ── 4. Convertir en actions replay normalisées ──
+    actions = []
+    last_ts = 0.0
+
+    for evt in merged_events:
+        evt_type = evt.get("type", "")
+        evt_ts = float(evt.get("timestamp", 0))
+
+        # Insérer un wait si pause significative (> 2s, cappé à 5s)
+        if last_ts > 0 and evt_ts > last_ts:
+            delta_ms = int((evt_ts - last_ts) * 1000)
+            if delta_ms > 2000:
+                capped_ms = min(delta_ms, 5000)
+                actions.append({
+                    "action_id": f"act_raw_{uuid.uuid4().hex[:8]}",
+                    "type": "wait",
+                    "duration_ms": capped_ms,
+                })
+
+        # Mettre à jour le timestamp
+        end_ts = float(evt.get("_end_ts", evt_ts))
+        last_ts = max(last_ts, end_ts if end_ts > 0 else evt_ts)
+
+        action = {"action_id": f"act_raw_{uuid.uuid4().hex[:8]}"}
+
+        if evt_type == "mouse_click":
+            pos = evt.get("pos", [])
+            if not pos or len(pos) != 2:
+                continue
+            action["type"] = "click"
+            action["x_pct"] = round(pos[0] / screen_w, 6)
+            action["y_pct"] = round(pos[1] / screen_h, 6)
+            action["button"] = evt.get("button", "left")
+            # Enrichir avec le titre de fenêtre si disponible
+            window = evt.get("window", {})
+            if window.get("title"):
+                action["window_title"] = window["title"]
+
+            # ── Visual replay : attacher le crop de référence (anchor) ──
+            if session_dir_path:
+                anchor_b64 = _load_crop_for_event(
+                    evt, session_dir_path, screen_w, screen_h,
+                )
+                if anchor_b64:
+                    action["visual_mode"] = True
+
+                    # Construire une description VLM riche pour le replay VLM-first
+                    window_title = window.get("title", "")
+                    x_pos, y_pos = pos[0], pos[1]
+
+                    # Position relative dans l'écran
+                    if screen_h > 0:
+                        y_relative = (
+                            "en bas" if y_pos / screen_h > 0.8
+                            else "en haut" if y_pos / screen_h < 0.2
+                            else "au milieu"
+                        )
+                    else:
+                        y_relative = ""
+                    if screen_w > 0:
+                        x_relative = (
+                            "à gauche" if x_pos / screen_w < 0.3
+                            else "à droite" if x_pos / screen_w > 0.7
+                            else "au centre"
+                        )
+                    else:
+                        x_relative = ""
+
+                    # Description riche pour le VLM
+                    vlm_parts = []
+                    if window_title:
+                        vlm_parts.append(
+                            f"Dans la fenêtre '{window_title}'"
+                        )
+                    position_desc = " ".join(
+                        p for p in [y_relative, x_relative] if p
+                    )
+                    if position_desc:
+                        vlm_parts.append(
+                            f"l'élément cliqué se trouve {position_desc} de l'écran"
+                        )
+                    # Ajouter le texte visible (vision_info ou OCR)
+                    vision_info = evt.get("vision_info", {})
+                    if isinstance(vision_info, dict):
+                        vis_text = vision_info.get("text", "")
+                        vis_type = vision_info.get("type", "")
+                        if vis_text:
+                            vlm_parts.append(
+                                f"le texte visible est '{vis_text}'"
+                            )
+                        if vis_type:
+                            vlm_parts.append(
+                                f"c'est un élément de type '{vis_type}'"
+                            )
+                    vlm_description = ", ".join(vlm_parts) if vlm_parts else ""
+
+                    action["target_spec"] = {
+                        "anchor_image_base64": anchor_b64,
+                        "vlm_description": vlm_description,
+                        "window_title": window_title,
+                        "original_position": {
+                            "x_relative": x_relative,
+                            "y_relative": y_relative,
+                        },
+                    }
+                    # Propager les infos textuelles pour compatibilité
+                    if window_title:
+                        action["target_spec"]["by_text"] = window_title
+
+        elif evt_type == "text_input":
+            text = evt.get("text", "")
+            if not text:
+                continue
+            action["type"] = "type"
+            action["text"] = text
+            # Propager les raw_keys pour le replay exact (solution AZERTY)
+            if evt.get("raw_keys"):
+                action["raw_keys"] = evt["raw_keys"]
+
+        elif evt_type in ("key_press", "key_combo"):
+            keys = evt.get("keys", [])
+            if not keys:
+                key = evt.get("key", "")
+                if key:
+                    keys = [key]
+            if not keys:
+                continue
+            keys = _sanitize_keys(keys)
+            if _is_modifier_only(keys):
+                continue
+            action["type"] = "key_combo"
+            action["keys"] = keys
+            # Propager les raw_keys pour le replay exact (solution AZERTY)
+            if evt.get("raw_keys"):
+                action["raw_keys"] = evt["raw_keys"]
+
+        elif evt_type == "scroll":
+            pos = evt.get("pos", [])
+            action["type"] = "scroll"
+            if pos and len(pos) == 2:
+                action["x_pct"] = round(pos[0] / screen_w, 6)
+                action["y_pct"] = round(pos[1] / screen_h, 6)
+            action["delta"] = evt.get("delta", -3)
+
+        else:
+            continue
+
+        actions.append(action)
+
+    # ── 5. Nettoyage global (dédup combos, sanitize, merge texte, waits) ──
+    actions = clean_enriched_actions(actions)
+
+    # ── 6. Insérer des waits contextuels après raccourcis critiques ──
+    final_actions = []
+    for action in actions:
+        final_actions.append(action)
+        post_wait = _needs_post_wait(action)
+        if post_wait > 0:
+            # Vérifier si un wait existe déjà juste après (sera ajouté au prochain tour)
+            final_actions.append({
+                "action_id": f"act_raw_{uuid.uuid4().hex[:8]}",
+                "type": "wait",
+                "duration_ms": post_wait,
+            })
+
+    # ── 7. Dernier nettoyage des waits consécutifs ──
+    result = []
+    for a in final_actions:
+        if (result
+                and a.get("type") == "wait"
+                and result[-1].get("type") == "wait"):
+            # Garder le plus long
+            if a.get("duration_ms", 0) > result[-1].get("duration_ms", 0):
+                result[-1] = a
+            continue
+        result.append(a)
+
+    # Stats visual replay
+    visual_clicks = sum(
+        1 for a in result
+        if a.get("type") == "click" and a.get("visual_mode")
+    )
+    total_clicks = sum(1 for a in result if a.get("type") == "click")
+    logger.info(
+        "build_replay_from_raw_events(%s) : %d actions propres produites "
+        "(%d/%d clics avec visual_mode)",
+        session_id, len(result), visual_clicks, total_clicks,
+    )
+    return result
+
 
 class StreamProcessor:
     """
@@ -49,6 +842,14 @@ class StreamProcessor:
         # Lock pour l'accès concurrent aux données de session (screen_states, embeddings, workflows)
         self._data_lock = threading.Lock()
 
+        # Lock pour l'accès FAISS (IndexFlat.add() n'est pas thread-safe)
+        self._faiss_lock = threading.Lock()
+
+        # Flag de suspension : quand un replay est actif, le worker se suspend
+        # pour libérer le GPU au resolve_target VLM du replay.
+        # Settée depuis api_stream.py via set_replay_flag().
+        self._replay_active_flag: Optional[threading.Event] = None
+
         # Résultats d'analyse par session
         self._screen_states: Dict[str, list] = {}  # session_id -> List[ScreenState]
         self._embeddings: Dict[str, list] = {}      # session_id -> List[np.ndarray]
@@ -102,6 +903,53 @@ class StreamProcessor:
         except ImportError:
             logger.debug("core.models.workflow_graph non disponible, skip chargement")
 
+    def set_replay_flag(self, flag: threading.Event):
+        """Associer le flag de replay actif (depuis api_stream.py).
+
+        Quand ce flag est set(), reprocess_session() se suspend entre chaque
+        screenshot pour libérer le GPU au replay (resolve_target VLM).
+        """
+        self._replay_active_flag = flag
+        logger.info("Flag de suspension replay configuré sur le StreamProcessor")
+
+    def _wait_if_replay_active(self, context: str = "") -> bool:
+        """Suspendre le traitement si un replay est en cours.
+
+        Vérifie le flag _replay_active_flag et attend qu'il se clear.
+        Timeout de sécurité : 60s max pour éviter un blocage si le replay
+        plante sans clear le flag.
+
+        Args:
+            context: Description pour les logs (ex: "screenshot 3/10").
+
+        Returns:
+            True si on a dû attendre (replay était actif), False sinon.
+        """
+        if not self._replay_active_flag or not self._replay_active_flag.is_set():
+            return False
+
+        import time
+        suspend_start = time.time()
+        waited = False
+        while self._replay_active_flag.is_set():
+            elapsed = time.time() - suspend_start
+            if elapsed > 60:
+                logger.warning(
+                    f"Worker : timeout suspension (60s), reprise forcée ({context})"
+                )
+                break
+            if not waited:
+                logger.info(f"Worker suspendu — replay en cours ({context})")
+                waited = True
+            time.sleep(2)
+
+        if waited:
+            total_wait = time.time() - suspend_start
+            logger.info(
+                f"Worker reprend après {total_wait:.1f}s de suspension ({context})"
+            )
+        return waited
+
     def _ensure_initialized(self):
         """Charger les composants core GPU si pas encore fait."""
         if self._initialized:
@@ -177,7 +1025,19 @@ class StreamProcessor:
     # =========================================================================
 
     def process_event(self, session_id: str, event_data: Dict[str, Any]) -> Dict[str, Any]:
-        """Enregistrer un événement dans la session live."""
+        """Enregistrer un événement dans la session live.
+
+        Filtre les événements parasites à la réception :
+        - key_combo/key_press avec uniquement des modificateurs seuls (ctrl, alt, shift, etc.)
+        - key_combo/key_press avec liste de touches vide
+        - text_input avec texte vide
+        """
+        if _is_parasitic_event(event_data):
+            logger.debug(
+                f"Événement parasite filtré (session {session_id}): "
+                f"type={event_data.get('type')}, data={event_data.get('keys', event_data.get('text', ''))}"
+            )
+            return {"status": "event_filtered", "session_id": session_id, "reason": "parasitic"}
         self.session_manager.add_event(session_id, event_data)
         return {"status": "event_recorded", "session_id": session_id}
 
@@ -214,7 +1074,12 @@ class StreamProcessor:
             return result
 
         session = self.session_manager.get_session(session_id)
-        window_info = session.last_window_info if session else {}
+        # Utiliser le mapping shot → window si disponible (reprocessing)
+        shot_map = getattr(session, '_shot_window_map', None) if session else None
+        if shot_map and shot_id in shot_map:
+            window_info = shot_map[shot_id]
+        else:
+            window_info = session.last_window_info if session else {}
 
         try:
             screen_state = self._screen_analyzer.analyze(
@@ -264,14 +1129,7 @@ class StreamProcessor:
                     f"fallback sur CLIP pur"
                 )
 
-        # Fallback 1 : embedding pré-calculé dans le ScreenState (si disponible)
-        if embedding_vector is None:
-            if hasattr(screen_state, "perception") and screen_state.perception:
-                emb_ref = getattr(screen_state.perception, "embedding", None)
-                if emb_ref and hasattr(emb_ref, "vector") and emb_ref.vector is not None:
-                    embedding_vector = np.array(emb_ref.vector, dtype=np.float32)
-
-        # Fallback 2 : utiliser le CLIPEmbedder singleton (embedding image seul)
+        # Fallback : utiliser le CLIPEmbedder singleton (embedding image seul)
         if embedding_vector is None and self._clip_embedder is not None:
             try:
                 from PIL import Image
@@ -287,18 +1145,19 @@ class StreamProcessor:
                     self._embeddings[session_id] = []
                 self._embeddings[session_id].append(embedding_vector)
 
-            # 3. Indexer dans FAISS
+            # 3. Indexer dans FAISS (protégé par _faiss_lock car IndexFlat.add n'est pas thread-safe)
             if self._faiss_manager is not None:
                 try:
-                    self._faiss_manager.add_embedding(
-                        embedding_id=screen_state.screen_state_id,
-                        vector=embedding_vector,
-                        metadata={
-                            "session_id": session_id,
-                            "shot_id": shot_id,
-                            "window_title": window_info.get("title", ""),
-                        },
-                    )
+                    with self._faiss_lock:
+                        self._faiss_manager.add_embedding(
+                            embedding_id=screen_state.screen_state_id,
+                            vector=embedding_vector,
+                            metadata={
+                                "session_id": session_id,
+                                "shot_id": shot_id,
+                                "window_title": window_info.get("title", ""),
+                            },
+                        )
                     result["embedding_indexed"] = True
                 except Exception as e:
                     logger.error(f"Erreur FAISS indexation: {e}")
@@ -373,22 +1232,29 @@ class StreamProcessor:
             from core.graph.graph_builder import GraphBuilder
 
             n = len(states)
-            min_reps = 2 if n < 10 else 3 if n <= 30 else min(5, n // 10)
+            min_reps = 1 if n < 6 else 2 if n <= 30 else min(3, n // 10)
 
             builder = GraphBuilder(
+                embedding_builder=self._state_embedding_builder,  # Réutiliser le même modèle CLIP
                 min_pattern_repetitions=min_reps,
-                clustering_eps=0.15,
+                clustering_eps=0.08,
                 clustering_min_samples=2,
             )
 
             # Nommer le workflow intelligemment à partir des titres de fenêtre
             workflow_name = self._generate_workflow_name(session_id)
 
-            # Injecter les ScreenStates pré-calculés pour éviter de re-analyser
+            # Récupérer les embeddings pré-calculés pendant le streaming
+            with self._data_lock:
+                precomputed_embs = list(self._embeddings.get(session_id, []))
+
+            # Injecter les ScreenStates et embeddings pré-calculés pour éviter
+            # de re-analyser et de recalculer les embeddings (triple calcul)
             workflow = builder.build_from_session(
                 raw_session,
                 workflow_name=workflow_name,
                 precomputed_states=states,
+                precomputed_embeddings=precomputed_embs if len(precomputed_embs) == len(states) else None,
             )
 
             with self._data_lock:
@@ -528,33 +1394,76 @@ class StreamProcessor:
         # S'assurer que la session est enregistrée dans le session_manager
         self.session_manager.get_or_create(session_id)
 
+        # Restaurer les window_info depuis live_events.jsonl
+        # pour que ScreenAnalyzer crée des ScreenStates avec les bons titres de fenêtre
+        self._restore_window_events(session_id, session_dir)
+
         # Nettoyer les données en mémoire (au cas où un traitement précédent a échoué)
         with self._data_lock:
             self._screen_states.pop(session_id, None)
             self._embeddings.pop(session_id, None)
 
-        # Analyser chaque screenshot sélectionné
+        # Analyser les screenshots en parallèle (2 workers max pour la VRAM)
+        # Chaque process_screenshot() est indépendant : ScreenAnalyzer + CLIP + FAISS
+        # Les structures partagées (_screen_states, _embeddings) sont protégées par _data_lock
+        max_parallel = min(2, total)
         errors = 0
-        for i, shot_file in enumerate(key_shots):
-            shot_id = shot_file.stem  # ex: "shot_0001_full"
-            file_path = str(shot_file)
+        processed_count = 0
 
-            if progress_callback:
-                try:
-                    progress_callback(session_id, i + 1, total, shot_id)
-                except Exception:
-                    pass
+        def _analyze_one(shot_file, index=0):
+            """Analyse un screenshot, retourne (shot_id, result_or_error).
 
+            Vérifie avant chaque analyse si un replay est en cours (flag GPU).
+            Si oui, attend que le replay termine avant de lancer le VLM.
+            """
+            shot_id = shot_file.stem
+            # Suspendre si un replay est en cours (libérer le GPU)
+            self._wait_if_replay_active(
+                context=f"screenshot {index + 1}/{total} ({shot_id})"
+            )
             try:
-                result = self.process_screenshot(session_id, shot_id, file_path)
-                if result.get("state_id") is None:
-                    logger.warning(
-                        f"Screenshot {shot_id} : analyse échouée (pas de state_id)"
-                    )
-                    errors += 1
+                res = self.process_screenshot(session_id, shot_id, str(shot_file))
+                return (shot_id, res, None)
             except Exception as e:
-                logger.error(f"Erreur analyse screenshot {shot_id}: {e}")
-                errors += 1
+                return (shot_id, None, str(e))
+
+        if total <= 1:
+            # Un seul screenshot — pas besoin de pool
+            for shot_file in key_shots:
+                shot_id, res, err = _analyze_one(shot_file, index=0)
+                processed_count += 1
+                if progress_callback:
+                    try:
+                        progress_callback(session_id, processed_count, total, shot_id)
+                    except Exception:
+                        pass
+                if err:
+                    logger.error(f"Erreur analyse screenshot {shot_id}: {err}")
+                    errors += 1
+                elif res and res.get("state_id") is None:
+                    logger.warning(f"Screenshot {shot_id} : analyse échouée (pas de state_id)")
+                    errors += 1
+        else:
+            # Traitement parallèle — 2 screenshots simultanés
+            # Note : _analyze_one vérifie le flag replay avant chaque VLM call,
+            # donc les workers se suspendent automatiquement si un replay est en cours.
+            logger.info(f"Traitement parallèle : {max_parallel} workers pour {total} screenshots")
+            with ThreadPoolExecutor(max_workers=max_parallel, thread_name_prefix="vlm") as pool:
+                futures = {pool.submit(_analyze_one, sf, i): sf for i, sf in enumerate(key_shots)}
+                for future in as_completed(futures):
+                    shot_id, res, err = future.result()
+                    processed_count += 1
+                    if progress_callback:
+                        try:
+                            progress_callback(session_id, processed_count, total, shot_id)
+                        except Exception:
+                            pass
+                    if err:
+                        logger.error(f"Erreur analyse screenshot {shot_id}: {err}")
+                        errors += 1
+                    elif res and res.get("state_id") is None:
+                        logger.warning(f"Screenshot {shot_id} : analyse échouée (pas de state_id)")
+                        errors += 1
 
         # Vérifier combien de states ont été produits
         with self._data_lock:
@@ -625,6 +1534,90 @@ class StreamProcessor:
 
         return selected
 
+    def _restore_window_events(self, session_id: str, session_dir: Path):
+        """Restaurer les window_info depuis live_events.jsonl lors du retraitement.
+
+        Crée un mapping chronologique timestamp → window_info pour que chaque
+        screenshot soit associé au bon titre de fenêtre. Stocké dans
+        session._shot_window_map : {shot_number → window_info}.
+
+        Le mapping est utilisé par process_screenshot() via last_window_info
+        qui est mis à jour avant chaque shot.
+        """
+        import json as _json
+        import re
+
+        events_file = session_dir / "live_events.jsonl"
+        if not events_file.exists():
+            logger.debug(f"Pas de live_events.jsonl pour {session_id}")
+            return
+
+        try:
+            # Collecter tous les window_focus_change avec leur timestamp
+            window_changes = []  # [(timestamp, {"title": ..., "app_name": ...})]
+
+            for line in events_file.read_text().splitlines():
+                if not line.strip():
+                    continue
+                try:
+                    evt = _json.loads(line)
+                except _json.JSONDecodeError:
+                    continue
+
+                event_data = evt.get("event", evt)
+                evt_type = event_data.get("type", "")
+                ts = float(event_data.get("timestamp", evt.get("timestamp", 0)))
+
+                if evt_type == "window_focus_change":
+                    to_info = event_data.get("to") or event_data.get("window") or {}
+                    title = to_info.get("title", "")
+                    if title:
+                        window_changes.append((ts, {
+                            "title": title,
+                            "app_name": to_info.get("app_name", "unknown"),
+                        }))
+
+            if not window_changes:
+                logger.debug(f"Pas de window_focus_change dans {session_id}")
+                return
+
+            window_changes.sort(key=lambda x: x[0])
+
+            # Mapper chaque shot_XXXX aux window_info par timestamp
+            shots_dir = session_dir / "shots"
+            shot_files = sorted(shots_dir.glob("shot_*_full.png"))
+
+            # Extraire le timestamp du nom de fichier (shot_XXXX_full.png → XXXX = index)
+            # ou du mtime du fichier
+            shot_window_map = {}
+            for shot_file in shot_files:
+                shot_ts = shot_file.stat().st_mtime
+                # Trouver le dernier window_change avant ce shot
+                best_window = window_changes[0][1]  # Premier par défaut
+                for wc_ts, wc_info in window_changes:
+                    if wc_ts <= shot_ts:
+                        best_window = wc_info
+                    else:
+                        break
+                shot_window_map[shot_file.stem] = best_window
+
+            # Stocker dans la session pour que process_screenshot() puisse l'utiliser
+            session = self.session_manager.get_session(session_id)
+            if session:
+                session._shot_window_map = shot_window_map
+                # Mettre le dernier titre connu comme fallback
+                session.last_window_info = window_changes[-1][1]
+
+            titles_seen = set(w.get("title", "") for w in shot_window_map.values())
+            logger.info(
+                f"Window events restaurés pour {session_id}: "
+                f"{len(shot_window_map)} shots mappés, "
+                f"{len(titles_seen)} titres uniques: {titles_seen}"
+            )
+
+        except Exception as e:
+            logger.warning(f"Erreur restauration window events pour {session_id}: {e}")
+
     def _find_session_dir(self, session_id: str) -> Optional[Path]:
         """Trouver le dossier d'une session sur disque.
 
@@ -686,7 +1679,7 @@ class StreamProcessor:
                 continue
 
             # Vérifier si un workflow existe déjà pour cette session
-            # (parcourir les workflows et checker la session_id dans les métadonnées)
+            # 1. Check en mémoire (attribut _source_session)
             with self._data_lock:
                 has_workflow = any(
                     getattr(wf, '_source_session', None) == sid
@@ -695,6 +1688,10 @@ class StreamProcessor:
             if has_workflow:
                 continue
 
+            # 2. Check sur disque (metadata.source_session_id dans les fichiers JSON)
+            if self._workflow_exists_on_disk(sid):
+                continue
+
             # Vérifier qu'il y a des screenshots full sur disque
             session_dir = self._find_session_dir(sid)
             if session_dir:
@@ -710,6 +1707,35 @@ class StreamProcessor:
 
         return pending
 
+    def _workflow_exists_on_disk(self, session_id: str) -> bool:
+        """Vérifier si un workflow a déjà été produit pour cette session.
+
+        Parcourt les fichiers JSON dans data/training/workflows/ et cherche
+        source_session_id dans les métadonnées.
+        Utilise un cache pour éviter de re-lire les fichiers à chaque appel.
+        """
+        import json as _json
+
+        if not hasattr(self, '_processed_sessions_cache'):
+            # Construire le cache au premier appel
+            self._processed_sessions_cache = set()
+            workflows_dir = self.data_dir / "workflows"
+            if workflows_dir.exists():
+                for wf_file in workflows_dir.rglob("*.json"):
+                    try:
+                        with open(wf_file, 'r') as f:
+                            data = _json.load(f)
+                        src = data.get('metadata', {}).get('source_session_id', '')
+                        if src:
+                            self._processed_sessions_cache.add(src)
+                    except Exception:
+                        continue
+            logger.info(
+                f"Cache sessions traitées : {len(self._processed_sessions_cache)} workflows existants"
+            )
+
+        return session_id in self._processed_sessions_cache
+
     def _cleanup_session_data(self, session_id: str):
         """Libérer la mémoire des ScreenStates et embeddings après finalization."""
         with self._data_lock:
@@ -905,11 +1931,15 @@ class StreamProcessor:
                 workflows_dir = self.data_dir / "workflows"
             workflows_dir.mkdir(parents=True, exist_ok=True)
             filepath = workflows_dir / f"{workflow.workflow_id}.json"
-            workflow.save_to_file(filepath)
-            # Stocker le machine_id dans le workflow pour référence
+            # Stocker le session_id source et machine_id dans les métadonnées
+            if hasattr(workflow, 'metadata') and isinstance(workflow.metadata, dict):
+                workflow.metadata['source_session_id'] = session_id
+                workflow.metadata['machine_id'] = machine_id
             if not hasattr(workflow, '_machine_id'):
                 workflow._machine_id = machine_id
-            logger.info(f"Workflow sauvegardé: {filepath} (machine={machine_id})")
+            workflow._source_session = session_id
+            workflow.save_to_file(filepath)
+            logger.info(f"Workflow sauvegardé: {filepath} (session={session_id}, machine={machine_id})")
             return filepath
         except Exception as e:
             logger.error(f"Erreur sauvegarde workflow {session_id}: {e}")
@@ -1023,6 +2053,1525 @@ class StreamProcessor:
         logger.info("Workflows rechargés depuis le disque : %d", count)
         return count
 
+    # =========================================================================
+    # Extraction d'actions enrichies depuis un workflow appris
+    # =========================================================================
+
+    def extract_enriched_actions(
+        self,
+        workflow,
+        params: Optional[Dict[str, Any]] = None,
+    ) -> List[Dict[str, Any]]:
+        """Extraire les actions enrichies d'un workflow appris (nodes + edges + events).
+
+        Parcourt le graphe en BFS depuis les entry_nodes et construit pour chaque edge
+        une action enrichie contenant :
+        - Les coordonnées normalisées (x_pct, y_pct) depuis les events originaux
+        - Les infos de ciblage visuel (by_text, by_role, window_title)
+        - Les identifiants de nodes (from_node, to_node) pour le pre-check et post-check
+        - Le flag visual_mode=True pour activer la résolution visuelle côté agent
+
+        Args:
+            workflow: Objet Workflow ou dict brut avec nodes/edges
+            params: Paramètres de substitution (variables ${var})
+
+        Returns:
+            Liste d'actions enrichies prêtes pour la queue de replay, ou liste vide
+            si le workflow n'a pas d'edges exploitables.
+        """
+        import uuid
+        from collections import defaultdict
+
+        params = params or {}
+
+        # Accéder aux données du workflow (objet ou dict)
+        if hasattr(workflow, 'edges'):
+            edges = workflow.edges
+            entry_nodes = workflow.entry_nodes if hasattr(workflow, 'entry_nodes') else []
+            nodes_list = workflow.nodes if hasattr(workflow, 'nodes') else []
+        elif isinstance(workflow, dict):
+            edges = workflow.get('edges', [])
+            entry_nodes = workflow.get('entry_nodes', [])
+            nodes_list = workflow.get('nodes', [])
+        else:
+            return []
+
+        if not edges:
+            return []
+
+        # Index des nodes par ID
+        node_index = {}
+        for n in nodes_list:
+            nid = n.node_id if hasattr(n, 'node_id') else n.get('node_id', '')
+            node_index[nid] = n
+
+        # Index des edges sortants par node
+        outgoing: Dict[str, list] = defaultdict(list)
+        for edge in edges:
+            fn = edge.from_node if hasattr(edge, 'from_node') else edge.get('from_node', '')
+            outgoing[fn].append(edge)
+
+        # Trouver les événements originaux de la session source
+        original_events = self._load_original_events_for_workflow(workflow)
+
+        # Trouver le dossier de la session source (pour les crops/anchors)
+        source_session_dir = self._find_session_dir_for_workflow(workflow)
+
+        # Inférer la résolution d'écran depuis les positions maximales des events
+        inferred_resolution = self._infer_screen_resolution(original_events)
+
+        # BFS depuis les entry_nodes
+        if not entry_nodes:
+            # Fallback : premier node des edges
+            first_edge = edges[0]
+            fn = first_edge.from_node if hasattr(first_edge, 'from_node') else first_edge.get('from_node', '')
+            entry_nodes = [fn]
+
+        visited = set()
+        queue = list(entry_nodes)
+        ordered_edges = []
+
+        while queue:
+            node_id = queue.pop(0)
+            if node_id in visited:
+                continue
+            visited.add(node_id)
+
+            for edge in outgoing.get(node_id, []):
+                ordered_edges.append(edge)
+                tn = edge.to_node if hasattr(edge, 'to_node') else edge.get('to_node', '')
+                if tn not in visited:
+                    queue.append(tn)
+
+        # Construire les actions enrichies depuis les edges ordonnés
+        actions = []
+        for edge in ordered_edges:
+            enriched = self._edge_to_enriched_action(
+                edge, node_index, original_events, params, inferred_resolution,
+                source_session_dir,
+            )
+            if enriched:
+                actions.extend(enriched)
+
+        # Nettoyage global : éliminer les actions parasites, fusionner les
+        # text_input consécutifs, dédupliquer les key_combo, etc.
+        raw_count = len(actions)
+        actions = clean_enriched_actions(actions)
+
+        logger.info(
+            "Actions enrichies extraites : %d actions (nettoyées depuis %d brutes) "
+            "depuis %d edges (events originaux : %d)",
+            len(actions), raw_count, len(ordered_edges), len(original_events),
+        )
+        return actions
+
+    def _load_original_events_for_workflow(self, workflow) -> List[Dict[str, Any]]:
+        """Charger les événements originaux (live_events.jsonl) liés à un workflow.
+
+        Stratégie de recherche :
+        1. metadata.source_session_id (si le workflow le stocke)
+        2. Parcourir les sessions existantes pour trouver un match temporel
+        3. Utiliser le workflow_id comme hint (parfois contient le session_id)
+
+        Returns:
+            Liste d'events bruts (dicts), ou liste vide si introuvable.
+        """
+        import json
+
+        # Stratégie 1 : metadata.source_session_id
+        metadata = workflow.metadata if hasattr(workflow, 'metadata') else (
+            workflow.get('metadata', {}) if isinstance(workflow, dict) else {}
+        )
+        source_sid = metadata.get('source_session_id', '')
+
+        if source_sid:
+            events = self._load_events_from_session(source_sid)
+            if events:
+                return events
+
+        # Stratégie 2 : workflow_id peut contenir ou être un session_id
+        wf_id = workflow.workflow_id if hasattr(workflow, 'workflow_id') else (
+            workflow.get('workflow_id', '') if isinstance(workflow, dict) else ''
+        )
+        if wf_id.startswith('sess_'):
+            events = self._load_events_from_session(wf_id)
+            if events:
+                return events
+
+        # Stratégie 3 : chercher les sessions les plus proches temporellement
+        created_at = None
+        if hasattr(workflow, 'created_at'):
+            created_at = workflow.created_at
+        elif isinstance(workflow, dict) and 'created_at' in workflow:
+            try:
+                from datetime import datetime
+                created_at = datetime.fromisoformat(workflow['created_at'])
+            except (ValueError, TypeError):
+                pass
+
+        if created_at:
+            events = self._find_closest_session_events(created_at)
+            if events:
+                return events
+
+        return []
+
+    def _find_session_dir_for_workflow(self, workflow) -> Optional[Path]:
+        """Trouver le dossier de la session source associée à un workflow.
+
+        Utilise la même logique de recherche que _load_original_events_for_workflow
+        mais retourne le chemin du dossier au lieu des événements. Nécessaire pour
+        accéder aux crops (anchor images) stockés dans {session_dir}/shots/.
+
+        Returns:
+            Path du dossier session, ou None si introuvable.
+        """
+        # Stratégie 1 : metadata.source_session_id
+        metadata = workflow.metadata if hasattr(workflow, 'metadata') else (
+            workflow.get('metadata', {}) if isinstance(workflow, dict) else {}
+        )
+        source_sid = metadata.get('source_session_id', '')
+
+        if source_sid:
+            session_dir = self._find_session_dir(source_sid)
+            if session_dir:
+                return session_dir
+
+        # Stratégie 2 : workflow_id peut contenir ou être un session_id
+        wf_id = workflow.workflow_id if hasattr(workflow, 'workflow_id') else (
+            workflow.get('workflow_id', '') if isinstance(workflow, dict) else ''
+        )
+        if wf_id.startswith('sess_'):
+            session_dir = self._find_session_dir(wf_id)
+            if session_dir:
+                return session_dir
+
+        # Stratégie 3 : chercher la session la plus proche temporellement
+        created_at = None
+        if hasattr(workflow, 'created_at'):
+            created_at = workflow.created_at
+        elif isinstance(workflow, dict) and 'created_at' in workflow:
+            try:
+                from datetime import datetime as _dt
+                created_at = _dt.fromisoformat(workflow['created_at'])
+            except (ValueError, TypeError):
+                pass
+
+        if created_at:
+            session_dir = self._find_closest_session_dir(created_at)
+            if session_dir:
+                return session_dir
+
+        return None
+
+    def _find_closest_session_dir(self, workflow_created_at) -> Optional[Path]:
+        """Trouver le dossier de la session la plus proche temporellement.
+
+        Même logique que _find_closest_session_events mais retourne le Path
+        du dossier au lieu de charger les événements.
+
+        Returns:
+            Path du dossier session, ou None si aucun match dans les 10 minutes.
+        """
+        from datetime import datetime as _dt
+
+        best_dir = None
+        best_delta = float('inf')
+
+        search_dirs = [self.data_dir]
+        if self.data_dir.exists():
+            for subdir in self.data_dir.iterdir():
+                if subdir.is_dir() and not subdir.name.startswith('.'):
+                    search_dirs.append(subdir)
+
+        for search_dir in search_dirs:
+            if not search_dir.exists():
+                continue
+            for session_dir in search_dir.iterdir():
+                if not session_dir.is_dir():
+                    continue
+                name = session_dir.name
+                if not name.startswith('sess_'):
+                    continue
+                try:
+                    ts_part = name.split('_')[1]
+                    session_dt = _dt.strptime(ts_part, '%Y%m%dT%H%M%S')
+                    delta = abs((workflow_created_at - session_dt).total_seconds())
+                    if delta < best_delta:
+                        best_delta = delta
+                        best_dir = session_dir
+                except (IndexError, ValueError):
+                    continue
+
+        if best_dir and best_delta < 600:
+            return best_dir
+
+        return None
+
+    def _load_anchor_crop(
+        self,
+        matched_event: Dict[str, Any],
+        session_dir: Path,
+    ) -> Optional[str]:
+        """Charger le crop de référence (anchor image) pour un événement clic.
+
+        Cherche le crop dans le dossier shots/ de la session source, en utilisant
+        le screenshot_id de l'événement original. Si le crop n'existe pas, tente
+        de le recréer à partir du screenshot full en croppant autour de la position
+        du clic.
+
+        Args:
+            matched_event: Événement original (dict avec screenshot_id et pos)
+            session_dir: Dossier de la session source
+
+        Returns:
+            Image crop encodée en base64, ou None si introuvable.
+        """
+        import base64
+
+        screenshot_id = matched_event.get('screenshot_id', '')
+        if not screenshot_id:
+            return None
+
+        shots_dir = session_dir / "shots"
+        if not shots_dir.exists():
+            return None
+
+        # Stratégie 1 : crop déjà capturé par l'agent (shot_XXXX_crop.png)
+        crop_path = shots_dir / f"{screenshot_id}_crop.png"
+        if crop_path.exists():
+            try:
+                crop_b64 = base64.b64encode(crop_path.read_bytes()).decode()
+                logger.debug("Anchor crop chargé : %s", crop_path.name)
+                return crop_b64
+            except Exception as e:
+                logger.warning("Erreur lecture crop %s : %s", crop_path, e)
+
+        # Stratégie 1b : crop en JPEG (compression possible côté agent)
+        crop_jpg = shots_dir / f"{screenshot_id}_crop.jpg"
+        if crop_jpg.exists():
+            try:
+                crop_b64 = base64.b64encode(crop_jpg.read_bytes()).decode()
+                logger.debug("Anchor crop JPEG chargé : %s", crop_jpg.name)
+                return crop_b64
+            except Exception as e:
+                logger.warning("Erreur lecture crop JPEG %s : %s", crop_jpg, e)
+
+        # Stratégie 2 : cropper le full screenshot autour de la position du clic
+        full_path = shots_dir / f"{screenshot_id}_full.png"
+        if not full_path.exists():
+            full_path = shots_dir / f"{screenshot_id}_full.jpg"
+        if not full_path.exists():
+            return None
+
+        pos = matched_event.get('pos', [])
+        if not pos or len(pos) < 2:
+            return None
+
+        try:
+            from PIL import Image
+            import io
+
+            img = Image.open(full_path)
+            x, y = int(pos[0]), int(pos[1])
+
+            # Crop 400x400 centré sur le clic (même taille que le captor)
+            crop_size = 200  # demi-côté
+            left = max(0, x - crop_size)
+            top = max(0, y - crop_size)
+            right = min(img.width, x + crop_size)
+            bottom = min(img.height, y + crop_size)
+
+            crop = img.crop((left, top, right, bottom))
+
+            buf = io.BytesIO()
+            crop.save(buf, format="PNG", optimize=True)
+            crop_b64 = base64.b64encode(buf.getvalue()).decode()
+
+            logger.debug(
+                "Anchor crop généré depuis %s (pos=%s, crop=%dx%d)",
+                full_path.name, pos, crop.width, crop.height,
+            )
+            return crop_b64
+        except Exception as e:
+            logger.warning("Erreur génération crop depuis %s : %s", full_path, e)
+            return None
+
+    def _load_events_from_session(self, session_id: str) -> List[Dict[str, Any]]:
+        """Charger les événements depuis le live_events.jsonl d'une session."""
+        import json
+
+        session_dir = self._find_session_dir(session_id)
+        if not session_dir:
+            return []
+
+        events_file = session_dir / "live_events.jsonl"
+        if not events_file.exists():
+            return []
+
+        events = []
+        try:
+            with open(events_file, 'r', encoding='utf-8') as f:
+                for line in f:
+                    line = line.strip()
+                    if not line:
+                        continue
+                    try:
+                        data = json.loads(line)
+                        event = data.get('event', data)
+                        events.append(event)
+                    except json.JSONDecodeError:
+                        continue
+        except Exception as e:
+            logger.warning("Erreur lecture events %s : %s", events_file, e)
+
+        return events
+
+    def _find_closest_session_events(self, workflow_created_at) -> List[Dict[str, Any]]:
+        """Trouver la session la plus proche temporellement du workflow.
+
+        Parcourt les dossiers de sessions sur disque et compare les dates
+        de création (encodées dans le nom du dossier sess_YYYYMMDDTHHMMSS_xxx).
+        """
+        import json
+        from datetime import datetime
+
+        best_dir = None
+        best_delta = float('inf')
+
+        # Chercher dans data_dir et ses sous-dossiers (machine_id)
+        search_dirs = [self.data_dir]
+        if self.data_dir.exists():
+            for subdir in self.data_dir.iterdir():
+                if subdir.is_dir() and not subdir.name.startswith('.'):
+                    search_dirs.append(subdir)
+
+        for search_dir in search_dirs:
+            for session_dir in search_dir.iterdir():
+                if not session_dir.is_dir():
+                    continue
+                name = session_dir.name
+                if not name.startswith('sess_'):
+                    continue
+                # Extraire le timestamp du nom : sess_YYYYMMDDTHHMMSS_xxx
+                try:
+                    ts_part = name.split('_')[1]  # YYYYMMDDTHHMMSS
+                    session_dt = datetime.strptime(ts_part, '%Y%m%dT%H%M%S')
+                    delta = abs((workflow_created_at - session_dt).total_seconds())
+                    if delta < best_delta:
+                        best_delta = delta
+                        best_dir = session_dir
+                except (IndexError, ValueError):
+                    continue
+
+        if best_dir and best_delta < 600:  # Max 10 minutes d'écart
+            events_file = best_dir / "live_events.jsonl"
+            if events_file.exists():
+                events = []
+                try:
+                    with open(events_file, 'r', encoding='utf-8') as f:
+                        for line in f:
+                            line = line.strip()
+                            if not line:
+                                continue
+                            try:
+                                data = json.loads(line)
+                                event = data.get('event', data)
+                                events.append(event)
+                            except json.JSONDecodeError:
+                                continue
+                    if events:
+                        logger.info(
+                            "Events originaux trouvés dans %s (delta=%ds, %d events)",
+                            best_dir.name, int(best_delta), len(events),
+                        )
+                        return events
+                except Exception as e:
+                    logger.warning("Erreur lecture events %s : %s", events_file, e)
+
+        return []
+
+    def _find_session_wide_search(
+        self,
+        workflow,
+        return_dir: bool = False,
+    ):
+        """Recherche élargie de la session source d'un workflow.
+
+        Utilisé quand ``_load_original_events_for_workflow`` échoue (pas de
+        ``source_session_id`` et fenêtre temporelle de 10 min trop serrée).
+
+        Élargit la fenêtre à 3 heures et utilise le ``_machine_id`` du workflow
+        pour filtrer les candidats.
+
+        Args:
+            workflow: Objet Workflow ou dict.
+            return_dir: Si True, retourne aussi le Path du dossier session.
+
+        Returns:
+            Si ``return_dir`` est False : (events_list, session_dir_path)
+            Si ``return_dir`` est True : (events_list, session_dir_path)
+        """
+        import json as _json
+        from datetime import datetime as _dt
+
+        created_at = None
+        if hasattr(workflow, 'created_at'):
+            created_at = workflow.created_at
+        elif isinstance(workflow, dict) and 'created_at' in workflow:
+            try:
+                created_at = _dt.fromisoformat(workflow['created_at'])
+            except (ValueError, TypeError):
+                pass
+
+        if not created_at:
+            return ([], None)
+
+        # Machine_id du workflow (peut être dans l'attribut privé ou dans les métadonnées)
+        machine_id = getattr(workflow, '_machine_id', None)
+        if not machine_id:
+            metadata = workflow.metadata if hasattr(workflow, 'metadata') else (
+                workflow.get('metadata', {}) if isinstance(workflow, dict) else {}
+            )
+            machine_id = metadata.get('machine_id', '')
+
+        best_dir = None
+        best_delta = float('inf')
+        max_delta = 10800  # 3 heures
+
+        search_dirs = [self.data_dir]
+        if self.data_dir.exists():
+            for subdir in self.data_dir.iterdir():
+                if subdir.is_dir() and not subdir.name.startswith('.'):
+                    search_dirs.append(subdir)
+
+        for search_dir in search_dirs:
+            if not search_dir.exists():
+                continue
+            for session_dir in search_dir.iterdir():
+                if not session_dir.is_dir():
+                    continue
+                name = session_dir.name
+                if not name.startswith('sess_'):
+                    continue
+
+                # Filtrer par machine_id si connu : le session_dir parent doit contenir
+                # le machine_id, ou être directement dans data_dir
+                if machine_id and machine_id != "default":
+                    parent_name = search_dir.name
+                    # Accepter si le parent est le machine_id ou si c'est le data_dir racine
+                    if parent_name != machine_id and search_dir != self.data_dir:
+                        continue
+
+                try:
+                    ts_part = name.split('_')[1]
+                    session_dt = _dt.strptime(ts_part, '%Y%m%dT%H%M%S')
+                    delta = abs((created_at - session_dt).total_seconds())
+                    if delta < best_delta:
+                        best_delta = delta
+                        best_dir = session_dir
+                except (IndexError, ValueError):
+                    continue
+
+        if best_dir and best_delta < max_delta:
+            events_file = best_dir / "live_events.jsonl"
+            if events_file.exists():
+                events = []
+                try:
+                    with open(events_file, 'r', encoding='utf-8') as f:
+                        for line in f:
+                            line = line.strip()
+                            if not line:
+                                continue
+                            try:
+                                data = _json.loads(line)
+                                event = data.get('event', data)
+                                events.append(event)
+                            except _json.JSONDecodeError:
+                                continue
+                    if events:
+                        logger.info(
+                            "Recherche élargie : session trouvée dans %s "
+                            "(delta=%ds, %d events, machine=%s)",
+                            best_dir.name, int(best_delta), len(events),
+                            machine_id or "?",
+                        )
+                        return (events, best_dir)
+                except Exception as e:
+                    logger.warning("Erreur lecture events %s : %s", events_file, e)
+
+        return ([], None)
+
+    @staticmethod
+    def _infer_screen_resolution(events: List[Dict[str, Any]]) -> tuple:
+        """Inférer la résolution d'écran depuis les positions maximales des events.
+
+        Analyse les coordonnées de tous les clics pour estimer la résolution
+        de l'écran source. Si un clic a x=1800 ou y=1500, la résolution
+        est au moins 1800+marge x 1500+marge.
+
+        Utilise une heuristique : arrondir vers la résolution standard la plus
+        proche parmi les plus courantes (1920x1080, 2560x1440, 2560x1600,
+        3840x2160, 1366x768, 1280x720).
+
+        Returns:
+            Tuple (width, height) de la résolution inférée, ou (1920, 1080) par défaut.
+        """
+        # Résolutions standard connues
+        STANDARD_RESOLUTIONS = [
+            (1280, 720), (1366, 768), (1440, 900), (1600, 900),
+            (1920, 1080), (1920, 1200), (2560, 1440), (2560, 1600),
+            (3440, 1440), (3840, 2160),
+        ]
+
+        max_x = 0
+        max_y = 0
+        for evt in events:
+            pos = evt.get('pos', [])
+            if pos and len(pos) == 2:
+                max_x = max(max_x, pos[0])
+                max_y = max(max_y, pos[1])
+
+        if max_x == 0 and max_y == 0:
+            return (1920, 1080)
+
+        # Trouver la résolution standard minimale qui contient tous les clics
+        for w, h in STANDARD_RESOLUTIONS:
+            if w >= max_x and h >= max_y:
+                return (w, h)
+
+        # Si aucune résolution standard ne convient, arrondir vers le haut
+        # par paliers de 100 pixels
+        inferred_w = ((max_x // 100) + 1) * 100
+        inferred_h = ((max_y // 100) + 1) * 100
+        return (inferred_w, inferred_h)
+
+    def _edge_to_enriched_action(
+        self,
+        edge,
+        node_index: Dict[str, Any],
+        original_events: List[Dict[str, Any]],
+        params: Dict[str, Any],
+        inferred_resolution: tuple = (1920, 1080),
+        source_session_dir: Optional[Path] = None,
+    ) -> List[Dict[str, Any]]:
+        """Convertir un edge de workflow en action(s) enrichie(s).
+
+        Enrichit chaque action avec :
+        - Coordonnées normalisées depuis les événements originaux
+        - Infos de ciblage visuel (by_text, by_role, window_title)
+        - Anchor image (crop) pour le template matching visuel
+        - Flag visual_mode pour la résolution visuelle côté agent
+        - Identifiants from_node/to_node pour pre-check et post-conditions
+
+        Args:
+            edge: WorkflowEdge (objet ou dict)
+            node_index: Index des nodes par ID
+            original_events: Événements originaux de la session
+            params: Variables de substitution
+            inferred_resolution: Résolution écran inférée
+            source_session_dir: Dossier de la session source (pour les crops/anchors)
+
+        Returns:
+            Liste d'actions enrichies (1 pour un edge simple, N pour un compound)
+        """
+        import uuid
+
+        # Extraire les données de l'edge (objet ou dict)
+        if hasattr(edge, 'edge_id'):
+            edge_id = edge.edge_id
+            from_node = edge.from_node
+            to_node = edge.to_node
+            action_obj = edge.action
+            edge_metadata = edge.metadata if hasattr(edge, 'metadata') else {}
+        else:
+            edge_id = edge.get('edge_id', '')
+            from_node = edge.get('from_node', '')
+            to_node = edge.get('to_node', '')
+            action_obj = edge.get('action', {})
+            edge_metadata = edge.get('metadata', {})
+
+        # Extraire les données de l'action
+        if hasattr(action_obj, 'type'):
+            action_type = action_obj.type
+            target = action_obj.target
+            action_params = action_obj.parameters or {}
+        elif isinstance(action_obj, dict):
+            action_type = action_obj.get('type', 'unknown')
+            target = action_obj.get('target', {})
+            action_params = action_obj.get('parameters', {})
+        else:
+            return []
+
+        # Extraire les infos du target
+        if hasattr(target, 'by_role'):
+            by_role = target.by_role or ''
+            by_text = target.by_text or ''
+            by_position = target.by_position
+        elif isinstance(target, dict):
+            by_role = target.get('by_role', '') or ''
+            by_text = target.get('by_text', '') or ''
+            by_position = target.get('by_position')
+        else:
+            by_role = ''
+            by_text = ''
+            by_position = None
+
+        # Données du node source (pour pre-check et window_title)
+        source_node = node_index.get(from_node)
+        target_node = node_index.get(to_node)
+
+        window_title = self._extract_window_title(source_node)
+        target_window_title = self._extract_window_title(target_node)
+
+        # Chercher l'événement original correspondant à cet edge
+        matched_event = self._match_edge_to_event(
+            edge_metadata, action_type, action_params, original_events
+        )
+
+        # Construire les coordonnées par ordre de priorité :
+        # 1. by_position du target (explicite, fiable)
+        # 2. position dans action_params (set par GraphBuilder depuis l'event original)
+        # 3. matched_event (recherche dans les events de la session - moins fiable)
+        # 4. (0, 0) → sera résolu visuellement par l'agent
+        x_pct = 0.0
+        y_pct = 0.0
+        text = ''
+        keys = []
+        button = action_params.get('button', 'left') if isinstance(action_params, dict) else 'left'
+
+        # Priorité 1 : by_position explicite du target
+        if by_position and isinstance(by_position, (list, tuple)) and len(by_position) == 2:
+            px, py = by_position
+            if px <= 1.0 and py <= 1.0:
+                x_pct = px
+                y_pct = py
+            elif px > 0 or py > 0:
+                rw = (action_params.get('ref_width', 1920) or 1920) if isinstance(action_params, dict) else 1920
+                rh = (action_params.get('ref_height', 1080) or 1080) if isinstance(action_params, dict) else 1080
+                x_pct = round(px / rw, 6)
+                y_pct = round(py / rh, 6)
+
+        # Priorité 2 : position dans action_params (de GraphBuilder)
+        if x_pct == 0.0 and y_pct == 0.0 and isinstance(action_params, dict):
+            pos = action_params.get('position', [])
+            if pos and len(pos) == 2 and (pos[0] > 0 or pos[1] > 0):
+                rw = (action_params.get('ref_width') or inferred_resolution[0]) if isinstance(action_params, dict) else inferred_resolution[0]
+                rh = (action_params.get('ref_height') or inferred_resolution[1]) if isinstance(action_params, dict) else inferred_resolution[1]
+                x_pct = round(pos[0] / rw, 6)
+                y_pct = round(pos[1] / rh, 6)
+
+        # Priorité 3 : matched_event (session la plus proche)
+        if x_pct == 0.0 and y_pct == 0.0 and matched_event:
+            pos = matched_event.get('pos', [])
+            if pos and len(pos) == 2:
+                ref_width = matched_event.get('screen_width') or inferred_resolution[0]
+                ref_height = matched_event.get('screen_height') or inferred_resolution[1]
+                x_pct = round(pos[0] / ref_width, 6)
+                y_pct = round(pos[1] / ref_height, 6)
+
+        # Sécurité : clamper à [0, 1]
+        x_pct = max(0.0, min(1.0, x_pct))
+        y_pct = max(0.0, min(1.0, y_pct))
+
+        # Texte et touches : action_params d'abord, matched_event en complément
+        if isinstance(action_params, dict):
+            text = action_params.get('text', '')
+            keys = action_params.get('keys', [])
+        if not text and matched_event:
+            text = matched_event.get('text', '')
+        if not keys and matched_event:
+            keys = matched_event.get('keys', [])
+        if matched_event:
+            button = matched_event.get('button', button)
+            # Enrichir le window_title si absent
+            event_window = matched_event.get('window', {})
+            if not window_title and event_window:
+                window_title = event_window.get('title', '')
+
+        # Sanitiser les touches : convertir les caractères de contrôle
+        if keys:
+            keys = _sanitize_keys(keys)
+            # Si ne reste que des modificateurs seuls → action parasite, skip
+            if _is_modifier_only(keys):
+                return []
+
+        # Substitution de variables dans le texte
+        if text and params:
+            text = self._substitute_vars(text, params, action_params)
+
+        # Déterminer le type d'action normalisé
+        if action_type == 'mouse_click':
+            norm_type = 'click'
+        elif action_type == 'text_input':
+            norm_type = 'type'
+        elif action_type == 'key_press':
+            norm_type = 'key_combo'
+        elif action_type == 'compound':
+            # Décomposer les compound en sous-actions
+            steps = action_params.get('steps', []) if isinstance(action_params, dict) else []
+            return self._expand_compound_enriched(
+                steps, edge_id, from_node, to_node, window_title, params
+            )
+        elif action_type in ('unknown', 'unknown_element'):
+            # Actions "unknown" : essayer de deviner depuis l'événement original
+            if matched_event:
+                evt_type = matched_event.get('type', '')
+                if evt_type == 'mouse_click':
+                    norm_type = 'click'
+                elif evt_type == 'text_input':
+                    norm_type = 'type'
+                elif evt_type == 'key_press':
+                    norm_type = 'key_combo'
+                else:
+                    # Event trouvé mais type non reconnu : défaut click
+                    norm_type = 'click'
+                    logger.debug(
+                        "Edge %s : action unknown, event type=%s -> défaut click",
+                        edge_id, evt_type,
+                    )
+            else:
+                # Pas d'événement original : défaut click (la transition entre
+                # deux états est presque toujours causée par un clic)
+                norm_type = 'click'
+                logger.debug(
+                    "Edge %s : action unknown, pas d'event original -> défaut click",
+                    edge_id,
+                )
+        else:
+            norm_type = action_type
+
+        # Construire le target_spec pour la résolution visuelle
+        target_spec = {}
+        if by_text and by_text not in ('', 'null', 'None'):
+            target_spec['by_text'] = by_text
+        if by_role and by_role not in ('', 'unknown', 'unknown_element', 'null'):
+            target_spec['by_role'] = by_role
+        if window_title:
+            target_spec['window_title'] = window_title
+
+        # Enrichir le target_spec avec les textes du node source (OCR)
+        source_texts = self._extract_required_texts(source_node)
+        if source_texts:
+            target_spec['context_hints'] = {'screen_texts': source_texts[:3]}
+
+        # Enrichir avec l'anchor image (crop de référence) pour les clics
+        if norm_type == 'click' and matched_event and source_session_dir:
+            anchor_b64 = self._load_anchor_crop(matched_event, source_session_dir)
+            if anchor_b64:
+                target_spec['anchor_image_base64'] = anchor_b64
+                logger.debug(
+                    "Anchor image chargée pour edge %s (screenshot_id=%s)",
+                    edge_id, matched_event.get('screenshot_id', '?'),
+                )
+
+        # Construire l'action enrichie
+        action = {
+            'action_id': f'act_{uuid.uuid4().hex[:8]}',
+            'type': norm_type,
+            'edge_id': edge_id,
+            'from_node': from_node,
+            'to_node': to_node,
+            'x_pct': x_pct,
+            'y_pct': y_pct,
+            'window_title': window_title,
+        }
+
+        # Ajouter les champs spécifiques au type d'action
+        if norm_type == 'click':
+            action['button'] = button
+        elif norm_type == 'type':
+            action['text'] = text
+        elif norm_type == 'key_combo':
+            action['keys'] = keys
+
+        # Activer la résolution visuelle si on a des critères sémantiques
+        # OU si les coordonnées sont nulles (nécessite une résolution)
+        if target_spec or (x_pct == 0.0 and y_pct == 0.0 and norm_type == 'click'):
+            action['visual_mode'] = True
+            action['target_spec'] = target_spec
+
+        return [action]
+
+    def _match_edge_to_event(
+        self,
+        edge_metadata: Dict[str, Any],
+        action_type: str,
+        action_params: Dict[str, Any],
+        original_events: List[Dict[str, Any]],
+    ) -> Optional[Dict[str, Any]]:
+        """Trouver l'événement original correspondant à un edge.
+
+        Stratégie de matching :
+        1. Par type d'action (mouse_click, text_input, key_press)
+        2. Par position approximative (si position dans action_params)
+        3. Par ordre chronologique (premier événement non-matché du bon type)
+
+        Returns:
+            L'événement matché ou None.
+        """
+        if not original_events:
+            return None
+
+        # Type d'événement attendu
+        expected_types = set()
+        if action_type in ('mouse_click', 'click', 'unknown'):
+            expected_types.add('mouse_click')
+        if action_type in ('text_input', 'type', 'unknown'):
+            expected_types.add('text_input')
+        if action_type in ('key_press', 'key_combo', 'unknown'):
+            expected_types.add('key_press')
+
+        if not expected_types:
+            expected_types = {'mouse_click', 'text_input', 'key_press'}
+
+        # Filtrer les événements du bon type
+        candidates = [
+            e for e in original_events
+            if e.get('type', '') in expected_types
+        ]
+
+        if not candidates:
+            return None
+
+        # Si on a une position dans action_params, chercher l'événement le plus proche
+        ref_pos = (action_params.get('position', []) if isinstance(action_params, dict) else [])
+        if ref_pos and len(ref_pos) == 2 and ref_pos[0] > 0:
+            best_event = None
+            best_dist = float('inf')
+            for evt in candidates:
+                evt_pos = evt.get('pos', [])
+                if evt_pos and len(evt_pos) == 2:
+                    dx = ref_pos[0] - evt_pos[0]
+                    dy = ref_pos[1] - evt_pos[1]
+                    dist = (dx * dx + dy * dy) ** 0.5
+                    if dist < best_dist:
+                        best_dist = dist
+                        best_event = evt
+            if best_event and best_dist < 200:  # Max 200px d'écart
+                return best_event
+
+        # Fallback : le premier événement du bon type
+        # On utilise created_from_event du edge metadata comme hint
+        created_from = edge_metadata.get('created_from_event', '')
+        if created_from:
+            for evt in candidates:
+                if evt.get('type') == created_from:
+                    return evt
+
+        return candidates[0] if candidates else None
+
+    def _extract_window_title(self, node) -> str:
+        """Extraire le titre de fenêtre depuis un node (objet ou dict)."""
+        if node is None:
+            return ''
+        if hasattr(node, 'template'):
+            tpl = node.template
+            if tpl and hasattr(tpl, 'window') and tpl.window:
+                return tpl.window.title_contains or tpl.window.title_pattern or ''
+        elif isinstance(node, dict):
+            template = node.get('template', {})
+            if isinstance(template, dict):
+                window = template.get('window', {})
+                if isinstance(window, dict):
+                    return window.get('title_contains', '') or window.get('title_pattern', '') or ''
+        return ''
+
+    def _extract_required_texts(self, node) -> List[str]:
+        """Extraire les textes requis depuis le template d'un node."""
+        if node is None:
+            return []
+        if hasattr(node, 'template'):
+            tpl = node.template
+            if tpl and hasattr(tpl, 'text') and tpl.text:
+                texts = tpl.text.required_texts or []
+                # Filtrer les textes trop courts ou trop longs
+                return [t for t in texts if 3 <= len(t) <= 80]
+        elif isinstance(node, dict):
+            template = node.get('template', {})
+            if isinstance(template, dict):
+                text_spec = template.get('text', {})
+                if isinstance(text_spec, dict):
+                    texts = text_spec.get('required_texts', [])
+                    return [t for t in texts if isinstance(t, str) and 3 <= len(t) <= 80]
+        return []
+
+    @staticmethod
+    def _substitute_vars(text: str, params: Dict[str, Any], action_params: Dict[str, Any]) -> str:
+        """Substituer les variables ${var} dans un texte."""
+        import re
+        defaults = action_params.get('defaults', {}) if isinstance(action_params, dict) else {}
+
+        def replacer(match):
+            var_name = match.group(1)
+            return str(params.get(var_name, defaults.get(var_name, match.group(0))))
+
+        return re.sub(r'\$\{(\w+)\}', replacer, text)
+
+    def _expand_compound_enriched(
+        self,
+        steps: List[Dict[str, Any]],
+        edge_id: str,
+        from_node: str,
+        to_node: str,
+        window_title: str,
+        params: Dict[str, Any],
+    ) -> List[Dict[str, Any]]:
+        """Décomposer un compound en actions enrichies individuelles.
+
+        Applique le nettoyage des steps parasites avant expansion :
+        - Suppression des modificateurs seuls (ctrl, alt, shift, etc.)
+        - Fusion des text_input consécutifs
+        - Déduplication des key_combo consécutifs identiques
+
+        Supporte les types de steps produits par GraphBuilder._build_compound_action() :
+        - mouse_click / click : clic souris avec x_pct/y_pct ou position/ref_*
+        - text_input / type : saisie de texte
+        - key_press / key_combo : combinaison de touches
+        - wait : pause entre actions
+        """
+        import uuid
+
+        # Nettoyage des steps parasites avant expansion
+        steps = clean_compound_steps(steps)
+
+        actions = []
+        for step in steps:
+            step_type = step.get('type', 'unknown')
+            action = {
+                'action_id': f'act_{uuid.uuid4().hex[:8]}',
+                'edge_id': edge_id,
+                'from_node': from_node,
+                'to_node': to_node,
+                'window_title': window_title,
+            }
+            if step_type in ('key_press', 'key_combo'):
+                action['type'] = 'key_combo'
+                keys = step.get('keys', [])
+                if not keys and step.get('key'):
+                    keys = [step['key']]
+                action['keys'] = keys
+            elif step_type in ('text_input', 'type'):
+                action['type'] = 'type'
+                text = step.get('text', '')
+                text = self._substitute_vars(text, params, step)
+                action['text'] = text
+            elif step_type == 'wait':
+                action['type'] = 'wait'
+                action['duration_ms'] = step.get('duration_ms', 500)
+            elif step_type in ('mouse_click', 'click'):
+                action['type'] = 'click'
+                # Coordonnées normalisées directes (x_pct/y_pct)
+                x_pct = step.get('x_pct', 0.0)
+                y_pct = step.get('y_pct', 0.0)
+                # Fallback : calculer depuis position absolue + résolution de référence
+                if x_pct == 0.0 and y_pct == 0.0:
+                    pos = step.get('position', [])
+                    if pos and len(pos) == 2 and (pos[0] > 0 or pos[1] > 0):
+                        rw = step.get('ref_width', 1920) or 1920
+                        rh = step.get('ref_height', 1080) or 1080
+                        x_pct = round(pos[0] / rw, 6)
+                        y_pct = round(pos[1] / rh, 6)
+                action['x_pct'] = x_pct
+                action['y_pct'] = y_pct
+                action['button'] = step.get('button', 'left')
+            else:
+                continue
+            actions.append(action)
+
+        # Nettoyage post-expansion des actions enrichies
+        return clean_enriched_actions(actions)
+
+    # =========================================================================
+    # Replay hybride : événements bruts + structure workflow
+    # =========================================================================
+
+    def build_hybrid_replay(
+        self,
+        workflow,
+        session_id: Optional[str] = None,
+    ) -> List[Dict[str, Any]]:
+        """Construire un replay hybride combinant événements bruts et structure workflow.
+
+        Le replay hybride utilise :
+        - Les événements bruts (live_events.jsonl) comme SOURCE D'ACTIONS
+        - La structure du workflow (nodes) comme STRUCTURE DE VÉRIFICATION
+
+        Les événements sont groupés par transition de node. Entre chaque groupe,
+        une action ``verify_screen`` est insérée pour vérifier que l'écran
+        correspond bien au node attendu avant de continuer.
+
+        Args:
+            workflow: Objet Workflow ou dict brut avec nodes/edges.
+            session_id: Identifiant de session explicite (optionnel, sinon
+                déduit depuis les métadonnées du workflow).
+
+        Returns:
+            Liste d'actions propres prêtes pour la queue de replay, avec des
+            ``verify_screen`` intercalés entre les groupes de transition.
+        """
+        import uuid
+
+        # 1. Charger les événements bruts
+        original_events = self._load_original_events_for_workflow(workflow)
+
+        # Si la recherche standard échoue, élargir la fenêtre temporelle.
+        # Le SessionWorker peut construire le workflow 1-2h après la capture
+        # (analyse VLM longue), donc la fenêtre de 10 min est trop serrée.
+        if not original_events:
+            original_events, session_dir_hint = self._find_session_wide_search(workflow)
+        else:
+            session_dir_hint = None
+
+        if not original_events:
+            logger.warning("build_hybrid_replay : aucun événement brut trouvé")
+            return []
+
+        # 2. Trouver le dossier de la session source
+        session_dir = session_dir_hint or self._find_session_dir_for_workflow(workflow)
+        if not session_dir:
+            # Fallback : chercher avec une fenêtre large
+            if not session_dir_hint:
+                _, session_dir = self._find_session_wide_search(workflow, return_dir=True)
+            if not session_dir:
+                logger.warning("build_hybrid_replay : dossier session introuvable")
+                return []
+
+        # 3. Mapper les screenshots aux nodes du workflow
+        node_timeline = self._map_screenshots_to_nodes(session_dir, workflow)
+        if not node_timeline:
+            logger.warning(
+                "build_hybrid_replay : impossible de mapper les screenshots aux nodes, "
+                "fallback sur extract_enriched_actions"
+            )
+            return []
+
+        # 4. Grouper les événements par transition de node
+        groups = self._group_events_by_transition(original_events, node_timeline)
+        if not groups:
+            logger.warning("build_hybrid_replay : aucun groupe de transition construit")
+            return []
+
+        # 5. Inférer la résolution d'écran
+        screen_w, screen_h = self._infer_screen_resolution(original_events)
+
+        # 6. Construire la liste d'actions
+        actions = []
+        for group_idx, group in enumerate(groups):
+            group_events = group["events"]
+            to_node = group["to_node"]
+
+            # Convertir les événements bruts en actions de replay
+            group_actions = self._events_to_replay_actions(
+                group_events, screen_w, screen_h, group_idx,
+            )
+
+            # Nettoyer le groupe (filtrer parasites, fusionner texte, dédupliquer combos, waits)
+            group_actions = clean_enriched_actions(group_actions)
+
+            actions.extend(group_actions)
+
+            # Insérer une vérification visuelle après chaque groupe
+            if to_node:
+                actions.append({
+                    "action_id": f"act_verify_{uuid.uuid4().hex[:8]}",
+                    "type": "verify_screen",
+                    "expected_node": to_node,
+                    "timeout_ms": 5000,
+                    "group": group_idx,
+                })
+
+        logger.info(
+            "Replay hybride construit : %d actions (%d groupes, %d events bruts, "
+            "résolution=%dx%d)",
+            len(actions), len(groups), len(original_events), screen_w, screen_h,
+        )
+        return actions
+
+    def _map_screenshots_to_nodes(
+        self,
+        session_dir: Path,
+        workflow,
+    ) -> List[tuple]:
+        """Mapper chaque screenshot de la session au node du workflow correspondant.
+
+        Utilise les window_focus_change events pour associer un timestamp à un
+        window_title, puis le window_title au node (via node.template.window.title_contains).
+
+        Returns:
+            Liste de (timestamp, node_id) triée chronologiquement, ou liste vide
+            si le mapping est impossible.
+        """
+        import json as _json
+
+        # Extraire les titres de fenêtre depuis les nodes du workflow
+        if hasattr(workflow, 'nodes'):
+            nodes = workflow.nodes
+        elif isinstance(workflow, dict):
+            nodes = workflow.get('nodes', [])
+        else:
+            return []
+
+        # Construire un index title_fragment → node_id
+        # Un node peut avoir un title_contains comme "Bloc-notes" ou "Sans titre"
+        title_to_node = {}
+        node_order = []  # Ordre topologique (par index)
+        for node in nodes:
+            nid = node.node_id if hasattr(node, 'node_id') else node.get('node_id', '')
+            node_order.append(nid)
+            win_title = self._extract_window_title(node)
+            if win_title:
+                title_to_node[win_title.lower()] = nid
+
+        if not title_to_node:
+            logger.debug("Aucun titre de fenêtre dans les nodes du workflow")
+            return []
+
+        # Charger les changements de fenêtre depuis live_events.jsonl
+        events_file = session_dir / "live_events.jsonl"
+        if not events_file.exists():
+            return []
+
+        window_changes = []  # [(timestamp, window_title)]
+        try:
+            for line in events_file.read_text(encoding="utf-8").splitlines():
+                if not line.strip():
+                    continue
+                try:
+                    evt = _json.loads(line)
+                except _json.JSONDecodeError:
+                    continue
+
+                event_data = evt.get("event", evt)
+                evt_type = event_data.get("type", "")
+                ts = float(event_data.get("timestamp", evt.get("timestamp", 0)))
+
+                if evt_type == "window_focus_change":
+                    to_info = event_data.get("to") or event_data.get("window") or {}
+                    title = to_info.get("title", "")
+                    if title:
+                        window_changes.append((ts, title))
+        except Exception as e:
+            logger.warning("Erreur lecture events pour mapping screenshots : %s", e)
+            return []
+
+        if not window_changes:
+            # Fallback : utiliser les timestamps des screenshots (mtime) et les
+            # mapper aux nodes dans l'ordre du workflow
+            return self._map_screenshots_by_order(session_dir, node_order)
+
+        # Associer chaque changement de fenêtre au node correspondant
+        timeline = []
+        last_node_id = None
+        for ts, title in sorted(window_changes, key=lambda x: x[0]):
+            matched_node = self._match_title_to_node(title, title_to_node)
+            if matched_node and matched_node != last_node_id:
+                timeline.append((ts, matched_node))
+                last_node_id = matched_node
+
+        # Si timeline est vide, essayer un mapping plus souple avec les screenshots
+        if not timeline:
+            return self._map_screenshots_by_order(session_dir, node_order)
+
+        logger.info(
+            "Timeline node mappée : %d transitions (%s)",
+            len(timeline),
+            " → ".join(nid for _, nid in timeline),
+        )
+        return timeline
+
+    def _match_title_to_node(self, window_title: str, title_to_node: dict) -> Optional[str]:
+        """Matcher un titre de fenêtre à un node via les fragments de titre.
+
+        Le matching est insensible à la casse et cherche si le fragment du node
+        est contenu dans le titre de la fenêtre.
+
+        Returns:
+            Le node_id correspondant, ou None.
+        """
+        title_lower = window_title.lower()
+        best_match = None
+        best_len = 0
+        for fragment, node_id in title_to_node.items():
+            if fragment in title_lower and len(fragment) > best_len:
+                best_match = node_id
+                best_len = len(fragment)
+        return best_match
+
+    def _map_screenshots_by_order(
+        self,
+        session_dir: Path,
+        node_order: List[str],
+    ) -> List[tuple]:
+        """Fallback : mapper les screenshots aux nodes dans l'ordre topologique.
+
+        Utilisé quand les window_focus_change ne sont pas disponibles. Distribue
+        les screenshots uniformément entre les nodes.
+
+        Returns:
+            Liste de (timestamp, node_id).
+        """
+        shots_dir = session_dir / "shots"
+        if not shots_dir.exists() or not node_order:
+            return []
+
+        shot_files = sorted(shots_dir.glob("shot_*_full.png"))
+        if not shot_files:
+            return []
+
+        timeline = []
+        shots_per_node = max(1, len(shot_files) // max(1, len(node_order)))
+
+        for i, node_id in enumerate(node_order):
+            shot_idx = min(i * shots_per_node, len(shot_files) - 1)
+            ts = shot_files[shot_idx].stat().st_mtime
+            timeline.append((ts, node_id))
+
+        return timeline
+
+    def _group_events_by_transition(
+        self,
+        events: List[Dict[str, Any]],
+        node_timeline: List[tuple],
+    ) -> List[Dict[str, Any]]:
+        """Grouper les événements bruts par transition de node.
+
+        Pour chaque événement, détermine dans quel intervalle de transition il
+        se situe (entre quel changement de node et le suivant).
+
+        Args:
+            events: Liste d'événements bruts (dicts avec ``timestamp`` et ``type``).
+            node_timeline: Liste de (timestamp, node_id) triée chronologiquement.
+
+        Returns:
+            Liste de groupes :
+            ``[{"from_node": "node_000", "to_node": "node_001", "events": [...]}, ...]``
+        """
+        if not node_timeline or not events:
+            return []
+
+        # Construire les intervalles de transition
+        # Chaque transition va du node_timeline[i] au node_timeline[i+1]
+        groups = []
+        for i in range(len(node_timeline)):
+            from_node = node_timeline[i][1]
+            to_node = node_timeline[i + 1][1] if i + 1 < len(node_timeline) else ""
+            start_ts = node_timeline[i][0]
+            end_ts = node_timeline[i + 1][0] if i + 1 < len(node_timeline) else float("inf")
+
+            # Collecter les événements dans cet intervalle
+            group_events = []
+            for evt in events:
+                evt_ts = float(evt.get("timestamp", 0))
+                evt_type = evt.get("type", "")
+
+                # Ignorer les événements non-actionnables
+                if evt_type in _PARASITIC_ACTION_TYPES:
+                    continue
+                if evt_type in ("window_focus_change", "screenshot", "heartbeat"):
+                    continue
+
+                if start_ts <= evt_ts < end_ts:
+                    group_events.append(evt)
+
+            if group_events:
+                groups.append({
+                    "from_node": from_node,
+                    "to_node": to_node,
+                    "events": group_events,
+                })
+
+        # Si aucun événement n'a de timestamp ou tout est tombé dans les parasites,
+        # essayer un groupement séquentiel sans timestamp
+        if not groups:
+            groups = self._group_events_sequential(events, node_timeline)
+
+        return groups
+
+    def _group_events_sequential(
+        self,
+        events: List[Dict[str, Any]],
+        node_timeline: List[tuple],
+    ) -> List[Dict[str, Any]]:
+        """Groupement séquentiel des événements quand les timestamps ne matchent pas.
+
+        Distribue les événements actionnables entre les transitions du workflow
+        de manière proportionnelle.
+
+        Returns:
+            Liste de groupes comme ``_group_events_by_transition``.
+        """
+        # Filtrer les événements actionnables
+        actionable_types = {
+            "mouse_click", "text_input", "key_press", "key_combo", "scroll",
+        }
+        actionable = [
+            e for e in events
+            if e.get("type", "") in actionable_types
+        ]
+
+        if not actionable or len(node_timeline) < 2:
+            # Un seul node → tout dans un seul groupe
+            if actionable and node_timeline:
+                return [{
+                    "from_node": node_timeline[0][1],
+                    "to_node": node_timeline[-1][1] if len(node_timeline) > 1 else "",
+                    "events": actionable,
+                }]
+            return []
+
+        # Distribuer proportionnellement
+        n_transitions = len(node_timeline) - 1
+        events_per_group = max(1, len(actionable) // n_transitions)
+        groups = []
+
+        for i in range(n_transitions):
+            start_idx = i * events_per_group
+            end_idx = (i + 1) * events_per_group if i < n_transitions - 1 else len(actionable)
+            group_events = actionable[start_idx:end_idx]
+            if group_events:
+                groups.append({
+                    "from_node": node_timeline[i][1],
+                    "to_node": node_timeline[i + 1][1],
+                    "events": group_events,
+                })
+
+        return groups
+
+    def _events_to_replay_actions(
+        self,
+        events: List[Dict[str, Any]],
+        screen_w: int,
+        screen_h: int,
+        group_idx: int,
+    ) -> List[Dict[str, Any]]:
+        """Convertir une liste d'événements bruts en actions de replay normalisées.
+
+        Pré-fusionne les text_input consécutifs (frappes individuelles) en un seul
+        bloc de texte avant de les convertir en actions avec timing. Cela évite
+        d'avoir des dizaines d'actions ``type`` d'un seul caractère.
+
+        Ajoute un ``wait`` entre les actions de types différents quand le délai
+        naturel est significatif (> 2s = pause de réflexion de l'utilisateur).
+
+        Args:
+            events: Événements bruts d'un groupe.
+            screen_w: Largeur de l'écran source (pixels).
+            screen_h: Hauteur de l'écran source (pixels).
+            group_idx: Index du groupe de transition.
+
+        Returns:
+            Liste d'actions normalisées pour le replay.
+        """
+        import uuid
+
+        # ── Phase 1 : pré-fusionner les événements text_input consécutifs ──
+        # Les frappes clavier produisent un text_input par caractère. On les
+        # fusionne en un seul événement avec le texte complet et le timestamp
+        # du premier caractère.
+        merged_events = []
+        for evt in events:
+            evt_type = evt.get("type", "")
+
+            # Filtrer les événements non-actionnables
+            if evt_type in _PARASITIC_ACTION_TYPES:
+                continue
+            if evt_type in ("window_focus_change", "screenshot", "heartbeat"):
+                continue
+
+            if evt_type == "text_input":
+                text = evt.get("text", "")
+                if not text:
+                    continue
+                # Fusionner avec le précédent si c'est aussi un text_input
+                if merged_events and merged_events[-1].get("type") == "text_input":
+                    merged_events[-1]["text"] = merged_events[-1].get("text", "") + text
+                    # Garder le timestamp le plus récent pour le calcul du delta suivant
+                    merged_events[-1]["_end_ts"] = float(evt.get("timestamp", 0))
+                    continue
+
+            merged_events.append(dict(evt))
+
+        # ── Phase 2 : convertir les événements fusionnés en actions replay ──
+        actions = []
+        last_action_end_ts = 0.0
+
+        for evt in merged_events:
+            evt_type = evt.get("type", "")
+            evt_ts = float(evt.get("timestamp", 0))
+
+            # Calculer le délai entre la fin de la dernière action et le début
+            # de celle-ci. Les waits ne sont insérés que pour les pauses
+            # significatives (> 2s), pas entre chaque frappe.
+            if last_action_end_ts > 0 and evt_ts > last_action_end_ts:
+                delta_ms = int((evt_ts - last_action_end_ts) * 1000)
+                if delta_ms > 2000:
+                    capped_ms = min(delta_ms, 5000)
+                    actions.append({
+                        "action_id": f"act_wait_{uuid.uuid4().hex[:8]}",
+                        "type": "wait",
+                        "duration_ms": capped_ms,
+                        "group": group_idx,
+                    })
+
+            # Mettre à jour le timestamp de fin
+            end_ts = float(evt.get("_end_ts", evt_ts))
+            if end_ts > 0:
+                last_action_end_ts = end_ts
+            elif evt_ts > 0:
+                last_action_end_ts = evt_ts
+
+            action = {
+                "action_id": f"act_hyb_{uuid.uuid4().hex[:8]}",
+                "group": group_idx,
+            }
+
+            if evt_type == "mouse_click":
+                pos = evt.get("pos", [])
+                if pos and len(pos) == 2:
+                    action["type"] = "click"
+                    action["x_pct"] = round(pos[0] / screen_w, 6)
+                    action["y_pct"] = round(pos[1] / screen_h, 6)
+                    action["button"] = evt.get("button", "left")
+                else:
+                    continue
+
+            elif evt_type == "text_input":
+                text = evt.get("text", "")
+                if not text:
+                    continue
+                action["type"] = "type"
+                action["text"] = text
+
+            elif evt_type in ("key_press", "key_combo"):
+                keys = evt.get("keys", [])
+                if not keys:
+                    key = evt.get("key", "")
+                    if key:
+                        keys = [key]
+                if not keys:
+                    continue
+                keys = _sanitize_keys(keys)
+                if _is_modifier_only(keys):
+                    continue
+                action["type"] = "key_combo"
+                action["keys"] = keys
+
+            elif evt_type == "scroll":
+                pos = evt.get("pos", [])
+                action["type"] = "scroll"
+                if pos and len(pos) == 2:
+                    action["x_pct"] = round(pos[0] / screen_w, 6)
+                    action["y_pct"] = round(pos[1] / screen_h, 6)
+                action["delta"] = evt.get("delta", -3)
+
+            else:
+                continue
+
+            actions.append(action)
+
+        return actions
+
     @property
     def stats(self) -> Dict[str, Any]:
         """Statistiques du processeur."""
diff --git a/core/auth/__init__.py b/core/auth/__init__.py
new file mode 100644
index 000000000..8fe957ff5
--- /dev/null
+++ b/core/auth/__init__.py
@@ -0,0 +1,6 @@
+# core/auth — Module d'authentification automatique pour Léa
+#
+# Fournit :
+# - CredentialVault : coffre-fort chiffré pour les credentials
+# - TOTPGenerator  : générateur TOTP RFC 6238 (sans dépendance externe)
+# - AuthHandler    : détection d'écrans d'auth et injection automatique
diff --git a/core/auth/auth_handler.py b/core/auth/auth_handler.py
new file mode 100644
index 000000000..4b158571f
--- /dev/null
+++ b/core/auth/auth_handler.py
@@ -0,0 +1,523 @@
+"""
+Gestionnaire d'authentification automatique pendant le replay.
+
+Détecte les écrans d'authentification et injecte les credentials appropriés.
+Fonctionne avec le ScreenState du core pipeline et le CredentialVault chiffré.
+
+Stratégie de détection :
+1. Analyse OCR : cherche des patterns textuels indicatifs d'un écran d'auth
+   ("mot de passe", "identifiant", "code de vérification", etc.)
+2. Analyse UI : cherche des éléments sémantiques typiques (champ password,
+   bouton "Se connecter", etc.)
+3. Identification de l'application : via window_title du ScreenState
+
+La confiance est calculée selon le nombre de signaux détectés :
+- 1 signal = 0.3 (faible)
+- 2 signaux = 0.6 (moyen)
+- 3+ signaux = 0.85+ (élevé)
+"""
+
+import logging
+import re
+import uuid
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional
+
+from .credential_vault import CredentialVault
+from .totp_generator import TOTPGenerator
+
+logger = logging.getLogger(__name__)
+
+# =========================================================================
+# Patterns de détection d'écrans d'authentification
+# =========================================================================
+
+# Patterns OCR (texte visible sur l'écran) — FR + EN pour support bilingue
+_AUTH_TEXT_PATTERNS = [
+    # Français
+    r"mot\s+de\s+passe",
+    r"identifiant",
+    r"nom\s+d'utilisateur",
+    r"connexion",
+    r"se\s+connecter",
+    r"authentification",
+    r"code\s+de\s+v[ée]rification",
+    r"code\s+otp",
+    r"double\s+authentification",
+    r"v[ée]rification\s+en\s+deux\s+[ée]tapes",
+    # Anglais
+    r"password",
+    r"username",
+    r"sign\s+in",
+    r"log\s*in",
+    r"verification\s+code",
+    r"two.factor",
+    r"2fa",
+    r"one.time\s+password",
+    r"enter\s+your\s+code",
+]
+
+# Patterns pour identifier spécifiquement un écran TOTP/2FA
+_TOTP_TEXT_PATTERNS = [
+    r"code\s+de\s+v[ée]rification",
+    r"code\s+otp",
+    r"double\s+authentification",
+    r"v[ée]rification\s+en\s+deux",
+    r"two.factor",
+    r"2fa",
+    r"one.time\s+password",
+    r"enter\s+your\s+code",
+    r"code\s+[àa]\s+\d+\s+chiffres",
+    r"authenticator",
+]
+
+# Libellés de boutons de validation
+_SUBMIT_BUTTON_PATTERNS = [
+    r"se\s+connecter",
+    r"connexion",
+    r"valider",
+    r"envoyer",
+    r"confirmer",
+    r"sign\s+in",
+    r"log\s*in",
+    r"submit",
+    r"verify",
+    r"ok",
+]
+
+# Compilations pour performance
+_AUTH_REGEXES = [re.compile(p, re.IGNORECASE) for p in _AUTH_TEXT_PATTERNS]
+_TOTP_REGEXES = [re.compile(p, re.IGNORECASE) for p in _TOTP_TEXT_PATTERNS]
+_SUBMIT_REGEXES = [re.compile(p, re.IGNORECASE) for p in _SUBMIT_BUTTON_PATTERNS]
+
+
+@dataclass
+class AuthRequest:
+    """Requête d'authentification détectée sur un écran.
+
+    Attributes:
+        auth_type: Type d'authentification détecté ("login", "totp", "login_and_totp").
+        app_name: Application identifiée (depuis window_title).
+        detected_fields: Champs détectés sur l'écran (positions, types).
+        confidence: Confiance de la détection (0.0 à 1.0).
+    """
+
+    auth_type: str  # "login", "totp", "login_and_totp"
+    app_name: str  # App identifiée (depuis window_title)
+    detected_fields: Dict[str, Any] = field(default_factory=dict)
+    confidence: float = 0.0
+
+
+class AuthHandler:
+    """Gestionnaire d'authentification automatique pour le replay.
+
+    Analyse les ScreenStates pour détecter les écrans d'authentification
+    et génère les actions de replay correspondantes.
+
+    Usage :
+        handler = AuthHandler(vault)
+        auth_req = handler.detect_auth_screen(screen_state)
+        if auth_req:
+            actions = handler.get_auth_actions(auth_req)
+            # Injecter les actions dans la queue de replay
+    """
+
+    def __init__(self, vault: CredentialVault):
+        """Initialise le gestionnaire d'authentification.
+
+        Args:
+            vault: Instance du coffre-fort de credentials.
+        """
+        self._vault = vault
+
+    def detect_auth_screen(self, screen_state: Any) -> Optional[AuthRequest]:
+        """Analyse un ScreenState pour détecter un écran d'authentification.
+
+        La détection combine plusieurs signaux :
+        - Textes OCR correspondant à des patterns d'auth
+        - Éléments UI de type password/text_input
+        - Boutons de validation ("Se connecter", "Valider")
+
+        Args:
+            screen_state: ScreenState du core pipeline (ou dict compatible).
+
+        Returns:
+            AuthRequest si un écran d'auth est détecté avec confiance > 0.3,
+            None sinon.
+        """
+        # Extraire les textes détectés et les éléments UI
+        texts = self._extract_texts(screen_state)
+        ui_elements = self._extract_ui_elements(screen_state)
+        app_name = self._extract_app_name(screen_state)
+
+        # Compteur de signaux de détection
+        signals: Dict[str, Any] = {}
+
+        # Signal 1 : Patterns textuels d'authentification
+        auth_text_matches = []
+        for text in texts:
+            for regex in _AUTH_REGEXES:
+                if regex.search(text):
+                    auth_text_matches.append(regex.pattern)
+        if auth_text_matches:
+            signals["auth_text"] = auth_text_matches
+
+        # Signal 2 : Patterns textuels TOTP/2FA
+        totp_text_matches = []
+        for text in texts:
+            for regex in _TOTP_REGEXES:
+                if regex.search(text):
+                    totp_text_matches.append(regex.pattern)
+        if totp_text_matches:
+            signals["totp_text"] = totp_text_matches
+
+        # Signal 3 : Champs UI de type password
+        password_fields = []
+        username_fields = []
+        submit_buttons = []
+        otp_fields = []
+
+        for elem in ui_elements:
+            elem_type = self._get_elem_attr(elem, "type", "")
+            elem_role = self._get_elem_attr(elem, "role", "")
+            elem_label = self._get_elem_attr(elem, "label", "").lower()
+            elem_tags = self._get_elem_attr(elem, "tags", [])
+
+            # Champ mot de passe
+            if elem_role == "password" or "password" in elem_tags:
+                password_fields.append(elem)
+            elif elem_type == "text_input" and any(
+                p in elem_label for p in ("mot de passe", "password", "mdp")
+            ):
+                password_fields.append(elem)
+
+            # Champ identifiant/username
+            if elem_type == "text_input" and any(
+                p in elem_label
+                for p in ("identifiant", "username", "utilisateur", "login", "email", "e-mail")
+            ):
+                username_fields.append(elem)
+
+            # Champ OTP
+            if elem_type == "text_input" and any(
+                p in elem_label for p in ("code", "otp", "vérification", "verification")
+            ):
+                otp_fields.append(elem)
+
+            # Bouton de validation
+            if elem_type == "button":
+                for regex in _SUBMIT_REGEXES:
+                    if regex.search(elem_label):
+                        submit_buttons.append(elem)
+                        break
+
+        if password_fields:
+            signals["password_field"] = len(password_fields)
+        if username_fields:
+            signals["username_field"] = len(username_fields)
+        if submit_buttons:
+            signals["submit_button"] = len(submit_buttons)
+        if otp_fields:
+            signals["otp_field"] = len(otp_fields)
+
+        # Pas assez de signaux → pas d'écran d'auth
+        if not signals:
+            return None
+
+        # Déterminer le type d'auth
+        # Les signaux textuels "auth_text" peuvent contenir des patterns ambigus
+        # (ex: "2fa" apparaît dans les deux listes). On ne compte comme signal
+        # login que les patterns auth_text qui ne sont PAS aussi des patterns TOTP.
+        auth_only_text = set(signals.get("auth_text", [])) - set(signals.get("totp_text", []))
+        has_login_signals = bool(
+            password_fields
+            or auth_only_text
+            or username_fields
+        )
+        has_totp_signals = bool(
+            otp_fields
+            or "totp_text" in signals
+        )
+
+        if has_login_signals and has_totp_signals:
+            auth_type = "login_and_totp"
+        elif has_totp_signals:
+            auth_type = "totp"
+        else:
+            auth_type = "login"
+
+        # Calculer la confiance (nombre de signaux distincts)
+        num_signals = len(signals)
+        if num_signals >= 4:
+            confidence = 0.95
+        elif num_signals >= 3:
+            confidence = 0.85
+        elif num_signals >= 2:
+            confidence = 0.6
+        else:
+            confidence = 0.3
+
+        # Construire les champs détectés
+        detected_fields: Dict[str, Any] = {}
+        if username_fields:
+            detected_fields["username_field"] = self._elem_to_dict(username_fields[0])
+        if password_fields:
+            detected_fields["password_field"] = self._elem_to_dict(password_fields[0])
+        if otp_fields:
+            detected_fields["otp_field"] = self._elem_to_dict(otp_fields[0])
+        if submit_buttons:
+            detected_fields["submit_button"] = self._elem_to_dict(submit_buttons[0])
+
+        auth_request = AuthRequest(
+            auth_type=auth_type,
+            app_name=app_name,
+            detected_fields=detected_fields,
+            confidence=confidence,
+        )
+
+        logger.info(
+            "Écran d'authentification détecté : type=%s app=%s confiance=%.2f signaux=%s",
+            auth_type,
+            app_name,
+            confidence,
+            list(signals.keys()),
+        )
+
+        return auth_request
+
+    def get_auth_actions(self, auth_request: AuthRequest) -> List[Dict[str, Any]]:
+        """Génère les actions de replay pour s'authentifier.
+
+        Produit une séquence d'actions que l'Agent V1 peut exécuter :
+        - click sur le champ username, type le login
+        - click sur le champ password, type le mot de passe
+        - (optionnel) type le code TOTP
+        - click sur le bouton de validation
+
+        Args:
+            auth_request: Requête d'authentification détectée.
+
+        Returns:
+            Liste d'actions de replay (format compatible avec la queue de replay).
+            Liste vide si les credentials ne sont pas trouvés dans le vault.
+        """
+        actions: List[Dict[str, Any]] = []
+        app_name = auth_request.app_name
+        fields = auth_request.detected_fields
+
+        # Générer un préfixe unique pour les action_ids
+        prefix = f"auth_{uuid.uuid4().hex[:6]}"
+
+        # ---- Login : username + password ----
+        if auth_request.auth_type in ("login", "login_and_totp"):
+            login_creds = self._vault.get_credential(app_name, "login")
+            if not login_creds:
+                logger.warning(
+                    "Pas de credential 'login' pour l'app '%s' dans le vault",
+                    app_name,
+                )
+                return []
+
+            # Action : cliquer sur le champ username et taper
+            username_field = fields.get("username_field")
+            if username_field:
+                actions.append({
+                    "action_id": f"{prefix}_click_username",
+                    "type": "click",
+                    "target": username_field.get("center", [0, 0]),
+                    "description": f"Clic champ identifiant ({app_name})",
+                    "_auth_action": True,
+                })
+                actions.append({
+                    "action_id": f"{prefix}_type_username",
+                    "type": "type_text",
+                    "text": login_creds.get("username", ""),
+                    "description": f"Saisie identifiant ({app_name})",
+                    "_auth_action": True,
+                })
+
+            # Action : cliquer sur le champ password et taper
+            password_field = fields.get("password_field")
+            if password_field:
+                actions.append({
+                    "action_id": f"{prefix}_click_password",
+                    "type": "click",
+                    "target": password_field.get("center", [0, 0]),
+                    "description": f"Clic champ mot de passe ({app_name})",
+                    "_auth_action": True,
+                })
+                actions.append({
+                    "action_id": f"{prefix}_type_password",
+                    "type": "type_text",
+                    "text": login_creds.get("password", ""),
+                    "description": f"Saisie mot de passe ({app_name})",
+                    "_auth_action": True,
+                })
+
+        # ---- TOTP : générer et taper le code ----
+        if auth_request.auth_type in ("totp", "login_and_totp"):
+            totp_creds = self._vault.get_credential(app_name, "totp_seed")
+            if not totp_creds:
+                logger.warning(
+                    "Pas de credential 'totp_seed' pour l'app '%s' dans le vault",
+                    app_name,
+                )
+                # On continue quand même si le login a été fait
+                if not actions:
+                    return []
+            else:
+                totp = TOTPGenerator(
+                    secret=totp_creds["secret"],
+                    digits=totp_creds.get("digits", 6),
+                    interval=totp_creds.get("interval", 30),
+                    algorithm=totp_creds.get("algorithm", "SHA1"),
+                )
+
+                # Attendre si le code expire dans moins de 5 secondes
+                remaining = totp.time_remaining()
+                if remaining < 5:
+                    actions.append({
+                        "action_id": f"{prefix}_wait_totp",
+                        "type": "wait",
+                        "duration_ms": (remaining + 1) * 1000,
+                        "reason": "attente_nouveau_code_totp",
+                        "description": f"Attente nouveau code TOTP ({remaining}s restantes)",
+                        "_auth_action": True,
+                    })
+
+                code = totp.generate()
+
+                otp_field = fields.get("otp_field")
+                if otp_field:
+                    actions.append({
+                        "action_id": f"{prefix}_click_otp",
+                        "type": "click",
+                        "target": otp_field.get("center", [0, 0]),
+                        "description": f"Clic champ OTP ({app_name})",
+                        "_auth_action": True,
+                    })
+
+                actions.append({
+                    "action_id": f"{prefix}_type_totp",
+                    "type": "type_text",
+                    "text": code,
+                    "description": f"Saisie code TOTP ({app_name})",
+                    "_auth_action": True,
+                })
+
+        # ---- Bouton de validation ----
+        submit_button = fields.get("submit_button")
+        if submit_button and actions:
+            actions.append({
+                "action_id": f"{prefix}_click_submit",
+                "type": "click",
+                "target": submit_button.get("center", [0, 0]),
+                "description": f"Clic validation ({app_name})",
+                "_auth_action": True,
+            })
+
+        # Pause après validation pour laisser l'app charger
+        if actions:
+            actions.append({
+                "action_id": f"{prefix}_wait_after_auth",
+                "type": "wait",
+                "duration_ms": 2000,
+                "reason": "attente_chargement_post_auth",
+                "description": f"Attente post-authentification ({app_name})",
+                "_auth_action": True,
+            })
+
+        logger.info(
+            "Actions d'authentification générées : %d actions pour %s (type=%s)",
+            len(actions),
+            app_name,
+            auth_request.auth_type,
+        )
+
+        return actions
+
+    # =========================================================================
+    # Méthodes d'extraction internes
+    # =========================================================================
+
+    def _extract_texts(self, screen_state: Any) -> List[str]:
+        """Extrait tous les textes détectés depuis un ScreenState.
+
+        Supporte les objets ScreenState du core et les dicts bruts.
+        """
+        texts: List[str] = []
+
+        # ScreenState core (dataclass)
+        if hasattr(screen_state, "perception") and hasattr(
+            screen_state.perception, "detected_text"
+        ):
+            texts.extend(screen_state.perception.detected_text)
+
+        # Dict brut (sessions streaming)
+        elif isinstance(screen_state, dict):
+            perception = screen_state.get("perception", {})
+            if isinstance(perception, dict):
+                texts.extend(perception.get("detected_text", []))
+            # Texte OCR brut
+            if "ocr_text" in screen_state:
+                texts.append(screen_state["ocr_text"])
+            # Textes des éléments UI
+            for elem in screen_state.get("ui_elements", []):
+                label = elem.get("label", "")
+                if label:
+                    texts.append(label)
+
+        # Textes des éléments UI (objets)
+        if hasattr(screen_state, "ui_elements"):
+            for elem in screen_state.ui_elements:
+                label = self._get_elem_attr(elem, "label", "")
+                if label:
+                    texts.append(label)
+
+        return texts
+
+    def _extract_ui_elements(self, screen_state: Any) -> List[Any]:
+        """Extrait les éléments UI depuis un ScreenState."""
+        if hasattr(screen_state, "ui_elements"):
+            return list(screen_state.ui_elements)
+        if isinstance(screen_state, dict):
+            return screen_state.get("ui_elements", [])
+        return []
+
+    def _extract_app_name(self, screen_state: Any) -> str:
+        """Extrait le nom de l'application depuis un ScreenState."""
+        # ScreenState core
+        if hasattr(screen_state, "window") and hasattr(screen_state.window, "app_name"):
+            return screen_state.window.app_name
+
+        # Dict brut
+        if isinstance(screen_state, dict):
+            window = screen_state.get("window", {})
+            if isinstance(window, dict):
+                return window.get("app_name", "unknown")
+
+        return "unknown"
+
+    @staticmethod
+    def _get_elem_attr(elem: Any, attr: str, default: Any = None) -> Any:
+        """Récupère un attribut d'un élément UI (objet ou dict)."""
+        if isinstance(elem, dict):
+            return elem.get(attr, default)
+        return getattr(elem, attr, default)
+
+    @staticmethod
+    def _elem_to_dict(elem: Any) -> Dict[str, Any]:
+        """Convertit un élément UI en dict minimal pour les detected_fields."""
+        if isinstance(elem, dict):
+            return {
+                "type": elem.get("type", ""),
+                "label": elem.get("label", ""),
+                "center": elem.get("center", [0, 0]),
+                "element_id": elem.get("element_id", ""),
+            }
+        return {
+            "type": getattr(elem, "type", ""),
+            "label": getattr(elem, "label", ""),
+            "center": list(getattr(elem, "center", (0, 0))),
+            "element_id": getattr(elem, "element_id", ""),
+        }
diff --git a/core/auth/credential_vault.py b/core/auth/credential_vault.py
new file mode 100644
index 000000000..35e1e4572
--- /dev/null
+++ b/core/auth/credential_vault.py
@@ -0,0 +1,298 @@
+"""
+Coffre-fort chiffré pour les credentials d'authentification.
+
+Stocke de façon sécurisée :
+- Comptes de service (login/password)
+- Seeds TOTP pour la 2FA
+- Tokens de session
+- Certificats client
+
+Le fichier vault est chiffré avec Fernet (AES-128-CBC + HMAC-SHA256).
+La clé est dérivée d'un mot de passe maître via PBKDF2 (600000 itérations).
+
+Choix de sécurité :
+- PBKDF2 avec 600 000 itérations : recommandation OWASP 2023 pour SHA-256.
+  Compromis acceptable entre temps de dérivation (~0.5s) et résistance au brute-force.
+- Fernet (AES-128-CBC + HMAC-SHA256) : chiffrement authentifié, empêche les
+  modifications silencieuses du fichier vault. Bibliothèque maintenue et auditée.
+- Salt aléatoire de 16 bytes : empêche les attaques par rainbow table.
+  Stocké en clair en préfixe du fichier (le salt n'est pas un secret).
+"""
+
+import base64
+import json
+import logging
+import os
+import warnings
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+logger = logging.getLogger(__name__)
+
+# Types de credentials supportés
+CREDENTIAL_TYPES = {"login", "totp_seed", "session_token", "certificate"}
+
+# Taille du salt en bytes
+SALT_SIZE = 16
+
+# Nombre d'itérations PBKDF2 — recommandation OWASP 2023 pour SHA-256
+PBKDF2_ITERATIONS = 600_000
+
+# Tentative d'import de cryptography pour le chiffrement Fernet
+_HAS_FERNET = False
+try:
+    from cryptography.fernet import Fernet, InvalidToken
+    from cryptography.hazmat.primitives import hashes
+    from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC
+
+    _HAS_FERNET = True
+except ImportError:
+    _HAS_FERNET = False
+    warnings.warn(
+        "Module 'cryptography' non disponible. Le vault utilisera un encodage "
+        "base64 NON SÉCURISÉ. NE PAS utiliser en production.",
+        stacklevel=2,
+    )
+
+
+class CredentialVault:
+    """Coffre-fort chiffré pour les credentials d'applications.
+
+    Usage :
+        vault = CredentialVault("/chemin/vault.enc", "mot_de_passe_maitre")
+        vault.add_credential("DPI_Crossway", "login", {
+            "username": "robot_lea", "password": "xxx", "domain": "HOPITAL"
+        })
+        vault.save()
+
+        creds = vault.get_credential("DPI_Crossway", "login")
+    """
+
+    def __init__(self, vault_path: str, master_password: str):
+        """Charge ou crée un vault chiffré.
+
+        Args:
+            vault_path: Chemin du fichier vault chiffré sur disque.
+            master_password: Mot de passe maître pour dériver la clé de chiffrement.
+        """
+        self._vault_path = Path(vault_path)
+        self._master_password = master_password
+        self._data: Dict[str, Any] = {
+            "version": "1.0",
+            "created_at": datetime.now(timezone.utc).isoformat(),
+            "credentials": {},
+        }
+
+        if self._vault_path.exists():
+            self._load()
+        else:
+            logger.info("Vault inexistant, création d'un nouveau vault : %s", vault_path)
+
+    # =========================================================================
+    # API publique
+    # =========================================================================
+
+    def add_credential(
+        self, app_name: str, credential_type: str, data: Dict[str, Any]
+    ) -> None:
+        """Ajoute ou met à jour un credential pour une application.
+
+        Args:
+            app_name: Nom de l'application (ex: "DPI_Crossway").
+            credential_type: Type parmi "login", "totp_seed", "session_token", "certificate".
+            data: Dictionnaire avec les champs spécifiques au type.
+
+        Raises:
+            ValueError: Si le credential_type n'est pas supporté.
+        """
+        if credential_type not in CREDENTIAL_TYPES:
+            raise ValueError(
+                f"Type de credential invalide : {credential_type!r}. "
+                f"Types supportés : {CREDENTIAL_TYPES}"
+            )
+
+        if app_name not in self._data["credentials"]:
+            self._data["credentials"][app_name] = {}
+
+        self._data["credentials"][app_name][credential_type] = data
+        logger.info(
+            "Credential ajouté : app=%s type=%s", app_name, credential_type
+        )
+
+    def get_credential(
+        self, app_name: str, credential_type: str
+    ) -> Optional[Dict[str, Any]]:
+        """Récupère un credential pour une application.
+
+        Args:
+            app_name: Nom de l'application.
+            credential_type: Type de credential recherché.
+
+        Returns:
+            Dictionnaire du credential, ou None si non trouvé.
+        """
+        app_creds = self._data["credentials"].get(app_name, {})
+        return app_creds.get(credential_type)
+
+    def remove_credential(self, app_name: str, credential_type: str) -> bool:
+        """Supprime un credential.
+
+        Args:
+            app_name: Nom de l'application.
+            credential_type: Type de credential à supprimer.
+
+        Returns:
+            True si supprimé, False si non trouvé.
+        """
+        app_creds = self._data["credentials"].get(app_name, {})
+        if credential_type in app_creds:
+            del app_creds[credential_type]
+            # Nettoyer l'app si plus de credentials
+            if not app_creds:
+                del self._data["credentials"][app_name]
+            logger.info(
+                "Credential supprimé : app=%s type=%s", app_name, credential_type
+            )
+            return True
+        return False
+
+    def list_apps(self) -> List[str]:
+        """Liste les noms d'applications configurées.
+
+        Returns:
+            Liste triée des noms d'applications.
+        """
+        return sorted(self._data["credentials"].keys())
+
+    def list_credential_types(self, app_name: str) -> List[str]:
+        """Liste les types de credentials pour une application.
+
+        Args:
+            app_name: Nom de l'application.
+
+        Returns:
+            Liste des types de credentials configurés.
+        """
+        return list(self._data["credentials"].get(app_name, {}).keys())
+
+    def save(self) -> None:
+        """Chiffre et sauvegarde le vault sur disque."""
+        plaintext = json.dumps(self._data, ensure_ascii=False, indent=2).encode("utf-8")
+        encrypted = self._encrypt(plaintext)
+
+        # Écriture atomique via fichier temporaire
+        tmp_path = self._vault_path.with_suffix(".tmp")
+        self._vault_path.parent.mkdir(parents=True, exist_ok=True)
+        tmp_path.write_bytes(encrypted)
+        tmp_path.rename(self._vault_path)
+
+        logger.info("Vault sauvegardé : %s (%d bytes)", self._vault_path, len(encrypted))
+
+    # =========================================================================
+    # Chiffrement / Déchiffrement
+    # =========================================================================
+
+    def _derive_key(self, password: str, salt: bytes) -> bytes:
+        """Dérive une clé Fernet à partir du mot de passe maître.
+
+        Utilise PBKDF2-HMAC-SHA256 avec 600 000 itérations (OWASP 2023).
+        La sortie est encodée en base64 URL-safe pour Fernet (32 bytes → 44 chars).
+
+        Args:
+            password: Mot de passe maître.
+            salt: Salt aléatoire (16 bytes minimum).
+
+        Returns:
+            Clé Fernet encodée en base64 URL-safe (44 bytes).
+        """
+        if _HAS_FERNET:
+            kdf = PBKDF2HMAC(
+                algorithm=hashes.SHA256(),
+                length=32,
+                salt=salt,
+                iterations=PBKDF2_ITERATIONS,
+            )
+            key = base64.urlsafe_b64encode(kdf.derive(password.encode("utf-8")))
+            return key
+        else:
+            # Fallback non sécurisé — simple hash pour le développement
+            import hashlib
+
+            dk = hashlib.pbkdf2_hmac(
+                "sha256", password.encode("utf-8"), salt, PBKDF2_ITERATIONS
+            )
+            return base64.urlsafe_b64encode(dk)
+
+    def _encrypt(self, plaintext: bytes) -> bytes:
+        """Chiffre les données avec Fernet (ou base64 en fallback).
+
+        Format du fichier vault :
+            [16 bytes salt][données chiffrées Fernet]
+
+        Args:
+            plaintext: Données en clair à chiffrer.
+
+        Returns:
+            Bytes chiffrés avec le salt en préfixe.
+        """
+        salt = os.urandom(SALT_SIZE)
+        key = self._derive_key(self._master_password, salt)
+
+        if _HAS_FERNET:
+            fernet = Fernet(key)
+            encrypted = fernet.encrypt(plaintext)
+        else:
+            # Fallback : base64 simple (NON sécurisé)
+            encrypted = base64.urlsafe_b64encode(plaintext)
+
+        return salt + encrypted
+
+    def _decrypt(self, encrypted_data: bytes) -> bytes:
+        """Déchiffre les données.
+
+        Args:
+            encrypted_data: Bytes chiffrés (salt + données Fernet).
+
+        Returns:
+            Données déchiffrées.
+
+        Raises:
+            ValueError: Si le mot de passe est incorrect ou les données corrompues.
+        """
+        if len(encrypted_data) < SALT_SIZE:
+            raise ValueError("Fichier vault corrompu (trop court)")
+
+        salt = encrypted_data[:SALT_SIZE]
+        ciphertext = encrypted_data[SALT_SIZE:]
+        key = self._derive_key(self._master_password, salt)
+
+        if _HAS_FERNET:
+            try:
+                fernet = Fernet(key)
+                return fernet.decrypt(ciphertext)
+            except InvalidToken:
+                raise ValueError(
+                    "Mot de passe maître incorrect ou fichier vault corrompu"
+                )
+        else:
+            # Fallback : base64 simple
+            return base64.urlsafe_b64decode(ciphertext)
+
+    # =========================================================================
+    # Chargement
+    # =========================================================================
+
+    def _load(self) -> None:
+        """Charge et déchiffre le vault depuis le disque."""
+        try:
+            encrypted_data = self._vault_path.read_bytes()
+            plaintext = self._decrypt(encrypted_data)
+            self._data = json.loads(plaintext.decode("utf-8"))
+            logger.info(
+                "Vault chargé : %s (%d apps)",
+                self._vault_path,
+                len(self._data.get("credentials", {})),
+            )
+        except (ValueError, json.JSONDecodeError) as e:
+            raise ValueError(f"Impossible de charger le vault : {e}") from e
diff --git a/core/auth/manage_vault.py b/core/auth/manage_vault.py
new file mode 100644
index 000000000..08f6b27bd
--- /dev/null
+++ b/core/auth/manage_vault.py
@@ -0,0 +1,213 @@
+#!/usr/bin/env python3
+"""
+CLI de gestion du coffre-fort de credentials (vault).
+
+Usage :
+    # Ajouter un login
+    python -m core.auth.manage_vault --vault /path/to/vault.enc --action add \
+        --app "DPI_Crossway" --type login \
+        --username "robot_lea" --password "xxx"
+
+    # Ajouter un seed TOTP
+    python -m core.auth.manage_vault --vault /path/to/vault.enc --action add \
+        --app "DPI_Crossway" --type totp_seed \
+        --secret "JBSWY3DPEHPK3PXP"
+
+    # Lister les applications configurées
+    python -m core.auth.manage_vault --vault /path/to/vault.enc --action list
+
+    # Générer un code TOTP
+    python -m core.auth.manage_vault --vault /path/to/vault.enc --action generate-totp \
+        --app "DPI_Crossway"
+
+    # Supprimer un credential
+    python -m core.auth.manage_vault --vault /path/to/vault.enc --action remove \
+        --app "DPI_Crossway" --type login
+
+Le mot de passe maître est demandé interactivement via getpass.
+"""
+
+import argparse
+import getpass
+import sys
+
+from .credential_vault import CredentialVault
+from .totp_generator import TOTPGenerator
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Gestionnaire de coffre-fort de credentials pour Léa.",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=__doc__,
+    )
+    parser.add_argument(
+        "--vault",
+        required=True,
+        help="Chemin du fichier vault chiffré",
+    )
+    parser.add_argument(
+        "--action",
+        required=True,
+        choices=["add", "list", "remove", "generate-totp", "show"],
+        help="Action à effectuer",
+    )
+    parser.add_argument("--app", help="Nom de l'application")
+    parser.add_argument(
+        "--type",
+        dest="cred_type",
+        choices=["login", "totp_seed", "session_token", "certificate"],
+        help="Type de credential",
+    )
+    # Champs pour le type "login"
+    parser.add_argument("--username", help="Nom d'utilisateur (type login)")
+    parser.add_argument("--password", help="Mot de passe (type login)")
+    parser.add_argument("--domain", help="Domaine Windows (type login, optionnel)")
+    # Champs pour le type "totp_seed"
+    parser.add_argument("--secret", help="Secret base32 (type totp_seed)")
+    parser.add_argument(
+        "--digits", type=int, default=6, help="Nombre de chiffres TOTP (défaut: 6)"
+    )
+    parser.add_argument(
+        "--interval", type=int, default=30, help="Intervalle TOTP en secondes (défaut: 30)"
+    )
+    parser.add_argument(
+        "--algorithm", default="SHA1", help="Algorithme HMAC (défaut: SHA1)"
+    )
+
+    args = parser.parse_args()
+
+    # Demander le mot de passe maître
+    master_password = getpass.getpass("Mot de passe maître : ")
+    if not master_password:
+        print("Erreur : mot de passe maître requis.", file=sys.stderr)
+        sys.exit(1)
+
+    try:
+        vault = CredentialVault(args.vault, master_password)
+    except ValueError as e:
+        print(f"Erreur d'ouverture du vault : {e}", file=sys.stderr)
+        sys.exit(1)
+
+    # ---- Actions ----
+
+    if args.action == "list":
+        apps = vault.list_apps()
+        if not apps:
+            print("Vault vide — aucune application configurée.")
+        else:
+            print(f"Applications configurées ({len(apps)}) :")
+            for app in apps:
+                types = vault.list_credential_types(app)
+                print(f"  {app} : {', '.join(types)}")
+
+    elif args.action == "add":
+        if not args.app:
+            print("Erreur : --app requis pour l'action 'add'.", file=sys.stderr)
+            sys.exit(1)
+        if not args.cred_type:
+            print("Erreur : --type requis pour l'action 'add'.", file=sys.stderr)
+            sys.exit(1)
+
+        if args.cred_type == "login":
+            if not args.username:
+                args.username = input("Username : ")
+            if not args.password:
+                args.password = getpass.getpass("Password : ")
+            data = {"username": args.username, "password": args.password}
+            if args.domain:
+                data["domain"] = args.domain
+
+        elif args.cred_type == "totp_seed":
+            if not args.secret:
+                args.secret = input("Secret base32 : ")
+            data = {
+                "secret": args.secret,
+                "digits": args.digits,
+                "interval": args.interval,
+                "algorithm": args.algorithm,
+            }
+
+        elif args.cred_type == "session_token":
+            token = input("Token de session : ")
+            data = {"token": token}
+
+        elif args.cred_type == "certificate":
+            cert_path = input("Chemin du certificat : ")
+            key_path = input("Chemin de la clé privée : ")
+            data = {"cert_path": cert_path, "key_path": key_path}
+
+        else:
+            print(f"Type non géré : {args.cred_type}", file=sys.stderr)
+            sys.exit(1)
+
+        vault.add_credential(args.app, args.cred_type, data)
+        vault.save()
+        print(f"Credential ajouté : {args.app} / {args.cred_type}")
+
+    elif args.action == "remove":
+        if not args.app or not args.cred_type:
+            print(
+                "Erreur : --app et --type requis pour l'action 'remove'.",
+                file=sys.stderr,
+            )
+            sys.exit(1)
+        removed = vault.remove_credential(args.app, args.cred_type)
+        if removed:
+            vault.save()
+            print(f"Credential supprimé : {args.app} / {args.cred_type}")
+        else:
+            print(f"Credential non trouvé : {args.app} / {args.cred_type}")
+
+    elif args.action == "generate-totp":
+        if not args.app:
+            print(
+                "Erreur : --app requis pour l'action 'generate-totp'.",
+                file=sys.stderr,
+            )
+            sys.exit(1)
+        totp_creds = vault.get_credential(args.app, "totp_seed")
+        if not totp_creds:
+            print(
+                f"Pas de seed TOTP configuré pour '{args.app}'.",
+                file=sys.stderr,
+            )
+            sys.exit(1)
+
+        totp = TOTPGenerator(
+            secret=totp_creds["secret"],
+            digits=totp_creds.get("digits", 6),
+            interval=totp_creds.get("interval", 30),
+            algorithm=totp_creds.get("algorithm", "SHA1"),
+        )
+        code = totp.generate()
+        remaining = totp.time_remaining()
+        print(f"Code TOTP : {code}")
+        print(f"Expire dans : {remaining}s")
+
+    elif args.action == "show":
+        if not args.app:
+            print(
+                "Erreur : --app requis pour l'action 'show'.",
+                file=sys.stderr,
+            )
+            sys.exit(1)
+        types = vault.list_credential_types(args.app)
+        if not types:
+            print(f"Aucun credential pour '{args.app}'.")
+        else:
+            print(f"Credentials pour '{args.app}' :")
+            for cred_type in types:
+                cred = vault.get_credential(args.app, cred_type)
+                # Masquer les mots de passe et secrets
+                display = {}
+                for k, v in (cred or {}).items():
+                    if k in ("password", "secret", "token"):
+                        display[k] = v[:3] + "***" if len(str(v)) > 3 else "***"
+                    else:
+                        display[k] = v
+                print(f"  {cred_type} : {display}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/core/auth/totp_generator.py b/core/auth/totp_generator.py
new file mode 100644
index 000000000..a0c1dcaa3
--- /dev/null
+++ b/core/auth/totp_generator.py
@@ -0,0 +1,183 @@
+"""
+Générateur TOTP (Time-based One-Time Password) pour l'authentification 2FA.
+
+Implémente RFC 6238 directement, sans dépendance externe.
+Compatible avec FreeOTP, Google Authenticator, Microsoft Authenticator.
+
+Algorithme (RFC 6238 / RFC 4226) :
+1. Décoder le secret partagé depuis base32
+2. Calculer le compteur temporel T = floor(unix_time / interval)
+3. Encoder T en big-endian 8 bytes
+4. Calculer HMAC-SHA1(secret, T) (ou SHA-256/SHA-512 selon config)
+5. Extraction dynamique (dynamic truncation) :
+   - offset = dernier octet du HMAC & 0x0F
+   - extraire 4 bytes à partir de offset
+   - masquer le bit de signe (& 0x7FFFFFFF)
+   - modulo 10^digits pour obtenir le code
+"""
+
+import base64
+import hashlib
+import hmac
+import logging
+import struct
+import time
+
+logger = logging.getLogger(__name__)
+
+# Mapping des algorithmes supportés
+_HASH_ALGORITHMS = {
+    "SHA1": hashlib.sha1,
+    "SHA256": hashlib.sha256,
+    "SHA512": hashlib.sha512,
+}
+
+
+class TOTPGenerator:
+    """Générateur de codes TOTP conformes à la RFC 6238.
+
+    Usage :
+        totp = TOTPGenerator("JBSWY3DPEHPK3PXP")
+        code = totp.generate()       # "492039"
+        remaining = totp.time_remaining()  # 17 (secondes)
+        valid = totp.verify("492039")  # True
+    """
+
+    def __init__(
+        self,
+        secret: str,
+        digits: int = 6,
+        interval: int = 30,
+        algorithm: str = "SHA1",
+    ):
+        """Initialise le générateur TOTP.
+
+        Args:
+            secret: Clé secrète encodée en base32 (standard TOTP).
+            digits: Nombre de chiffres du code (6 ou 8, défaut 6).
+            interval: Intervalle en secondes entre deux codes (défaut 30).
+            algorithm: Algorithme HMAC ("SHA1", "SHA256", "SHA512").
+
+        Raises:
+            ValueError: Si le secret n'est pas du base32 valide ou l'algorithme inconnu.
+        """
+        # Normaliser et décoder le secret base32
+        # Les secrets TOTP peuvent contenir des espaces pour la lisibilité
+        clean_secret = secret.upper().replace(" ", "")
+        # Ajouter du padding base32 si nécessaire
+        padding = (8 - len(clean_secret) % 8) % 8
+        clean_secret += "=" * padding
+
+        try:
+            self._secret_bytes = base64.b32decode(clean_secret)
+        except Exception as e:
+            raise ValueError(f"Secret base32 invalide : {e}") from e
+
+        if algorithm.upper() not in _HASH_ALGORITHMS:
+            raise ValueError(
+                f"Algorithme non supporté : {algorithm!r}. "
+                f"Valeurs acceptées : {list(_HASH_ALGORITHMS.keys())}"
+            )
+
+        self._digits = digits
+        self._interval = interval
+        self._algorithm = algorithm.upper()
+
+    def generate(self, timestamp: float | None = None) -> str:
+        """Génère le code TOTP pour l'instant présent (ou un timestamp donné).
+
+        Args:
+            timestamp: Timestamp Unix optionnel (pour les tests). Si None, utilise time.time().
+
+        Returns:
+            Code TOTP sous forme de chaîne zero-padded (ex: "003271").
+        """
+        if timestamp is None:
+            timestamp = time.time()
+
+        counter = int(timestamp) // self._interval
+        return self._generate_hotp(counter)
+
+    def time_remaining(self) -> int:
+        """Nombre de secondes avant expiration du code actuel.
+
+        Returns:
+            Secondes restantes (entre 1 et interval).
+        """
+        return self._interval - (int(time.time()) % self._interval)
+
+    def verify(self, code: str, timestamp: float | None = None, window: int = 1) -> bool:
+        """Vérifie un code TOTP avec une fenêtre de tolérance.
+
+        La fenêtre permet de compenser le décalage d'horloge entre client et serveur.
+        Avec window=1, on vérifie le code actuel, le précédent et le suivant.
+
+        Args:
+            code: Code TOTP à vérifier.
+            timestamp: Timestamp Unix optionnel.
+            window: Nombre d'intervalles de tolérance de chaque côté (défaut 1).
+
+        Returns:
+            True si le code correspond à un intervalle dans la fenêtre.
+        """
+        if timestamp is None:
+            timestamp = time.time()
+
+        counter = int(timestamp) // self._interval
+
+        for offset in range(-window, window + 1):
+            check_counter = counter + offset
+            if check_counter < 0:
+                continue  # Compteur négatif impossible
+            expected = self._generate_hotp(check_counter)
+            # Comparaison en temps constant pour éviter les timing attacks
+            if hmac.compare_digest(code, expected):
+                return True
+
+        return False
+
+    # =========================================================================
+    # Implémentation interne HOTP (RFC 4226)
+    # =========================================================================
+
+    def _generate_hotp(self, counter: int) -> str:
+        """Génère un code HOTP pour un compteur donné.
+
+        Implémentation conforme à la RFC 4226 section 5.3 :
+        1. Encoder le compteur en big-endian 8 bytes
+        2. HMAC avec l'algorithme configuré
+        3. Truncation dynamique
+        4. Réduction modulo 10^digits
+
+        Args:
+            counter: Valeur du compteur (entier 64 bits).
+
+        Returns:
+            Code HOTP zero-padded.
+        """
+        # Étape 1 : Compteur en big-endian 8 bytes
+        counter_bytes = struct.pack(">Q", counter)
+
+        # Étape 2 : HMAC
+        hash_func = _HASH_ALGORITHMS[self._algorithm]
+        hmac_digest = hmac.new(
+            self._secret_bytes, counter_bytes, hash_func
+        ).digest()
+
+        # Étape 3 : Truncation dynamique (RFC 4226 section 5.4)
+        # L'offset est déterminé par les 4 bits de poids faible du dernier octet
+        offset = hmac_digest[-1] & 0x0F
+
+        # Extraire 4 bytes à partir de l'offset et masquer le bit de signe
+        truncated = (
+            ((hmac_digest[offset] & 0x7F) << 24)
+            | ((hmac_digest[offset + 1] & 0xFF) << 16)
+            | ((hmac_digest[offset + 2] & 0xFF) << 8)
+            | (hmac_digest[offset + 3] & 0xFF)
+        )
+
+        # Étape 4 : Réduction modulo pour obtenir le nombre de chiffres voulu
+        code = truncated % (10 ** self._digits)
+
+        # Zero-padding pour garantir la longueur
+        return str(code).zfill(self._digits)
diff --git a/core/detection/ollama_client.py b/core/detection/ollama_client.py
index 3de20377d..4eaab7171 100644
--- a/core/detection/ollama_client.py
+++ b/core/detection/ollama_client.py
@@ -26,7 +26,7 @@ class OllamaClient:
     def __init__(self, 
                  endpoint: str = "http://localhost:11434",
                  model: str = "qwen3-vl:8b",
-                 timeout: int = 60):
+                 timeout: int = 180):
         """
         Initialiser le client Ollama
         
@@ -63,14 +63,21 @@ class OllamaClient:
                 system_prompt: Optional[str] = None,
                 temperature: float = 0.1,
                 max_tokens: int = 500,
-                force_json: bool = False) -> Dict[str, Any]:
+                force_json: bool = False,
+                assistant_prefill: Optional[str] = None,
+                num_ctx: Optional[int] = None,
+                extra_images_b64: Optional[List[str]] = None) -> Dict[str, Any]:
         """
         Générer une réponse du VLM via l'API chat d'Ollama.
 
-        Note: On utilise /api/chat au lieu de /api/generate car qwen3-vl
-        avec /api/generate consomme tous les tokens en thinking interne
-        et retourne une réponse vide. L'API chat gère correctement
-        le mode /no_think et sépare thinking/réponse.
+        Pour les modèles thinking (qwen3-vl), on utilise la technique du
+        "assistant prefill" : un message assistant pré-rempli est ajouté
+        après le message user, forçant le modèle à continuer directement
+        sans phase de thinking. Cela résout le bug Ollama 0.18.x où
+        think=false est ignoré par le renderer qwen3-vl-thinking.
+
+        Sans prefill : le modèle pense 500+ tokens puis répond (~180s)
+        Avec prefill : le modèle répond directement (~1-5s)
 
         Args:
             prompt: Prompt textuel
@@ -80,6 +87,11 @@ class OllamaClient:
             temperature: Température de génération
             max_tokens: Nombre max de tokens
             force_json: Forcer la sortie JSON (non recommandé pour qwen3-vl)
+            assistant_prefill: Début de réponse pré-rempli (auto-détecté si None)
+            num_ctx: Context window (défaut 2048, augmenter pour batch)
+            extra_images_b64: Images supplémentaires en base64 à envoyer avec le prompt.
+                Ajoutées après l'image principale. Utile pour le VLM multi-image
+                (ex: screenshot + crop de référence).
 
         Returns:
             Dict avec 'response', 'success', 'error'
@@ -93,17 +105,19 @@ class OllamaClient:
                 image_data = self._encode_image_from_pil(image)
 
             # Nettoyer le prompt — retirer /no_think et /nothink du texte
-            # car le mode thinking est contrôlé via le paramètre think=false
-            # de l'API chat. Les préfixes /no_think dans le prompt causent
-            # paradoxalement PLUS de thinking interne chez qwen3-vl.
             effective_prompt = prompt.replace("/no_think\n", "").replace("/no_think", "")
             effective_prompt = effective_prompt.replace("/nothink ", "").replace("/nothink", "")
             effective_prompt = effective_prompt.strip()
 
             # Construire le message utilisateur
             user_message = {"role": "user", "content": effective_prompt}
+            all_images = []
             if image_data:
-                user_message["images"] = [image_data]
+                all_images.append(image_data)
+            if extra_images_b64:
+                all_images.extend(extra_images_b64)
+            if all_images:
+                user_message["images"] = all_images
 
             # Construire les messages
             messages = []
@@ -111,9 +125,37 @@ class OllamaClient:
                 messages.append({"role": "system", "content": system_prompt})
             messages.append(user_message)
 
-            # Déterminer si le modèle supporte le thinking
+            # Déterminer si le modèle est un modèle thinking (qwen3)
             is_thinking_model = "qwen3" in self.model.lower()
 
+            # WORKAROUND Ollama 0.18.x : think=false est ignoré par le
+            # renderer qwen3-vl-thinking. On utilise un assistant prefill
+            # pour forcer le modèle à skip le thinking et répondre directement.
+            # Le prefill est choisi en fonction du format attendu.
+            # IMPORTANT : avec image, sans prefill le thinking dépasse 180s.
+            prefill_used = None
+            if is_thinking_model:
+                if assistant_prefill is not None:
+                    prefill_used = assistant_prefill
+                elif force_json:
+                    prefill_used = "{"
+                elif all_images:
+                    # Avec image(s), le thinking est catastrophique (>180s).
+                    # Prefill générique pour forcer une réponse directe.
+                    prefill_used = "Based on the image,"
+
+                if prefill_used is not None:
+                    messages.append({
+                        "role": "assistant",
+                        "content": prefill_used
+                    })
+
+            # num_ctx par défaut à 2048 (correspondant au default du modèle
+            # chargé en mémoire). Changer num_ctx force un rechargement du
+            # KV cache (~30s de pénalité), donc ne l'augmenter que pour les
+            # requêtes batch qui dépassent la limite (image + prompt long).
+            effective_num_ctx = num_ctx or 2048
+
             payload = {
                 "model": self.model,
                 "messages": messages,
@@ -121,13 +163,13 @@ class OllamaClient:
                 "options": {
                     "temperature": temperature,
                     "num_predict": max_tokens,
-                    "num_ctx": 2048,
+                    "num_ctx": effective_num_ctx,
                     "top_k": 1
                 }
             }
 
-            # Désactiver le thinking pour les modèles qui le supportent
-            # Cela réduit drastiquement la consommation de tokens et le temps
+            # Garder think=false au cas où une future version d'Ollama le
+            # corrige — le prefill reste le mécanisme principal
             if is_thinking_model:
                 payload["think"] = False
 
@@ -144,6 +186,11 @@ class OllamaClient:
             if response.status_code == 200:
                 result = response.json()
                 content = result.get("message", {}).get("content", "")
+
+                # Reconstituer la réponse complète en ajoutant le prefill
+                if prefill_used and content:
+                    content = prefill_used + content
+
                 return {
                     "response": content,
                     "success": True,
@@ -181,8 +228,11 @@ For each element, provide:
 - Semantic role (primary_action, cancel, submit, form_input, search_field, navigation, settings, close)
 
 Format your response as JSON."""
-        
-        result = self.generate(prompt, image_path=image_path, temperature=0.1)
+
+        result = self.generate(
+            prompt, image_path=image_path, temperature=0.1,
+            assistant_prefill="[",
+        )
         
         if result["success"]:
             try:
@@ -214,14 +264,21 @@ Format your response as JSON."""
 Choose ONLY ONE from: {types_list}
 
 Respond with just the type name, nothing else."""
-        
+
         if context:
             prompt += f"\n\nContext: {context}"
-        
-        result = self.generate(prompt, image=element_image, temperature=0.1)
+
+        result = self.generate(
+            prompt, image=element_image, temperature=0.1,
+            assistant_prefill="The type is:",
+        )
         
         if result["success"]:
-            element_type = result["response"].strip().lower()
+            # Retirer le prefill du début pour extraire le type
+            raw = result["response"]
+            if raw.startswith("The type is:"):
+                raw = raw[len("The type is:"):]
+            element_type = raw.strip().lower()
             # Valider que c'est un type connu
             valid_types = types_list.split(", ")
             if element_type in valid_types:
@@ -255,14 +312,21 @@ Respond with just the type name, nothing else."""
 Choose ONLY ONE from: {roles_list}
 
 Respond with just the role name, nothing else."""
-        
+
         if context:
             prompt += f"\n\nContext: {context}"
-        
-        result = self.generate(prompt, image=element_image, temperature=0.1)
+
+        result = self.generate(
+            prompt, image=element_image, temperature=0.1,
+            assistant_prefill="The role is:",
+        )
         
         if result["success"]:
-            role = result["response"].strip().lower()
+            # Retirer le prefill du début pour extraire le rôle
+            raw = result["response"]
+            if raw.startswith("The role is:"):
+                raw = raw[len("The role is:"):]
+            role = raw.strip().lower()
             # Valider que c'est un rôle connu
             valid_roles = roles_list.split(", ")
             if role in valid_roles:
@@ -286,12 +350,19 @@ Respond with just the role name, nothing else."""
             Dict avec 'text' extrait
         """
         prompt = "Extract all visible text from this image. Return only the text, nothing else."
-        
-        result = self.generate(prompt, image=image, temperature=0.1)
+
+        result = self.generate(
+            prompt, image=image, temperature=0.1,
+            assistant_prefill="Text:",
+        )
         
         if result["success"]:
-            return {"text": result["response"].strip(), "success": True}
-        
+            # Retirer le prefill du début pour extraire le texte
+            raw = result["response"]
+            if raw.startswith("Text:"):
+                raw = raw[len("Text:"):]
+            return {"text": raw.strip(), "success": True}
+
         return {"text": "", "success": False, "error": result["error"]}
 
     # Taille minimum pour une classification fiable par le VLM
@@ -346,7 +417,8 @@ Your answer:"""
                 system_prompt=system_prompt,
                 temperature=0.1,
                 max_tokens=300,
-                force_json=False
+                force_json=False,
+                assistant_prefill="{"
             )
 
             if not result["success"]:
diff --git a/core/detection/ui_detector.py b/core/detection/ui_detector.py
index 52723857c..adda88a75 100644
--- a/core/detection/ui_detector.py
+++ b/core/detection/ui_detector.py
@@ -220,7 +220,7 @@ class UIDetector:
         # des centaines d'appels VLM inutiles (~2-3s chacun).
         # On garde max 80 candidats — suffisant pour obtenir ~50 éléments
         # après filtrage par confiance, tout en gardant un temps raisonnable.
-        max_candidates = 30  # 30 suffisent pour les éléments principaux (~6min/screenshot au lieu de 17)
+        max_candidates = 10  # 10 régions : compact, rapide (~5-10s avec prefill)
         if len(regions) > max_candidates:
             # Trier par confiance décroissante, puis par surface décroissante
             regions.sort(key=lambda r: (r.confidence, r.w * r.h), reverse=True)
@@ -489,32 +489,18 @@ class UIDetector:
         if not self.vlm_client or not regions:
             return None
 
-        # Construire la description des régions pour le prompt
+        # Construire une description compacte des régions (économise les tokens)
         regions_desc_lines = []
         for i, r in enumerate(regions):
-            regions_desc_lines.append(
-                f"  #{i}: position=({r.x},{r.y}), size={r.w}x{r.h}, source={r.source}"
-            )
-        regions_description = "\n".join(regions_desc_lines)
+            regions_desc_lines.append(f"#{i}:({r.x},{r.y},{r.w}x{r.h})")
+        regions_description = " ".join(regions_desc_lines)
 
-        prompt = f"""Analyze this screenshot. I have detected UI elements at these positions:
-{regions_description}
+        prompt = f"""Classify UI elements at: {regions_description}
+Types: button,text_input,checkbox,radio,dropdown,tab,link,icon,table_row,menu_item
+Roles: primary_action,cancel,submit,form_input,search_field,navigation,settings,close,delete,edit,save
+JSON array: [{{"id":0,"type":"...","role":"...","text":"..."}}]"""
 
-For each element, classify it as a JSON array. Each entry must have:
-- "id": the element number (matching # above)
-- "type": one of button, text_input, checkbox, radio, dropdown, tab, link, icon, table_row, menu_item
-- "role": one of primary_action, cancel, submit, form_input, search_field, navigation, settings, close, delete, edit, save
-- "text": visible text on the element (empty string if none)
-
-Return ONLY the JSON array, nothing else. Example:
-[{{"id": 0, "type": "button", "role": "submit", "text": "OK"}}, {{"id": 1, "type": "text_input", "role": "form_input", "text": ""}}]
-
-Your answer:"""
-
-        system_prompt = (
-            "You are a JSON-only UI classifier. No thinking. No explanation. "
-            "Output a raw JSON array only."
-        )
+        system_prompt = "JSON-only UI classifier. No explanation."
 
         # Appel VLM unique avec le screenshot complet
         for attempt in range(2):
@@ -523,8 +509,10 @@ Your answer:"""
                 image=pil_image,
                 system_prompt=system_prompt,
                 temperature=0.1,
-                max_tokens=2000,  # Plus de tokens car réponse groupée
+                max_tokens=1500,  # ~100 tokens/element * 10 elements + marge
                 force_json=False,
+                assistant_prefill="[",  # Force JSON array direct, skip thinking
+                num_ctx=2048,  # 2048 suffit pour 10 régions compactes + image
             )
 
             if not result["success"]:
diff --git a/core/detection/ui_detector_old.py.bak b/core/detection/ui_detector_old.py.bak
new file mode 100644
index 000000000..ac9a793d1
--- /dev/null
+++ b/core/detection/ui_detector_old.py.bak
@@ -0,0 +1,622 @@
+"""
+UIDetector - Détection Sémantique d'Éléments UI avec VLM
+
+Utilise un Vision-Language Model (VLM) pour détecter et classifier
+les éléments UI avec leurs types et rôles sémantiques.
+"""
+
+from typing import List, Dict, Optional, Any, Tuple
+from pathlib import Path
+from dataclasses import dataclass
+import numpy as np
+from PIL import Image
+import json
+import re
+
+from ..models.ui_element import UIElement, UIElementEmbeddings, VisualFeatures
+from .ollama_client import OllamaClient, check_ollama_available
+
+
+@dataclass
+class DetectionConfig:
+    """Configuration de la détection UI"""
+    vlm_model: str = "qwen3-vl:8b"  # Modèle VLM à utiliser (qwen3-vl:8b recommandé)
+    vlm_endpoint: str = "http://localhost:11434"  # Endpoint Ollama
+    confidence_threshold: float = 0.7  # Seuil de confiance minimum
+    max_elements: int = 50  # Nombre max d'éléments à détecter
+    detect_regions: bool = True  # Détecter régions d'intérêt d'abord
+    use_embeddings: bool = True  # Générer embeddings duaux
+
+
+class UIDetector:
+    """
+    Détecteur d'éléments UI sémantique
+    
+    Utilise un VLM (Vision-Language Model) pour :
+    1. Détecter les régions d'intérêt dans un screenshot
+    2. Classifier le type de chaque élément UI
+    3. Déterminer le rôle sémantique
+    4. Extraire les features visuelles
+    5. Générer des embeddings duaux (image + texte)
+    """
+    
+    def __init__(self, config: Optional[DetectionConfig] = None):
+        """
+        Initialiser le détecteur
+        
+        Args:
+            config: Configuration (utilise config par défaut si None)
+        """
+        self.config = config or DetectionConfig()
+        self.vlm_client = None
+        self._initialize_vlm()
+    
+    def _initialize_vlm(self) -> None:
+        """Initialiser le client VLM (Ollama)"""
+        try:
+            # Vérifier si Ollama est disponible
+            if check_ollama_available(self.config.vlm_endpoint):
+                self.vlm_client = OllamaClient(
+                    endpoint=self.config.vlm_endpoint,
+                    model=self.config.vlm_model
+                )
+                print(f"✓ VLM initialized: {self.config.vlm_model} at {self.config.vlm_endpoint}")
+            else:
+                print(f"⚠ Ollama not available at {self.config.vlm_endpoint}, using simulation mode")
+                self.vlm_client = None
+        except Exception as e:
+            print(f"⚠ Failed to initialize VLM: {e}, using simulation mode")
+            self.vlm_client = None
+    
+    def detect(self, 
+              screenshot_path: str,
+              window_context: Optional[Dict[str, Any]] = None) -> List[UIElement]:
+        """
+        Détecter tous les éléments UI dans un screenshot
+        
+        Args:
+            screenshot_path: Chemin vers le screenshot
+            window_context: Contexte de la fenêtre (titre, process, etc.)
+        
+        Returns:
+            Liste d'UIElements détectés
+        """
+        # Charger image
+        image = self._load_image(screenshot_path)
+        if image is None:
+            return []
+        
+        # Détecter régions d'intérêt si activé
+        if self.config.detect_regions:
+            regions = self._detect_regions_of_interest(image, window_context)
+        else:
+            # Utiliser image complète
+            regions = [{"bbox": (0, 0, image.width, image.height), "confidence": 1.0}]
+        
+        # Détecter éléments UI dans chaque région
+        ui_elements = []
+        for region in regions:
+            elements = self._detect_elements_in_region(
+                image, 
+                region, 
+                screenshot_path,
+                window_context
+            )
+            ui_elements.extend(elements)
+        
+        # Filtrer par confiance
+        ui_elements = [
+            el for el in ui_elements 
+            if el.confidence >= self.config.confidence_threshold
+        ]
+        
+        # Limiter nombre d'éléments
+        if len(ui_elements) > self.config.max_elements:
+            # Trier par confiance et garder les meilleurs
+            ui_elements.sort(key=lambda x: x.confidence, reverse=True)
+            ui_elements = ui_elements[:self.config.max_elements]
+        
+        return ui_elements
+    
+    def _load_image(self, screenshot_path: str) -> Optional[Image.Image]:
+        """Charger une image depuis un fichier"""
+        try:
+            return Image.open(screenshot_path)
+        except Exception as e:
+            print(f"Error loading image {screenshot_path}: {e}")
+            return None
+    
+    def _detect_regions_of_interest(self,
+                                    image: Image.Image,
+                                    window_context: Optional[Dict] = None) -> List[Dict]:
+        """
+        Détecter les régions d'intérêt dans l'image
+        
+        Utilise le VLM pour identifier les zones contenant des éléments UI.
+        
+        Args:
+            image: Image PIL
+            window_context: Contexte de la fenêtre
+        
+        Returns:
+            Liste de régions {bbox: (x, y, w, h), confidence: float}
+        """
+        if self.vlm_client is None:
+            # Mode simulation : diviser l'image en grille
+            return self._simulate_region_detection(image)
+        
+        # Utiliser VLM pour détecter régions
+        # Pour l'instant, on utilise l'image complète (plus simple et efficace)
+        width, height = image.size
+        return [{
+            "bbox": (0, 0, width, height),
+            "confidence": 1.0
+        }]
+    
+    def _simulate_region_detection(self, image: Image.Image) -> List[Dict]:
+        """Simulation de détection de régions (pour développement)"""
+        width, height = image.size
+        
+        # Diviser en grille 3x3 pour simulation
+        regions = []
+        grid_size = 3
+        cell_w = width // grid_size
+        cell_h = height // grid_size
+        
+        for i in range(grid_size):
+            for j in range(grid_size):
+                regions.append({
+                    "bbox": (j * cell_w, i * cell_h, cell_w, cell_h),
+                    "confidence": 0.8
+                })
+        
+        return regions
+    
+    def _detect_elements_in_region(self,
+                                   image: Image.Image,
+                                   region: Dict,
+                                   screenshot_path: str,
+                                   window_context: Optional[Dict] = None) -> List[UIElement]:
+        """
+        Détecter éléments UI dans une région spécifique
+        
+        Args:
+            image: Image complète
+            region: Région à analyser
+            screenshot_path: Chemin du screenshot
+            window_context: Contexte de la fenêtre
+        
+        Returns:
+            Liste d'UIElements dans cette région
+        """
+        bbox = region["bbox"]
+        x, y, w, h = bbox
+        
+        # Extraire crop de la région
+        region_image = image.crop((x, y, x + w, y + h))
+        
+        # Détecter éléments avec VLM
+        if self.vlm_client is None:
+            # Mode simulation
+            return self._simulate_element_detection(
+                region_image, bbox, screenshot_path, window_context
+            )
+        
+        # Vraie détection avec VLM !
+        return self._detect_with_vlm(
+            region_image, bbox, screenshot_path, window_context
+        )
+    
+    def _detect_with_vlm(self,
+                        region_image: Image.Image,
+                        region_bbox: Tuple[int, int, int, int],
+                        screenshot_path: str,
+                        window_context: Optional[Dict] = None) -> List[UIElement]:
+        """
+        Détecter éléments UI avec le VLM (vraie détection)
+        
+        Args:
+            region_image: Image de la région
+            region_bbox: Bbox de la région (x, y, w, h)
+            screenshot_path: Chemin du screenshot
+            window_context: Contexte de la fenêtre
+        
+        Returns:
+            Liste d'UIElements détectés
+        """
+        x_offset, y_offset, w, h = region_bbox
+        
+        # Construire le prompt pour le VLM
+        context_str = ""
+        if window_context:
+            context_str = f"\nWindow context: {window_context.get('title', 'Unknown')}"
+        
+        # Approche simplifiée : demander une description structurée
+        prompt = f"""List all interactive UI elements in this screenshot.{context_str}
+
+For each element, provide:
+- type (button, text_input, checkbox, link, etc.)
+- label (visible text)
+- approximate position (top/middle/bottom, left/center/right)
+
+Format as JSON array:
+[{{"type": "button", "label": "Submit", "position": "middle-center"}}]
+
+Return ONLY the JSON array, no other text."""
+        
+        # Appeler le VLM
+        # Note: Utiliser le chemin du screenshot complet plutôt que le crop
+        # car certains VLM gèrent mieux les fichiers que les images PIL
+        result = self.vlm_client.generate(
+            prompt=prompt,
+            image_path=screenshot_path,  # Utiliser le chemin au lieu de l'image PIL
+            temperature=0.1,
+            max_tokens=1000
+        )
+        
+        if not result["success"]:
+            print(f"❌ VLM detection failed: {result.get('error', 'Unknown error')}")
+            return []
+        
+        if not result["response"] or len(result["response"].strip()) == 0:
+            print(f"⚠ VLM returned empty response")
+            return []
+        
+        # Parser la réponse JSON
+        elements = self._parse_vlm_response(
+            result["response"], 
+            region_bbox, 
+            screenshot_path,
+            window_context
+        )
+        
+        return elements
+    
+    def _parse_vlm_response(self,
+                           response: str,
+                           region_bbox: Tuple[int, int, int, int],
+                           screenshot_path: str,
+                           window_context: Optional[Dict] = None) -> List[UIElement]:
+        """
+        Parser la réponse JSON du VLM
+        
+        Args:
+            response: Réponse texte du VLM
+            region_bbox: Bbox de la région
+            screenshot_path: Chemin du screenshot
+            window_context: Contexte de la fenêtre
+        
+        Returns:
+            Liste d'UIElements
+        """
+        x_offset, y_offset, region_w, region_h = region_bbox
+        
+        try:
+            # Extraire le JSON de la réponse (peut contenir du texte avant/après)
+            json_match = re.search(r'\[.*\]', response, re.DOTALL)
+            if not json_match:
+                print(f"No JSON array found in VLM response")
+                print(f"VLM response was: {response[:500]}...")
+                return []
+            
+            elements_data = json.loads(json_match.group(0))
+            
+            if not isinstance(elements_data, list):
+                print(f"VLM response is not a JSON array")
+                return []
+            
+            elements = []
+            for i, elem_data in enumerate(elements_data):
+                try:
+                    # Gérer les positions (pourcentages ou textuelles)
+                    if 'x' in elem_data and 'y' in elem_data:
+                        # Format avec pourcentages
+                        x_pct = float(elem_data.get('x', 0))
+                        y_pct = float(elem_data.get('y', 0))
+                        w_pct = float(elem_data.get('width', 10))
+                        h_pct = float(elem_data.get('height', 5))
+                        
+                        elem_x = x_offset + int(region_w * x_pct / 100)
+                        elem_y = y_offset + int(region_h * y_pct / 100)
+                        elem_w = int(region_w * w_pct / 100)
+                        elem_h = int(region_h * h_pct / 100)
+                    else:
+                        # Format avec position textuelle (top/middle/bottom, left/center/right)
+                        position = elem_data.get('position', 'middle-center').lower()
+                        
+                        # Parser la position
+                        if 'top' in position:
+                            elem_y = y_offset + region_h // 4
+                        elif 'bottom' in position:
+                            elem_y = y_offset + 3 * region_h // 4
+                        else:  # middle
+                            elem_y = y_offset + region_h // 2
+                        
+                        if 'left' in position:
+                            elem_x = x_offset + region_w // 4
+                        elif 'right' in position:
+                            elem_x = x_offset + 3 * region_w // 4
+                        else:  # center
+                            elem_x = x_offset + region_w // 2
+                        
+                        # Taille par défaut basée sur le type
+                        elem_type = elem_data.get('type', 'button')
+                        if elem_type == 'button':
+                            elem_w, elem_h = 100, 40
+                        elif elem_type == 'text_input':
+                            elem_w, elem_h = 200, 35
+                        elif elem_type == 'checkbox':
+                            elem_w, elem_h = 25, 25
+                        else:
+                            elem_w, elem_h = 80, 30
+                    
+                    # Créer l'UIElement
+                    element = UIElement(
+                        element_id=f"vlm_{elem_x}_{elem_y}",
+                        type=elem_data.get('type', 'unknown'),
+                        role=elem_data.get('role', 'unknown'),
+                        bbox=(elem_x, elem_y, elem_w, elem_h),
+                        center=(elem_x + elem_w // 2, elem_y + elem_h // 2),
+                        label=elem_data.get('label', ''),
+                        label_confidence=0.85,  # Confiance par défaut pour VLM
+                        embeddings=UIElementEmbeddings(),
+                        visual_features=VisualFeatures(
+                            dominant_color="rgb(128, 128, 128)",
+                            has_icon=elem_data.get('type') == 'icon',
+                            shape="rectangle",
+                            size_category="medium"
+                        ),
+                        confidence=0.85,  # Confiance par défaut pour VLM
+                        metadata={
+                            "detected_by": "vlm",
+                            "model": self.config.vlm_model,
+                            "screenshot_path": screenshot_path
+                        }
+                    )
+                    
+                    elements.append(element)
+                    
+                except (KeyError, ValueError, TypeError) as e:
+                    print(f"Error parsing element {i}: {e}")
+                    continue
+            
+            return elements
+            
+        except json.JSONDecodeError as e:
+            print(f"Failed to parse VLM JSON response: {e}")
+            print(f"Response was: {response[:200]}...")
+            return []
+    
+    def _simulate_element_detection(self,
+                                    region_image: Image.Image,
+                                    region_bbox: Tuple[int, int, int, int],
+                                    screenshot_path: str,
+                                    window_context: Optional[Dict] = None) -> List[UIElement]:
+        """Simulation de détection d'éléments (pour développement)"""
+        # Pour simulation, créer quelques éléments fictifs
+        elements = []
+        
+        x_offset, y_offset, w, h = region_bbox
+        
+        # Simuler 2-3 éléments par région
+        num_elements = np.random.randint(2, 4)
+        
+        for i in range(num_elements):
+            # Position aléatoire dans la région
+            elem_w = np.random.randint(50, 150)
+            elem_h = np.random.randint(20, 60)
+            elem_x = x_offset + np.random.randint(0, max(1, w - elem_w))
+            elem_y = y_offset + np.random.randint(0, max(1, h - elem_h))
+            
+            # Type et rôle aléatoires
+            types = ["button", "text_input", "checkbox", "link", "icon"]
+            roles = ["primary_action", "cancel", "submit", "form_input", "navigation"]
+            
+            element = UIElement(
+                element_id=f"elem_{elem_x}_{elem_y}",
+                type=np.random.choice(types),
+                role=np.random.choice(roles),
+                bbox=(elem_x, elem_y, elem_w, elem_h),
+                center=(elem_x + elem_w // 2, elem_y + elem_h // 2),
+                label=f"Element {i}",
+                label_confidence=np.random.uniform(0.7, 0.95),
+                embeddings=UIElementEmbeddings(),  # Embeddings vides
+                visual_features=VisualFeatures(
+                    dominant_color="rgb(128, 128, 128)",
+                    has_icon=np.random.choice([True, False]),
+                    shape="rectangle",
+                    size_category="medium"
+                ),
+                confidence=np.random.uniform(0.7, 0.95),
+                metadata={"simulated": True, "screenshot_path": screenshot_path}
+            )
+            
+            elements.append(element)
+        
+        return elements
+    
+    def classify_type(self, 
+                     element_image: Image.Image,
+                     context: Optional[Dict] = None) -> Tuple[str, float]:
+        """
+        Classifier le type d'un élément UI
+        
+        Args:
+            element_image: Image de l'élément
+            context: Contexte additionnel
+        
+        Returns:
+            (type, confidence)
+        """
+        if self.vlm_client is None:
+            # Simulation
+            types = ["button", "text_input", "checkbox", "radio", "dropdown", 
+                    "tab", "link", "icon", "table_row", "menu_item"]
+            return np.random.choice(types), np.random.uniform(0.7, 0.95)
+        
+        # Vraie classification avec VLM
+        result = self.vlm_client.classify_element_type(element_image, context)
+        
+        if result["success"]:
+            return result["type"], result["confidence"]
+        
+        return "unknown", 0.0
+    
+    def classify_role(self,
+                     element_image: Image.Image,
+                     element_type: str,
+                     context: Optional[Dict] = None) -> Tuple[str, float]:
+        """
+        Classifier le rôle sémantique d'un élément
+        
+        Args:
+            element_image: Image de l'élément
+            element_type: Type de l'élément
+            context: Contexte additionnel
+        
+        Returns:
+            (role, confidence)
+        """
+        if self.vlm_client is None:
+            # Simulation
+            roles = ["primary_action", "cancel", "submit", "form_input", 
+                    "search_field", "navigation", "settings", "close"]
+            return np.random.choice(roles), np.random.uniform(0.7, 0.95)
+        
+        # Vraie classification avec VLM
+        result = self.vlm_client.classify_element_role(
+            element_image, 
+            element_type,
+            context
+        )
+        
+        if result["success"]:
+            return result["role"], result["confidence"]
+        
+        return "unknown", 0.0
+    
+    def extract_visual_features(self,
+                               element_image: Image.Image) -> VisualFeatures:
+        """
+        Extraire les features visuelles d'un élément
+        
+        Args:
+            element_image: Image de l'élément
+        
+        Returns:
+            VisualFeatures
+        """
+        # Calculer couleur dominante
+        img_array = np.array(element_image)
+        if len(img_array.shape) == 3:
+            # Moyenne des couleurs
+            dominant_color = tuple(img_array.mean(axis=(0, 1)).astype(int).tolist())
+        else:
+            dominant_color = (128, 128, 128)
+        
+        # Déterminer forme (simplifié)
+        width, height = element_image.size
+        aspect_ratio = width / height if height > 0 else 1.0
+        
+        if aspect_ratio > 3:
+            shape = "horizontal_bar"
+        elif aspect_ratio < 0.33:
+            shape = "vertical_bar"
+        elif 0.8 <= aspect_ratio <= 1.2:
+            shape = "square"
+        else:
+            shape = "rectangle"
+        
+        # Catégorie de taille
+        area = width * height
+        if area < 1000:
+            size_category = "small"
+        elif area < 10000:
+            size_category = "medium"
+        else:
+            size_category = "large"
+        
+        # Détection d'icône (simplifié)
+        has_icon = width < 100 and height < 100 and 0.8 <= aspect_ratio <= 1.2
+        
+        return VisualFeatures(
+            dominant_color=dominant_color,
+            has_icon=has_icon,
+            shape=shape,
+            size_category=size_category
+        )
+    
+    def generate_embeddings(self,
+                          element_image: Image.Image,
+                          element_label: str,
+                          embedder: Optional[Any] = None) -> Optional[UIElementEmbeddings]:
+        """
+        Générer embeddings duaux (image + texte) pour un élément
+        
+        Args:
+            element_image: Image de l'élément
+            element_label: Label textuel de l'élément
+            embedder: Embedder à utiliser (optionnel)
+        
+        Returns:
+            UIElementEmbeddings ou None
+        """
+        if not self.config.use_embeddings or embedder is None:
+            return None
+        
+        try:
+            # Générer embedding image
+            image_embedding_id = None
+            if hasattr(embedder, 'embed_image'):
+                # Sauvegarder temporairement l'image
+                # TODO: Implémenter sauvegarde et embedding
+                pass
+            
+            # Générer embedding texte
+            text_embedding_id = None
+            if element_label and hasattr(embedder, 'embed_text'):
+                # TODO: Implémenter embedding texte
+                pass
+            
+            if image_embedding_id or text_embedding_id:
+                return UIElementEmbeddings(
+                    image_embedding_id=image_embedding_id,
+                    text_embedding_id=text_embedding_id,
+                    provider="openclip_ViT-B-32",
+                    dimensions=512
+                )
+        except Exception as e:
+            print(f"Warning: Failed to generate embeddings: {e}")
+        
+        return None
+    
+    def set_vlm_client(self, client: Any) -> None:
+        """Définir le client VLM"""
+        self.vlm_client = client
+    
+    def get_config(self) -> DetectionConfig:
+        """Récupérer la configuration"""
+        return self.config
+
+
+# ============================================================================
+# Fonctions utilitaires
+# ============================================================================
+
+def create_detector(vlm_model: str = "qwen3-vl:8b",
+                   confidence_threshold: float = 0.7) -> UIDetector:
+    """
+    Créer un UIDetector avec configuration personnalisée
+    
+    Args:
+        vlm_model: Modèle VLM à utiliser
+        confidence_threshold: Seuil de confiance
+    
+    Returns:
+        UIDetector configuré
+    """
+    config = DetectionConfig(
+        vlm_model=vlm_model,
+        confidence_threshold=confidence_threshold
+    )
+    return UIDetector(config)
diff --git a/core/embedding/fusion_engine.py b/core/embedding/fusion_engine.py
index 997af584d..3184a70b8 100644
--- a/core/embedding/fusion_engine.py
+++ b/core/embedding/fusion_engine.py
@@ -125,18 +125,32 @@ class FusionEngine:
                       weights: Dict[str, float]) -> np.ndarray:
         """
         Fusion pondérée simple : somme pondérée des vecteurs
-        
+
         fused = w1*v1 + w2*v2 + w3*v3 + w4*v4
+
+        Les poids sont renormalisés en fonction des modalités effectivement
+        présentes, pour que la somme des poids effectifs = 1.0.
+        Exemple : si seuls image (0.5) et text (0.3) sont fournis,
+        les poids deviennent image=0.625, text=0.375.
         """
         # Initialiser vecteur résultat
         first_vector = next(iter(embeddings.values()))
         fused = np.zeros_like(first_vector, dtype=np.float32)
-        
-        # Somme pondérée
+
+        # Calculer la somme des poids des modalités présentes pour renormaliser
+        present_weight_sum = sum(
+            weights.get(modality, 0.0) for modality in embeddings
+        )
+
+        # Somme pondérée avec renormalisation
         for modality, vector in embeddings.items():
-            weight = weights.get(modality, 0.0)
-            fused += weight * vector
-        
+            raw_weight = weights.get(modality, 0.0)
+            if present_weight_sum > 1e-10:
+                effective_weight = raw_weight / present_weight_sum
+            else:
+                effective_weight = 1.0 / len(embeddings)
+            fused += effective_weight * vector
+
         return fused
     
     def _fuse_concat_projection(self,
diff --git a/core/embedding/state_embedding_builder.py b/core/embedding/state_embedding_builder.py
index a88656c05..26657fc54 100644
--- a/core/embedding/state_embedding_builder.py
+++ b/core/embedding/state_embedding_builder.py
@@ -112,7 +112,7 @@ class StateEmbeddingBuilder:
             metadata={
                 "screen_state_id": screen_state.screen_state_id,
                 "timestamp": screen_state.timestamp.isoformat(),
-                "window_title": getattr(screen_state.window, 'title', ''),
+                "window_title": getattr(screen_state.window, 'window_title', ''),
                 "created_at": datetime.now().isoformat()
             }
         )
@@ -160,15 +160,16 @@ class StateEmbeddingBuilder:
             if ui_emb is not None:
                 embeddings["ui"] = ui_emb
         
-        # Si aucun embedding calculé, créer des vecteurs par défaut
+        # Si aucun embedding calculé, retourner un vecteur zéro unique
+        # (sera ignoré par DBSCAN → noise, comportement correct)
         if not embeddings:
-            # Utiliser dimensions par défaut (512)
             default_dim = 512
+            logger.warning(
+                "Aucun embedding calculé pour ce ScreenState — "
+                "retour d'un vecteur zéro (sera traité comme noise par DBSCAN)"
+            )
             embeddings = {
-                "image": np.random.randn(default_dim).astype(np.float32),
-                "text": np.random.randn(default_dim).astype(np.float32),
-                "title": np.random.randn(default_dim).astype(np.float32),
-                "ui": np.random.randn(default_dim).astype(np.float32)
+                "image": np.zeros(default_dim, dtype=np.float32)
             }
         
         return embeddings
@@ -243,7 +244,7 @@ class StateEmbeddingBuilder:
         
         try:
             embedder = self.embedders["title"]
-            title = getattr(screen_state.window, 'title', '')
+            title = getattr(screen_state.window, 'window_title', '')
             
             if not title:
                 return None
diff --git a/core/federation/__init__.py b/core/federation/__init__.py
new file mode 100644
index 000000000..7c2b20fdc
--- /dev/null
+++ b/core/federation/__init__.py
@@ -0,0 +1,24 @@
+"""
+core.federation — Fédération des apprentissages entre clients.
+
+Exporte les connaissances anonymisées (Learning Packs) de chaque site client,
+les fusionne sur un serveur central, et redistribue le modèle enrichi.
+
+Modules :
+    learning_pack   — Format d'export, exportation, fusion
+    faiss_global    — Index FAISS global multi-clients
+"""
+
+from .learning_pack import (
+    LearningPack,
+    LearningPackExporter,
+    LearningPackMerger,
+)
+from .faiss_global import GlobalFAISSIndex
+
+__all__ = [
+    "LearningPack",
+    "LearningPackExporter",
+    "LearningPackMerger",
+    "GlobalFAISSIndex",
+]
diff --git a/core/federation/faiss_global.py b/core/federation/faiss_global.py
new file mode 100644
index 000000000..cbfbf2b31
--- /dev/null
+++ b/core/federation/faiss_global.py
@@ -0,0 +1,354 @@
+"""
+GlobalFAISSIndex — Index FAISS global fédérant les prototypes de tous les clients.
+
+Construit un index de recherche vectorielle à partir des Learning Packs
+reçus de multiples sites clients. Chaque vecteur indexé porte des métadonnées
+permettant de retrouver le pack source, le workflow et l'application d'origine.
+
+Cet index est utilisé par le serveur central (DGX Spark) pour :
+- Reconnaître instantanément un écran déjà vu chez un autre client
+- Proposer des workflows existants quand un nouveau client rencontre un écran familier
+- Mesurer la couverture applicative globale de Léa
+
+Auteur : Dom, Claude — 19 mars 2026
+"""
+
+import json
+import logging
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+import numpy as np
+
+from .learning_pack import LearningPack, ScreenPrototype
+
+logger = logging.getLogger(__name__)
+
+# Dimensions par défaut des embeddings CLIP (ViT-B-32)
+DEFAULT_DIMENSIONS = 512
+
+try:
+    import faiss
+    FAISS_AVAILABLE = True
+except ImportError:
+    FAISS_AVAILABLE = False
+    logger.warning("FAISS non installé — GlobalFAISSIndex désactivé. pip install faiss-cpu")
+
+
+@dataclass
+class GlobalSearchResult:
+    """Résultat d'une recherche dans l'index global."""
+    prototype_id: str
+    similarity: float
+    pack_source_hash: str
+    workflow_skeleton_id: str
+    node_name: str
+    app_name: str
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+class GlobalFAISSIndex:
+    """
+    Index FAISS global contenant les prototypes d'écran de tous les clients.
+
+    Chaque vecteur est associé à des métadonnées :
+        - pack_source_hash : hash du client source
+        - workflow_skeleton_id : ID du workflow d'origine
+        - node_name : nom du nœud (écran) dans le workflow
+        - app_name : nom de l'application
+
+    Usage :
+        >>> index = GlobalFAISSIndex()
+        >>> index.build_from_packs([pack_a, pack_b])
+        >>> results = index.search(query_vector, k=5)
+        >>> index.save(Path("global/faiss_index"))
+    """
+
+    def __init__(self, dimensions: int = DEFAULT_DIMENSIONS):
+        """
+        Initialiser l'index global.
+
+        Args:
+            dimensions: Nombre de dimensions des vecteurs (512 pour CLIP ViT-B-32).
+        """
+        if not FAISS_AVAILABLE:
+            raise ImportError(
+                "FAISS est requis pour GlobalFAISSIndex. "
+                "Installer avec : pip install faiss-cpu"
+            )
+
+        self.dimensions = dimensions
+        self.index: Optional["faiss.IndexFlatIP"] = None
+        self._metadata: List[Dict[str, Any]] = []
+        self._rebuild_index()
+
+    def _rebuild_index(self) -> None:
+        """Créer ou recréer l'index FAISS vide."""
+        # IndexFlatIP pour similarité cosinus (vecteurs normalisés)
+        self.index = faiss.IndexFlatIP(self.dimensions)
+        self._metadata = []
+
+    @property
+    def total_vectors(self) -> int:
+        """Nombre de vecteurs dans l'index."""
+        return self.index.ntotal if self.index is not None else 0
+
+    # ------------------------------------------------------------------
+    # Construction depuis les Learning Packs
+    # ------------------------------------------------------------------
+
+    def build_from_packs(self, packs: List[LearningPack]) -> int:
+        """
+        Construire l'index à partir d'une liste de Learning Packs.
+
+        Remplace le contenu existant de l'index.
+
+        Args:
+            packs: Liste de LearningPacks à indexer.
+
+        Returns:
+            Nombre de vecteurs ajoutés à l'index.
+        """
+        self._rebuild_index()
+
+        vectors = []
+        metadata_list = []
+
+        for pack in packs:
+            for proto in pack.screen_prototypes:
+                vec = self._proto_to_vector(proto)
+                if vec is None:
+                    continue
+
+                meta = {
+                    "prototype_id": proto.prototype_id,
+                    "pack_source_hash": pack.source_hash,
+                    "workflow_skeleton_id": self._extract_skeleton_id(proto),
+                    "node_name": self._extract_node_name(proto),
+                    "app_name": proto.app_name or "",
+                }
+                vectors.append(vec)
+                metadata_list.append(meta)
+
+        if not vectors:
+            logger.info("Aucun vecteur valide trouvé dans les packs.")
+            return 0
+
+        # Empiler et normaliser les vecteurs
+        matrix = np.array(vectors, dtype=np.float32)
+        faiss.normalize_L2(matrix)
+
+        # Ajouter à l'index
+        self.index.add(matrix)
+        self._metadata = metadata_list
+
+        logger.info(
+            "Index global construit : %d vecteurs depuis %d packs",
+            len(vectors), len(packs),
+        )
+        return len(vectors)
+
+    def add_pack(self, pack: LearningPack) -> int:
+        """
+        Ajouter les prototypes d'un pack à l'index existant (incrémental).
+
+        Args:
+            pack: LearningPack à ajouter.
+
+        Returns:
+            Nombre de vecteurs ajoutés.
+        """
+        vectors = []
+        metadata_list = []
+
+        for proto in pack.screen_prototypes:
+            vec = self._proto_to_vector(proto)
+            if vec is None:
+                continue
+
+            meta = {
+                "prototype_id": proto.prototype_id,
+                "pack_source_hash": pack.source_hash,
+                "workflow_skeleton_id": self._extract_skeleton_id(proto),
+                "node_name": self._extract_node_name(proto),
+                "app_name": proto.app_name or "",
+            }
+            vectors.append(vec)
+            metadata_list.append(meta)
+
+        if not vectors:
+            return 0
+
+        matrix = np.array(vectors, dtype=np.float32)
+        faiss.normalize_L2(matrix)
+
+        self.index.add(matrix)
+        self._metadata.extend(metadata_list)
+
+        logger.info(
+            "Pack ajouté à l'index global : +%d vecteurs (total=%d)",
+            len(vectors), self.total_vectors,
+        )
+        return len(vectors)
+
+    # ------------------------------------------------------------------
+    # Recherche
+    # ------------------------------------------------------------------
+
+    def search(
+        self, query_vector: np.ndarray, k: int = 5
+    ) -> List[GlobalSearchResult]:
+        """
+        Chercher les k écrans les plus similaires dans l'index global.
+
+        Args:
+            query_vector: Vecteur de requête (même dimension que l'index).
+            k: Nombre de résultats à retourner.
+
+        Returns:
+            Liste de GlobalSearchResult triée par similarité décroissante.
+        """
+        if self.total_vectors == 0:
+            return []
+
+        # Préparer le vecteur
+        q = np.array(query_vector, dtype=np.float32).reshape(1, -1)
+        faiss.normalize_L2(q)
+
+        k = min(k, self.total_vectors)
+        distances, indices = self.index.search(q, k)
+
+        results = []
+        for dist, idx in zip(distances[0], indices[0]):
+            if idx < 0 or idx >= len(self._metadata):
+                continue
+
+            meta = self._metadata[int(idx)]
+            results.append(GlobalSearchResult(
+                prototype_id=meta["prototype_id"],
+                similarity=float(dist),
+                pack_source_hash=meta["pack_source_hash"],
+                workflow_skeleton_id=meta["workflow_skeleton_id"],
+                node_name=meta["node_name"],
+                app_name=meta["app_name"],
+                metadata=meta,
+            ))
+
+        return results
+
+    # ------------------------------------------------------------------
+    # Persistance
+    # ------------------------------------------------------------------
+
+    def save(self, path: Path) -> None:
+        """
+        Sauvegarder l'index et ses métadonnées.
+
+        Crée deux fichiers :
+            - ``{path}.faiss``  — index FAISS binaire
+            - ``{path}.meta.json`` — métadonnées JSON
+
+        Args:
+            path: Chemin de base (sans extension).
+        """
+        path = Path(path)
+        path.parent.mkdir(parents=True, exist_ok=True)
+
+        index_path = path.with_suffix(".faiss")
+        meta_path = path.with_suffix(".meta.json")
+
+        faiss.write_index(self.index, str(index_path))
+
+        meta_data = {
+            "dimensions": self.dimensions,
+            "total_vectors": self.total_vectors,
+            "entries": self._metadata,
+        }
+        with open(meta_path, "w", encoding="utf-8") as fh:
+            json.dump(meta_data, fh, indent=2, ensure_ascii=False)
+
+        logger.info(
+            "Index global sauvegardé : %s (%d vecteurs)",
+            index_path, self.total_vectors,
+        )
+
+    @classmethod
+    def load(cls, path: Path) -> "GlobalFAISSIndex":
+        """
+        Charger un index depuis le disque.
+
+        Args:
+            path: Chemin de base (sans extension).
+
+        Returns:
+            GlobalFAISSIndex chargé et prêt à l'emploi.
+        """
+        if not FAISS_AVAILABLE:
+            raise ImportError("FAISS requis pour charger l'index global.")
+
+        path = Path(path)
+        index_path = path.with_suffix(".faiss")
+        meta_path = path.with_suffix(".meta.json")
+
+        with open(meta_path, "r", encoding="utf-8") as fh:
+            meta_data = json.load(fh)
+
+        dimensions = meta_data.get("dimensions", DEFAULT_DIMENSIONS)
+        instance = cls.__new__(cls)
+        instance.dimensions = dimensions
+        instance.index = faiss.read_index(str(index_path))
+        instance._metadata = meta_data.get("entries", [])
+
+        logger.info(
+            "Index global chargé : %s (%d vecteurs, %dd)",
+            index_path, instance.total_vectors, dimensions,
+        )
+        return instance
+
+    def get_stats(self) -> Dict[str, Any]:
+        """Statistiques de l'index global."""
+        source_hashes = set()
+        app_names = set()
+        for meta in self._metadata:
+            source_hashes.add(meta.get("pack_source_hash", ""))
+            app_name = meta.get("app_name", "")
+            if app_name:
+                app_names.add(app_name)
+
+        return {
+            "dimensions": self.dimensions,
+            "total_vectors": self.total_vectors,
+            "unique_sources": len(source_hashes),
+            "unique_apps": sorted(app_names),
+        }
+
+    # ------------------------------------------------------------------
+    # Utilitaires internes
+    # ------------------------------------------------------------------
+
+    def _proto_to_vector(self, proto: ScreenPrototype) -> Optional[np.ndarray]:
+        """Convertir un ScreenPrototype en vecteur numpy, ou None si absent."""
+        if proto.vector is None or len(proto.vector) == 0:
+            return None
+
+        vec = np.array(proto.vector, dtype=np.float32)
+        if vec.shape[0] != self.dimensions:
+            logger.warning(
+                "Prototype %s : dimensions incorrectes (%d != %d), ignoré",
+                proto.prototype_id, vec.shape[0], self.dimensions,
+            )
+            return None
+        return vec
+
+    @staticmethod
+    def _extract_skeleton_id(proto: ScreenPrototype) -> str:
+        """Extraire le workflow_id depuis le prototype_id (format: workflow_id__node_id)."""
+        parts = proto.prototype_id.split("__", 1)
+        return parts[0] if len(parts) >= 1 else ""
+
+    @staticmethod
+    def _extract_node_name(proto: ScreenPrototype) -> str:
+        """Extraire le node_id depuis le prototype_id."""
+        parts = proto.prototype_id.split("__", 1)
+        return parts[1] if len(parts) >= 2 else proto.prototype_id
diff --git a/core/federation/learning_pack.py b/core/federation/learning_pack.py
new file mode 100644
index 000000000..c61d4fb01
--- /dev/null
+++ b/core/federation/learning_pack.py
@@ -0,0 +1,961 @@
+"""
+Learning Pack — Format d'export anonymisé des apprentissages.
+
+Un LearningPack contient les connaissances extraites des workflows
+d'un client, sans aucune donnée personnelle ou sensible.
+
+Ce qu'on exporte (anonymisé) :
+    - Embeddings CLIP des prototypes d'écran (vecteurs 512d — pas réversibles)
+    - ScreenTemplates (contraintes UI : titres fenêtres, rôles éléments)
+    - Structure des workflows (nodes/edges, actions, contraintes)
+    - Patterns d'erreur rencontrés
+    - Signatures d'applications (app_name, version)
+
+Ce qu'on N'exporte PAS :
+    - Screenshots bruts
+    - Textes OCR bruts (données patient potentielles)
+    - Événements clavier bruts (mots de passe potentiels)
+    - machine_id, hostname, IP (identification du client)
+
+Structure JSON :
+    {
+        "version": "1.0",
+        "created_at": "2026-03-19T...",
+        "source_hash": "abc123...",      # SHA-256 anonyme du client
+        "pack_id": "lp_xxx",
+        "stats": { ... },
+        "app_signatures": [ ... ],
+        "screen_prototypes": [ ... ],
+        "workflow_skeletons": [ ... ],
+        "ui_patterns": [ ... ],
+        "error_patterns": [ ... ],
+        "edge_statistics": [ ... ],
+    }
+
+Auteur : Dom, Claude — 19 mars 2026
+"""
+
+import hashlib
+import json
+import logging
+import uuid
+from dataclasses import dataclass, field
+from datetime import datetime
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple
+
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+# Version du format Learning Pack
+LEARNING_PACK_VERSION = "1.0"
+
+# Seuil de similarité cosinus pour considérer deux prototypes comme identiques
+DEDUP_COSINE_THRESHOLD = 0.95
+
+# Longueur maximale d'un texte avant d'être considéré comme donnée OCR sensible
+MAX_SAFE_TEXT_LENGTH = 120
+
+# Champs de métadonnées à exclure (données sensibles)
+_SENSITIVE_METADATA_KEYS = frozenset({
+    "screenshot_path", "screenshot", "ocr_text", "ocr_raw",
+    "raw_text", "keyboard_events", "key_events", "input_text",
+    "machine_id", "hostname", "ip_address", "user", "username",
+    "patient", "patient_id", "dossier", "nip", "ipp",
+})
+
+
+# ============================================================================
+# Structures de données du Learning Pack
+# ============================================================================
+
+@dataclass
+class AppSignature:
+    """Signature d'une application observée."""
+    app_name: str
+    version: Optional[str] = None
+    window_title_patterns: List[str] = field(default_factory=list)
+    observation_count: int = 1
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "app_name": self.app_name,
+            "version": self.version,
+            "window_title_patterns": self.window_title_patterns,
+            "observation_count": self.observation_count,
+        }
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "AppSignature":
+        return cls(
+            app_name=data["app_name"],
+            version=data.get("version"),
+            window_title_patterns=data.get("window_title_patterns", []),
+            observation_count=data.get("observation_count", 1),
+        )
+
+
+@dataclass
+class ScreenPrototype:
+    """Prototype d'écran anonymisé (embedding + contraintes UI)."""
+    prototype_id: str
+    vector: Optional[List[float]] = None  # Vecteur 512d sérialisé en liste
+    provider: str = "openclip_ViT-B-32"
+    app_name: Optional[str] = None
+    window_constraints: Optional[Dict[str, Any]] = None
+    text_constraints: Optional[Dict[str, Any]] = None
+    ui_constraints: Optional[Dict[str, Any]] = None
+    sample_count: int = 1
+    source_hashes: List[str] = field(default_factory=list)  # Packs d'origine
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "prototype_id": self.prototype_id,
+            "vector": self.vector,
+            "provider": self.provider,
+            "app_name": self.app_name,
+            "window_constraints": self.window_constraints,
+            "text_constraints": self.text_constraints,
+            "ui_constraints": self.ui_constraints,
+            "sample_count": self.sample_count,
+            "source_hashes": self.source_hashes,
+        }
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "ScreenPrototype":
+        return cls(
+            prototype_id=data["prototype_id"],
+            vector=data.get("vector"),
+            provider=data.get("provider", "openclip_ViT-B-32"),
+            app_name=data.get("app_name"),
+            window_constraints=data.get("window_constraints"),
+            text_constraints=data.get("text_constraints"),
+            ui_constraints=data.get("ui_constraints"),
+            sample_count=data.get("sample_count", 1),
+            source_hashes=data.get("source_hashes", []),
+        )
+
+
+@dataclass
+class WorkflowSkeleton:
+    """Structure anonymisée d'un workflow (sans données sensibles)."""
+    skeleton_id: str
+    name: str
+    description: str
+    learning_state: str
+    node_names: List[str]
+    edge_summaries: List[Dict[str, Any]]  # from_node, to_node, action_type, target_role
+    entry_nodes: List[str]
+    end_nodes: List[str]
+    node_count: int = 0
+    edge_count: int = 0
+    app_names: List[str] = field(default_factory=list)
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "skeleton_id": self.skeleton_id,
+            "name": self.name,
+            "description": self.description,
+            "learning_state": self.learning_state,
+            "node_names": self.node_names,
+            "edge_summaries": self.edge_summaries,
+            "entry_nodes": self.entry_nodes,
+            "end_nodes": self.end_nodes,
+            "node_count": self.node_count,
+            "edge_count": self.edge_count,
+            "app_names": self.app_names,
+        }
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "WorkflowSkeleton":
+        return cls(
+            skeleton_id=data["skeleton_id"],
+            name=data["name"],
+            description=data.get("description", ""),
+            learning_state=data.get("learning_state", "OBSERVATION"),
+            node_names=data.get("node_names", []),
+            edge_summaries=data.get("edge_summaries", []),
+            entry_nodes=data.get("entry_nodes", []),
+            end_nodes=data.get("end_nodes", []),
+            node_count=data.get("node_count", 0),
+            edge_count=data.get("edge_count", 0),
+            app_names=data.get("app_names", []),
+        )
+
+
+@dataclass
+class UIPattern:
+    """Pattern UI universel (bouton Enregistrer, menu Fichier, etc.)."""
+    pattern_id: str
+    role: str                                  # button, textfield, menu, etc.
+    context_description: str                   # description du contexte
+    window_title_patterns: List[str] = field(default_factory=list)
+    observation_count: int = 1
+    cross_client_count: int = 1                # Nb de clients différents l'ayant vu
+    confidence: float = 0.0
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "pattern_id": self.pattern_id,
+            "role": self.role,
+            "context_description": self.context_description,
+            "window_title_patterns": self.window_title_patterns,
+            "observation_count": self.observation_count,
+            "cross_client_count": self.cross_client_count,
+            "confidence": self.confidence,
+        }
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "UIPattern":
+        return cls(
+            pattern_id=data["pattern_id"],
+            role=data.get("role", "unknown"),
+            context_description=data.get("context_description", ""),
+            window_title_patterns=data.get("window_title_patterns", []),
+            observation_count=data.get("observation_count", 1),
+            cross_client_count=data.get("cross_client_count", 1),
+            confidence=data.get("confidence", 0.0),
+        )
+
+
+@dataclass
+class ErrorPattern:
+    """Pattern d'erreur rencontré (texte d'erreur, contexte, fréquence)."""
+    pattern_id: str
+    error_text: str
+    kind: str = "text_present"                 # kind du PostConditionCheck source
+    app_name: Optional[str] = None
+    observation_count: int = 1
+    cross_client_count: int = 1
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "pattern_id": self.pattern_id,
+            "error_text": self.error_text,
+            "kind": self.kind,
+            "app_name": self.app_name,
+            "observation_count": self.observation_count,
+            "cross_client_count": self.cross_client_count,
+        }
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "ErrorPattern":
+        return cls(
+            pattern_id=data["pattern_id"],
+            error_text=data["error_text"],
+            kind=data.get("kind", "text_present"),
+            app_name=data.get("app_name"),
+            observation_count=data.get("observation_count", 1),
+            cross_client_count=data.get("cross_client_count", 1),
+        )
+
+
+@dataclass
+class EdgeStatistic:
+    """Statistiques anonymisées d'une transition entre écrans."""
+    from_node_name: str
+    to_node_name: str
+    action_type: str
+    target_role: Optional[str] = None
+    execution_count: int = 0
+    success_rate: float = 0.0
+    avg_execution_time_ms: float = 0.0
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "from_node_name": self.from_node_name,
+            "to_node_name": self.to_node_name,
+            "action_type": self.action_type,
+            "target_role": self.target_role,
+            "execution_count": self.execution_count,
+            "success_rate": self.success_rate,
+            "avg_execution_time_ms": self.avg_execution_time_ms,
+        }
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "EdgeStatistic":
+        return cls(
+            from_node_name=data["from_node_name"],
+            to_node_name=data["to_node_name"],
+            action_type=data["action_type"],
+            target_role=data.get("target_role"),
+            execution_count=data.get("execution_count", 0),
+            success_rate=data.get("success_rate", 0.0),
+            avg_execution_time_ms=data.get("avg_execution_time_ms", 0.0),
+        )
+
+
+# ============================================================================
+# LearningPack — conteneur principal
+# ============================================================================
+
+@dataclass
+class LearningPack:
+    """
+    Pack d'apprentissage anonymisé prêt à être échangé entre sites.
+
+    Peut être sérialisé en JSON (``to_dict`` / ``from_dict``)
+    ou sauvegardé / chargé depuis un fichier (``save`` / ``load``).
+    """
+
+    version: str = LEARNING_PACK_VERSION
+    created_at: str = ""
+    source_hash: str = ""
+    pack_id: str = ""
+    stats: Dict[str, Any] = field(default_factory=dict)
+    app_signatures: List[AppSignature] = field(default_factory=list)
+    screen_prototypes: List[ScreenPrototype] = field(default_factory=list)
+    workflow_skeletons: List[WorkflowSkeleton] = field(default_factory=list)
+    ui_patterns: List[UIPattern] = field(default_factory=list)
+    error_patterns: List[ErrorPattern] = field(default_factory=list)
+    edge_statistics: List[EdgeStatistic] = field(default_factory=list)
+
+    # --- Sérialisation -------------------------------------------------------
+
+    def to_dict(self) -> Dict[str, Any]:
+        """Convertir en dictionnaire JSON-sérialisable."""
+        return {
+            "version": self.version,
+            "created_at": self.created_at,
+            "source_hash": self.source_hash,
+            "pack_id": self.pack_id,
+            "stats": self.stats,
+            "app_signatures": [a.to_dict() for a in self.app_signatures],
+            "screen_prototypes": [p.to_dict() for p in self.screen_prototypes],
+            "workflow_skeletons": [s.to_dict() for s in self.workflow_skeletons],
+            "ui_patterns": [u.to_dict() for u in self.ui_patterns],
+            "error_patterns": [e.to_dict() for e in self.error_patterns],
+            "edge_statistics": [e.to_dict() for e in self.edge_statistics],
+        }
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "LearningPack":
+        """Reconstruire depuis un dictionnaire."""
+        return cls(
+            version=data.get("version", LEARNING_PACK_VERSION),
+            created_at=data.get("created_at", ""),
+            source_hash=data.get("source_hash", ""),
+            pack_id=data.get("pack_id", ""),
+            stats=data.get("stats", {}),
+            app_signatures=[
+                AppSignature.from_dict(a) for a in data.get("app_signatures", [])
+            ],
+            screen_prototypes=[
+                ScreenPrototype.from_dict(p) for p in data.get("screen_prototypes", [])
+            ],
+            workflow_skeletons=[
+                WorkflowSkeleton.from_dict(s) for s in data.get("workflow_skeletons", [])
+            ],
+            ui_patterns=[
+                UIPattern.from_dict(u) for u in data.get("ui_patterns", [])
+            ],
+            error_patterns=[
+                ErrorPattern.from_dict(e) for e in data.get("error_patterns", [])
+            ],
+            edge_statistics=[
+                EdgeStatistic.from_dict(e) for e in data.get("edge_statistics", [])
+            ],
+        )
+
+    # --- Persistance fichier --------------------------------------------------
+
+    def save(self, path: Path) -> None:
+        """Sauvegarder le pack au format JSON compressé."""
+        path = Path(path)
+        path.parent.mkdir(parents=True, exist_ok=True)
+        with open(path, "w", encoding="utf-8") as fh:
+            json.dump(self.to_dict(), fh, indent=2, ensure_ascii=False)
+        logger.info("Learning pack sauvegardé : %s (%d prototypes, %d skeletons)",
+                     path, len(self.screen_prototypes), len(self.workflow_skeletons))
+
+    @classmethod
+    def load(cls, path: Path) -> "LearningPack":
+        """Charger un pack depuis un fichier JSON."""
+        path = Path(path)
+        with open(path, "r", encoding="utf-8") as fh:
+            data = json.load(fh)
+        pack = cls.from_dict(data)
+        logger.info("Learning pack chargé : %s (v%s, %d prototypes)",
+                     path, pack.version, len(pack.screen_prototypes))
+        return pack
+
+
+# ============================================================================
+# Fonctions utilitaires d'anonymisation
+# ============================================================================
+
+def _hash_client_id(client_id: str) -> str:
+    """Hacher un identifiant client via SHA-256 (irréversible)."""
+    return hashlib.sha256(client_id.encode("utf-8")).hexdigest()
+
+
+def _sanitize_text(text: str) -> Optional[str]:
+    """
+    Nettoyer un texte pour l'export.
+
+    Retourne None si le texte est trop long (probable donnée OCR sensible)
+    ou s'il contient des patterns suspects (numéros de dossier, etc.).
+    """
+    if not text or len(text) > MAX_SAFE_TEXT_LENGTH:
+        return None
+    # Filtrer les textes qui ressemblent à des identifiants patients
+    lower = text.lower()
+    for suspect in ("patient", "nip:", "ipp:", "dossier n", "numéro de"):
+        if suspect in lower:
+            return None
+    return text
+
+
+def _clean_metadata(metadata: Dict[str, Any]) -> Dict[str, Any]:
+    """Retirer les clés sensibles d'un dictionnaire de métadonnées."""
+    return {
+        k: v for k, v in metadata.items()
+        if k.lower() not in _SENSITIVE_METADATA_KEYS
+    }
+
+
+def _extract_prototype_vector(node) -> Optional[List[float]]:
+    """
+    Extraire le vecteur prototype d'un WorkflowNode.
+
+    Cherche dans ``node.metadata["_prototype_vector"]`` (numpy array ou liste)
+    puis tente de charger depuis le fichier .npy référencé par le template.
+    """
+    # 1. Vecteur directement stocké dans les métadonnées
+    vec = node.metadata.get("_prototype_vector")
+    if vec is not None:
+        if isinstance(vec, np.ndarray):
+            return vec.tolist()
+        if isinstance(vec, list):
+            return vec
+
+    # 2. Fichier .npy référencé par le template embedding
+    vector_id = node.template.embedding.vector_id
+    if vector_id:
+        npy_path = Path(vector_id)
+        if npy_path.exists() and npy_path.suffix == ".npy":
+            try:
+                arr = np.load(str(npy_path))
+                return arr.tolist()
+            except Exception as exc:
+                logger.debug("Impossible de charger %s : %s", npy_path, exc)
+
+    return None
+
+
+# ============================================================================
+# LearningPackExporter
+# ============================================================================
+
+class LearningPackExporter:
+    """
+    Produit un LearningPack anonymisé à partir d'une liste de Workflows.
+
+    Usage :
+        >>> from core.models.workflow_graph import Workflow
+        >>> exporter = LearningPackExporter()
+        >>> pack = exporter.export(workflows, client_id="CHU-Lyon-001")
+        >>> pack.save(Path("export/chu_lyon.json"))
+    """
+
+    def export(self, workflows, client_id: str) -> LearningPack:
+        """
+        Exporter les workflows d'un client en un LearningPack anonymisé.
+
+        Args:
+            workflows: Liste d'objets ``Workflow`` (core.models.workflow_graph).
+            client_id: Identifiant en clair du client (sera haché).
+
+        Returns:
+            LearningPack prêt à être sauvegardé ou envoyé au serveur central.
+        """
+        source_hash = _hash_client_id(client_id)
+        pack_id = f"lp_{uuid.uuid4().hex[:12]}"
+
+        app_sigs: Dict[str, AppSignature] = {}
+        prototypes: List[ScreenPrototype] = []
+        skeletons: List[WorkflowSkeleton] = []
+        ui_patterns_map: Dict[str, UIPattern] = {}
+        error_patterns_map: Dict[str, ErrorPattern] = {}
+        edge_stats: List[EdgeStatistic] = []
+
+        total_nodes = 0
+        total_edges = 0
+
+        for wf in workflows:
+            # --- Skeleton ---
+            skeleton = self._extract_skeleton(wf)
+            skeletons.append(skeleton)
+
+            total_nodes += len(wf.nodes)
+            total_edges += len(wf.edges)
+
+            # --- Nodes : prototypes + app signatures + UI patterns ---
+            for node in wf.nodes:
+                proto = self._extract_prototype(node, source_hash, wf.workflow_id)
+                if proto is not None:
+                    prototypes.append(proto)
+
+                self._collect_app_signature(node, app_sigs)
+                self._collect_ui_patterns(node, ui_patterns_map)
+
+            # --- Edges : actions + error patterns + stats ---
+            for edge in wf.edges:
+                self._collect_error_patterns(edge, error_patterns_map, wf)
+                stat = self._extract_edge_statistic(edge, wf)
+                if stat is not None:
+                    edge_stats.append(stat)
+
+        apps_seen = sorted(app_sigs.keys())
+
+        pack = LearningPack(
+            version=LEARNING_PACK_VERSION,
+            created_at=datetime.utcnow().isoformat(),
+            source_hash=source_hash,
+            pack_id=pack_id,
+            stats={
+                "workflows_count": len(workflows),
+                "total_nodes": total_nodes,
+                "total_edges": total_edges,
+                "apps_seen": apps_seen,
+                "prototypes_exported": len(prototypes),
+            },
+            app_signatures=list(app_sigs.values()),
+            screen_prototypes=prototypes,
+            workflow_skeletons=skeletons,
+            ui_patterns=list(ui_patterns_map.values()),
+            error_patterns=list(error_patterns_map.values()),
+            edge_statistics=edge_stats,
+        )
+
+        logger.info(
+            "Learning pack exporté : %s — %d workflows, %d prototypes, %d error patterns",
+            pack_id, len(workflows), len(prototypes), len(error_patterns_map),
+        )
+        return pack
+
+    # ------------------------------------------------------------------
+    # Extraction unitaire
+    # ------------------------------------------------------------------
+
+    def _extract_skeleton(self, wf) -> WorkflowSkeleton:
+        """Extraire le squelette anonymisé d'un workflow."""
+        node_names = [n.name for n in wf.nodes]
+        app_names = set()
+
+        edge_summaries = []
+        for edge in wf.edges:
+            summary: Dict[str, Any] = {
+                "from_node": edge.from_node,
+                "to_node": edge.to_node,
+                "action_type": edge.action.type,
+                "target_role": edge.action.target.by_role,
+            }
+            edge_summaries.append(summary)
+
+        for node in wf.nodes:
+            proc = node.template.window.process_name
+            if proc:
+                app_names.add(proc)
+
+        return WorkflowSkeleton(
+            skeleton_id=wf.workflow_id,
+            name=wf.name,
+            description=wf.description,
+            learning_state=wf.learning_state,
+            node_names=node_names,
+            edge_summaries=edge_summaries,
+            entry_nodes=wf.entry_nodes,
+            end_nodes=wf.end_nodes,
+            node_count=len(wf.nodes),
+            edge_count=len(wf.edges),
+            app_names=sorted(app_names),
+        )
+
+    def _extract_prototype(
+        self, node, source_hash: str, workflow_id: str
+    ) -> Optional[ScreenPrototype]:
+        """Extraire un ScreenPrototype anonymisé depuis un WorkflowNode."""
+        vector = _extract_prototype_vector(node)
+        # On exporte même sans vecteur : les contraintes UI ont de la valeur
+        app_name = node.template.window.process_name
+
+        # Construire les contraintes nettoyées
+        window_constraints = node.template.window.to_dict()
+        text_constraints = self._sanitize_text_constraints(node.template.text.to_dict())
+        ui_constraints = node.template.ui.to_dict()
+
+        return ScreenPrototype(
+            prototype_id=f"{workflow_id}__{node.node_id}",
+            vector=vector,
+            provider=node.template.embedding.provider,
+            app_name=app_name,
+            window_constraints=window_constraints,
+            text_constraints=text_constraints,
+            ui_constraints=ui_constraints,
+            sample_count=node.template.embedding.sample_count,
+            source_hashes=[source_hash],
+        )
+
+    def _sanitize_text_constraints(self, text_dict: Dict[str, Any]) -> Dict[str, Any]:
+        """Nettoyer les contraintes texte en retirant les textes trop longs / sensibles."""
+        required = [
+            t for t in text_dict.get("required_texts", [])
+            if _sanitize_text(t) is not None
+        ]
+        forbidden = [
+            t for t in text_dict.get("forbidden_texts", [])
+            if _sanitize_text(t) is not None
+        ]
+        return {"required_texts": required, "forbidden_texts": forbidden}
+
+    def _collect_app_signature(
+        self, node, app_sigs: Dict[str, AppSignature]
+    ) -> None:
+        """Collecter la signature d'application depuis un node."""
+        proc = node.template.window.process_name
+        if not proc:
+            return
+
+        if proc in app_sigs:
+            app_sigs[proc].observation_count += 1
+        else:
+            title_pattern = node.template.window.title_pattern
+            patterns = [title_pattern] if title_pattern else []
+            app_sigs[proc] = AppSignature(
+                app_name=proc,
+                window_title_patterns=patterns,
+            )
+
+        # Ajouter le pattern de titre s'il est nouveau
+        title_pattern = node.template.window.title_pattern
+        if title_pattern and title_pattern not in app_sigs[proc].window_title_patterns:
+            app_sigs[proc].window_title_patterns.append(title_pattern)
+
+    def _collect_ui_patterns(
+        self, node, patterns: Dict[str, UIPattern]
+    ) -> None:
+        """Collecter les patterns UI depuis les contraintes d'un node."""
+        for role in node.template.ui.required_roles:
+            key = role
+            if key in patterns:
+                patterns[key].observation_count += 1
+            else:
+                title_pattern = node.template.window.title_pattern
+                title_patterns = [title_pattern] if title_pattern else []
+                patterns[key] = UIPattern(
+                    pattern_id=f"uip_{role}",
+                    role=role,
+                    context_description=f"Rôle UI requis : {role}",
+                    window_title_patterns=title_patterns,
+                )
+
+    def _collect_error_patterns(
+        self, edge, patterns: Dict[str, ErrorPattern], wf
+    ) -> None:
+        """Extraire les patterns d'erreur depuis les PostConditions.fail_fast."""
+        for check in edge.post_conditions.fail_fast:
+            if check.value and _sanitize_text(check.value) is not None:
+                key = check.value
+                if key in patterns:
+                    patterns[key].observation_count += 1
+                else:
+                    # Trouver l'app_name du node source
+                    source_node = wf.get_node(edge.from_node)
+                    app_name = None
+                    if source_node:
+                        app_name = source_node.template.window.process_name
+
+                    patterns[key] = ErrorPattern(
+                        pattern_id=f"err_{hashlib.md5(key.encode()).hexdigest()[:8]}",
+                        error_text=check.value,
+                        kind=check.kind,
+                        app_name=app_name,
+                    )
+
+    def _extract_edge_statistic(self, edge, wf) -> Optional[EdgeStatistic]:
+        """Extraire les statistiques anonymisées d'un edge."""
+        source_node = wf.get_node(edge.from_node)
+        target_node = wf.get_node(edge.to_node)
+
+        from_name = source_node.name if source_node else edge.from_node
+        to_name = target_node.name if target_node else edge.to_node
+
+        return EdgeStatistic(
+            from_node_name=from_name,
+            to_node_name=to_name,
+            action_type=edge.action.type,
+            target_role=edge.action.target.by_role,
+            execution_count=edge.stats.execution_count,
+            success_rate=edge.stats.success_rate,
+            avg_execution_time_ms=edge.stats.avg_execution_time_ms,
+        )
+
+
+# ============================================================================
+# LearningPackMerger
+# ============================================================================
+
+class LearningPackMerger:
+    """
+    Fusionne plusieurs LearningPacks en un seul pack consolidé.
+
+    La fusion :
+    - Déduplique les prototypes similaires (cosine > 0.95 = même écran)
+    - Fusionne les signatures d'application (union)
+    - Fusionne les patterns d'erreur (union, comptage cross-clients)
+    - Calcule les occurrences cross-clients (haute confiance si vu par N clients)
+
+    Usage :
+        >>> merger = LearningPackMerger()
+        >>> merged = merger.merge([pack_a, pack_b, pack_c])
+        >>> merged.save(Path("global/merged_pack.json"))
+    """
+
+    def __init__(self, dedup_threshold: float = DEDUP_COSINE_THRESHOLD):
+        self.dedup_threshold = dedup_threshold
+
+    def merge(self, packs: List[LearningPack]) -> LearningPack:
+        """
+        Fusionner plusieurs packs en un pack global consolidé.
+
+        Args:
+            packs: Liste de LearningPacks à fusionner.
+
+        Returns:
+            LearningPack consolidé avec déduplication et comptage cross-clients.
+        """
+        if not packs:
+            return LearningPack(
+                created_at=datetime.utcnow().isoformat(),
+                pack_id=f"lp_merged_{uuid.uuid4().hex[:8]}",
+            )
+
+        if len(packs) == 1:
+            # Un seul pack : on le retourne avec un nouveau pack_id
+            merged = LearningPack.from_dict(packs[0].to_dict())
+            merged.pack_id = f"lp_merged_{uuid.uuid4().hex[:8]}"
+            return merged
+
+        merged_id = f"lp_merged_{uuid.uuid4().hex[:8]}"
+        source_hashes = list({p.source_hash for p in packs if p.source_hash})
+
+        # Fusionner chaque catégorie
+        app_sigs = self._merge_app_signatures(packs)
+        prototypes = self._merge_prototypes(packs)
+        skeletons = self._merge_skeletons(packs)
+        ui_patterns = self._merge_ui_patterns(packs)
+        error_patterns = self._merge_error_patterns(packs)
+        edge_stats = self._merge_edge_statistics(packs)
+
+        # Calculer les stats globales
+        total_wf = sum(p.stats.get("workflows_count", 0) for p in packs)
+        total_nodes = sum(p.stats.get("total_nodes", 0) for p in packs)
+        total_edges = sum(p.stats.get("total_edges", 0) for p in packs)
+        all_apps = set()
+        for p in packs:
+            all_apps.update(p.stats.get("apps_seen", []))
+
+        return LearningPack(
+            version=LEARNING_PACK_VERSION,
+            created_at=datetime.utcnow().isoformat(),
+            source_hash=",".join(sorted(source_hashes)),
+            pack_id=merged_id,
+            stats={
+                "workflows_count": total_wf,
+                "total_nodes": total_nodes,
+                "total_edges": total_edges,
+                "apps_seen": sorted(all_apps),
+                "prototypes_exported": len(prototypes),
+                "source_packs_count": len(packs),
+                "source_hashes": source_hashes,
+            },
+            app_signatures=app_sigs,
+            screen_prototypes=prototypes,
+            workflow_skeletons=skeletons,
+            ui_patterns=ui_patterns,
+            error_patterns=error_patterns,
+            edge_statistics=edge_stats,
+        )
+
+    # ------------------------------------------------------------------
+    # Fusion par catégorie
+    # ------------------------------------------------------------------
+
+    def _merge_app_signatures(self, packs: List[LearningPack]) -> List[AppSignature]:
+        """Union des signatures d'application, cumul des compteurs."""
+        merged: Dict[str, AppSignature] = {}
+        for pack in packs:
+            for sig in pack.app_signatures:
+                if sig.app_name in merged:
+                    existing = merged[sig.app_name]
+                    existing.observation_count += sig.observation_count
+                    for pat in sig.window_title_patterns:
+                        if pat not in existing.window_title_patterns:
+                            existing.window_title_patterns.append(pat)
+                else:
+                    merged[sig.app_name] = AppSignature.from_dict(sig.to_dict())
+        return list(merged.values())
+
+    def _merge_prototypes(self, packs: List[LearningPack]) -> List[ScreenPrototype]:
+        """
+        Fusionner les prototypes avec déduplication par similarité cosinus.
+
+        Deux prototypes avec cosine > ``self.dedup_threshold`` sont considérés
+        comme le même écran. On conserve celui avec le plus d'échantillons
+        et on fusionne les source_hashes.
+        """
+        all_protos: List[ScreenPrototype] = []
+        for pack in packs:
+            all_protos.extend(pack.screen_prototypes)
+
+        if not all_protos:
+            return []
+
+        # Séparer les prototypes avec et sans vecteur
+        with_vec: List[Tuple[ScreenPrototype, np.ndarray]] = []
+        without_vec: List[ScreenPrototype] = []
+
+        for proto in all_protos:
+            if proto.vector is not None and len(proto.vector) > 0:
+                vec = np.array(proto.vector, dtype=np.float32)
+                norm = np.linalg.norm(vec)
+                if norm > 0:
+                    vec = vec / norm
+                with_vec.append((proto, vec))
+            else:
+                without_vec.append(proto)
+
+        # Déduplication greedy par similarité cosinus
+        merged: List[ScreenPrototype] = []
+        used = [False] * len(with_vec)
+
+        for i, (proto_i, vec_i) in enumerate(with_vec):
+            if used[i]:
+                continue
+            used[i] = True
+
+            # Chercher les prototypes similaires
+            group_sources = set(proto_i.source_hashes)
+            best_sample_count = proto_i.sample_count
+            best_proto = proto_i
+
+            for j in range(i + 1, len(with_vec)):
+                if used[j]:
+                    continue
+                proto_j, vec_j = with_vec[j]
+                cosine_sim = float(np.dot(vec_i, vec_j))
+
+                if cosine_sim >= self.dedup_threshold:
+                    used[j] = True
+                    group_sources.update(proto_j.source_hashes)
+                    if proto_j.sample_count > best_sample_count:
+                        best_sample_count = proto_j.sample_count
+                        best_proto = proto_j
+
+            # Construire le prototype consolidé
+            consolidated = ScreenPrototype.from_dict(best_proto.to_dict())
+            consolidated.source_hashes = sorted(group_sources)
+            consolidated.sample_count = best_sample_count
+            merged.append(consolidated)
+
+        # Ajouter les prototypes sans vecteur (pas de déduplication possible)
+        merged.extend(without_vec)
+
+        logger.info(
+            "Fusion prototypes : %d entrées → %d après déduplication (seuil=%.2f)",
+            len(all_protos), len(merged), self.dedup_threshold,
+        )
+        return merged
+
+    def _merge_skeletons(self, packs: List[LearningPack]) -> List[WorkflowSkeleton]:
+        """Union des skeletons de workflows (dédupliqués par skeleton_id)."""
+        merged: Dict[str, WorkflowSkeleton] = {}
+        for pack in packs:
+            for skel in pack.workflow_skeletons:
+                if skel.skeleton_id not in merged:
+                    merged[skel.skeleton_id] = skel
+        return list(merged.values())
+
+    def _merge_ui_patterns(self, packs: List[LearningPack]) -> List[UIPattern]:
+        """Fusionner les patterns UI avec comptage cross-clients."""
+        merged: Dict[str, UIPattern] = {}
+        # Suivre quels source_hashes ont vu chaque pattern
+        pattern_sources: Dict[str, set] = {}
+
+        for pack in packs:
+            for pattern in pack.ui_patterns:
+                key = pattern.role
+                if key in merged:
+                    merged[key].observation_count += pattern.observation_count
+                    for pat in pattern.window_title_patterns:
+                        if pat not in merged[key].window_title_patterns:
+                            merged[key].window_title_patterns.append(pat)
+                else:
+                    merged[key] = UIPattern.from_dict(pattern.to_dict())
+                    pattern_sources[key] = set()
+                if pack.source_hash:
+                    pattern_sources.setdefault(key, set()).add(pack.source_hash)
+
+        # Mettre à jour le cross_client_count
+        for key, pattern in merged.items():
+            sources = pattern_sources.get(key, set())
+            pattern.cross_client_count = len(sources)
+            # Confiance = proportion de clients ayant vu le pattern
+            total_clients = len({p.source_hash for p in packs if p.source_hash})
+            pattern.confidence = (
+                len(sources) / total_clients if total_clients > 0 else 0.0
+            )
+
+        return list(merged.values())
+
+    def _merge_error_patterns(self, packs: List[LearningPack]) -> List[ErrorPattern]:
+        """Fusionner les patterns d'erreur avec comptage cross-clients."""
+        merged: Dict[str, ErrorPattern] = {}
+        pattern_sources: Dict[str, set] = {}
+
+        for pack in packs:
+            for pattern in pack.error_patterns:
+                key = pattern.error_text
+                if key in merged:
+                    merged[key].observation_count += pattern.observation_count
+                else:
+                    merged[key] = ErrorPattern.from_dict(pattern.to_dict())
+                    pattern_sources[key] = set()
+                if pack.source_hash:
+                    pattern_sources.setdefault(key, set()).add(pack.source_hash)
+
+        for key, pattern in merged.items():
+            pattern.cross_client_count = len(pattern_sources.get(key, set()))
+
+        return list(merged.values())
+
+    def _merge_edge_statistics(
+        self, packs: List[LearningPack]
+    ) -> List[EdgeStatistic]:
+        """Fusionner les statistiques de transitions."""
+        merged: Dict[str, EdgeStatistic] = {}
+
+        for pack in packs:
+            for stat in pack.edge_statistics:
+                key = f"{stat.from_node_name}→{stat.to_node_name}→{stat.action_type}"
+                if key in merged:
+                    existing = merged[key]
+                    total_exec = existing.execution_count + stat.execution_count
+                    if total_exec > 0:
+                        # Moyenne pondérée du success_rate
+                        existing.success_rate = (
+                            existing.success_rate * existing.execution_count
+                            + stat.success_rate * stat.execution_count
+                        ) / total_exec
+                        # Moyenne pondérée du temps d'exécution
+                        existing.avg_execution_time_ms = (
+                            existing.avg_execution_time_ms * existing.execution_count
+                            + stat.avg_execution_time_ms * stat.execution_count
+                        ) / total_exec
+                    existing.execution_count = total_exec
+                else:
+                    merged[key] = EdgeStatistic.from_dict(stat.to_dict())
+
+        return list(merged.values())
diff --git a/core/graph/graph_builder.py b/core/graph/graph_builder.py
index b36b60c6d..4bc606c3b 100644
--- a/core/graph/graph_builder.py
+++ b/core/graph/graph_builder.py
@@ -47,6 +47,7 @@ from core.models.workflow_graph import (
     TargetSpec,
     EdgeConstraints,
     PostConditions,
+    PostConditionCheck,
     WindowConstraint,
     TextConstraint,
     UIConstraint,
@@ -58,6 +59,91 @@ from core.training.quality_validator import TrainingQualityValidator, QualityRep
 
 logger = logging.getLogger(__name__)
 
+# ---------------------------------------------------------------------------
+# Filtrage des événements parasites (modificateurs seuls, etc.)
+# Appliqué dans _find_transition_events et _build_compound_action.
+# ---------------------------------------------------------------------------
+
+_MODIFIER_ONLY_KEYS = {
+    "ctrl", "ctrl_l", "ctrl_r",
+    "alt", "alt_l", "alt_r",
+    "shift", "shift_l", "shift_r",
+    "win", "cmd", "cmd_l", "cmd_r",
+    "meta", "super", "super_l", "super_r",
+}
+
+
+def _is_modifier_only(keys: list) -> bool:
+    """Retourne True si la liste de touches ne contient que des modificateurs."""
+    if not keys:
+        return True
+    return all(k.lower() in _MODIFIER_ONLY_KEYS for k in keys)
+
+
+def _is_parasitic_event(event: Event) -> bool:
+    """Retourne True si l'événement est parasite (modificateur seul, texte vide).
+
+    Filtrage appliqué aux événements de session avant construction des edges.
+    """
+    if event.type == "key_press":
+        keys = event.data.get("keys", [])
+        if not keys or _is_modifier_only(keys):
+            return True
+    elif event.type == "text_input":
+        text = event.data.get("text", "")
+        if not text:
+            return True
+    return False
+
+
+def _merge_consecutive_text_steps(steps: list) -> list:
+    """Fusionne les text_input consécutifs en un seul."""
+    merged = []
+    for step in steps:
+        if (step.get("type") in ("text_input", "type")
+                and merged
+                and merged[-1].get("type") in ("text_input", "type")):
+            merged[-1]["text"] = merged[-1].get("text", "") + step.get("text", "")
+        else:
+            merged.append(dict(step))
+    return merged
+
+
+def _dedup_consecutive_combos(steps: list) -> list:
+    """Supprime les key_combo/key_press dupliqués consécutifs."""
+    deduped = []
+    for step in steps:
+        if (step.get("type") in ("key_combo", "key_press")
+                and deduped
+                and deduped[-1].get("type") in ("key_combo", "key_press")
+                and deduped[-1].get("keys") == step.get("keys")):
+            continue
+        deduped.append(step)
+    return deduped
+
+
+def _filter_modifier_only_steps(steps: list) -> list:
+    """Supprime les steps key_combo/key_press avec uniquement des modificateurs."""
+    return [
+        s for s in steps
+        if not (
+            s.get("type") in ("key_combo", "key_press")
+            and _is_modifier_only(s.get("keys", []))
+        )
+    ]
+
+
+def _ensure_min_waits(steps: list, min_wait_ms: int = 300) -> list:
+    """Ajoute un wait entre les steps si aucun wait n'existe."""
+    if not steps:
+        return steps
+    result = [steps[0]]
+    for step in steps[1:]:
+        if result[-1].get("type") != "wait" and step.get("type") != "wait":
+            result.append({"type": "wait", "duration_ms": min_wait_ms})
+        result.append(step)
+    return result
+
 
 class GraphBuilder:
     """
@@ -83,8 +169,8 @@ class GraphBuilder:
         embedding_builder: Optional[StateEmbeddingBuilder] = None,
         faiss_manager: Optional[FAISSManager] = None,
         quality_validator: Optional[TrainingQualityValidator] = None,
-        min_pattern_repetitions: int = 3,
-        clustering_eps: float = 0.15,
+        min_pattern_repetitions: int = 2,
+        clustering_eps: float = 0.08,
         clustering_min_samples: int = 2,
         enable_quality_validation: bool = True,
     ):
@@ -122,13 +208,14 @@ class GraphBuilder:
         session: RawSession,
         workflow_name: Optional[str] = None,
         precomputed_states: Optional[List["ScreenState"]] = None,
+        precomputed_embeddings: Optional[List] = None,
     ) -> Workflow:
         """
         Construire un Workflow complet depuis une RawSession.
 
         Processus:
             1. Créer ScreenStates depuis screenshots (ou utiliser precomputed_states)
-            2. Calculer embeddings pour chaque état
+            2. Calculer embeddings pour chaque état (ou réutiliser precomputed_embeddings)
             3. Détecter patterns via clustering
             4. Construire nodes depuis clusters
             5. Construire edges depuis transitions
@@ -138,6 +225,9 @@ class GraphBuilder:
             workflow_name: Nom du workflow (généré si None)
             precomputed_states: ScreenStates déjà analysés (streaming).
                 Si fourni, saute l'étape 1 (pas de re-analyse via ScreenAnalyzer).
+            precomputed_embeddings: Embeddings déjà calculés (streaming).
+                Si fourni et de la bonne longueur (= len(screen_states)),
+                saute l'étape 2 (pas de recalcul CLIP).
 
         Returns:
             Workflow construit avec nodes et edges
@@ -162,20 +252,30 @@ class GraphBuilder:
             screen_states = self._create_screen_states(session)
             logger.debug(f"Created {len(screen_states)} screen states")
 
-        # Étape 2: Calculer embeddings
-        embeddings = self._compute_embeddings(screen_states)
-        logger.debug(f"Computed {len(embeddings)} embeddings")
-        
+        # Étape 2: Calculer embeddings (ou réutiliser ceux pré-calculés)
+        if precomputed_embeddings is not None and len(precomputed_embeddings) == len(screen_states):
+            embeddings = [np.asarray(e, dtype=np.float32) for e in precomputed_embeddings]
+            logger.debug(f"Using {len(embeddings)} precomputed embeddings (skip CLIP)")
+        else:
+            if precomputed_embeddings is not None:
+                logger.warning(
+                    f"precomputed_embeddings length mismatch "
+                    f"({len(precomputed_embeddings)} vs {len(screen_states)} states), "
+                    f"recalculating..."
+                )
+            embeddings = self._compute_embeddings(screen_states)
+            logger.debug(f"Computed {len(embeddings)} embeddings")
+
         # Étape 3: Détecter patterns
         clusters = self._detect_patterns(embeddings, screen_states)
         logger.info(f"Detected {len(clusters)} patterns")
-        
+
         # Étape 4: Construire nodes
         nodes = self._build_nodes(clusters, screen_states, embeddings)
         logger.info(f"Built {len(nodes)} workflow nodes")
-        
-        # Étape 5: Construire edges
-        edges = self._build_edges(nodes, screen_states, session)
+
+        # Étape 5: Construire edges (passer les embeddings pour éviter recalcul)
+        edges = self._build_edges(nodes, screen_states, session, embeddings=embeddings)
         logger.info(f"Built {len(edges)} workflow edges")
         
         # Créer Workflow
@@ -300,19 +400,31 @@ class GraphBuilder:
             event = screenshot_to_event.get(screenshot.screenshot_id)
             
             # Créer WindowContext depuis l'événement
+            screen_env = session.environment.get("screen", {})
+            screen_res = screen_env.get("primary_resolution", [1920, 1080])
             if event and event.window:
                 window = WindowContext(
                     app_name=event.window.app_name,
                     window_title=event.window.title,
-                    screen_resolution=session.environment.get("screen", {}).get("primary_resolution", [1920, 1080]),
-                    workspace="main"
+                    screen_resolution=screen_res,
+                    workspace="main",
+                    monitor_index=screen_env.get("monitor_index", 0),
+                    dpi_scale=screen_env.get("dpi_scale", 100),
+                    monitors=screen_env.get("monitors"),
+                    os_theme=session.environment.get("os_theme", "unknown"),
+                    os_language=session.environment.get("os_language", "unknown"),
                 )
             else:
                 window = WindowContext(
                     app_name="unknown",
                     window_title="Unknown",
-                    screen_resolution=[1920, 1080],
-                    workspace="main"
+                    screen_resolution=screen_res,
+                    workspace="main",
+                    monitor_index=screen_env.get("monitor_index", 0),
+                    dpi_scale=screen_env.get("dpi_scale", 100),
+                    monitors=screen_env.get("monitors"),
+                    os_theme=session.environment.get("os_theme", "unknown"),
+                    os_language=session.environment.get("os_language", "unknown"),
                 )
             
             # Créer RawLevel
@@ -429,23 +541,23 @@ class GraphBuilder:
         screen_states: List[ScreenState],
     ) -> Dict[int, List[int]]:
         """
-        Détecter patterns répétés via clustering DBSCAN.
-        
+        Détecter patterns répétés via clustering hybride.
+
         Algorithme:
-            1. Convertir embeddings en matrice numpy
-            2. Appliquer DBSCAN avec métrique cosinus
-            3. Grouper états par cluster
-            4. Filtrer clusters avec assez de répétitions
-        
+            1. Pré-clustering par titre de fenêtre (discriminant fort)
+            2. DBSCAN au sein de chaque groupe de titres (affine par vision)
+            3. Filtrer clusters trop petits
+
+        Le titre de fenêtre est le signal le plus discriminant entre écrans.
+        CLIP/embeddings seul ne distingue pas assez les screenshots d'apps différentes
+        (distance cosine max ~0.12 entre Notepad et Explorer).
+
         Args:
             embeddings: Vecteurs d'embeddings
             screen_states: ScreenStates correspondants
-        
+
         Returns:
             Dictionnaire {cluster_id: [indices des états]}
-            
-        Note:
-            Les états non assignés (bruit) ont label=-1 et sont ignorés
         """
         if len(embeddings) < self.min_pattern_repetitions:
             logger.warning(
@@ -453,41 +565,135 @@ class GraphBuilder:
                 f"(minimum: {self.min_pattern_repetitions})"
             )
             return {}
-        
-        # Convertir en matrice numpy
+
+        # Étape 1 : Pré-clustering par titre de fenêtre (ou app_name)
+        title_groups = defaultdict(list)
+        for idx, state in enumerate(screen_states):
+            # Extraire le titre de fenêtre — le meilleur discriminant
+            title = ""
+            if hasattr(state, 'window') and state.window:
+                title = getattr(state.window, 'window_title', '') or ''
+            # Normaliser : garder le nom de l'app (partie avant le tiret)
+            # Ex: "Sans titre - Bloc-notes" → "Bloc-notes"
+            # Ex: "Google - Chrome" → "Chrome"
+            app_key = self._normalize_window_title(title) if title else "__no_title__"
+            title_groups[app_key].append(idx)
+
+        n_title_groups = len(title_groups)
+        logger.info(
+            f"Window title pre-clustering: {n_title_groups} groups "
+            f"({', '.join(f'{k}:{len(v)}' for k, v in title_groups.items())})"
+        )
+
+        # Étape 2 : Si les titres distinguent bien les écrans, utiliser directement
+        # Si un seul groupe (titres identiques ou absents), fallback DBSCAN pur
+        if n_title_groups <= 1:
+            return self._dbscan_clustering(embeddings, screen_states)
+
+        # Étape 3 : Chaque groupe de titres devient un cluster
+        # Optionnellement, DBSCAN affine les groupes avec >5 screenshots
+        final_clusters = {}
+        cluster_id = 0
+        noise_count = 0
+
+        for title_key, indices in title_groups.items():
+            if len(indices) < self.min_pattern_repetitions:
+                noise_count += len(indices)
+                continue
+
+            if len(indices) > 5:
+                # Sous-clustering DBSCAN pour les gros groupes
+                sub_embeddings = [embeddings[i] for i in indices]
+                sub_X = np.array(sub_embeddings)
+
+                sub_clustering = DBSCAN(
+                    eps=self.clustering_eps,
+                    min_samples=self.clustering_min_samples,
+                    metric="cosine",
+                )
+                sub_labels = sub_clustering.fit_predict(sub_X)
+
+                sub_clusters = defaultdict(list)
+                for local_idx, label in enumerate(sub_labels):
+                    if label == -1:
+                        noise_count += 1
+                    else:
+                        sub_clusters[label].append(indices[local_idx])
+
+                for sub_indices in sub_clusters.values():
+                    if len(sub_indices) >= self.min_pattern_repetitions:
+                        final_clusters[cluster_id] = sub_indices
+                        cluster_id += 1
+            else:
+                # Petit groupe → 1 cluster direct
+                final_clusters[cluster_id] = indices
+                cluster_id += 1
+
+        logger.info(
+            f"Hybrid clustering results: {len(final_clusters)} patterns "
+            f"(from {n_title_groups} title groups), {noise_count} noise points"
+        )
+
+        return final_clusters
+
+    def _normalize_window_title(self, title: str) -> str:
+        """Normaliser un titre de fenêtre pour le clustering.
+
+        Extrait le nom de l'application (partie significative) en ignorant
+        le contenu variable (nom de fichier, URL, etc.).
+
+        Ex: "document.txt - Bloc-notes" → "Bloc-notes"
+        Ex: "Google - Chrome" → "Chrome"
+        Ex: "C:\\Users\\... - Explorateur" → "Explorateur"
+        """
+        if not title:
+            return "__no_title__"
+
+        # Séparateurs courants dans les titres Windows
+        for sep in [" - ", " — ", " – ", " | "]:
+            if sep in title:
+                parts = title.split(sep)
+                # Le nom de l'app est généralement le dernier segment
+                return parts[-1].strip()
+
+        return title.strip()
+
+    def _dbscan_clustering(
+        self,
+        embeddings: List[np.ndarray],
+        screen_states: List[ScreenState],
+    ) -> Dict[int, List[int]]:
+        """Clustering DBSCAN pur (fallback quand les titres ne discriminent pas)."""
         X = np.array(embeddings)
-        
-        # Clustering DBSCAN
+
         clustering = DBSCAN(
             eps=self.clustering_eps,
             min_samples=self.clustering_min_samples,
             metric="cosine",
         )
         labels = clustering.fit_predict(X)
-        
-        # Grouper par cluster
+
         clusters = defaultdict(list)
         noise_count = 0
-        
+
         for idx, label in enumerate(labels):
             if label == -1:
                 noise_count += 1
             else:
                 clusters[label].append(idx)
-        
-        # Filtrer clusters avec assez de répétitions
+
         filtered_clusters = {
             cluster_id: indices
             for cluster_id, indices in clusters.items()
             if len(indices) >= self.min_pattern_repetitions
         }
-        
+
         logger.info(
-            f"Clustering results: {len(filtered_clusters)} patterns, "
+            f"DBSCAN clustering results: {len(filtered_clusters)} patterns, "
             f"{noise_count} noise points, "
             f"{len(clusters) - len(filtered_clusters)} small clusters filtered"
         )
-        
+
         return filtered_clusters
     
     def _build_nodes(
@@ -718,92 +924,125 @@ class GraphBuilder:
         constraints.sort(key=lambda c: role_counts.get(c.get("role", ""), 0), reverse=True)
         return constraints[:8]
     
+    # Patterns d'erreur courants pour la détection fail_fast
+    _ERROR_PATTERNS = [
+        "erreur", "error", "échec", "failed", "impossible",
+        "accès refusé", "access denied", "not found",
+        "timeout", "connexion perdue", "session expirée",
+    ]
+
     def _build_edges(
         self,
         nodes: List[WorkflowNode],
         screen_states: List[ScreenState],
         session: RawSession,
+        embeddings: Optional[List[np.ndarray]] = None,
     ) -> List[WorkflowEdge]:
         """
         Construire WorkflowEdges depuis les transitions observées.
-        
+
         Algorithme:
             1. Mapper chaque ScreenState vers son node (via embedding similarity)
             2. Identifier les transitions (state_i -> state_j où node change)
             3. Extraire l'action depuis l'événement entre les deux états
-            4. Créer WorkflowEdge avec action et conditions
-        
+            4. Créer WorkflowEdge avec action, pré-conditions et post-conditions
+
+        Les EdgeConstraints sont peuplées depuis le ScreenTemplate du node source :
+            - required_window_title, required_app_name, min_source_similarity
+            - required_texts (textes OCR fréquents dans le cluster source)
+
+        Les PostConditions sont peuplées depuis le ScreenTemplate du node cible :
+            - expected_window_title, expected_app_name, min_target_similarity
+            - expected_texts + forbidden_texts (patterns d'erreur courants)
+
         Args:
             nodes: WorkflowNodes construits
             screen_states: ScreenStates
             session: Session brute (pour événements)
-        
+            embeddings: Embeddings pré-calculés (évite un recalcul dans _map_states_to_nodes)
+
         Returns:
             Liste de WorkflowEdges
         """
         if not nodes or len(screen_states) < 2:
             logger.warning("Not enough data to build edges")
             return []
-        
+
         edges = []
         edge_counts = defaultdict(int)  # Pour compter les occurrences de chaque transition
-        
+
+        # Index des nodes par ID pour accès rapide
+        node_by_id = {node.node_id: node for node in nodes}
+
         # Étape 1: Mapper chaque état vers son node
-        state_to_node = self._map_states_to_nodes(screen_states, nodes)
-        
-        # Étape 2: Créer un mapping screenshot_id -> événement
-        screenshot_to_event = {}
-        for event in session.events:
-            if event.screenshot_id:
-                screenshot_to_event[event.screenshot_id] = event
-        
+        state_to_node = self._map_states_to_nodes(screen_states, nodes, embeddings=embeddings)
+
+        # Étape 2: Récupérer la résolution d'écran pour normaliser les coordonnées
+        screen_env = session.environment.get("screen", {})
+        screen_resolution = tuple(screen_env.get("primary_resolution", [1920, 1080]))
+
         # Étape 3: Parcourir les transitions
         for i in range(len(screen_states) - 1):
             current_state = screen_states[i]
             next_state = screen_states[i + 1]
-            
+
             current_node_id = state_to_node.get(current_state.screen_state_id)
             next_node_id = state_to_node.get(next_state.screen_state_id)
-            
+
             # Si les deux états sont dans des nodes différents, c'est une transition
             if current_node_id and next_node_id and current_node_id != next_node_id:
-                # Trouver l'événement qui a causé la transition
-                event = self._find_transition_event(
+                # Trouver TOUS les événements entre les deux états
+                transition_events = self._find_transition_events(
                     current_state, next_state, session.events
                 )
-                
+
                 # Créer l'edge
                 edge_key = f"{current_node_id}_to_{next_node_id}"
                 edge_counts[edge_key] += 1
-                
+
                 # Ne créer l'edge qu'une fois, mais compter les occurrences
                 if edge_counts[edge_key] == 1:
+                    source_node = node_by_id.get(current_node_id)
+                    target_node = node_by_id.get(next_node_id)
                     edge = self._create_edge(
-                        current_node_id, next_node_id, event, edge_key
+                        current_node_id, next_node_id,
+                        transition_events[-1] if transition_events else None,
+                        edge_key,
+                        source_node=source_node,
+                        target_node=target_node,
+                        all_events=transition_events,
+                        screen_resolution=screen_resolution,
                     )
                     edges.append(edge)
-        
+
         # Mettre à jour les stats des edges avec les comptages
         for edge in edges:
             edge_key = f"{edge.from_node}_to_{edge.to_node}"
             edge.stats.execution_count = edge_counts[edge_key]
             edge.stats.success_count = edge_counts[edge_key]
-        
+
         logger.info(f"Built {len(edges)} edges from {sum(edge_counts.values())} transitions")
         return edges
     
     def _map_states_to_nodes(
         self,
         screen_states: List[ScreenState],
-        nodes: List[WorkflowNode]
+        nodes: List[WorkflowNode],
+        embeddings: Optional[List[np.ndarray]] = None,
     ) -> Dict[str, str]:
         """
         Mapper chaque ScreenState vers le node le plus proche.
-        
+
         Utilise la similarité d'embedding pour trouver le meilleur match.
+
+        Args:
+            screen_states: Liste de ScreenStates à mapper.
+            nodes: WorkflowNodes cibles.
+            embeddings: Embeddings pré-calculés (même ordre que screen_states).
+                Si fourni, évite de recalculer via self.embedding_builder.build().
         """
         state_to_node = {}
-        
+
         # Récupérer les embeddings des prototypes de nodes
         node_prototypes = {}
         for node in nodes:
@@ -815,36 +1054,82 @@ class GraphBuilder:
                 proto_path = Path(node.template.embedding.vector_id)
                 if proto_path.exists():
                     node_prototypes[node.node_id] = np.load(proto_path)
-        
+
         if not node_prototypes:
             logger.warning("No node prototypes available for mapping")
             return state_to_node
-        
+
+        # Vérifier si les embeddings pré-calculés sont utilisables
+        use_precomputed = (
+            embeddings is not None and len(embeddings) == len(screen_states)
+        )
+        if use_precomputed:
+            logger.debug("_map_states_to_nodes: using precomputed embeddings")
+
         # Pour chaque état, trouver le node le plus proche
-        for state in screen_states:
-            # Calculer embedding de l'état
+        for i, state in enumerate(screen_states):
             try:
-                state_embedding = self.embedding_builder.build(state)
-                state_vector = state_embedding.get_vector()
-                
+                # Utiliser l'embedding pré-calculé ou recalculer
+                if use_precomputed:
+                    state_vector = np.asarray(embeddings[i], dtype=np.float32)
+                else:
+                    state_embedding = self.embedding_builder.build(state)
+                    state_vector = state_embedding.get_vector()
+
                 # Trouver le node avec la meilleure similarité
                 best_node_id = None
                 best_similarity = -1
-                
+
                 for node_id, prototype in node_prototypes.items():
                     similarity = np.dot(state_vector, prototype)
                     if similarity > best_similarity:
                         best_similarity = similarity
                         best_node_id = node_id
-                
+
                 if best_node_id and best_similarity > 0.7:  # Seuil minimum
                     state_to_node[state.screen_state_id] = best_node_id
-                    
+
             except Exception as e:
                 logger.warning(f"Failed to map state {state.screen_state_id}: {e}")
-        
+
         return state_to_node
     
+    def _find_transition_events(
+        self,
+        current_state: ScreenState,
+        next_state: ScreenState,
+        events: List[Event],
+    ) -> List[Event]:
+        """
+        Trouver TOUS les événements entre deux états (pas juste le dernier).
+
+        Collecte toutes les actions utilisateur (clics, frappes, saisie texte)
+        qui se sont produites entre les timestamps des deux ScreenStates.
+        C'est essentiel pour le replay : une transition peut nécessiter
+        plusieurs actions (ex: Win+R → taper "notepad" → Entrée).
+
+        Args:
+            current_state: État source
+            next_state: État cible
+            events: Tous les événements de la session
+
+        Returns:
+            Liste ordonnée (par timestamp) de tous les événements d'action
+            entre les deux états. Peut être vide.
+        """
+        current_time = current_state.metadata.get("event_time", 0)
+        next_time = next_state.metadata.get("event_time", float('inf'))
+
+        action_events = []
+        for event in events:
+            if current_time <= event.t < next_time:
+                if event.type in ["mouse_click", "key_press", "text_input"]:
+                    # Filtrer les événements parasites (modificateurs seuls, texte vide)
+                    if not _is_parasitic_event(event):
+                        action_events.append(event)
+
+        return action_events
+
     def _find_transition_event(
         self,
         current_state: ScreenState,
@@ -853,98 +1138,92 @@ class GraphBuilder:
     ) -> Optional[Event]:
         """
         Trouver l'événement qui a causé la transition entre deux états.
-        
-        Cherche l'événement (clic, frappe) qui s'est produit entre les deux screenshots.
+
+        DEPRECATED: Utiliser _find_transition_events() pour obtenir TOUS les événements.
+        Conservé pour rétrocompatibilité.
         """
-        current_time = current_state.metadata.get("event_time", 0)
-        next_time = next_state.metadata.get("event_time", float('inf'))
-        
-        # Chercher les événements d'action entre les deux timestamps
-        action_events = []
-        for event in events:
-            if current_time <= event.t < next_time:
-                if event.type in ["mouse_click", "key_press", "text_input"]:
-                    action_events.append(event)
-        
-        # Retourner le dernier événement d'action (celui qui a probablement causé la transition)
-        if action_events:
-            return action_events[-1]
-        
-        return None
+        all_events = self._find_transition_events(current_state, next_state, events)
+        return all_events[-1] if all_events else None
     
     def _create_edge(
         self,
         from_node: str,
         to_node: str,
         event: Optional[Event],
-        edge_id: str
+        edge_id: str,
+        source_node: Optional[WorkflowNode] = None,
+        target_node: Optional[WorkflowNode] = None,
+        all_events: Optional[List[Event]] = None,
+        screen_resolution: Tuple[int, int] = (1920, 1080),
     ) -> WorkflowEdge:
         """
         Créer un WorkflowEdge depuis une transition observée.
+
+        Peuple les EdgeConstraints depuis le template du node source et
+        les PostConditions depuis le template du node cible.
+
+        Si plusieurs événements sont associés à la transition (all_events),
+        crée une action "compound" contenant toutes les étapes (steps),
+        ce qui permet au replay de reproduire fidèlement la séquence complète
+        (ex: Win+R → taper "notepad" → Entrée).
+
+        Args:
+            from_node: ID du node source
+            to_node: ID du node cible
+            event: Dernier événement de la transition (rétrocompatibilité)
+            edge_id: Identifiant de l'edge
+            source_node: Node source (pour extraire les pré-conditions)
+            target_node: Node cible (pour extraire les post-conditions)
+            all_events: TOUS les événements entre les deux états (si disponible)
+            screen_resolution: Résolution de référence pour normaliser les coordonnées
         """
-        # Déterminer le type d'action
-        if event:
-            action_type = event.type
-            action_params = {}
-            
-            if action_type == "mouse_click":
-                action_params = {
-                    "button": event.data.get("button", "left"),
-                    "position": event.data.get("pos", [0, 0]),
-                    "wait_after_ms": 500
-                }
-                target_role = "unknown_element"  # Sera affiné avec détection UI
-                
-            elif action_type == "key_press":
-                action_params = {
-                    "keys": event.data.get("keys", []),
-                    "wait_after_ms": 200
-                }
-                target_role = "keyboard_input"
-                
-            elif action_type == "text_input":
-                action_params = {
-                    "text": event.data.get("text", ""),
-                    "wait_after_ms": 300
-                }
-                target_role = "text_field"
-            else:
-                action_params = {}
-                target_role = "unknown"
+        # Si on a plusieurs événements, créer une action compound
+        events_to_use = all_events or ([event] if event else [])
+
+        if len(events_to_use) > 1:
+            action = self._build_compound_action(
+                events_to_use, screen_resolution
+            )
+        elif len(events_to_use) == 1:
+            action = self._build_single_action(events_to_use[0])
         else:
-            action_type = "unknown"
-            action_params = {}
-            target_role = "unknown"
-        
-        # Créer l'action
-        action = Action(
-            type=action_type,
-            target=TargetSpec(
-                by_role=target_role,
-                selection_policy="first",
-                fallback_strategy="visual_similarity"
-            ),
-            parameters=action_params
+            action = Action(
+                type="unknown",
+                target=TargetSpec(
+                    by_role="unknown",
+                    selection_policy="first",
+                    fallback_strategy="visual_similarity",
+                ),
+                parameters={},
+            )
+
+        # ---------------------------------------------------------------
+        # Pré-conditions (EdgeConstraints) depuis le template du node source
+        # ---------------------------------------------------------------
+        constraints = self._build_edge_constraints(source_node)
+
+        # ---------------------------------------------------------------
+        # Post-conditions depuis le template du node cible
+        # ---------------------------------------------------------------
+        post_conditions = self._build_post_conditions(
+            to_node, source_node, target_node
         )
-        
-        # Créer les contraintes
-        constraints = EdgeConstraints(
-            pre_conditions={},
-            required_confidence=0.8,
-            max_wait_time_ms=5000
-        )
-        
-        # Créer les post-conditions
-        post_conditions = PostConditions(
-            expected_node=to_node,
-            window_change_expected=False,
-            new_ui_elements_expected=[],
-            timeout_ms=3000
-        )
-        
+
         # Créer l'edge
         from core.models.workflow_graph import EdgeStats
-        
+
+        # Metadata enrichie pour le diagnostic
+        edge_metadata = {
+            "auto_generated": True,
+            "event_count": len(events_to_use),
+        }
+        if events_to_use:
+            edge_metadata["created_from_event"] = events_to_use[-1].type
+            if len(events_to_use) > 1:
+                edge_metadata["event_types"] = [e.type for e in events_to_use]
+        else:
+            edge_metadata["created_from_event"] = None
+
         return WorkflowEdge(
             edge_id=edge_id,
             from_node=from_node,
@@ -953,10 +1232,306 @@ class GraphBuilder:
             constraints=constraints,
             post_conditions=post_conditions,
             stats=EdgeStats(),
-            metadata={
-                "created_from_event": event.type if event else None,
-                "auto_generated": True
+            metadata=edge_metadata,
+        )
+
+    def _build_single_action(self, event: Event) -> Action:
+        """
+        Construire une Action simple depuis un seul événement.
+
+        Rétrocompatible avec l'ancien format : un type d'action direct
+        (mouse_click, key_press, text_input) avec ses paramètres.
+        """
+        action_type = event.type
+        action_params = {}
+
+        if action_type == "mouse_click":
+            action_params = {
+                "button": event.data.get("button", "left"),
+                "position": event.data.get("pos", [0, 0]),
+                "wait_after_ms": 500,
             }
+            target_role = "unknown_element"
+
+        elif action_type == "key_press":
+            action_params = {
+                "keys": event.data.get("keys", []),
+                "wait_after_ms": 200,
+            }
+            target_role = "keyboard_input"
+
+        elif action_type == "text_input":
+            action_params = {
+                "text": event.data.get("text", ""),
+                "wait_after_ms": 300,
+            }
+            target_role = "text_field"
+        else:
+            action_params = {}
+            target_role = "unknown"
+
+        return Action(
+            type=action_type,
+            target=TargetSpec(
+                by_role=target_role,
+                selection_policy="first",
+                fallback_strategy="visual_similarity",
+            ),
+            parameters=action_params,
+        )
+
+    def _build_compound_action(
+        self,
+        events: List[Event],
+        screen_resolution: Tuple[int, int] = (1920, 1080),
+    ) -> Action:
+        """
+        Construire une Action compound (multi-étapes) depuis plusieurs événements.
+
+        Quand l'utilisateur fait plusieurs actions entre deux changements d'écran
+        (ex: Win+R → taper "notepad" → Entrée), on les regroupe en une seule
+        action compound avec une liste de steps ordonnés.
+
+        Des waits sont insérés automatiquement entre les étapes quand le délai
+        réel dépasse 500ms (plafonné à 5000ms).
+
+        Args:
+            events: Liste ordonnée d'événements (>= 2)
+            screen_resolution: Résolution de référence (largeur, hauteur)
+
+        Returns:
+            Action de type "compound" avec steps dans parameters
+        """
+        steps = []
+        ref_w, ref_h = screen_resolution
+
+        for i, event in enumerate(events):
+            # Insérer un wait si le délai entre deux événements est > 500ms
+            if i > 0:
+                delta_ms = int((event.t - events[i - 1].t) * 1000)
+                if delta_ms > 500:
+                    steps.append({
+                        "type": "wait",
+                        "duration_ms": min(delta_ms, 5000),
+                    })
+
+            if event.type == "mouse_click":
+                pos = event.data.get("pos", [0, 0])
+                steps.append({
+                    "type": "mouse_click",
+                    "position": list(pos),
+                    "x_pct": round(pos[0] / ref_w, 6) if ref_w > 0 else 0.0,
+                    "y_pct": round(pos[1] / ref_h, 6) if ref_h > 0 else 0.0,
+                    "button": event.data.get("button", "left"),
+                    "ref_width": ref_w,
+                    "ref_height": ref_h,
+                })
+            elif event.type == "text_input":
+                steps.append({
+                    "type": "text_input",
+                    "text": event.data.get("text", ""),
+                })
+            elif event.type == "key_press":
+                keys = event.data.get("keys", [])
+                if keys:
+                    steps.append({
+                        "type": "key_press",
+                        "keys": keys,
+                    })
+            else:
+                # Type inconnu : stocker quand même pour ne rien perdre
+                steps.append({
+                    "type": event.type,
+                    "data": dict(event.data) if event.data else {},
+                })
+
+        # ---------------------------------------------------------------
+        # Nettoyage des steps parasites :
+        # 1. Supprimer les modificateurs seuls (ctrl, alt, shift, etc.)
+        # 2. Fusionner les text_input consécutifs (ex: "No","m",":"," " → "Nom: ")
+        # 3. Dédupliquer les key_combo consécutifs identiques
+        # 4. Garantir un wait minimum de 300ms entre chaque step
+        # ---------------------------------------------------------------
+        steps = _filter_modifier_only_steps(steps)
+        steps = _merge_consecutive_text_steps(steps)
+        steps = _dedup_consecutive_combos(steps)
+        steps = _ensure_min_waits(steps)
+
+        # La cible du compound = cible de la dernière action (le clic final, etc.)
+        last_event = events[-1]
+        if last_event.type == "mouse_click":
+            target_role = "unknown_element"
+        elif last_event.type == "text_input":
+            target_role = "text_field"
+        elif last_event.type == "key_press":
+            target_role = "keyboard_input"
+        else:
+            target_role = "unknown"
+
+        return Action(
+            type="compound",
+            target=TargetSpec(
+                by_role=target_role,
+                selection_policy="first",
+                fallback_strategy="visual_similarity",
+            ),
+            parameters={
+                "steps": steps,
+                "step_count": len(steps),
+                "ref_width": ref_w,
+                "ref_height": ref_h,
+            },
+        )
+
+    def _build_edge_constraints(
+        self,
+        source_node: Optional[WorkflowNode],
+    ) -> EdgeConstraints:
+        """
+        Construire les EdgeConstraints (pré-conditions) depuis le node source.
+
+        Extrait du ScreenTemplate du node source :
+        - required_window_title : titre de fenêtre attendu
+        - required_app_name : nom de l'application
+        - min_source_similarity : seuil de similarité embedding
+        - window / text : contraintes WindowConstraint et TextConstraint
+        """
+        if not source_node or not source_node.template:
+            return EdgeConstraints(
+                pre_conditions={},
+                required_confidence=0.8,
+                max_wait_time_ms=5000,
+            )
+
+        tpl = source_node.template
+
+        # Extraire le titre de fenêtre depuis le WindowConstraint
+        req_title = None
+        req_app = None
+        window_constraint = None
+
+        if tpl.window:
+            req_title = tpl.window.title_contains or tpl.window.title_pattern
+            req_app = tpl.window.process_name
+            # Copier la contrainte de fenêtre complète
+            window_constraint = WindowConstraint(
+                title_pattern=tpl.window.title_pattern,
+                title_contains=tpl.window.title_contains,
+                process_name=tpl.window.process_name,
+            )
+
+        # Extraire la contrainte de texte (required_texts du template)
+        text_constraint = None
+        if tpl.text and tpl.text.required_texts:
+            # Limiter à 5 textes les plus significatifs (>= 3 car, <= 100 car)
+            filtered = [
+                t for t in tpl.text.required_texts
+                if 3 <= len(t) <= 100
+            ][:5]
+            if filtered:
+                text_constraint = TextConstraint(
+                    required_texts=filtered,
+                )
+
+        # Seuil de similarité depuis l'embedding prototype
+        min_sim = 0.80
+        if tpl.embedding and tpl.embedding.min_cosine_similarity:
+            min_sim = tpl.embedding.min_cosine_similarity
+
+        return EdgeConstraints(
+            pre_conditions={},
+            required_confidence=min_sim,
+            max_wait_time_ms=5000,
+            window=window_constraint,
+            text=text_constraint,
+            min_source_similarity=min_sim,
+            required_app_name=req_app,
+            required_window_title=req_title,
+        )
+
+    def _build_post_conditions(
+        self,
+        to_node_id: str,
+        source_node: Optional[WorkflowNode],
+        target_node: Optional[WorkflowNode],
+    ) -> PostConditions:
+        """
+        Construire les PostConditions depuis le node cible.
+
+        Peuple :
+        - expected_window_title / expected_app_name depuis le template cible
+        - success checks : textes attendus (required_texts du template cible)
+                         + window_title_contains si le titre change
+        - fail_fast checks : patterns d'erreur courants (forbidden_texts)
+        - min_target_similarity depuis l'embedding prototype cible
+        """
+        if not target_node or not target_node.template:
+            return PostConditions(
+                expected_node=to_node_id,
+                window_change_expected=False,
+                new_ui_elements_expected=[],
+                timeout_ms=10000,
+            )
+
+        tpl = target_node.template
+
+        # Extraire les infos du node cible
+        expected_title = None
+        expected_app = None
+        if tpl.window:
+            expected_title = tpl.window.title_contains or tpl.window.title_pattern
+            expected_app = tpl.window.process_name
+
+        # Seuil de similarité cible
+        min_sim = 0.80
+        if tpl.embedding and tpl.embedding.min_cosine_similarity:
+            min_sim = tpl.embedding.min_cosine_similarity
+
+        # Détecter si le titre de fenêtre change entre source et cible
+        window_change = False
+        if source_node and source_node.template and source_node.template.window:
+            src_title = source_node.template.window.title_contains or source_node.template.window.title_pattern
+            if src_title and expected_title and src_title != expected_title:
+                window_change = True
+
+        # --- Construire les checks de succès ---
+        success_checks = []
+
+        # Check sur le titre de fenêtre cible (si défini)
+        if expected_title:
+            success_checks.append(
+                PostConditionCheck(kind="window_title_contains", value=expected_title)
+            )
+
+        # Checks sur les textes attendus dans l'écran cible
+        if tpl.text and tpl.text.required_texts:
+            for text in tpl.text.required_texts[:5]:
+                if 3 <= len(text) <= 100:
+                    success_checks.append(
+                        PostConditionCheck(kind="text_present", value=text)
+                    )
+
+        # --- Construire les checks fail_fast (détection d'erreurs) ---
+        fail_fast_checks = []
+        for pattern in self._ERROR_PATTERNS:
+            fail_fast_checks.append(
+                PostConditionCheck(kind="text_present", value=pattern)
+            )
+
+        return PostConditions(
+            success_mode="all",
+            timeout_ms=10000,
+            poll_ms=200,
+            success=success_checks,
+            fail_fast=fail_fast_checks,
+            retries=2,
+            backoff_ms=150,
+            expected_window_title=expected_title,
+            expected_app_name=expected_app,
+            min_target_similarity=min_sim,
+            expected_node=to_node_id,
+            window_change_expected=window_change,
+            new_ui_elements_expected=[],
         )
 
 
diff --git a/core/models/screen_state.py b/core/models/screen_state.py
index e70ec3487..b9d0fcb95 100644
--- a/core/models/screen_state.py
+++ b/core/models/screen_state.py
@@ -135,27 +135,48 @@ class ContextLevel:
 
 @dataclass
 class WindowContext:
-    """Contexte de fenêtre"""
+    """Contexte de fenêtre avec métadonnées d'environnement graphique"""
     app_name: str
     window_title: str
     screen_resolution: List[int]
     workspace: str = "main"
-    
+    monitor_index: int = 0                              # Index du moniteur (0 = principal)
+    dpi_scale: int = 100                                # Facteur DPI en % (100 = normal, 150 = haute résolution)
+    window_bounds: Optional[List[int]] = None           # [x, y, width, height] de la fenêtre
+    monitors: Optional[List[Dict[str, int]]] = None     # Liste des moniteurs [{width, height, x, y}]
+    os_theme: str = "unknown"                           # "light", "dark", "unknown"
+    os_language: str = "unknown"                        # Code langue (fr, en, de...)
+
     def to_dict(self) -> Dict[str, Any]:
-        return {
+        result = {
             "app_name": self.app_name,
             "window_title": self.window_title,
             "screen_resolution": self.screen_resolution,
-            "workspace": self.workspace
+            "workspace": self.workspace,
+            "monitor_index": self.monitor_index,
+            "dpi_scale": self.dpi_scale,
+            "os_theme": self.os_theme,
+            "os_language": self.os_language,
         }
-    
+        if self.window_bounds is not None:
+            result["window_bounds"] = self.window_bounds
+        if self.monitors is not None:
+            result["monitors"] = self.monitors
+        return result
+
     @classmethod
     def from_dict(cls, data: Dict[str, Any]) -> 'WindowContext':
         return cls(
             app_name=data["app_name"],
             window_title=data["window_title"],
             screen_resolution=data["screen_resolution"],
-            workspace=data.get("workspace", "main")
+            workspace=data.get("workspace", "main"),
+            monitor_index=data.get("monitor_index", 0),
+            dpi_scale=data.get("dpi_scale", 100),
+            window_bounds=data.get("window_bounds"),
+            monitors=data.get("monitors"),
+            os_theme=data.get("os_theme", "unknown"),
+            os_language=data.get("os_language", "unknown"),
         )
 
 
diff --git a/core/models/workflow_graph.py b/core/models/workflow_graph.py
index 6ad5b85b5..7af4520ac 100644
--- a/core/models/workflow_graph.py
+++ b/core/models/workflow_graph.py
@@ -304,7 +304,7 @@ class ScreenTemplate:
         
         # Vérifier contraintes de fenêtre
         if hasattr(screen_state, 'window'):
-            window_title = getattr(screen_state.window, 'title', '')
+            window_title = getattr(screen_state.window, 'window_title', '')
             process = getattr(screen_state.window, 'process', '')
             if not self.window.matches(window_title, process):
                 return False, 0.0
@@ -672,24 +672,94 @@ class Action:
 
 @dataclass
 class EdgeConstraints:
-    """Contraintes pour l'exécution d'un edge"""
+    """Contraintes pour l'exécution d'un edge (pré-conditions avant l'action)"""
     pre_conditions: Dict[str, Any] = field(default_factory=dict)
     required_confidence: float = 0.8
     max_wait_time_ms: int = 5000
-    
+
+    # Contraintes enrichies extraites du node source
+    window: Optional[WindowConstraint] = None
+    text: Optional[TextConstraint] = None
+    min_source_similarity: float = 0.80
+    required_app_name: Optional[str] = None
+    required_window_title: Optional[str] = None
+
+    def check_preconditions(
+        self, window_title: str = "", app_name: str = "",
+        detected_texts: Optional[List[str]] = None,
+        source_similarity: float = 1.0,
+    ) -> Tuple[bool, str]:
+        """
+        Vérifier si toutes les pré-conditions sont satisfaites.
+
+        Returns:
+            (ok: bool, reason: str)
+        """
+        # Vérifier similarité minimale avec le node source
+        if source_similarity < self.min_source_similarity:
+            return False, (
+                f"Similarité source insuffisante: {source_similarity:.2f} "
+                f"< {self.min_source_similarity:.2f}"
+            )
+
+        # Vérifier titre de fenêtre
+        if self.required_window_title and window_title:
+            if self.required_window_title not in window_title:
+                return False, (
+                    f"Titre de fenêtre incorrect: '{window_title}' "
+                    f"ne contient pas '{self.required_window_title}'"
+                )
+
+        # Vérifier nom d'application
+        if self.required_app_name and app_name:
+            if self.required_app_name.lower() not in app_name.lower():
+                return False, (
+                    f"Application incorrecte: '{app_name}' "
+                    f"ne correspond pas à '{self.required_app_name}'"
+                )
+
+        # Vérifier contrainte de fenêtre (objet WindowConstraint)
+        if self.window:
+            if not self.window.matches(window_title, app_name):
+                return False, f"Contrainte de fenêtre non satisfaite"
+
+        # Vérifier contrainte de texte (objet TextConstraint)
+        if self.text and detected_texts is not None:
+            if not self.text.matches(detected_texts):
+                return False, f"Contrainte de texte non satisfaite"
+
+        return True, "OK"
+
     def to_dict(self) -> Dict[str, Any]:
         return {
             "pre_conditions": self.pre_conditions,
             "required_confidence": self.required_confidence,
-            "max_wait_time_ms": self.max_wait_time_ms
+            "max_wait_time_ms": self.max_wait_time_ms,
+            "window": self.window.to_dict() if self.window else None,
+            "text": self.text.to_dict() if self.text else None,
+            "min_source_similarity": self.min_source_similarity,
+            "required_app_name": self.required_app_name,
+            "required_window_title": self.required_window_title,
         }
-    
+
     @classmethod
     def from_dict(cls, data: Dict[str, Any]) -> 'EdgeConstraints':
+        window = None
+        if data.get("window"):
+            window = WindowConstraint.from_dict(data["window"])
+        text = None
+        if data.get("text"):
+            text = TextConstraint.from_dict(data["text"])
+
         return cls(
             pre_conditions=data.get("pre_conditions", {}),
             required_confidence=data.get("required_confidence", 0.8),
-            max_wait_time_ms=data.get("max_wait_time_ms", 5000)
+            max_wait_time_ms=data.get("max_wait_time_ms", 5000),
+            window=window,
+            text=text,
+            min_source_similarity=data.get("min_source_similarity", 0.80),
+            required_app_name=data.get("required_app_name"),
+            required_window_title=data.get("required_window_title"),
         )
 
 
@@ -709,23 +779,101 @@ class PostConditionCheck:
 @dataclass
 class PostConditions:
     """Post-conditions attendues après exécution - Fiche #9"""
-    # (garde tes champs existants si tu en as déjà, et ajoute ceux-ci)
-    
+
     success_mode: str = "all"      # "all" | "any"
     timeout_ms: int = 2500
     poll_ms: int = 200
-    
+
     success: List[PostConditionCheck] = field(default_factory=list)
     fail_fast: List[PostConditionCheck] = field(default_factory=list)
-    
+
     retries: int = 2               # nb de tentatives après échec post-conditions
     backoff_ms: int = 150          # 150, 300, 600...
-    
+
+    # Contraintes enrichies extraites du node cible
+    expected_window_title: Optional[str] = None
+    expected_app_name: Optional[str] = None
+    min_target_similarity: float = 0.80
+
     # Legacy fields (garde compatibilité)
     expected_node: Optional[str] = None  # Node attendu après action
     window_change_expected: bool = False
     new_ui_elements_expected: List[str] = field(default_factory=list)
-    
+
+    def check_postconditions(
+        self, window_title: str = "", app_name: str = "",
+        detected_texts: Optional[List[str]] = None,
+        target_similarity: float = 1.0,
+    ) -> Tuple[bool, str]:
+        """
+        Vérifier si les post-conditions sont satisfaites après l'action.
+
+        Returns:
+            (ok: bool, reason: str)
+        """
+        # Vérifier similarité minimale avec le node cible
+        if target_similarity < self.min_target_similarity:
+            return False, (
+                f"Similarité cible insuffisante: {target_similarity:.2f} "
+                f"< {self.min_target_similarity:.2f}"
+            )
+
+        # Vérifier titre de fenêtre attendu
+        if self.expected_window_title and window_title:
+            if self.expected_window_title not in window_title:
+                return False, (
+                    f"Titre de fenêtre post-action incorrect: '{window_title}' "
+                    f"ne contient pas '{self.expected_window_title}'"
+                )
+
+        # Vérifier application attendue
+        if self.expected_app_name and app_name:
+            if self.expected_app_name.lower() not in app_name.lower():
+                return False, (
+                    f"Application post-action incorrecte: '{app_name}' "
+                    f"ne correspond pas à '{self.expected_app_name}'"
+                )
+
+        # Vérifier les checks de succès (PostConditionCheck)
+        if self.success:
+            results = []
+            for check in self.success:
+                ok = self._evaluate_check(check, window_title, detected_texts or [])
+                results.append(ok)
+
+            if self.success_mode == "all" and not all(results):
+                return False, "Certaines post-conditions de succès non satisfaites"
+            if self.success_mode == "any" and not any(results):
+                return False, "Aucune post-condition de succès satisfaite"
+
+        # Vérifier fail_fast (si un pattern d'erreur est détecté, échec immédiat)
+        if self.fail_fast and detected_texts:
+            for check in self.fail_fast:
+                if self._evaluate_check(check, window_title, detected_texts):
+                    return False, (
+                        f"Condition d'échec détectée: {check.kind}={check.value}"
+                    )
+
+        return True, "OK"
+
+    @staticmethod
+    def _evaluate_check(
+        check: PostConditionCheck,
+        window_title: str,
+        detected_texts: List[str],
+    ) -> bool:
+        """Évaluer un PostConditionCheck individuel."""
+        texts_lower = [t.lower() for t in detected_texts]
+
+        if check.kind == "text_present":
+            return any(check.value.lower() in t for t in texts_lower) if check.value else False
+        elif check.kind == "text_absent":
+            return not any(check.value.lower() in t for t in texts_lower) if check.value else True
+        elif check.kind == "window_title_contains":
+            return check.value.lower() in window_title.lower() if check.value else False
+        # Autres types de checks non gérés ici → considérés comme OK
+        return True
+
     def to_dict(self) -> Dict[str, Any]:
         return {
             "success_mode": self.success_mode,
@@ -735,24 +883,28 @@ class PostConditions:
             "fail_fast": [{"kind": c.kind, "value": c.value, "target": c.target.to_dict() if c.target else None} for c in self.fail_fast],
             "retries": self.retries,
             "backoff_ms": self.backoff_ms,
+            # Contraintes enrichies
+            "expected_window_title": self.expected_window_title,
+            "expected_app_name": self.expected_app_name,
+            "min_target_similarity": self.min_target_similarity,
             # Legacy
             "expected_node": self.expected_node,
             "window_change_expected": self.window_change_expected,
             "new_ui_elements_expected": self.new_ui_elements_expected
         }
-    
+
     @classmethod
     def from_dict(cls, data: Dict[str, Any]) -> 'PostConditions':
         success_checks = []
         for c in data.get("success", []):
             target = TargetSpec.from_dict(c["target"]) if c.get("target") else None
             success_checks.append(PostConditionCheck(kind=c["kind"], value=c.get("value"), target=target))
-        
+
         fail_fast_checks = []
         for c in data.get("fail_fast", []):
             target = TargetSpec.from_dict(c["target"]) if c.get("target") else None
             fail_fast_checks.append(PostConditionCheck(kind=c["kind"], value=c.get("value"), target=target))
-        
+
         return cls(
             success_mode=data.get("success_mode", "all"),
             timeout_ms=data.get("timeout_ms", 2500),
@@ -761,6 +913,10 @@ class PostConditions:
             fail_fast=fail_fast_checks,
             retries=data.get("retries", 2),
             backoff_ms=data.get("backoff_ms", 150),
+            # Contraintes enrichies
+            expected_window_title=data.get("expected_window_title"),
+            expected_app_name=data.get("expected_app_name"),
+            min_target_similarity=data.get("min_target_similarity", 0.80),
             # Legacy
             expected_node=data.get("expected_node"),
             window_change_expected=data.get("window_change_expected", False),
diff --git a/core/pipeline/screen_analyzer.py b/core/pipeline/screen_analyzer.py
index acf7d6c96..89f617654 100644
--- a/core/pipeline/screen_analyzer.py
+++ b/core/pipeline/screen_analyzer.py
@@ -321,6 +321,12 @@ class ScreenAnalyzer:
                 window_title=window_info.get("title", "Unknown"),
                 screen_resolution=window_info.get("screen_resolution", [1920, 1080]),
                 workspace=window_info.get("workspace", "main"),
+                monitor_index=window_info.get("monitor_index", 0),
+                dpi_scale=window_info.get("dpi_scale", 100),
+                window_bounds=window_info.get("window_bounds"),
+                monitors=window_info.get("monitors"),
+                os_theme=window_info.get("os_theme", "unknown"),
+                os_language=window_info.get("os_language", "unknown"),
             )
         return WindowContext(
             app_name="unknown",
diff --git a/deploy/build_lea_exe.sh b/deploy/build_lea_exe.sh
new file mode 100755
index 000000000..e2e48b121
--- /dev/null
+++ b/deploy/build_lea_exe.sh
@@ -0,0 +1,275 @@
+#!/bin/bash
+# ============================================================
+# build_lea_exe.sh — Cree un executable Windows autonome via PyInstaller
+#
+# IMPORTANT : Ce script doit tourner SUR WINDOWS (ou dans Wine/WSL
+# avec acces a un Python Windows). PyInstaller ne peut pas produire
+# un .exe Windows depuis Linux natif.
+#
+# Procedure recommandee :
+#   1. Sur le PC Windows (192.168.1.11 ou autre) :
+#      - Installer Python 3.12 (https://python.org)
+#      - pip install pyinstaller
+#   2. Copier ce script et le dossier agent_v0/ sur le PC Windows
+#   3. Executer depuis PowerShell/cmd :
+#      python -m PyInstaller --onefile --windowed ^
+#        --name "Lea" ^
+#        --add-data "agent_v1;agent_v1" ^
+#        --add-data "lea_ui;lea_ui" ^
+#        --add-data "config.txt;." ^
+#        --hidden-import "pynput.keyboard._win32" ^
+#        --hidden-import "pynput.mouse._win32" ^
+#        --hidden-import "pystray._win32" ^
+#        --hidden-import "plyer.platforms.win.notification" ^
+#        --hidden-import "win32api" ^
+#        --hidden-import "win32con" ^
+#        --hidden-import "win32gui" ^
+#        run_agent_v1.py
+#
+# Le .exe resultant sera dans dist/Lea.exe (~50-100 MB)
+#
+# ============================================================
+#
+# OPTION ALTERNATIVE : Python Embedded (recommandee)
+#
+# Python Embedded est un Python portable officiel (pas d'installation).
+# Combine avec le code source, c'est la methode la plus fiable
+# pour les non-informaticiens.
+#
+# Sur une machine Windows :
+#   1. Telecharger Python Embedded 3.12 :
+#      https://www.python.org/ftp/python/3.12.9/python-3.12.9-embed-amd64.zip
+#
+#   2. Dezipper dans un dossier temporaire
+#
+#   3. Activer pip dans Python Embedded :
+#      - Editer python312._pth, decommenter "import site"
+#      - Telecharger get-pip.py : https://bootstrap.pypa.io/get-pip.py
+#      - Executer : python.exe get-pip.py
+#
+#   4. Installer les dependances :
+#      python.exe -m pip install -r requirements_agent.txt
+#
+#   5. Copier le code source (agent_v1/, lea_ui/, run_agent_v1.py)
+#
+#   6. Zipper le tout → Lea_Portable.zip (~40-60 MB)
+#
+#   Le Lea.bat dans ce cas utiliserait :
+#      python\python.exe run_agent_v1.py
+#   au lieu de .venv\Scripts\python.exe
+#
+# ============================================================
+
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+PROJECT_ROOT="$(dirname "$SCRIPT_DIR")"
+
+echo "============================================================"
+echo "  Build Lea.exe (PyInstaller)"
+echo "============================================================"
+echo ""
+echo "  Ce script ne peut pas produire un .exe Windows depuis Linux."
+echo ""
+echo "  OPTIONS DISPONIBLES :"
+echo ""
+echo "  1. OPTION VIA PC WINDOWS (recommandee pour .exe) :"
+echo "     Copiez le dossier deploy/ sur le PC Windows"
+echo "     puis lancez la commande PyInstaller ci-dessous."
+echo ""
+echo "  2. OPTION ZIP + VENV (recommandee pour deploiement rapide) :"
+echo "     Lancez ./deploy/build_package.sh"
+echo "     Le zip resultant contient install.bat + Lea.bat"
+echo ""
+echo "  3. OPTION PYTHON EMBEDDED (recommandee pour zero install) :"
+echo "     Suivez les instructions dans ce script (section ALTERNATIVE)"
+echo ""
+echo "============================================================"
+echo ""
+
+# Generer le .spec PyInstaller pour reference
+SPEC_FILE="$SCRIPT_DIR/Lea.spec"
+cat > "$SPEC_FILE" << 'PYINSTALLER_SPEC'
+# -*- mode: python ; coding: utf-8 -*-
+# Lea.spec — Configuration PyInstaller pour l'agent Lea
+#
+# Usage sur Windows :
+#   pip install pyinstaller
+#   pyinstaller Lea.spec
+#
+# Le .exe resultant sera dans dist/Lea.exe
+
+import os
+import sys
+
+block_cipher = None
+
+# Repertoire de travail (ou se trouve ce .spec)
+SPEC_DIR = os.path.dirname(os.path.abspath(SPEC())) if 'SPEC' in dir() else '.'
+
+a = Analysis(
+    ['run_agent_v1.py'],
+    pathex=['.'],
+    binaries=[],
+    datas=[
+        ('agent_v1', 'agent_v1'),
+        ('lea_ui', 'lea_ui'),
+        ('config.txt', '.'),
+        ('LISEZMOI.txt', '.'),
+    ],
+    hiddenimports=[
+        # pynput backends Windows
+        'pynput.keyboard._win32',
+        'pynput.mouse._win32',
+        # pystray backend Windows
+        'pystray._win32',
+        # plyer notification Windows
+        'plyer.platforms.win',
+        'plyer.platforms.win.notification',
+        # pywin32
+        'win32api',
+        'win32con',
+        'win32gui',
+        'win32com',
+        'pythoncom',
+        # tkinter (stdlib, parfois manquant dans PyInstaller)
+        'tkinter',
+        'tkinter.simpledialog',
+        'tkinter.messagebox',
+        'tkinter.filedialog',
+    ],
+    hookspath=[],
+    hooksconfig={},
+    runtime_hooks=[],
+    excludes=[
+        # Exclure les modules lourds non necessaires cote client
+        'torch',
+        'torchvision',
+        'transformers',
+        'clip',
+        'open_clip',
+        'faiss',
+        'cv2',         # opencv pas obligatoire (blur_sensitive a un fallback)
+        'numpy',       # requis par PIL mais pas directement
+        'scipy',
+        'sklearn',
+        'matplotlib',
+        'pandas',
+        'tensorflow',
+    ],
+    win_no_prefer_redirects=False,
+    win_private_assemblies=False,
+    cipher=block_cipher,
+    noarchive=False,
+)
+
+pyz = PYZ(a.pure, a.zipped_data, cipher=block_cipher)
+
+exe = EXE(
+    pyz,
+    a.scripts,
+    a.binaries,
+    a.zipfiles,
+    a.datas,
+    [],
+    name='Lea',
+    debug=False,
+    bootloader_ignore_signals=False,
+    strip=False,
+    upx=True,
+    upx_exclude=[],
+    runtime_tmpdir=None,
+    console=False,  # --windowed : pas de console visible
+    disable_windowed_traceback=False,
+    argv_emulation=False,
+    target_arch=None,
+    codesign_identity=None,
+    entitlements_file=None,
+    # icon='assets/lea_icon.ico',  # Decommenter quand l'icone sera creee
+)
+PYINSTALLER_SPEC
+
+echo "  Fichier Lea.spec genere dans : $SPEC_FILE"
+echo ""
+echo "  Pour builder sur Windows :"
+echo "    1. Copier le dossier Lea/ (apres build_package.sh) sur le PC Windows"
+echo "    2. pip install pyinstaller"
+echo "    3. cd Lea"
+echo "    4. pyinstaller ../Lea.spec"
+echo "    5. Le .exe sera dans dist/Lea.exe"
+echo ""
+
+# Generer aussi un script batch pour builder sur Windows
+WIN_BUILD="$SCRIPT_DIR/build_exe_windows.bat"
+cat > "$WIN_BUILD" << 'WIN_BATCH'
+@echo off
+chcp 65001 >nul 2>&1
+title Build Lea.exe
+
+echo ============================================================
+echo  Build Lea.exe (PyInstaller)
+echo ============================================================
+echo.
+
+:: Verifier PyInstaller
+pip show pyinstaller >nul 2>&1
+if errorlevel 1 (
+    echo Installation de PyInstaller...
+    pip install pyinstaller
+)
+
+:: Builder
+echo Build en cours (cela prend 2-5 minutes)...
+echo.
+
+pyinstaller --onefile --windowed ^
+    --name "Lea" ^
+    --add-data "agent_v1;agent_v1" ^
+    --add-data "lea_ui;lea_ui" ^
+    --add-data "config.txt;." ^
+    --add-data "LISEZMOI.txt;." ^
+    --hidden-import "pynput.keyboard._win32" ^
+    --hidden-import "pynput.mouse._win32" ^
+    --hidden-import "pystray._win32" ^
+    --hidden-import "plyer.platforms.win.notification" ^
+    --hidden-import "win32api" ^
+    --hidden-import "win32con" ^
+    --hidden-import "win32gui" ^
+    --hidden-import "tkinter" ^
+    --hidden-import "tkinter.simpledialog" ^
+    --hidden-import "tkinter.messagebox" ^
+    --exclude-module "torch" ^
+    --exclude-module "torchvision" ^
+    --exclude-module "transformers" ^
+    --exclude-module "clip" ^
+    --exclude-module "faiss" ^
+    --exclude-module "scipy" ^
+    --exclude-module "sklearn" ^
+    --exclude-module "matplotlib" ^
+    --exclude-module "pandas" ^
+    --exclude-module "tensorflow" ^
+    run_agent_v1.py
+
+if errorlevel 1 (
+    echo.
+    echo  ERREUR : Le build a echoue.
+    pause
+    exit /b 1
+)
+
+echo.
+echo ============================================================
+echo  Build termine !
+echo.
+echo  Lea.exe est dans le dossier dist\
+echo  Taille :
+dir dist\Lea.exe | findstr "Lea.exe"
+echo.
+echo  Pour deployer : copiez dist\Lea.exe + config.txt + LISEZMOI.txt
+echo ============================================================
+pause
+WIN_BATCH
+
+echo "  Script Windows genere : $WIN_BUILD"
+echo ""
+echo "============================================================"
diff --git a/deploy/build_package.sh b/deploy/build_package.sh
new file mode 100755
index 000000000..46e5877e4
--- /dev/null
+++ b/deploy/build_package.sh
@@ -0,0 +1,166 @@
+#!/bin/bash
+# ============================================================
+# build_package.sh — Assemble le package Lea pour deploiement Windows
+#
+# Produit : Lea_v<version>.zip (< 5 MB sans venv)
+#
+# Usage :
+#   ./deploy/build_package.sh          # Package standard
+#   ./deploy/build_package.sh --clean   # Nettoyer avant de builder
+#
+# Le zip contient tout ce qu'il faut pour un deploiement :
+#   - install.bat (premiere installation)
+#   - Lea.bat (lancement quotidien)
+#   - config.txt (parametres serveur)
+#   - LISEZMOI.txt (documentation utilisateur)
+#   - Code Python de l'agent
+# ============================================================
+
+set -euo pipefail
+
+# Couleurs pour les messages
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+RED='\033[0;31m'
+NC='\033[0m' # No Color
+
+# Repertoire racine du projet
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+PROJECT_ROOT="$(dirname "$SCRIPT_DIR")"
+
+# Version (lue depuis config.py de l'agent)
+VERSION=$(grep -oP 'AGENT_VERSION\s*=\s*"([^"]+)"' "$PROJECT_ROOT/agent_v0/agent_v1/config.py" | grep -oP '"[^"]+"' | tr -d '"' || echo "1.0.0")
+
+# Dossier de sortie
+BUILD_DIR="$SCRIPT_DIR/build"
+PACKAGE_DIR="$BUILD_DIR/Lea"
+OUTPUT_ZIP="$SCRIPT_DIR/Lea_v${VERSION}.zip"
+
+echo -e "${GREEN}============================================================${NC}"
+echo -e "${GREEN}  Build du package Lea v${VERSION}${NC}"
+echo -e "${GREEN}============================================================${NC}"
+echo ""
+
+# ---------------------------------------------------------------
+# Option --clean
+# ---------------------------------------------------------------
+if [[ "${1:-}" == "--clean" ]]; then
+    echo -e "${YELLOW}Nettoyage du build precedent...${NC}"
+    rm -rf "$BUILD_DIR"
+    rm -f "$SCRIPT_DIR"/Lea_v*.zip
+    echo "  OK"
+    echo ""
+fi
+
+# ---------------------------------------------------------------
+# 1. Creer le dossier de build
+# ---------------------------------------------------------------
+echo "[1/7] Preparation du dossier de build..."
+rm -rf "$PACKAGE_DIR"
+mkdir -p "$PACKAGE_DIR"
+echo "  $PACKAGE_DIR cree"
+echo ""
+
+# ---------------------------------------------------------------
+# 2. Copier les fichiers de deploiement (bat, config, readme)
+# ---------------------------------------------------------------
+echo "[2/7] Copie des fichiers de deploiement..."
+cp "$SCRIPT_DIR/lea_package/Lea.bat"              "$PACKAGE_DIR/"
+cp "$SCRIPT_DIR/lea_package/install.bat"           "$PACKAGE_DIR/"
+cp "$SCRIPT_DIR/lea_package/config.txt"            "$PACKAGE_DIR/"
+cp "$SCRIPT_DIR/lea_package/LISEZMOI.txt"          "$PACKAGE_DIR/"
+cp "$SCRIPT_DIR/lea_package/requirements_agent.txt" "$PACKAGE_DIR/"
+echo "  5 fichiers copies"
+echo ""
+
+# ---------------------------------------------------------------
+# 3. Copier le point d'entree
+# ---------------------------------------------------------------
+echo "[3/7] Copie du point d'entree..."
+cp "$PROJECT_ROOT/agent_v0/run_agent_v1.py" "$PACKAGE_DIR/"
+echo "  run_agent_v1.py copie"
+echo ""
+
+# ---------------------------------------------------------------
+# 4. Copier le package agent_v1 (code Python)
+# ---------------------------------------------------------------
+echo "[4/7] Copie du code agent_v1..."
+# Copier tout le dossier en excluant les fichiers inutiles
+rsync -a \
+    --exclude='__pycache__' \
+    --exclude='*.pyc' \
+    --exclude='.pytest_cache' \
+    --exclude='sessions/' \
+    --exclude='logs/*.log' \
+    --exclude='.hypothesis' \
+    "$PROJECT_ROOT/agent_v0/agent_v1/" \
+    "$PACKAGE_DIR/agent_v1/"
+
+# Creer les dossiers necessaires (vides)
+mkdir -p "$PACKAGE_DIR/agent_v1/sessions"
+mkdir -p "$PACKAGE_DIR/agent_v1/logs"
+
+echo "  agent_v1/ copie ($(find "$PACKAGE_DIR/agent_v1" -name "*.py" | wc -l) fichiers Python)"
+echo ""
+
+# ---------------------------------------------------------------
+# 5. Copier le module lea_ui (client serveur pour le chat)
+# ---------------------------------------------------------------
+echo "[5/7] Copie du module lea_ui..."
+mkdir -p "$PACKAGE_DIR/lea_ui"
+cp "$PROJECT_ROOT/agent_v0/lea_ui/"*.py "$PACKAGE_DIR/lea_ui/"
+echo "  lea_ui/ copie ($(ls "$PACKAGE_DIR/lea_ui/"*.py | wc -l) fichiers)"
+echo ""
+
+# ---------------------------------------------------------------
+# 6. Copier le __init__.py racine (pour les imports relatifs)
+# ---------------------------------------------------------------
+echo "[6/7] Configuration des packages Python..."
+# Le __init__.py au niveau racine du package (agent_v0 level)
+# n'est PAS necessaire car run_agent_v1.py est au meme niveau que agent_v1/
+# Mais lea_ui est importe avec un import relatif depuis agent_v1/main.py
+# via `from ..lea_ui.server_client import LeaServerClient`
+# Cet import fonctionne uniquement si l'arborescence est un package.
+# Or, dans le deploiement, lea_ui est au meme niveau que agent_v1,
+# et le fallback dans main.py fait `from lea_ui.server_client import LeaServerClient`
+# qui fonctionne car run_agent_v1.py ajoute current_dir au sys.path.
+echo "  Structure d'imports verifiee"
+echo ""
+
+# ---------------------------------------------------------------
+# 7. Creer le zip
+# ---------------------------------------------------------------
+echo "[7/7] Creation du zip..."
+cd "$BUILD_DIR"
+rm -f "$OUTPUT_ZIP"
+zip -r "$OUTPUT_ZIP" "Lea/" -x "Lea/.venv/*" "Lea/__pycache__/*" "Lea/*/__pycache__/*"
+cd "$PROJECT_ROOT"
+
+# Taille du zip
+ZIP_SIZE=$(du -h "$OUTPUT_ZIP" | cut -f1)
+
+echo ""
+echo -e "${GREEN}============================================================${NC}"
+echo -e "${GREEN}  Build termine !${NC}"
+echo -e "${GREEN}============================================================${NC}"
+echo ""
+echo "  Package : $OUTPUT_ZIP"
+echo "  Taille  : $ZIP_SIZE"
+echo "  Version : $VERSION"
+echo ""
+echo "  Contenu du package :"
+echo "  --------------------"
+echo ""
+
+# Lister le contenu du zip (structure lisible)
+unzip -l "$OUTPUT_ZIP" | tail -n +4 | head -n -2 | awk '{print "    " $4}'
+
+echo ""
+echo -e "${YELLOW}  Deploiement :${NC}"
+echo "  1. Copier le zip sur le PC Windows du collaborateur"
+echo "  2. Dezipper dans un dossier (ex: C:\\Lea)"
+echo "  3. Editer config.txt si besoin (adresse serveur, token)"
+echo "  4. Double-cliquer install.bat (une seule fois)"
+echo "  5. Double-cliquer Lea.bat pour lancer"
+echo ""
+echo -e "${GREEN}============================================================${NC}"
diff --git a/deploy/lea_package/LISEZMOI.txt b/deploy/lea_package/LISEZMOI.txt
new file mode 100644
index 000000000..3489d6c01
--- /dev/null
+++ b/deploy/lea_package/LISEZMOI.txt
@@ -0,0 +1,85 @@
+============================================================
+          Lea - Votre assistante intelligente
+============================================================
+
+Bienvenue ! Lea est une assistante qui apprend vos taches
+repetitives sur l'ordinateur et peut les refaire a votre place.
+
+
+PREMIERE INSTALLATION
+---------------------
+
+1. Double-cliquez sur "install.bat"
+   (cela prend 2-3 minutes, une seule fois)
+
+2. Si une fenetre vous demande d'autoriser Python,
+   cliquez "Oui" ou "Autoriser".
+
+3. A la fin, vous verrez "Installation terminee !"
+
+
+LANCER LEA
+----------
+
+Double-cliquez sur "Lea.bat"
+
+Lea apparait en bas a droite de votre ecran, dans la barre
+des taches (petite icone ronde, a cote de l'horloge).
+
+Clic droit sur l'icone pour ouvrir le menu :
+
+  - "Apprenez-moi une tache" : Lea observe ce que vous faites
+    et memorise les etapes.
+
+  - "Mes taches" : Liste des taches que Lea a apprises.
+    Cliquez sur une tache pour que Lea la refasse.
+
+  - "Discuter avec Lea" : Ouvre une fenetre de discussion
+    pour poser des questions ou donner des instructions.
+
+  - "ARRET D'URGENCE" : Arrete immediatement tout ce que
+    Lea est en train de faire.
+
+  - "Quitter Lea" : Ferme le programme.
+
+
+CONFIGURATION
+-------------
+
+Si vous devez modifier l'adresse du serveur, ouvrez le fichier
+"config.txt" avec le Bloc-notes et changez les valeurs.
+
+Ne modifiez rien d'autre sans l'accord de votre administrateur.
+
+
+EN CAS DE PROBLEME
+-------------------
+
+  - "Python n'est pas installe" : Demandez a votre
+    service informatique d'installer Python 3.10
+    depuis https://python.org
+
+  - Lea ne demarre pas : Relancez "install.bat" puis
+    relancez "Lea.bat"
+
+  - Lea est deconnectee : Verifiez votre connexion
+    internet/reseau. Le serveur est peut-etre en
+    maintenance.
+
+  - En cas de doute, contactez votre administrateur.
+
+
+INFORMATIONS
+------------
+
+Lea est un systeme base sur l'intelligence artificielle.
+
+Quand Lea enregistre vos actions, elle capture votre ecran,
+vos clics et vos frappes clavier. Les donnees sensibles
+(mots de passe, informations medicales) sont automatiquement
+floutees avant envoi.
+
+Vous pouvez arreter l'enregistrement ou le replay a tout
+moment via le menu ou le bouton "ARRET D'URGENCE".
+
+============================================================
diff --git a/deploy/lea_package/Lea.bat b/deploy/lea_package/Lea.bat
new file mode 100644
index 000000000..dd4e17306
--- /dev/null
+++ b/deploy/lea_package/Lea.bat
@@ -0,0 +1,54 @@
+@echo off
+chcp 65001 >nul 2>&1
+title Lea - Assistante IA
+
+:: ---------------------------------------------------------------
+:: Se placer dans le dossier du script (important pour les chemins)
+:: ---------------------------------------------------------------
+cd /d "%~dp0"
+
+:: ---------------------------------------------------------------
+:: Verifier que l'installation a ete faite
+:: ---------------------------------------------------------------
+if not exist ".venv\Scripts\python.exe" (
+    echo.
+    echo  Lea n'est pas encore installee.
+    echo  Lancez d'abord "install.bat" puis revenez ici.
+    echo.
+    pause
+    exit /b 1
+)
+
+:: ---------------------------------------------------------------
+:: Charger la configuration depuis config.txt
+:: Les lignes commencant par # sont ignorees (commentaires)
+:: Format attendu : NOM_VARIABLE=valeur
+:: ---------------------------------------------------------------
+if exist "config.txt" (
+    for /f "usebackq eol=# tokens=1,* delims==" %%a in ("config.txt") do (
+        if not "%%a"=="" if not "%%b"=="" set "%%a=%%b"
+    )
+) else (
+    echo  ATTENTION : config.txt introuvable, utilisation des valeurs par defaut.
+)
+
+:: ---------------------------------------------------------------
+:: Lancer Lea
+:: ---------------------------------------------------------------
+echo.
+echo  Demarrage de Lea...
+echo  (Lea apparait dans la barre des taches, en bas a droite)
+echo.
+echo  Pour arreter Lea : clic droit sur l'icone ^> "Quitter Lea"
+echo  Vous pouvez fermer cette fenetre.
+echo.
+
+.venv\Scripts\pythonw.exe run_agent_v1.py
+if errorlevel 1 (
+    echo.
+    echo  Lea a rencontre un probleme au demarrage.
+    echo  Tentative avec affichage des erreurs...
+    echo.
+    .venv\Scripts\python.exe run_agent_v1.py
+    pause
+)
diff --git a/deploy/lea_package/config.txt b/deploy/lea_package/config.txt
new file mode 100644
index 000000000..b1388e094
--- /dev/null
+++ b/deploy/lea_package/config.txt
@@ -0,0 +1,31 @@
+# ============================================================
+#  Configuration Lea
+# ============================================================
+#
+#  Ce fichier contient les parametres de connexion au serveur.
+#  Modifiez uniquement les valeurs apres le signe =
+#  Ne touchez pas aux noms des parametres (a gauche du =).
+#
+#  Les lignes commencant par # sont des commentaires (ignorees).
+#
+# ============================================================
+
+# Adresse du serveur Lea (URL complete avec /api/v1)
+RPA_SERVER_URL=https://lea.labs.laurinebazin.design/api/v1
+
+# Cle d'authentification (fournie par l'administrateur)
+RPA_API_TOKEN=86031addb338e449fccdb1a983f61807aec15d42d482b9c7748ad607dc23caab
+
+# Nom du serveur (sans https://, sans /api/v1)
+RPA_SERVER_HOST=lea.labs.laurinebazin.design
+
+# ============================================================
+#  Parametres avances (ne pas modifier sauf indication)
+# ============================================================
+
+# Flouter les zones de texte dans les captures (securite donnees)
+# Mettre false uniquement pour le developpement/tests
+RPA_BLUR_SENSITIVE=true
+
+# Duree de conservation des logs en jours (minimum 180 pour conformite)
+RPA_LOG_RETENTION_DAYS=180
diff --git a/deploy/lea_package/install.bat b/deploy/lea_package/install.bat
new file mode 100644
index 000000000..0cdca81ca
--- /dev/null
+++ b/deploy/lea_package/install.bat
@@ -0,0 +1,127 @@
+@echo off
+chcp 65001 >nul 2>&1
+title Lea - Installation
+
+echo.
+echo ============================================================
+echo          Lea - Installation
+echo ============================================================
+echo.
+echo  Cette installation prend 2-3 minutes.
+echo  Ne fermez pas cette fenetre.
+echo.
+echo ============================================================
+echo.
+
+:: ---------------------------------------------------------------
+:: 0. Verifier que Python est installe
+:: ---------------------------------------------------------------
+echo [1/5] Verification de Python...
+
+python --version >nul 2>&1
+if errorlevel 1 (
+    echo.
+    echo  ERREUR : Python n'est pas installe sur cet ordinateur.
+    echo.
+    echo  Pour installer Python :
+    echo    1. Allez sur https://python.org
+    echo    2. Cliquez "Download Python 3.12"
+    echo    3. IMPORTANT : cochez "Add Python to PATH" pendant l'installation
+    echo    4. Relancez install.bat apres l'installation de Python
+    echo.
+    pause
+    exit /b 1
+)
+
+:: Afficher la version Python detectee
+for /f "tokens=*" %%v in ('python --version 2^>^&1') do echo        %%v detecte - OK
+echo.
+
+:: ---------------------------------------------------------------
+:: 1. Creer l'environnement virtuel
+:: ---------------------------------------------------------------
+if not exist ".venv" (
+    echo [2/5] Creation de l'environnement isole...
+    python -m venv .venv
+    if errorlevel 1 (
+        echo  ERREUR : Impossible de creer l'environnement virtuel.
+        echo  Verifiez que Python est correctement installe.
+        pause
+        exit /b 1
+    )
+    echo        Environnement cree - OK
+) else (
+    echo [2/5] Environnement existant detecte - OK
+)
+echo.
+
+:: ---------------------------------------------------------------
+:: 2. Activer l'environnement
+:: ---------------------------------------------------------------
+echo [3/5] Activation de l'environnement...
+call .venv\Scripts\activate.bat
+echo        Active - OK
+echo.
+
+:: ---------------------------------------------------------------
+:: 3. Installer les dependances
+:: ---------------------------------------------------------------
+echo [4/5] Installation des composants (cela peut prendre 1-2 min)...
+python -m pip install --upgrade pip --quiet 2>nul
+pip install -r requirements_agent.txt --quiet 2>nul
+if errorlevel 1 (
+    echo.
+    echo  ATTENTION : Certains composants n'ont pas pu etre installes.
+    echo  Nouvelle tentative avec affichage des details...
+    echo.
+    pip install -r requirements_agent.txt
+    if errorlevel 1 (
+        echo.
+        echo  ERREUR : L'installation a echoue.
+        echo  Verifiez votre connexion internet et reessayez.
+        pause
+        exit /b 1
+    )
+)
+echo        Composants installes - OK
+echo.
+
+:: ---------------------------------------------------------------
+:: 4. Post-installation Windows (pywin32)
+:: ---------------------------------------------------------------
+echo [5/5] Configuration Windows...
+python -c "import win32api" >nul 2>&1
+if errorlevel 1 (
+    python .venv\Scripts\pywin32_postinstall.py -install >nul 2>&1
+)
+echo        Configuration terminee - OK
+echo.
+
+:: ---------------------------------------------------------------
+:: 5. Verification finale
+:: ---------------------------------------------------------------
+echo ============================================================
+echo  Verification finale...
+echo ============================================================
+echo.
+
+python -c "import mss; import pynput; import pystray; import plyer; import requests; import PIL; print('  Tous les composants sont OK !')"
+if errorlevel 1 (
+    echo.
+    echo  ATTENTION : Certains composants manquent.
+    echo  Essayez de relancer install.bat.
+    echo  Si le probleme persiste, contactez votre administrateur.
+    pause
+    exit /b 1
+)
+
+echo.
+echo ============================================================
+echo.
+echo  Installation terminee !
+echo.
+echo  Pour lancer Lea, double-cliquez sur "Lea.bat"
+echo.
+echo ============================================================
+echo.
+pause
diff --git a/deploy/lea_package/requirements_agent.txt b/deploy/lea_package/requirements_agent.txt
new file mode 100644
index 000000000..dc942086e
--- /dev/null
+++ b/deploy/lea_package/requirements_agent.txt
@@ -0,0 +1,13 @@
+# Dependances Lea Agent (client leger)
+# Pas de CLIP, PyTorch, ou modele lourd - tout le calcul est sur le serveur
+
+mss>=9.0.1              # Capture d'ecran haute performance
+pynput>=1.7.7           # Clavier/Souris
+Pillow>=10.0.0          # Traitement image (crops, compression)
+requests>=2.31.0        # Communication serveur
+psutil>=5.9.0           # Monitoring CPU/RAM
+pystray>=0.19.5         # Icone systray
+plyer>=2.1.0            # Notifications toast natives
+
+# Windows specifique
+pywin32>=306 ; sys_platform == 'win32'
diff --git a/docs/RAPPORT_CONFORMITE_AI_ACT.md b/docs/RAPPORT_CONFORMITE_AI_ACT.md
new file mode 100644
index 000000000..8aad6d5d3
--- /dev/null
+++ b/docs/RAPPORT_CONFORMITE_AI_ACT.md
@@ -0,0 +1 @@
+{"parentUuid":"27762a0f-3d42-438c-81c7-ff3201308904","isSidechain":true,"agentId":"a89e576514eb3a4ec","message":{"model":"claude-opus-4-6","id":"msg_014RyauXc2qaRQ25JJuargBZ","type":"message","role":"assistant","content":[{"type":"text","text":"I now have comprehensive information from both web research and codebase analysis. Here is the full compliance report.\n\n---\n\n# EU AI Act Compliance Assessment Report -- RPA Vision V3\n\n**Date**: 18 March 2026\n**Regulation**: Regulation (EU) 2024/1689 (EU Artificial Intelligence Act)\n**System assessed**: RPA Vision V3 -- Visual RPA with AI-based UI understanding\n**Assessor**: Claude Opus 4.6 (automated compliance research)\n\n---\n\n## 1. PRELIMINARY QUESTION: Does RPA Vision V3 Qualify as an \"AI System\"?\n\n**Answer: YES, unambiguously.**\n\nArticle 3(1) defines an AI system as: *\"a machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.\"*\n\nWhile traditional rule-based RPA falls outside this definition, RPA Vision V3 is fundamentally different. It uses:\n- **CLIP** (contrastive language-image pre-training) for semantic UI understanding and embedding\n- **Qwen3-VL** (vision-language model) for visual inference on screenshots\n- **FAISS** for similarity search and state matching\n- **OWLv2** for object detection\n- **Machine learning inference** to match screen states, resolve targets semantically, and make decisions about workflow progression\n\nThe system **infers** how to interact with UIs from visual observations, operates with **autonomy** during replay mode, and exhibits **adaptiveness** through its learning pipeline. This clearly meets the AI system definition.\n\n---\n\n## 2. RISK CLASSIFICATION\n\n### 2.1 Primary Classification: HIGH-RISK (Annex III, Point 4(b))\n\n**Annex III, Point 4(b)** classifies as high-risk: *\"AI intended to be used for making decisions on [...] task allocation and for monitoring and evaluating performance and behavior of persons in [work-related] relationships.\"*\n\nRPA Vision V3 triggers this because:\n- It **observes and records worker behavior** (screenshots, mouse clicks, keyboard inputs) during the Shadow/learning phase\n- It captures **behavioral patterns** of how employees perform tasks\n- In autonomous mode, it **takes over task execution**, which constitutes AI-driven task allocation/execution\n- The system's learning pipeline creates a **behavioral model** of worker actions\n- It operates in the **workplace** (hospitals, clinics) targeting healthcare professionals and administrative staff\n\n### 2.2 Does the Article 6(3) Exception Apply?\n\nArticle 6(3) allows an Annex III system to be classified as non-high-risk if it does not pose a significant risk of harm. However, **Article 6(3) last paragraph** states: *\"an AI system referred to in Annex III shall always be considered to be high-risk where the AI system performs profiling of natural persons.\"*\n\n**RPA Vision V3 performs profiling** under the GDPR definition (Article 4(4) GDPR, referenced by the AI Act): it processes personal data through automated means to analyze or predict aspects related to a person's **behavior** and **work performance**. The system captures exactly how a person works -- what they click, what they type, what screens they visit, and in what order.\n\n**Conclusion: The Article 6(3) exception does NOT apply. The system is definitively high-risk.**\n\n### 2.3 Additional High-Risk Triggers\n\n| Annex III Category | Applicability | Rationale |\n|---|---|---|\n| **4(b) Employment/worker monitoring** | **YES** | Core function: observe and learn from worker behavior |\n| **5(a) Essential public services (healthcare)** | **POSSIBLE** | If the system is used to process/manage patient-related workflows, it touches healthcare service delivery |\n| **1 Biometrics** | **NO** | No biometric identification/categorization |\n\n### 2.4 Prohibited Practices Check (Article 5)\n\n| Prohibition | Risk | Assessment |\n|---|---|---|\n| **Art. 5(1)(f): Emotion recognition in workplace** | **LOW but must verify** | The system captures screenshots and analyzes UI elements, not facial expressions or emotions. However, if screenshots contain webcam feeds or the VLM is ever prompted to infer user emotional state, this would be **PROHIBITED**. Currently not implemented, but no technical safeguard prevents it. |\n| **Art. 5(1)(a): Subliminal manipulation** | **NO** | System does not manipulate user behavior |\n| **Art. 5(1)(c): Social scoring** | **NO** | No scoring of individuals |\n| **Art. 5(1)(d): Predictive policing** | **NO** | Not applicable |\n\n---\n\n## 3. COMPLIANCE OBLIGATIONS FOR HIGH-RISK AI SYSTEMS\n\n### 3.1 Required by the AI Act (Provider Obligations -- Articles 8-25)\n\n| Requirement | Article | Current State | Gap |\n|---|---|---|---|\n| **Risk Management System** | Art. 9 | **ABSENT** | No formal risk management process documented. No continuous lifecycle risk assessment. |\n| **Data Governance** | Art. 10 | **ABSENT** | No data governance framework. Screenshots containing patient data are stored without classification. No bias detection. |\n| **Technical Documentation** | Art. 11 + Annex IV | **ABSENT** | No Annex IV-compliant technical documentation exists. The codebase has inline docstrings but no formal regulatory documentation. |\n| **Record-keeping / Logging** | Art. 12 | **PARTIAL** | Python `logging` module is used throughout, but there is no structured audit trail, no immutable logs, no mandatory 6-month retention policy for AI decisions. |\n| **Transparency & User Information** | Art. 13 | **PARTIAL** | The \"Lea\" chatbot says \"Je suis Lea\" but does not explicitly disclose it is an AI system. No documentation provided to deployers about capabilities, limitations, or risks. |\n| **Human Oversight** | Art. 14 | **PARTIAL** | The system has Shadow/Copilot/Autonomous modes suggesting graduated autonomy. However, no formal \"stop button\", no human-in-the-loop override mechanism is documented, no ability for a human to systematically review before actions are executed in autonomous mode. |\n| **Accuracy, Robustness, Cybersecurity** | Art. 15 | **PARTIAL** | Match confidence scores exist (`match_confidence` in `StepResult`), but no formal accuracy benchmarks, no adversarial testing, no cybersecurity assessment. Default security keys in config (`dev_secret_key_not_for_production`). CORS set to `*` by default. |\n| **Quality Management System** | Art. 17 | **ABSENT** | No QMS. No documented development procedures, no validation processes, no incident reporting framework. |\n| **EU Declaration of Conformity** | Art. 47 | **ABSENT** | No conformity declaration exists. |\n| **CE Marking** | Art. 48 | **ABSENT** | No CE marking process. |\n| **Registration in EU Database** | Art. 49 | **ABSENT** | Not registered. |\n| **Post-market Monitoring** | Art. 72 | **ABSENT** | No post-market monitoring plan. |\n\n### 3.2 Deployer Obligations (Article 26 -- for hospitals/clinics using the system)\n\n| Requirement | Status | Gap |\n|---|---|---|\n| Use according to instructions | **NO instructions exist** | No user manual or instructions for use compliant with Art. 13 |\n| Human oversight by competent persons | **NOT FORMALIZED** | No training program, no competency requirements defined |\n| Monitor operation | **PARTIAL** | Dashboard exists (port 5001) but lacks compliance-oriented monitoring |\n| Inform workers' representatives | **NOT ADDRESSED** | No guidance provided to deployers about worker notification obligations |\n| Inform affected workers | **NOT ADDRESSED** | Workers being observed are not systematically informed the AI is recording and learning from their actions |\n| DPIA (GDPR Art. 35) | **NOT PERFORMED** | Mandatory given the processing of health data and employee monitoring |\n| Keep logs for 6 months minimum | **NOT GUARANTEED** | `SessionStorage` has `retention_days=1` by default -- this VIOLATES the minimum 6-month retention requirement |\n\n---\n\n## 4. GDPR INTERSECTION -- CRITICAL ISSUES\n\n### 4.1 Personal Data Processing\n\nRPA Vision V3 captures and processes:\n- **Screenshots** containing potentially: patient names, medical records, diagnoses, treatments, prescriptions, personal addresses, phone numbers, social security numbers, financial data\n- **Keyboard inputs** (keystrokes) which may include passwords, medical notes, personal communications\n- **Mouse movements and click patterns** which constitute behavioral data\n\n### 4.2 GDPR Classification\n\n| Data Category | GDPR Classification | Impact |\n|---|---|---|\n| Patient medical records in screenshots | **Special category data** (Art. 9 GDPR -- health data) | Requires explicit consent or specific legal basis. Additional safeguards mandatory. |\n| Employee behavior patterns | **Personal data** (Art. 4(1) GDPR) + **Profiling** (Art. 4(4) GDPR) | Subject to GDPR profiling rules. Right to object under Art. 21. |\n| Keyboard inputs | **Personal data**, potentially **special category** if medical notes | High sensitivity. May capture passwords. |\n\n### 4.3 Legal Basis Analysis\n\nThe system currently has **no documented legal basis** for processing any of this data. Potential bases:\n\n- **Art. 6(1)(a) Consent**: Would require explicit, informed, freely given consent from both employees being observed AND patients whose data appears in screenshots. In an employment context, consent is generally NOT considered freely given (power imbalance).\n- **Art. 6(1)(f) Legitimate interest**: Requires a documented Legitimate Interest Assessment (LIA). The employer's interest in automation must be balanced against employee privacy rights and patient data protection.\n- **Art. 9(2) Health data**: Requires one of the specific Art. 9(2) exceptions. Processing patient health data visible in screenshots for RPA training purposes does not easily fit any exception.\n\n### 4.4 Current Data Protection Measures -- INSUFFICIENT\n\n| Measure | Current State | Required |\n|---|---|---|\n| Anonymization of screenshots | **Optional** (`anonymize=False` by default in `capture_dual()`) -- only Gaussian blur on crop, not on full screenshot | Mandatory for screenshots containing health data. Should be default-on with opt-out requiring justification. |\n| Encryption at rest | **Mentioned in code comments** but not implemented | Screenshots containing patient data must be encrypted |\n| Data minimization | **NOT IMPLEMENTED** | System captures full screenshots. No mechanism to mask/redact sensitive areas before storage. |\n| Purpose limitation | **NOT DOCUMENTED** | Data captured for \"learning\" is stored without purpose limitation controls |\n| Storage limitation | `retention_days=1` default, but AI Act requires 6-month log retention | Conflicting requirements need resolution through tiered retention |\n| Right to erasure | **NOT IMPLEMENTED** | No mechanism for a patient or employee to request deletion of their data from training sets, FAISS indexes, or learned workflows |\n\n---\n\n## 5. TRANSPARENCY OBLIGATIONS (Article 50)\n\n### 5.1 Chatbot \"Lea\" -- Article 50(1)\n\nArticle 50(1) requires: *\"Providers shall ensure that AI systems intended to interact directly with natural persons are designed and developed in such a way that the natural persons concerned are informed that they are interacting with an AI system, unless this is obvious from the circumstances and the context of use.\"*\n\n**Current state**: \"Lea\" introduces herself as *\"Bonjour ! Je suis Lea. Je peux apprendre vos taches repetitives et les refaire a votre place.\"* This is insufficient because:\n- It does not explicitly state she is an **AI system / artificial intelligence**\n- A user unfamiliar with the product might believe \"Lea\" is a human assistant\n- The disclosure must be **unambiguous**: \"Je suis Lea, une assistante basee sur l'intelligence artificielle\"\n\n### 5.2 Autonomous Execution Disclosure\n\nWhen the system operates autonomously (replaying workflows), users whose computer is being controlled should be clearly informed that an AI system is performing actions on their screen.\n\n---\n\n## 6. COMPLIANCE TIMELINE\n\n| Date | Obligation | Status |\n|---|---|---|\n| **2 Feb 2025** (PAST) | Prohibited AI practices in force | Must verify no emotion recognition in screenshots |\n| **2 Feb 2025** (PAST) | AI literacy obligations (Art. 4) | No AI literacy training program exists |\n| **2 Aug 2025** (PAST) | GPAI model obligations | Applies if Qwen3-VL or CLIP are considered GPAI -- primarily a concern for the model providers (Meta, OpenAI), not for deployers |\n| **2 Aug 2026** (5 months away) | **High-risk AI system obligations FULLY APPLICABLE** | **CRITICAL DEADLINE**: All Articles 8-15, 17, Annex IV documentation, risk management, conformity assessment, EU database registration |\n| **2 Aug 2026** | Transparency obligations (Art. 50) | Lea chatbot disclosure must be compliant |\n| **2 Aug 2027** | Final deadline for certain existing systems | Grace period for some legacy high-risk systems already on market |\n\n---\n\n## 7. COMPLIANCE GAPS SUMMARY (Prioritized)\n\n### CRITICAL (must fix before August 2026)\n\n1. **No Risk Management System** (Art. 9) -- Must establish a documented, continuous risk management process\n2. **No Technical Documentation** (Art. 11 + Annex IV) -- Must produce comprehensive documentation before market placement\n3. **No DPIA** (GDPR Art. 35) -- Mandatory for AI processing health data and monitoring employees\n4. **No legal basis for data processing** -- Must establish and document GDPR-compliant legal bases\n5. **Screenshots with patient data are unprotected** -- Default anonymization OFF, no encryption at rest, no data minimization\n6. **Log retention of 1 day** violates the 6-month minimum (Art. 12 + Art. 26(6))\n7. **No Quality Management System** (Art. 17)\n8. **No EU Declaration of Conformity** (Art. 47)\n9. **No registration in EU database** (Art. 49)\n\n### HIGH (should fix before August 2026)\n\n10. **Worker notification gap** -- No mechanism to inform employees they are being observed by an AI\n11. **No human oversight mechanism for autonomous mode** -- No documented kill switch / override / review-before-execute\n12. **Lea chatbot does not disclose AI nature** clearly (Art. 50)\n13. **No post-market monitoring plan** (Art. 72)\n14. **No incident reporting framework**\n15. **Security hardening needed** -- default dev keys, CORS `*`, no TLS enforcement\n\n### MEDIUM (recommended)\n\n16. **No bias detection or fairness testing** (Art. 10)\n17. **No accuracy benchmarks published** (Art. 15)\n18. **No adversarial robustness testing** (Art. 15)\n19. **No formal instructions for deployers** (Art. 13)\n20. **No data subject rights implementation** (GDPR Art. 15-22)\n\n---\n\n## 8. REQUIRED ACTIONS -- ROADMAP\n\n### Phase 1: Immediate (March-April 2026)\n\n1. **Perform a Data Protection Impact Assessment (DPIA)** -- This is overdue given the system processes health data. Engage a DPO.\n2. **Document the legal basis** for all personal data processing activities.\n3. **Enable screenshot anonymization by default** -- change `anonymize=False` to `anonymize=True` in `capturer.py`. Implement full-screenshot redaction (not just crop blur).\n4. **Fix log retention** -- increase minimum to 6 months for AI decision logs while implementing tiered retention (short for raw screenshots with patient data, long for AI audit logs).\n5. **Add explicit AI disclosure to Lea**: *\"Je suis Lea, une assistante propulsee par l'intelligence artificielle.\"*\n6. **Add worker notification feature** -- Before recording begins, display clear notification that AI is observing.\n\n### Phase 2: Documentation (April-June 2026)\n\n7. **Draft Annex IV Technical Documentation** including: system description, intended purpose, design specifications, data requirements, risk management, testing results, performance metrics.\n8. **Establish Risk Management System** (Art. 9) -- continuous, documented, covering the full lifecycle.\n9. **Create Quality Management System** (Art. 17) -- development procedures, testing, validation, incident reporting.\n10. **Write Instructions for Use** for deployers (hospital IT departments).\n11. **Create Human Oversight documentation** -- define who oversees the system, what competencies are needed, how to intervene.\n\n### Phase 3: Conformity (June-August 2026)\n\n12. **Conduct conformity assessment** (self-assessment for Annex III systems under Art. 43).\n13. **Draft EU Declaration of Conformity** (Art. 47).\n14. **Register in the EU database** (Art. 49).\n15. **Implement post-market monitoring plan** (Art. 72).\n16. **Conduct accuracy and robustness testing** with documented benchmarks.\n17. **Implement encryption at rest** for all stored screenshots.\n18. **Implement data subject rights** (access, erasure, objection to profiling).\n\n---\n\n## 9. KEY RECOMMENDATIONS\n\n1. **Engage a specialized AI Act compliance consultant or law firm** -- The intersection of AI Act + GDPR + healthcare data + worker monitoring creates a complex regulatory landscape that requires legal expertise.\n\n2. **Consider appointing a \"responsible AI officer\"** -- Given the high-risk classification, having a designated person accountable for AI compliance is strongly recommended.\n\n3. **Implement Privacy by Design** -- The current architecture stores raw screenshots by default. Redesign to:\n   - Detect and redact PII/PHI in screenshots before storage (using the VLM itself)\n   - Store only anonymized versions for training\n   - Keep identifiable data only for the minimum necessary period\n   - Encrypt all data at rest\n\n4. **Consider the SME simplified documentation** -- Article 11 and Annex IV allow SMEs to use a simplified technical documentation form. The European Commission is expected to publish this template.\n\n5. **Document the \"no emotion recognition\" guarantee** -- Add a technical safeguard (code-level) that prevents the VLM from being prompted with emotion-detection queries on workplace screenshots. This eliminates any Art. 5(1)(f) risk.\n\n6. **Separate AI logs from personal data logs** -- Create a tiered retention system:\n   - AI decision audit logs: 6+ months (Art. 12/26 compliance)\n   - Raw screenshots with personal data: minimum necessary, encrypted, access-controlled\n   - Anonymized training data: longer retention acceptable\n\n7. **The \"on-premise / no cloud\" architecture is a positive** -- Local processing via Ollama significantly reduces data transfer risks and simplifies GDPR compliance. Highlight this in documentation.\n\n---\n\n## 10. PENALTIES FOR NON-COMPLIANCE\n\n| Violation | Maximum Fine |\n|---|---|\n| Prohibited practices (Art. 5) | EUR 35 million or 7% of global turnover |\n| High-risk system obligations (Art. 8-15, 17) | EUR 15 million or 3% of global turnover |\n| Deployer obligations (Art. 26) | EUR 15 million or 3% of global turnover |\n| Transparency obligations (Art. 50) | EUR 15 million or 3% of global turnover |\n| Incorrect information to authorities | EUR 7.5 million or 1% of global turnover |\n| GDPR violations (separate) | EUR 20 million or 4% of global turnover |\n\n---\n\n## Sources\n\n- [Article 3: Definitions | EU AI Act](https://artificialintelligenceact.eu/article/3/)\n- [Article 5: Prohibited AI Practices | EU AI Act](https://artificialintelligenceact.eu/article/5/)\n- [Article 6: Classification Rules for High-Risk AI Systems](https://artificialintelligenceact.eu/article/6/)\n- [Article 9: Risk Management System](https://artificialintelligenceact.eu/article/9/)\n- [Article 14: Human Oversight](https://artificialintelligenceact.eu/article/14/)\n- [Article 17: Quality Management System](https://artificialintelligenceact.eu/article/17/)\n- [Article 26: Obligations of Deployers of High-Risk AI Systems](https://artificialintelligenceact.eu/article/26/)\n- [Article 50: Transparency Obligations](https://artificialintelligenceact.eu/article/50/)\n- [Annex III: High-Risk AI Systems](https://artificialintelligenceact.eu/annex/3/)\n- [Annex IV: Technical Documentation](https://artificialintelligenceact.eu/annex/4/)\n- [Implementation Timeline | EU AI Act](https://artificialintelligenceact.eu/implementation-timeline/)\n- [High-Level Summary of the AI Act](https://artificialintelligenceact.eu/high-level-summary/)\n- [EU AI Act Brief - Pt. 4, AI at Work (CDT)](https://cdt.org/insights/eu-ai-act-brief-pt-4-ai-at-work/)\n- [Prohibition of AI Emotion Recognition in the Workplace](https://legalblogs.wolterskluwer.com/global-workplace-law-and-policy/the-prohibition-of-ai-emotion-recognition-technologies-in-the-workplace-under-the-ai-act/)\n- [AI and Human Resources in the EU: 2026 Legal Overview](https://www.crowell.com/en/insights/client-alerts/artificial-intelligence-and-human-resources-in-the-eu-a-2026-legal-overview)\n- [Navigating the EU AI Act for Healthcare (PMC)](https://pmc.ncbi.nlm.nih.gov/articles/PMC11319791/)\n- [GDPR and AI Act Interplay (IAPP)](https://iapp.org/resources/article/top-impacts-eu-ai-act-leveraging-gdpr-compliance)\n- [EU AI Act 2026 Compliance Guide](https://secureprivacy.ai/blog/eu-ai-act-2026-compliance)\n- [High-Risk Escape Route via Article 6(3)](https://www.ictrechtswijzer.be/en/high-risk-ai-under-the-eu-ai-act-there-is-an-escape-route-through-article-63/)\n- [EU Commission Clarifies Definition of AI Systems](https://www.orrick.com/en/Insights/2025/04/EU-Commission-Clarifies-Definition-of-AI-Systems)\n- [Regulation (EU) 2024/1689 Full Text (EUR-Lex)](https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=OJ:L_202401689)\n\n---\n\n**Key project files examined during this assessment:**\n- `/home/dom/ai/rpa_vision_v3/core/config.py` -- Central configuration (security defaults, no privacy controls)\n- `/home/dom/ai/rpa_vision_v3/agent_v0/agent_v1/vision/capturer.py` -- Screenshot capture (anonymize=False default)\n- `/home/dom/ai/rpa_vision_v3/agent_v0/agent_v1/session/storage.py` -- Session storage (retention_days=1, violates 6-month minimum)\n- `/home/dom/ai/rpa_vision_v3/agent_v0/agent_v1/ui/chat_window.py` -- Lea chatbot (insufficient AI disclosure)\n- `/home/dom/ai/rpa_vision_v3/core/execution/workflow_runner.py` -- Autonomous replay (no human oversight mechanism)\n- `/home/dom/ai/rpa_vision_v3/core/extraction/extraction_engine.py` -- Data extraction from screens (patient data risk)\n\n**Bottom line**: RPA Vision V3 is classified as a **high-risk AI system** under the EU AI Act. The August 2, 2026 deadline for full compliance is **5 months away**. The project currently has **zero** of the required formal compliance artifacts (risk management system, technical documentation, QMS, declaration of conformity, EU database registration). Significant engineering work is also needed for data protection (anonymization defaults, encryption, retention policies, data subject rights). Engaging legal/compliance expertise is strongly recommended."}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1117,"cache_read_input_tokens":50463,"output_tokens":6018,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1117},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZAhNymwQrUGNrLd85zEj","type":"assistant","uuid":"782ef93a-c6d8-4ee0-9a66-937f38d0d196","timestamp":"2026-03-18T14:25:39.474Z","userType":"external","cwd":"/home/dom/ai/rpa_vision_v3","sessionId":"e3cde4b8-cef7-4e9d-8596-3d411082503d","version":"2.1.76","gitBranch":"main","slug":"whimsical-herding-kazoo"}
diff --git a/docs/architecture_data_extraction.md b/docs/architecture_data_extraction.md
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@@ -0,0 +1,92 @@
+{"parentUuid":null,"isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"user","message":{"role":"user","content":"Tu es architecte pour le projet RPA Vision V3. L'utilisateur veut une fonctionnalité de **scraping visuel structuré** : le système apprend à naviguer dans une application métier (ex: DPI — Dossier Patient Informatisé), ouvre chaque dossier un par un, lit visuellement les informations à l'écran (OCR + VLM), et les structure dans une base de données.\n\nContexte du projet :\n- Architecture client/serveur : Agent V1 (Windows, no GPU) ↔ Streaming Server (Linux, RTX 5070)\n- Pipeline existant : ScreenAnalyzer (OCR docTR + UIDetector + VLM qwen2.5vl) → ScreenState → StateEmbedding → WorkflowGraph\n- Replay visuel déjà implémenté : le système sait exécuter des workflows avec résolution visuelle des cibles UI\n- 3 phases : Shadow (observation) → Copilot (supervision humaine) → Autonomous (chat-driven)\n- Chat interface sur port 5004 (agent_chat/app.py)\n- Workflows stockés en JSON dans data/training/workflows/\n\nFichiers clés à explorer :\n- agent_v0/server_v1/api_stream.py — API streaming, endpoints replay\n- agent_v0/server_v1/stream_processor.py — ScreenAnalyzer, pipeline GPU\n- agent_v0/agent_v1/core/executor.py — exécution physique d'actions\n- core/models/raw_session.py — modèles de données session\n- core/graph/graph_builder.py — construction de workflows\n- core/analysis/screen_analyzer.py — analyse d'écran (OCR, UI detection)\n- agent_chat/app.py — interface chat\n\nProduis un plan d'architecture détaillé pour cette feature d'extraction de données :\n\n1. **Nouveau type d'action \"extract\"** dans les workflows — comment l'intégrer dans le format workflow JSON existant\n2. **Schéma d'extraction configurable** — comment l'utilisateur définit quels champs extraire de quel écran\n3. **Pipeline d'extraction** — comment utiliser le VLM (qwen2.5vl ou plus gros) pour extraire des données structurées d'un screenshot\n4. **Itération/pagination** — comment boucler sur une liste de dossiers (concept de \"for each\" dans un workflow)\n5. **Stockage base de données** — SQLite d'abord, extensible PostgreSQL\n6. **Intégration dans le cycle Shadow→Copilot→Autonomous** — comment l'utilisateur enseigne l'extraction\n\nSois concret : propose des structures JSON, des noms de fichiers, des endpoints API. Pense aux edge cases (écrans variables, données manquantes, erreurs de navigation).\n\nNE PAS écrire de code. Uniquement un plan d'architecture avec structures de données et diagrammes.\n"},"uuid":"8032329c-101e-4013-aa60-3e5a601109b2","timestamp":"2026-03-14T21:14:09.841Z"}
+{"parentUuid":"8032329c-101e-4013-aa60-3e5a601109b2","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","message":{"model":"claude-opus-4-6","id":"msg_018zaGgwBpWGBr2DDyh32bYQ","type":"message","role":"assistant","content":[{"type":"text","text":"\n\nI'll start by thoroughly exploring the existing codebase to understand the current architecture before designing the plan."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":12328,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":12328,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZ3fTXbxvgvjif9CfKQnR","type":"assistant","uuid":"7242631e-42b7-4f9c-91dd-ce498fe556dc","timestamp":"2026-03-14T21:14:14.282Z"}
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+{"parentUuid":"0f5a1970-9274-4d59-bf2d-830f444cb4bd","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QZuwrY1gP7RTveNCV1iozY","type":"tool_result","content":"     1→# agent_v0/server_v1/api_stream.py\n     2→\"\"\"\n     3→API de Streaming Temps Réel pour RPA Vision V3.\n     4→\n     5→Connecte l'Agent V1 au core pipeline via StreamProcessor.\n     6→Tous les calculs GPU (ScreenAnalyzer, CLIP, FAISS) tournent ici sur le serveur.\n     7→\n     8→Inclut les endpoints de replay pour renvoyer des ordres d'exécution à l'Agent V1.\n     9→\"\"\"\n    10→\n    11→import json\n    12→import logging\n    13→import threading\n    14→import uuid\n    15→from collections import defaultdict\n    16→from concurrent.futures import ThreadPoolExecutor\n    17→from pathlib import Path\n    18→from typing import Any, Dict, List, Optional\n    19→\n    20→from fastapi import BackgroundTasks, FastAPI, File, HTTPException, UploadFile\n    21→from pydantic import BaseModel\n    22→\n    23→from .stream_processor import StreamProcessor\n    24→from .worker_stream import StreamWorker\n    25→\n    26→logger = logging.getLogger(\"api_stream\")\n    27→app = FastAPI(title=\"RPA Vision V3 - Streaming API v1\")\n    28→\n    29→# Dossier des sessions live\n    30→ROOT_DIR = Path(__file__).parent.parent.parent\n    31→LIVE_SESSIONS_DIR = ROOT_DIR / \"data\" / \"training\" / \"live_sessions\"\n    32→LIVE_SESSIONS_DIR.mkdir(parents=True, exist_ok=True)\n    33→\n    34→# Instance globale partagée\n    35→processor = StreamProcessor(data_dir=str(LIVE_SESSIONS_DIR))\n    36→worker = StreamWorker(live_dir=str(LIVE_SESSIONS_DIR), processor=processor)\n    37→\n    38→# =========================================================================\n    39→# Compteur d'analyses en cours par session (pour attendre avant finalize)\n    40→# =========================================================================\n    41→_pending_analyses: Dict[str, int] = defaultdict(int)\n    42→_pending_lock = threading.Lock()\n    43→\n    44→# =========================================================================\n    45→# File d'attente de replay par session\n    46→# Chaque session a une queue d'actions à exécuter et un état de replay\n    47→# =========================================================================\n    48→_replay_lock = threading.Lock()\n    49→# session_id -> liste d'actions en attente (FIFO)\n    50→_replay_queues: Dict[str, List[Dict[str, Any]]] = defaultdict(list)\n    51→# replay_id -> état du replay (workflow_id, session_id, status, progress)\n    52→_replay_states: Dict[str, Dict[str, Any]] = {}\n    53→\n    54→\n    55→class StreamEvent(BaseModel):\n    56→    session_id: str\n    57→    timestamp: float\n    58→    event: Dict[str, Any]\n    59→\n    60→\n    61→class ReplayRequest(BaseModel):\n    62→    \"\"\"Requête de lancement de replay d'un workflow.\"\"\"\n    63→    workflow_id: str\n    64→    session_id: str\n    65→    params: Optional[Dict[str, Any]] = None\n    66→\n    67→\n    68→class ReplayResultReport(BaseModel):\n    69→    \"\"\"Rapport de résultat d'exécution d'une action par l'Agent V1.\"\"\"\n    70→    session_id: str\n    71→    action_id: str\n    72→    success: bool\n    73→    error: Optional[str] = None\n    74→    screenshot: Optional[str] = None  # Chemin ou base64 du screenshot post-action\n    75→\n    76→\n    77→@app.on_event(\"startup\")\n    78→async def startup():\n    79→    \"\"\"Démarrer le worker et charger les workflows existants.\"\"\"\n    80→    worker.start(blocking=False)\n    81→\n    82→    # Charger les workflows existants depuis le disque\n    83→    _load_existing_workflows()\n    84→    logger.info(\"API Streaming démarrée — StreamProcessor et Worker prêts.\")\n    85→\n    86→\n    87→def _load_existing_workflows():\n    88→    \"\"\"Charger les workflows JSON existants dans processor._workflows.\n    89→\n    90→    Supporte deux formats :\n    91→    - Workflow.load_from_file (format complet avec workflow_id)\n    92→    - JSON brut avec clé 'name' (format simplifié VWB/manuels)\n    93→    \"\"\"\n    94→    from core.models.workflow_graph import Workflow\n    95→\n    96→    workflow_dirs = [\n    97→        ROOT_DIR / \"data\" / \"workflows\",\n    98→        ROOT_DIR / \"data\" / \"training\" / \"workflows\",\n    99→        LIVE_SESSIONS_DIR / \"workflows\",\n   100→    ]\n   101→\n   102→    loaded = 0\n   103→    for wf_dir in workflow_dirs:\n   104→        if not wf_dir.exists():\n   105→            continue\n   106→        for wf_file in wf_dir.glob(\"*.json\"):\n   107→            try:\n   108→                wf = Workflow.load_from_file(str(wf_file))\n   109→                if wf and hasattr(wf, 'workflow_id'):\n   110→                    with processor._data_lock:\n   111→                        processor._workflows[wf.workflow_id] = wf\n   112→                    loaded += 1\n   113→                    continue\n   114→            except Exception:\n   115→                pass\n   116→\n   117→            # Fallback : charger comme JSON brut et injecter un workflow_id\n   118→            try:\n   119→                wf_data = json.loads(wf_file.read_text(encoding=\"utf-8\"))\n   120→                wf_id = wf_data.get(\"workflow_id\") or wf_file.stem\n   121→                # Stocker le dict brut (suffisant pour _workflow_to_actions)\n   122→                with processor._data_lock:\n   123→                    processor._workflows[wf_id] = wf_data\n   124→                loaded += 1\n   125→            except Exception as e:\n   126→                logger.debug(f\"Skip workflow {wf_file.name}: {e}\")\n   127→\n   128→    logger.info(f\"Workflows chargés depuis disque: {loaded}\")\n   129→\n   130→\n   131→@app.on_event(\"shutdown\")\n   132→async def shutdown():\n   133→    worker.stop()\n   134→    logger.info(\"API Streaming arrêtée.\")\n   135→\n   136→\n   137→# =========================================================================\n   138→# Session management\n   139→# =========================================================================\n   140→\n   141→@app.post(\"/api/v1/traces/stream/register\")\n   142→async def register_session(session_id: str):\n   143→    \"\"\"Enregistrer une nouvelle session de streaming.\"\"\"\n   144→    processor.session_manager.register_session(session_id)\n   145→    # Reset des compteurs pour cette session (évite les reliquats d'une session précédente)\n   146→    with _pending_lock:\n   147→        _pending_analyses[session_id] = 0\n   148→        _analyzed_shots[session_id] = set()\n   149→    logger.info(f\"Session {session_id} enregistrée (compteurs réinitialisés)\")\n   150→    return {\"status\": \"session_registered\", \"session_id\": session_id}\n   151→\n   152→\n   153→def _ensure_session_registered(session_id: str):\n   154→    \"\"\"Auto-enregistrer une session si elle n'existe pas encore.\n   155→\n   156→    Robustesse au redémarrage du serveur : l'Agent V1 ne re-register pas\n   157→    sa session, mais continue d'envoyer des events/images. On l'enregistre\n   158→    automatiquement à la première réception.\n   159→    \"\"\"\n   160→    session = processor.session_manager.get_session(session_id)\n   161→    if session is None:\n   162→        logger.info(f\"Auto-enregistrement de la session {session_id}\")\n   163→        processor.session_manager.register_session(session_id)\n   164→        with _pending_lock:\n   165→            _pending_analyses[session_id] = 0\n   166→            _analyzed_shots[session_id] = set()\n   167→\n   168→\n   169→# =========================================================================\n   170→# Événements\n   171→# =========================================================================\n   172→\n   173→@app.post(\"/api/v1/traces/stream/event\")\n   174→async def stream_event(data: StreamEvent):\n   175→    \"\"\"Reçoit un événement et l'enregistre dans la session.\"\"\"\n   176→    session_id = data.session_id\n   177→\n   178→    # Auto-enregistrer la session si inconnue (robustesse au redémarrage serveur)\n   179→    _ensure_session_registered(session_id)\n   180→\n   181→    # Persister sur disque (journal JSONL)\n   182→    session_path = LIVE_SESSIONS_DIR / session_id\n   183→    session_path.mkdir(exist_ok=True)\n   184→    event_file = session_path / \"live_events.jsonl\"\n   185→    with open(event_file, \"a\", encoding=\"utf-8\") as f:\n   186→        f.write(json.dumps(data.dict()) + \"\\n\")\n   187→\n   188→    # Traitement direct via StreamProcessor\n   189→    result = worker.process_event_direct(session_id, data.event)\n   190→    return {\"status\": \"event_synced\", \"session_id\": session_id, **result}\n   191→\n   192→\n   193→# =========================================================================\n   194→# Images\n   195→# =========================================================================\n   196→\n   197→# Ensemble des screenshots déjà analysés (évite les doublons de retry)\n   198→_analyzed_shots: Dict[str, set] = defaultdict(set)\n   199→\n   200→# ThreadPool pour l'analyse GPU (évite de bloquer le event loop async)\n   201→_gpu_executor = ThreadPoolExecutor(max_workers=1, thread_name_prefix=\"gpu_analysis\")\n   202→\n   203→\n   204→@app.post(\"/api/v1/traces/stream/image\")\n   205→async def stream_image(\n   206→    session_id: str,\n   207→    shot_id: str,\n   208→    file: UploadFile = File(...),\n   209→    background_tasks: BackgroundTasks = None,\n   210→):\n   211→    \"\"\"Reçoit une image et déclenche l'analyse via le core pipeline.\"\"\"\n   212→    # Auto-enregistrer la session si inconnue (robustesse au redémarrage serveur)\n   213→    _ensure_session_registered(session_id)\n   214→\n   215→    # Sauvegarder sur disque\n   216→    session_path = LIVE_SESSIONS_DIR / session_id\n   217→    shots_dir = session_path / \"shots\"\n   218→    shots_dir.mkdir(parents=True, exist_ok=True)\n   219→\n   220→    file_path = shots_dir / f\"{shot_id}.png\"\n   221→    content = await file.read()\n   222→    with open(file_path, \"wb\") as f:\n   223→        f.write(content)\n   224→\n   225→    file_path_str = str(file_path)\n   226→\n   227→    # Crops : traitement léger (pas d'analyse ScreenAnalyzer)\n   228→    if \"_crop\" in shot_id:\n   229→        result = worker.process_crop_direct(session_id, shot_id, file_path_str)\n   230→        return {\"status\": \"crop_stored\", \"shot_id\": shot_id, **result}\n   231→\n   232→    # Filtrer les screenshots qui ne nécessitent PAS d'analyse GPU.\n   233→    # Seuls les shot_XXXX_full (screenshots d'action) sont analysés.\n   234→    # Les autres (heartbeat, focus, res_shot) sont stockés sur disque\n   235→    # mais pas envoyés au GPU — sinon le ThreadPool (1 worker, ~10-30s/analyse)\n   236→    # est submergé et la finalisation timeout avec 0 states.\n   237→    if shot_id.startswith(\"heartbeat_\"):\n   238→        return {\"status\": \"heartbeat_stored\", \"shot_id\": shot_id}\n   239→    if shot_id.startswith(\"focus_\"):\n   240→        return {\"status\": \"focus_stored\", \"shot_id\": shot_id}\n   241→    if shot_id.startswith(\"res_shot_\"):\n   242→        return {\"status\": \"res_stored\", \"shot_id\": shot_id}\n   243→    if not shot_id.startswith(\"shot_\") or \"_full\" not in shot_id:\n   244→        # Tout ce qui n'est pas shot_XXXX_full → stocker sans analyser\n   245→        logger.debug(f\"Screenshot {shot_id} stocké sans analyse GPU\")\n   246→        return {\"status\": \"stored_no_analysis\", \"shot_id\": shot_id}\n   247→\n   248→    # Déduplication : ne pas réanalyser un screenshot déjà traité\n   249→    with _pending_lock:\n   250→        if shot_id in _analyzed_shots[session_id]:\n   251→            logger.debug(f\"Screenshot {shot_id} déjà analysé, skip\")\n   252→            return {\"status\": \"already_analyzed\", \"shot_id\": shot_id}\n   253→        _analyzed_shots[session_id].add(shot_id)\n   254→\n   255→    # Screenshots full : analyse GPU dans un thread séparé (ne bloque pas l'event loop)\n   256→    with _pending_lock:\n   257→        _pending_analyses[session_id] += 1\n   258→    _gpu_executor.submit(_process_screenshot_thread, session_id, shot_id, file_path_str)\n   259→    return {\"status\": \"image_queued\", \"shot_id\": shot_id}\n   260→\n   261→\n   262→def _process_screenshot_thread(session_id: str, shot_id: str, path: str):\n   263→    \"\"\"Analyse GPU d'un screenshot dans un thread séparé (ne bloque pas FastAPI).\"\"\"\n   264→    try:\n   265→        result = worker.process_screenshot_direct(session_id, shot_id, path)\n   266→        logger.info(\n   267→            f\"Screenshot {shot_id} analysé en fond: \"\n   268→            f\"{result.get('ui_elements_count', 0)} UI, \"\n   269→            f\"{result.get('text_detected', 0)} textes, \"\n   270→            f\"indexed={result.get('embedding_indexed', False)}\"\n   271→        )\n   272→    except Exception as e:\n   273→        logger.error(f\"Erreur analyse screenshot {shot_id}: {e}\")\n   274→    finally:\n   275→        with _pending_lock:\n   276→            _pending_analyses[session_id] = max(0, _pending_analyses[session_id] - 1)\n   277→\n   278→\n   279→# =========================================================================\n   280→# Finalisation\n   281→# =========================================================================\n   282→\n   283→@app.post(\"/api/v1/traces/stream/finalize\")\n   284→async def finalize(session_id: str):\n   285→    \"\"\"Clôture la session et construit le workflow via GraphBuilder.\n   286→\n   287→    Attend que toutes les analyses de screenshots en cours soient terminées\n   288→    avant de lancer la construction du workflow (timeout 60s).\n   289→    \"\"\"\n   290→    import asyncio\n   291→\n   292→    # Attendre que les analyses background soient terminées (max 60s)\n   293→    max_wait = 60\n   294→    waited = 0\n   295→    while waited < max_wait:\n   296→        with _pending_lock:\n   297→            pending = _pending_analyses.get(session_id, 0)\n   298→        if pending <= 0:\n   299→            break\n   300→        logger.info(\n   301→            f\"Finalisation {session_id}: attente de {pending} analyses en cours...\"\n   302→        )\n   303→        await asyncio.sleep(1.0)\n   304→        waited += 1\n   305→\n   306→    if waited > 0:\n   307→        logger.info(\n   308→            f\"Finalisation {session_id}: analyses terminées après {waited}s d'attente\"\n   309→        )\n   310→\n   311→    logger.info(f\"Finalisation de la session {session_id}\")\n   312→    result = worker.finalize_session(session_id)\n   313→    return {\"status\": \"finalized\", **result}\n   314→\n   315→\n   316→# =========================================================================\n   317→# Monitoring\n   318→# =========================================================================\n   319→\n   320→@app.get(\"/api/v1/traces/stream/stats\")\n   321→async def get_stats():\n   322→    \"\"\"Statistiques du serveur de streaming.\"\"\"\n   323→    return worker.stats\n   324→\n   325→\n   326→@app.get(\"/api/v1/traces/stream/sessions\")\n   327→async def list_sessions():\n   328→    \"\"\"Lister toutes les sessions (actives et finalisées).\"\"\"\n   329→    return {\"sessions\": processor.list_sessions()}\n   330→\n   331→\n   332→@app.get(\"/api/v1/traces/stream/workflows\")\n   333→async def list_workflows():\n   334→    \"\"\"Lister tous les workflows construits.\"\"\"\n   335→    return {\"workflows\": processor.list_workflows()}\n   336→\n   337→\n   338→@app.get(\"/api/v1/traces/stream/session/{session_id}\")\n   339→async def get_session(session_id: str):\n   340→    \"\"\"État d'une session.\"\"\"\n   341→    session = processor.session_manager.get_session(session_id)\n   342→    if not session:\n   343→        raise HTTPException(status_code=404, detail=f\"Session {session_id} non trouvée\")\n   344→    return {\n   345→        \"session_id\": session.session_id,\n   346→        \"events_count\": len(session.events),\n   347→        \"screenshots_count\": len(session.shot_paths),\n   348→        \"last_window\": session.last_window_info,\n   349→        \"created_at\": session.created_at.isoformat(),\n   350→        \"last_activity\": session.last_activity.isoformat(),\n   351→        \"finalized\": session.finalized,\n   352→    }\n   353→\n   354→\n   355→# =========================================================================\n   356→# Replay — Exécution de workflows sur l'Agent V1\n   357→# =========================================================================\n   358→\n   359→\n   360→def _find_active_agent_session() -> Optional[str]:\n   361→    \"\"\"Trouver la dernière session Agent V1 pour le replay.\n   362→\n   363→    Stratégie en 2 passes :\n   364→    1. D'abord chercher une session non-finalisée (Agent V1 actif)\n   365→    2. Sinon, prendre la plus récente même finalisée (Agent V1 peut avoir\n   366→       redémarré et créé une nouvelle session, ou la session a été finalisée\n   367→       par timeout mais l'agent est toujours là)\n   368→\n   369→    Dans les deux cas, on ne considère que les sessions 'sess_*' (Agent V1).\n   370→    \"\"\"\n   371→    with processor.session_manager._lock:\n   372→        all_agent_sessions = [\n   373→            s for s in processor.session_manager._sessions.values()\n   374→            if s.session_id.startswith(\"sess_\")\n   375→        ]\n   376→\n   377→    if not all_agent_sessions:\n   378→        return None\n   379→\n   380→    # Trier par session_id (contient un timestamp) — plus récent d'abord\n   381→    all_agent_sessions.sort(key=lambda s: s.session_id, reverse=True)\n   382→\n   383→    # Passe 1 : préférer une session non-finalisée\n   384→    for s in all_agent_sessions:\n   385→        if not s.finalized:\n   386→            return s.session_id\n   387→\n   388→    # Passe 2 : fallback sur la plus récente (même finalisée)\n   389→    # L'Agent V1 poll /replay/next indépendamment de l'état finalized\n   390→    return all_agent_sessions[0].session_id\n   391→\n   392→\n   393→def _workflow_to_actions(workflow, params: Optional[Dict[str, Any]] = None) -> List[Dict[str, Any]]:\n   394→    \"\"\"\n   395→    Convertir un workflow (nodes + edges ordonnés) en liste d'actions normalisées.\n   396→\n   397→    Parcourt le graphe depuis les entry_nodes en suivant les edges.\n   398→    Chaque edge produit une action normalisée avec coordonnées en pourcentage.\n   399→    \"\"\"\n   400→    actions = []\n   401→    params = params or {}\n   402→\n   403→    # Construire un index des edges sortants par node\n   404→    outgoing: Dict[str, list] = defaultdict(list)\n   405→    for edge in workflow.edges:\n   406→        outgoing[edge.from_node].append(edge)\n   407→\n   408→    # Parcours linéaire depuis le premier entry_node\n   409→    visited = set()\n   410→    current_nodes = list(workflow.entry_nodes) if workflow.entry_nodes else []\n   411→\n   412→    # Fallback : si pas d'entry_nodes, prendre le premier node\n   413→    if not current_nodes and workflow.nodes:\n   414→        current_nodes = [workflow.nodes[0].node_id]\n   415→\n   416→    while current_nodes:\n   417→        node_id = current_nodes.pop(0)\n   418→        if node_id in visited:\n   419→            continue\n   420→        visited.add(node_id)\n   421→\n   422→        edges = outgoing.get(node_id, [])\n   423→        for edge in edges:\n   424→            edge_actions = _edge_to_normalized_actions(edge, params)\n   425→            actions.extend(edge_actions)\n   426→            # Suivre le graphe vers le prochain node\n   427→            if edge.to_node not in visited:\n   428→                current_nodes.append(edge.to_node)\n   429→\n   430→    return actions\n   431→\n   432→\n   433→def _edge_to_normalized_actions(edge, params: Dict[str, Any]) -> List[Dict[str, Any]]:\n   434→    \"\"\"\n   435→    Convertir un WorkflowEdge en liste d'actions normalisées pour l'Agent V1.\n   436→\n   437→    Un edge simple produit 1 action, un edge compound produit N actions (une par step).\n   438→    \"\"\"\n   439→    action = edge.action\n   440→    action_type = action.type\n   441→    target = action.target\n   442→    action_params = action.parameters or {}\n   443→\n   444→    # Extraire les coordonnées normalisées depuis TargetSpec.by_position\n   445→    x_pct = 0.0\n   446→    y_pct = 0.0\n   447→    if target.by_position:\n   448→        px, py = target.by_position\n   449→        if px <= 1.0 and py <= 1.0:\n   450→            x_pct = px\n   451→            y_pct = py\n   452→        else:\n   453→            ref_w = action_params.get(\"ref_width\", 1920)\n   454→            ref_h = action_params.get(\"ref_height\", 1080)\n   455→            x_pct = round(px / ref_w, 6)\n   456→            y_pct = round(py / ref_h, 6)\n   457→\n   458→    base = {\"edge_id\": edge.edge_id, \"from_node\": edge.from_node, \"to_node\": edge.to_node}\n   459→\n   460→    # Compound : décomposer en actions individuelles\n   461→    if action_type == \"compound\":\n   462→        return _expand_compound_steps(action_params.get(\"steps\", []), base, params)\n   463→\n   464→    # Actions simples\n   465→    normalized = {**base, \"action_id\": f\"act_{uuid.uuid4().hex[:8]}\"}\n   466→\n   467→    if action_type == \"mouse_click\":\n   468→        normalized[\"type\"] = \"click\"\n   469→        normalized[\"x_pct\"] = x_pct\n   470→        normalized[\"y_pct\"] = y_pct\n   471→        normalized[\"button\"] = action_params.get(\"button\", \"left\")\n   472→\n   473→    elif action_type == \"text_input\":\n   474→        normalized[\"type\"] = \"type\"\n   475→        text = action_params.get(\"text\", \"\")\n   476→        text = _substitute_variables(text, params, action_params.get(\"defaults\", {}))\n   477→        normalized[\"text\"] = text\n   478→        normalized[\"x_pct\"] = x_pct\n   479→        normalized[\"y_pct\"] = y_pct\n   480→\n   481→    elif action_type == \"key_press\":\n   482→        normalized[\"type\"] = \"key_combo\"\n   483→        keys = action_params.get(\"keys\", [])\n   484→        if not keys and action_params.get(\"key\"):\n   485→            keys = [action_params[\"key\"]]\n   486→        normalized[\"keys\"] = keys\n   487→\n   488→    else:\n   489→        logger.warning(f\"Type d'action inconnu : {action_type}\")\n   490→        return []\n   491→\n   492→    # Ajouter le target_spec complet pour la résolution visuelle\n   493→    target_spec = {}\n   494→    if target.by_role:\n   495→        target_spec[\"by_role\"] = target.by_role\n   496→        normalized[\"target_role\"] = target.by_role  # Compat debug\n   497→    if target.by_text:\n   498→        target_spec[\"by_text\"] = target.by_text\n   499→        normalized[\"target_text\"] = target.by_text  # Compat debug\n   500→    if hasattr(target, 'context_hints') and target.context_hints:\n   501→        target_spec[\"context_hints\"] = target.context_hints\n   502→    if target_spec:\n   503→        normalized[\"target_spec\"] = target_spec\n   504→        normalized[\"visual_mode\"] = True  # Signal à l'agent d'utiliser la résolution visuelle\n   505→\n   506→    return [normalized]\n   507→\n   508→\n   509→def _substitute_variables(text: str, params: Dict[str, Any], defaults: Dict[str, Any]) -> str:\n   510→    \"\"\"Substituer les variables ${var} dans un texte.\n   511→\n   512→    Priorité : params utilisateur > defaults du workflow > texte brut inchangé.\n   513→    Supporte ${var} dans un texte plus long (ex: \"${expression}=\").\n   514→    \"\"\"\n   515→    import re\n   516→\n   517→    def replacer(match):\n   518→        var_name = match.group(1)\n   519→        return str(params.get(var_name, defaults.get(var_name, match.group(0))))\n   520→\n   521→    return re.sub(r'\\$\\{(\\w+)\\}', replacer, text)\n   522→\n   523→\n   524→def _expand_compound_steps(\n   525→    steps: List[Dict[str, Any]], base: Dict[str, Any], params: Dict[str, Any]\n   526→) -> List[Dict[str, Any]]:\n   527→    \"\"\"Décomposer les steps d'un compound en actions individuelles.\"\"\"\n   528→    actions = []\n   529→    for step in steps:\n   530→        step_type = step.get(\"type\", \"unknown\")\n   531→        action = {\n   532→            **base,\n   533→            \"action_id\": f\"act_{uuid.uuid4().hex[:8]}\",\n   534→        }\n   535→\n   536→        if step_type == \"key_press\":\n   537→            action[\"type\"] = \"key_combo\"\n   538→            keys = step.get(\"keys\", [])\n   539→            if not keys and step.get(\"key\"):\n   540→                keys = [step[\"key\"]]\n   541→            action[\"keys\"] = keys\n   542→\n   543→        elif step_type == \"text_input\":\n   544→            action[\"type\"] = \"type\"\n   545→            text = step.get(\"text\", \"\")\n   546→            text = _substitute_variables(text, params, {})\n   547→            action[\"text\"] = text\n   548→\n   549→        elif step_type == \"wait\":\n   550→            action[\"type\"] = \"wait\"\n   551→            action[\"duration_ms\"] = step.get(\"duration_ms\", 500)\n   552→\n   553→        elif step_type == \"mouse_click\":\n   554→            action[\"type\"] = \"click\"\n   555→            action[\"x_pct\"] = step.get(\"x_pct\", 0.0)\n   556→            action[\"y_pct\"] = step.get(\"y_pct\", 0.0)\n   557→            action[\"button\"] = step.get(\"button\", \"left\")\n   558→\n   559→        else:\n   560→            logger.debug(f\"Step compound inconnu : {step_type}\")\n   561→            continue\n   562→\n   563→        actions.append(action)\n   564→\n   565→    return actions\n   566→\n   567→\n   568→@app.post(\"/api/v1/traces/stream/replay\")\n   569→async def start_replay(request: ReplayRequest):\n   570→    \"\"\"\n   571→    Lancer le replay d'un workflow sur une session Agent V1 active.\n   572→\n   573→    Le serveur charge le workflow, le convertit en liste d'actions normalisées,\n   574→    et les place dans la queue de la session. L'Agent V1 les récupérera\n   575→    via GET /replay/next (modèle pull).\n   576→\n   577→    Si session_id commence par \"chat_\" ou est vide, on détecte automatiquement\n   578→    la dernière session Agent V1 active (non finalisée, préfixe \"sess_\").\n   579→    \"\"\"\n   580→    workflow_id = request.workflow_id\n   581→    session_id = request.session_id\n   582→    params = request.params or {}\n   583→\n   584→    # Auto-détection de la session Agent V1 active\n   585→    if not session_id or session_id.startswith(\"chat_\"):\n   586→        active_session = _find_active_agent_session()\n   587→        if active_session:\n   588→            logger.info(\n   589→                f\"Auto-détection session Agent V1 : {active_session} \"\n   590→                f\"(demandé: {session_id})\"\n   591→            )\n   592→            session_id = active_session\n   593→        else:\n   594→            raise HTTPException(\n   595→                status_code=404,\n   596→                detail=\"Aucune session Agent V1 active. \"\n   597→                       \"Lancez l'Agent V1 et démarrez une session d'abord.\"\n   598→            )\n   599→\n   600→    # Vérifier que le workflow existe\n   601→    with processor._data_lock:\n   602→        workflow = processor._workflows.get(workflow_id)\n   603→\n   604→    if not workflow:\n   605→        raise HTTPException(\n   606→            status_code=404,\n   607→            detail=f\"Workflow '{workflow_id}' non trouvé. \"\n   608→                   f\"Workflows disponibles : {list(processor._workflows.keys())}\"\n   609→        )\n   610→\n   611→    # Convertir le workflow en actions normalisées\n   612→    actions = _workflow_to_actions(workflow, params)\n   613→    if not actions:\n   614→        raise HTTPException(\n   615→            status_code=400,\n   616→            detail=f\"Le workflow '{workflow_id}' ne contient aucune action exécutable.\"\n   617→        )\n   618→\n   619→    # Créer l'identifiant de replay\n   620→    replay_id = f\"replay_{uuid.uuid4().hex[:8]}\"\n   621→\n   622→    # Injecter les actions dans la queue de la session\n   623→    with _replay_lock:\n   624→        _replay_queues[session_id] = list(actions)  # Remplacer la queue existante\n   625→        _replay_states[replay_id] = {\n   626→            \"replay_id\": replay_id,\n   627→            \"workflow_id\": workflow_id,\n   628→            \"session_id\": session_id,\n   629→            \"status\": \"running\",\n   630→            \"total_actions\": len(actions),\n   631→            \"completed_actions\": 0,\n   632→            \"failed_actions\": 0,\n   633→            \"current_action_index\": 0,\n   634→            \"params\": params,\n   635→            \"results\": [],  # Historique des résultats action par action\n   636→        }\n   637→\n   638→    logger.info(\n   639→        f\"Replay démarré : {replay_id} | workflow={workflow_id} | \"\n   640→        f\"session={session_id} | {len(actions)} actions à exécuter\"\n   641→    )\n   642→\n   643→    return {\n   644→        \"replay_id\": replay_id,\n   645→        \"status\": \"running\",\n   646→        \"workflow_id\": workflow_id,\n   647→        \"session_id\": session_id,\n   648→        \"total_actions\": len(actions),\n   649→    }\n   650→\n   651→\n   652→@app.get(\"/api/v1/traces/stream/replay/next\")\n   653→async def get_next_action(session_id: str):\n   654→    \"\"\"\n   655→    L'Agent V1 poll cet endpoint pour récupérer la prochaine action à exécuter.\n   656→\n   657→    Retourne la prochaine action de la queue ou {\"action\": null} si rien.\n   658→    Modèle pull : l'agent demande, pas de WebSocket nécessaire.\n   659→\n   660→    Si la session de l'agent n'a pas d'actions en attente, cherche dans les\n   661→    autres queues (le replay peut avoir été assigné à une session différente\n   662→    suite à un redémarrage serveur/agent).\n   663→    \"\"\"\n   664→    with _replay_lock:\n   665→        queue = _replay_queues.get(session_id, [])\n   666→\n   667→        if not queue:\n   668→            # Chercher dans les autres queues (mismatch de session après redémarrage)\n   669→            for other_sid, other_queue in _replay_queues.items():\n   670→                if other_queue and other_sid != session_id:\n   671→                    logger.info(\n   672→                        f\"Replay cross-session: {session_id} récupère une action \"\n   673→                        f\"de la queue {other_sid}\"\n   674→                    )\n   675→                    queue = other_queue\n   676→                    # Mettre à jour le replay_state pour pointer vers la bonne session\n   677→                    for state in _replay_states.values():\n   678→                        if state[\"session_id\"] == other_sid and state[\"status\"] == \"running\":\n   679→                            state[\"session_id\"] = session_id\n   680→                    # Transférer la queue\n   681→                    _replay_queues[session_id] = other_queue\n   682→                    del _replay_queues[other_sid]\n   683→                    break\n   684→\n   685→        if not queue:\n   686→            return {\"action\": None, \"session_id\": session_id}\n   687→\n   688→        # Retirer la première action de la queue (FIFO)\n   689→        action = queue.pop(0)\n   690→\n   691→    logger.info(\n   692→        f\"Action envoyée à {session_id} : {action.get('type')} \"\n   693→        f\"(id={action.get('action_id')})\"\n   694→    )\n   695→\n   696→    return {\"action\": action, \"session_id\": session_id}\n   697→\n   698→\n   699→@app.post(\"/api/v1/traces/stream/replay/result\")\n   700→async def report_action_result(report: ReplayResultReport):\n   701→    \"\"\"\n   702→    L'Agent V1 renvoie le résultat d'exécution d'une action.\n   703→\n   704→    Permet au serveur de suivre la progression et de détecter les échecs.\n   705→    En cas d'échec, le replay est marqué en erreur (pas de retry automatique).\n   706→    \"\"\"\n   707→    session_id = report.session_id\n   708→    action_id = report.action_id\n   709→\n   710→    # Trouver le replay correspondant à cette session\n   711→    with _replay_lock:\n   712→        replay_state = None\n   713→        for state in _replay_states.values():\n   714→            if state[\"session_id\"] == session_id and state[\"status\"] == \"running\":\n   715→                replay_state = state\n   716→                break\n   717→\n   718→        if not replay_state:\n   719→            logger.warning(\n   720→                f\"Résultat reçu pour session {session_id} mais aucun replay actif\"\n   721→            )\n   722→            return {\"status\": \"no_active_replay\", \"session_id\": session_id}\n   723→\n   724→        # Enregistrer le résultat\n   725→        result_entry = {\n   726→            \"action_id\": action_id,\n   727→            \"success\": report.success,\n   728→            \"error\": report.error,\n   729→            \"has_screenshot\": bool(report.screenshot),\n   730→        }\n   731→        replay_state[\"results\"].append(result_entry)\n   732→\n   733→        if report.success:\n   734→            replay_state[\"completed_actions\"] += 1\n   735→        else:\n   736→            replay_state[\"failed_actions\"] += 1\n   737→            # En cas d'échec, on marque le replay en erreur et on vide la queue\n   738→            replay_state[\"status\"] = \"error\"\n   739→            _replay_queues[session_id] = []\n   740→            logger.error(\n   741→                f\"Replay {replay_state['replay_id']} échoué à l'action {action_id} : \"\n   742→                f\"{report.error}\"\n   743→            )\n   744→\n   745→        # Vérifier si le replay est terminé (queue vide + dernière action réussie)\n   746→        remaining = len(_replay_queues.get(session_id, []))\n   747→        if remaining == 0 and replay_state[\"status\"] == \"running\":\n   748→            replay_state[\"status\"] = \"completed\"\n   749→            logger.info(\n   750→                f\"Replay {replay_state['replay_id']} terminé avec succès : \"\n   751→                f\"{replay_state['completed_actions']}/{replay_state['total_actions']} actions\"\n   752→            )\n   753→\n   754→    return {\n   755→        \"status\": \"recorded\",\n   756→        \"action_id\": action_id,\n   757→        \"success\": report.success,\n   758→        \"replay_status\": replay_state[\"status\"],\n   759→        \"remaining_actions\": remaining,\n   760→    }\n   761→\n   762→\n   763→@app.get(\"/api/v1/traces/stream/replay/{replay_id}\")\n   764→async def get_replay_status(replay_id: str):\n   765→    \"\"\"Consulter l'état d'un replay en cours ou terminé.\"\"\"\n   766→    with _replay_lock:\n   767→        state = _replay_states.get(replay_id)\n   768→\n   769→    if not state:\n   770→        raise HTTPException(\n   771→            status_code=404, detail=f\"Replay '{replay_id}' non trouvé\"\n   772→        )\n   773→\n   774→    return state\n   775→\n   776→\n   777→@app.get(\"/api/v1/traces/stream/replays\")\n   778→async def list_replays():\n   779→    \"\"\"Lister tous les replays (actifs, terminés, en erreur).\"\"\"\n   780→    with _replay_lock:\n   781→        return {\"replays\": list(_replay_states.values())}\n   782→\n   783→\n   784→# =========================================================================\n   785→# Visual Replay — Résolution visuelle des cibles\n   786→# =========================================================================\n   787→\n   788→\n   789→class ResolveTargetRequest(BaseModel):\n   790→    \"\"\"Requête de résolution visuelle d'une cible.\"\"\"\n   791→    session_id: str\n   792→    screenshot_b64: str  # Screenshot JPEG en base64\n   793→    target_spec: Dict[str, Any]  # {by_role, by_text, by_position, ...}\n   794→    fallback_x_pct: float = 0.0  # Coordonnées de fallback\n   795→    fallback_y_pct: float = 0.0\n   796→    screen_width: int = 1920\n   797→    screen_height: int = 1080\n   798→\n   799→\n   800→@app.post(\"/api/v1/traces/stream/replay/resolve_target\")\n   801→async def resolve_target(request: ResolveTargetRequest):\n   802→    \"\"\"\n   803→    Résoudre visuellement une cible UI à partir d'un screenshot.\n   804→\n   805→    L'Agent V1 envoie un screenshot + target_spec AVANT d'exécuter l'action.\n   806→    Le serveur analyse l'image avec UIDetector/OCR et retourne les coordonnées\n   807→    de l'élément trouvé.\n   808→\n   809→    Stratégie de matching (par priorité) :\n   810→    1. by_text — chercher un élément dont le label contient le texte\n   811→    2. by_role — chercher un élément avec le bon rôle sémantique\n   812→    3. by_text + by_role — intersection des deux\n   813→    4. fallback — utiliser les coordonnées statiques\n   814→    \"\"\"\n   815→    import base64\n   816→    import io\n   817→    import tempfile\n   818→\n   819→    from PIL import Image\n   820→\n   821→    # Décoder le screenshot\n   822→    try:\n   823→        img_bytes = base64.b64decode(request.screenshot_b64)\n   824→        img = Image.open(io.BytesIO(img_bytes))\n   825→    except Exception as e:\n   826→        logger.error(f\"Décodage screenshot échoué: {e}\")\n   827→        return _fallback_response(request, \"decode_error\", str(e))\n   828→\n   829→    # Sauver temporairement pour les analyseurs (ils attendent un chemin fichier)\n   830→    with tempfile.NamedTemporaryFile(suffix=\".jpg\", delete=False) as tmp:\n   831→        img.save(tmp, format=\"JPEG\", quality=90)\n   832→        tmp_path = tmp.name\n   833→\n   834→    try:\n   835→        # Lancer la résolution visuelle dans le thread GPU\n   836→        import asyncio\n   837→        loop = asyncio.get_event_loop()\n   838→        result = await loop.run_in_executor(\n   839→            _gpu_executor,\n   840→            _resolve_target_sync,\n   841→            tmp_path,\n   842→            request.target_spec,\n   843→            request.screen_width,\n   844→            request.screen_height,\n   845→            request.fallback_x_pct,\n   846→            request.fallback_y_pct,\n   847→        )\n   848→        return result\n   849→    except Exception as e:\n   850→        logger.error(f\"Résolution visuelle échouée: {e}\")\n   851→        return _fallback_response(request, \"analysis_error\", str(e))\n   852→    finally:\n   853→        import os\n   854→        try:\n   855→            os.unlink(tmp_path)\n   856→        except OSError:\n   857→            pass\n   858→\n   859→\n   860→def _resolve_target_sync(\n   861→    screenshot_path: str,\n   862→    target_spec: Dict[str, Any],\n   863→    screen_width: int,\n   864→    screen_height: int,\n   865→    fallback_x_pct: float,\n   866→    fallback_y_pct: float,\n   867→) -> Dict[str, Any]:\n   868→    \"\"\"Résoudre la cible visuellement (exécuté dans le thread GPU).\"\"\"\n   869→    processor._ensure_initialized()\n   870→\n   871→    if processor._screen_analyzer is None:\n   872→        return {\n   873→            \"resolved\": False,\n   874→            \"method\": \"fallback\",\n   875→            \"reason\": \"screen_analyzer_unavailable\",\n   876→            \"x_pct\": fallback_x_pct,\n   877→            \"y_pct\": fallback_y_pct,\n   878→        }\n   879→\n   880→    # Analyser le screenshot (Niveaux 1-3 : raw, OCR, UI elements)\n   881→    try:\n   882→        screen_state = processor._screen_analyzer.analyze(screenshot_path)\n   883→    except Exception as e:\n   884→        logger.warning(f\"Analyse screenshot échouée: {e}\")\n   885→        return {\n   886→            \"resolved\": False,\n   887→            \"method\": \"fallback\",\n   888→            \"reason\": f\"analysis_failed: {e}\",\n   889→            \"x_pct\": fallback_x_pct,\n   890→            \"y_pct\": fallback_y_pct,\n   891→        }\n   892→\n   893→    ui_elements = screen_state.ui_elements or []\n   894→    if not ui_elements:\n   895→        logger.info(\"Aucun élément UI détecté, fallback coordonnées\")\n   896→        return {\n   897→            \"resolved\": False,\n   898→            \"method\": \"fallback\",\n   899→            \"reason\": \"no_ui_elements\",\n   900→            \"x_pct\": fallback_x_pct,\n   901→            \"y_pct\": fallback_y_pct,\n   902→        }\n   903→\n   904→    # Matching de la cible parmi les éléments détectés\n   905→    by_text = target_spec.get(\"by_text\", \"\")\n   906→    by_role = target_spec.get(\"by_role\", \"\")\n   907→    candidates = []\n   908→\n   909→    for elem in ui_elements:\n   910→        score = 0.0\n   911→\n   912→        # Score par texte (label)\n   913→        if by_text and elem.label:\n   914→            text_lower = by_text.lower()\n   915→            label_lower = elem.label.lower()\n   916→            if text_lower in label_lower or label_lower in text_lower:\n   917→                score += 0.6\n   918→            elif _fuzzy_match(text_lower, label_lower):\n   919→                score += 0.3\n   920→\n   921→        # Score par rôle\n   922→        if by_role:\n   923→            role_lower = by_role.lower()\n   924→            if elem.role and role_lower in elem.role.lower():\n   925→                score += 0.3\n   926→            if elem.type and role_lower in elem.type.lower():\n   927→                score += 0.2\n   928→\n   929→        if score > 0:\n   930→            candidates.append((elem, score))\n   931→\n   932→    if not candidates:\n   933→        logger.info(\n   934→            f\"Aucun match visuel pour target(text='{by_text}', role='{by_role}') \"\n   935→            f\"parmi {len(ui_elements)} éléments\"\n   936→        )\n   937→        return {\n   938→            \"resolved\": False,\n   939→            \"method\": \"fallback\",\n   940→            \"reason\": \"no_match\",\n   941→            \"x_pct\": fallback_x_pct,\n   942→            \"y_pct\": fallback_y_pct,\n   943→            \"ui_elements_count\": len(ui_elements),\n   944→        }\n   945→\n   946→    # Trier par score décroissant et prendre le meilleur\n   947→    candidates.sort(key=lambda c: c[1], reverse=True)\n   948→    best_elem, best_score = candidates[0]\n   949→\n   950→    # Convertir les coordonnées pixel en proportions\n   951→    cx, cy = best_elem.center\n   952→    x_pct = round(cx / screen_width, 6) if screen_width > 0 else 0.0\n   953→    y_pct = round(cy / screen_height, 6) if screen_height > 0 else 0.0\n   954→\n   955→    logger.info(\n   956→        f\"Cible résolue visuellement: '{best_elem.label}' ({best_elem.type}/{best_elem.role}) \"\n   957→        f\"score={best_score:.2f} → ({x_pct:.4f}, {y_pct:.4f})\"\n   958→    )\n   959→\n   960→    return {\n   961→        \"resolved\": True,\n   962→        \"method\": \"visual\",\n   963→        \"x_pct\": x_pct,\n   964→        \"y_pct\": y_pct,\n   965→        \"matched_element\": {\n   966→            \"label\": best_elem.label,\n   967→            \"type\": best_elem.type,\n   968→            \"role\": best_elem.role,\n   969→            \"center\": list(best_elem.center),\n   970→            \"confidence\": best_elem.label_confidence,\n   971→        },\n   972→        \"score\": best_score,\n   973→        \"candidates_count\": len(candidates),\n   974→        \"ui_elements_count\": len(ui_elements),\n   975→    }\n   976→\n   977→\n   978→def _fuzzy_match(a: str, b: str, threshold: float = 0.6) -> bool:\n   979→    \"\"\"Match approximatif par ratio de caractères communs.\"\"\"\n   980→    if not a or not b:\n   981→        return False\n   982→    common = sum(1 for c in a if c in b)\n   983→    return (common / max(len(a), len(b))) >= threshold\n   984→\n   985→\n   986→def _fallback_response(request: ResolveTargetRequest, reason: str, detail: str) -> Dict:\n   987→    \"\"\"Réponse de fallback quand la résolution visuelle échoue.\"\"\"\n   988→    return {\n   989→        \"resolved\": False,\n   990→        \"method\": \"fallback\",\n   991→        \"reason\": reason,\n   992→        \"detail\": detail,\n   993→        \"x_pct\": request.fallback_x_pct,\n   994→        \"y_pct\": request.fallback_y_pct,\n   995→    }\n   996→\n   997→\n   998→if __name__ == \"__main__\":\n   999→    import uvicorn\n  1000→\n  1001→    logging.basicConfig(\n  1002→        level=logging.INFO,\n  1003→        format=\"%(asctime)s [API-STREAM] %(message)s\",\n  1004→    )\n  1005→    uvicorn.run(app, host=\"0.0.0.0\", port=5005)\n  1006→"}]},"uuid":"140e8a34-f399-4ce3-a0b1-be995603f6ef","timestamp":"2026-03-14T21:14:15.413Z","sourceToolAssistantUUID":"0f5a1970-9274-4d59-bf2d-830f444cb4bd"}
+{"parentUuid":"140e8a34-f399-4ce3-a0b1-be995603f6ef","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_01L2iYUGSiD4WG1ca5Q47rCm","toolUseID":"toolu_01L2iYUGSiD4WG1ca5Q47rCm","timestamp":"2026-03-14T21:14:15.256Z","uuid":"61628ca4-d96c-43a2-b999-0e3cdb14563f"}
+{"parentUuid":"6144986b-9ed4-40b9-b99f-c0102f036a25","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01L2iYUGSiD4WG1ca5Q47rCm","type":"tool_result","content":"     1→\"\"\"\n     2→StreamProcessor — Pont entre le streaming Agent V1 et le core pipeline RPA Vision V3.\n     3→\n     4→Orchestre les composants core (ScreenAnalyzer, CLIP, FAISS, GraphBuilder)\n     5→pour traiter en temps réel les screenshots et événements reçus via fibre.\n     6→\n     7→Tous les calculs GPU tournent ici (serveur RTX 5070).\n     8→\"\"\"\n     9→\n    10→import logging\n    11→import threading\n    12→from datetime import datetime\n    13→from pathlib import Path\n    14→from typing import Any, Dict, List, Optional\n    15→\n    16→import numpy as np\n    17→\n    18→from .live_session_manager import LiveSessionManager\n    19→\n    20→logger = logging.getLogger(__name__)\n    21→\n    22→\n    23→class StreamProcessor:\n    24→    \"\"\"\n    25→    Processeur de streaming qui connecte les données Agent V1 au core pipeline.\n    26→\n    27→    Cycle de vie :\n    28→        1. register_session() — crée l'état mémoire\n    29→        2. process_event() — accumule événements, extrait contexte fenêtre\n    30→        3. process_screenshot() — analyse via ScreenAnalyzer + CLIP embedding\n    31→        4. finalize_session() — construit le Workflow via GraphBuilder (DBSCAN)\n    32→    \"\"\"\n    33→\n    34→    def __init__(self, data_dir: str = \"data/training\"):\n    35→        self.data_dir = Path(data_dir)\n    36→        self.session_manager = LiveSessionManager()\n    37→        self._lock = threading.Lock()\n    38→\n    39→        # Core components (chargés paresseusement pour éviter les imports lourds au démarrage)\n    40→        self._screen_analyzer = None\n    41→        self._clip_embedder = None\n    42→        self._state_embedding_builder = None  # P0-3 : pipeline d'embedding unifié (fusion multi-modale)\n    43→        self._faiss_manager = None\n    44→        self._initialized = False\n    45→\n    46→        # Lock pour l'accès concurrent aux données de session (screen_states, embeddings, workflows)\n    47→        self._data_lock = threading.Lock()\n    48→\n    49→        # Résultats d'analyse par session\n    50→        self._screen_states: Dict[str, list] = {}  # session_id -> List[ScreenState]\n    51→        self._embeddings: Dict[str, list] = {}      # session_id -> List[np.ndarray]\n    52→\n    53→        # Workflows construits (pour le matching)\n    54→        self._workflows: Dict[str, Any] = {}\n    55→\n    56→        # Charger les workflows existants depuis le disque\n    57→        self._load_persisted_workflows()\n    58→\n    59→    def _load_persisted_workflows(self):\n    60→        \"\"\"Charger les workflows sauvegardés depuis le disque au démarrage.\"\"\"\n    61→        workflows_dir = self.data_dir / \"workflows\"\n    62→        if not workflows_dir.exists():\n    63→            return\n    64→\n    65→        try:\n    66→            from core.models.workflow_graph import Workflow\n    67→\n    68→            count = 0\n    69→            for wf_file in sorted(workflows_dir.glob(\"*.json\")):\n    70→                try:\n    71→                    wf = Workflow.load_from_file(wf_file)\n    72→                    self._workflows[wf.workflow_id] = wf\n    73→                    count += 1\n    74→                except Exception as e:\n    75→                    logger.warning(f\"Impossible de charger {wf_file.name}: {e}\")\n    76→\n    77→            if count:\n    78→                logger.info(f\"{count} workflow(s) chargé(s) depuis {workflows_dir}\")\n    79→        except ImportError:\n    80→            logger.debug(\"core.models.workflow_graph non disponible, skip chargement\")\n    81→\n    82→    def _ensure_initialized(self):\n    83→        \"\"\"Charger les composants core GPU si pas encore fait.\"\"\"\n    84→        if self._initialized:\n    85→            return\n    86→\n    87→        with self._lock:\n    88→            if self._initialized:\n    89→                return\n    90→\n    91→            logger.info(\"Initialisation des composants core (GPU)...\")\n    92→\n    93→            try:\n    94→                from core.pipeline.screen_analyzer import ScreenAnalyzer\n    95→                self._screen_analyzer = ScreenAnalyzer(session_id=\"stream_server\")\n    96→                logger.info(\"  ScreenAnalyzer prêt\")\n    97→            except Exception as e:\n    98→                logger.error(f\"  Erreur init ScreenAnalyzer: {e}\")\n    99→                self._screen_analyzer = None\n   100→\n   101→            try:\n   102→                from core.embedding.clip_embedder import CLIPEmbedder\n   103→                self._clip_embedder = CLIPEmbedder()\n   104→                logger.info(\"  CLIPEmbedder prêt (singleton, ne sera plus rechargé)\")\n   105→            except Exception as e:\n   106→                logger.error(f\"  Erreur init CLIPEmbedder: {e}\")\n   107→                self._clip_embedder = None\n   108→\n   109→            # P0-3 : Initialiser le StateEmbeddingBuilder pour unifier l'espace d'embedding\n   110→            # Utilise le même CLIPEmbedder (pas de rechargement du modèle) + FusionEngine\n   111→            # pour produire des vecteurs fusionnés (image+text+title+ui) identiques à GraphBuilder\n   112→            try:\n   113→                from core.embedding.state_embedding_builder import StateEmbeddingBuilder\n   114→                if self._clip_embedder is not None:\n   115→                    # Injecter le CLIPEmbedder déjà chargé pour éviter un double chargement\n   116→                    self._state_embedding_builder = StateEmbeddingBuilder(\n   117→                        embedders={\n   118→                            \"image\": self._clip_embedder,\n   119→                            \"text\": self._clip_embedder,\n   120→                            \"title\": self._clip_embedder,\n   121→                            \"ui\": self._clip_embedder,\n   122→                        },\n   123→                        output_dir=self.data_dir / \"embeddings\",\n   124→                        use_clip=False,  # Pas besoin, on fournit les embedders directement\n   125→                    )\n   126→                else:\n   127→                    # Fallback : laisser le builder créer son propre CLIPEmbedder\n   128→                    self._state_embedding_builder = StateEmbeddingBuilder(\n   129→                        output_dir=self.data_dir / \"embeddings\",\n   130→                        use_clip=True,\n   131→                    )\n   132→                logger.info(\"  StateEmbeddingBuilder prêt (fusion multi-modale unifiée)\")\n   133→            except Exception as e:\n   134→                logger.warning(f\"  StateEmbeddingBuilder non disponible, fallback CLIP pur: {e}\")\n   135→                self._state_embedding_builder = None\n   136→\n   137→            try:\n   138→                from core.embedding.faiss_manager import FAISSManager\n   139→                self._faiss_manager = FAISSManager(\n   140→                    dimensions=512,\n   141→                    index_type=\"Flat\",\n   142→                    metric=\"cosine\",\n   143→                )\n   144→                logger.info(\"  FAISSManager prêt (512 dims, cosine)\")\n   145→            except Exception as e:\n   146→                logger.error(f\"  Erreur init FAISSManager: {e}\")\n   147→                self._faiss_manager = None\n   148→\n   149→            self._initialized = True\n   150→            logger.info(\"Composants core initialisés.\")\n   151→\n   152→    # =========================================================================\n   153→    # Événements\n   154→    # =========================================================================\n   155→\n   156→    def process_event(self, session_id: str, event_data: Dict[str, Any]) -> Dict[str, Any]:\n   157→        \"\"\"Enregistrer un événement dans la session live.\"\"\"\n   158→        self.session_manager.add_event(session_id, event_data)\n   159→        return {\"status\": \"event_recorded\", \"session_id\": session_id}\n   160→\n   161→    # =========================================================================\n   162→    # Screenshots\n   163→    # =========================================================================\n   164→\n   165→    def process_screenshot(self, session_id: str, shot_id: str, file_path: str) -> Dict[str, Any]:\n   166→        \"\"\"\n   167→        Analyser un screenshot full via le core pipeline.\n   168→\n   169→        1. ScreenAnalyzer → ScreenState (OCR, UI detection)\n   170→        2. StateEmbeddingBuilder → vecteur fusionné 512d (image+text+title+ui)\n   171→           Même espace d'embedding que GraphBuilder (P0-3)\n   172→           Fallback : CLIP embed_image() si StateEmbeddingBuilder échoue\n   173→        3. FAISS indexation → matching temps réel\n   174→        \"\"\"\n   175→        self._ensure_initialized()\n   176→        self.session_manager.add_screenshot(session_id, shot_id, file_path)\n   177→\n   178→        result = {\n   179→            \"shot_id\": shot_id,\n   180→            \"session_id\": session_id,\n   181→            \"state_id\": None,\n   182→            \"ui_elements_count\": 0,\n   183→            \"text_detected\": 0,\n   184→            \"embedding_indexed\": False,\n   185→            \"match\": None,\n   186→        }\n   187→\n   188→        # 1. Construire le ScreenState\n   189→        if self._screen_analyzer is None:\n   190→            logger.warning(\"ScreenAnalyzer non disponible, skip analyse\")\n   191→            return result\n   192→\n   193→        session = self.session_manager.get_session(session_id)\n   194→        window_info = session.last_window_info if session else {}\n   195→\n   196→        try:\n   197→            screen_state = self._screen_analyzer.analyze(\n   198→                screenshot_path=file_path,\n   199→                window_info=window_info,\n   200→            )\n   201→            result[\"state_id\"] = screen_state.screen_state_id\n   202→            result[\"ui_elements_count\"] = len(screen_state.ui_elements)\n   203→            result[\"text_detected\"] = len(\n   204→                getattr(screen_state.perception, \"detected_text\", [])\n   205→            )\n   206→\n   207→            # Stocker le ScreenState pour le build final\n   208→            with self._data_lock:\n   209→                if session_id not in self._screen_states:\n   210→                    self._screen_states[session_id] = []\n   211→                self._screen_states[session_id].append(screen_state)\n   212→\n   213→            logger.info(\n   214→                f\"Screenshot analysé: {shot_id} | \"\n   215→                f\"{result['ui_elements_count']} UI elements, \"\n   216→                f\"{result['text_detected']} textes\"\n   217→            )\n   218→        except Exception as e:\n   219→            logger.error(f\"Erreur analyse screenshot {shot_id}: {e}\")\n   220→            return result\n   221→\n   222→        # 2. Construire l'embedding fusionné via StateEmbeddingBuilder (P0-3)\n   223→        # Utilise le même pipeline que GraphBuilder : fusion image+text+title+ui\n   224→        # pour garantir que les vecteurs FAISS sont dans le même espace d'embedding\n   225→        embedding_vector = None\n   226→\n   227→        if self._state_embedding_builder is not None:\n   228→            try:\n   229→                state_embedding = self._state_embedding_builder.build(screen_state)\n   230→                # Récupérer le vecteur fusionné depuis le StateEmbedding\n   231→                fused_vec = state_embedding.get_vector()\n   232→                if fused_vec is not None:\n   233→                    embedding_vector = fused_vec.astype(np.float32)\n   234→                    logger.debug(\n   235→                        f\"Embedding fusionné multi-modal calculé pour {shot_id} \"\n   236→                        f\"(dim={embedding_vector.shape[0]})\"\n   237→                    )\n   238→            except Exception as e:\n   239→                logger.warning(\n   240→                    f\"StateEmbeddingBuilder échoué pour {shot_id}: {e}, \"\n   241→                    f\"fallback sur CLIP pur\"\n   242→                )\n   243→\n   244→        # Fallback 1 : embedding pré-calculé dans le ScreenState (si disponible)\n   245→        if embedding_vector is None:\n   246→            if hasattr(screen_state, \"perception\") and screen_state.perception:\n   247→                emb_ref = getattr(screen_state.perception, \"embedding\", None)\n   248→                if emb_ref and hasattr(emb_ref, \"vector\") and emb_ref.vector is not None:\n   249→                    embedding_vector = np.array(emb_ref.vector, dtype=np.float32)\n   250→\n   251→        # Fallback 2 : utiliser le CLIPEmbedder singleton (embedding image seul)\n   252→        if embedding_vector is None and self._clip_embedder is not None:\n   253→            try:\n   254→                from PIL import Image\n   255→                pil_image = Image.open(file_path)\n   256→                embedding_vector = self._clip_embedder.embed_image(pil_image)\n   257→            except Exception as e:\n   258→                logger.debug(f\"CLIP embedding échoué: {e}\")\n   259→\n   260→        if embedding_vector is not None:\n   261→            # Stocker pour le build final\n   262→            with self._data_lock:\n   263→                if session_id not in self._embeddings:\n   264→                    self._embeddings[session_id] = []\n   265→                self._embeddings[session_id].append(embedding_vector)\n   266→\n   267→            # 3. Indexer dans FAISS\n   268→            if self._faiss_manager is not None:\n   269→                try:\n   270→                    self._faiss_manager.add_embedding(\n   271→                        embedding_id=screen_state.screen_state_id,\n   272→                        vector=embedding_vector,\n   273→                        metadata={\n   274→                            \"session_id\": session_id,\n   275→                            \"shot_id\": shot_id,\n   276→                            \"window_title\": window_info.get(\"title\", \"\"),\n   277→                        },\n   278→                    )\n   279→                    result[\"embedding_indexed\"] = True\n   280→                except Exception as e:\n   281→                    logger.error(f\"Erreur FAISS indexation: {e}\")\n   282→\n   283→        # 4. Matching temps réel contre les workflows connus\n   284→        with self._data_lock:\n   285→            has_workflows = bool(self._workflows)\n   286→        if embedding_vector is not None and has_workflows:\n   287→            result[\"match\"] = self._try_match(embedding_vector)\n   288→\n   289→        return result\n   290→\n   291→    def process_crop(self, session_id: str, shot_id: str, file_path: str) -> Dict[str, Any]:\n   292→        \"\"\"\n   293→        Enregistrer un crop (400x400). Pas d'analyse ScreenAnalyzer\n   294→        (un crop est un fragment, pas un écran complet).\n   295→        \"\"\"\n   296→        self.session_manager.add_screenshot(session_id, shot_id, file_path)\n   297→        return {\"status\": \"crop_stored\", \"shot_id\": shot_id}\n   298→\n   299→    # =========================================================================\n   300→    # Finalisation\n   301→    # =========================================================================\n   302→\n   303→    def finalize_session(self, session_id: str) -> Dict[str, Any]:\n   304→        \"\"\"\n   305→        Construire un Workflow depuis les données accumulées.\n   306→\n   307→        Utilise le GraphBuilder du core avec les ScreenStates et embeddings\n   308→        collectés pendant le streaming.\n   309→        \"\"\"\n   310→        self._ensure_initialized()\n   311→\n   312→        session = self.session_manager.finalize(session_id)\n   313→        if not session:\n   314→            return {\"error\": f\"Session {session_id} non trouvée\"}\n   315→\n   316→        with self._data_lock:\n   317→            states = list(self._screen_states.get(session_id, []))\n   318→            embeddings = list(self._embeddings.get(session_id, []))\n   319→\n   320→        if len(states) < 2:\n   321→            logger.warning(\n   322→                f\"Session {session_id}: seulement {len(states)} states, \"\n   323→                f\"pas assez pour construire un workflow\"\n   324→            )\n   325→            return {\n   326→                \"session_id\": session_id,\n   327→                \"status\": \"insufficient_data\",\n   328→                \"states_count\": len(states),\n   329→                \"min_required\": 2,\n   330→            }\n   331→\n   332→        # Convertir en RawSession pour le GraphBuilder\n   333→        raw_dict = self.session_manager.to_raw_session(session_id)\n   334→        if not raw_dict:\n   335→            return {\"error\": \"Conversion RawSession échouée\"}\n   336→\n   337→        try:\n   338→            from core.models.raw_session import RawSession\n   339→            raw_session = RawSession.from_dict(raw_dict)\n   340→        except Exception as e:\n   341→            logger.error(f\"Erreur construction RawSession: {e}\")\n   342→            # Fallback : construire manuellement\n   343→            try:\n   344→                raw_session = self._build_raw_session_fallback(session, raw_dict)\n   345→            except Exception as e2:\n   346→                return {\"error\": f\"Erreur RawSession: {e2}\"}\n   347→\n   348→        # Construire le workflow via GraphBuilder\n   349→        try:\n   350→            from core.graph.graph_builder import GraphBuilder\n   351→\n   352→            n = len(states)\n   353→            min_reps = 2 if n < 10 else 3 if n <= 30 else min(5, n // 10)\n   354→\n   355→            builder = GraphBuilder(\n   356→                min_pattern_repetitions=min_reps,\n   357→                clustering_eps=0.15,\n   358→                clustering_min_samples=2,\n   359→            )\n   360→\n   361→            # Injecter les ScreenStates pré-calculés pour éviter de re-analyser\n   362→            workflow = builder.build_from_session(\n   363→                raw_session,\n   364→                workflow_name=f\"stream_{session_id}\",\n   365→                precomputed_states=states,\n   366→            )\n   367→\n   368→            with self._data_lock:\n   369→                self._workflows[workflow.workflow_id] = workflow\n   370→\n   371→            # Persister sur disque\n   372→            saved_path = self._persist_workflow(workflow, session_id)\n   373→\n   374→            result = {\n   375→                \"session_id\": session_id,\n   376→                \"status\": \"workflow_built\",\n   377→                \"workflow_id\": workflow.workflow_id,\n   378→                \"nodes\": len(workflow.nodes),\n   379→                \"edges\": len(workflow.edges),\n   380→                \"states_analyzed\": len(states),\n   381→                \"embeddings_indexed\": len(embeddings),\n   382→                \"saved_path\": str(saved_path) if saved_path else None,\n   383→            }\n   384→\n   385→            logger.info(\n   386→                f\"Workflow construit: {workflow.workflow_id} | \"\n   387→                f\"{result['nodes']} nodes, {result['edges']} edges\"\n   388→            )\n   389→            return result\n   390→\n   391→        except Exception as e:\n   392→            logger.error(f\"Erreur construction workflow: {e}\")\n   393→            return {\"error\": f\"GraphBuilder: {e}\", \"session_id\": session_id}\n   394→\n   395→    # =========================================================================\n   396→    # Matching\n   397→    # =========================================================================\n   398→\n   399→    def _try_match(self, embedding_vector: np.ndarray) -> Optional[Dict[str, Any]]:\n   400→        \"\"\"Matcher un embedding contre les workflows connus.\"\"\"\n   401→        if self._faiss_manager is None or self._faiss_manager.index.ntotal == 0:\n   402→            return None\n   403→\n   404→        try:\n   405→            results = self._faiss_manager.search_similar(\n   406→                query_vector=embedding_vector,\n   407→                k=1,\n   408→                min_similarity=0.85,\n   409→            )\n   410→            if results:\n   411→                best = results[0]\n   412→                return {\n   413→                    \"matched_id\": best.embedding_id,\n   414→                    \"similarity\": round(best.similarity, 4),\n   415→                    \"metadata\": best.metadata,\n   416→                }\n   417→        except Exception as e:\n   418→            logger.debug(f\"Erreur matching: {e}\")\n   419→\n   420→        return None\n   421→\n   422→    # =========================================================================\n   423→    # Helpers\n   424→    # =========================================================================\n   425→\n   426→    def _persist_workflow(self, workflow, session_id: str) -> Optional[Path]:\n   427→        \"\"\"Sauvegarder le workflow JSON sur disque.\"\"\"\n   428→        try:\n   429→            workflows_dir = self.data_dir / \"workflows\"\n   430→            workflows_dir.mkdir(parents=True, exist_ok=True)\n   431→            filepath = workflows_dir / f\"{workflow.workflow_id}.json\"\n   432→            workflow.save_to_file(filepath)\n   433→            logger.info(f\"Workflow sauvegardé: {filepath}\")\n   434→            return filepath\n   435→        except Exception as e:\n   436→            logger.error(f\"Erreur sauvegarde workflow {session_id}: {e}\")\n   437→            return None\n   438→\n   439→    def _build_raw_session_fallback(self, session, raw_dict):\n   440→        \"\"\"Construire un RawSession manuellement si from_dict échoue.\"\"\"\n   441→        from core.models.raw_session import RawSession, Event, Screenshot, RawWindowContext\n   442→\n   443→        events = []\n   444→        for evt_dict in raw_dict.get(\"events\", []):\n   445→            window_data = evt_dict.get(\"window\", {\"title\": \"\", \"app_name\": \"unknown\"})\n   446→            window = RawWindowContext(\n   447→                title=window_data.get(\"title\", \"\"),\n   448→                app_name=window_data.get(\"app_name\", \"unknown\"),\n   449→            )\n   450→            events.append(Event(\n   451→                t=evt_dict.get(\"t\", 0.0),\n   452→                type=evt_dict.get(\"type\", \"unknown\"),\n   453→                window=window,\n   454→                data={k: v for k, v in evt_dict.items()\n   455→                      if k not in (\"t\", \"type\", \"window\", \"screenshot_id\")},\n   456→                screenshot_id=evt_dict.get(\"screenshot_id\"),\n   457→            ))\n   458→\n   459→        screenshots = []\n   460→        for ss_dict in raw_dict.get(\"screenshots\", []):\n   461→            screenshots.append(Screenshot(\n   462→                screenshot_id=ss_dict[\"screenshot_id\"],\n   463→                relative_path=ss_dict.get(\"relative_path\", ss_dict.get(\"path\", \"\")),\n   464→                captured_at=ss_dict.get(\"captured_at\", datetime.now().isoformat()),\n   465→            ))\n   466→\n   467→        return RawSession(\n   468→            session_id=session.session_id,\n   469→            agent_version=\"agent_v1_stream\",\n   470→            environment=raw_dict.get(\"environment\", {}),\n   471→            user=raw_dict.get(\"user\", {\"id\": \"remote_agent\"}),\n   472→            context=raw_dict.get(\"context\", {}),\n   473→            started_at=session.created_at,\n   474→            ended_at=datetime.now(),\n   475→            events=events,\n   476→            screenshots=screenshots,\n   477→        )\n   478→\n   479→    def list_sessions(self) -> List[Dict[str, Any]]:\n   480→        \"\"\"Lister toutes les sessions avec leur état.\"\"\"\n   481→        sessions = []\n   482→        for sid in self.session_manager.session_ids:\n   483→            session = self.session_manager.get_session(sid)\n   484→            if session is None:\n   485→                continue\n   486→            with self._data_lock:\n   487→                states_count = len(self._screen_states.get(sid, []))\n   488→                embeddings_count = len(self._embeddings.get(sid, []))\n   489→            sessions.append({\n   490→                \"session_id\": session.session_id,\n   491→                \"events_count\": len(session.events),\n   492→                \"screenshots_count\": len(session.shot_paths),\n   493→                \"states_count\": states_count,\n   494→                \"embeddings_count\": embeddings_count,\n   495→                \"last_window\": session.last_window_info,\n   496→                \"created_at\": session.created_at.isoformat(),\n   497→                \"last_activity\": session.last_activity.isoformat(),\n   498→                \"finalized\": session.finalized,\n   499→            })\n   500→        return sessions\n   501→\n   502→    def list_workflows(self) -> List[Dict[str, Any]]:\n   503→        \"\"\"Lister tous les workflows construits.\"\"\"\n   504→        with self._data_lock:\n   505→            workflows_snapshot = list(self._workflows.items())\n   506→        result = []\n   507→        for wf_id, wf in workflows_snapshot:\n   508→            result.append({\n   509→                \"workflow_id\": wf_id,\n   510→                \"nodes\": len(wf.nodes) if hasattr(wf, \"nodes\") else 0,\n   511→                \"edges\": len(wf.edges) if hasattr(wf, \"edges\") else 0,\n   512→                \"name\": getattr(wf, \"name\", wf_id),\n   513→            })\n   514→        return result\n   515→\n   516→    @property\n   517→    def stats(self) -> Dict[str, Any]:\n   518→        \"\"\"Statistiques du processeur.\"\"\"\n   519→        with self._data_lock:\n   520→            total_workflows = len(self._workflows)\n   521→        return {\n   522→            \"active_sessions\": self.session_manager.active_session_count,\n   523→            \"total_sessions\": len(self.session_manager.session_ids),\n   524→            \"total_workflows\": total_workflows,\n   525→            \"faiss_vectors\": self._faiss_manager.index.ntotal if self._faiss_manager else 0,\n   526→            \"initialized\": self._initialized,\n   527→        }\n   528→"}]},"uuid":"7889bcc6-0d4b-432c-aa8f-771b40dae957","timestamp":"2026-03-14T21:14:15.256Z","sourceToolAssistantUUID":"6144986b-9ed4-40b9-b99f-c0102f036a25"}
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+{"parentUuid":"965a25a8-ce5b-449f-ab60-0118efc7e487","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Nt69baC9LKRR8LxPsKXyDw","type":"tool_result","content":"     1→# agent_v1/core/executor.py\n     2→\"\"\"\n     3→Executeur d'actions visuelles pour Agent V1.\n     4→Opere par coordonnees normalisees (proportions) pour le rejeu en univers ferme (VM).\n     5→\n     6→Supporte deux modes :\n     7→  - Watchdog fichier (command.json) — legacy\n     8→  - Polling serveur (GET /replay/next) — mode replay P0-5\n     9→\"\"\"\n    10→\n    11→import base64\n    12→import io\n    13→import time\n    14→import logging\n    15→\n    16→import mss\n    17→from pynput.mouse import Button, Controller as MouseController\n    18→from pynput.keyboard import Controller as KeyboardController, Key\n    19→\n    20→logger = logging.getLogger(__name__)\n    21→\n    22→# Mapping des noms de touches spéciales vers pynput.Key\n    23→_SPECIAL_KEYS = {\n    24→    \"enter\": Key.enter,\n    25→    \"return\": Key.enter,\n    26→    \"tab\": Key.tab,\n    27→    \"escape\": Key.esc,\n    28→    \"esc\": Key.esc,\n    29→    \"backspace\": Key.backspace,\n    30→    \"delete\": Key.delete,\n    31→    \"space\": Key.space,\n    32→    \"up\": Key.up,\n    33→    \"down\": Key.down,\n    34→    \"left\": Key.left,\n    35→    \"right\": Key.right,\n    36→    \"home\": Key.home,\n    37→    \"end\": Key.end,\n    38→    \"page_up\": Key.page_up,\n    39→    \"page_down\": Key.page_down,\n    40→    \"f1\": Key.f1, \"f2\": Key.f2, \"f3\": Key.f3, \"f4\": Key.f4,\n    41→    \"f5\": Key.f5, \"f6\": Key.f6, \"f7\": Key.f7, \"f8\": Key.f8,\n    42→    \"f9\": Key.f9, \"f10\": Key.f10, \"f11\": Key.f11, \"f12\": Key.f12,\n    43→    \"ctrl\": Key.ctrl, \"ctrl_l\": Key.ctrl_l, \"ctrl_r\": Key.ctrl_r,\n    44→    \"alt\": Key.alt, \"alt_l\": Key.alt_l, \"alt_r\": Key.alt_r,\n    45→    \"shift\": Key.shift, \"shift_l\": Key.shift_l, \"shift_r\": Key.shift_r,\n    46→    \"cmd\": Key.cmd, \"win\": Key.cmd,\n    47→    \"super\": Key.cmd, \"super_l\": Key.cmd, \"super_r\": Key.cmd,\n    48→    \"windows\": Key.cmd, \"meta\": Key.cmd,\n    49→    \"insert\": Key.insert, \"print_screen\": Key.print_screen,\n    50→    \"caps_lock\": Key.caps_lock, \"num_lock\": Key.num_lock,\n    51→}\n    52→\n    53→\n    54→class ActionExecutorV1:\n    55→    def __init__(self):\n    56→        self.mouse = MouseController()\n    57→        self.keyboard = KeyboardController()\n    58→        self.sct = mss.mss()\n    59→        self.running = True\n    60→\n    61→    # =========================================================================\n    62→    # Execution legacy (watchdog command.json)\n    63→    # =========================================================================\n    64→\n    65→    def execute_normalized_order(self, order: dict):\n    66→        \"\"\"\n    67→        Execute un ordre base sur des proportions (0.0 a 1.0).\n    68→        Ex: {\"action\": \"mouse_click\", \"x_pct\": 0.5, \"y_pct\": 0.5} (Clic au centre)\n    69→        \"\"\"\n    70→        action = order.get(\"action\")\n    71→        try:\n    72→            # Recuperation de la resolution actuelle de la VM\n    73→            monitor = self.sct.monitors[1]\n    74→            width, height = monitor[\"width\"], monitor[\"height\"]\n    75→\n    76→            if action == \"mouse_click\":\n    77→                # Traduction Proportions -> Pixels reels de la VM\n    78→                real_x = int(order.get(\"x_pct\", 0) * width)\n    79→                real_y = int(order.get(\"y_pct\", 0) * height)\n    80→                self._click((real_x, real_y), order.get(\"button\", \"left\"))\n    81→\n    82→            elif action == \"text_input\":\n    83→                self.keyboard.type(order.get(\"text\", \"\"))\n    84→\n    85→            logger.info(f\"Ordre Visuel execute : {action} sur ({width}x{height})\")\n    86→        except Exception as e:\n    87→            logger.error(f\"Echec de l'ordre {action} : {e}\")\n    88→\n    89→    # =========================================================================\n    90→    # Execution replay (polling serveur)\n    91→    # =========================================================================\n    92→\n    93→    def execute_replay_action(self, action: dict, server_url: str = \"\") -> dict:\n    94→        \"\"\"\n    95→        Execute une action normalisee recue du serveur de replay.\n    96→\n    97→        Supporte deux modes :\n    98→        - Visual mode (visual_mode=True + target_spec) : capture un screenshot,\n    99→          l'envoie au serveur pour resolution visuelle, puis execute a la position trouvee.\n   100→        - Blind mode (defaut) : utilise les coordonnees statiques x_pct/y_pct.\n   101→\n   102→        Format d'entree :\n   103→        {\n   104→            \"action_id\": \"act_xxxx\",\n   105→            \"type\": \"click|type|key_combo|scroll|wait\",\n   106→            \"x_pct\": 0.5,\n   107→            \"y_pct\": 0.3,\n   108→            \"text\": \"...\",\n   109→            \"keys\": [...],\n   110→            \"button\": \"left\",\n   111→            \"duration_ms\": 500,\n   112→            \"visual_mode\": true,\n   113→            \"target_spec\": {\"by_role\": \"button\", \"by_text\": \"Submit\"}\n   114→        }\n   115→\n   116→        Retourne :\n   117→        {\n   118→            \"action_id\": \"act_xxxx\",\n   119→            \"success\": True/False,\n   120→            \"error\": None ou message,\n   121→            \"screenshot\": base64 du screenshot post-action,\n   122→            \"visual_resolved\": True/False\n   123→        }\n   124→        \"\"\"\n   125→        action_id = action.get(\"action_id\", \"unknown\")\n   126→        action_type = action.get(\"type\", \"unknown\")\n   127→        visual_mode = action.get(\"visual_mode\", False)\n   128→        target_spec = action.get(\"target_spec\", {})\n   129→        result = {\n   130→            \"action_id\": action_id,\n   131→            \"success\": False,\n   132→            \"error\": None,\n   133→            \"screenshot\": None,\n   134→            \"visual_resolved\": False,\n   135→        }\n   136→\n   137→        try:\n   138→            monitor = self.sct.monitors[1]\n   139→            width, height = monitor[\"width\"], monitor[\"height\"]\n   140→\n   141→            # Resolution visuelle des coordonnees si demande\n   142→            x_pct = action.get(\"x_pct\", 0.0)\n   143→            y_pct = action.get(\"y_pct\", 0.0)\n   144→\n   145→            if visual_mode and target_spec and server_url:\n   146→                resolved = self._resolve_target_visual(\n   147→                    server_url, target_spec, x_pct, y_pct, width, height\n   148→                )\n   149→                if resolved:\n   150→                    x_pct = resolved[\"x_pct\"]\n   151→                    y_pct = resolved[\"y_pct\"]\n   152→                    result[\"visual_resolved\"] = resolved.get(\"resolved\", False)\n   153→                    if resolved.get(\"resolved\"):\n   154→                        logger.info(\n   155→                            f\"Visual resolve OK: {resolved.get('matched_element', {}).get('label', '?')} \"\n   156→                            f\"-> ({x_pct:.4f}, {y_pct:.4f})\"\n   157→                        )\n   158→\n   159→            if action_type == \"click\":\n   160→                real_x = int(x_pct * width)\n   161→                real_y = int(y_pct * height)\n   162→                button = action.get(\"button\", \"left\")\n   163→                self._click((real_x, real_y), button)\n   164→                mode = \"VISUAL\" if result[\"visual_resolved\"] else \"BLIND\"\n   165→                logger.info(\n   166→                    f\"Replay click [{mode}] : ({x_pct:.3f}, {y_pct:.3f}) -> \"\n   167→                    f\"({real_x}, {real_y}) sur ({width}x{height})\"\n   168→                )\n   169→\n   170→            elif action_type == \"type\":\n   171→                text = action.get(\"text\", \"\")\n   172→                # Cliquer sur le champ avant de taper (si coordonnees disponibles)\n   173→                if x_pct > 0 and y_pct > 0:\n   174→                    real_x = int(x_pct * width)\n   175→                    real_y = int(y_pct * height)\n   176→                    self._click((real_x, real_y), \"left\")\n   177→                    time.sleep(0.3)\n   178→                self.keyboard.type(text)\n   179→                logger.info(f\"Replay type : '{text[:30]}...' ({len(text)} chars)\")\n   180→\n   181→            elif action_type == \"key_combo\":\n   182→                keys = action.get(\"keys\", [])\n   183→                self._execute_key_combo(keys)\n   184→                logger.info(f\"Replay key_combo : {keys}\")\n   185→\n   186→            elif action_type == \"scroll\":\n   187→                real_x = int(x_pct * width) if x_pct > 0 else int(0.5 * width)\n   188→                real_y = int(y_pct * height) if y_pct > 0 else int(0.5 * height)\n   189→                delta = action.get(\"delta\", -3)\n   190→                self.mouse.position = (real_x, real_y)\n   191→                time.sleep(0.05)\n   192→                self.mouse.scroll(0, delta)\n   193→                logger.info(f\"Replay scroll : delta={delta} a ({real_x}, {real_y})\")\n   194→\n   195→            elif action_type == \"wait\":\n   196→                duration_ms = action.get(\"duration_ms\", 500)\n   197→                time.sleep(duration_ms / 1000.0)\n   198→                logger.info(f\"Replay wait : {duration_ms}ms\")\n   199→\n   200→            else:\n   201→                result[\"error\"] = f\"Type d'action inconnu : {action_type}\"\n   202→                logger.warning(result[\"error\"])\n   203→                return result\n   204→\n   205→            result[\"success\"] = True\n   206→\n   207→            # Capturer un screenshot post-action\n   208→            time.sleep(0.5)\n   209→            result[\"screenshot\"] = self._capture_screenshot_b64()\n   210→\n   211→        except Exception as e:\n   212→            result[\"error\"] = str(e)\n   213→            logger.error(f\"Echec replay action {action_id} ({action_type}) : {e}\")\n   214→\n   215→        return result\n   216→\n   217→    def _resolve_target_visual(\n   218→        self, server_url: str, target_spec: dict,\n   219→        fallback_x: float, fallback_y: float,\n   220→        screen_width: int, screen_height: int,\n   221→    ) -> dict:\n   222→        \"\"\"\n   223→        Envoyer un screenshot au serveur pour resolution visuelle de la cible.\n   224→\n   225→        Capture l'ecran, l'encode en base64 JPEG, et POST au endpoint\n   226→        /replay/resolve_target. Retourne les coordonnees resolues.\n   227→        \"\"\"\n   228→        import requests\n   229→\n   230→        try:\n   231→            # Capturer le screenshot actuel\n   232→            screenshot_b64 = self._capture_screenshot_b64()\n   233→            if not screenshot_b64:\n   234→                logger.warning(\"Capture screenshot echouee pour visual resolve\")\n   235→                return None\n   236→\n   237→            # Appel au serveur\n   238→            resolve_url = f\"{server_url}/traces/stream/replay/resolve_target\"\n   239→            payload = {\n   240→                \"session_id\": \"\",  # Pas critique pour la resolution\n   241→                \"screenshot_b64\": screenshot_b64,\n   242→                \"target_spec\": target_spec,\n   243→                \"fallback_x_pct\": fallback_x,\n   244→                \"fallback_y_pct\": fallback_y,\n   245→                \"screen_width\": screen_width,\n   246→                \"screen_height\": screen_height,\n   247→            }\n   248→\n   249→            resp = requests.post(resolve_url, json=payload, timeout=30)\n   250→            if resp.ok:\n   251→                return resp.json()\n   252→            else:\n   253→                logger.warning(f\"Visual resolve HTTP {resp.status_code}: {resp.text[:200]}\")\n   254→                return None\n   255→\n   256→        except requests.exceptions.Timeout:\n   257→            logger.warning(\"Visual resolve timeout (30s)\")\n   258→            return None\n   259→        except Exception as e:\n   260→            logger.warning(f\"Visual resolve echoue: {e}\")\n   261→            return None\n   262→\n   263→    def poll_and_execute(self, session_id: str, server_url: str) -> bool:\n   264→        \"\"\"\n   265→        Poll le serveur pour recuperer et executer la prochaine action.\n   266→\n   267→        1. GET /replay/next pour recuperer l'action\n   268→        2. Execute l'action (clic, texte, etc.)\n   269→        3. POST /replay/result avec le resultat + screenshot\n   270→\n   271→        Retourne True si une action a ete executee, False sinon.\n   272→        \"\"\"\n   273→        import requests\n   274→\n   275→        replay_next_url = f\"{server_url}/traces/stream/replay/next\"\n   276→        replay_result_url = f\"{server_url}/traces/stream/replay/result\"\n   277→\n   278→        try:\n   279→            # 1. Recuperer la prochaine action\n   280→            resp = requests.get(\n   281→                replay_next_url,\n   282→                params={\"session_id\": session_id},\n   283→                timeout=5,\n   284→            )\n   285→            if not resp.ok:\n   286→                logger.debug(f\"Poll replay echoue : HTTP {resp.status_code}\")\n   287→                return False\n   288→\n   289→            data = resp.json()\n   290→            action = data.get(\"action\")\n   291→            if action is None:\n   292→                # Pas d'action en attente — rien a faire\n   293→                return False\n   294→\n   295→            logger.info(\n   296→                f\"Action de replay recue : {action.get('type')} \"\n   297→                f\"(id={action.get('action_id')})\"\n   298→            )\n   299→\n   300→            # 2. Executer l'action (avec server_url pour la resolution visuelle)\n   301→            result = self.execute_replay_action(action, server_url=server_url)\n   302→\n   303→            # 3. Rapporter le resultat au serveur\n   304→            report = {\n   305→                \"session_id\": session_id,\n   306→                \"action_id\": result[\"action_id\"],\n   307→                \"success\": result[\"success\"],\n   308→                \"error\": result.get(\"error\"),\n   309→                \"screenshot\": result.get(\"screenshot\"),\n   310→            }\n   311→            try:\n   312→                resp2 = requests.post(\n   313→                    replay_result_url,\n   314→                    json=report,\n   315→                    timeout=5,\n   316→                )\n   317→                if resp2.ok:\n   318→                    server_resp = resp2.json()\n   319→                    logger.info(\n   320→                        f\"Resultat rapporte : replay_status={server_resp.get('replay_status')}, \"\n   321→                        f\"restant={server_resp.get('remaining_actions')}\"\n   322→                    )\n   323→                else:\n   324→                    logger.warning(f\"Rapport resultat echoue : HTTP {resp2.status_code}\")\n   325→            except Exception as e:\n   326→                logger.warning(f\"Impossible de rapporter le resultat : {e}\")\n   327→\n   328→            return True\n   329→\n   330→        except (requests.exceptions.ConnectionError, requests.exceptions.Timeout):\n   331→            # Serveur non disponible ou occupé — pas de log spam\n   332→            return False\n   333→        except Exception as e:\n   334→            logger.error(f\"Erreur poll_and_execute : {e}\")\n   335→            return False\n   336→\n   337→    # =========================================================================\n   338→    # Helpers\n   339→    # =========================================================================\n   340→\n   341→    def _click(self, pos, button_name):\n   342→        \"\"\"Deplacer la souris et cliquer.\"\"\"\n   343→        self.mouse.position = pos\n   344→        time.sleep(0.1)  # Delai pour simuler le temps de reaction humain\n   345→        btn = Button.left if button_name == \"left\" else Button.right\n   346→        self.mouse.click(btn)\n   347→\n   348→    def _execute_key_combo(self, keys: list):\n   349→        \"\"\"\n   350→        Executer une combinaison de touches.\n   351→        Ex: [\"ctrl\", \"a\"] -> Ctrl+A\n   352→        Ex: [\"enter\"] -> Enter\n   353→        \"\"\"\n   354→        if not keys:\n   355→            return\n   356→\n   357→        # Resoudre les noms de touches vers les objets pynput\n   358→        resolved = []\n   359→        for key_name in keys:\n   360→            key_lower = key_name.lower()\n   361→            if key_lower in _SPECIAL_KEYS:\n   362→                resolved.append(_SPECIAL_KEYS[key_lower])\n   363→            elif len(key_name) == 1:\n   364→                resolved.append(key_name)\n   365→            else:\n   366→                logger.warning(f\"Touche inconnue : '{key_name}', ignoree\")\n   367→\n   368→        if not resolved:\n   369→            return\n   370→\n   371→        # Si une seule touche, simple press\n   372→        if len(resolved) == 1:\n   373→            self.keyboard.press(resolved[0])\n   374→            self.keyboard.release(resolved[0])\n   375→            return\n   376→\n   377→        # Combo : maintenir les modificateurs, taper la derniere touche\n   378→        modifiers = resolved[:-1]\n   379→        final_key = resolved[-1]\n   380→\n   381→        for mod in modifiers:\n   382→            self.keyboard.press(mod)\n   383→        time.sleep(0.05)\n   384→\n   385→        self.keyboard.press(final_key)\n   386→        self.keyboard.release(final_key)\n   387→\n   388→        for mod in reversed(modifiers):\n   389→            self.keyboard.release(mod)\n   390→\n   391→    def _capture_screenshot_b64(self) -> str:\n   392→        \"\"\"\n   393→        Capturer l'ecran et retourner le screenshot en base64.\n   394→        Utilise une qualite reduite pour limiter la taille du payload.\n   395→        \"\"\"\n   396→        try:\n   397→            from PIL import Image\n   398→\n   399→            monitor = self.sct.monitors[1]\n   400→            raw = self.sct.grab(monitor)\n   401→            img = Image.frombytes(\"RGB\", raw.size, raw.bgra, \"raw\", \"BGRX\")\n   402→\n   403→            # Redimensionner pour limiter la taille (max 800px de large)\n   404→            max_w = 800\n   405→            if img.width > max_w:\n   406→                ratio = max_w / img.width\n   407→                new_h = int(img.height * ratio)\n   408→                img = img.resize((max_w, new_h), Image.LANCZOS)\n   409→\n   410→            buffer = io.BytesIO()\n   411→            img.save(buffer, format=\"JPEG\", quality=60)\n   412→            return base64.b64encode(buffer.getvalue()).decode(\"utf-8\")\n   413→        except ImportError:\n   414→            # PIL non disponible — retourner None\n   415→            logger.debug(\"PIL non disponible, pas de screenshot base64\")\n   416→            return \"\"\n   417→        except Exception as e:\n   418→            logger.debug(f\"Capture screenshot base64 echouee : {e}\")\n   419→            return \"\"\n   420→"}]},"uuid":"46b0b6f6-9e78-47d1-84a6-0d64abd3e078","timestamp":"2026-03-14T21:14:15.671Z","sourceToolAssistantUUID":"965a25a8-ce5b-449f-ab60-0118efc7e487"}
+{"parentUuid":"46b0b6f6-9e78-47d1-84a6-0d64abd3e078","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","message":{"model":"claude-opus-4-6","id":"msg_018zaGgwBpWGBr2DDyh32bYQ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01W38MzfzZfph6YQYX97tRxJ","name":"Read","input":{"file_path":"/home/dom/ai/rpa_vision_v3/core/models/raw_session.py"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":12328,"cache_read_input_tokens":0,"output_tokens":294,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":12328},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZ3fTXbxvgvjif9CfKQnR","type":"assistant","uuid":"74035396-1344-40a9-a424-1b708980a39d","timestamp":"2026-03-14T21:14:16.064Z"}
+{"parentUuid":"74035396-1344-40a9-a424-1b708980a39d","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_01W38MzfzZfph6YQYX97tRxJ","toolUseID":"toolu_01W38MzfzZfph6YQYX97tRxJ","timestamp":"2026-03-14T21:14:16.066Z","uuid":"a741bc4f-7d13-46e6-9b2d-aa01ac47b75e"}
+{"parentUuid":"74035396-1344-40a9-a424-1b708980a39d","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01W38MzfzZfph6YQYX97tRxJ","type":"tool_result","content":"     1→\"\"\"\n     2→RawSession - Couche 0 : Capture Brute\n     3→\n     4→Enregistre fidèlement toutes les interactions utilisateur avec horodatage précis\n     5→et contexte complet. C'est la fondation du système RPA Vision V3.\n     6→\"\"\"\n     7→\n     8→from dataclasses import dataclass, field\n     9→from datetime import datetime\n    10→from typing import Dict, List, Optional, Any\n    11→from pathlib import Path\n    12→import json\n    13→\n    14→\n    15→@dataclass\n    16→class RawWindowContext:\n    17→    \"\"\"\n    18→    Contexte de fenêtre pour un événement (RawSession)\n    19→    \n    20→    Renommé de WindowContext pour éviter collision avec ScreenState.WindowContext\n    21→    Auteur: Dom, Alice Kiro - 15 décembre 2024\n    22→    \"\"\"\n    23→    title: str\n    24→    app_name: str\n    25→    \n    26→    def to_dict(self) -> Dict[str, str]:\n    27→        return {\n    28→            \"title\": self.title,\n    29→            \"app_name\": self.app_name\n    30→        }\n    31→    \n    32→    @classmethod\n    33→    def from_dict(cls, data: Dict[str, str]) -> 'RawWindowContext':\n    34→        return cls(\n    35→            title=data[\"title\"],\n    36→            app_name=data[\"app_name\"]\n    37→        )\n    38→\n    39→\n    40→# Alias de compatibilité pour migration douce\n    41→WindowContext = RawWindowContext\n    42→\n    43→\n    44→@dataclass\n    45→class Event:\n    46→    \"\"\"\n    47→    Événement utilisateur capturé\n    48→    \n    49→    Types supportés:\n    50→    - mouse_click, mouse_move, mouse_scroll\n    51→    - key_press, key_release, text_input\n    52→    - window_change, screen_change\n    53→    \"\"\"\n    54→    t: float  # Timestamp relatif en secondes depuis début session\n    55→    type: str  # Type d'événement\n    56→    window: RawWindowContext\n    57→    screenshot_id: Optional[str] = None\n    58→    data: Dict[str, Any] = field(default_factory=dict)  # Données spécifiques au type\n    59→    \n    60→    def to_dict(self) -> Dict[str, Any]:\n    61→        result = {\n    62→            \"t\": self.t,\n    63→            \"type\": self.type,\n    64→            \"window\": self.window.to_dict(),\n    65→        }\n    66→        if self.screenshot_id:\n    67→            result[\"screenshot_id\"] = self.screenshot_id\n    68→        # Ajouter les données spécifiques\n    69→        result.update(self.data)\n    70→        return result\n    71→    \n    72→    @classmethod\n    73→    def from_dict(cls, data: Dict[str, Any]) -> 'Event':\n    74→        # Extraire les champs de base\n    75→        t = data[\"t\"]\n    76→        event_type = data[\"type\"]\n    77→        window = RawWindowContext.from_dict(data[\"window\"])\n    78→        screenshot_id = data.get(\"screenshot_id\")\n    79→        \n    80→        # Le reste va dans data\n    81→        event_data = {k: v for k, v in data.items() \n    82→                     if k not in [\"t\", \"type\", \"window\", \"screenshot_id\"]}\n    83→        \n    84→        return cls(\n    85→            t=t,\n    86→            type=event_type,\n    87→            window=window,\n    88→            screenshot_id=screenshot_id,\n    89→            data=event_data\n    90→        )\n    91→\n    92→\n    93→@dataclass\n    94→class Screenshot:\n    95→    \"\"\"Référence à un screenshot capturé\"\"\"\n    96→    screenshot_id: str\n    97→    relative_path: str\n    98→    captured_at: str  # ISO format timestamp\n    99→    \n   100→    def to_dict(self) -> Dict[str, str]:\n   101→        return {\n   102→            \"screenshot_id\": self.screenshot_id,\n   103→            \"relative_path\": self.relative_path,\n   104→            \"captured_at\": self.captured_at\n   105→        }\n   106→    \n   107→    @classmethod\n   108→    def from_dict(cls, data: Dict[str, str]) -> 'Screenshot':\n   109→        return cls(\n   110→            screenshot_id=data[\"screenshot_id\"],\n   111→            relative_path=data[\"relative_path\"],\n   112→            captured_at=data[\"captured_at\"]\n   113→        )\n   114→\n   115→\n   116→@dataclass\n   117→class RawSession:\n   118→    \"\"\"\n   119→    Session brute capturant tous les événements utilisateur\n   120→    \n   121→    Format: rawsession_v1\n   122→    \"\"\"\n   123→    session_id: str\n   124→    agent_version: str\n   125→    environment: Dict[str, Any]\n   126→    user: Dict[str, str]\n   127→    context: Dict[str, str]\n   128→    started_at: datetime\n   129→    ended_at: Optional[datetime] = None\n   130→    events: List[Event] = field(default_factory=list)\n   131→    screenshots: List[Screenshot] = field(default_factory=list)\n   132→    schema_version: str = \"rawsession_v1\"\n   133→    \n   134→    def add_event(self, event: Event) -> None:\n   135→        \"\"\"Ajouter un événement à la session\"\"\"\n   136→        self.events.append(event)\n   137→    \n   138→    def add_screenshot(self, screenshot: Screenshot) -> None:\n   139→        \"\"\"Ajouter un screenshot à la session\"\"\"\n   140→        self.screenshots.append(screenshot)\n   141→    \n   142→    def to_json(self) -> Dict[str, Any]:\n   143→        \"\"\"Sérialiser en JSON\"\"\"\n   144→        return {\n   145→            \"schema_version\": self.schema_version,\n   146→            \"session_id\": self.session_id,\n   147→            \"agent_version\": self.agent_version,\n   148→            \"environment\": self.environment,\n   149→            \"user\": self.user,\n   150→            \"context\": self.context,\n   151→            \"started_at\": self.started_at.isoformat(),\n   152→            \"ended_at\": self.ended_at.isoformat() if self.ended_at else None,\n   153→            \"events\": [event.to_dict() for event in self.events],\n   154→            \"screenshots\": [screenshot.to_dict() for screenshot in self.screenshots]\n   155→        }\n   156→    \n   157→    @classmethod\n   158→    def from_json(cls, data: Dict[str, Any]) -> 'RawSession':\n   159→        \"\"\"Désérialiser depuis JSON\"\"\"\n   160→        # Valider schéma\n   161→        schema_version = data.get(\"schema_version\")\n   162→        if schema_version != \"rawsession_v1\":\n   163→            raise ValueError(\n   164→                f\"Unsupported schema version: {schema_version}. \"\n   165→                f\"Expected: rawsession_v1\"\n   166→            )\n   167→        \n   168→        # Parser dates\n   169→        started_at = datetime.fromisoformat(data[\"started_at\"])\n   170→        ended_at = datetime.fromisoformat(data[\"ended_at\"]) if data.get(\"ended_at\") else None\n   171→        \n   172→        # Parser events et screenshots\n   173→        events = [Event.from_dict(e) for e in data.get(\"events\", [])]\n   174→        screenshots = [Screenshot.from_dict(s) for s in data.get(\"screenshots\", [])]\n   175→        \n   176→        return cls(\n   177→            schema_version=schema_version,\n   178→            session_id=data[\"session_id\"],\n   179→            agent_version=data[\"agent_version\"],\n   180→            environment=data[\"environment\"],\n   181→            user=data[\"user\"],\n   182→            context=data[\"context\"],\n   183→            started_at=started_at,\n   184→            ended_at=ended_at,\n   185→            events=events,\n   186→            screenshots=screenshots\n   187→        )\n   188→    \n   189→    def save_to_file(self, filepath: Path) -> None:\n   190→        \"\"\"Sauvegarder dans un fichier JSON\"\"\"\n   191→        filepath.parent.mkdir(parents=True, exist_ok=True)\n   192→        with open(filepath, 'w', encoding='utf-8') as f:\n   193→            json.dump(self.to_json(), f, indent=2, ensure_ascii=False)\n   194→    \n   195→    @classmethod\n   196→    def load_from_file(cls, filepath: Path) -> 'RawSession':\n   197→        \"\"\"Charger depuis un fichier JSON\"\"\"\n   198→        with open(filepath, 'r', encoding='utf-8') as f:\n   199→            data = json.load(f)\n   200→        return cls.from_json(data)\n   201→"}]},"uuid":"6cb44275-219c-4d0e-9293-504a98648f63","timestamp":"2026-03-14T21:14:16.066Z","sourceToolAssistantUUID":"74035396-1344-40a9-a424-1b708980a39d"}
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+{"parentUuid":"44291c11-e293-4eb3-8257-9ab8a6b59276","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_015sEk5WTTPnQvM6rvUNNHaZ","toolUseID":"toolu_015sEk5WTTPnQvM6rvUNNHaZ","timestamp":"2026-03-14T21:14:20.393Z","uuid":"fe54ed18-a675-4b01-87b9-336c0c578408"}
+{"parentUuid":"44291c11-e293-4eb3-8257-9ab8a6b59276","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015sEk5WTTPnQvM6rvUNNHaZ","type":"tool_result","content":"     1→\"\"\"\n     2→GraphBuilder - Construction Automatique de Workflow Graphs\n     3→\n     4→Ce module implémente la construction automatique de graphes de workflows\n     5→en analysant les sessions enregistrées et en détectant les patterns répétés.\n     6→\n     7→Architecture:\n     8→    1. Création de ScreenStates depuis RawSession\n     9→    2. Calcul de State Embeddings pour tous les états\n    10→    3. Détection de patterns via clustering DBSCAN\n    11→    4. Construction de WorkflowNodes depuis clusters\n    12→    5. Construction de WorkflowEdges depuis transitions\n    13→\n    14→Algorithme de Détection de Patterns:\n    15→    - Utilise DBSCAN (Density-Based Spatial Clustering of Applications with Noise)\n    16→    - Métrique: similarité cosinus entre embeddings\n    17→    - Filtre les clusters avec moins de N répétitions\n    18→    - Calcule un prototype (moyenne) pour chaque cluster\n    19→\n    20→Example:\n    21→    >>> builder = GraphBuilder(min_pattern_repetitions=3)\n    22→    >>> workflow = builder.build_from_session(raw_session)\n    23→    >>> print(f\"Workflow with {len(workflow.nodes)} nodes\")\n    24→\"\"\"\n    25→\n    26→import logging\n    27→import os\n    28→from typing import List, Dict, Optional, Tuple, Any\n    29→from collections import defaultdict, Counter\n    30→from datetime import datetime\n    31→from pathlib import Path\n    32→\n    33→import numpy as np\n    34→from sklearn.cluster import DBSCAN\n    35→\n    36→from core.models.raw_session import RawSession, Event\n    37→from core.models.screen_state import (\n    38→    ScreenState, WindowContext, RawLevel, PerceptionLevel, \n    39→    ContextLevel, EmbeddingRef\n    40→)\n    41→from core.models.workflow_graph import (\n    42→    Workflow,\n    43→    WorkflowNode,\n    44→    WorkflowEdge,\n    45→    ScreenTemplate,\n    46→    Action,\n    47→    TargetSpec,\n    48→    EdgeConstraints,\n    49→    PostConditions,\n    50→    WindowConstraint,\n    51→    TextConstraint,\n    52→    UIConstraint,\n    53→    EmbeddingPrototype,\n    54→)\n    55→from core.embedding.state_embedding_builder import StateEmbeddingBuilder\n    56→from core.embedding.faiss_manager import FAISSManager\n    57→from core.training.quality_validator import TrainingQualityValidator, QualityReport\n    58→\n    59→logger = logging.getLogger(__name__)\n    60→\n    61→\n    62→class GraphBuilder:\n    63→    \"\"\"\n    64→    Constructeur de graphes de workflows depuis sessions brutes.\n    65→    \n    66→    Cette classe analyse une RawSession pour construire automatiquement\n    67→    un Workflow avec ses nodes et edges en détectant les patterns répétés.\n    68→    \n    69→    Attributes:\n    70→        embedding_builder: Builder pour calculer les State Embeddings\n    71→        faiss_manager: Manager FAISS pour indexation (optionnel)\n    72→        min_pattern_repetitions: Nombre minimum de répétitions pour un pattern\n    73→        clustering_eps: Distance maximum entre points pour DBSCAN\n    74→        clustering_min_samples: Nombre minimum d'échantillons par cluster\n    75→    \n    76→    Example:\n    77→        >>> builder = GraphBuilder(min_pattern_repetitions=3)\n    78→        >>> workflow = builder.build_from_session(session, \"Login Workflow\")\n    79→    \"\"\"\n    80→    \n    81→    def __init__(\n    82→        self,\n    83→        embedding_builder: Optional[StateEmbeddingBuilder] = None,\n    84→        faiss_manager: Optional[FAISSManager] = None,\n    85→        quality_validator: Optional[TrainingQualityValidator] = None,\n    86→        min_pattern_repetitions: int = 3,\n    87→        clustering_eps: float = 0.15,\n    88→        clustering_min_samples: int = 2,\n    89→        enable_quality_validation: bool = True,\n    90→    ):\n    91→        \"\"\"\n    92→        Initialiser le GraphBuilder.\n    93→        \n    94→        Args:\n    95→            embedding_builder: Builder pour State Embeddings (créé si None)\n    96→            faiss_manager: Manager FAISS pour indexation (optionnel)\n    97→            quality_validator: Validateur de qualité (créé si None)\n    98→            min_pattern_repetitions: Nombre minimum de répétitions pour un pattern\n    99→            clustering_eps: Epsilon pour DBSCAN (distance max entre points)\n   100→            clustering_min_samples: Nombre minimum d'échantillons pour un cluster\n   101→            enable_quality_validation: Activer la validation de qualité\n   102→        \"\"\"\n   103→        self.embedding_builder = embedding_builder or StateEmbeddingBuilder()\n   104→        self.faiss_manager = faiss_manager\n   105→        self.quality_validator = quality_validator or TrainingQualityValidator()\n   106→        self.min_pattern_repetitions = min_pattern_repetitions\n   107→        self.clustering_eps = clustering_eps\n   108→        self.clustering_min_samples = clustering_min_samples\n   109→        self.enable_quality_validation = enable_quality_validation\n   110→        self._screen_analyzer = None  # ScreenAnalyzer (lazy import)\n   111→        \n   112→        logger.info(\n   113→            f\"GraphBuilder initialized: \"\n   114→            f\"min_repetitions={min_pattern_repetitions}, \"\n   115→            f\"eps={clustering_eps}, \"\n   116→            f\"min_samples={clustering_min_samples}, \"\n   117→            f\"quality_validation={enable_quality_validation}\"\n   118→        )\n   119→    \n   120→    def build_from_session(\n   121→        self,\n   122→        session: RawSession,\n   123→        workflow_name: Optional[str] = None,\n   124→        precomputed_states: Optional[List[\"ScreenState\"]] = None,\n   125→    ) -> Workflow:\n   126→        \"\"\"\n   127→        Construire un Workflow complet depuis une RawSession.\n   128→\n   129→        Processus:\n   130→            1. Créer ScreenStates depuis screenshots (ou utiliser precomputed_states)\n   131→            2. Calculer embeddings pour chaque état\n   132→            3. Détecter patterns via clustering\n   133→            4. Construire nodes depuis clusters\n   134→            5. Construire edges depuis transitions\n   135→\n   136→        Args:\n   137→            session: Session brute à analyser\n   138→            workflow_name: Nom du workflow (généré si None)\n   139→            precomputed_states: ScreenStates déjà analysés (streaming).\n   140→                Si fourni, saute l'étape 1 (pas de re-analyse via ScreenAnalyzer).\n   141→\n   142→        Returns:\n   143→            Workflow construit avec nodes et edges\n   144→\n   145→        Raises:\n   146→            ValueError: Si la session est vide ou invalide\n   147→        \"\"\"\n   148→        if not precomputed_states and not session.screenshots:\n   149→            raise ValueError(\"Session has no screenshots and no precomputed states\")\n   150→\n   151→        logger.info(\n   152→            f\"Building workflow from session {session.session_id} \"\n   153→            f\"with {len(precomputed_states or session.screenshots)} \"\n   154→            f\"{'precomputed states' if precomputed_states else 'screenshots'}\"\n   155→        )\n   156→\n   157→        # Étape 1: Créer ScreenStates (ou réutiliser ceux pré-calculés)\n   158→        if precomputed_states:\n   159→            screen_states = precomputed_states\n   160→            logger.debug(f\"Using {len(screen_states)} precomputed screen states\")\n   161→        else:\n   162→            screen_states = self._create_screen_states(session)\n   163→            logger.debug(f\"Created {len(screen_states)} screen states\")\n   164→\n   165→        # Étape 2: Calculer embeddings\n   166→        embeddings = self._compute_embeddings(screen_states)\n   167→        logger.debug(f\"Computed {len(embeddings)} embeddings\")\n   168→        \n   169→        # Étape 3: Détecter patterns\n   170→        clusters = self._detect_patterns(embeddings, screen_states)\n   171→        logger.info(f\"Detected {len(clusters)} patterns\")\n   172→        \n   173→        # Étape 4: Construire nodes\n   174→        nodes = self._build_nodes(clusters, screen_states, embeddings)\n   175→        logger.info(f\"Built {len(nodes)} workflow nodes\")\n   176→        \n   177→        # Étape 5: Construire edges\n   178→        edges = self._build_edges(nodes, screen_states, session)\n   179→        logger.info(f\"Built {len(edges)} workflow edges\")\n   180→        \n   181→        # Créer Workflow\n   182→        from core.models.workflow_graph import WorkflowStats, SafetyRules, LearningConfig\n   183→        \n   184→        workflow = Workflow(\n   185→            workflow_id=workflow_name or f\"workflow_{session.session_id}\",\n   186→            name=workflow_name or \"Unnamed Workflow\",\n   187→            description=\"Auto-generated workflow\",\n   188→            version=1,\n   189→            learning_state=\"OBSERVATION\",\n   190→            created_at=datetime.now(),\n   191→            updated_at=datetime.now(),\n   192→            entry_nodes=[nodes[0].node_id] if nodes else [],\n   193→            end_nodes=[],\n   194→            nodes=nodes,\n   195→            edges=edges,\n   196→            safety_rules=SafetyRules(),\n   197→            stats=WorkflowStats(),\n   198→            learning=LearningConfig()\n   199→        )\n   200→        \n   201→        # Étape 6: Validation de qualité\n   202→        quality_report = None\n   203→        if self.enable_quality_validation and screen_states:\n   204→            quality_report = self._validate_workflow_quality(\n   205→                workflow, screen_states, embeddings, clusters\n   206→            )\n   207→            \n   208→            # Stocker le rapport dans les métadonnées du workflow\n   209→            workflow.metadata = workflow.metadata or {}\n   210→            workflow.metadata['quality_report'] = quality_report.to_dict()\n   211→            \n   212→            # Ajuster learning_state basé sur la qualité\n   213→            if quality_report.is_production_ready:\n   214→                workflow.learning_state = \"AUTO_CANDIDATE\"\n   215→                logger.info(\"Workflow qualité suffisante -> AUTO_CANDIDATE\")\n   216→            else:\n   217→                workflow.learning_state = \"OBSERVATION\"\n   218→                logger.warning(\n   219→                    f\"Qualité insuffisante ({quality_report.overall_score:.3f}), \"\n   220→                    f\"workflow reste en OBSERVATION\"\n   221→                )\n   222→        \n   223→        logger.info(\n   224→            f\"Workflow '{workflow.name}' built successfully: \"\n   225→            f\"{len(nodes)} nodes, {len(edges)} edges\"\n   226→        )\n   227→        \n   228→        return workflow\n   229→    \n   230→    def _validate_workflow_quality(\n   231→        self,\n   232→        workflow: Workflow,\n   233→        screen_states: List[ScreenState],\n   234→        embeddings: List[np.ndarray],\n   235→        clusters: Dict[int, List[int]]\n   236→    ) -> QualityReport:\n   237→        \"\"\"\n   238→        Valider la qualité du workflow construit.\n   239→        \n   240→        Args:\n   241→            workflow: Workflow à valider\n   242→            screen_states: États d'écran utilisés\n   243→            embeddings: Embeddings calculés\n   244→            clusters: Clusters détectés\n   245→        \n   246→        Returns:\n   247→            QualityReport avec métriques et recommandations\n   248→        \"\"\"\n   249→        logger.info(f\"Validation qualité du workflow {workflow.workflow_id}\")\n   250→        \n   251→        # Préparer les données pour le validateur\n   252→        embeddings_array = np.array(embeddings)\n   253→        \n   254→        # Créer labels depuis les clusters\n   255→        labels = np.full(len(embeddings), -1)  # -1 = bruit\n   256→        for cluster_id, indices in clusters.items():\n   257→            for idx in indices:\n   258→                labels[idx] = cluster_id\n   259→        \n   260→        # Valider avec le TrainingQualityValidator\n   261→        report = self.quality_validator.validate_workflow(\n   262→            workflow=workflow,\n   263→            observations=screen_states,\n   264→            embeddings=embeddings_array,\n   265→            labels=labels\n   266→        )\n   267→        \n   268→        logger.info(\n   269→            f\"Validation terminée: score={report.overall_score:.3f}, \"\n   270→            f\"production_ready={report.is_production_ready}\"\n   271→        )\n   272→        \n   273→        return report\n   274→    \n   275→    def _create_screen_states(self, session: RawSession) -> List[ScreenState]:\n   276→        \"\"\"\n   277→        Créer ScreenStates enrichis depuis les screenshots de la session.\n   278→        \n   279→        Pour chaque screenshot:\n   280→        1. Trouver l'événement associé pour le contexte de fenêtre\n   281→        2. Créer les 4 niveaux du ScreenState\n   282→        3. Optionnellement détecter les éléments UI\n   283→        \n   284→        Args:\n   285→            session: Session brute\n   286→        \n   287→        Returns:\n   288→            Liste de ScreenStates enrichis\n   289→        \"\"\"\n   290→        screen_states = []\n   291→        \n   292→        # Créer un mapping screenshot_id -> événement\n   293→        screenshot_to_event = {}\n   294→        for event in session.events:\n   295→            if event.screenshot_id:\n   296→                screenshot_to_event[event.screenshot_id] = event\n   297→        \n   298→        for i, screenshot in enumerate(session.screenshots):\n   299→            # Trouver l'événement associé\n   300→            event = screenshot_to_event.get(screenshot.screenshot_id)\n   301→            \n   302→            # Créer WindowContext depuis l'événement\n   303→            if event and event.window:\n   304→                window = WindowContext(\n   305→                    app_name=event.window.app_name,\n   306→                    window_title=event.window.title,\n   307→                    screen_resolution=session.environment.get(\"screen\", {}).get(\"primary_resolution\", [1920, 1080]),\n   308→                    workspace=\"main\"\n   309→                )\n   310→            else:\n   311→                window = WindowContext(\n   312→                    app_name=\"unknown\",\n   313→                    window_title=\"Unknown\",\n   314→                    screen_resolution=[1920, 1080],\n   315→                    workspace=\"main\"\n   316→                )\n   317→            \n   318→            # Créer RawLevel\n   319→            # Construire chemin absolu : data/training/sessions/{session_id}/{session_id}/{relative_path}\n   320→            screenshot_absolute_path = f\"data/training/sessions/{session.session_id}/{session.session_id}/{screenshot.relative_path}\"\n   321→            screenshot_path = Path(screenshot_absolute_path)\n   322→            raw = RawLevel(\n   323→                screenshot_path=str(screenshot_path),\n   324→                capture_method=\"mss\",\n   325→                file_size_bytes=screenshot_path.stat().st_size if screenshot_path.exists() else 0\n   326→            )\n   327→            \n   328→            # Créer PerceptionLevel — enrichir avec OCR si le screenshot existe\n   329→            detected_text = []\n   330→            text_method = \"none\"\n   331→\n   332→            if screenshot_path.exists():\n   333→                try:\n   334→                    if self._screen_analyzer is None:\n   335→                        from core.pipeline.screen_analyzer import ScreenAnalyzer\n   336→                        self._screen_analyzer = ScreenAnalyzer(session_id=session.session_id)\n   337→                    extracted = self._screen_analyzer._extract_text(str(screenshot_path))\n   338→                    if extracted:\n   339→                        detected_text = extracted\n   340→                        text_method = self._screen_analyzer._get_ocr_method_name()\n   341→                except Exception as e:\n   342→                    logger.debug(f\"OCR échoué pour {screenshot_path}: {e}\")\n   343→\n   344→            perception = PerceptionLevel(\n   345→                embedding=EmbeddingRef(\n   346→                    provider=\"openclip_ViT-B-32\",\n   347→                    vector_id=f\"data/embeddings/screens/{session.session_id}_state_{i:04d}.npy\",\n   348→                    dimensions=512\n   349→                ),\n   350→                detected_text=detected_text,\n   351→                text_detection_method=text_method,\n   352→                confidence_avg=0.85 if detected_text else 0.0\n   353→            )\n   354→            \n   355→            # Créer ContextLevel\n   356→            context = ContextLevel(\n   357→                current_workflow_candidate=None,\n   358→                workflow_step=i,\n   359→                user_id=session.user.get(\"id\", \"unknown\"),\n   360→                tags=list(session.context.get(\"tags\", [])) if isinstance(session.context.get(\"tags\"), list) else [],\n   361→                business_variables={}\n   362→            )\n   363→            \n   364→            # Parser timestamp\n   365→            if isinstance(screenshot.captured_at, str):\n   366→                timestamp = datetime.fromisoformat(screenshot.captured_at.replace('Z', '+00:00'))\n   367→            else:\n   368→                timestamp = screenshot.captured_at\n   369→            \n   370→            # Créer ScreenState complet\n   371→            state = ScreenState(\n   372→                screen_state_id=f\"{session.session_id}_state_{i:04d}\",\n   373→                timestamp=timestamp,\n   374→                session_id=session.session_id,\n   375→                window=window,\n   376→                raw=raw,\n   377→                perception=perception,\n   378→                context=context,\n   379→                metadata={\n   380→                    \"screenshot_id\": screenshot.screenshot_id,\n   381→                    \"event_type\": event.type if event else None,\n   382→                    \"event_time\": event.t if event else None\n   383→                },\n   384→                ui_elements=[]  # Sera rempli par UIDetector si disponible\n   385→            )\n   386→            \n   387→            screen_states.append(state)\n   388→        \n   389→        logger.info(f\"Created {len(screen_states)} enriched screen states\")\n   390→        return screen_states\n   391→    \n   392→    def _compute_embeddings(\n   393→        self, screen_states: List[ScreenState]\n   394→    ) -> List[np.ndarray]:\n   395→        \"\"\"\n   396→        Calculer State Embeddings pour tous les états.\n   397→        \n   398→        Utilise StateEmbeddingBuilder pour générer des embeddings\n   399→        multi-modaux (image + texte + UI). Ajoute optionnellement\n   400→        les embeddings à l'index FAISS.\n   401→        \n   402→        Args:\n   403→            screen_states: Liste de ScreenStates\n   404→        \n   405→        Returns:\n   406→            Liste de vecteurs d'embeddings (numpy arrays)\n   407→        \"\"\"\n   408→        embeddings = []\n   409→        \n   410→        for state in screen_states:\n   411→            # Construire embedding\n   412→            state_embedding = self.embedding_builder.build(state)\n   413→            vector = state_embedding.get_vector()\n   414→            embeddings.append(vector)\n   415→            \n   416→            # Ajouter à FAISS si disponible\n   417→            if self.faiss_manager:\n   418→                self.faiss_manager.add_embedding(\n   419→                    state.screen_state_id,\n   420→                    vector,\n   421→                    {\"state_id\": state.screen_state_id},\n   422→                )\n   423→        \n   424→        return embeddings\n   425→    \n   426→    def _detect_patterns(\n   427→        self,\n   428→        embeddings: List[np.ndarray],\n   429→        screen_states: List[ScreenState],\n   430→    ) -> Dict[int, List[int]]:\n   431→        \"\"\"\n   432→        Détecter patterns répétés via clustering DBSCAN.\n   433→        \n   434→        Algorithme:\n   435→            1. Convertir embeddings en matrice numpy\n   436→            2. Appliquer DBSCAN avec métrique cosinus\n   437→            3. Grouper états par cluster\n   438→            4. Filtrer clusters avec assez de répétitions\n   439→        \n   440→        Args:\n   441→            embeddings: Vecteurs d'embeddings\n   442→            screen_states: ScreenStates correspondants\n   443→        \n   444→        Returns:\n   445→            Dictionnaire {cluster_id: [indices des états]}\n   446→            \n   447→        Note:\n   448→            Les états non assignés (bruit) ont label=-1 et sont ignorés\n   449→        \"\"\"\n   450→        if len(embeddings) < self.min_pattern_repetitions:\n   451→            logger.warning(\n   452→                f\"Not enough states ({len(embeddings)}) for pattern detection \"\n   453→                f\"(minimum: {self.min_pattern_repetitions})\"\n   454→            )\n   455→            return {}\n   456→        \n   457→        # Convertir en matrice numpy\n   458→        X = np.array(embeddings)\n   459→        \n   460→        # Clustering DBSCAN\n   461→        clustering = DBSCAN(\n   462→            eps=self.clustering_eps,\n   463→            min_samples=self.clustering_min_samples,\n   464→            metric=\"cosine\",\n   465→        )\n   466→        labels = clustering.fit_predict(X)\n   467→        \n   468→        # Grouper par cluster\n   469→        clusters = defaultdict(list)\n   470→        noise_count = 0\n   471→        \n   472→        for idx, label in enumerate(labels):\n   473→            if label == -1:\n   474→                noise_count += 1\n   475→            else:\n   476→                clusters[label].append(idx)\n   477→        \n   478→        # Filtrer clusters avec assez de répétitions\n   479→        filtered_clusters = {\n   480→            cluster_id: indices\n   481→            for cluster_id, indices in clusters.items()\n   482→            if len(indices) >= self.min_pattern_repetitions\n   483→        }\n   484→        \n   485→        logger.info(\n   486→            f\"Clustering results: {len(filtered_clusters)} patterns, \"\n   487→            f\"{noise_count} noise points, \"\n   488→            f\"{len(clusters) - len(filtered_clusters)} small clusters filtered\"\n   489→        )\n   490→        \n   491→        return filtered_clusters\n   492→    \n   493→    def _build_nodes(\n   494→        self,\n   495→        clusters: Dict[int, List[int]],\n   496→        screen_states: List[ScreenState],\n   497→        embeddings: List[np.ndarray],\n   498→    ) -> List[WorkflowNode]:\n   499→        \"\"\"\n   500→        Construire WorkflowNodes depuis les clusters détectés.\n   501→        \n   502→        Pour chaque cluster:\n   503→            1. Calculer embedding prototype (moyenne normalisée)\n   504→            2. Extraire contraintes depuis états du cluster\n   505→            3. Créer ScreenTemplate\n   506→            4. Créer WorkflowNode\n   507→        \n   508→        Args:\n   509→            clusters: Clusters détectés {cluster_id: [indices]}\n   510→            screen_states: ScreenStates\n   511→            embeddings: Embeddings\n   512→        \n   513→        Returns:\n   514→            Liste de WorkflowNodes\n   515→        \"\"\"\n   516→        nodes = []\n   517→        \n   518→        for cluster_id, indices in clusters.items():\n   519→            # Calculer embedding prototype (moyenne)\n   520→            cluster_embeddings = [embeddings[i] for i in indices]\n   521→            prototype = np.mean(cluster_embeddings, axis=0)\n   522→            prototype = prototype / np.linalg.norm(prototype)  # Normaliser\n   523→            \n   524→            # Extraire contraintes depuis les états du cluster\n   525→            cluster_states = [screen_states[i] for i in indices]\n   526→            template = self._create_screen_template(cluster_states, prototype)\n   527→            \n   528→            # Créer node\n   529→            node = WorkflowNode(\n   530→                node_id=f\"node_{cluster_id:03d}\",\n   531→                name=f\"State Pattern {cluster_id}\",\n   532→                description=f\"Pattern auto-détecté ({len(indices)} observations)\",\n   533→                template=template,\n   534→                metadata={\n   535→                    \"observation_count\": len(indices),\n   536→                    \"_prototype_vector\": prototype.tolist(),\n   537→                },\n   538→            )\n   539→            \n   540→            nodes.append(node)\n   541→            logger.debug(\n   542→                f\"Created node {node.node_id} with {len(indices)} observations\"\n   543→            )\n   544→        \n   545→        return nodes\n   546→    \n   547→    def _create_screen_template(\n   548→        self,\n   549→        states: List[ScreenState],\n   550→        prototype_embedding: np.ndarray,\n   551→    ) -> ScreenTemplate:\n   552→        \"\"\"\n   553→        Créer un ScreenTemplate depuis un cluster d'états.\n   554→\n   555→        Extrait les contraintes communes à tous les états du cluster :\n   556→            - window_title_pattern : titre de fenêtre commun\n   557→            - required_text_patterns : textes présents dans la majorité des états\n   558→            - required_ui_elements : rôles/types UI récurrents\n   559→\n   560→        Args:\n   561→            states: États du cluster\n   562→            prototype_embedding: Embedding prototype\n   563→\n   564→        Returns:\n   565→            ScreenTemplate avec contraintes extraites\n   566→        \"\"\"\n   567→        # --- Extraction du titre de fenêtre commun ---\n   568→        window_title_pattern = self._extract_window_pattern(states)\n   569→\n   570→        # --- Extraction des textes récurrents ---\n   571→        required_text_patterns = self._extract_common_texts(states)\n   572→\n   573→        # --- Extraction des éléments UI récurrents ---\n   574→        required_ui_elements = self._extract_common_ui_elements(states)\n   575→\n   576→        # Construire les sous-objets de contraintes\n   577→        window_constraint = WindowConstraint(\n   578→            title_pattern=window_title_pattern,\n   579→            title_contains=window_title_pattern,\n   580→        )\n   581→\n   582→        text_constraint = TextConstraint(\n   583→            required_texts=required_text_patterns,\n   584→        )\n   585→\n   586→        ui_roles = [\n   587→            e.get(\"role\", \"\") for e in required_ui_elements if e.get(\"role\")\n   588→        ]\n   589→        ui_constraint = UIConstraint(\n   590→            required_roles=ui_roles,\n   591→        )\n   592→\n   593→        embedding_proto = EmbeddingPrototype(\n   594→            provider=\"openclip_ViT-B-32\",\n   595→            vector_id=\"\",  # Le vecteur est stocké dans node.metadata._prototype_vector\n   596→            min_cosine_similarity=0.85,\n   597→            sample_count=len(states),\n   598→        )\n   599→\n   600→        return ScreenTemplate(\n   601→            window=window_constraint,\n   602→            text=text_constraint,\n   603→            ui=ui_constraint,\n   604→            embedding=embedding_proto,\n   605→        )\n   606→\n   607→    def _extract_window_pattern(self, states: List[ScreenState]) -> Optional[str]:\n   608→        \"\"\"Extraire un pattern de titre de fenêtre commun aux états du cluster.\"\"\"\n   609→        titles = [s.window.window_title for s in states if s.window.window_title]\n   610→        if not titles:\n   611→            return None\n   612→\n   613→        # Si tous les titres sont identiques, retourner directement\n   614→        if len(set(titles)) == 1:\n   615→            return titles[0]\n   616→\n   617→        # Trouver le préfixe commun le plus long\n   618→        prefix = os.path.commonprefix(titles)\n   619→        if len(prefix) >= 5:\n   620→            return prefix.rstrip(\" -–—|\")\n   621→\n   622→        # Fallback: le titre le plus fréquent\n   623→        from collections import Counter\n   624→        most_common = Counter(titles).most_common(1)[0][0]\n   625→        return most_common\n   626→\n   627→    def _extract_common_texts(\n   628→        self, states: List[ScreenState], min_presence_ratio: float = 0.6\n   629→    ) -> List[str]:\n   630→        \"\"\"\n   631→        Extraire les textes présents dans la majorité des états du cluster.\n   632→\n   633→        Args:\n   634→            states: États du cluster\n   635→            min_presence_ratio: Proportion minimale de présence (0.6 = 60% des états)\n   636→        \"\"\"\n   637→        if not states:\n   638→            return []\n   639→\n   640→        # Collecter les textes de chaque état\n   641→        text_counts: Dict[str, int] = defaultdict(int)\n   642→        states_with_text = 0\n   643→\n   644→        for state in states:\n   645→            if hasattr(state.perception, 'detected_text') and state.perception.detected_text:\n   646→                states_with_text += 1\n   647→                seen_in_state = set()\n   648→                for text in state.perception.detected_text:\n   649→                    normalized = text.strip().lower()\n   650→                    if len(normalized) >= 3 and normalized not in seen_in_state:\n   651→                        text_counts[normalized] += 1\n   652→                        seen_in_state.add(normalized)\n   653→\n   654→        if states_with_text == 0:\n   655→            return []\n   656→\n   657→        # Garder les textes présents dans au moins min_presence_ratio des états\n   658→        threshold = max(2, int(states_with_text * min_presence_ratio))\n   659→        common_texts = [\n   660→            text for text, count in text_counts.items()\n   661→            if count >= threshold\n   662→        ]\n   663→\n   664→        # Limiter à 10 textes les plus fréquents\n   665→        common_texts.sort(key=lambda t: text_counts[t], reverse=True)\n   666→        return common_texts[:10]\n   667→\n   668→    def _extract_common_ui_elements(\n   669→        self, states: List[ScreenState], min_presence_ratio: float = 0.5\n   670→    ) -> List[Dict[str, Any]]:\n   671→        \"\"\"\n   672→        Extraire les types/rôles d'éléments UI récurrents dans le cluster.\n   673→\n   674→        Retourne une liste de contraintes UI au format:\n   675→            [{\"type\": \"button\", \"role\": \"validate\", \"min_count\": 1}, ...]\n   676→        \"\"\"\n   677→        if not states:\n   678→            return []\n   679→\n   680→        # Compter les paires (type, role) dans chaque état\n   681→        role_counts: Dict[str, int] = defaultdict(int)\n   682→        type_counts: Dict[str, int] = defaultdict(int)\n   683→        states_with_ui = 0\n   684→\n   685→        for state in states:\n   686→            if state.ui_elements:\n   687→                states_with_ui += 1\n   688→                seen_roles = set()\n   689→                seen_types = set()\n   690→                for el in state.ui_elements:\n   691→                    el_type = getattr(el, 'type', 'unknown')\n   692→                    el_role = getattr(el, 'role', 'unknown')\n   693→\n   694→                    if el_role != 'unknown' and el_role not in seen_roles:\n   695→                        role_counts[el_role] += 1\n   696→                        seen_roles.add(el_role)\n   697→\n   698→                    if el_type != 'unknown' and el_type not in seen_types:\n   699→                        type_counts[el_type] += 1\n   700→                        seen_types.add(el_type)\n   701→\n   702→        if states_with_ui == 0:\n   703→            return []\n   704→\n   705→        threshold = max(2, int(states_with_ui * min_presence_ratio))\n   706→\n   707→        constraints = []\n   708→\n   709→        # Ajouter les rôles récurrents\n   710→        for role, count in role_counts.items():\n   711→            if count >= threshold:\n   712→                constraints.append({\n   713→                    \"role\": role,\n   714→                    \"min_count\": 1,\n   715→                })\n   716→\n   717→        # Limiter à 8 contraintes\n   718→        constraints.sort(key=lambda c: role_counts.get(c.get(\"role\", \"\"), 0), reverse=True)\n   719→        return constraints[:8]\n   720→    \n   721→    def _build_edges(\n   722→        self,\n   723→        nodes: List[WorkflowNode],\n   724→        screen_states: List[ScreenState],\n   725→        session: RawSession,\n   726→    ) -> List[WorkflowEdge]:\n   727→        \"\"\"\n   728→        Construire WorkflowEdges depuis les transitions observées.\n   729→        \n   730→        Algorithme:\n   731→            1. Mapper chaque ScreenState vers son node (via embedding similarity)\n   732→            2. Identifier les transitions (state_i -> state_j où node change)\n   733→            3. Extraire l'action depuis l'événement entre les deux états\n   734→            4. Créer WorkflowEdge avec action et conditions\n   735→        \n   736→        Args:\n   737→            nodes: WorkflowNodes construits\n   738→            screen_states: ScreenStates\n   739→            session: Session brute (pour événements)\n   740→        \n   741→        Returns:\n   742→            Liste de WorkflowEdges\n   743→        \"\"\"\n   744→        if not nodes or len(screen_states) < 2:\n   745→            logger.warning(\"Not enough data to build edges\")\n   746→            return []\n   747→        \n   748→        edges = []\n   749→        edge_counts = defaultdict(int)  # Pour compter les occurrences de chaque transition\n   750→        \n   751→        # Étape 1: Mapper chaque état vers son node\n   752→        state_to_node = self._map_states_to_nodes(screen_states, nodes)\n   753→        \n   754→        # Étape 2: Créer un mapping screenshot_id -> événement\n   755→        screenshot_to_event = {}\n   756→        for event in session.events:\n   757→            if event.screenshot_id:\n   758→                screenshot_to_event[event.screenshot_id] = event\n   759→        \n   760→        # Étape 3: Parcourir les transitions\n   761→        for i in range(len(screen_states) - 1):\n   762→            current_state = screen_states[i]\n   763→            next_state = screen_states[i + 1]\n   764→            \n   765→            current_node_id = state_to_node.get(current_state.screen_state_id)\n   766→            next_node_id = state_to_node.get(next_state.screen_state_id)\n   767→            \n   768→            # Si les deux états sont dans des nodes différents, c'est une transition\n   769→            if current_node_id and next_node_id and current_node_id != next_node_id:\n   770→                # Trouver l'événement qui a causé la transition\n   771→                event = self._find_transition_event(\n   772→                    current_state, next_state, session.events\n   773→                )\n   774→                \n   775→                # Créer l'edge\n   776→                edge_key = f\"{current_node_id}_to_{next_node_id}\"\n   777→                edge_counts[edge_key] += 1\n   778→                \n   779→                # Ne créer l'edge qu'une fois, mais compter les occurrences\n   780→                if edge_counts[edge_key] == 1:\n   781→                    edge = self._create_edge(\n   782→                        current_node_id, next_node_id, event, edge_key\n   783→                    )\n   784→                    edges.append(edge)\n   785→        \n   786→        # Mettre à jour les stats des edges avec les comptages\n   787→        for edge in edges:\n   788→            edge_key = f\"{edge.from_node}_to_{edge.to_node}\"\n   789→            edge.stats.execution_count = edge_counts[edge_key]\n   790→            edge.stats.success_count = edge_counts[edge_key]\n   791→        \n   792→        logger.info(f\"Built {len(edges)} edges from {sum(edge_counts.values())} transitions\")\n   793→        return edges\n   794→    \n   795→    def _map_states_to_nodes(\n   796→        self,\n   797→        screen_states: List[ScreenState],\n   798→        nodes: List[WorkflowNode]\n   799→    ) -> Dict[str, str]:\n   800→        \"\"\"\n   801→        Mapper chaque ScreenState vers le node le plus proche.\n   802→        \n   803→        Utilise la similarité d'embedding pour trouver le meilleur match.\n   804→        \"\"\"\n   805→        state_to_node = {}\n   806→        \n   807→        # Récupérer les embeddings des prototypes de nodes\n   808→        node_prototypes = {}\n   809→        for node in nodes:\n   810→            # Priorité : vecteur en mémoire (metadata), sinon chargement depuis disque\n   811→            proto_list = node.metadata.get(\"_prototype_vector\")\n   812→            if proto_list is not None:\n   813→                node_prototypes[node.node_id] = np.array(proto_list, dtype=np.float32)\n   814→            elif node.template and node.template.embedding and node.template.embedding.vector_id:\n   815→                proto_path = Path(node.template.embedding.vector_id)\n   816→                if proto_path.exists():\n   817→                    node_prototypes[node.node_id] = np.load(proto_path)\n   818→        \n   819→        if not node_prototypes:\n   820→            logger.warning(\"No node prototypes available for mapping\")\n   821→            return state_to_node\n   822→        \n   823→        # Pour chaque état, trouver le node le plus proche\n   824→        for state in screen_states:\n   825→            # Calculer embedding de l'état\n   826→            try:\n   827→                state_embedding = self.embedding_builder.build(state)\n   828→                state_vector = state_embedding.get_vector()\n   829→                \n   830→                # Trouver le node avec la meilleure similarité\n   831→                best_node_id = None\n   832→                best_similarity = -1\n   833→                \n   834→                for node_id, prototype in node_prototypes.items():\n   835→                    similarity = np.dot(state_vector, prototype)\n   836→                    if similarity > best_similarity:\n   837→                        best_similarity = similarity\n   838→                        best_node_id = node_id\n   839→                \n   840→                if best_node_id and best_similarity > 0.7:  # Seuil minimum\n   841→                    state_to_node[state.screen_state_id] = best_node_id\n   842→                    \n   843→            except Exception as e:\n   844→                logger.warning(f\"Failed to map state {state.screen_state_id}: {e}\")\n   845→        \n   846→        return state_to_node\n   847→    \n   848→    def _find_transition_event(\n   849→        self,\n   850→        current_state: ScreenState,\n   851→        next_state: ScreenState,\n   852→        events: List[Event]\n   853→    ) -> Optional[Event]:\n   854→        \"\"\"\n   855→        Trouver l'événement qui a causé la transition entre deux états.\n   856→        \n   857→        Cherche l'événement (clic, frappe) qui s'est produit entre les deux screenshots.\n   858→        \"\"\"\n   859→        current_time = current_state.metadata.get(\"event_time\", 0)\n   860→        next_time = next_state.metadata.get(\"event_time\", float('inf'))\n   861→        \n   862→        # Chercher les événements d'action entre les deux timestamps\n   863→        action_events = []\n   864→        for event in events:\n   865→            if current_time <= event.t < next_time:\n   866→                if event.type in [\"mouse_click\", \"key_press\", \"text_input\"]:\n   867→                    action_events.append(event)\n   868→        \n   869→        # Retourner le dernier événement d'action (celui qui a probablement causé la transition)\n   870→        if action_events:\n   871→            return action_events[-1]\n   872→        \n   873→        return None\n   874→    \n   875→    def _create_edge(\n   876→        self,\n   877→        from_node: str,\n   878→        to_node: str,\n   879→        event: Optional[Event],\n   880→        edge_id: str\n   881→    ) -> WorkflowEdge:\n   882→        \"\"\"\n   883→        Créer un WorkflowEdge depuis une transition observée.\n   884→        \"\"\"\n   885→        # Déterminer le type d'action\n   886→        if event:\n   887→            action_type = event.type\n   888→            action_params = {}\n   889→            \n   890→            if action_type == \"mouse_click\":\n   891→                action_params = {\n   892→                    \"button\": event.data.get(\"button\", \"left\"),\n   893→                    \"position\": event.data.get(\"pos\", [0, 0]),\n   894→                    \"wait_after_ms\": 500\n   895→                }\n   896→                target_role = \"unknown_element\"  # Sera affiné avec détection UI\n   897→                \n   898→            elif action_type == \"key_press\":\n   899→                action_params = {\n   900→                    \"keys\": event.data.get(\"keys\", []),\n   901→                    \"wait_after_ms\": 200\n   902→                }\n   903→                target_role = \"keyboard_input\"\n   904→                \n   905→            elif action_type == \"text_input\":\n   906→                action_params = {\n   907→                    \"text\": event.data.get(\"text\", \"\"),\n   908→                    \"wait_after_ms\": 300\n   909→                }\n   910→                target_role = \"text_field\"\n   911→            else:\n   912→                action_params = {}\n   913→                target_role = \"unknown\"\n   914→        else:\n   915→            action_type = \"unknown\"\n   916→            action_params = {}\n   917→            target_role = \"unknown\"\n   918→        \n   919→        # Créer l'action\n   920→        action = Action(\n   921→            type=action_type,\n   922→            target=TargetSpec(\n   923→                by_role=target_role,\n   924→                selection_policy=\"first\",\n   925→                fallback_strategy=\"visual_similarity\"\n   926→            ),\n   927→            parameters=action_params\n   928→        )\n   929→        \n   930→        # Créer les contraintes\n   931→        constraints = EdgeConstraints(\n   932→            pre_conditions={},\n   933→            required_confidence=0.8,\n   934→            max_wait_time_ms=5000\n   935→        )\n   936→        \n   937→        # Créer les post-conditions\n   938→        post_conditions = PostConditions(\n   939→            expected_node=to_node,\n   940→            window_change_expected=False,\n   941→            new_ui_elements_expected=[],\n   942→            timeout_ms=3000\n   943→        )\n   944→        \n   945→        # Créer l'edge\n   946→        from core.models.workflow_graph import EdgeStats\n   947→        \n   948→        return WorkflowEdge(\n   949→            edge_id=edge_id,\n   950→            from_node=from_node,\n   951→            to_node=to_node,\n   952→            action=action,\n   953→            constraints=constraints,\n   954→            post_conditions=post_conditions,\n   955→            stats=EdgeStats(),\n   956→            metadata={\n   957→                \"created_from_event\": event.type if event else None,\n   958→                \"auto_generated\": True\n   959→            }\n   960→        )\n   961→\n   962→\n   963→def main():\n   964→    \"\"\"Point d'entrée pour tests manuels.\"\"\"\n   965→    logging.basicConfig(\n   966→        level=logging.INFO,\n   967→        format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n   968→    )\n   969→    \n   970→    builder = GraphBuilder(min_pattern_repetitions=3)\n   971→    logger.info(f\"GraphBuilder initialized: {builder}\")\n   972→    logger.info(\"Ready to build workflows from sessions\")\n   973→\n   974→\n   975→if __name__ == \"__main__\":\n   976→    main()\n   977→"}]},"uuid":"9efb7a10-5357-418d-8251-93e4f23d9909","timestamp":"2026-03-14T21:14:20.393Z","sourceToolAssistantUUID":"44291c11-e293-4eb3-8257-9ab8a6b59276"}
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+{"parentUuid":"e2f56cab-9423-4d72-9dc4-ad0d440c4278","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_013QiUXY1fNBCUn45oLh4J12","toolUseID":"toolu_013QiUXY1fNBCUn45oLh4J12","timestamp":"2026-03-14T21:14:23.766Z","uuid":"51ae516d-6b5b-4178-941a-a0dc9e48d6b9"}
+{"parentUuid":"e2f56cab-9423-4d72-9dc4-ad0d440c4278","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013QiUXY1fNBCUn45oLh4J12","type":"tool_result","content":"<persisted-output>\nOutput too large (61.9KB). Full output saved to: /home/dom/.claude/projects/-home-dom-ai-rpa-vision-v3/eec8c668-e1f8-4599-9dd5-42170a166ba4/tool-results/toolu_013QiUXY1fNBCUn45oLh4J12.txt\n\nPreview (first 2KB):\n     1→#!/usr/bin/env python3\n     2→\"\"\"\n     3→RPA Vision V3 - Agent Chat\n     4→\n     5→Interface conversationnelle pour communiquer avec le système RPA.\n     6→Style \"Spotlight/Alfred\" - minimaliste et efficace.\n     7→\n     8→Composants intégrés:\n     9→- IntentParser: Compréhension des intentions utilisateur\n    10→- ConfirmationLoop: Validation avant actions critiques\n    11→- ResponseGenerator: Réponses en langage naturel\n    12→- ConversationManager: Contexte multi-tour\n    13→\n    14→Usage:\n    15→    python agent_chat/app.py\n    16→\n    17→Puis ouvrir: http://localhost:5004\n    18→\n    19→Auteur: Dom - Janvier 2026\n    20→\"\"\"\n    21→\n    22→import asyncio\n    23→import json\n    24→import logging\n    25→import os\n    26→import sys\n    27→from pathlib import Path\n    28→from datetime import datetime\n    29→from typing import Dict, Any, List, Optional\n    30→\n    31→import requests as http_requests  # Pour les appels au streaming server\n    32→\n    33→from flask import Flask, render_template, request, jsonify\n    34→from flask_socketio import SocketIO, emit\n    35→\n    36→# Add project root to path\n    37→sys.path.insert(0, str(Path(__file__).parent.parent))\n    38→\n    39→from core.workflow import SemanticMatcher, VariableManager\n    40→\n    41→# Import des composants conversationnels\n    42→from .intent_parser import IntentParser, IntentType, get_intent_parser\n    43→from .confirmation import ConfirmationLoop, ConfirmationStatus, RiskLevel, get_confirmation_loop\n    44→from .response_generator import ResponseGenerator, get_response_generator\n    45→from .conversation_manager import ConversationManager, get_conversation_manager\n    46→from .autonomous_planner import AutonomousPlanner, get_autonomous_planner, ExecutionPlan\n    47→\n    48→# GPU Resource Manager (optional)\n    49→try:\n    50→    from core.gpu import get_gpu_resource_manager, ExecutionMode\n    51→    GPU_AVAILABLE = True\n    52→except ImportError:\n    53→    GPU_AVAILABLE = False\n    54→\n...\n</persisted-output>"}]},"uuid":"c16ab019-9353-48bf-9486-83378ee2b611","timestamp":"2026-03-14T21:14:23.766Z","sourceToolAssistantUUID":"e2f56cab-9423-4d72-9dc4-ad0d440c4278"}
+{"parentUuid":"c16ab019-9353-48bf-9486-83378ee2b611","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","message":{"model":"claude-opus-4-6","id":"msg_01E4BR695FSRsgCqtQGDUC31","type":"message","role":"assistant","content":[{"type":"text","text":"Let me continue exploring the key files."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":13624,"cache_read_input_tokens":42317,"cache_creation":{"ephemeral_5m_input_tokens":13624,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZ3fUZQ8qJncoiWbXi5B2","type":"assistant","uuid":"e046efef-f76f-4804-8108-ed5a384d1316","timestamp":"2026-03-14T21:14:26.901Z"}
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+{"parentUuid":"b4be59d4-ae9b-40c9-a94d-9501793254ca","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UZvAHPPoSdzKG2ZSU9kZsR","type":"tool_result","content":"     1→     1→#!/usr/bin/env python3\n     2→     2→\"\"\"\n     3→     3→RPA Vision V3 - Agent Chat\n     4→     4→\n     5→     5→Interface conversationnelle pour communiquer avec le système RPA.\n     6→     6→Style \"Spotlight/Alfred\" - minimaliste et efficace.\n     7→     7→\n     8→     8→Composants intégrés:\n     9→     9→- IntentParser: Compréhension des intentions utilisateur\n    10→    10→- ConfirmationLoop: Validation avant actions critiques\n    11→    11→- ResponseGenerator: Réponses en langage naturel\n    12→    12→- ConversationManager: Contexte multi-tour\n    13→    13→\n    14→    14→Usage:\n    15→    15→    python agent_chat/app.py\n    16→    16→\n    17→    17→Puis ouvrir: http://localhost:5004\n    18→    18→\n    19→    19→Auteur: Dom - Janvier 2026\n    20→    20→\"\"\"\n    21→    21→\n    22→    22→import asyncio\n    23→    23→import json\n    24→    24→import logging\n    25→    25→import os\n    26→    26→import sys\n    27→    27→from pathlib import Path\n    28→    28→from datetime import datetime\n    29→    29→from typing import Dict, Any, List, Optional\n    30→    30→\n    31→    31→import requests as http_requests  # Pour les appels au streaming server\n    32→    32→\n    33→    33→from flask import Flask, render_template, request, jsonify\n    34→    34→from flask_socketio import SocketIO, emit\n    35→    35→\n    36→    36→# Add project root to path\n    37→    37→sys.path.insert(0, str(Path(__file__).parent.parent))\n    38→    38→\n    39→    39→from core.workflow import SemanticMatcher, VariableManager\n    40→    40→\n    41→    41→# Import des composants conversationnels\n    42→    42→from .intent_parser import IntentParser, IntentType, get_intent_parser\n    43→    43→from .confirmation import ConfirmationLoop, ConfirmationStatus, RiskLevel, get_confirmation_loop\n    44→    44→from .response_generator import ResponseGenerator, get_response_generator\n    45→    45→from .conversation_manager import ConversationManager, get_conversation_manager\n    46→    46→from .autonomous_planner import AutonomousPlanner, get_autonomous_planner, ExecutionPlan\n    47→    47→\n    48→    48→# GPU Resource Manager (optional)\n    49→    49→try:\n    50→    50→    from core.gpu import get_gpu_resource_manager, ExecutionMode\n    51→    51→    GPU_AVAILABLE = True\n    52→    52→except ImportError:\n    53→    53→    GPU_AVAILABLE = False\n    54→    54→\n    55→    55→# Execution components (optional - pour exécution réelle)\n    56→    56→try:\n    57→    57→    from core.execution import ActionExecutor, TargetResolver, ErrorHandler\n    58→    58→    from core.execution.execution_loop import ExecutionLoop, ExecutionMode as ExecMode, ExecutionState\n    59→    59→    from core.pipeline.workflow_pipeline import WorkflowPipeline\n    60→    60→    from core.capture import ScreenCapturer\n    61→    61→    EXECUTION_AVAILABLE = True\n    62→    62→except ImportError as e:\n    63→    63→    logger.warning(f\"Composants d'exécution non disponibles: {e}\")\n    64→    64→    EXECUTION_AVAILABLE = False\n    65→    65→\n    66→    66→logging.basicConfig(level=logging.INFO)\n    67→    67→logger = logging.getLogger(__name__)\n    68→    68→\n    69→    69→app = Flask(__name__)\n    70→    70→app.config['SECRET_KEY'] = 'rpa-vision-v3-secret'\n    71→    71→socketio = SocketIO(app, cors_allowed_origins=\"*\")\n    72→    72→\n    73→    73→# Global state\n    74→    74→matcher: Optional[SemanticMatcher] = None\n    75→    75→gpu_manager = None\n    76→    76→intent_parser: Optional[IntentParser] = None\n    77→    77→confirmation_loop: Optional[ConfirmationLoop] = None\n    78→    78→response_generator: Optional[ResponseGenerator] = None\n    79→    79→conversation_manager: Optional[ConversationManager] = None\n    80→    80→autonomous_planner: Optional[AutonomousPlanner] = None\n    81→    81→\n    82→    82→# Execution components\n    83→    83→workflow_pipeline = None\n    84→    84→action_executor = None\n    85→    85→execution_loop = None\n    86→    86→screen_capturer = None\n    87→    87→\n    88→    88→# URL du streaming server (Agent V1) pour l'exécution distante\n    89→    89→STREAMING_SERVER_URL = os.environ.get(\n    90→    90→    \"RPA_STREAMING_URL\", \"http://localhost:5005\"\n    91→    91→)\n    92→    92→\n    93→    93→execution_status = {\n    94→    94→    \"running\": False,\n    95→    95→    \"workflow\": None,\n    96→    96→    \"progress\": 0,\n    97→    97→    \"message\": \"\",\n    98→    98→    \"can_minimize\": True\n    99→    99→}\n   100→   100→command_history: List[Dict[str, Any]] = []\n   101→   101→\n   102→   102→\n   103→   103→def init_system():\n   104→   104→    \"\"\"Initialiser tous les composants du système.\"\"\"\n   105→   105→    global matcher, gpu_manager\n   106→   106→    global intent_parser, confirmation_loop, response_generator, conversation_manager\n   107→   107→    global autonomous_planner\n   108→   108→\n   109→   109→    # 1. SemanticMatcher — multi-répertoires (P0-6) + matching LLM (P0-7)\n   110→   110→    # Scan data/workflows/ + data/training/workflows/ + data/training/live_sessions/workflows/\n   111→   111→    try:\n   112→   112→        matcher = SemanticMatcher(\n   113→   113→            workflows_dir=None,  # None = scan tous les répertoires par défaut\n   114→   114→            use_llm=True,        # Matching sémantique via Ollama (P0-7)\n   115→   115→            llm_model=\"qwen2.5:7b\",\n   116→   116→        )\n   117→   117→        dirs_info = matcher.get_directories()\n   118→   118→        dirs_summary = \", \".join(\n   119→   119→            f\"{d['path']}({d['workflow_count']})\" for d in dirs_info if d['exists']\n   120→   120→        )\n   121→   121→        logger.info(\n   122→   122→            f\"✓ SemanticMatcher: {len(matcher.get_all_workflows())} workflows \"\n   123→   123→            f\"[{dirs_summary}]\"\n   124→   124→        )\n   125→   125→    except Exception as e:\n   126→   126→        logger.error(f\"✗ SemanticMatcher: {e}\")\n   127→   127→        matcher = None\n   128→   128→\n   129→   129→    # 2. GPU Resource Manager\n   130→   130→    if GPU_AVAILABLE:\n   131→   131→        try:\n   132→   132→            gpu_manager = get_gpu_resource_manager()\n   133→   133→            logger.info(\"✓ GPU Resource Manager connected\")\n   134→   134→        except Exception as e:\n   135→   135→            logger.warning(f\"⚠ GPU Resource Manager: {e}\")\n   136→   136→            gpu_manager = None\n   137→   137→\n   138→   138→    # 3. Composants conversationnels\n   139→   139→    try:\n   140→   140→        intent_parser = get_intent_parser(use_llm=True)  # LLM activé (Ollama)\n   141→   141→        confirmation_loop = get_confirmation_loop()\n   142→   142→        response_generator = get_response_generator()\n   143→   143→        conversation_manager = get_conversation_manager()\n   144→   144→\n   145→   145→        # Injecter les workflows dans l'intent_parser pour contexte LLM\n   146→   146→        if matcher and intent_parser:\n   147→   147→            workflows_for_llm = [\n   148→   148→                {\"name\": wf.name, \"description\": wf.description, \"tags\": wf.tags}\n   149→   149→                for wf in matcher.get_all_workflows()\n   150→   150→            ]\n   151→   151→            intent_parser.set_workflows(workflows_for_llm)\n   152→   152→\n   153→   153→        logger.info(\"✓ Composants conversationnels initialisés (LLM activé)\")\n   154→   154→    except Exception as e:\n   155→   155→        logger.error(f\"✗ Composants conversationnels: {e}\")\n   156→   156→        # Fallback aux composants de base\n   157→   157→        intent_parser = IntentParser(use_llm=False)\n   158→   158→        confirmation_loop = ConfirmationLoop()\n   159→   159→        response_generator = ResponseGenerator()\n   160→   160→        conversation_manager = ConversationManager()\n   161→   161→\n   162→   162→    # 4. Composants d'exécution réelle\n   163→   163→    global workflow_pipeline, action_executor, execution_loop, screen_capturer\n   164→   164→    if EXECUTION_AVAILABLE:\n   165→   165→        try:\n   166→   166→            # Pipeline de workflow (matching + actions)\n   167→   167→            workflow_pipeline = WorkflowPipeline()\n   168→   168→            logger.info(\"✓ WorkflowPipeline initialisé\")\n   169→   169→\n   170→   170→            # Capture d'écran\n   171→   171→            screen_capturer = ScreenCapturer()\n   172→   172→            logger.info(\"✓ ScreenCapturer initialisé\")\n   173→   173→\n   174→   174→            # Résolveur de cibles et gestionnaire d'erreurs\n   175→   175→            target_resolver = TargetResolver()\n   176→   176→            error_handler = ErrorHandler()\n   177→   177→\n   178→   178→            # Exécuteur d'actions\n   179→   179→            action_executor = ActionExecutor(\n   180→   180→                target_resolver=target_resolver,\n   181→   181→                error_handler=error_handler,\n   182→   182→                verify_postconditions=True\n   183→   183→            )\n   184→   184→            logger.info(\"✓ ActionExecutor initialisé\")\n   185→   185→\n   186→   186→            # Boucle d'exécution (pour mode automatique)\n   187→   187→            execution_loop = ExecutionLoop(\n   188→   188→                pipeline=workflow_pipeline,\n   189→   189→                action_executor=action_executor,\n   190→   190→                screen_capturer=screen_capturer\n   191→   191→            )\n   192→   192→            logger.info(\"✓ ExecutionLoop initialisé\")\n   193→   193→\n   194→   194→        except Exception as e:\n   195→   195→            logger.warning(f\"⚠ Composants d'exécution partiels: {e}\")\n   196→   196→            # Mode dégradé: simulation uniquement\n   197→   197→            workflow_pipeline = None\n   198→   198→            action_executor = None\n   199→   199→            execution_loop = None\n   200→   200→    else:"}]},"uuid":"4d23fdf7-7fac-49e8-bf9b-bc55d6ce7c68","timestamp":"2026-03-14T21:14:28.330Z","sourceToolAssistantUUID":"b4be59d4-ae9b-40c9-a94d-9501793254ca"}
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+{"parentUuid":"86b034b8-df96-4ad9-8577-80771fab7eee","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_01X8WDVqsezUXH1vkcskBwyT","toolUseID":"toolu_01X8WDVqsezUXH1vkcskBwyT","timestamp":"2026-03-14T21:14:28.919Z","uuid":"33b89291-7cc2-4657-aa5a-da22d51764bc"}
+{"parentUuid":"86b034b8-df96-4ad9-8577-80771fab7eee","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01X8WDVqsezUXH1vkcskBwyT","type":"tool_result","content":"<persisted-output>\nOutput too large (51KB). Full output saved to: /home/dom/.claude/projects/-home-dom-ai-rpa-vision-v3/eec8c668-e1f8-4599-9dd5-42170a166ba4/tool-results/toolu_01X8WDVqsezUXH1vkcskBwyT.txt\n\nPreview (first 2KB):\n     1→\"\"\"\n     2→Workflow Graph - Couche 4 : Graphes de Workflows Sémantiques\n     3→\n     4→Représente des workflows appris comme des graphes de transitions entre états d'écran.\n     5→Chaque node est un template d'écran, chaque edge est une action sémantique.\n     6→\n     7→Auteur : Dom, Alice Kiro\n     8→Date : 15 décembre 2024\n     9→\"\"\"\n    10→\n    11→from dataclasses import dataclass, field\n    12→from typing import Dict, List, Optional, Any, Tuple\n    13→from datetime import datetime\n    14→from pathlib import Path\n    15→import json\n    16→from enum import Enum\n    17→\n    18→\n    19→# ============================================================================\n    20→# Enums et Types\n    21→# ============================================================================\n    22→\n    23→class LearningState(str, Enum):\n    24→    \"\"\"États d'apprentissage d'un workflow\"\"\"\n    25→    OBSERVATION = \"OBSERVATION\"  # Observation passive\n    26→    COACHING = \"COACHING\"  # Suggestions avec confirmation\n    27→    AUTO_CANDIDATE = \"AUTO_CANDIDATE\"  # Exécution auto en test\n    28→    AUTO_CONFIRMED = \"AUTO_CONFIRMED\"  # Exécution auto validée\n    29→\n    30→\n    31→class ActionType(str, Enum):\n    32→    \"\"\"Types d'actions possibles\"\"\"\n    33→    MOUSE_CLICK = \"mouse_click\"\n    34→    TEXT_INPUT = \"text_input\"\n    35→    KEY_PRESS = \"key_press\"\n    36→    COMPOUND = \"compound\"  # Séquence d'actions\n    37→\n    38→\n    39→class SelectionPolicy(str, Enum):\n    40→    \"\"\"Politiques de sélection d'éléments UI\"\"\"\n    41→    FIRST = \"first\"\n    42→    LAST = \"last\"\n    43→    BY_SIMILARITY = \"by_similarity\"\n    44→    BY_POSITION = \"by_position\"\n    45→\n    46→\n    47→# ============================================================================\n    48→# Classes de Support\n    49→# ============================================================================\n    50→\n    51→@dataclass\n    52→class WindowConstraint:\n    53→    \"\"\"Contrainte sur la fenêtre active\"\"\"\n...\n</persisted-output>"}]},"uuid":"ee1a0f2b-bf62-489f-ad20-df7e84a3ce4b","timestamp":"2026-03-14T21:14:28.918Z","sourceToolAssistantUUID":"86b034b8-df96-4ad9-8577-80771fab7eee"}
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+{"parentUuid":"f8385088-af9a-4979-8239-d835ae08618b","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BY4fJMFkEofT5DjaGkduho","type":"tool_result","content":"     1→\"\"\"\n     2→ScreenAnalyzer - Construction complète d'un ScreenState depuis un screenshot\n     3→\n     4→Orchestre les 4 niveaux du ScreenState :\n     5→  Niveau 1 (Raw)       : métadonnées de l'image\n     6→  Niveau 2 (Perception): OCR + embedding global\n     7→  Niveau 3 (UI)        : détection d'éléments UI\n     8→  Niveau 4 (Contexte)  : fenêtre active, workflow en cours\n     9→\n    10→Ce module comble le chaînon manquant entre la capture brute (Couche 0)\n    11→et la construction d'embeddings (Couche 3).\n    12→\"\"\"\n    13→\n    14→import logging\n    15→import os\n    16→from datetime import datetime\n    17→from pathlib import Path\n    18→from typing import Optional, Dict, Any, List\n    19→\n    20→from PIL import Image\n    21→\n    22→from core.models.screen_state import (\n    23→    ScreenState,\n    24→    RawLevel,\n    25→    PerceptionLevel,\n    26→    ContextLevel,\n    27→    WindowContext,\n    28→    EmbeddingRef,\n    29→)\n    30→from core.models.ui_element import UIElement\n    31→\n    32→logger = logging.getLogger(__name__)\n    33→\n    34→\n    35→class ScreenAnalyzer:\n    36→    \"\"\"\n    37→    Construit un ScreenState complet (4 niveaux) depuis un screenshot.\n    38→\n    39→    Utilise le UIDetector pour la détection d'éléments et un OCR\n    40→    (docTR ou Tesseract) pour l'extraction de texte.\n    41→\n    42→    Example:\n    43→        >>> analyzer = ScreenAnalyzer()\n    44→        >>> state = analyzer.analyze(\"/path/to/screenshot.png\")\n    45→        >>> print(state.perception.detected_text)\n    46→        >>> print(len(state.ui_elements))\n    47→    \"\"\"\n    48→\n    49→    def __init__(\n    50→        self,\n    51→        ui_detector=None,\n    52→        ocr_engine: Optional[str] = None,\n    53→        session_id: str = \"\",\n    54→    ):\n    55→        \"\"\"\n    56→        Args:\n    57→            ui_detector: Instance de UIDetector (créé si None)\n    58→            ocr_engine: Moteur OCR à utiliser (\"doctr\", \"tesseract\", None=auto)\n    59→            session_id: ID de la session en cours\n    60→        \"\"\"\n    61→        self._ui_detector = ui_detector\n    62→        self._ocr_engine_name = ocr_engine\n    63→        self._ocr = None\n    64→        self.session_id = session_id\n    65→        self._state_counter = 0\n    66→\n    67→        # Initialisation lazy pour éviter les imports lourds au démarrage\n    68→        self._ui_detector_initialized = ui_detector is not None\n    69→        self._ocr_initialized = False\n    70→\n    71→    # =========================================================================\n    72→    # API publique\n    73→    # =========================================================================\n    74→\n    75→    def analyze(\n    76→        self,\n    77→        screenshot_path: str,\n    78→        window_info: Optional[Dict[str, Any]] = None,\n    79→        context: Optional[Dict[str, Any]] = None,\n    80→    ) -> ScreenState:\n    81→        \"\"\"\n    82→        Analyser un screenshot et construire un ScreenState complet.\n    83→\n    84→        Args:\n    85→            screenshot_path: Chemin vers le fichier image\n    86→            window_info: Infos fenêtre active {\"title\": ..., \"app_name\": ...}\n    87→            context: Contexte métier optionnel\n    88→\n    89→        Returns:\n    90→            ScreenState avec les 4 niveaux remplis\n    91→        \"\"\"\n    92→        screenshot_path = str(screenshot_path)\n    93→        self._state_counter += 1\n    94→\n    95→        state_id = f\"{self.session_id}_state_{self._state_counter:04d}\" if self.session_id else f\"state_{self._state_counter:04d}\"\n    96→\n    97→        # Niveau 1 : Raw\n    98→        raw = self._build_raw_level(screenshot_path)\n    99→\n   100→        # Niveau 2 : Perception (OCR)\n   101→        detected_text = self._extract_text(screenshot_path)\n   102→        perception = PerceptionLevel(\n   103→            embedding=EmbeddingRef(\n   104→                provider=\"openclip_ViT-B-32\",\n   105→                vector_id=f\"data/embeddings/screens/{state_id}.npy\",\n   106→                dimensions=512,\n   107→            ),\n   108→            detected_text=detected_text,\n   109→            text_detection_method=self._get_ocr_method_name(),\n   110→            confidence_avg=0.85 if detected_text else 0.0,\n   111→        )\n   112→\n   113→        # Niveau 3 : UI Elements\n   114→        ui_elements = self._detect_ui_elements(screenshot_path, window_info)\n   115→\n   116→        # Niveau 4 : Contexte\n   117→        window_ctx = self._build_window_context(window_info)\n   118→        context_level = self._build_context_level(context)\n   119→\n   120→        state = ScreenState(\n   121→            screen_state_id=state_id,\n   122→            timestamp=datetime.now(),\n   123→            session_id=self.session_id,\n   124→            window=window_ctx,\n   125→            raw=raw,\n   126→            perception=perception,\n   127→            context=context_level,\n   128→            metadata={\n   129→                \"analyzer_version\": \"1.0\",\n   130→                \"ui_elements_count\": len(ui_elements),\n   131→                \"text_regions_count\": len(detected_text),\n   132→            },\n   133→            ui_elements=ui_elements,\n   134→        )\n   135→\n   136→        logger.info(\n   137→            f\"ScreenState {state_id} construit: \"\n   138→            f\"{len(ui_elements)} éléments UI, {len(detected_text)} textes détectés\"\n   139→        )\n   140→        return state\n   141→\n   142→    def analyze_image(\n   143→        self,\n   144→        image: Image.Image,\n   145→        save_dir: str = \"data/screens\",\n   146→        window_info: Optional[Dict[str, Any]] = None,\n   147→        context: Optional[Dict[str, Any]] = None,\n   148→    ) -> ScreenState:\n   149→        \"\"\"\n   150→        Analyser une PIL Image (utile quand on a déjà l'image en mémoire).\n   151→\n   152→        Sauvegarde l'image sur disque puis appelle analyze().\n   153→        \"\"\"\n   154→        save_path = Path(save_dir)\n   155→        save_path.mkdir(parents=True, exist_ok=True)\n   156→\n   157→        timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S_%f\")\n   158→        filename = f\"screen_{timestamp}.png\"\n   159→        filepath = save_path / filename\n   160→\n   161→        image.save(str(filepath))\n   162→        return self.analyze(str(filepath), window_info=window_info, context=context)\n   163→\n   164→    # =========================================================================\n   165→    # Niveau 1 : Raw\n   166→    # =========================================================================\n   167→\n   168→    def _build_raw_level(self, screenshot_path: str) -> RawLevel:\n   169→        file_size = 0\n   170→        try:\n   171→            file_size = os.path.getsize(screenshot_path)\n   172→        except OSError:\n   173→            pass\n   174→\n   175→        return RawLevel(\n   176→            screenshot_path=screenshot_path,\n   177→            capture_method=\"mss\",\n   178→            file_size_bytes=file_size,\n   179→        )\n   180→\n   181→    # =========================================================================\n   182→    # Niveau 2 : Perception — OCR\n   183→    # =========================================================================\n   184→\n   185→    def _extract_text(self, screenshot_path: str) -> List[str]:\n   186→        \"\"\"Extraire le texte d'un screenshot via OCR.\"\"\"\n   187→        self._ensure_ocr()\n   188→\n   189→        if self._ocr is None:\n   190→            return []\n   191→\n   192→        try:\n   193→            return self._ocr(screenshot_path)\n   194→        except Exception as e:\n   195→            logger.warning(f\"OCR échoué: {e}\")\n   196→            return []\n   197→\n   198→    def _ensure_ocr(self) -> None:\n   199→        \"\"\"Initialiser le moteur OCR (lazy).\"\"\"\n   200→        if self._ocr_initialized:\n   201→            return\n   202→        self._ocr_initialized = True\n   203→\n   204→        engine = self._ocr_engine_name\n   205→\n   206→        # Auto-détection : essayer docTR puis Tesseract\n   207→        if engine is None or engine == \"doctr\":\n   208→            try:\n   209→                self._ocr = self._create_doctr_ocr()\n   210→                logger.info(\"OCR initialisé avec docTR\")\n   211→                return\n   212→            except Exception as e:\n   213→                if engine == \"doctr\":\n   214→                    logger.warning(f\"docTR non disponible: {e}\")\n   215→                    return\n   216→\n   217→        if engine is None or engine == \"tesseract\":\n   218→            try:\n   219→                self._ocr = self._create_tesseract_ocr()\n   220→                logger.info(\"OCR initialisé avec Tesseract\")\n   221→                return\n   222→            except Exception as e:\n   223→                logger.warning(f\"Tesseract non disponible: {e}\")\n   224→\n   225→        logger.warning(\"Aucun moteur OCR disponible — detected_text sera vide\")\n   226→\n   227→    def _create_doctr_ocr(self):\n   228→        \"\"\"Créer une fonction OCR basée sur docTR.\"\"\"\n   229→        from doctr.io import DocumentFile\n   230→        from doctr.models import ocr_predictor\n   231→\n   232→        predictor = ocr_predictor(det_arch=\"db_resnet50\", reco_arch=\"crnn_vgg16_bn\", pretrained=True)\n   233→\n   234→        def ocr_func(image_path: str) -> List[str]:\n   235→            doc = DocumentFile.from_images(image_path)\n   236→            result = predictor(doc)\n   237→            texts = []\n   238→            for page in result.pages:\n   239→                for block in page.blocks:\n   240→                    for line in block.lines:\n   241→                        line_text = \" \".join(word.value for word in line.words)\n   242→                        if line_text.strip():\n   243→                            texts.append(line_text.strip())\n   244→            return texts\n   245→\n   246→        return ocr_func\n   247→\n   248→    def _create_tesseract_ocr(self):\n   249→        \"\"\"Créer une fonction OCR basée sur Tesseract.\"\"\"\n   250→        import pytesseract\n   251→\n   252→        def ocr_func(image_path: str) -> List[str]:\n   253→            img = Image.open(image_path)\n   254→            raw_text = pytesseract.image_to_string(img, lang=\"fra+eng\")\n   255→            lines = [line.strip() for line in raw_text.split(\"\\n\") if line.strip()]\n   256→            return lines\n   257→\n   258→        return ocr_func\n   259→\n   260→    def _get_ocr_method_name(self) -> str:\n   261→        if self._ocr is None:\n   262→            return \"none\"\n   263→        if self._ocr_engine_name:\n   264→            return self._ocr_engine_name\n   265→        return \"doctr\"\n   266→\n   267→    # =========================================================================\n   268→    # Niveau 3 : UI Elements\n   269→    # =========================================================================\n   270→\n   271→    def _detect_ui_elements(\n   272→        self,\n   273→        screenshot_path: str,\n   274→        window_info: Optional[Dict[str, Any]] = None,\n   275→    ) -> List[UIElement]:\n   276→        \"\"\"Détecter les éléments UI dans le screenshot.\"\"\"\n   277→        self._ensure_ui_detector()\n   278→\n   279→        if self._ui_detector is None:\n   280→            return []\n   281→\n   282→        try:\n   283→            elements = self._ui_detector.detect(\n   284→                screenshot_path, window_context=window_info\n   285→            )\n   286→            return elements\n   287→        except Exception as e:\n   288→            logger.warning(f\"Détection UI échouée: {e}\")\n   289→            return []\n   290→\n   291→    def _ensure_ui_detector(self) -> None:\n   292→        \"\"\"Initialiser le UIDetector (lazy).\"\"\"\n   293→        if self._ui_detector_initialized:\n   294→            return\n   295→        self._ui_detector_initialized = True\n   296→\n   297→        try:\n   298→            from core.detection.ui_detector import UIDetector, DetectionConfig\n   299→\n   300→            config = DetectionConfig(\n   301→                use_owl_detection=False,  # Désactiver OWL par défaut (lourd)\n   302→                use_vlm_classification=True,\n   303→                confidence_threshold=0.6,\n   304→            )\n   305→            self._ui_detector = UIDetector(config)\n   306→            logger.info(\"UIDetector initialisé\")\n   307→        except Exception as e:\n   308→            logger.warning(f\"UIDetector non disponible: {e}\")\n   309→            self._ui_detector = None\n   310→\n   311→    # =========================================================================\n   312→    # Niveau 4 : Contexte\n   313→    # =========================================================================\n   314→\n   315→    def _build_window_context(\n   316→        self, window_info: Optional[Dict[str, Any]] = None\n   317→    ) -> WindowContext:\n   318→        if window_info:\n   319→            return WindowContext(\n   320→                app_name=window_info.get(\"app_name\", \"unknown\"),\n   321→                window_title=window_info.get(\"title\", \"Unknown\"),\n   322→                screen_resolution=window_info.get(\"screen_resolution\", [1920, 1080]),\n   323→                workspace=window_info.get(\"workspace\", \"main\"),\n   324→            )\n   325→        return WindowContext(\n   326→            app_name=\"unknown\",\n   327→            window_title=\"Unknown\",\n   328→            screen_resolution=[1920, 1080],\n   329→            workspace=\"main\",\n   330→        )\n   331→\n   332→    def _build_context_level(\n   333→        self, context: Optional[Dict[str, Any]] = None\n   334→    ) -> ContextLevel:\n   335→        if context:\n   336→            return ContextLevel(\n   337→                current_workflow_candidate=context.get(\"workflow_candidate\"),\n   338→                workflow_step=context.get(\"workflow_step\"),\n   339→                user_id=context.get(\"user_id\", \"\"),\n   340→                tags=context.get(\"tags\", []),\n   341→                business_variables=context.get(\"business_variables\", {}),\n   342→            )\n   343→        return ContextLevel()\n   344→"}]},"uuid":"42e8800d-8a2a-47ad-bafd-8f5638a40ea6","timestamp":"2026-03-14T21:14:29.235Z","sourceToolAssistantUUID":"f8385088-af9a-4979-8239-d835ae08618b"}
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+{"parentUuid":"43d78478-d3c5-426f-adfc-162c756b10f1","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01AwBh7WHUpEG7p7mcNnXamQ","type":"tool_result","content":"     1→     1→\"\"\"\n     2→     2→Workflow Graph - Couche 4 : Graphes de Workflows Sémantiques\n     3→     3→\n     4→     4→Représente des workflows appris comme des graphes de transitions entre états d'écran.\n     5→     5→Chaque node est un template d'écran, chaque edge est une action sémantique.\n     6→     6→\n     7→     7→Auteur : Dom, Alice Kiro\n     8→     8→Date : 15 décembre 2024\n     9→     9→\"\"\"\n    10→    10→\n    11→    11→from dataclasses import dataclass, field\n    12→    12→from typing import Dict, List, Optional, Any, Tuple\n    13→    13→from datetime import datetime\n    14→    14→from pathlib import Path\n    15→    15→import json\n    16→    16→from enum import Enum\n    17→    17→\n    18→    18→\n    19→    19→# ============================================================================\n    20→    20→# Enums et Types\n    21→    21→# ============================================================================\n    22→    22→\n    23→    23→class LearningState(str, Enum):\n    24→    24→    \"\"\"États d'apprentissage d'un workflow\"\"\"\n    25→    25→    OBSERVATION = \"OBSERVATION\"  # Observation passive\n    26→    26→    COACHING = \"COACHING\"  # Suggestions avec confirmation\n    27→    27→    AUTO_CANDIDATE = \"AUTO_CANDIDATE\"  # Exécution auto en test\n    28→    28→    AUTO_CONFIRMED = \"AUTO_CONFIRMED\"  # Exécution auto validée\n    29→    29→\n    30→    30→\n    31→    31→class ActionType(str, Enum):\n    32→    32→    \"\"\"Types d'actions possibles\"\"\"\n    33→    33→    MOUSE_CLICK = \"mouse_click\"\n    34→    34→    TEXT_INPUT = \"text_input\"\n    35→    35→    KEY_PRESS = \"key_press\"\n    36→    36→    COMPOUND = \"compound\"  # Séquence d'actions\n    37→    37→\n    38→    38→\n    39→    39→class SelectionPolicy(str, Enum):\n    40→    40→    \"\"\"Politiques de sélection d'éléments UI\"\"\"\n    41→    41→    FIRST = \"first\"\n    42→    42→    LAST = \"last\"\n    43→    43→    BY_SIMILARITY = \"by_similarity\"\n    44→    44→    BY_POSITION = \"by_position\"\n    45→    45→\n    46→    46→\n    47→    47→# ============================================================================\n    48→    48→# Classes de Support\n    49→    49→# ============================================================================\n    50→    50→\n    51→    51→@dataclass\n    52→    52→class WindowConstraint:\n    53→    53→    \"\"\"Contrainte sur la fenêtre active\"\"\"\n    54→    54→    title_pattern: Optional[str] = None\n    55→    55→    title_contains: Optional[str] = None\n    56→    56→    process_name: Optional[str] = None\n    57→    57→    \n    58→    58→    def matches(self, window_title: str, process: str) -> bool:\n    59→    59→        \"\"\"Vérifier si la fenêtre correspond aux contraintes\"\"\"\n    60→    60→        if self.title_contains and self.title_contains not in window_title:\n    61→    61→            return False\n    62→    62→        if self.process_name and self.process_name != process:\n    63→    63→            return False\n    64→    64→        return True\n    65→    65→    \n    66→    66→    def to_dict(self) -> Dict[str, Any]:\n    67→    67→        return {\n    68→    68→            \"title_pattern\": self.title_pattern,\n    69→    69→            \"title_contains\": self.title_contains,\n    70→    70→            \"process_name\": self.process_name\n    71→    71→        }\n    72→    72→    \n    73→    73→    @classmethod\n    74→    74→    def from_dict(cls, data: Dict[str, Any]) -> 'WindowConstraint':\n    75→    75→        return cls(\n    76→    76→            title_pattern=data.get(\"title_pattern\"),\n    77→    77→            title_contains=data.get(\"title_contains\"),\n    78→    78→            process_name=data.get(\"process_name\")\n    79→    79→        )\n    80→    80→\n    81→    81→\n    82→    82→@dataclass\n    83→    83→class TextConstraint:\n    84→    84→    \"\"\"Contrainte sur le texte présent à l'écran\"\"\"\n    85→    85→    required_texts: List[str] = field(default_factory=list)\n    86→    86→    forbidden_texts: List[str] = field(default_factory=list)\n    87→    87→    \n    88→    88→    def matches(self, detected_texts: List[str]) -> bool:\n    89→    89→        \"\"\"Vérifier si les textes correspondent aux contraintes\"\"\"\n    90→    90→        # Vérifier textes requis\n    91→    91→        for required in self.required_texts:\n    92→    92→            if not any(required in text for text in detected_texts):\n    93→    93→                return False\n    94→    94→        \n    95→    95→        # Vérifier textes interdits\n    96→    96→        for forbidden in self.forbidden_texts:\n    97→    97→            if any(forbidden in text for text in detected_texts):\n    98→    98→                return False\n    99→    99→        \n   100→   100→        return True\n   101→   101→    \n   102→   102→    def to_dict(self) -> Dict[str, Any]:\n   103→   103→        return {\n   104→   104→            \"required_texts\": self.required_texts,\n   105→   105→            \"forbidden_texts\": self.forbidden_texts\n   106→   106→        }\n   107→   107→    \n   108→   108→    @classmethod\n   109→   109→    def from_dict(cls, data: Dict[str, Any]) -> 'TextConstraint':\n   110→   110→        return cls(\n   111→   111→            required_texts=data.get(\"required_texts\", []),\n   112→   112→            forbidden_texts=data.get(\"forbidden_texts\", [])\n   113→   113→        )\n   114→   114→\n   115→   115→\n   116→   116→@dataclass\n   117→   117→class UIConstraint:\n   118→   118→    \"\"\"Contrainte sur les éléments UI présents\"\"\"\n   119→   119→    required_roles: List[str] = field(default_factory=list)\n   120→   120→    required_types: List[str] = field(default_factory=list)\n   121→   121→    min_element_count: int = 0\n   122→   122→    \n   123→   123→    def matches(self, ui_elements: List[Any]) -> bool:\n   124→   124→        \"\"\"Vérifier si les éléments UI correspondent aux contraintes\"\"\"\n   125→   125→        if len(ui_elements) < self.min_element_count:\n   126→   126→            return False\n   127→   127→        \n   128→   128→        # Vérifier rôles requis\n   129→   129→        element_roles = [el.role for el in ui_elements if hasattr(el, 'role')]\n   130→   130→        for required_role in self.required_roles:\n   131→   131→            if required_role not in element_roles:\n   132→   132→                return False\n   133→   133→        \n   134→   134→        # Vérifier types requis\n   135→   135→        element_types = [el.type for el in ui_elements if hasattr(el, 'type')]\n   136→   136→        for required_type in self.required_types:\n   137→   137→            if required_type not in element_types:\n   138→   138→                return False\n   139→   139→        \n   140→   140→        return True\n   141→   141→    \n   142→   142→    def to_dict(self) -> Dict[str, Any]:\n   143→   143→        return {\n   144→   144→            \"required_roles\": self.required_roles,\n   145→   145→            \"required_types\": self.required_types,\n   146→   146→            \"min_element_count\": self.min_element_count\n   147→   147→        }\n   148→   148→    \n   149→   149→    @classmethod\n   150→   150→    def from_dict(cls, data: Dict[str, Any]) -> 'UIConstraint':\n   151→   151→        return cls(\n   152→   152→            required_roles=data.get(\"required_roles\", []),\n   153→   153→            required_types=data.get(\"required_types\", []),\n   154→   154→            min_element_count=data.get(\"min_element_count\", 0)\n   155→   155→        )\n   156→   156→\n   157→   157→\n   158→   158→@dataclass\n   159→   159→class EmbeddingPrototype:\n   160→   160→    \"\"\"Prototype d'embedding pour un node\"\"\"\n   161→   161→    provider: str  # e.g., \"openclip_ViT-B-32\"\n   162→   162→    vector_id: str  # Chemin vers .npy du prototype\n   163→   163→    min_cosine_similarity: float  # Seuil de similarité\n   164→   164→    sample_count: int  # Nombre d'échantillons utilisés\n   165→   165→    \n   166→   166→    def to_dict(self) -> Dict[str, Any]:\n   167→   167→        return {\n   168→   168→            \"provider\": self.provider,\n   169→   169→            \"vector_id\": self.vector_id,\n   170→   170→            \"min_cosine_similarity\": self.min_cosine_similarity,\n   171→   171→            \"sample_count\": self.sample_count\n   172→   172→        }\n   173→   173→    \n   174→   174→    @classmethod\n   175→   175→    def from_dict(cls, data: Dict[str, Any]) -> 'EmbeddingPrototype':\n   176→   176→        return cls(\n   177→   177→            provider=data[\"provider\"],\n   178→   178→            vector_id=data[\"vector_id\"],\n   179→   179→            min_cosine_similarity=data[\"min_cosine_similarity\"],\n   180→   180→            sample_count=data[\"sample_count\"]\n   181→   181→        )\n   182→   182→\n   183→   183→\n   184→   184→@dataclass\n   185→   185→class NodeVariant:\n   186→   186→    \"\"\"Variante d'un node de workflow (pour gérer les variations UI)\"\"\"\n   187→   187→    variant_id: str\n   188→   188→    embedding_path: str  # Chemin vers l'embedding .npy\n   189→   189→    similarity_to_primary: float  # Similarité avec le prototype principal\n   190→   190→    observation_count: int = 1\n   191→   191→    success_rate: float = 0.0\n   192→   192→    ui_state: Optional[str] = None  # enabled, disabled, checked, loading, error\n   193→   193→    created_at: datetime = field(default_factory=datetime.now)\n   194→   194→    metadata: Dict[str, Any] = field(default_factory=dict)\n   195→   195→    \n   196→   196→    def to_dict(self) -> Dict[str, Any]:\n   197→   197→        return {\n   198→   198→            \"variant_id\": self.variant_id,\n   199→   199→            \"embedding_path\": self.embedding_path,\n   200→   200→            \"similarity_to_primary\": self.similarity_to_primary,\n   201→   201→            \"observation_count\": self.observation_count,\n   202→   202→            \"success_rate\": self.success_rate,\n   203→   203→            \"ui_state\": self.ui_state,\n   204→   204→            \"created_at\": self.created_at.isoformat(),\n   205→   205→            \"metadata\": self.metadata\n   206→   206→        }\n   207→   207→    \n   208→   208→    @classmethod\n   209→   209→    def from_dict(cls, data: Dict[str, Any]) -> 'NodeVariant':\n   210→   210→        created_at = datetime.now()\n   211→   211→        if data.get(\"created_at\"):\n   212→   212→            created_at = datetime.fromisoformat(data[\"created_at\"])\n   213→   213→        return cls(\n   214→   214→            variant_id=data[\"variant_id\"],\n   215→   215→            embedding_path=data[\"embedding_path\"],\n   216→   216→            similarity_to_primary=data[\"similarity_to_primary\"],\n   217→   217→            observation_count=data.get(\"observation_count\", 1),\n   218→   218→            success_rate=data.get(\"success_rate\", 0.0),\n   219→   219→            ui_state=data.get(\"ui_state\"),\n   220→   220→            created_at=created_at,\n   221→   221→            metadata=data.get(\"metadata\", {})\n   222→   222→        )\n   223→   223→\n   224→   224→\n   225→   225→@dataclass\n   226→   226→class PrototypeVersion:\n   227→   227→    \"\"\"Version d'un prototype pour le versioning\"\"\"\n   228→   228→    version_id: str\n   229→   229→    embedding_path: str\n   230→   230→    created_at: datetime\n   231→   231→    reason: str  # \"initial\", \"drift_update\", \"manual_update\", \"consolidation\"\n   232→   232→    confidence_at_creation: float = 0.0\n   233→   233→    \n   234→   234→    def to_dict(self) -> Dict[str, Any]:\n   235→   235→        return {\n   236→   236→            \"version_id\": self.version_id,\n   237→   237→            \"embedding_path\": self.embedding_path,\n   238→   238→            \"created_at\": self.created_at.isoformat(),\n   239→   239→            \"reason\": self.reason,\n   240→   240→            \"confidence_at_creation\": self.confidence_at_creation\n   241→   241→        }\n   242→   242→    \n   243→   243→    @classmethod\n   244→   244→    def from_dict(cls, data: Dict[str, Any]) -> 'PrototypeVersion':\n   245→   245→        return cls(\n   246→   246→            version_id=data[\"version_id\"],\n   247→   247→            embedding_path=data[\"embedding_path\"],\n   248→   248→            created_at=datetime.fromisoformat(data[\"created_at\"]),\n   249→   249→            reason=data.get(\"reason\", \"unknown\"),\n   250→   250→            confidence_at_creation=data.get(\"confidence_at_creation\", 0.0)"}]},"uuid":"42af3b85-3c1a-46f9-88dd-31da2be06090","timestamp":"2026-03-14T21:14:37.032Z","sourceToolAssistantUUID":"43d78478-d3c5-426f-adfc-162c756b10f1"}
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+{"parentUuid":"2cd48daa-b380-46a3-8bad-1b3141a8e4b9","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MRd4TnHK7Y1aRdbinTQr2M","type":"tool_result","content":"   250→   250→            confidence_at_creation=data.get(\"confidence_at_creation\", 0.0)\n   251→   251→        )\n   252→   252→\n   253→   253→\n   254→   254→@dataclass\n   255→   255→class SpatialRelation:\n   256→   256→    \"\"\"Relation spatiale entre éléments UI\"\"\"\n   257→   257→    relation_id: str\n   258→   258→    source_element_id: str\n   259→   259→    target_element_id: str\n   260→   260→    relation_type: str  # above, below, left_of, right_of, inside, contains\n   261→   261→    distance_pixels: float = 0.0\n   262→   262→    confidence: float = 1.0\n   263→   263→    \n   264→   264→    def to_dict(self) -> Dict[str, Any]:\n   265→   265→        return {\n   266→   266→            \"relation_id\": self.relation_id,\n   267→   267→            \"source_element_id\": self.source_element_id,\n   268→   268→            \"target_element_id\": self.target_element_id,\n   269→   269→            \"relation_type\": self.relation_type,\n   270→   270→            \"distance_pixels\": self.distance_pixels,\n   271→   271→            \"confidence\": self.confidence\n   272→   272→        }\n   273→   273→    \n   274→   274→    @classmethod\n   275→   275→    def from_dict(cls, data: Dict[str, Any]) -> 'SpatialRelation':\n   276→   276→        return cls(\n   277→   277→            relation_id=data[\"relation_id\"],\n   278→   278→            source_element_id=data[\"source_element_id\"],\n   279→   279→            target_element_id=data[\"target_element_id\"],\n   280→   280→            relation_type=data[\"relation_type\"],\n   281→   281→            distance_pixels=data.get(\"distance_pixels\", 0.0),\n   282→   282→            confidence=data.get(\"confidence\", 1.0)\n   283→   283→        )\n   284→   284→\n   285→   285→\n   286→   286→@dataclass\n   287→   287→class ScreenTemplate:\n   288→   288→    \"\"\"Template d'écran pour un WorkflowNode\"\"\"\n   289→   289→    window: WindowConstraint\n   290→   290→    text: TextConstraint\n   291→   291→    ui: UIConstraint\n   292→   292→    embedding: EmbeddingPrototype\n   293→   293→    \n   294→   294→    def matches(self, screen_state: Any, embedding_similarity: float) -> Tuple[bool, float]:\n   295→   295→        \"\"\"\n   296→   296→        Vérifier si un ScreenState correspond à ce template\n   297→   297→        \n   298→   298→        Returns:\n   299→   299→            (matches: bool, confidence: float)\n   300→   300→        \"\"\"\n   301→   301→        # Vérifier embedding similarity\n   302→   302→        if embedding_similarity < self.embedding.min_cosine_similarity:\n   303→   303→            return False, embedding_similarity\n   304→   304→        \n   305→   305→        # Vérifier contraintes de fenêtre\n   306→   306→        if hasattr(screen_state, 'window'):\n   307→   307→            window_title = getattr(screen_state.window, 'title', '')\n   308→   308→            process = getattr(screen_state.window, 'process', '')\n   309→   309→            if not self.window.matches(window_title, process):\n   310→   310→                return False, 0.0\n   311→   311→        \n   312→   312→        # Vérifier contraintes de texte\n   313→   313→        if hasattr(screen_state, 'perception'):\n   314→   314→            detected_text = getattr(screen_state.perception, 'detected_text', [])\n   315→   315→            if not self.text.matches(detected_text):\n   316→   316→                return False, 0.0\n   317→   317→        \n   318→   318→        # Vérifier contraintes UI\n   319→   319→        if hasattr(screen_state, 'ui_elements'):\n   320→   320→            if not self.ui.matches(screen_state.ui_elements):\n   321→   321→                return False, 0.0\n   322→   322→        \n   323→   323→        # Calculer confiance globale\n   324→   324→        confidence = embedding_similarity\n   325→   325→        \n   326→   326→        return True, confidence\n   327→   327→    \n   328→   328→    def to_dict(self) -> Dict[str, Any]:\n   329→   329→        return {\n   330→   330→            \"window\": self.window.to_dict(),\n   331→   331→            \"text\": self.text.to_dict(),\n   332→   332→            \"ui\": self.ui.to_dict(),\n   333→   333→            \"embedding\": self.embedding.to_dict()\n   334→   334→        }\n   335→   335→    \n   336→   336→    @classmethod\n   337→   337→    def from_dict(cls, data: Dict[str, Any]) -> 'ScreenTemplate':\n   338→   338→        # Support format simplifié ou complet\n   339→   339→        if \"window\" in data:\n   340→   340→            window = WindowConstraint.from_dict(data[\"window\"])\n   341→   341→        else:\n   342→   342→            # Format simplifié avec title_pattern\n   343→   343→            window = WindowConstraint(\n   344→   344→                title_pattern=data.get(\"title_pattern\", \".*\"),\n   345→   345→                process_name=data.get(\"process_name\")\n   346→   346→            )\n   347→   347→        \n   348→   348→        if \"text\" in data:\n   349→   349→            text = TextConstraint.from_dict(data[\"text\"])\n   350→   350→        else:\n   351→   351→            text = TextConstraint(\n   352→   352→                required_texts=data.get(\"required_text\", []),\n   353→   353→                forbidden_texts=data.get(\"forbidden_text\", [])\n   354→   354→            )\n   355→   355→        \n   356→   356→        if \"ui\" in data:\n   357→   357→            ui = UIConstraint.from_dict(data[\"ui\"])\n   358→   358→        else:\n   359→   359→            ui = UIConstraint(\n   360→   360→                required_roles=data.get(\"required_elements\", []),\n   361→   361→                required_types=data.get(\"required_types\", []),\n   362→   362→                min_element_count=data.get(\"min_element_count\", 0)\n   363→   363→            )\n   364→   364→        \n   365→   365→        if \"embedding\" in data and data[\"embedding\"]:\n   366→   366→            embedding = EmbeddingPrototype.from_dict(data[\"embedding\"])\n   367→   367→        else:\n   368→   368→            # Créer un prototype vide/par défaut\n   369→   369→            embedding = EmbeddingPrototype(\n   370→   370→                provider=\"none\",\n   371→   371→                vector_id=\"\",\n   372→   372→                min_cosine_similarity=data.get(\"similarity_threshold\", 0.85),\n   373→   373→                sample_count=0\n   374→   374→            )\n   375→   375→        \n   376→   376→        return cls(window=window, text=text, ui=ui, embedding=embedding)\n   377→   377→\n   378→   378→\n   379→   379→# ============================================================================\n   380→   380→# WorkflowNode\n   381→   381→# ============================================================================\n   382→   382→\n   383→   383→@dataclass\n   384→   384→class WorkflowNode:\n   385→   385→    \"\"\"\n   386→   386→    Node dans un Workflow Graph\n   387→   387→    \n   388→   388→    Représente un état d'écran reconnaissable par son template.\n   389→   389→    Supporte les variantes pour gérer les variations UI.\n   390→   390→    \"\"\"\n   391→   391→    node_id: str\n   392→   392→    name: str\n   393→   393→    description: str\n   394→   394→    template: ScreenTemplate\n   395→   395→    is_entry: bool = False\n   396→   396→    is_end: bool = False\n   397→   397→    \n   398→   398→    # Support des variantes (Exigences 4.1, 4.2, 4.3)\n   399→   399→    variants: List[NodeVariant] = field(default_factory=list)\n   400→   400→    primary_variant_id: Optional[str] = None  # ID de la variante principale\n   401→   401→    max_variants: int = 5  # Limite de variantes avant consolidation\n   402→   402→    \n   403→   403→    # Versioning du prototype (Exigence 3.5)\n   404→   404→    prototype_version: Optional[str] = None  # Version actuelle\n   405→   405→    prototype_history: List[PrototypeVersion] = field(default_factory=list)\n   406→   406→    \n   407→   407→    # Score de qualité (Exigences 1.1, 1.4)\n   408→   408→    quality_score: float = 0.0  # Score de qualité du cluster\n   409→   409→    cluster_metrics: Dict[str, float] = field(default_factory=dict)\n   410→   410→    \n   411→   411→    # Relations spatiales (Exigence 5.1)\n   412→   412→    spatial_relations: List[SpatialRelation] = field(default_factory=list)\n   413→   413→    container_type: Optional[str] = None  # form, menu, toolbar, dialog, list\n   414→   414→    \n   415→   415→    metadata: Dict[str, Any] = field(default_factory=dict)\n   416→   416→    \n   417→   417→    def matches(self, screen_state: Any, state_embedding: Any) -> Tuple[bool, float]:\n   418→   418→        \"\"\"\n   419→   419→        Vérifier si un ScreenState correspond à ce node\n   420→   420→        \n   421→   421→        Returns:\n   422→   422→            (matches: bool, confidence: float)\n   423→   423→        \"\"\"\n   424→   424→        # Calculer similarité d'embedding\n   425→   425→        if hasattr(state_embedding, 'compute_similarity'):\n   426→   426→            # Charger prototype embedding\n   427→   427→            from .state_embedding import StateEmbedding\n   428→   428→            prototype_path = Path(self.template.embedding.vector_id)\n   429→   429→            if prototype_path.exists():\n   430→   430→                # Créer un StateEmbedding temporaire pour le prototype\n   431→   431→                prototype_emb = StateEmbedding(\n   432→   432→                    embedding_id=f\"{self.node_id}_prototype\",\n   433→   433→                    vector_id=self.template.embedding.vector_id,\n   434→   434→                    dimensions=state_embedding.dimensions,\n   435→   435→                    fusion_method=\"weighted\",\n   436→   436→                    components={}\n   437→   437→                )\n   438→   438→                embedding_similarity = state_embedding.compute_similarity(prototype_emb)\n   439→   439→            else:\n   440→   440→                embedding_similarity = 0.0\n   441→   441→        else:\n   442→   442→            embedding_similarity = 0.0\n   443→   443→        \n   444→   444→        # Vérifier template\n   445→   445→        return self.template.matches(screen_state, embedding_similarity)\n   446→   446→    \n   447→   447→    def to_dict(self) -> Dict[str, Any]:\n   448→   448→        return {\n   449→   449→            \"node_id\": self.node_id,\n   450→   450→            \"name\": self.name,\n   451→   451→            \"description\": self.description,\n   452→   452→            \"template\": self.template.to_dict(),\n   453→   453→            \"is_entry\": self.is_entry,\n   454→   454→            \"is_end\": self.is_end,\n   455→   455→            \"variants\": [v.to_dict() for v in self.variants],\n   456→   456→            \"primary_variant_id\": self.primary_variant_id,\n   457→   457→            \"max_variants\": self.max_variants,\n   458→   458→            \"prototype_version\": self.prototype_version,\n   459→   459→            \"prototype_history\": [p.to_dict() for p in self.prototype_history],\n   460→   460→            \"quality_score\": self.quality_score,\n   461→   461→            \"cluster_metrics\": self.cluster_metrics,\n   462→   462→            \"spatial_relations\": [r.to_dict() for r in self.spatial_relations],\n   463→   463→            \"container_type\": self.container_type,\n   464→   464→            \"metadata\": self.metadata\n   465→   465→        }\n   466→   466→    \n   467→   467→    @classmethod\n   468→   468→    def from_dict(cls, data: Dict[str, Any]) -> 'WorkflowNode':\n   469→   469→        variants = [NodeVariant.from_dict(v) for v in data.get(\"variants\", [])]\n   470→   470→        prototype_history = [PrototypeVersion.from_dict(p) for p in data.get(\"prototype_history\", [])]\n   471→   471→        spatial_relations = [SpatialRelation.from_dict(r) for r in data.get(\"spatial_relations\", [])]\n   472→   472→        \n   473→   473→        return cls(\n   474→   474→            node_id=data[\"node_id\"],\n   475→   475→            name=data[\"name\"],\n   476→   476→            description=data[\"description\"],\n   477→   477→            template=ScreenTemplate.from_dict(data[\"template\"]),\n   478→   478→            is_entry=data.get(\"is_entry\", False),\n   479→   479→            is_end=data.get(\"is_end\", False),\n   480→   480→            variants=variants,\n   481→   481→            primary_variant_id=data.get(\"primary_variant_id\"),\n   482→   482→            max_variants=data.get(\"max_variants\", 5),\n   483→   483→            prototype_version=data.get(\"prototype_version\"),\n   484→   484→            prototype_history=prototype_history,\n   485→   485→            quality_score=data.get(\"quality_score\", 0.0),\n   486→   486→            cluster_metrics=data.get(\"cluster_metrics\", {}),\n   487→   487→            spatial_relations=spatial_relations,\n   488→   488→            container_type=data.get(\"container_type\"),\n   489→   489→            metadata=data.get(\"metadata\", {})\n   490→   490→        )\n   491→   491→    \n   492→   492→    # =========================================================================\n   493→   493→    # Méthodes de gestion des variantes\n   494→   494→    # =========================================================================\n   495→   495→    \n   496→   496→    def add_variant(self, variant: NodeVariant) -> bool:\n   497→   497→        \"\"\"\n   498→   498→        Ajouter une variante au node.\n   499→   499→        \n   500→   500→        Returns:\n   501→   501→            True si ajoutée, False si limite atteinte ou déjà existante\n   502→   502→        \"\"\"\n   503→   503→        # Vérifier si variante existe déjà\n   504→   504→        for existing in self.variants:\n   505→   505→            if existing.variant_id == variant.variant_id:\n   506→   506→                return False\n   507→   507→        \n   508→   508→        # Vérifier limite\n   509→   509→        if len(self.variants) >= self.max_variants:\n   510→   510→            return False\n   511→   511→        \n   512→   512→        self.variants.append(variant)\n   513→   513→        return True\n   514→   514→    \n   515→   515→    def remove_variant(self, variant_id: str) -> bool:\n   516→   516→        \"\"\"Supprimer une variante par ID\"\"\"\n   517→   517→        for i, variant in enumerate(self.variants):\n   518→   518→            if variant.variant_id == variant_id:\n   519→   519→                del self.variants[i]\n   520→   520→                return True\n   521→   521→        return False\n   522→   522→    \n   523→   523→    def get_variant(self, variant_id: str) -> Optional[NodeVariant]:\n   524→   524→        \"\"\"Récupérer une variante par ID\"\"\"\n   525→   525→        for variant in self.variants:\n   526→   526→            if variant.variant_id == variant_id:\n   527→   527→                return variant\n   528→   528→        return None\n   529→   529→    \n   530→   530→    def get_best_variant(self) -> Optional[NodeVariant]:\n   531→   531→        \"\"\"Récupérer la variante avec le meilleur taux de succès\"\"\"\n   532→   532→        if not self.variants:\n   533→   533→            return None\n   534→   534→        return max(self.variants, key=lambda v: v.success_rate * v.observation_count)\n   535→   535→    \n   536→   536→    def needs_consolidation(self) -> bool:\n   537→   537→        \"\"\"Vérifier si les variantes doivent être consolidées\"\"\"\n   538→   538→        return len(self.variants) >= self.max_variants\n   539→   539→    \n   540→   540→    def add_prototype_version(self, version: PrototypeVersion) -> None:\n   541→   541→        \"\"\"Ajouter une version de prototype à l'historique\"\"\"\n   542→   542→        self.prototype_history.append(version)\n   543→   543→        self.prototype_version = version.version_id\n   544→   544→    \n   545→   545→    def get_prototype_version(self, version_id: str) -> Optional[PrototypeVersion]:\n   546→   546→        \"\"\"Récupérer une version de prototype par ID\"\"\"\n   547→   547→        for version in self.prototype_history:\n   548→   548→            if version.version_id == version_id:\n   549→   549→                return version"}]},"uuid":"8fd5fb77-1f36-49fc-9553-b5abbdf0c7d1","timestamp":"2026-03-14T21:14:46.233Z","sourceToolAssistantUUID":"2cd48daa-b380-46a3-8bad-1b3141a8e4b9"}
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+{"parentUuid":"e6c09744-f7f8-4f2e-b18d-b260036d6f58","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_017njNfQLQ3Ymdsstbzwwr3k","toolUseID":"toolu_017njNfQLQ3Ymdsstbzwwr3k","timestamp":"2026-03-14T21:14:47.061Z","uuid":"665864b1-5093-4ab1-a058-419aa38d1311"}
+{"parentUuid":"e6c09744-f7f8-4f2e-b18d-b260036d6f58","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017njNfQLQ3Ymdsstbzwwr3k","type":"tool_result","content":"     1→#!/usr/bin/env python3\n     2→\"\"\"\n     3→Autonomous Planner - Agent Libre pour RPA Vision V3\n     4→\n     5→Ce module permet d'exécuter des tâches sans workflow pré-enregistré.\n     6→Il utilise un LLM (Qwen via Ollama) pour :\n     7→1. Comprendre l'intention utilisateur\n     8→2. Décomposer en étapes d'actions\n     9→3. Adapter dynamiquement selon le contexte visuel\n    10→\n    11→Auteur: Dom - Janvier 2026\n    12→\"\"\"\n    13→\n    14→import json\n    15→import logging\n    16→import time\n    17→import re\n    18→import sys\n    19→import os\n    20→from dataclasses import dataclass, field\n    21→from typing import List, Dict, Any, Optional, Callable, Tuple\n    22→from enum import Enum\n    23→from datetime import datetime\n    24→\n    25→import requests\n    26→\n    27→# Ajouter le chemin du projet pour les imports core\n    28→sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\n    29→\n    30→logger = logging.getLogger(__name__)\n    31→\n    32→# Essayer d'importer les composants de détection visuelle\n    33→try:\n    34→    from core.detection.owl_detector import OwlDetector, OWL_AVAILABLE\n    35→    VISUAL_DETECTION_AVAILABLE = OWL_AVAILABLE\n    36→except ImportError:\n    37→    VISUAL_DETECTION_AVAILABLE = False\n    38→    OwlDetector = None\n    39→\n    40→# Essayer d'importer le client VLM pour analyse intelligente\n    41→try:\n    42→    from core.detection.ollama_client import OllamaClient\n    43→    VLM_AVAILABLE = True\n    44→except ImportError:\n    45→    VLM_AVAILABLE = False\n    46→    OllamaClient = None\n    47→\n    48→try:\n    49→    from PIL import Image as PILImage\n    50→    import pyautogui\n    51→    PYAUTOGUI_AVAILABLE = True\n    52→except ImportError:\n    53→    PYAUTOGUI_AVAILABLE = False\n    54→    PILImage = None\n    55→    pyautogui = None\n    56→\n    57→\n    58→class ActionType(Enum):\n    59→    \"\"\"Types d'actions supportées par l'agent autonome.\"\"\"\n    60→    CLICK = \"click\"\n    61→    TYPE_TEXT = \"type_text\"\n    62→    HOTKEY = \"hotkey\"\n    63→    SCROLL = \"scroll\"\n    64→    WAIT = \"wait\"\n    65→    OPEN_APP = \"open_app\"\n    66→    OPEN_URL = \"open_url\"\n    67→    SCREENSHOT = \"screenshot\"\n    68→    FIND_ELEMENT = \"find_element\"\n    69→\n    70→\n    71→@dataclass\n    72→class PlannedAction:\n    73→    \"\"\"Une action planifiée par le LLM.\"\"\"\n    74→    step_number: int\n    75→    action_type: ActionType\n    76→    description: str\n    77→    target: Optional[str] = None  # Description textuelle de la cible\n    78→    parameters: Dict[str, Any] = field(default_factory=dict)\n    79→    expected_result: Optional[str] = None\n    80→    fallback: Optional[str] = None\n    81→\n    82→\n    83→@dataclass\n    84→class ExecutionPlan:\n    85→    \"\"\"Plan d'exécution complet généré par le LLM.\"\"\"\n    86→    task_description: str\n    87→    steps: List[PlannedAction]\n    88→    estimated_duration_seconds: int = 30\n    89→    requires_confirmation: bool = True\n    90→    risk_level: str = \"low\"  # low, medium, high\n    91→    created_at: datetime = field(default_factory=datetime.now)\n    92→\n    93→\n    94→@dataclass\n    95→class ActionResult:\n    96→    \"\"\"Résultat d'une action exécutée.\"\"\"\n    97→    success: bool\n    98→    action: PlannedAction\n    99→    message: str\n   100→    screenshot_path: Optional[str] = None\n   101→    duration_ms: int = 0\n   102→    error: Optional[str] = None\n   103→\n   104→\n   105→class AutonomousPlanner:\n   106→    \"\"\"\n   107→    Planificateur autonome utilisant LLM pour générer des plans d'action.\n   108→    \"\"\"\n   109→\n   110→    def __init__(\n   111→        self,\n   112→        llm_endpoint: str = \"http://localhost:11434/api/generate\",\n   113→        llm_model: str = \"qwen2.5:7b\",\n   114→        timeout: int = 60\n   115→    ):\n   116→        self.llm_endpoint = llm_endpoint\n   117→        self.llm_model = llm_model\n   118→        self.timeout = timeout\n   119→        self.llm_available = self._check_llm()\n   120→\n   121→        # Callbacks pour l'exécution\n   122→        self._action_executor: Optional[Callable] = None\n   123→        self._screen_capturer: Optional[Callable] = None\n   124→        self._progress_callback: Optional[Callable] = None\n   125→\n   126→        # Détecteur visuel (OWL-v2)\n   127→        self._owl_detector: Optional[OwlDetector] = None\n   128→        self._init_visual_detection()\n   129→\n   130→        # Client VLM pour analyse intelligente d'écran\n   131→        self._vlm_client = None\n   132→        self._init_vlm_client()\n   133→\n   134→        logger.info(f\"AutonomousPlanner initialized (LLM: {self.llm_model}, available: {self.llm_available}, visual: {self._owl_detector is not None}, vlm: {self._vlm_client is not None})\")\n   135→\n   136→    def _init_visual_detection(self):\n   137→        \"\"\"Initialise le détecteur visuel OWL-v2.\"\"\"\n   138→        if VISUAL_DETECTION_AVAILABLE and OwlDetector:\n   139→            try:\n   140→                self._owl_detector = OwlDetector(confidence_threshold=0.1)\n   141→                logger.info(\"OWL-v2 visual detector initialized\")\n   142→            except Exception as e:\n   143→                logger.warning(f\"Could not initialize OWL detector: {e}\")\n   144→                self._owl_detector = None\n   145→\n   146→    def _init_vlm_client(self):\n   147→        \"\"\"Initialise le client VLM pour analyse intelligente.\"\"\"\n   148→        if VLM_AVAILABLE and OllamaClient:\n   149→            try:\n   150→                self._vlm_client = OllamaClient(model=\"qwen2.5vl:7b\")\n   151→                logger.info(\"VLM client initialized (qwen2.5vl:7b)\")\n   152→            except Exception as e:\n   153→                logger.warning(f\"Could not initialize VLM client: {e}\")\n   154→                self._vlm_client = None\n   155→\n   156→    def _analyze_screen_for_element(\n   157→        self,\n   158→        description: str,\n   159→        screenshot=None\n   160→    ) -> Optional[Tuple[int, int]]:\n   161→        \"\"\"\n   162→        Utilise le VLM pour analyser l'écran et trouver un élément.\n   163→        Plus intelligent que OWL car peut comprendre le contexte.\n   164→\n   165→        Args:\n   166→            description: Description de l'élément à trouver\n   167→            screenshot: Capture d'écran (capturée si non fournie)\n   168→\n   169→        Returns:\n   170→            Tuple (x, y) des coordonnées ou None\n   171→        \"\"\"\n   172→        if not self._vlm_client:\n   173→            return None\n   174→\n   175→        if screenshot is None:\n   176→            screenshot = self._capture_screen()\n   177→\n   178→        if screenshot is None:\n   179→            return None\n   180→\n   181→        # Prompt pour le VLM\n   182→        prompt = f\"\"\"Analyse cette capture d'écran et trouve l'élément suivant: \"{description}\"\n   183→\n   184→IMPORTANT:\n   185→- Ignore les publicités et les éléments sponsorisés\n   186→- Cherche un vrai résultat de recherche, pas une annonce\n   187→- Si c'est une vidéo, cherche une miniature de vidéo avec un titre pertinent\n   188→\n   189→Réponds UNIQUEMENT avec les coordonnées X,Y du CENTRE de l'élément au format:\n   190→COORDINATES: X, Y\n   191→\n   192→Si tu ne trouves pas l'élément, réponds:\n   193→NOT_FOUND\"\"\"\n   194→\n   195→        try:\n   196→            result = self._vlm_client.generate(\n   197→                prompt=prompt,\n   198→                image=screenshot,\n   199→                temperature=0.1,\n   200→                max_tokens=100\n   201→            )\n   202→\n   203→            if result.get('success'):\n   204→                response = result.get('response', '')\n   205→                logger.info(f\"VLM response: {response}\")\n   206→\n   207→                # Parser les coordonnées\n   208→                coord_match = re.search(r'COORDINATES:\\s*(\\d+)\\s*,\\s*(\\d+)', response)\n   209→                if coord_match:\n   210→                    x = int(coord_match.group(1))\n   211→                    y = int(coord_match.group(2))\n   212→                    logger.info(f\"VLM found element at ({x}, {y})\")\n   213→                    return (x, y)\n   214→\n   215→        except Exception as e:\n   216→            logger.warning(f\"VLM analysis failed: {e}\")\n   217→\n   218→        return None\n   219→\n   220→    def _capture_screen(self):\n   221→        \"\"\"Capture l'écran actuel. Retourne PIL.Image ou None.\"\"\"\n   222→        if not PYAUTOGUI_AVAILABLE:\n   223→            return None\n   224→        try:\n   225→            screenshot = pyautogui.screenshot()\n   226→            return screenshot\n   227→        except Exception as e:\n   228→            logger.warning(f\"Screen capture failed: {e}\")\n   229→            return None\n   230→\n   231→    def _find_element_by_description(\n   232→        self,\n   233→        description: str,\n   234→        screenshot=None\n   235→    ) -> Optional[Tuple[int, int]]:\n   236→        \"\"\"\n   237→        Trouve un élément à l'écran par sa description textuelle.\n   238→\n   239→        Args:\n   240→            description: Description de l'élément (ex: \"search bar\", \"play button\")\n   241→            screenshot: Capture d'écran (capturée si non fournie)\n   242→\n   243→        Returns:\n   244→            Tuple (x, y) des coordonnées du centre de l'élément, ou None si non trouvé\n   245→        \"\"\"\n   246→        if screenshot is None:\n   247→            screenshot = self._capture_screen()\n   248→\n   249→        if screenshot is None:\n   250→            return None\n   251→\n   252→        # Essayer avec OWL-v2 si disponible\n   253→        if self._owl_detector:\n   254→            try:\n   255→                # Créer des variantes de la requête pour OWL\n   256→                queries = self._generate_owl_queries(description)\n   257→                logger.info(f\"OWL searching for: {queries}\")\n   258→\n   259→                detections = self._owl_detector.detect(screenshot, queries, confidence_threshold=0.05)\n   260→\n   261→                if detections:\n   262→                    # Prendre la détection avec le meilleur score\n   263→                    best = max(detections, key=lambda d: d['confidence'])\n   264→                    center = best.get('center')\n   265→                    if center:\n   266→                        logger.info(f\"OWL found '{best['label']}' at {center} (conf: {best['confidence']:.2f})\")\n   267→                        return (int(center[0]), int(center[1]))\n   268→            except Exception as e:\n   269→                logger.warning(f\"OWL detection failed: {e}\")\n   270→\n   271→        # Fallback 2: Utiliser le VLM pour analyse intelligente\n   272→        # Particulièrement utile pour distinguer les pubs des vrais résultats\n   273→        if self._vlm_client and (\"vidéo\" in description.lower() or \"video\" in description.lower()):\n   274→            logger.info(\"Trying VLM analysis for intelligent element detection...\")\n   275→            vlm_result = self._analyze_screen_for_element(description, screenshot)\n   276→            if vlm_result:\n   277→                return vlm_result\n   278→\n   279→        # Fallback 3: positions heuristiques basées sur la description\n   280→        return self._heuristic_position(description, screenshot.size if screenshot else (1920, 1080))\n   281→\n   282→    def _generate_owl_queries(self, description: str) -> List[str]:\n   283→        \"\"\"Génère des requêtes OWL à partir d'une description.\"\"\"\n   284→        desc_lower = description.lower()\n   285→        queries = []\n   286→\n   287→        # Mapping description -> termes OWL\n   288→        if \"recherche\" in desc_lower or \"search\" in desc_lower:\n   289→            queries.extend([\"search bar\", \"search box\", \"text input\", \"search field\"])\n   290→        elif \"vidéo\" in desc_lower or \"video\" in desc_lower or \"miniature\" in desc_lower:\n   291→            queries.extend([\"video thumbnail\", \"video preview\", \"video card\"])\n   292→        elif \"bouton\" in desc_lower or \"button\" in desc_lower:\n   293→            queries.extend([\"button\", \"click button\", \"submit button\"])\n   294→        elif \"play\" in desc_lower or \"lecture\" in desc_lower:\n   295→            queries.extend([\"play button\", \"video player\"])\n   296→        else:\n   297→            # Utiliser la description directement\n   298→            queries.append(description)\n   299→\n   300→        return queries\n   301→\n   302→    def _heuristic_position(\n   303→        self,\n   304→        description: str,\n   305→        screen_size: Tuple[int, int]\n   306→    ) -> Optional[Tuple[int, int]]:\n   307→        \"\"\"Position heuristique basée sur la description (fallback).\"\"\"\n   308→        width, height = screen_size\n   309→        desc_lower = description.lower()\n   310→\n   311→        # YouTube specific heuristics (après scroll, les vidéos sont plus visibles)\n   312→        if \"recherche\" in desc_lower or \"search\" in desc_lower:\n   313→            # Barre de recherche YouTube: centre-haut\n   314→            return (width // 2, 60)\n   315→        elif (\"vidéo\" in desc_lower or \"video\" in desc_lower or \"miniature\" in desc_lower) and (\"musique\" in desc_lower or \"music\" in desc_lower or \"première\" in desc_lower or \"visible\" in desc_lower):\n   316→            # Première vidéo YouTube visible (après scroll, au milieu de l'écran)\n   317→            # Position adaptée pour différentes résolutions\n   318→            x = width // 4  # Colonne gauche où sont les miniatures\n   319→            y = height // 3  # Tiers supérieur après scroll\n   320→            logger.info(f\"Heuristic video position: ({x}, {y}) for screen {width}x{height}\")\n   321→            return (x, y)\n   322→        elif \"bouton\" in desc_lower and \"recherche\" in desc_lower:\n   323→            # Bouton de recherche YouTube (loupe)\n   324→            return (width // 2 + 280, 60)\n   325→\n   326→        # Position par défaut: centre\n   327→        return (width // 2, height // 2)\n   328→\n   329→    def _check_llm(self) -> bool:\n   330→        \"\"\"Vérifie si le LLM est disponible.\"\"\"\n   331→        try:\n   332→            response = requests.get(\n   333→                \"http://localhost:11434/api/tags\",\n   334→                timeout=5\n   335→            )\n   336→            if response.status_code == 200:\n   337→                models = response.json().get('models', [])\n   338→                model_names = [m['name'] for m in models]\n   339→                # Vérifier si notre modèle est disponible\n   340→                if any(self.llm_model in name for name in model_names):\n   341→                    return True\n   342→                logger.warning(f\"Model {self.llm_model} not found. Available: {model_names}\")\n   343→            return False\n   344→        except Exception as e:\n   345→            logger.warning(f\"LLM check failed: {e}\")\n   346→            return False\n   347→\n   348→    def set_action_executor(self, executor: Callable):\n   349→        \"\"\"Configure l'exécuteur d'actions.\"\"\"\n   350→        self._action_executor = executor\n   351→\n   352→    def set_screen_capturer(self, capturer: Callable):\n   353→        \"\"\"Configure le capteur d'écran.\"\"\"\n   354→        self._screen_capturer = capturer\n   355→\n   356→    def set_progress_callback(self, callback: Callable):\n   357→        \"\"\"Configure le callback de progression.\"\"\"\n   358→        self._progress_callback = callback\n   359→\n   360→    def _notify_progress(self, step: int, total: int, message: str, status: str = \"running\"):\n   361→        \"\"\"Notifie la progression.\"\"\"\n   362→        if self._progress_callback:\n   363→            self._progress_callback({\n   364→                \"step\": step,\n   365→                \"total\": total,\n   366→                \"message\": message,\n   367→                \"status\": status,\n   368→                \"percent\": int((step / total) * 100) if total > 0 else 0\n   369→            })\n   370→\n   371→    def plan(self, user_request: str, context: Optional[Dict] = None) -> ExecutionPlan:\n   372→        \"\"\"\n   373→        Génère un plan d'exécution à partir d'une requête utilisateur.\n   374→\n   375→        Args:\n   376→            user_request: La demande en langage naturel\n   377→            context: Contexte optionnel (écran actuel, apps ouvertes, etc.)\n   378→\n   379→        Returns:\n   380→            ExecutionPlan avec les étapes à exécuter\n   381→        \"\"\"\n   382→        if not self.llm_available:\n   383→            return self._fallback_plan(user_request)\n   384→\n   385→        # Construire le prompt\n   386→        prompt = self._build_planning_prompt(user_request, context)\n   387→\n   388→        try:\n   389→            # Appeler le LLM\n   390→            response = requests.post(\n   391→                self.llm_endpoint,\n   392→                json={\n   393→                    \"model\": self.llm_model,\n   394→                    \"prompt\": prompt,\n   395→                    \"stream\": False,\n   396→                    \"options\": {\n   397→                        \"temperature\": 0.3,  # Déterministe\n   398→                        \"num_predict\": 1500\n   399→                    }\n   400→                },\n   401→                timeout=self.timeout\n   402→            )\n   403→\n   404→            if response.status_code == 200:\n   405→                result = response.json()\n   406→                llm_response = result.get('response', '')\n   407→                return self._parse_plan(user_request, llm_response)\n   408→            else:\n   409→                logger.error(f\"LLM request failed: {response.status_code}\")\n   410→                return self._fallback_plan(user_request)\n   411→\n   412→        except Exception as e:\n   413→            logger.error(f\"Planning error: {e}\")\n   414→            return self._fallback_plan(user_request)\n   415→\n   416→    def _build_planning_prompt(self, user_request: str, context: Optional[Dict] = None) -> str:\n   417→        \"\"\"Construit le prompt pour le LLM.\"\"\"\n   418→\n   419→        context_info = \"\"\n   420→        if context:\n   421→            context_info = f\"\"\"\n   422→Contexte actuel:\n   423→- Application active: {context.get('active_app', 'inconnue')}\n   424→- Fenêtre: {context.get('window_title', 'inconnue')}\n   425→- Écran: {context.get('screen_info', 'standard')}\n   426→\"\"\"\n   427→\n   428→        prompt = f\"\"\"Tu es un assistant RPA intelligent qui planifie des actions sur ordinateur.\n   429→\n   430→TÂCHE DEMANDÉE: {user_request}\n   431→{context_info}\n   432→\n   433→ACTIONS DISPONIBLES:\n   434→- open_app: Ouvrir une application (params: app_name - ex: \"firefox\", \"terminal\", \"code\")\n   435→- open_url: Ouvrir une URL dans le navigateur (params: url)\n   436→- click: Cliquer sur un élément visuel (target: description précise de l'élément à cliquer)\n   437→- type_text: Taper du texte (target: description du champ, params: text)\n   438→- hotkey: Raccourci clavier (params: keys - ex: \"ctrl+t\", \"ctrl+l\", \"Return\", \"Escape\", \"Tab\")\n   439→- scroll: Défiler (params: direction - \"up\"/\"down\", amount - nombre de scrolls)\n   440→- wait: Attendre (params: seconds)\n   441→\n   442→PRINCIPES DE PLANIFICATION:\n   443→1. Décompose la tâche en étapes simples, une action par étape\n   444→2. Ajoute wait (2-3s) après open_url/open_app pour laisser le temps au chargement\n   445→3. Pour taper dans un champ web, utilise d'abord click sur le champ OU hotkey pour focus (ex: ctrl+l pour barre d'adresse)\n   446→4. Après avoir tapé dans un champ de recherche, ajoute hotkey \"Return\" pour valider\n   447→5. Pour click, décris précisément l'élément visuel (ex: \"bouton bleu Envoyer\", \"première image de la liste\")\n   448→6. Si la page peut avoir des publicités, ajoute un scroll pour les passer avant de cliquer\n   449→\n   450→RACCOURCIS UTILES:\n   451→- ctrl+l : Focus sur la barre d'adresse du navigateur\n   452→- ctrl+t : Nouvel onglet\n   453→- ctrl+f : Rechercher dans la page\n   454→- Return : Valider/Entrée\n   455→- Escape : Annuler/Fermer\n   456→\n   457→RÉPONDS EN JSON UNIQUEMENT:\n   458→{{\n   459→  \"steps\": [\n   460→    {{\n   461→      \"step\": 1,\n   462→      \"action\": \"open_url|click|type_text|hotkey|wait|scroll\",\n   463→      \"target\": \"description visuelle de la cible\",\n   464→      \"params\": {{\"url\": \"...\", \"text\": \"...\", \"keys\": \"...\", \"seconds\": 2}},\n   465→      \"description\": \"Ce que fait cette étape\",\n   466→      \"expected_result\": \"Résultat attendu\"\n   467→    }}\n   468→  ],\n   469→  \"risk_level\": \"low|medium|high\",\n   470→  \"estimated_seconds\": 30\n   471→}}\n   472→\n   473→JSON:\"\"\"\n   474→\n   475→        return prompt\n   476→\n   477→    def _parse_plan(self, user_request: str, llm_response: str) -> ExecutionPlan:\n   478→        \"\"\"Parse la réponse du LLM en ExecutionPlan.\"\"\"\n   479→\n   480→        try:\n   481→            # Extraire le JSON de la réponse\n   482→            json_match = re.search(r'\\{[\\s\\S]*\\}', llm_response)\n   483→            if not json_match:\n   484→                logger.warning(\"No JSON found in LLM response\")\n   485→                return self._fallback_plan(user_request)\n   486→\n   487→            data = json.loads(json_match.group())\n   488→\n   489→            steps = []\n   490→            for step_data in data.get('steps', []):\n   491→                action_type_str = step_data.get('action', 'click')\n   492→\n   493→                # Mapper vers ActionType\n   494→                action_type_map = {\n   495→                    'open_app': ActionType.OPEN_APP,\n   496→                    'open_url': ActionType.OPEN_URL,\n   497→                    'click': ActionType.CLICK,\n   498→                    'type_text': ActionType.TYPE_TEXT,\n   499→                    'hotkey': ActionType.HOTKEY,\n   500→                    'scroll': ActionType.SCROLL,\n   501→                    'wait': ActionType.WAIT,\n   502→                    'screenshot': ActionType.SCREENSHOT,\n   503→                    'find_element': ActionType.FIND_ELEMENT\n   504→                }\n   505→\n   506→                action_type = action_type_map.get(action_type_str, ActionType.CLICK)\n   507→\n   508→                steps.append(PlannedAction(\n   509→                    step_number=step_data.get('step', len(steps) + 1),\n   510→                    action_type=action_type,\n   511→                    description=step_data.get('description', ''),\n   512→                    target=step_data.get('target'),\n   513→                    parameters=step_data.get('params', {}),\n   514→                    expected_result=step_data.get('expected_result')\n   515→                ))\n   516→\n   517→            return ExecutionPlan(\n   518→                task_description=user_request,\n   519→                steps=steps,\n   520→                estimated_duration_seconds=data.get('estimated_seconds', 30),\n   521→                risk_level=data.get('risk_level', 'low'),\n   522→                requires_confirmation=data.get('risk_level', 'low') != 'low'\n   523→            )\n   524→\n   525→        except json.JSONDecodeError as e:\n   526→            logger.error(f\"JSON parse error: {e}\")\n   527→            return self._fallback_plan(user_request)\n   528→\n   529→    def _fallback_plan(self, user_request: str) -> ExecutionPlan:\n   530→        \"\"\"Plan de fallback si le LLM n'est pas disponible.\"\"\"\n   531→\n   532→        # Détection simple de patterns\n   533→        request_lower = user_request.lower()\n   534→        steps = []\n   535→\n   536→        # Pattern: ouvrir navigateur/browser\n   537→        if any(word in request_lower for word in ['navigateur', 'browser', 'chrome', 'firefox']):\n   538→            steps.append(PlannedAction(\n   539→                step_number=1,\n   540→                action_type=ActionType.OPEN_APP,\n   541→                description=\"Ouvrir le navigateur\",\n   542→                parameters={\"app_name\": \"firefox\"}\n   543→            ))\n   544→\n   545→        # Pattern: aller sur / ouvrir site\n   546→        url_patterns = [\n   547→            (r'youtube', 'https://www.youtube.com'),\n   548→            (r'google', 'https://www.google.com'),\n   549→            (r'github', 'https://www.github.com'),\n   550→        ]\n   551→\n   552→        for pattern, url in url_patterns:\n   553→            if pattern in request_lower:\n   554→                steps.append(PlannedAction(\n   555→                    step_number=len(steps) + 1,\n   556→                    action_type=ActionType.OPEN_URL,\n   557→                    description=f\"Ouvrir {pattern}\",\n   558→                    parameters={\"url\": url}\n   559→                ))\n   560→                break\n   561→\n   562→        # Pattern: chercher / rechercher\n   563→        if any(word in request_lower for word in ['chercher', 'rechercher', 'search']):\n   564→            # Extraire le terme de recherche\n   565→            search_term = request_lower\n   566→            for remove in ['cherche', 'recherche', 'search', 'sur youtube', 'sur google', 'une vidéo', 'video']:\n   567→                search_term = search_term.replace(remove, '')\n   568→            search_term = search_term.strip()\n   569→\n   570→            if search_term:\n   571→                steps.append(PlannedAction(\n   572→                    step_number=len(steps) + 1,\n   573→                    action_type=ActionType.CLICK,\n   574→                    description=\"Cliquer sur la barre de recherche\",\n   575→                    target=\"barre de recherche\"\n   576→                ))\n   577→                steps.append(PlannedAction(\n   578→                    step_number=len(steps) + 1,\n   579→                    action_type=ActionType.TYPE_TEXT,\n   580→                    description=f\"Taper '{search_term}'\",\n   581→                    parameters={\"text\": search_term}\n   582→                ))\n   583→                steps.append(PlannedAction(\n   584→                    step_number=len(steps) + 1,\n   585→                    action_type=ActionType.HOTKEY,\n   586→                    description=\"Appuyer sur Entrée\",\n   587→                    parameters={\"keys\": \"Return\"}\n   588→                ))\n   589→\n   590→        # Pattern: lancer / jouer vidéo\n   591→        if any(word in request_lower for word in ['lancer', 'jouer', 'play', 'regarder']):\n   592→            steps.append(PlannedAction(\n   593→                step_number=len(steps) + 1,\n   594→                action_type=ActionType.WAIT,\n   595→                description=\"Attendre le chargement\",\n   596→                parameters={\"seconds\": 2}\n   597→            ))\n   598→            steps.append(PlannedAction(\n   599→                step_number=len(steps) + 1,\n   600→                action_type=ActionType.CLICK,\n   601→                description=\"Cliquer sur la première vidéo\",\n   602→                target=\"première miniature de vidéo\"\n   603→            ))\n   604→\n   605→        if not steps:\n   606→            # Plan générique\n   607→            steps.append(PlannedAction(\n   608→                step_number=1,\n   609→                action_type=ActionType.SCREENSHOT,\n   610→                description=\"Capturer l'écran actuel\",\n   611→                parameters={}\n   612→            ))\n   613→\n   614→        return ExecutionPlan(\n   615→            task_description=user_request,\n   616→            steps=steps,\n   617→            estimated_duration_seconds=len(steps) * 5,\n   618→            risk_level=\"low\"\n   619→        )\n   620→\n   621→    async def execute_plan(self, plan: ExecutionPlan) -> List[ActionResult]:\n   622→        \"\"\"\n   623→        Exécute un plan d'action.\n   624→\n   625→        Args:\n   626→            plan: Le plan à exécuter\n   627→\n   628→        Returns:\n   629→            Liste des résultats d'actions\n   630→        \"\"\"\n   631→        results = []\n   632→        total_steps = len(plan.steps)\n   633→\n   634→        self._notify_progress(0, total_steps, \"Démarrage de l'exécution...\", \"starting\")\n   635→\n   636→        for i, action in enumerate(plan.steps):\n   637→            step_num = i + 1\n   638→            self._notify_progress(\n   639→                step_num,\n   640→                total_steps,\n   641→                f\"Étape {step_num}/{total_steps}: {action.description}\",\n   642→                \"running\"\n   643→            )\n   644→\n   645→            try:\n   646→                result = await self._execute_action(action)\n   647→                results.append(result)\n   648→\n   649→                if not result.success:\n   650→                    logger.warning(f\"Step {step_num} failed: {result.message}\")\n   651→                    # Continuer malgré l'échec (best effort)\n   652→\n   653→            except Exception as e:\n   654→                logger.error(f\"Step {step_num} error: {e}\")\n   655→                results.append(ActionResult(\n   656→                    success=False,\n   657→                    action=action,\n   658→                    message=f\"Erreur: {str(e)}\",\n   659→                    error=str(e)\n   660→                ))\n   661→\n   662→            # Petit délai entre les actions\n   663→            time.sleep(0.3)\n   664→\n   665→        success_count = sum(1 for r in results if r.success)\n   666→        self._notify_progress(\n   667→            total_steps,\n   668→            total_steps,\n   669→            f\"Terminé: {success_count}/{total_steps} étapes réussies\",\n   670→            \"completed\" if success_count == total_steps else \"partial\"\n   671→        )\n   672→\n   673→        return results\n   674→\n   675→    async def _execute_action(self, action: PlannedAction) -> ActionResult:\n   676→        \"\"\"Exécute une action individuelle.\"\"\"\n   677→\n   678→        start_time = time.time()\n   679→\n   680→        try:\n   681→            if action.action_type == ActionType.OPEN_APP:\n   682→                return await self._exec_open_app(action)\n   683→\n   684→            elif action.action_type == ActionType.OPEN_URL:\n   685→                return await self._exec_open_url(action)\n   686→\n   687→            elif action.action_type == ActionType.CLICK:\n   688→                return await self._exec_click(action)\n   689→\n   690→            elif action.action_type == ActionType.TYPE_TEXT:\n   691→                return await self._exec_type_text(action)\n   692→\n   693→            elif action.action_type == ActionType.HOTKEY:\n   694→                return await self._exec_hotkey(action)\n   695→\n   696→            elif action.action_type == ActionType.SCROLL:\n   697→                return await self._exec_scroll(action)\n   698→\n   699→            elif action.action_type == ActionType.WAIT:\n   700→                return await self._exec_wait(action)\n   701→\n   702→            elif action.action_type == ActionType.SCREENSHOT:\n   703→                return await self._exec_screenshot(action)\n   704→\n   705→            else:\n   706→                return ActionResult(\n   707→                    success=False,\n   708→                    action=action,\n   709→                    message=f\"Type d'action non supporté: {action.action_type}\",\n   710→                    duration_ms=int((time.time() - start_time) * 1000)\n   711→                )\n   712→\n   713→        except Exception as e:\n   714→            return ActionResult(\n   715→                success=False,\n   716→                action=action,\n   717→                message=f\"Erreur: {str(e)}\",\n   718→                error=str(e),\n   719→                duration_ms=int((time.time() - start_time) * 1000)\n   720→            )\n   721→\n   722→    async def _exec_open_app(self, action: PlannedAction) -> ActionResult:\n   723→        \"\"\"Ouvre une application.\"\"\"\n   724→        import subprocess\n   725→\n   726→        app_name = action.parameters.get('app_name', 'firefox')\n   727→\n   728→        # Mapping des noms courants\n   729→        app_commands = {\n   730→            'firefox': 'firefox',\n   731→            'chrome': 'google-chrome',\n   732→            'navigateur': 'firefox',\n   733→            'browser': 'firefox',\n   734→            'terminal': 'gnome-terminal',\n   735→            'files': 'nautilus',\n   736→            'code': 'code',\n   737→            'vscode': 'code'\n   738→        }\n   739→\n   740→        cmd = app_commands.get(app_name.lower(), app_name)\n   741→\n   742→        try:\n   743→            subprocess.Popen([cmd], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)\n   744→            time.sleep(1)  # Attendre que l'app démarre\n   745→\n   746→            return ActionResult(\n   747→                success=True,\n   748→                action=action,\n   749→                message=f\"Application '{app_name}' ouverte\"\n   750→            )\n   751→        except FileNotFoundError:\n   752→            return ActionResult(\n   753→                success=False,\n   754→                action=action,\n   755→                message=f\"Application '{app_name}' non trouvée\"\n   756→            )\n   757→\n   758→    async def _exec_open_url(self, action: PlannedAction) -> ActionResult:\n   759→        \"\"\"Ouvre une URL dans le navigateur.\"\"\"\n   760→        import subprocess\n   761→\n   762→        url = action.parameters.get('url', 'https://www.google.com')\n   763→\n   764→        try:\n   765→            subprocess.Popen(['xdg-open', url], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)\n   766→            time.sleep(2)  # Attendre le chargement\n   767→\n   768→            return ActionResult(\n   769→                success=True,\n   770→                action=action,\n   771→                message=f\"URL '{url}' ouverte\"\n   772→            )\n   773→        except Exception as e:\n   774→            return ActionResult(\n   775→                success=False,\n   776→                action=action,\n   777→                message=f\"Erreur ouverture URL: {e}\"\n   778→            )\n   779→\n   780→    async def _exec_click(self, action: PlannedAction) -> ActionResult:\n   781→        \"\"\"Exécute un clic sur un élément avec détection visuelle.\"\"\"\n   782→\n   783→        target = action.target or action.parameters.get('target', '')\n   784→\n   785→        # Si on a un ActionExecutor configuré, l'utiliser en priorité\n   786→        if self._action_executor:\n   787→            try:\n   788→                screenshot = None\n   789→                if self._screen_capturer:\n   790→                    screenshot = self._screen_capturer()\n   791→\n   792→                result = self._action_executor(\n   793→                    action_type=\"click\",\n   794→                    target=target,\n   795→                    screenshot=screenshot\n   796→                )\n   797→\n   798→                return ActionResult(\n   799→                    success=result.get('success', False),\n   800→                    action=action,\n   801→                    message=result.get('message', 'Clic effectué')\n   802→                )\n   803→            except Exception as e:\n   804→                logger.warning(f\"ActionExecutor failed: {e}, using visual detection\")\n   805→\n   806→        # Utiliser la détection visuelle\n   807→        try:\n   808→            if not PYAUTOGUI_AVAILABLE:\n   809→                return ActionResult(\n   810→                    success=False,\n   811→                    action=action,\n   812→                    message=\"pyautogui non disponible\"\n   813→                )\n   814→\n   815→            # Capturer l'écran\n   816→            screenshot = self._capture_screen()\n   817→\n   818→            # Trouver l'élément par sa description\n   819→            position = self._find_element_by_description(target, screenshot)\n   820→\n   821→            if position:\n   822→                x, y = position\n   823→                logger.info(f\"Clicking at ({x}, {y}) for target: '{target}'\")\n   824→                pyautogui.click(x, y)\n   825→\n   826→                return ActionResult(\n   827→                    success=True,\n   828→                    action=action,\n   829→                    message=f\"Clic à ({x}, {y}) pour '{target}'\"\n   830→                )\n   831→            else:\n   832→                return ActionResult(\n   833→                    success=False,\n   834→                    action=action,\n   835→                    message=f\"Élément non trouvé: '{target}'\"\n   836→                )\n   837→\n   838→        except Exception as e:\n   839→            logger.error(f\"Click error: {e}\")\n   840→            return ActionResult(\n   841→                success=False,\n   842→                action=action,\n   843→                message=f\"Erreur clic: {e}\"\n   844→            )\n   845→\n   846→    async def _exec_type_text(self, action: PlannedAction) -> ActionResult:\n   847→        \"\"\"Tape du texte dans un champ (clique d'abord sur le champ si spécifié).\"\"\"\n   848→        try:\n   849→            if not PYAUTOGUI_AVAILABLE:\n   850→                return ActionResult(\n   851→                    success=False,\n   852→                    action=action,\n   853→                    message=\"pyautogui non disponible\"\n   854→                )\n   855→\n   856→            text = action.parameters.get('text', '')\n   857→            target = action.target or action.parameters.get('target', '')\n   858→\n   859→            # Si une cible est spécifiée, cliquer dessus d'abord\n   860→            if target:\n   861→                screenshot = self._capture_screen()\n   862→                position = self._find_element_by_description(target, screenshot)\n   863→\n   864→                if position:\n   865→                    x, y = position\n   866→                    logger.info(f\"Clicking on input field at ({x}, {y}) before typing\")\n   867→                    pyautogui.click(x, y)\n   868→                    time.sleep(0.5)  # Attendre que le focus soit acquis\n   869→                else:\n   870→                    # Champ non trouvé - on continue car le focus est peut-être déjà au bon endroit\n   871→                    # (ex: après un hotkey ctrl+l ou ctrl+k)\n   872→                    logger.info(f\"Target '{target}' not found visually, assuming focus is already correct\")\n   873→\n   874→            # Taper le texte\n   875→            time.sleep(0.2)\n   876→\n   877→            # Utiliser write() pour le texte Unicode (français, etc.)\n   878→            if text.isascii():\n   879→                pyautogui.typewrite(text, interval=0.03)\n   880→            else:\n   881→                # Pour les caractères non-ASCII, utiliser le presse-papier\n   882→                import subprocess\n   883→                # Copier dans le presse-papier\n   884→                process = subprocess.Popen(['xclip', '-selection', 'clipboard'], stdin=subprocess.PIPE)\n   885→                process.communicate(text.encode('utf-8'))\n   886→                # Coller avec Ctrl+V\n   887→                pyautogui.hotkey('ctrl', 'v')\n   888→\n   889→            return ActionResult(\n   890→                success=True,\n   891→                action=action,\n   892→                message=f\"Texte tapé: '{text[:30]}...'\" if len(text) > 30 else f\"Texte tapé: '{text}'\"\n   893→            )\n   894→\n   895→        except Exception as e:\n   896→            logger.error(f\"Type text error: {e}\")\n   897→            return ActionResult(\n   898→                success=False,\n   899→                action=action,\n   900→                message=f\"Erreur frappe: {e}\"\n   901→            )\n   902→\n   903→    async def _exec_hotkey(self, action: PlannedAction) -> ActionResult:\n   904→        \"\"\"Exécute un raccourci clavier.\"\"\"\n   905→        try:\n   906→            import pyautogui\n   907→\n   908→            keys = action.parameters.get('keys', 'Return')\n   909→\n   910→            # Parser les combinaisons de touches\n   911→            if '+' in keys:\n   912→                key_list = [k.strip().lower() for k in keys.split('+')]\n   913→                pyautogui.hotkey(*key_list)\n   914→            else:\n   915→                pyautogui.press(keys.lower())\n   916→\n   917→            return ActionResult(\n   918→                success=True,\n   919→                action=action,\n   920→                message=f\"Touche(s) '{keys}' pressée(s)\"\n   921→            )\n   922→\n   923→        except Exception as e:\n   924→            return ActionResult(\n   925→                success=False,\n   926→                action=action,\n   927→                message=f\"Erreur hotkey: {e}\"\n   928→            )\n   929→\n   930→    async def _exec_scroll(self, action: PlannedAction) -> ActionResult:\n   931→        \"\"\"Effectue un scroll.\"\"\"\n   932→        try:\n   933→            import pyautogui\n   934→\n   935→            direction = action.parameters.get('direction', 'down')\n   936→            amount = action.parameters.get('amount', 3)\n   937→\n   938→            scroll_amount = amount if direction == 'up' else -amount\n   939→            pyautogui.scroll(scroll_amount)\n   940→\n   941→            return ActionResult(\n   942→                success=True,\n   943→                action=action,\n   944→                message=f\"Scroll {direction} de {amount}\"\n   945→            )\n   946→\n   947→        except Exception as e:\n   948→            return ActionResult(\n   949→                success=False,\n   950→                action=action,\n   951→                message=f\"Erreur scroll: {e}\"\n   952→            )\n   953→\n   954→    async def _exec_wait(self, action: PlannedAction) -> ActionResult:\n   955→        \"\"\"Attend un certain temps.\"\"\"\n   956→        seconds = action.parameters.get('seconds', 1)\n   957→        time.sleep(seconds)\n   958→\n   959→        return ActionResult(\n   960→            success=True,\n   961→            action=action,\n   962→            message=f\"Attente de {seconds}s terminée\"\n   963→        )\n   964→\n   965→    async def _exec_screenshot(self, action: PlannedAction) -> ActionResult:\n   966→        \"\"\"Capture l'écran.\"\"\"\n   967→        try:\n   968→            if self._screen_capturer:\n   969→                screenshot = self._screen_capturer()\n   970→                # Sauvegarder si nécessaire\n   971→                path = f\"/tmp/screenshot_{datetime.now().strftime('%H%M%S')}.png\"\n   972→\n   973→                return ActionResult(\n   974→                    success=True,\n   975→                    action=action,\n   976→                    message=\"Screenshot capturé\",\n   977→                    screenshot_path=path\n   978→                )\n   979→            else:\n   980→                import pyautogui\n   981→                screenshot = pyautogui.screenshot()\n   982→                path = f\"/tmp/screenshot_{datetime.now().strftime('%H%M%S')}.png\"\n   983→                screenshot.save(path)\n   984→\n   985→                return ActionResult(\n   986→                    success=True,\n   987→                    action=action,\n   988→                    message=\"Screenshot capturé\",\n   989→                    screenshot_path=path\n   990→                )\n   991→\n   992→        except Exception as e:\n   993→            return ActionResult(\n   994→                success=False,\n   995→                action=action,\n   996→                message=f\"Erreur screenshot: {e}\"\n   997→            )\n   998→\n   999→\n  1000→# Singleton\n  1001→_planner_instance: Optional[AutonomousPlanner] = None\n  1002→\n  1003→\n  1004→def get_autonomous_planner(\n  1005→    llm_model: str = \"qwen2.5:7b\"\n  1006→) -> AutonomousPlanner:\n  1007→    \"\"\"Retourne l'instance singleton du planner.\"\"\"\n  1008→    global _planner_instance\n  1009→\n  1010→    if _planner_instance is None:\n  1011→        _planner_instance = AutonomousPlanner(llm_model=llm_model)\n  1012→\n  1013→    return _planner_instance\n  1014→"}]},"uuid":"11f4ac18-a8f3-44aa-9c09-180da7980968","timestamp":"2026-03-14T21:14:47.061Z","sourceToolAssistantUUID":"e6c09744-f7f8-4f2e-b18d-b260036d6f58"}
+{"parentUuid":"11f4ac18-a8f3-44aa-9c09-180da7980968","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","message":{"model":"claude-opus-4-6","id":"msg_01H8ZjFUhrqBrpRGKpFeQHu8","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me check the remaining workflow_graph.py model code (edges, Action, TargetSpec, Workflow class) and the OllamaClient for VLM."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":19869,"cache_read_input_tokens":73649,"cache_creation":{"ephemeral_5m_input_tokens":19869,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZ3fWGtGR8nfcrwezUJkN","type":"assistant","uuid":"87ad57d3-2eac-4f51-abdf-6ffbe836a341","timestamp":"2026-03-14T21:14:51.234Z"}
+{"parentUuid":"87ad57d3-2eac-4f51-abdf-6ffbe836a341","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","message":{"model":"claude-opus-4-6","id":"msg_01H8ZjFUhrqBrpRGKpFeQHu8","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013nBBeHdHp9v2zyV7mDi9tj","name":"Read","input":{"file_path":"/home/dom/.claude/projects/-home-dom-ai-rpa-vision-v3/eec8c668-e1f8-4599-9dd5-42170a166ba4/tool-results/toolu_01X8WDVqsezUXH1vkcskBwyT.txt","offset":550,"limit":350},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":19869,"cache_read_input_tokens":73649,"cache_creation":{"ephemeral_5m_input_tokens":19869,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZ3fWGtGR8nfcrwezUJkN","type":"assistant","uuid":"97aaadb2-200e-4567-b137-6a689a807e14","timestamp":"2026-03-14T21:14:55.188Z"}
+{"parentUuid":"97aaadb2-200e-4567-b137-6a689a807e14","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_013nBBeHdHp9v2zyV7mDi9tj","toolUseID":"toolu_013nBBeHdHp9v2zyV7mDi9tj","timestamp":"2026-03-14T21:14:55.212Z","uuid":"99b6d6fb-7717-490f-a2ca-b02075b058c7"}
+{"parentUuid":"97aaadb2-200e-4567-b137-6a689a807e14","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013nBBeHdHp9v2zyV7mDi9tj","type":"tool_result","content":"   550→   550→        return None\n   551→   551→    \n   552→   552→    def add_spatial_relation(self, relation: SpatialRelation) -> None:\n   553→   553→        \"\"\"Ajouter une relation spatiale\"\"\"\n   554→   554→        # Éviter les doublons\n   555→   555→        for existing in self.spatial_relations:\n   556→   556→            if (existing.source_element_id == relation.source_element_id and\n   557→   557→                existing.target_element_id == relation.target_element_id and\n   558→   558→                existing.relation_type == relation.relation_type):\n   559→   559→                return\n   560→   560→        self.spatial_relations.append(relation)\n   561→   561→    \n   562→   562→    def get_relations_for_element(self, element_id: str) -> List[SpatialRelation]:\n   563→   563→        \"\"\"Récupérer toutes les relations impliquant un élément\"\"\"\n   564→   564→        return [r for r in self.spatial_relations \n   565→   565→                if r.source_element_id == element_id or r.target_element_id == element_id]\n   566→   566→\n   567→   567→\n   568→   568→# ============================================================================\n   569→   569→# WorkflowEdge et Actions\n   570→   570→# ============================================================================\n   571→   571→\n   572→   572→@dataclass\n   573→   573→class TargetSpec:\n   574→   574→    \"\"\"\n   575→   575→    Spécification de la cible d'une action.\n   576→   576→    \n   577→   577→    Supporte plusieurs stratégies de résolution:\n   578→   578→    - by_role: Recherche par rôle sémantique\n   579→   579→    - by_text: Recherche par texte exact ou partiel\n   580→   580→    - by_position: Recherche par coordonnées\n   581→   581→    - embedding_ref: Recherche par similarité visuelle\n   582→   582→    \n   583→   583→    Fiche #11 - Multi-anchor + contraintes combinées:\n   584→   584→    - hard_constraints: Contraintes strictes (within_container_text, min_area)\n   585→   585→    - weights: Pondération des critères de scoring\n   586→   586→    \"\"\"\n   587→   587→    # Stratégies de recherche (au moins une requise)\n   588→   588→    by_role: Optional[str] = None  # Rôle sémantique (e.g., \"button\", \"textfield\")\n   589→   589→    by_text: Optional[str] = None  # Texte à rechercher\n   590→   590→    by_position: Optional[Tuple[int, int]] = None  # Coordonnées (x, y)\n   591→   591→    \n   592→   592→    # Configuration\n   593→   593→    selection_policy: str = \"first\"  # first, last, by_similarity, largest, nearest_to_previous\n   594→   594→    fallback_strategy: str = \"visual_similarity\"  # visual_similarity, position\n   595→   595→    embedding_ref: Optional[Any] = None  # Embedding de référence pour similarité\n   596→   596→    \n   597→   597→    # Contexte spatial (optionnel)\n   598→   598→    context_hints: Dict[str, Any] = field(default_factory=dict)  # near_text, below_text, right_of_text\n   599→   599→    \n   600→   600→    # Fiche #11: Contraintes & pondérations (facultatif, backward compatible)\n   601→   601→    hard_constraints: Dict[str, Any] = field(default_factory=dict)  # within_container_text, min_area\n   602→   602→    weights: Dict[str, float] = field(default_factory=dict)  # proximity, alignment, container, roi_iou\n   603→   603→    \n   604→   604→    # Legacy support\n   605→   605→    @property\n   606→   606→    def role(self) -> Optional[str]:\n   607→   607→        \"\"\"Alias pour by_role (compatibilité)\"\"\"\n   608→   608→        return self.by_role\n   609→   609→    \n   610→   610→    def to_dict(self) -> Dict[str, Any]:\n   611→   611→        return {\n   612→   612→            \"by_role\": self.by_role,\n   613→   613→            \"by_text\": self.by_text,\n   614→   614→            \"by_position\": list(self.by_position) if self.by_position else None,\n   615→   615→            \"selection_policy\": self.selection_policy,\n   616→   616→            \"fallback_strategy\": self.fallback_strategy,\n   617→   617→            \"context_hints\": self.context_hints,\n   618→   618→            # Fiche #11: Nouveaux champs\n   619→   619→            \"hard_constraints\": self.hard_constraints,\n   620→   620→            \"weights\": self.weights\n   621→   621→        }\n   622→   622→    \n   623→   623→    @classmethod\n   624→   624→    def from_dict(cls, data: Dict[str, Any]) -> 'TargetSpec':\n   625→   625→        by_position = data.get(\"by_position\")\n   626→   626→        if by_position:\n   627→   627→            by_position = tuple(by_position)\n   628→   628→        \n   629→   629→        return cls(\n   630→   630→            by_role=data.get(\"by_role\") or data.get(\"role\"),  # Support legacy \"role\"\n   631→   631→            by_text=data.get(\"by_text\"),\n   632→   632→            by_position=by_position,\n   633→   633→            selection_policy=data.get(\"selection_policy\", \"first\"),\n   634→   634→            fallback_strategy=data.get(\"fallback_strategy\", \"visual_similarity\"),\n   635→   635→            context_hints=data.get(\"context_hints\", {}),\n   636→   636→            # Fiche #11: Nouveaux champs avec defaults safe\n   637→   637→            hard_constraints=data.get(\"hard_constraints\", {}),\n   638→   638→            weights=data.get(\"weights\", {})\n   639→   639→        )\n   640→   640→\n   641→   641→\n   642→   642→@dataclass\n   643→   643→class Action:\n   644→   644→    \"\"\"Action à exécuter sur un élément UI\"\"\"\n   645→   645→    type: str  # mouse_click, text_input, key_press, compound\n   646→   646→    target: TargetSpec\n   647→   647→    parameters: Dict[str, Any] = field(default_factory=dict)\n   648→   648→    \n   649→   649→    @property\n   650→   650→    def params(self) -> Dict[str, Any]:\n   651→   651→        return self.parameters\n   652→   652→\n   653→   653→    @params.setter\n   654→   654→    def params(self, value: Dict[str, Any]) -> None:\n   655→   655→        self.parameters = value\n   656→   656→    \n   657→   657→    def to_dict(self) -> Dict[str, Any]:\n   658→   658→        return {\n   659→   659→            \"type\": self.type,\n   660→   660→            \"target\": self.target.to_dict(),\n   661→   661→            \"parameters\": self.parameters\n   662→   662→        }\n   663→   663→    \n   664→   664→    @classmethod\n   665→   665→    def from_dict(cls, data: Dict[str, Any]) -> 'Action':\n   666→   666→        return cls(\n   667→   667→            type=data[\"type\"],\n   668→   668→            target=TargetSpec.from_dict(data[\"target\"]),\n   669→   669→            parameters=data.get(\"parameters\", {})\n   670→   670→        )\n   671→   671→\n   672→   672→\n   673→   673→@dataclass\n   674→   674→class EdgeConstraints:\n   675→   675→    \"\"\"Contraintes pour l'exécution d'un edge\"\"\"\n   676→   676→    pre_conditions: Dict[str, Any] = field(default_factory=dict)\n   677→   677→    required_confidence: float = 0.8\n   678→   678→    max_wait_time_ms: int = 5000\n   679→   679→    \n   680→   680→    def to_dict(self) -> Dict[str, Any]:\n   681→   681→        return {\n   682→   682→            \"pre_conditions\": self.pre_conditions,\n   683→   683→            \"required_confidence\": self.required_confidence,\n   684→   684→            \"max_wait_time_ms\": self.max_wait_time_ms\n   685→   685→        }\n   686→   686→    \n   687→   687→    @classmethod\n   688→   688→    def from_dict(cls, data: Dict[str, Any]) -> 'EdgeConstraints':\n   689→   689→        return cls(\n   690→   690→            pre_conditions=data.get(\"pre_conditions\", {}),\n   691→   691→            required_confidence=data.get(\"required_confidence\", 0.8),\n   692→   692→            max_wait_time_ms=data.get(\"max_wait_time_ms\", 5000)\n   693→   693→        )\n   694→   694→\n   695→   695→\n   696→   696→@dataclass\n   697→   697→class PostConditionCheck:\n   698→   698→    \"\"\"\n   699→   699→    kind:       - \"text_present\": value=str\n   700→   700→                - \"text_absent\": value=str\n   701→   701→                - \"element_present\": target=TargetSpec\n   702→   702→                - \"window_title_contains\": value=str\n   703→   703→    \"\"\"\n   704→   704→    kind: str\n   705→   705→    value: Optional[str] = None\n   706→   706→    target: Optional[\"TargetSpec\"] = None\n   707→   707→\n   708→   708→\n   709→   709→@dataclass\n   710→   710→class PostConditions:\n   711→   711→    \"\"\"Post-conditions attendues après exécution - Fiche #9\"\"\"\n   712→   712→    # (garde tes champs existants si tu en as déjà, et ajoute ceux-ci)\n   713→   713→    \n   714→   714→    success_mode: str = \"all\"      # \"all\" | \"any\"\n   715→   715→    timeout_ms: int = 2500\n   716→   716→    poll_ms: int = 200\n   717→   717→    \n   718→   718→    success: List[PostConditionCheck] = field(default_factory=list)\n   719→   719→    fail_fast: List[PostConditionCheck] = field(default_factory=list)\n   720→   720→    \n   721→   721→    retries: int = 2               # nb de tentatives après échec post-conditions\n   722→   722→    backoff_ms: int = 150          # 150, 300, 600...\n   723→   723→    \n   724→   724→    # Legacy fields (garde compatibilité)\n   725→   725→    expected_node: Optional[str] = None  # Node attendu après action\n   726→   726→    window_change_expected: bool = False\n   727→   727→    new_ui_elements_expected: List[str] = field(default_factory=list)\n   728→   728→    \n   729→   729→    def to_dict(self) -> Dict[str, Any]:\n   730→   730→        return {\n   731→   731→            \"success_mode\": self.success_mode,\n   732→   732→            \"timeout_ms\": self.timeout_ms,\n   733→   733→            \"poll_ms\": self.poll_ms,\n   734→   734→            \"success\": [{\"kind\": c.kind, \"value\": c.value, \"target\": c.target.to_dict() if c.target else None} for c in self.success],\n   735→   735→            \"fail_fast\": [{\"kind\": c.kind, \"value\": c.value, \"target\": c.target.to_dict() if c.target else None} for c in self.fail_fast],\n   736→   736→            \"retries\": self.retries,\n   737→   737→            \"backoff_ms\": self.backoff_ms,\n   738→   738→            # Legacy\n   739→   739→            \"expected_node\": self.expected_node,\n   740→   740→            \"window_change_expected\": self.window_change_expected,\n   741→   741→            \"new_ui_elements_expected\": self.new_ui_elements_expected\n   742→   742→        }\n   743→   743→    \n   744→   744→    @classmethod\n   745→   745→    def from_dict(cls, data: Dict[str, Any]) -> 'PostConditions':\n   746→   746→        success_checks = []\n   747→   747→        for c in data.get(\"success\", []):\n   748→   748→            target = TargetSpec.from_dict(c[\"target\"]) if c.get(\"target\") else None\n   749→   749→            success_checks.append(PostConditionCheck(kind=c[\"kind\"], value=c.get(\"value\"), target=target))\n   750→   750→        \n   751→   751→        fail_fast_checks = []\n   752→   752→        for c in data.get(\"fail_fast\", []):\n   753→   753→            target = TargetSpec.from_dict(c[\"target\"]) if c.get(\"target\") else None\n   754→   754→            fail_fast_checks.append(PostConditionCheck(kind=c[\"kind\"], value=c.get(\"value\"), target=target))\n   755→   755→        \n   756→   756→        return cls(\n   757→   757→            success_mode=data.get(\"success_mode\", \"all\"),\n   758→   758→            timeout_ms=data.get(\"timeout_ms\", 2500),\n   759→   759→            poll_ms=data.get(\"poll_ms\", 200),\n   760→   760→            success=success_checks,\n   761→   761→            fail_fast=fail_fast_checks,\n   762→   762→            retries=data.get(\"retries\", 2),\n   763→   763→            backoff_ms=data.get(\"backoff_ms\", 150),\n   764→   764→            # Legacy\n   765→   765→            expected_node=data.get(\"expected_node\"),\n   766→   766→            window_change_expected=data.get(\"window_change_expected\", False),\n   767→   767→            new_ui_elements_expected=data.get(\"new_ui_elements_expected\", [])\n   768→   768→        )\n   769→   769→\n   770→   770→\n   771→   771→@dataclass\n   772→   772→class EdgeStats:\n   773→   773→    \"\"\"Statistiques d'exécution d'un edge\"\"\"\n   774→   774→    execution_count: int = 0\n   775→   775→    success_count: int = 0\n   776→   776→    failure_count: int = 0\n   777→   777→    avg_execution_time_ms: float = 0.0\n   778→   778→    last_executed: Optional[datetime] = None\n   779→   779→    \n   780→   780→    @property\n   781→   781→    def success_rate(self) -> float:\n   782→   782→        if self.execution_count == 0:\n   783→   783→            return 0.0\n   784→   784→        return self.success_count / self.execution_count\n   785→   785→    \n   786→   786→    def to_dict(self) -> Dict[str, Any]:\n   787→   787→        return {\n   788→   788→            \"execution_count\": self.execution_count,\n   789→   789→            \"success_count\": self.success_count,\n   790→   790→            \"failure_count\": self.failure_count,\n   791→   791→            \"avg_execution_time_ms\": self.avg_execution_time_ms,\n   792→   792→            \"last_executed\": self.last_executed.isoformat() if self.last_executed else None\n   793→   793→        }\n   794→   794→    \n   795→   795→    @classmethod\n   796→   796→    def from_dict(cls, data: Dict[str, Any]) -> 'EdgeStats':\n   797→   797→        last_executed = None\n   798→   798→        if data.get(\"last_executed\"):\n   799→   799→            last_executed = datetime.fromisoformat(data[\"last_executed\"])\n   800→   800→        \n   801→   801→        return cls(\n   802→   802→            execution_count=data.get(\"execution_count\", 0),\n   803→   803→            success_count=data.get(\"success_count\", 0),\n   804→   804→            failure_count=data.get(\"failure_count\", 0),\n   805→   805→            avg_execution_time_ms=data.get(\"avg_execution_time_ms\", 0.0),\n   806→   806→            last_executed=last_executed\n   807→   807→        )\n   808→   808→\n   809→   809→\n   810→   810→@dataclass\n   811→   811→class WorkflowEdge:\n   812→   812→    \"\"\"\n   813→   813→    Edge dans un Workflow Graph\n   814→   814→    \n   815→   815→    Représente une transition entre deux états via une action.\n   816→   816→    \"\"\"\n   817→   817→    edge_id: str\n   818→   818→    from_node: str\n   819→   819→    to_node: str\n   820→   820→    action: Action\n   821→   821→    constraints: EdgeConstraints\n   822→   822→    post_conditions: PostConditions\n   823→   823→    stats: EdgeStats = field(default_factory=EdgeStats)\n   824→   824→    metadata: Dict[str, Any] = field(default_factory=dict)\n   825→   825→    \n   826→   826→    def can_execute(self, current_state: Any) -> Tuple[bool, str]:\n   827→   827→        \"\"\"\n   828→   828→        Vérifier si l'edge peut être exécuté\n   829→   829→        \n   830→   830→        Returns:\n   831→   831→            (can_execute: bool, reason: str)\n   832→   832→        \"\"\"\n   833→   833→        # Vérifier pre-conditions\n   834→   834→        for key, value in self.constraints.pre_conditions.items():\n   835→   835→            if not hasattr(current_state, key):\n   836→   836→                return False, f\"Missing pre-condition: {key}\"\n   837→   837→            if getattr(current_state, key) != value:\n   838→   838→                return False, f\"Pre-condition not met: {key}\"\n   839→   839→        \n   840→   840→        return True, \"OK\"\n   841→   841→    \n   842→   842→    def to_dict(self) -> Dict[str, Any]:\n   843→   843→        return {\n   844→   844→            \"edge_id\": self.edge_id,\n   845→   845→            \"from_node\": self.from_node,\n   846→   846→            \"to_node\": self.to_node,\n   847→   847→            \"action\": self.action.to_dict(),\n   848→   848→            \"constraints\": self.constraints.to_dict(),\n   849→   849→            \"post_conditions\": self.post_conditions.to_dict(),\n   850→   850→            \"stats\": self.stats.to_dict(),\n   851→   851→            \"metadata\": self.metadata\n   852→   852→        }\n   853→   853→    \n   854→   854→    @classmethod\n   855→   855→    def from_dict(cls, data: Dict[str, Any]) -> 'WorkflowEdge':\n   856→   856→        # Support both naming conventions\n   857→   857→        from_node = data.get(\"from_node\") or data.get(\"source_node\")\n   858→   858→        to_node = data.get(\"to_node\") or data.get(\"target_node\")\n   859→   859→        \n   860→   860→        return cls(\n   861→   861→            edge_id=data[\"edge_id\"],\n   862→   862→            from_node=from_node,\n   863→   863→            to_node=to_node,\n   864→   864→            action=Action.from_dict(data[\"action\"]),\n   865→   865→            constraints=EdgeConstraints.from_dict(data.get(\"constraints\", {})),\n   866→   866→            post_conditions=PostConditions.from_dict(data.get(\"post_conditions\", {})),\n   867→   867→            stats=EdgeStats.from_dict(data.get(\"stats\", {})),\n   868→   868→            metadata=data.get(\"metadata\", {})\n   869→   869→        )\n   870→   870→\n   871→   871→\n   872→   872→# ============================================================================\n   873→   873→# Workflow\n   874→   874→# ============================================================================\n   875→   875→\n   876→   876→@dataclass\n   877→   877→class SafetyRules:\n   878→   878→    \"\"\"Règles de sécurité pour un workflow\"\"\"\n   879→   879→    # Pas de limite d'exécutions - les workflows peuvent tourner indéfiniment\n   880→   880→    require_confirmation_for: List[str] = field(default_factory=list)\n   881→   881→    forbidden_windows: List[str] = field(default_factory=list)\n   882→   882→    # Timeout global optionnel (0 = pas de limite)\n   883→   883→    execution_timeout_minutes: int = 0\n   884→   884→    \n   885→   885→    def to_dict(self) -> Dict[str, Any]:\n   886→   886→        return {\n   887→   887→            \"require_confirmation_for\": self.require_confirmation_for,\n   888→   888→            \"forbidden_windows\": self.forbidden_windows,\n   889→   889→            \"execution_timeout_minutes\": self.execution_timeout_minutes\n   890→   890→        }\n   891→   891→    \n   892→   892→    @classmethod\n   893→   893→    def from_dict(cls, data: Dict[str, Any]) -> 'SafetyRules':\n   894→   894→        return cls(\n   895→   895→            require_confirmation_for=data.get(\"require_confirmation_for\", []),\n   896→   896→            forbidden_windows=data.get(\"forbidden_windows\", []),\n   897→   897→            execution_timeout_minutes=data.get(\"execution_timeout_minutes\", 0)\n   898→   898→        )\n   899→   899→"}]},"uuid":"67d8f0e2-1545-467f-a589-4e5660418e44","timestamp":"2026-03-14T21:14:55.210Z","sourceToolAssistantUUID":"97aaadb2-200e-4567-b137-6a689a807e14"}
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+{"parentUuid":"4e8b7992-eb31-4cf0-aecf-1654710832ae","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BSoCow5m65osTC3pYXWYiK","type":"tool_result","content":"     1→\"\"\"\n     2→OllamaClient - Client pour Vision-Language Models via Ollama\n     3→\n     4→Interface pour communiquer avec des VLM (Qwen, LLaVA, etc.) via Ollama.\n     5→\"\"\"\n     6→\n     7→import logging\n     8→from typing import Dict, List, Optional, Any\n     9→import requests\n    10→import json\n    11→import base64\n    12→from pathlib import Path\n    13→from PIL import Image\n    14→import io\n    15→\n    16→logger = logging.getLogger(__name__)\n    17→\n    18→\n    19→class OllamaClient:\n    20→    \"\"\"\n    21→    Client Ollama pour VLM\n    22→    \n    23→    Permet d'envoyer des images et prompts à un VLM via l'API Ollama.\n    24→    \"\"\"\n    25→    \n    26→    def __init__(self, \n    27→                 endpoint: str = \"http://localhost:11434\",\n    28→                 model: str = \"qwen3-vl:8b\",\n    29→                 timeout: int = 60):\n    30→        \"\"\"\n    31→        Initialiser le client Ollama\n    32→        \n    33→        Args:\n    34→            endpoint: URL de l'API Ollama\n    35→            model: Nom du modèle VLM à utiliser\n    36→            timeout: Timeout en secondes\n    37→        \"\"\"\n    38→        self.endpoint = endpoint.rstrip('/')\n    39→        self.model = model\n    40→        self.timeout = timeout\n    41→        self._check_connection()\n    42→    \n    43→    def _check_connection(self) -> bool:\n    44→        \"\"\"Vérifier la connexion à Ollama\"\"\"\n    45→        try:\n    46→            response = requests.get(f\"{self.endpoint}/api/tags\", timeout=5)\n    47→            if response.status_code == 200:\n    48→                models = response.json().get('models', [])\n    49→                model_names = [m['name'] for m in models]\n    50→                if self.model not in model_names:\n    51→                    logger.warning(f\" Model '{self.model}' not found in Ollama\")\n    52→                    logger.info(f\"Available models: {model_names}\")\n    53→                return True\n    54→        except Exception as e:\n    55→            logger.warning(f\" Cannot connect to Ollama at {self.endpoint}: {e}\")\n    56→            return False\n    57→        return False\n    58→    \n    59→    def generate(self,\n    60→                prompt: str,\n    61→                image_path: Optional[str] = None,\n    62→                image: Optional[Image.Image] = None,\n    63→                system_prompt: Optional[str] = None,\n    64→                temperature: float = 0.1,\n    65→                max_tokens: int = 500,\n    66→                force_json: bool = False) -> Dict[str, Any]:\n    67→        \"\"\"\n    68→        Générer une réponse du VLM\n    69→        \n    70→        Args:\n    71→            prompt: Prompt textuel\n    72→            image_path: Chemin vers une image (optionnel)\n    73→            image: Image PIL (optionnel)\n    74→            system_prompt: Prompt système (optionnel)\n    75→            temperature: Température de génération\n    76→            max_tokens: Nombre max de tokens\n    77→        \n    78→        Returns:\n    79→            Dict avec 'response', 'success', 'error'\n    80→        \"\"\"\n    81→        try:\n    82→            # Préparer l'image si fournie\n    83→            image_data = None\n    84→            if image_path:\n    85→                image_data = self._encode_image_from_path(image_path)\n    86→            elif image:\n    87→                image_data = self._encode_image_from_pil(image)\n    88→            \n    89→            # Construire la requête avec thinking mode désactivé\n    90→            # Pour Qwen3, utiliser /nothink au début du prompt\n    91→            effective_prompt = prompt\n    92→            if \"qwen\" in self.model.lower():\n    93→                effective_prompt = f\"/nothink {prompt}\"\n    94→            \n    95→            payload = {\n    96→                \"model\": self.model,\n    97→                \"prompt\": effective_prompt,\n    98→                \"stream\": False,\n    99→                \"options\": {\n   100→                    \"temperature\": temperature,\n   101→                    \"num_predict\": max_tokens,\n   102→                    \"num_ctx\": 2048,  # Contexte réduit pour plus de vitesse\n   103→                    \"top_k\": 1  # Plus rapide pour les tâches de classification\n   104→                }\n   105→            }\n   106→\n   107→            # Forcer la sortie JSON si demandé (réduit drastiquement les erreurs de parsing)\n   108→            if force_json:\n   109→                payload[\"format\"] = \"json\"\n   110→\n   111→            if system_prompt:\n   112→                payload[\"system\"] = system_prompt\n   113→\n   114→            if image_data:\n   115→                payload[\"images\"] = [image_data]\n   116→            \n   117→            # Envoyer la requête\n   118→            response = requests.post(\n   119→                f\"{self.endpoint}/api/generate\",\n   120→                json=payload,\n   121→                timeout=self.timeout\n   122→            )\n   123→            \n   124→            if response.status_code == 200:\n   125→                result = response.json()\n   126→                return {\n   127→                    \"response\": result.get(\"response\", \"\"),\n   128→                    \"success\": True,\n   129→                    \"error\": None\n   130→                }\n   131→            else:\n   132→                return {\n   133→                    \"response\": \"\",\n   134→                    \"success\": False,\n   135→                    \"error\": f\"HTTP {response.status_code}: {response.text}\"\n   136→                }\n   137→        \n   138→        except Exception as e:\n   139→            return {\n   140→                \"response\": \"\",\n   141→                \"success\": False,\n   142→                \"error\": str(e)\n   143→            }\n   144→    \n   145→    def detect_ui_elements(self, image_path: str) -> Dict[str, Any]:\n   146→        \"\"\"\n   147→        Détecter les éléments UI dans une image\n   148→        \n   149→        Args:\n   150→            image_path: Chemin vers le screenshot\n   151→        \n   152→        Returns:\n   153→            Dict avec liste d'éléments détectés\n   154→        \"\"\"\n   155→        prompt = \"\"\"Analyze this screenshot and list all interactive UI elements you can see.\n   156→For each element, provide:\n   157→- Type (button, text_input, checkbox, radio, dropdown, tab, link, icon, table_row, menu_item)\n   158→- Position (approximate x, y coordinates)\n   159→- Label or text content\n   160→- Semantic role (primary_action, cancel, submit, form_input, search_field, navigation, settings, close)\n   161→\n   162→Format your response as JSON.\"\"\"\n   163→        \n   164→        result = self.generate(prompt, image_path=image_path, temperature=0.1)\n   165→        \n   166→        if result[\"success\"]:\n   167→            try:\n   168→                # Parser la réponse JSON\n   169→                elements = json.loads(result[\"response\"])\n   170→                return {\"elements\": elements, \"success\": True}\n   171→            except json.JSONDecodeError:\n   172→                # Si pas JSON valide, retourner texte brut\n   173→                return {\"elements\": [], \"success\": False, \"raw_response\": result[\"response\"]}\n   174→        \n   175→        return {\"elements\": [], \"success\": False, \"error\": result[\"error\"]}\n   176→    \n   177→    def classify_element_type(self, \n   178→                             element_image: Image.Image,\n   179→                             context: Optional[str] = None) -> Dict[str, Any]:\n   180→        \"\"\"\n   181→        Classifier le type d'un élément UI\n   182→        \n   183→        Args:\n   184→            element_image: Image de l'élément\n   185→            context: Contexte additionnel\n   186→        \n   187→        Returns:\n   188→            Dict avec 'type' et 'confidence'\n   189→        \"\"\"\n   190→        types_list = \"button, text_input, checkbox, radio, dropdown, tab, link, icon, table_row, menu_item\"\n   191→        \n   192→        prompt = f\"\"\"What type of UI element is this?\n   193→Choose ONLY ONE from: {types_list}\n   194→\n   195→Respond with just the type name, nothing else.\"\"\"\n   196→        \n   197→        if context:\n   198→            prompt += f\"\\n\\nContext: {context}\"\n   199→        \n   200→        result = self.generate(prompt, image=element_image, temperature=0.0)\n   201→        \n   202→        if result[\"success\"]:\n   203→            element_type = result[\"response\"].strip().lower()\n   204→            # Valider que c'est un type connu\n   205→            valid_types = types_list.split(\", \")\n   206→            if element_type in valid_types:\n   207→                return {\"type\": element_type, \"confidence\": 0.9, \"success\": True}\n   208→            else:\n   209→                # Essayer de trouver le type le plus proche\n   210→                for vtype in valid_types:\n   211→                    if vtype in element_type:\n   212→                        return {\"type\": vtype, \"confidence\": 0.7, \"success\": True}\n   213→        \n   214→        return {\"type\": \"unknown\", \"confidence\": 0.0, \"success\": False}\n   215→    \n   216→    def classify_element_role(self,\n   217→                             element_image: Image.Image,\n   218→                             element_type: str,\n   219→                             context: Optional[str] = None) -> Dict[str, Any]:\n   220→        \"\"\"\n   221→        Classifier le rôle sémantique d'un élément\n   222→        \n   223→        Args:\n   224→            element_image: Image de l'élément\n   225→            element_type: Type de l'élément\n   226→            context: Contexte additionnel\n   227→        \n   228→        Returns:\n   229→            Dict avec 'role' et 'confidence'\n   230→        \"\"\"\n   231→        roles_list = \"primary_action, cancel, submit, form_input, search_field, navigation, settings, close, delete, edit, save\"\n   232→        \n   233→        prompt = f\"\"\"This is a {element_type}. What is its semantic role or purpose?\n   234→Choose ONLY ONE from: {roles_list}\n   235→\n   236→Respond with just the role name, nothing else.\"\"\"\n   237→        \n   238→        if context:\n   239→            prompt += f\"\\n\\nContext: {context}\"\n   240→        \n   241→        result = self.generate(prompt, image=element_image, temperature=0.0)\n   242→        \n   243→        if result[\"success\"]:\n   244→            role = result[\"response\"].strip().lower()\n   245→            # Valider que c'est un rôle connu\n   246→            valid_roles = roles_list.split(\", \")\n   247→            if role in valid_roles:\n   248→                return {\"role\": role, \"confidence\": 0.9, \"success\": True}\n   249→            else:\n   250→                # Essayer de trouver le rôle le plus proche\n   251→                for vrole in valid_roles:\n   252→                    if vrole in role:\n   253→                        return {\"role\": vrole, \"confidence\": 0.7, \"success\": True}\n   254→        \n   255→        return {\"role\": \"unknown\", \"confidence\": 0.0, \"success\": False}\n   256→    \n   257→    def extract_text(self, image: Image.Image) -> Dict[str, Any]:\n   258→        \"\"\"\n   259→        Extraire le texte d'une image\n   260→        \n   261→        Args:\n   262→            image: Image PIL\n   263→        \n   264→        Returns:\n   265→            Dict avec 'text' extrait\n   266→        \"\"\"\n   267→        prompt = \"Extract all visible text from this image. Return only the text, nothing else.\"\n   268→        \n   269→        result = self.generate(prompt, image=image, temperature=0.0)\n   270→        \n   271→        if result[\"success\"]:\n   272→            return {\"text\": result[\"response\"].strip(), \"success\": True}\n   273→        \n   274→        return {\"text\": \"\", \"success\": False, \"error\": result[\"error\"]}\n   275→\n   276→    def classify_element_complete(self, element_image: Image.Image) -> Dict[str, Any]:\n   277→        \"\"\"\n   278→        Classifier complètement un élément UI en UN SEUL appel VLM (optimisé)\n   279→\n   280→        Au lieu de 3 appels séparés (type, role, text), cette méthode\n   281→        fait UN SEUL appel pour obtenir toutes les informations.\n   282→\n   283→        Réduction de performance: 3 appels → 1 appel = 66% plus rapide\n   284→\n   285→        Args:\n   286→            element_image: Image PIL de l'élément\n   287→\n   288→        Returns:\n   289→            Dict avec 'type', 'role', 'text', 'confidence', 'success'\n   290→        \"\"\"\n   291→        # System prompt \"zéro tolérance\" - Force le VLM à NE produire QUE du JSON\n   292→        system_prompt = \"\"\"You are a UI element classifier.\n   293→Your ONLY task is to output valid JSON. Never explain. Never comment. Never discuss.\n   294→Expected format:\n   295→{\"type\": \"...\", \"role\": \"...\", \"text\": \"...\"}\"\"\"\n   296→\n   297→        # User prompt simplifié et direct\n   298→        prompt = \"\"\"Classify this UI element:\n   299→- Type: Choose ONE from [button, text_input, checkbox, radio, dropdown, tab, link, icon, table_row, menu_item]\n   300→- Role: Choose ONE from [primary_action, cancel, submit, form_input, search_field, navigation, settings, close, delete, edit, save]\n   301→- Text: Any visible text (empty string if none)\n   302→\n   303→Output JSON only.\"\"\"\n   304→\n   305→        result = self.generate(\n   306→            prompt,\n   307→            image=element_image,\n   308→            system_prompt=system_prompt,\n   309→            temperature=0.0,\n   310→            max_tokens=150,\n   311→            force_json=True\n   312→        )\n   313→\n   314→        if result[\"success\"]:\n   315→            try:\n   316→                # Parser la réponse JSON\n   317→                response_text = result[\"response\"].strip()\n   318→\n   319→                # Nettoyer la réponse si elle contient du markdown\n   320→                if response_text.startswith(\"```\"):\n   321→                    lines = response_text.split(\"\\n\")\n   322→                    response_text = \"\\n\".join([l for l in lines if not l.startswith(\"```\")])\n   323→                    response_text = response_text.strip()\n   324→\n   325→                data = json.loads(response_text)\n   326→\n   327→                # Valider les valeurs\n   328→                valid_types = [\"button\", \"text_input\", \"checkbox\", \"radio\", \"dropdown\",\n   329→                              \"tab\", \"link\", \"icon\", \"table_row\", \"menu_item\"]\n   330→                valid_roles = [\"primary_action\", \"cancel\", \"submit\", \"form_input\",\n   331→                              \"search_field\", \"navigation\", \"settings\", \"close\",\n   332→                              \"delete\", \"edit\", \"save\"]\n   333→\n   334→                elem_type = data.get(\"type\", \"unknown\").lower()\n   335→                elem_role = data.get(\"role\", \"unknown\").lower()\n   336→                elem_text = data.get(\"text\", \"\")\n   337→\n   338→                # Fallback si type/role invalides\n   339→                if elem_type not in valid_types:\n   340→                    elem_type = \"unknown\"\n   341→                if elem_role not in valid_roles:\n   342→                    elem_role = \"unknown\"\n   343→\n   344→                return {\n   345→                    \"type\": elem_type,\n   346→                    \"role\": elem_role,\n   347→                    \"text\": elem_text,\n   348→                    \"confidence\": 0.85,\n   349→                    \"success\": True\n   350→                }\n   351→\n   352→            except json.JSONDecodeError as e:\n   353→                logger.warning(f\"JSON parse error in classify_element_complete: {e}\")\n   354→                logger.debug(f\"Raw response: {result['response'][:200]}\")\n   355→                return {\n   356→                    \"type\": \"unknown\",\n   357→                    \"role\": \"unknown\",\n   358→                    \"text\": \"\",\n   359→                    \"confidence\": 0.0,\n   360→                    \"success\": False,\n   361→                    \"error\": f\"JSON parse error: {e}\"\n   362→                }\n   363→\n   364→        return {\n   365→            \"type\": \"unknown\",\n   366→            \"role\": \"unknown\",\n   367→            \"text\": \"\",\n   368→            \"confidence\": 0.0,\n   369→            \"success\": False,\n   370→            \"error\": result.get(\"error\", \"VLM call failed\")\n   371→        }\n   372→\n   373→    def _encode_image_from_path(self, image_path: str) -> str:\n   374→        \"\"\"Encoder une image depuis un fichier en base64\"\"\"\n   375→        with open(image_path, 'rb') as f:\n   376→            return base64.b64encode(f.read()).decode('utf-8')\n   377→    \n   378→    def _encode_image_from_pil(self, image: Image.Image) -> str:\n   379→        \"\"\"Encoder une image PIL en base64 avec prétraitement optimisé\"\"\"\n   380→        # 1. Convertir en RGB si nécessaire (évite erreurs PNG transparent)\n   381→        if image.mode != 'RGB':\n   382→            image = image.convert('RGB')\n   383→\n   384→        # 2. Redimensionnement intelligent : max 1280px sur le côté long\n   385→        max_size = 1280\n   386→        if max(image.size) > max_size:\n   387→            ratio = max_size / max(image.size)\n   388→            new_size = (int(image.size[0] * ratio), int(image.size[1] * ratio))\n   389→            image = image.resize(new_size, Image.Resampling.LANCZOS)\n   390→\n   391→        # 3. Sauvegarder en JPEG qualité 90 (plus léger, meilleur pour VLM)\n   392→        buffer = io.BytesIO()\n   393→        image.save(buffer, format='JPEG', quality=90)\n   394→        return base64.b64encode(buffer.getvalue()).decode('utf-8')\n   395→    \n   396→    def list_models(self) -> List[str]:\n   397→        \"\"\"Lister les modèles disponibles dans Ollama\"\"\"\n   398→        try:\n   399→            response = requests.get(f\"{self.endpoint}/api/tags\", timeout=5)\n   400→            if response.status_code == 200:\n   401→                models = response.json().get('models', [])\n   402→                return [m['name'] for m in models]\n   403→        except Exception as e:\n   404→            logger.error(f\"Error listing models: {e}\")\n   405→        return []\n   406→    \n   407→    def pull_model(self, model_name: str) -> bool:\n   408→        \"\"\"\n   409→        Télécharger un modèle dans Ollama\n   410→        \n   411→        Args:\n   412→            model_name: Nom du modèle à télécharger\n   413→        \n   414→        Returns:\n   415→            True si succès\n   416→        \"\"\"\n   417→        try:\n   418→            logger.info(f\"Pulling model {model_name}...\")\n   419→            response = requests.post(\n   420→                f\"{self.endpoint}/api/pull\",\n   421→                json={\"name\": model_name},\n   422→                stream=True,\n   423→                timeout=600\n   424→            )\n   425→            \n   426→            if response.status_code == 200:\n   427→                for line in response.iter_lines():\n   428→                    if line:\n   429→                        data = json.loads(line)\n   430→                        if 'status' in data:\n   431→                            logger.info(f\"  {data['status']}\")\n   432→                return True\n   433→        except Exception as e:\n   434→            logger.error(f\"Error pulling model: {e}\")\n   435→        return False\n   436→\n   437→\n   438→# ============================================================================\n   439→# Fonctions utilitaires\n   440→# ============================================================================\n   441→\n   442→def create_ollama_client(model: str = \"qwen3-vl:8b\",\n   443→                        endpoint: str = \"http://localhost:11434\") -> OllamaClient:\n   444→    \"\"\"\n   445→    Créer un client Ollama\n   446→    \n   447→    Args:\n   448→        model: Nom du modèle VLM\n   449→        endpoint: URL de l'API Ollama\n   450→    \n   451→    Returns:\n   452→        OllamaClient configuré\n   453→    \"\"\"\n   454→    return OllamaClient(endpoint=endpoint, model=model)\n   455→\n   456→\n   457→def check_ollama_available(endpoint: str = \"http://localhost:11434\") -> bool:\n   458→    \"\"\"\n   459→    Vérifier si Ollama est disponible\n   460→    \n   461→    Args:\n   462→        endpoint: URL de l'API Ollama\n   463→    \n   464→    Returns:\n   465→        True si disponible\n   466→    \"\"\"\n   467→    try:\n   468→        response = requests.get(f\"{endpoint}/api/tags\", timeout=5)\n   469→        return response.status_code == 200\n   470→    except (requests.RequestException, ConnectionError, TimeoutError):\n   471→        return False\n   472→"}]},"uuid":"1e809a55-98f5-4c1b-9b65-ea248fc23a5e","timestamp":"2026-03-14T21:15:01.926Z","sourceToolAssistantUUID":"4e8b7992-eb31-4cf0-aecf-1654710832ae"}
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+{"parentUuid":"6f9761c0-a7c4-47d8-aa44-f5224c70a3b0","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JJD3buRZPXasnoS3YezSgZ","type":"tool_result","content":"   899→   899→\n   900→   900→\n   901→   901→@dataclass\n   902→   902→class WorkflowStats:\n   903→   903→    \"\"\"Statistiques globales d'un workflow\"\"\"\n   904→   904→    total_executions: int = 0\n   905→   905→    success_count: int = 0\n   906→   906→    failure_count: int = 0\n   907→   907→    observed_runs: int = 0\n   908→   908→    assist_runs: int = 0\n   909→   909→    auto_candidate_runs: int = 0\n   910→   910→    auto_confirmed_runs: int = 0\n   911→   911→    avg_similarity: float = 0.0\n   912→   912→    recent_confidence: float = 0.0\n   913→   913→    last_executed: Optional[datetime] = None\n   914→   914→    \n   915→   915→    @property\n   916→   916→    def success_rate(self) -> float:\n   917→   917→        if self.total_executions == 0:\n   918→   918→            return 0.0\n   919→   919→        return self.success_count / self.total_executions\n   920→   920→    \n   921→   921→    def to_dict(self) -> Dict[str, Any]:\n   922→   922→        return {\n   923→   923→            \"total_executions\": self.total_executions,\n   924→   924→            \"success_count\": self.success_count,\n   925→   925→            \"failure_count\": self.failure_count,\n   926→   926→            \"observed_runs\": self.observed_runs,\n   927→   927→            \"assist_runs\": self.assist_runs,\n   928→   928→            \"auto_candidate_runs\": self.auto_candidate_runs,\n   929→   929→            \"auto_confirmed_runs\": self.auto_confirmed_runs,\n   930→   930→            \"avg_similarity\": self.avg_similarity,\n   931→   931→            \"recent_confidence\": self.recent_confidence,\n   932→   932→            \"last_executed\": self.last_executed.isoformat() if self.last_executed else None\n   933→   933→        }\n   934→   934→    \n   935→   935→    @classmethod\n   936→   936→    def from_dict(cls, data: Dict[str, Any]) -> 'WorkflowStats':\n   937→   937→        last_executed = None\n   938→   938→        if data.get(\"last_executed\"):\n   939→   939→            last_executed = datetime.fromisoformat(data[\"last_executed\"])\n   940→   940→        \n   941→   941→        return cls(\n   942→   942→            total_executions=data.get(\"total_executions\", 0),\n   943→   943→            success_count=data.get(\"success_count\", 0),\n   944→   944→            failure_count=data.get(\"failure_count\", 0),\n   945→   945→            observed_runs=data.get(\"observed_runs\", 0),\n   946→   946→            assist_runs=data.get(\"assist_runs\", 0),\n   947→   947→            auto_candidate_runs=data.get(\"auto_candidate_runs\", 0),\n   948→   948→            auto_confirmed_runs=data.get(\"auto_confirmed_runs\", 0),\n   949→   949→            avg_similarity=data.get(\"avg_similarity\", 0.0),\n   950→   950→            recent_confidence=data.get(\"recent_confidence\", 0.0),\n   951→   951→            last_executed=last_executed\n   952→   952→        )\n   953→   953→\n   954→   954→\n   955→   955→@dataclass\n   956→   956→class LearningConfig:\n   957→   957→    \"\"\"Configuration de l'apprentissage\"\"\"\n   958→   958→    observation_threshold: int = 5\n   959→   959→    coaching_threshold: int = 10\n   960→   960→    auto_candidate_threshold: int = 20\n   961→   961→    min_similarity_coaching: float = 0.90\n   962→   962→    min_success_rate_auto: float = 0.90\n   963→   963→    min_success_rate_confirmed: float = 0.95\n   964→   964→    rollback_confidence_threshold: float = 0.90\n   965→   965→    \n   966→   966→    def to_dict(self) -> Dict[str, Any]:\n   967→   967→        return {\n   968→   968→            \"observation_threshold\": self.observation_threshold,\n   969→   969→            \"coaching_threshold\": self.coaching_threshold,\n   970→   970→            \"auto_candidate_threshold\": self.auto_candidate_threshold,\n   971→   971→            \"min_similarity_coaching\": self.min_similarity_coaching,\n   972→   972→            \"min_success_rate_auto\": self.min_success_rate_auto,\n   973→   973→            \"min_success_rate_confirmed\": self.min_success_rate_confirmed,\n   974→   974→            \"rollback_confidence_threshold\": self.rollback_confidence_threshold\n   975→   975→        }\n   976→   976→    \n   977→   977→    @classmethod\n   978→   978→    def from_dict(cls, data: Dict[str, Any]) -> 'LearningConfig':\n   979→   979→        return cls(\n   980→   980→            observation_threshold=data.get(\"observation_threshold\", 5),\n   981→   981→            coaching_threshold=data.get(\"coaching_threshold\", 10),\n   982→   982→            auto_candidate_threshold=data.get(\"auto_candidate_threshold\", 20),\n   983→   983→            min_similarity_coaching=data.get(\"min_similarity_coaching\", 0.90),\n   984→   984→            min_success_rate_auto=data.get(\"min_success_rate_auto\", 0.90),\n   985→   985→            min_success_rate_confirmed=data.get(\"min_success_rate_confirmed\", 0.95),\n   986→   986→            rollback_confidence_threshold=data.get(\"rollback_confidence_threshold\", 0.90)\n   987→   987→        )\n   988→   988→\n   989→   989→\n   990→   990→@dataclass\n   991→   991→class Workflow:\n   992→   992→    \"\"\"\n   993→   993→    Workflow Graph complet\n   994→   994→    \n   995→   995→    Représente un workflow appris comme un graphe de transitions.\n   996→   996→    Supporte la composition: boucles, conditions, références à sous-workflows.\n   997→   997→    \"\"\"\n   998→   998→    workflow_id: str\n   999→   999→    name: str\n  1000→  1000→    description: str\n  1001→  1001→    version: int\n  1002→  1002→    learning_state: str  # OBSERVATION, COACHING, AUTO_CANDIDATE, AUTO_CONFIRMÉ\n  1003→  1003→    created_at: datetime\n  1004→  1004→    updated_at: datetime\n  1005→  1005→    entry_nodes: List[str]\n  1006→  1006→    end_nodes: List[str]\n  1007→  1007→    nodes: List[WorkflowNode]\n  1008→  1008→    edges: List[WorkflowEdge]\n  1009→  1009→    safety_rules: SafetyRules\n  1010→  1010→    stats: WorkflowStats\n  1011→  1011→    learning: LearningConfig\n  1012→  1012→    metadata: Dict[str, Any] = field(default_factory=dict)\n  1013→  1013→    # Composition fields\n  1014→  1014→    loops: Dict[str, Any] = field(default_factory=dict)  # loop_id -> LoopConfig\n  1015→  1015→    conditionals: Dict[str, Any] = field(default_factory=dict)  # node_id -> ConditionalNode\n  1016→  1016→    references: List[Any] = field(default_factory=list)  # List of ReferenceNode\n  1017→  1017→    chain_config: Optional[Dict[str, Any]] = None  # ChainConfig if part of a chain\n  1018→  1018→    \n  1019→  1019→    def get_node(self, node_id: str) -> Optional[WorkflowNode]:\n  1020→  1020→        \"\"\"Récupérer un node par ID\"\"\"\n  1021→  1021→        for node in self.nodes:\n  1022→  1022→            if node.node_id == node_id:\n  1023→  1023→                return node\n  1024→  1024→        return None\n  1025→  1025→    \n  1026→  1026→    def get_edge(self, edge_id: str) -> Optional[WorkflowEdge]:\n  1027→  1027→        \"\"\"Récupérer un edge par ID\"\"\"\n  1028→  1028→        for edge in self.edges:\n  1029→  1029→            if edge.edge_id == edge_id:\n  1030→  1030→                return edge\n  1031→  1031→        return None\n  1032→  1032→    \n  1033→  1033→    def get_outgoing_edges(self, node_id: str) -> List[WorkflowEdge]:\n  1034→  1034→        \"\"\"Récupérer tous les edges sortants d'un node\"\"\"\n  1035→  1035→        return [edge for edge in self.edges if edge.from_node == node_id]\n  1036→  1036→    \n  1037→  1037→    def get_incoming_edges(self, node_id: str) -> List[WorkflowEdge]:\n  1038→  1038→        \"\"\"Récupérer tous les edges entrants d'un node\"\"\"\n  1039→  1039→        return [edge for edge in self.edges if edge.to_node == node_id]\n  1040→  1040→    \n  1041→  1041→    # =========================================================================\n  1042→  1042→    # Méthodes de gestion des variantes au niveau workflow\n  1043→  1043→    # =========================================================================\n  1044→  1044→    \n  1045→  1045→    def add_variant_to_node(self, node_id: str, variant: NodeVariant) -> bool:\n  1046→  1046→        \"\"\"Ajouter une variante à un node spécifique\"\"\"\n  1047→  1047→        node = self.get_node(node_id)\n  1048→  1048→        if not node:\n  1049→  1049→            return False\n  1050→  1050→        return node.add_variant(variant)\n  1051→  1051→    \n  1052→  1052→    def remove_variant_from_node(self, node_id: str, variant_id: str) -> bool:\n  1053→  1053→        \"\"\"Supprimer une variante d'un node\"\"\"\n  1054→  1054→        node = self.get_node(node_id)\n  1055→  1055→        if not node:\n  1056→  1056→            return False\n  1057→  1057→        return node.remove_variant(variant_id)\n  1058→  1058→    \n  1059→  1059→    def get_node_variants(self, node_id: str) -> List[NodeVariant]:\n  1060→  1060→        \"\"\"Récupérer toutes les variantes d'un node\"\"\"\n  1061→  1061→        node = self.get_node(node_id)\n  1062→  1062→        return node.variants if node else []\n  1063→  1063→    \n  1064→  1064→    def get_nodes_needing_consolidation(self) -> List[WorkflowNode]:\n  1065→  1065→        \"\"\"Récupérer les nodes qui ont besoin de consolidation de variantes\"\"\"\n  1066→  1066→        return [node for node in self.nodes if node.needs_consolidation()]\n  1067→  1067→    \n  1068→  1068→    def get_low_quality_nodes(self, threshold: float = 0.7) -> List[WorkflowNode]:\n  1069→  1069→        \"\"\"Récupérer les nodes avec un score de qualité faible\"\"\"\n  1070→  1070→        return [node for node in self.nodes if node.quality_score < threshold]\n  1071→  1071→    \n  1072→  1072→    def to_dict(self) -> Dict[str, Any]:\n  1073→  1073→        \"\"\"Sérialiser en JSON\"\"\"\n  1074→  1074→        result = {\n  1075→  1075→            \"workflow_id\": self.workflow_id,\n  1076→  1076→            \"name\": self.name,\n  1077→  1077→            \"description\": self.description,\n  1078→  1078→            \"version\": self.version,\n  1079→  1079→            \"learning_state\": self.learning_state,\n  1080→  1080→            \"created_at\": self.created_at.isoformat(),\n  1081→  1081→            \"updated_at\": self.updated_at.isoformat(),\n  1082→  1082→            \"entry_nodes\": self.entry_nodes,\n  1083→  1083→            \"end_nodes\": self.end_nodes,\n  1084→  1084→            \"nodes\": [node.to_dict() for node in self.nodes],\n  1085→  1085→            \"edges\": [edge.to_dict() for edge in self.edges],\n  1086→  1086→            \"safety_rules\": self.safety_rules.to_dict(),\n  1087→  1087→            \"stats\": self.stats.to_dict(),\n  1088→  1088→            \"learning\": self.learning.to_dict(),\n  1089→  1089→            \"metadata\": self.metadata,\n  1090→  1090→            # Composition fields\n  1091→  1091→            \"loops\": {k: v.to_dict() if hasattr(v, 'to_dict') else v for k, v in self.loops.items()},\n  1092→  1092→            \"conditionals\": {k: v.to_dict() if hasattr(v, 'to_dict') else v for k, v in self.conditionals.items()},\n  1093→  1093→            \"references\": [r.to_dict() if hasattr(r, 'to_dict') else r for r in self.references],\n  1094→  1094→        }\n  1095→  1095→        if self.chain_config:\n  1096→  1096→            result[\"chain_config\"] = self.chain_config.to_dict() if hasattr(self.chain_config, 'to_dict') else self.chain_config\n  1097→  1097→        return result\n  1098→  1098→    "}]},"uuid":"b05e89cc-66de-42d3-bc66-4e48958071fa","timestamp":"2026-03-14T21:15:05.475Z","sourceToolAssistantUUID":"6f9761c0-a7c4-47d8-aa44-f5224c70a3b0"}
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+{"parentUuid":"81d6cb2f-8c11-496b-917d-1a5f676e95fc","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KwS6L4KBKL49DW6d5uZWQV","type":"tool_result","content":"     1→\"\"\"\n     2→Composition Models - Modèles de données pour la composition de workflows\n     3→\n     4→Ce module contient les dataclasses de configuration pour:\n     5→- Chaînage de workflows (ChainConfig)\n     6→- Boucles (LoopConfig, LoopState)\n     7→- Conditions (ConditionalNode, BranchConfig, VisualCondition)\n     8→- Déclencheurs (ScheduleTrigger, FileTrigger, VisualTrigger, TriggerContext)\n     9→- Sous-workflows (SubWorkflowDefinition, ReferenceNode, ParameterDef)\n    10→- Contexte d'exécution (ExecutionContext, LogEntry)\n    11→- Validation (ValidationResult)\n    12→- Fusion (MergeCandidate, NodeConflict)\n    13→- Séquences (CommonSequence, SequenceOccurrence)\n    14→\n    15→Chaque classe inclut les méthodes to_dict() et from_dict() pour la sérialisation.\n    16→\"\"\"\n    17→\n    18→from dataclasses import dataclass, field\n    19→from datetime import datetime\n    20→from typing import Dict, List, Optional, Any, Literal, Union\n    21→from enum import Enum\n    22→\n    23→\n    24→# ============================================================================\n    25→# Enums\n    26→# ============================================================================\n    27→\n    28→class FailureAction(str, Enum):\n    29→    \"\"\"Actions possibles en cas d'échec dans une chaîne\"\"\"\n    30→    RETRY = \"retry\"\n    31→    SKIP = \"skip\"\n    32→    ABORT = \"abort\"\n    33→\n    34→\n    35→class LoopType(str, Enum):\n    36→    \"\"\"Types de boucles\"\"\"\n    37→    COUNT = \"count\"\n    38→    CONDITION = \"condition\"\n    39→\n    40→\n    41→class ConditionType(str, Enum):\n    42→    \"\"\"Types de conditions visuelles\"\"\"\n    43→    ELEMENT_PRESENT = \"element_present\"\n    44→    ELEMENT_ABSENT = \"element_absent\"\n    45→    TEXT_EQUALS = \"text_equals\"\n    46→    TEXT_CONTAINS = \"text_contains\"\n    47→\n    48→\n    49→class TriggerType(str, Enum):\n    50→    \"\"\"Types de déclencheurs\"\"\"\n    51→    SCHEDULE = \"schedule\"\n    52→    FILE = \"file\"\n    53→    VISUAL = \"visual\"\n    54→\n    55→\n    56→class ConcurrencyMode(str, Enum):\n    57→    \"\"\"Modes de gestion de la concurrence\"\"\"\n    58→    CONCURRENT = \"concurrent\"\n    59→    QUEUE = \"queue\"\n    60→\n    61→\n    62→class SequencePriority(str, Enum):\n    63→    \"\"\"Priorité des séquences pour extraction\"\"\"\n    64→    HIGH = \"high\"\n    65→    MEDIUM = \"medium\"\n    66→    LOW = \"low\"\n    67→\n    68→\n    69→# ============================================================================\n    70→# Conditions Visuelles\n    71→# ============================================================================\n    72→\n    73→@dataclass\n    74→class VisualCondition:\n    75→    \"\"\"\n    76→    Condition basée sur l'état visuel de l'écran.\n    77→    \n    78→    Utilisée pour les boucles conditionnelles et les branchements.\n    79→    \"\"\"\n    80→    condition_type: str  # element_present, element_absent, text_equals, text_contains\n    81→    target_element: Optional[str] = None  # ID ou template de l'élément cible\n    82→    expected_text: Optional[str] = None  # Texte attendu pour les conditions textuelles\n    83→    \n    84→    def to_dict(self) -> Dict[str, Any]:\n    85→        return {\n    86→            \"condition_type\": self.condition_type,\n    87→            \"target_element\": self.target_element,\n    88→            \"expected_text\": self.expected_text\n    89→        }\n    90→    \n    91→    @classmethod\n    92→    def from_dict(cls, data: Dict[str, Any]) -> 'VisualCondition':\n    93→        return cls(\n    94→            condition_type=data[\"condition_type\"],\n    95→            target_element=data.get(\"target_element\"),\n    96→            expected_text=data.get(\"expected_text\")\n    97→        )\n    98→\n    99→\n   100→# ============================================================================\n   101→# Configuration de Chaînage\n   102→# ============================================================================\n   103→\n   104→@dataclass\n   105→class ChainConfig:\n   106→    \"\"\"\n   107→    Configuration de chaînage entre deux workflows.\n   108→    \n   109→    Définit comment un workflow se connecte au suivant dans une chaîne.\n   110→    \"\"\"\n   111→    source_workflow_id: str\n   112→    target_workflow_id: str\n   113→    variable_mapping: Dict[str, str] = field(default_factory=dict)  # source_var -> target_var\n   114→    on_failure: str = \"abort\"  # retry, skip, abort\n   115→    max_retries: int = 3\n   116→    \n   117→    def to_dict(self) -> Dict[str, Any]:\n   118→        return {\n   119→            \"source_workflow_id\": self.source_workflow_id,\n   120→            \"target_workflow_id\": self.target_workflow_id,\n   121→            \"variable_mapping\": self.variable_mapping,\n   122→            \"on_failure\": self.on_failure,\n   123→            \"max_retries\": self.max_retries\n   124→        }\n   125→    \n   126→    @classmethod\n   127→    def from_dict(cls, data: Dict[str, Any]) -> 'ChainConfig':\n   128→        return cls(\n   129→            source_workflow_id=data[\"source_workflow_id\"],\n   130→            target_workflow_id=data[\"target_workflow_id\"],\n   131→            variable_mapping=data.get(\"variable_mapping\", {}),\n   132→            on_failure=data.get(\"on_failure\", \"abort\"),\n   133→            max_retries=data.get(\"max_retries\", 3)\n   134→        )\n   135→\n   136→\n   137→# ============================================================================\n   138→# Configuration de Boucles\n   139→# ============================================================================\n   140→\n   141→@dataclass\n   142→class LoopConfig:\n   143→    \"\"\"\n   144→    Configuration d'une boucle dans un workflow.\n   145→    \n   146→    Supporte les boucles par compteur (count) ou par condition visuelle (condition).\n   147→    \"\"\"\n   148→    loop_id: str\n   149→    loop_type: str  # count, condition\n   150→    max_iterations: Optional[int] = None  # Pour type \"count\"\n   151→    exit_condition: Optional[VisualCondition] = None  # Pour type \"condition\"\n   152→    body_nodes: List[str] = field(default_factory=list)\n   153→    safety_limit: int = 1000  # Garde de sécurité\n   154→    \n   155→    def to_dict(self) -> Dict[str, Any]:\n   156→        return {\n   157→            \"loop_id\": self.loop_id,\n   158→            \"loop_type\": self.loop_type,\n   159→            \"max_iterations\": self.max_iterations,\n   160→            \"exit_condition\": self.exit_condition.to_dict() if self.exit_condition else None,\n   161→            \"body_nodes\": self.body_nodes,\n   162→            \"safety_limit\": self.safety_limit\n   163→        }\n   164→    \n   165→    @classmethod\n   166→    def from_dict(cls, data: Dict[str, Any]) -> 'LoopConfig':\n   167→        exit_condition = None\n   168→        if data.get(\"exit_condition\"):\n   169→            exit_condition = VisualCondition.from_dict(data[\"exit_condition\"])\n   170→        \n   171→        return cls(\n   172→            loop_id=data[\"loop_id\"],\n   173→            loop_type=data[\"loop_type\"],\n   174→            max_iterations=data.get(\"max_iterations\"),\n   175→            exit_condition=exit_condition,\n   176→            body_nodes=data.get(\"body_nodes\", []),\n   177→            safety_limit=data.get(\"safety_limit\", 1000)\n   178→        )\n   179→\n   180→\n   181→@dataclass\n   182→class LoopState:\n   183→    \"\"\"\n   184→    État d'exécution d'une boucle.\n   185→    \n   186→    Maintient le compteur et l'état courant pendant l'exécution.\n   187→    \"\"\"\n   188→    loop_id: str\n   189→    current_iteration: int = 0\n   190→    started_at: datetime = field(default_factory=datetime.now)\n   191→    last_condition_result: Optional[bool] = None\n   192→    \n   193→    def to_dict(self) -> Dict[str, Any]:\n   194→        return {\n   195→            \"loop_id\": self.loop_id,\n   196→            \"current_iteration\": self.current_iteration,\n   197→            \"started_at\": self.started_at.isoformat(),\n   198→            \"last_condition_result\": self.last_condition_result\n   199→        }\n   200→    \n   201→    @classmethod\n   202→    def from_dict(cls, data: Dict[str, Any]) -> 'LoopState':\n   203→        started_at = datetime.now()\n   204→        if data.get(\"started_at\"):\n   205→            started_at = datetime.fromisoformat(data[\"started_at\"])\n   206→        \n   207→        return cls(\n   208→            loop_id=data[\"loop_id\"],\n   209→            current_iteration=data.get(\"current_iteration\", 0),\n   210→            started_at=started_at,\n   211→            last_condition_result=data.get(\"last_condition_result\")\n   212→        )\n   213→\n   214→\n   215→# ============================================================================\n   216→# Configuration de Branchements Conditionnels\n   217→# ============================================================================\n   218→\n   219→@dataclass\n   220→class BranchConfig:\n   221→    \"\"\"\n   222→    Configuration d'une branche conditionnelle.\n   223→    \n   224→    Définit une condition et le node cible si la condition est vraie.\n   225→    \"\"\"\n   226→    branch_id: str\n   227→    condition: VisualCondition\n   228→    target_node: str\n   229→    priority: int = 0  # Ordre d'évaluation (plus petit = évalué en premier)\n   230→    \n   231→    def to_dict(self) -> Dict[str, Any]:\n   232→        return {\n   233→            \"branch_id\": self.branch_id,\n   234→            \"condition\": self.condition.to_dict(),\n   235→            \"target_node\": self.target_node,\n   236→            \"priority\": self.priority\n   237→        }\n   238→    \n   239→    @classmethod\n   240→    def from_dict(cls, data: Dict[str, Any]) -> 'BranchConfig':\n   241→        return cls(\n   242→            branch_id=data[\"branch_id\"],\n   243→            condition=VisualCondition.from_dict(data[\"condition\"]),\n   244→            target_node=data[\"target_node\"],\n   245→            priority=data.get(\"priority\", 0)\n   246→        )\n   247→\n   248→\n   249→@dataclass\n   250→class ConditionalNode:\n   251→    \"\"\"\n   252→    Node conditionnel avec plusieurs branches.\n   253→    \n   254→    Évalue les branches par ordre de priorité et exécute la première qui match.\n   255→    \"\"\"\n   256→    node_id: str\n   257→    branches: List[BranchConfig] = field(default_factory=list)\n   258→    default_branch: Optional[str] = None  # Node par défaut si aucune condition ne match\n   259→    \n   260→    def to_dict(self) -> Dict[str, Any]:\n   261→        return {\n   262→            \"node_id\": self.node_id,\n   263→            \"branches\": [b.to_dict() for b in self.branches],\n   264→            \"default_branch\": self.default_branch\n   265→        }\n   266→    \n   267→    @classmethod\n   268→    def from_dict(cls, data: Dict[str, Any]) -> 'ConditionalNode':\n   269→        branches = [BranchConfig.from_dict(b) for b in data.get(\"branches\", [])]\n   270→        return cls(\n   271→            node_id=data[\"node_id\"],\n   272→            branches=branches,\n   273→            default_branch=data.get(\"default_branch\")\n   274→        )\n   275→\n   276→\n   277→# ============================================================================\n   278→# Configuration des Déclencheurs\n   279→# ============================================================================\n   280→\n   281→@dataclass\n   282→class ScheduleTrigger:\n   283→    \"\"\"\n   284→    Déclencheur basé sur un horaire.\n   285→    \n   286→    Supporte les expressions cron ou les intervalles en secondes.\n   287→    \"\"\"\n   288→    trigger_id: str\n   289→    workflow_id: str\n   290→    cron_expression: Optional[str] = None\n   291→    interval_seconds: Optional[int] = None\n   292→    enabled: bool = True\n   293→    \n   294→    def to_dict(self) -> Dict[str, Any]:\n   295→        return {\n   296→            \"trigger_id\": self.trigger_id,\n   297→            \"trigger_type\": \"schedule\",\n   298→            \"workflow_id\": self.workflow_id,\n   299→            \"cron_expression\": self.cron_expression,\n   300→            \"interval_seconds\": self.interval_seconds,\n   301→            \"enabled\": self.enabled\n   302→        }\n   303→    \n   304→    @classmethod\n   305→    def from_dict(cls, data: Dict[str, Any]) -> 'ScheduleTrigger':\n   306→        return cls(\n   307→            trigger_id=data[\"trigger_id\"],\n   308→            workflow_id=data[\"workflow_id\"],\n   309→            cron_expression=data.get(\"cron_expression\"),\n   310→            interval_seconds=data.get(\"interval_seconds\"),\n   311→            enabled=data.get(\"enabled\", True)\n   312→        )\n   313→\n   314→\n   315→@dataclass\n   316→class FileTrigger:\n   317→    \"\"\"\n   318→    Déclencheur basé sur l'apparition d'un fichier.\n   319→    \n   320→    Surveille un répertoire et déclenche le workflow quand un fichier correspondant apparaît.\n   321→    \"\"\"\n   322→    trigger_id: str\n   323→    workflow_id: str\n   324→    watch_directory: str\n   325→    file_pattern: str  # Pattern glob (ex: \"*.csv\", \"report_*.xlsx\")\n   326→    enabled: bool = True\n   327→    \n   328→    def to_dict(self) -> Dict[str, Any]:\n   329→        return {\n   330→            \"trigger_id\": self.trigger_id,\n   331→            \"trigger_type\": \"file\",\n   332→            \"workflow_id\": self.workflow_id,\n   333→            \"watch_directory\": self.watch_directory,\n   334→            \"file_pattern\": self.file_pattern,\n   335→            \"enabled\": self.enabled\n   336→        }\n   337→    \n   338→    @classmethod\n   339→    def from_dict(cls, data: Dict[str, Any]) -> 'FileTrigger':\n   340→        return cls(\n   341→            trigger_id=data[\"trigger_id\"],\n   342→            workflow_id=data[\"workflow_id\"],\n   343→            watch_directory=data[\"watch_directory\"],\n   344→            file_pattern=data[\"file_pattern\"],\n   345→            enabled=data.get(\"enabled\", True)\n   346→        )\n   347→\n   348→\n   349→@dataclass\n   350→class VisualTrigger:\n   351→    \"\"\"\n   352→    Déclencheur basé sur la détection visuelle.\n   353→    \n   354→    Capture périodiquement l'écran et déclenche le workflow quand l'élément cible est détecté.\n   355→    \"\"\"\n   356→    trigger_id: str\n   357→    workflow_id: str\n   358→    target_element: str  # ID ou template de l'élément à détecter\n   359→    check_interval_seconds: int = 5\n   360→    enabled: bool = True\n   361→    \n   362→    def to_dict(self) -> Dict[str, Any]:\n   363→        return {\n   364→            \"trigger_id\": self.trigger_id,\n   365→            \"trigger_type\": \"visual\",\n   366→            \"workflow_id\": self.workflow_id,\n   367→            \"target_element\": self.target_element,\n   368→            \"check_interval_seconds\": self.check_interval_seconds,\n   369→            \"enabled\": self.enabled\n   370→        }\n   371→    \n   372→    @classmethod\n   373→    def from_dict(cls, data: Dict[str, Any]) -> 'VisualTrigger':\n   374→        return cls(\n   375→            trigger_id=data[\"trigger_id\"],\n   376→            workflow_id=data[\"workflow_id\"],\n   377→            target_element=data[\"target_element\"],\n   378→            check_interval_seconds=data.get(\"check_interval_seconds\", 5),\n   379→            enabled=data.get(\"enabled\", True)\n   380→        )\n   381→\n   382→\n   383→@dataclass\n   384→class TriggerContext:\n   385→    \"\"\"\n   386→    Contexte passé au workflow lors du déclenchement.\n   387→    \n   388→    Contient les informations sur l'événement qui a déclenché le workflow.\n   389→    \"\"\"\n   390→    trigger_id: str\n   391→    trigger_type: str  # schedule, file, visual\n   392→    fired_at: datetime = field(default_factory=datetime.now)\n   393→    file_path: Optional[str] = None  # Pour FileTrigger\n   394→    detected_element: Optional[Dict[str, Any]] = None  # Pour VisualTrigger\n   395→    \n   396→    def to_dict(self) -> Dict[str, Any]:\n   397→        return {\n   398→            \"trigger_id\": self.trigger_id,\n   399→            \"trigger_type\": self.trigger_type,\n   400→            \"fired_at\": self.fired_at.isoformat(),\n   401→            \"file_path\": self.file_path,\n   402→            \"detected_element\": self.detected_element\n   403→        }\n   404→    \n   405→    @classmethod\n   406→    def from_dict(cls, data: Dict[str, Any]) -> 'TriggerContext':\n   407→        fired_at = datetime.now()\n   408→        if data.get(\"fired_at\"):\n   409→            fired_at = datetime.fromisoformat(data[\"fired_at\"])\n   410→        \n   411→        return cls(\n   412→            trigger_id=data[\"trigger_id\"],\n   413→            trigger_type=data[\"trigger_type\"],\n   414→            fired_at=fired_at,\n   415→            file_path=data.get(\"file_path\"),\n   416→            detected_element=data.get(\"detected_element\")\n   417→        )\n   418→\n   419→\n   420→# Type alias pour les triggers\n   421→TriggerConfig = Union[ScheduleTrigger, FileTrigger, VisualTrigger]\n   422→\n   423→\n   424→def trigger_from_dict(data: Dict[str, Any]) -> TriggerConfig:\n   425→    \"\"\"Factory pour créer le bon type de trigger depuis un dictionnaire.\"\"\"\n   426→    trigger_type = data.get(\"trigger_type\", \"schedule\")\n   427→    \n   428→    if trigger_type == \"schedule\":\n   429→        return ScheduleTrigger.from_dict(data)\n   430→    elif trigger_type == \"file\":\n   431→        return FileTrigger.from_dict(data)\n   432→    elif trigger_type == \"visual\":\n   433→        return VisualTrigger.from_dict(data)\n   434→    else:\n   435→        raise ValueError(f\"Unknown trigger type: {trigger_type}\")\n   436→\n   437→\n   438→# ============================================================================\n   439→# Configuration des Sous-Workflows\n   440→# ============================================================================\n   441→\n   442→@dataclass\n   443→class ParameterDef:\n   444→    \"\"\"\n   445→    Définition d'un paramètre d'entrée ou de sortie pour un sous-workflow.\n   446→    \"\"\"\n   447→    name: str\n   448→    param_type: str = \"string\"  # string, number, boolean, list, dict\n   449→    required: bool = True\n   450→    default_value: Optional[Any] = None\n   451→    description: str = \"\"\n   452→    \n   453→    def to_dict(self) -> Dict[str, Any]:\n   454→        return {\n   455→            \"name\": self.name,\n   456→            \"param_type\": self.param_type,\n   457→            \"required\": self.required,\n   458→            \"default_value\": self.default_value,\n   459→            \"description\": self.description\n   460→        }\n   461→    \n   462→    @classmethod\n   463→    def from_dict(cls, data: Dict[str, Any]) -> 'ParameterDef':\n   464→        return cls(\n   465→            name=data[\"name\"],\n   466→            param_type=data.get(\"param_type\", \"string\"),\n   467→            required=data.get(\"required\", True),\n   468→            default_value=data.get(\"default_value\"),\n   469→            description=data.get(\"description\", \"\")\n   470→        )\n   471→\n   472→\n   473→@dataclass\n   474→class SubWorkflowDefinition:\n   475→    \"\"\"\n   476→    Définition d'un sous-workflow réutilisable.\n   477→    \n   478→    Permet de définir les paramètres d'entrée et de sortie.\n   479→    \"\"\"\n   480→    workflow_id: str\n   481→    name: str\n   482→    input_parameters: List[ParameterDef] = field(default_factory=list)\n   483→    output_values: List[ParameterDef] = field(default_factory=list)\n   484→    description: str = \"\"\n   485→    \n   486→    def to_dict(self) -> Dict[str, Any]:\n   487→        return {\n   488→            \"workflow_id\": self.workflow_id,\n   489→            \"name\": self.name,\n   490→            \"input_parameters\": [p.to_dict() for p in self.input_parameters],\n   491→            \"output_values\": [p.to_dict() for p in self.output_values],\n   492→            \"description\": self.description\n   493→        }\n   494→    \n   495→    @classmethod\n   496→    def from_dict(cls, data: Dict[str, Any]) -> 'SubWorkflowDefinition':\n   497→        input_params = [ParameterDef.from_dict(p) for p in data.get(\"input_parameters\", [])]\n   498→        output_vals = [ParameterDef.from_dict(p) for p in data.get(\"output_values\", [])]\n   499→        \n   500→        return cls(\n   501→            workflow_id=data[\"workflow_id\"],\n   502→            name=data[\"name\"],\n   503→            input_parameters=input_params,\n   504→            output_values=output_vals,\n   505→            description=data.get(\"description\", \"\")\n   506→        )\n   507→\n   508→\n   509→@dataclass\n   510→class ReferenceNode:\n   511→    \"\"\"\n   512→    Node qui référence un sous-workflow.\n   513→    \n   514→    Permet d'appeler un sous-workflow depuis un workflow parent.\n   515→    \"\"\"\n   516→    node_id: str\n   517→    sub_workflow_id: str\n   518→    input_bindings: Dict[str, str] = field(default_factory=dict)  # param_name -> variable_name\n   519→    output_bindings: Dict[str, str] = field(default_factory=dict)  # param_name -> variable_name\n   520→    \n   521→    def to_dict(self) -> Dict[str, Any]:\n   522→        return {\n   523→            \"node_id\": self.node_id,\n   524→            \"sub_workflow_id\": self.sub_workflow_id,\n   525→            \"input_bindings\": self.input_bindings,\n   526→            \"output_bindings\": self.output_bindings\n   527→        }\n   528→    \n   529→    @classmethod\n   530→    def from_dict(cls, data: Dict[str, Any]) -> 'ReferenceNode':\n   531→        return cls(\n   532→            node_id=data[\"node_id\"],\n   533→            sub_workflow_id=data[\"sub_workflow_id\"],\n   534→            input_bindings=data.get(\"input_bindings\", {}),\n   535→            output_bindings=data.get(\"output_bindings\", {})\n   536→        )\n   537→\n   538→\n   539→# ============================================================================\n   540→# Contexte d'Exécution et Logging\n   541→# ============================================================================\n   542→\n   543→@dataclass\n   544→class LogEntry:\n   545→    \"\"\"\n   546→    Entrée du log d'exécution unifié.\n   547→    \n   548→    Enregistre un événement pendant l'exécution d'une chaîne de workflows.\n   549→    \"\"\"\n   550→    timestamp: datetime\n   551→    workflow_id: str\n   552→    node_id: str\n   553→    event_type: str  # start, end, action, error, warning, info\n   554→    details: Dict[str, Any] = field(default_factory=dict)\n   555→    \n   556→    def to_dict(self) -> Dict[str, Any]:\n   557→        return {\n   558→            \"timestamp\": self.timestamp.isoformat(),\n   559→            \"workflow_id\": self.workflow_id,\n   560→            \"node_id\": self.node_id,\n   561→            \"event_type\": self.event_type,\n   562→            \"details\": self.details\n   563→        }\n   564→    \n   565→    @classmethod\n   566→    def from_dict(cls, data: Dict[str, Any]) -> 'LogEntry':\n   567→        timestamp = datetime.now()\n   568→        if data.get(\"timestamp\"):\n   569→            timestamp = datetime.fromisoformat(data[\"timestamp\"])\n   570→        \n   571→        return cls(\n   572→            timestamp=timestamp,\n   573→            workflow_id=data[\"workflow_id\"],\n   574→            node_id=data[\"node_id\"],\n   575→            event_type=data[\"event_type\"],\n   576→            details=data.get(\"details\", {})\n   577→        )\n   578→\n   579→\n   580→@dataclass\n   581→class VariableOverride:\n   582→    \"\"\"\n   583→    Enregistrement d'un override de variable.\n   584→    \n   585→    Utilisé pour tracer les conflits de noms de variables.\n   586→    \"\"\"\n   587→    variable_name: str\n   588→    old_value: Any\n   589→    new_value: Any\n   590→    source_workflow: str\n   591→    timestamp: datetime = field(default_factory=datetime.now)\n   592→    \n   593→    def to_dict(self) -> Dict[str, Any]:\n   594→        return {\n   595→            \"variable_name\": self.variable_name,\n   596→            \"old_value\": self.old_value,\n   597→            \"new_value\": self.new_value,\n   598→            \"source_workflow\": self.source_workflow,\n   599→            \"timestamp\": self.timestamp.isoformat()\n   600→        }\n   601→    \n   602→    @classmethod\n   603→    def from_dict(cls, data: Dict[str, Any]) -> 'VariableOverride':\n   604→        timestamp = datetime.now()\n   605→        if data.get(\"timestamp\"):\n   606→            timestamp = datetime.fromisoformat(data[\"timestamp\"])\n   607→        \n   608→        return cls(\n   609→            variable_name=data[\"variable_name\"],\n   610→            old_value=data.get(\"old_value\"),\n   611→            new_value=data.get(\"new_value\"),\n   612→            source_workflow=data[\"source_workflow\"],\n   613→            timestamp=timestamp\n   614→        )\n   615→\n   616→\n   617→@dataclass\n   618→class ExecutionContext:\n   619→    \"\"\"\n   620→    Contexte d'exécution partagé entre workflows d'une chaîne.\n   621→    \n   622→    Maintient l'état global pendant l'exécution.\n   623→    \"\"\"\n   624→    chain_id: str\n   625→    current_workflow_id: str\n   626→    global_variables: Dict[str, Any] = field(default_factory=dict)\n   627→    execution_log: List[LogEntry] = field(default_factory=list)\n   628→    trigger_context: Optional[TriggerContext] = None\n   629→    started_at: datetime = field(default_factory=datetime.now)\n   630→    \n   631→    def to_dict(self) -> Dict[str, Any]:\n   632→        return {\n   633→            \"chain_id\": self.chain_id,\n   634→            \"current_workflow_id\": self.current_workflow_id,\n   635→            \"global_variables\": self.global_variables,\n   636→            \"execution_log\": [e.to_dict() for e in self.execution_log],\n   637→            \"trigger_context\": self.trigger_context.to_dict() if self.trigger_context else None,\n   638→            \"started_at\": self.started_at.isoformat()\n   639→        }\n   640→    \n   641→    @classmethod\n   642→    def from_dict(cls, data: Dict[str, Any]) -> 'ExecutionContext':\n   643→        log_entries = [LogEntry.from_dict(e) for e in data.get(\"execution_log\", [])]\n   644→        trigger_ctx = None\n   645→        if data.get(\"trigger_context\"):\n   646→            trigger_ctx = TriggerContext.from_dict(data[\"trigger_context\"])\n   647→        \n   648→        started_at = datetime.now()\n   649→        if data.get(\"started_at\"):\n   650→            started_at = datetime.fromisoformat(data[\"started_at\"])\n   651→        \n   652→        return cls(\n   653→            chain_id=data[\"chain_id\"],\n   654→            current_workflow_id=data[\"current_workflow_id\"],\n   655→            global_variables=data.get(\"global_variables\", {}),\n   656→            execution_log=log_entries,\n   657→            trigger_context=trigger_ctx,\n   658→            started_at=started_at\n   659→        )\n   660→\n   661→\n   662→# ============================================================================\n   663→# Validation\n   664→# ============================================================================\n   665→\n   666→@dataclass\n   667→class ValidationResult:\n   668→    \"\"\"\n   669→    Résultat de validation d'une configuration.\n   670→    \n   671→    Utilisé pour valider les chaînes, dépendances, etc.\n   672→    \"\"\"\n   673→    is_valid: bool\n   674→    errors: List[str] = field(default_factory=list)\n   675→    warnings: List[str] = field(default_factory=list)\n   676→    \n   677→    def to_dict(self) -> Dict[str, Any]:\n   678→        return {\n   679→            \"is_valid\": self.is_valid,\n   680→            \"errors\": self.errors,\n   681→            \"warnings\": self.warnings\n   682→        }\n   683→    \n   684→    @classmethod\n   685→    def from_dict(cls, data: Dict[str, Any]) -> 'ValidationResult':\n   686→        return cls(\n   687→            is_valid=data[\"is_valid\"],\n   688→            errors=data.get(\"errors\", []),\n   689→            warnings=data.get(\"warnings\", [])\n   690→        )\n   691→\n   692→\n   693→# ============================================================================\n   694→# Fusion de Workflows\n   695→# ============================================================================\n   696→\n   697→@dataclass\n   698→class NodeConflict:\n   699→    \"\"\"\n   700→    Conflit entre deux nodes lors d'une fusion.\n   701→    \n   702→    Indique que deux workflows ont des actions différentes sur le même node.\n   703→    \"\"\"\n   704→    node_id: str\n   705→    action_a: str\n   706→    action_b: str\n   707→    \n   708→    def to_dict(self) -> Dict[str, Any]:\n   709→        return {\n   710→            \"node_id\": self.node_id,\n   711→            \"action_a\": self.action_a,\n   712→            \"action_b\": self.action_b\n   713→        }\n   714→    \n   715→    @classmethod\n   716→    def from_dict(cls, data: Dict[str, Any]) -> 'NodeConflict':\n   717→        return cls(\n   718→            node_id=data[\"node_id\"],\n   719→            action_a=data[\"action_a\"],\n   720→            action_b=data[\"action_b\"]\n   721→        )\n   722→\n   723→\n   724→@dataclass\n   725→class MergeCandidate:\n   726→    \"\"\"\n   727→    Candidat à la fusion de deux workflows.\n   728→    \n   729→    Contient les informations sur la similarité et les conflits potentiels.\n   730→    \"\"\"\n   731→    workflow_a_id: str\n   732→    workflow_b_id: str\n   733→    similarity_score: float\n   734→    shared_nodes: List[str] = field(default_factory=list)\n   735→    conflicts: List[NodeConflict] = field(default_factory=list)\n   736→    \n   737→    def to_dict(self) -> Dict[str, Any]:\n   738→        return {\n   739→            \"workflow_a_id\": self.workflow_a_id,\n   740→            \"workflow_b_id\": self.workflow_b_id,\n   741→            \"similarity_score\": self.similarity_score,\n   742→            \"shared_nodes\": self.shared_nodes,\n   743→            \"conflicts\": [c.to_dict() for c in self.conflicts]\n   744→        }\n   745→    \n   746→    @classmethod\n   747→    def from_dict(cls, data: Dict[str, Any]) -> 'MergeCandidate':\n   748→        conflicts = [NodeConflict.from_dict(c) for c in data.get(\"conflicts\", [])]\n   749→        return cls(\n   750→            workflow_a_id=data[\"workflow_a_id\"],\n   751→            workflow_b_id=data[\"workflow_b_id\"],\n   752→            similarity_score=data[\"similarity_score\"],\n   753→            shared_nodes=data.get(\"shared_nodes\", []),\n   754→            conflicts=conflicts\n   755→        )\n   756→\n   757→\n   758→# ============================================================================\n   759→# Extraction de Séquences\n   760→# ============================================================================\n   761→\n   762→@dataclass\n   763→class SequenceOccurrence:\n   764→    \"\"\"\n   765→    Occurrence d'une séquence dans un workflow.\n   766→    \n   767→    Indique où une séquence commune a été trouvée.\n   768→    \"\"\"\n   769→    workflow_id: str\n   770→    start_index: int\n   771→    end_index: int\n   772→    \n   773→    def to_dict(self) -> Dict[str, Any]:\n   774→        return {\n   775→            \"workflow_id\": self.workflow_id,\n   776→            \"start_index\": self.start_index,\n   777→            \"end_index\": self.end_index\n   778→        }\n   779→    \n   780→    @classmethod\n   781→    def from_dict(cls, data: Dict[str, Any]) -> 'SequenceOccurrence':\n   782→        return cls(\n   783→            workflow_id=data[\"workflow_id\"],\n   784→            start_index=data[\"start_index\"],\n   785→            end_index=data[\"end_index\"]\n   786→        )\n   787→\n   788→\n   789→@dataclass\n   790→class CommonSequence:\n   791→    \"\"\"\n   792→    Séquence commune détectée dans plusieurs workflows.\n   793→    \n   794→    Candidate à l'extraction en sous-workflow.\n   795→    \"\"\"\n   796→    nodes: List[str]\n   797→    occurrences: List[SequenceOccurrence] = field(default_factory=list)\n   798→    priority: str = \"medium\"  # high, medium, low\n   799→    \n   800→    def to_dict(self) -> Dict[str, Any]:\n   801→        return {\n   802→            \"nodes\": self.nodes,\n   803→            \"occurrences\": [o.to_dict() for o in self.occurrences],\n   804→            \"priority\": self.priority\n   805→        }\n   806→    \n   807→    @classmethod\n   808→    def from_dict(cls, data: Dict[str, Any]) -> 'CommonSequence':\n   809→        occurrences = [SequenceOccurrence.from_dict(o) for o in data.get(\"occurrences\", [])]\n   810→        return cls(\n   811→            nodes=data[\"nodes\"],\n   812→            occurrences=occurrences,\n   813→            priority=data.get(\"priority\", \"medium\")\n   814→        )\n   815→\n   816→\n   817→# ============================================================================\n   818→# Configuration Complète de Composition\n   819→# ============================================================================\n   820→\n   821→@dataclass\n   822→class WorkflowCompositionConfig:\n   823→    \"\"\"\n   824→    Configuration complète de composition pour un ensemble de workflows.\n   825→    \n   826→    Regroupe toutes les configurations de chaînage, boucles, conditions, etc.\n   827→    \"\"\"\n   828→    chains: List[ChainConfig] = field(default_factory=list)\n   829→    sub_workflows: Dict[str, SubWorkflowDefinition] = field(default_factory=dict)\n   830→    references: Dict[str, List[ReferenceNode]] = field(default_factory=dict)\n   831→    loops: Dict[str, LoopConfig] = field(default_factory=dict)\n   832→    conditionals: Dict[str, ConditionalNode] = field(default_factory=dict)\n   833→    triggers: List[TriggerConfig] = field(default_factory=list)\n   834→    \n   835→    def to_dict(self) -> Dict[str, Any]:\n   836→        return {\n   837→            \"chains\": [c.to_dict() for c in self.chains],\n   838→            \"sub_workflows\": {k: v.to_dict() for k, v in self.sub_workflows.items()},\n   839→            \"references\": {k: [r.to_dict() for r in v] for k, v in self.references.items()},\n   840→            \"loops\": {k: v.to_dict() for k, v in self.loops.items()},\n   841→            \"conditionals\": {k: v.to_dict() for k, v in self.conditionals.items()},\n   842→            \"triggers\": [t.to_dict() for t in self.triggers]\n   843→        }\n   844→    \n   845→    @classmethod\n   846→    def from_dict(cls, data: Dict[str, Any]) -> 'WorkflowCompositionConfig':\n   847→        chains = [ChainConfig.from_dict(c) for c in data.get(\"chains\", [])]\n   848→        sub_workflows = {\n   849→            k: SubWorkflowDefinition.from_dict(v) \n   850→            for k, v in data.get(\"sub_workflows\", {}).items()\n   851→        }\n   852→        references = {\n   853→            k: [ReferenceNode.from_dict(r) for r in v]\n   854→            for k, v in data.get(\"references\", {}).items()\n   855→        }\n   856→        loops = {\n   857→            k: LoopConfig.from_dict(v)\n   858→            for k, v in data.get(\"loops\", {}).items()\n   859→        }\n   860→        conditionals = {\n   861→            k: ConditionalNode.from_dict(v)\n   862→            for k, v in data.get(\"conditionals\", {}).items()\n   863→        }\n   864→        triggers = [trigger_from_dict(t) for t in data.get(\"triggers\", [])]\n   865→        \n   866→        return cls(\n   867→            chains=chains,\n   868→            sub_workflows=sub_workflows,\n   869→            references=references,\n   870→            loops=loops,\n   871→            conditionals=conditionals,\n   872→            triggers=triggers\n   873→        )\n   874→\n   875→\n   876→# ============================================================================\n   877→# Résultats d'Exécution\n   878→# ============================================================================\n   879→\n   880→@dataclass\n   881→class ChainResult:\n   882→    \"\"\"\n   883→    Résultat de l'exécution d'une chaîne de workflows.\n   884→    \"\"\"\n   885→    chain_id: str\n   886→    success: bool\n   887→    workflows_executed: List[str] = field(default_factory=list)\n   888→    final_variables: Dict[str, Any] = field(default_factory=dict)\n   889→    execution_log: List[LogEntry] = field(default_factory=list)\n   890→    error_message: Optional[str] = None\n   891→    started_at: datetime = field(default_factory=datetime.now)\n   892→    completed_at: Optional[datetime] = None\n   893→    \n   894→    def to_dict(self) -> Dict[str, Any]:\n   895→        return {\n   896→            \"chain_id\": self.chain_id,\n   897→            \"success\": self.success,\n   898→            \"workflows_executed\": self.workflows_executed,\n   899→            \"final_variables\": self.final_variables,\n   900→            \"execution_log\": [e.to_dict() for e in self.execution_log],\n   901→            \"error_message\": self.error_message,\n   902→            \"started_at\": self.started_at.isoformat(),\n   903→            \"completed_at\": self.completed_at.isoformat() if self.completed_at else None\n   904→        }\n   905→    \n   906→    @classmethod\n   907→    def from_dict(cls, data: Dict[str, Any]) -> 'ChainResult':\n   908→        log_entries = [LogEntry.from_dict(e) for e in data.get(\"execution_log\", [])]\n   909→        \n   910→        started_at = datetime.now()\n   911→        if data.get(\"started_at\"):\n   912→            started_at = datetime.fromisoformat(data[\"started_at\"])\n   913→        \n   914→        completed_at = None\n   915→        if data.get(\"completed_at\"):\n   916→            completed_at = datetime.fromisoformat(data[\"completed_at\"])\n   917→        \n   918→        return cls(\n   919→            chain_id=data[\"chain_id\"],\n   920→            success=data[\"success\"],\n   921→            workflows_executed=data.get(\"workflows_executed\", []),\n   922→            final_variables=data.get(\"final_variables\", {}),\n   923→            execution_log=log_entries,\n   924→            error_message=data.get(\"error_message\"),\n   925→            started_at=started_at,\n   926→            completed_at=completed_at\n   927→        )\n   928→\n   929→\n   930→@dataclass\n   931→class IterationResult:\n   932→    \"\"\"\n   933→    Résultat d'une itération de boucle.\n   934→    \"\"\"\n   935→    loop_id: str\n   936→    iteration: int\n   937→    success: bool\n   938→    should_continue: bool\n   939→    error_message: Optional[str] = None\n   940→    \n   941→    def to_dict(self) -> Dict[str, Any]:\n   942→        return {\n   943→            \"loop_id\": self.loop_id,\n   944→            \"iteration\": self.iteration,\n   945→            \"success\": self.success,\n   946→            \"should_continue\": self.should_continue,\n   947→            \"error_message\": self.error_message\n   948→        }\n   949→    \n   950→    @classmethod\n   951→    def from_dict(cls, data: Dict[str, Any]) -> 'IterationResult':\n   952→        return cls(\n   953→            loop_id=data[\"loop_id\"],\n   954→            iteration=data[\"iteration\"],\n   955→            success=data[\"success\"],\n   956→            should_continue=data[\"should_continue\"],\n   957→            error_message=data.get(\"error_message\")\n   958→        )\n   959→\n   960→\n   961→@dataclass\n   962→class ExecutionResult:\n   963→    \"\"\"\n   964→    Résultat générique d'exécution (sous-workflow, action, etc.).\n   965→    \"\"\"\n   966→    success: bool\n   967→    output_values: Dict[str, Any] = field(default_factory=dict)\n   968→    error_message: Optional[str] = None\n   969→    error_context: Optional[Dict[str, Any]] = None\n   970→    \n   971→    def to_dict(self) -> Dict[str, Any]:\n   972→        return {\n   973→            \"success\": self.success,\n   974→            \"output_values\": self.output_values,\n   975→            \"error_message\": self.error_message,\n   976→            \"error_context\": self.error_context\n   977→        }\n   978→    \n   979→    @classmethod\n   980→    def from_dict(cls, data: Dict[str, Any]) -> 'ExecutionResult':\n   981→        return cls(\n   982→            success=data[\"success\"],\n   983→            output_values=data.get(\"output_values\", {}),\n   984→            error_message=data.get(\"error_message\"),\n   985→            error_context=data.get(\"error_context\")\n   986→        )\n   987→"}]},"uuid":"344960d3-48e3-40a6-b0e0-d76abe19ffbc","timestamp":"2026-03-14T21:15:16.524Z","sourceToolAssistantUUID":"81d6cb2f-8c11-496b-917d-1a5f676e95fc"}
+{"parentUuid":"344960d3-48e3-40a6-b0e0-d76abe19ffbc","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","message":{"model":"claude-opus-4-6","id":"msg_011dgNsq4ex2V2dJ1prfZqvd","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01N3a4Q5Zhv2Re9qBSokyFny","name":"Read","input":{"file_path":"/home/dom/ai/rpa_vision_v3/core/workflow/loop_executor.py"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":923,"cache_read_input_tokens":110420,"output_tokens":135,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":923},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZ3fY8trukCFqLqmt9G82","type":"assistant","uuid":"5d60c146-eece-4229-bf4e-6ee747bd79ce","timestamp":"2026-03-14T21:15:16.730Z"}
+{"parentUuid":"5d60c146-eece-4229-bf4e-6ee747bd79ce","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Read","command":"callback"},"parentToolUseID":"toolu_01N3a4Q5Zhv2Re9qBSokyFny","toolUseID":"toolu_01N3a4Q5Zhv2Re9qBSokyFny","timestamp":"2026-03-14T21:15:16.737Z","uuid":"2a21ca4c-c2e9-423b-9dcf-5da8dbc48ec9"}
+{"parentUuid":"5d60c146-eece-4229-bf4e-6ee747bd79ce","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01N3a4Q5Zhv2Re9qBSokyFny","type":"tool_result","content":"     1→\"\"\"\n     2→Loop Executor - Gestion de l'exécution des boucles dans les workflows\n     3→\n     4→Ce module gère l'exécution des boucles (count et condition) avec:\n     5→- Compteur d'itérations\n     6→- Évaluation des conditions de sortie\n     7→- Garde de sécurité (limite max d'itérations)\n     8→\"\"\"\n     9→\n    10→import logging\n    11→from typing import Dict, Any, Optional\n    12→from datetime import datetime\n    13→\n    14→from .composition_models import (\n    15→    LoopConfig, LoopState, IterationResult, VisualCondition\n    16→)\n    17→\n    18→logger = logging.getLogger(__name__)\n    19→\n    20→\n    21→class LoopSafetyLimitError(Exception):\n    22→    \"\"\"Erreur levée quand la limite de sécurité est atteinte.\"\"\"\n    23→    \n    24→    def __init__(self, loop_id: str, iterations: int, limit: int):\n    25→        self.loop_id = loop_id\n    26→        self.iterations = iterations\n    27→        self.limit = limit\n    28→        super().__init__(\n    29→            f\"Boucle '{loop_id}' a atteint la limite de sécurité: \"\n    30→            f\"{iterations} itérations (limite: {limit})\"\n    31→        )\n    32→\n    33→\n    34→class LoopExecutor:\n    35→    \"\"\"\n    36→    Gère l'exécution des boucles dans les workflows.\n    37→    \n    38→    Supporte deux types de boucles:\n    39→    - count: Nombre fixe d'itérations\n    40→    - condition: Itère jusqu'à ce qu'une condition visuelle soit vraie\n    41→    \"\"\"\n    42→    \n    43→    DEFAULT_SAFETY_LIMIT = 1000\n    44→\n    45→    \n    46→    def __init__(self, safety_limit: int = DEFAULT_SAFETY_LIMIT):\n    47→        \"\"\"\n    48→        Initialise le LoopExecutor.\n    49→        \n    50→        Args:\n    51→            safety_limit: Limite maximale d'itérations par défaut\n    52→        \"\"\"\n    53→        self.safety_limit = safety_limit\n    54→        self._active_loops: Dict[str, LoopState] = {}\n    55→        self._configs: Dict[str, LoopConfig] = {}\n    56→    \n    57→    def start_loop(self, config: LoopConfig) -> LoopState:\n    58→        \"\"\"\n    59→        Démarre une nouvelle boucle.\n    60→        \n    61→        Args:\n    62→            config: Configuration de la boucle\n    63→            \n    64→        Returns:\n    65→            État initial de la boucle\n    66→        \"\"\"\n    67→        state = LoopState(\n    68→            loop_id=config.loop_id,\n    69→            current_iteration=0,\n    70→            started_at=datetime.now(),\n    71→            last_condition_result=None\n    72→        )\n    73→        \n    74→        self._active_loops[config.loop_id] = state\n    75→        self._configs[config.loop_id] = config\n    76→        \n    77→        logger.info(f\"Boucle '{config.loop_id}' démarrée (type: {config.loop_type})\")\n    78→        return state\n    79→    \n    80→    def execute_iteration(self, loop_id: str) -> IterationResult:\n    81→        \"\"\"\n    82→        Exécute une itération de la boucle.\n    83→        \n    84→        Args:\n    85→            loop_id: ID de la boucle\n    86→            \n    87→        Returns:\n    88→            Résultat de l'itération\n    89→            \n    90→        Raises:\n    91→            LoopSafetyLimitError: Si la limite de sécurité est atteinte\n    92→        \"\"\"\n    93→        if loop_id not in self._active_loops:\n    94→            return IterationResult(\n    95→                loop_id=loop_id,\n    96→                iteration=0,\n    97→                success=False,\n    98→                should_continue=False,\n    99→                error_message=f\"Boucle '{loop_id}' non trouvée\"\n   100→            )\n   101→        \n   102→        state = self._active_loops[loop_id]\n   103→        config = self._configs[loop_id]\n   104→        \n   105→        # Incrémenter le compteur\n   106→        new_iteration = self.increment_counter(loop_id)\n   107→        \n   108→        # Vérifier la limite de sécurité\n   109→        effective_limit = min(config.safety_limit, self.safety_limit)\n   110→        if new_iteration > effective_limit:\n   111→            logger.warning(\n   112→                f\"Boucle '{loop_id}' a atteint la limite de sécurité ({effective_limit})\"\n   113→            )\n   114→            raise LoopSafetyLimitError(loop_id, new_iteration, effective_limit)\n   115→        \n   116→        # Déterminer si on doit continuer\n   117→        should_continue = self.should_continue(loop_id)\n   118→        \n   119→        return IterationResult(\n   120→            loop_id=loop_id,\n   121→            iteration=new_iteration,\n   122→            success=True,\n   123→            should_continue=should_continue\n   124→        )\n   125→\n   126→    \n   127→    def should_continue(self, loop_id: str, condition_result: Optional[bool] = None) -> bool:\n   128→        \"\"\"\n   129→        Évalue si la boucle doit continuer.\n   130→        \n   131→        Args:\n   132→            loop_id: ID de la boucle\n   133→            condition_result: Résultat de l'évaluation de la condition (pour type condition)\n   134→            \n   135→        Returns:\n   136→            True si la boucle doit continuer, False sinon\n   137→        \"\"\"\n   138→        if loop_id not in self._active_loops:\n   139→            return False\n   140→        \n   141→        state = self._active_loops[loop_id]\n   142→        config = self._configs[loop_id]\n   143→        \n   144→        # Mettre à jour le résultat de condition si fourni\n   145→        if condition_result is not None:\n   146→            state.last_condition_result = condition_result\n   147→        \n   148→        if config.loop_type == \"count\":\n   149→            # Boucle par compteur\n   150→            if config.max_iterations is None:\n   151→                return True  # Pas de limite (dangereux, mais la garde de sécurité protège)\n   152→            return state.current_iteration < config.max_iterations\n   153→        \n   154→        elif config.loop_type == \"condition\":\n   155→            # Boucle par condition\n   156→            # Continue tant que la condition de sortie n'est pas vraie\n   157→            if state.last_condition_result is None:\n   158→                return True  # Pas encore évalué, continuer\n   159→            return not state.last_condition_result  # Sortir quand la condition est vraie\n   160→        \n   161→        return False\n   162→    \n   163→    def increment_counter(self, loop_id: str) -> int:\n   164→        \"\"\"\n   165→        Incrémente le compteur de la boucle.\n   166→        \n   167→        Args:\n   168→            loop_id: ID de la boucle\n   169→            \n   170→        Returns:\n   171→            Nouvelle valeur du compteur\n   172→        \"\"\"\n   173→        if loop_id not in self._active_loops:\n   174→            return 0\n   175→        \n   176→        state = self._active_loops[loop_id]\n   177→        state.current_iteration += 1\n   178→        \n   179→        logger.debug(f\"Boucle '{loop_id}': itération {state.current_iteration}\")\n   180→        return state.current_iteration\n   181→    \n   182→    def get_state(self, loop_id: str) -> Optional[LoopState]:\n   183→        \"\"\"Retourne l'état actuel d'une boucle.\"\"\"\n   184→        return self._active_loops.get(loop_id)\n   185→    \n   186→    def get_iteration(self, loop_id: str) -> int:\n   187→        \"\"\"Retourne le numéro d'itération actuel.\"\"\"\n   188→        state = self._active_loops.get(loop_id)\n   189→        return state.current_iteration if state else 0\n   190→    \n   191→    def end_loop(self, loop_id: str) -> Optional[LoopState]:\n   192→        \"\"\"\n   193→        Termine une boucle et retourne son état final.\n   194→        \n   195→        Args:\n   196→            loop_id: ID de la boucle\n   197→            \n   198→        Returns:\n   199→            État final de la boucle ou None si non trouvée\n   200→        \"\"\"\n   201→        state = self._active_loops.pop(loop_id, None)\n   202→        self._configs.pop(loop_id, None)\n   203→        \n   204→        if state:\n   205→            logger.info(\n   206→                f\"Boucle '{loop_id}' terminée après {state.current_iteration} itérations\"\n   207→            )\n   208→        \n   209→        return state\n   210→    \n   211→    def is_active(self, loop_id: str) -> bool:\n   212→        \"\"\"Vérifie si une boucle est active.\"\"\"\n   213→        return loop_id in self._active_loops\n   214→    \n   215→    def get_active_loops(self) -> Dict[str, LoopState]:\n   216→        \"\"\"Retourne toutes les boucles actives.\"\"\"\n   217→        return self._active_loops.copy()\n   218→    \n   219→    def to_dict(self) -> Dict[str, Any]:\n   220→        \"\"\"Sérialise l'état des boucles.\"\"\"\n   221→        return {\n   222→            \"safety_limit\": self.safety_limit,\n   223→            \"active_loops\": {k: v.to_dict() for k, v in self._active_loops.items()},\n   224→            \"configs\": {k: v.to_dict() for k, v in self._configs.items()}\n   225→        }\n   226→    \n   227→    @classmethod\n   228→    def from_dict(cls, data: Dict[str, Any]) -> 'LoopExecutor':\n   229→        \"\"\"Désérialise l'état des boucles.\"\"\"\n   230→        executor = cls(safety_limit=data.get(\"safety_limit\", cls.DEFAULT_SAFETY_LIMIT))\n   231→        \n   232→        for loop_id, state_data in data.get(\"active_loops\", {}).items():\n   233→            executor._active_loops[loop_id] = LoopState.from_dict(state_data)\n   234→        \n   235→        for loop_id, config_data in data.get(\"configs\", {}).items():\n   236→            executor._configs[loop_id] = LoopConfig.from_dict(config_data)\n   237→        \n   238→        return executor\n   239→"}]},"uuid":"2fb302e2-0589-460e-ba91-9ac20c38e6b9","timestamp":"2026-03-14T21:15:16.736Z","sourceToolAssistantUUID":"5d60c146-eece-4229-bf4e-6ee747bd79ce"}
+{"parentUuid":"2fb302e2-0589-460e-ba91-9ac20c38e6b9","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","message":{"model":"claude-opus-4-6","id":"msg_01WW297niaHDCxDbdLcyBMLs","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me generate the Mermaid diagram for the architecture."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":15676,"cache_read_input_tokens":111343,"cache_creation":{"ephemeral_5m_input_tokens":15676,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZ3fYTmfW8g6ebBpnWCFZ","type":"assistant","uuid":"436511e2-5fea-4685-a4ee-3ba4031eca7f","timestamp":"2026-03-14T21:15:22.647Z"}
+{"parentUuid":"436511e2-5fea-4685-a4ee-3ba4031eca7f","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","message":{"model":"claude-opus-4-6","id":"msg_01WW297niaHDCxDbdLcyBMLs","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DE5FdR375WTciFL6tCmMus","name":"ToolSearch","input":{"query":"select:mcp__claude_ai_Mermaid_Chart__validate_and_render_mermaid_diagram","max_results":1},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":15676,"cache_read_input_tokens":111343,"output_tokens":113,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":15676},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZ3fYTmfW8g6ebBpnWCFZ","type":"assistant","uuid":"4308fb99-9ec2-4ddc-9bf7-c2fc06041255","timestamp":"2026-03-14T21:15:23.548Z"}
+{"parentUuid":"4308fb99-9ec2-4ddc-9bf7-c2fc06041255","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"user","message":{"role":"user","content":[{"type":"tool_result","tool_use_id":"toolu_01DE5FdR375WTciFL6tCmMus","content":[{"type":"tool_reference","tool_name":"mcp__claude_ai_Mermaid_Chart__validate_and_render_mermaid_diagram"}]}]},"uuid":"7e6dce52-282c-42cf-8b6d-bc96447bd44d","timestamp":"2026-03-14T21:15:23.550Z","sourceToolAssistantUUID":"4308fb99-9ec2-4ddc-9bf7-c2fc06041255"}
+{"parentUuid":"7e6dce52-282c-42cf-8b6d-bc96447bd44d","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","message":{"model":"claude-opus-4-6","id":"msg_01Y5CbzzhD4pZNB1tswKymfM","type":"message","role":"assistant","content":[{"type":"text","text":"\n\nI now have a thorough understanding of the entire codebase. Let me generate the architecture diagrams and then produce the complete plan."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":511,"cache_read_input_tokens":127019,"cache_creation":{"ephemeral_5m_input_tokens":511,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CZ3fYxzpiJVk6ihJZEbgL","type":"assistant","uuid":"d54b44db-003a-45be-96a5-46065185bc79","timestamp":"2026-03-14T21:15:28.730Z"}
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+{"parentUuid":"4d0c6c6e-410c-4e71-9ec9-042e96dac140","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3/agent_v0","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Streamable HTTP error: Error POSTing to endpoint: event: message\r\ndata: {\"jsonrpc\":\"2.0\",\"error\":{\"code\":-32600,\"message\":\"Invalid Request\"},\"id\":3}","is_error":true,"tool_use_id":"toolu_01XtbwSBJTjy5HFjKX7VN51F"}]},"uuid":"852b44d7-0c60-4b44-b01f-11a17485923f","timestamp":"2026-03-14T21:15:50.283Z","toolUseResult":"Error: Streamable HTTP error: Error POSTing to endpoint: event: message\r\ndata: {\"jsonrpc\":\"2.0\",\"error\":{\"code\":-32600,\"message\":\"Invalid Request\"},\"id\":3}","sourceToolAssistantUUID":"4d0c6c6e-410c-4e71-9ec9-042e96dac140"}
+{"parentUuid":"852b44d7-0c60-4b44-b01f-11a17485923f","isSidechain":true,"userType":"external","cwd":"/home/dom/ai/rpa_vision_v3","sessionId":"eec8c668-e1f8-4599-9dd5-42170a166ba4","version":"2.1.72","gitBranch":"master","agentId":"a44ee2e574a8117a1","slug":"noble-roaming-floyd","message":{"model":"claude-opus-4-6","id":"msg_01WPVn22tg27JyHuVTpmXeCk","type":"message","role":"assistant","content":[{"type":"text","text":"The Mermaid tool appears to be unavailable. I will include the diagrams as text in the plan. I have now gathered everything I need. Here is the complete architecture plan.\n\n---\n\n# Plan d'Architecture : Scraping Visuel Structure\n\n## 1. Vue d'ensemble et positionnement dans l'existant\n\nLe systeme existant a deja tous les composants fondamentaux pour cette fonctionnalite :\n\n- **Pipeline d'analyse** : `ScreenAnalyzer` (OCR docTR + UIDetector + VLM qwen2.5vl) produit des `ScreenState` enrichis (textes detectes, elements UI, embeddings).\n- **Execution distante** : Le cycle `api_stream.py` replay queue / `ActionExecutorV1` poll+execute permet d'envoyer des ordres a l'Agent V1 Windows et de recevoir les screenshots de resultat.\n- **Resolution visuelle** : L'endpoint `/replay/resolve_target` sait deja trouver un element UI par texte/role dans un screenshot.\n- **Boucles** : `LoopConfig`, `LoopState`, `LoopExecutor` existent deja dans `core/workflow/composition_models.py` et `loop_executor.py`. Ils supportent des boucles `count` et `condition`.\n- **VLM** : `OllamaClient` dans `core/detection/ollama_client.py` sait envoyer une image + prompt a Ollama et recevoir du JSON structure (`force_json=True`).\n- **Variables** : Le systeme `_substitute_variables` dans `api_stream.py` gere deja `${var}` dans les textes d'actions.\n\nCe qu'il manque : un **type d'action \"extract\"** qui, au lieu de cliquer ou taper, **lit l'ecran et structure les donnees**, un **schema d'extraction configurable**, un **controleur d'iteration** specifique au scraping, et un **stockage base de donnees**.\n\n---\n\n## 2. Nouveau type d'action \"extract\" dans les workflows\n\n### 2.1 Extension du modele ActionType\n\nDans `/home/dom/ai/rpa_vision_v3/core/models/workflow_graph.py`, la classe `ActionType` (ligne 32) definit actuellement :\n\n```python\nclass ActionType(str, Enum):\n    MOUSE_CLICK = \"mouse_click\"\n    TEXT_INPUT = \"text_input\"\n    KEY_PRESS = \"key_press\"\n    COMPOUND = \"compound\"\n```\n\nAjouter deux nouveaux types :\n\n```\nEXTRACT = \"extract\"          # Lire l'ecran et extraire des donnees structurees\nEXTRACT_LIST = \"extract_list\" # Lire une liste d'elements visibles (table, liste de resultats)\n```\n\n### 2.2 Structure JSON d'un edge \"extract\" dans un workflow\n\nL'action `extract` est un edge comme les autres. Elle a un `from_node` (l'ecran a lire) et un `to_node` (l'ecran apres lecture, souvent le meme). Sa particularite : elle ne modifie pas l'UI, elle **lit** l'ecran.\n\n```json\n{\n    \"edge_id\": \"e_extract_patient\",\n    \"from_node\": \"patient_detail\",\n    \"to_node\": \"patient_detail\",\n    \"action\": {\n        \"type\": \"extract\",\n        \"target\": {\n            \"by_role\": \"screen\",\n            \"selection_policy\": \"full_screen\"\n        },\n        \"parameters\": {\n            \"schema_id\": \"schema_patient_dpi\",\n            \"extraction_mode\": \"vlm\",\n            \"store_result\": true,\n            \"variable_prefix\": \"patient\"\n        }\n    }\n}\n```\n\nLe champ `parameters.schema_id` fait reference a un schema d'extraction (voir section 3). Le `variable_prefix` permet de stocker les resultats dans les variables du workflow (`${patient.nom}`, `${patient.nip}`, etc.) pour les reutiliser dans les etapes suivantes.\n\n### 2.3 Integration dans le replay pipeline\n\nDans `/home/dom/ai/rpa_vision_v3/agent_v0/server_v1/api_stream.py`, la fonction `_edge_to_normalized_actions` (ligne 433) doit etre etendue pour generer une action normalisee de type `\"extract\"`. Contrairement aux actions physiques (click, type), l'action extract ne sera **pas executee par l'Agent V1**. C'est le **serveur** qui la traite directement en analysant le screenshot courant.\n\nLe flux est :\n\n```\n                   api_stream.py                          Agent V1\n                       |                                     |\n  1. Enqueue action    |  {\"type\":\"extract\", ...}            |\n     type=extract      |------------------------------------>|\n                       |                                     |\n  2. Agent V1 recoit   |  Capture screenshot,               |\n     l'action          |  POST /replay/result               |\n                       |  avec screenshot_b64               |\n                       |<------------------------------------|\n                       |                                     |\n  3. Serveur recoit    |  ExtractionEngine.extract(          |\n     le screenshot     |    screenshot, schema)              |\n     + action extract  |                                     |\n                       |                                     |\n  4. Resultat stocke   |  DataStore.insert_row(...)          |\n     en DB             |                                     |\n```\n\nLe point critique : quand le serveur recoit le `ReplayResultReport` pour une action `extract`, il doit intercepter le screenshot joint et le passer au moteur d'extraction au lieu de simplement logger le resultat. Cela necessite une extension de `report_action_result` dans `api_stream.py` (ligne 699).\n\nAlternative preferee (plus propre) : l'action `extract` n'est **jamais envoyee a l'Agent V1**. Le serveur la detecte dans `get_next_action` (ligne 652), capture l'analyse du **dernier screenshot** recu de l'Agent, et fait l'extraction cote serveur. L'Agent V1 ne sait meme pas qu'il y a eu une extraction.\n\n---\n\n## 3. Schema d'extraction configurable\n\n### 3.1 Nouveau fichier : `core/extraction/schema.py`\n\nLe schema d'extraction definit **quels champs** extraire et **comment les valider**. Il est cree par l'utilisateur via le Chat (mode Shadow) ou le VWB (Visual Workflow Builder).\n\nStructure JSON du schema :\n\n```json\n{\n    \"schema_id\": \"schema_patient_dpi\",\n    \"name\": \"Fiche Patient DPI\",\n    \"version\": 1,\n    \"description\": \"Extraction des informations patient depuis le Dossier Patient Informatise\",\n    \"target_app\": \"DPI Hopital*\",\n    \"fields\": [\n        {\n            \"field_id\": \"nip\",\n            \"label\": \"NIP (Numero d'Identification Patient)\",\n            \"type\": \"string\",\n            \"required\": true,\n            \"validation\": {\n                \"pattern\": \"^[0-9]{8,12}$\",\n                \"description\": \"8 a 12 chiffres\"\n            },\n            \"extraction_hint\": \"Numero a 8-12 chiffres, souvent en haut a droite, pres de 'NIP' ou 'IPP'\",\n            \"zone_hint\": \"top_right\"\n        },\n        {\n            \"field_id\": \"nom\",\n            \"label\": \"Nom du patient\",\n            \"type\": \"string\",\n            \"required\": true,\n            \"validation\": null,\n            \"extraction_hint\": \"Nom de famille en majuscules, souvent le premier champ visible\",\n            \"zone_hint\": \"top_left\"\n        },\n        {\n            \"field_id\": \"prenom\",\n            \"label\": \"Prenom du patient\",\n            \"type\": \"string\",\n            \"required\": true,\n            \"validation\": null,\n            \"extraction_hint\": \"Prenom apres le nom\",\n            \"zone_hint\": \"top_left\"\n        },\n        {\n            \"field_id\": \"date_naissance\",\n            \"label\": \"Date de naissance\",\n            \"type\": \"date\",\n            \"required\": true,\n            \"validation\": {\n                \"format\": \"DD/MM/YYYY\"\n            },\n            \"extraction_hint\": \"Date au format JJ/MM/AAAA pres de 'Ne(e) le'\",\n            \"zone_hint\": null\n        },\n        {\n            \"field_id\": \"diagnostic_principal\",\n            \"label\": \"Diagnostic principal\",\n            \"type\": \"string\",\n            \"required\": false,\n            \"validation\": null,\n            \"extraction_hint\": \"Texte libre dans la section 'Diagnostic' ou 'Motif'\",\n            \"zone_hint\": \"center\"\n        },\n        {\n            \"field_id\": \"code_cim10\",\n            \"label\": \"Code CIM-10\",\n            \"type\": \"string\",\n            \"required\": false,\n            \"validation\": {\n                \"pattern\": \"^[A-Z][0-9]{2}(\\\\.[0-9]{1,2})?$\"\n            },\n            \"extraction_hint\": \"Code au format lettre + 2 chiffres, ex: J18.9, I25.0\",\n            \"zone_hint\": null\n        }\n    ],\n    \"created_at\": \"2026-03-14T10:00:00\",\n    \"learning_examples\": []\n}\n```\n\nPoints de design importants :\n\n- **`extraction_hint`** : description en langage naturel utilisee dans le prompt VLM. C'est la que l'utilisateur \"enseigne\" au systeme ou trouver l'information.\n- **`zone_hint`** : indication spatiale optionnelle (`top_left`, `top_right`, `center`, `bottom`, null). Permet de pre-decouper le screenshot pour limiter le bruit. Non obligatoire.\n- **`validation`** : regex ou format attendu. Permet de detecter les extractions ratees et de demander un retry.\n- **`type`** : `string`, `number`, `date`, `boolean`, `list`. Determine le post-traitement et le schema de la colonne en DB.\n- **`learning_examples`** : liste de paires `(screenshot_path, expected_values)` accumulees en mode Shadow pour affiner le prompt VLM.\n\n### 3.2 Stockage des schemas\n\nLes schemas sont stockes en JSON dans `data/extraction/schemas/` :\n\n```\ndata/extraction/\n    schemas/\n        schema_patient_dpi.json\n        schema_labo_resultats.json\n    runs/\n        run_20260314_143000/\n            metadata.json\n            screenshots/\n            results.jsonl\n```\n\n---\n\n## 4. Pipeline d'extraction (ExtractionEngine)\n\n### 4.1 Nouveau fichier : `core/extraction/engine.py`\n\nC'est le coeur du systeme. Il recoit un screenshot (chemin ou PIL Image) + un `ExtractionSchema` et retourne des donnees structurees.\n\nStrategie en 3 niveaux (du plus rapide au plus lent) :\n\n**Niveau 1 : OCR + heuristiques** (< 1s)\n- Utiliser le `ScreenState.perception.detected_text` deja produit par le ScreenAnalyzer.\n- Pour chaque champ du schema, chercher le label puis le texte adjacent. Par exemple, si `extraction_hint` dit \"pres de NIP\", chercher le mot \"NIP\" dans les textes detectes et prendre la valeur a droite.\n- Convient pour les champs bien structures (labels + valeurs alignes en tableau).\n\n**Niveau 2 : VLM extraction structuree** (5-15s)\n- Envoyer le screenshot complet (ou une zone decoupee via `zone_hint`) a `OllamaClient.generate()` avec un prompt construit a partir du schema.\n- Utiliser `force_json=True` pour forcer la sortie JSON.\n- Prompt type :\n\n```\nAnalyse cette capture d'ecran d'un dossier patient informatise.\nExtrais les informations suivantes au format JSON :\n\n- \"nip\": NIP (8 a 12 chiffres, souvent en haut a droite)\n- \"nom\": Nom de famille en majuscules\n- \"prenom\": Prenom\n- \"date_naissance\": Date au format DD/MM/YYYY\n- \"diagnostic_principal\": Texte du diagnostic principal\n- \"code_cim10\": Code CIM-10 (format X00.0)\n\nSi un champ n'est pas visible, utilise null.\nReponds UNIQUEMENT en JSON.\n```\n\n**Niveau 3 : VLM + retry cible** (10-30s)\n- Si le niveau 2 retourne des champs manquants ou echouant la validation, re-envoyer des requetes ciblees pour les champs en erreur, eventuellement avec un crop de la zone pertinente.\n\nLe choix du niveau est configurable dans les `parameters` de l'action extract :\n- `\"extraction_mode\": \"fast\"` -> Niveau 1 seulement\n- `\"extraction_mode\": \"vlm\"` -> Niveau 1 + 2\n- `\"extraction_mode\": \"thorough\"` -> Niveaux 1 + 2 + 3\n\n### 4.2 Interface de l'ExtractionEngine\n\n```\nclass ExtractionEngine:\n    def __init__(self, ollama_client, screen_analyzer):\n        ...\n\n    def extract(\n        self,\n        screenshot_path: str,\n        schema: ExtractionSchema,\n        mode: str = \"vlm\",\n        screen_state: ScreenState = None,  # reutiliser si deja analyse\n        context: dict = None,  # variables du workflow\n    ) -> ExtractionResult:\n        ...\n\n@dataclass\nclass ExtractionResult:\n    schema_id: str\n    fields: Dict[str, Any]           # {\"nip\": \"12345678\", \"nom\": \"DUPONT\", ...}\n    confidence: Dict[str, float]     # {\"nip\": 0.95, \"nom\": 0.88, ...}\n    validation_errors: Dict[str, str] # {\"code_cim10\": \"Pattern mismatch\"}\n    extraction_mode_used: str         # \"ocr\", \"vlm\", \"vlm_retry\"\n    screenshot_path: str\n    duration_ms: int\n    raw_vlm_response: str = None      # Pour debug/audit\n```\n\n### 4.3 Integration dans le StreamProcessor\n\nLe `StreamProcessor` (dans `/home/dom/ai/rpa_vision_v3/agent_v0/server_v1/stream_processor.py`) a deja un `_screen_analyzer` et peut creer un `OllamaClient`. L'`ExtractionEngine` sera instancie paresseusement dans le StreamProcessor, comme les autres composants GPU :\n\n```\nself._extraction_engine = None  # Lazy init dans _ensure_initialized()\n```\n\n---\n\n## 5. Iteration / Pagination (for-each dans un workflow)\n\n### 5.1 Concept : LoopConfig etendu avec `loop_type = \"visual_list\"`\n\nLe `LoopConfig` existant dans `composition_models.py` (ligne 142) supporte `count` et `condition`. Il faut ajouter un troisieme type : `\"visual_list\"` -- une boucle qui itere visuellement sur une liste d'elements a l'ecran.\n\n**Nouveau type de boucle** : `ForEachVisualConfig`\n\n```json\n{\n    \"loop_id\": \"loop_patients\",\n    \"loop_type\": \"visual_list\",\n    \"list_detection\": {\n        \"method\": \"table_rows\",\n        \"list_node\": \"patient_list_screen\",\n        \"item_target\": {\n            \"by_role\": \"table_row\",\n            \"by_text\": null\n        },\n        \"pagination\": {\n            \"has_pagination\": true,\n            \"next_page_target\": {\n                \"by_role\": \"button\",\n                \"by_text\": \"Suivant\"\n            },\n            \"end_condition\": {\n                \"condition_type\": \"element_absent\",\n                \"target_element\": \"Suivant\"\n            }\n        }\n    },\n    \"body_nodes\": [\"click_item\", \"patient_detail\", \"extract_patient\", \"back_to_list\"],\n    \"safety_limit\": 500,\n    \"extract_schema_id\": \"schema_patient_dpi\"\n}\n```\n\n### 5.2 Cycle d'execution d'une boucle visual_list\n\n```\nBOUCLE (pour chaque page) :\n  |\n  1. ANALYSER l'ecran de liste (ScreenAnalyzer)\n  |     -> Detecter les elements de type table_row\n  |     -> Compter N items visibles\n  |\n  2. POUR chaque item i de 1 a N :\n  |     a. Cliquer sur l'item i (action click, resolve par position relative)\n  |     b. Attendre l'ecran de detail (post_conditions)\n  |     c. Executer l'action EXTRACT (extraire les donnees)\n  |     d. Stocker le resultat en DB\n  |     e. Revenir a la liste (action click \"Retour\" ou Back)\n  |     f. Verifier qu'on est sur la liste (post_conditions)\n  |\n  3. PAGINATION :\n  |     a. Verifier si le bouton \"Suivant\" existe (visual check)\n  |     b. Si oui : cliquer sur Suivant, retour a 1\n  |     c. Si non : FIN DE BOUCLE\n```\n\n### 5.3 Nouveau fichier : `core/extraction/iteration_controller.py`\n\nCe module orchestre le cycle ci-dessus. Il utilise le `LoopExecutor` existant pour le comptage et la guard de securite, mais ajoute la logique specifique au scraping visuel :\n\n- **Detection des items** : demander au `ScreenAnalyzer` de detecter les `table_row` ou utiliser le VLM pour compter les elements visibles.\n- **Navigation item par item** : generer dynamiquement les actions de clic (positions relatives calculees a partir des bounding boxes des elements detectes).\n- **Gestion de la pagination** : apres avoir traite tous les items visibles, chercher et cliquer le bouton \"Suivant\".\n- **Gestion des erreurs** : si un item echoue (ecran inattendu, timeout), loguer l'erreur, revenir a la liste, et continuer avec l'item suivant. Ne pas arreter toute la boucle.\n- **Resume** : sauvegarder periodiquement l'etat (dernier item traite, page courante) pour pouvoir reprendre apres un crash.\n\n### 5.4 Representation dans le workflow JSON complet\n\nVoici a quoi ressemble un workflow complet de scraping d'un DPI :\n\n```json\n{\n    \"workflow_id\": \"scraping_dpi_patients\",\n    \"name\": \"Extraction donnees patients DPI\",\n    \"description\": \"Ouvre le DPI, parcourt la liste des patients, extrait les infos de chaque fiche\",\n    \"version\": 1,\n    \"learning_state\": \"COACHING\",\n    \"variables\": [\n        {\"name\": \"service\", \"type\": \"string\", \"required\": true, \"description\": \"Service hospitalier\"},\n        {\"name\": \"date_debut\", \"type\": \"date\", \"required\": false, \"default_value\": \"today\"}\n    ],\n    \"nodes\": [\n        {\"node_id\": \"dpi_home\", \"name\": \"Accueil DPI\", \"type\": \"entry\"},\n        {\"node_id\": \"service_select\", \"name\": \"Selection service\"},\n        {\"node_id\": \"patient_list\", \"name\": \"Liste patients\"},\n        {\"node_id\": \"patient_detail\", \"name\": \"Fiche patient\"},\n        {\"node_id\": \"extraction_done\", \"name\": \"Extraction terminee\", \"type\": \"end\"}\n    ],\n    \"edges\": [\n        {\n            \"edge_id\": \"e1\", \"from_node\": \"dpi_home\", \"to_node\": \"service_select\",\n            \"action\": {\"type\": \"mouse_click\", \"target\": {\"by_text\": \"Patients\", \"by_role\": \"navigation\"}}\n        },\n        {\n            \"edge_id\": \"e2\", \"from_node\": \"service_select\", \"to_node\": \"patient_list\",\n            \"action\": {\"type\": \"text_input\", \"target\": {\"by_role\": \"search_field\"}, \"parameters\": {\"text\": \"${service}\"}}\n        },\n        {\n            \"edge_id\": \"e3_open\", \"from_node\": \"patient_list\", \"to_node\": \"patient_detail\",\n            \"action\": {\"type\": \"mouse_click\", \"target\": {\"by_role\": \"table_row\"}, \"parameters\": {\"select_nth\": \"${_loop_index}\"}}\n        },\n        {\n            \"edge_id\": \"e4_extract\", \"from_node\": \"patient_detail\", \"to_node\": \"patient_detail\",\n            \"action\": {\"type\": \"extract\", \"target\": {\"by_role\": \"screen\"}, \"parameters\": {\"schema_id\": \"schema_patient_dpi\", \"extraction_mode\": \"vlm\", \"store_result\": true}}\n        },\n        {\n            \"edge_id\": \"e5_back\", \"from_node\": \"patient_detail\", \"to_node\": \"patient_list\",\n            \"action\": {\"type\": \"mouse_click\", \"target\": {\"by_text\": \"Retour\", \"by_role\": \"navigation\"}}\n        }\n    ],\n    \"entry_nodes\": [\"dpi_home\"],\n    \"end_nodes\": [\"extraction_done\"],\n    \"loops\": {\n        \"loop_patients\": {\n            \"loop_id\": \"loop_patients\",\n            \"loop_type\": \"visual_list\",\n            \"body_nodes\": [\"patient_list\", \"patient_detail\"],\n            \"body_edges\": [\"e3_open\", \"e4_extract\", \"e5_back\"],\n            \"list_detection\": {\n                \"method\": \"table_rows\",\n                \"list_node\": \"patient_list\",\n                \"item_target\": {\"by_role\": \"table_row\"}\n            },\n            \"pagination\": {\n                \"next_page_target\": {\"by_text\": \"Suivant\"},\n                \"end_condition\": {\"condition_type\": \"element_absent\", \"target_element\": \"Suivant\"}\n            },\n            \"safety_limit\": 500,\n            \"extract_schema_id\": \"schema_patient_dpi\"\n        }\n    }\n}\n```\n\nLa variable speciale `${_loop_index}` est injectee automatiquement par l'`IterationController` et represente l'index de l'item courant dans la liste visible (0-based).\n\n---\n\n## 6. Stockage base de donnees\n\n### 6.1 Nouveau fichier : `core/extraction/datastore.py`\n\nSQLite d'abord, avec abstraction pour PostgreSQL plus tard.\n\n**Schema SQL de base :**\n\n```sql\n-- Table des runs d'extraction (une execution complete du workflow)\nCREATE TABLE extraction_runs (\n    run_id TEXT PRIMARY KEY,\n    workflow_id TEXT NOT NULL,\n    schema_id TEXT NOT NULL,\n    started_at TEXT NOT NULL,\n    completed_at TEXT,\n    status TEXT DEFAULT 'running',  -- running, completed, failed, paused\n    total_items INTEGER DEFAULT 0,\n    extracted_items INTEGER DEFAULT 0,\n    failed_items INTEGER DEFAULT 0,\n    params TEXT,  -- JSON des parametres du workflow\n    resume_state TEXT  -- JSON pour reprendre apres interruption\n);\n\n-- Table des donnees extraites (une ligne par item extrait)\nCREATE TABLE extracted_data (\n    row_id INTEGER PRIMARY KEY AUTOINCREMENT,\n    run_id TEXT NOT NULL REFERENCES extraction_runs(run_id),\n    schema_id TEXT NOT NULL,\n    item_index INTEGER NOT NULL,  -- Position dans la liste source\n    extracted_at TEXT NOT NULL,\n    data TEXT NOT NULL,  -- JSON des champs extraits\n    confidence TEXT,  -- JSON des scores de confiance par champ\n    validation_errors TEXT,  -- JSON des erreurs de validation\n    screenshot_path TEXT,  -- Chemin du screenshot source\n    extraction_mode TEXT,  -- \"ocr\", \"vlm\", \"vlm_retry\"\n    duration_ms INTEGER,\n    status TEXT DEFAULT 'ok'  -- ok, validation_warning, extraction_failed\n);\n\n-- Index pour les requetes frequentes\nCREATE INDEX idx_extracted_run ON extracted_data(run_id);\nCREATE INDEX idx_extracted_schema ON extracted_data(schema_id);\nCREATE INDEX idx_extracted_status ON extracted_data(status);\n\n-- Table de metadata (schemas, configs)\nCREATE TABLE extraction_schemas (\n    schema_id TEXT PRIMARY KEY,\n    name TEXT NOT NULL,\n    version INTEGER DEFAULT 1,\n    schema_json TEXT NOT NULL,  -- Le schema complet en JSON\n    created_at TEXT NOT NULL,\n    updated_at TEXT\n);\n```\n\n### 6.2 Interface DataStore\n\n```\nclass DataStore:\n    def __init__(self, db_path: str = \"data/extraction/extraction_db.sqlite\"):\n        ...\n\n    def create_run(self, workflow_id, schema_id, params) -> str:\n        # Retourne run_id\n\n    def insert_row(self, run_id, item_index, extraction_result: ExtractionResult):\n        ...\n\n    def update_run_status(self, run_id, status, resume_state=None):\n        ...\n\n    def get_run(self, run_id) -> dict:\n        ...\n\n    def get_run_data(self, run_id, status_filter=None) -> List[dict]:\n        ...\n\n    def export_csv(self, run_id, output_path) -> str:\n        ...\n\n    def export_json(self, run_id, output_path) -> str:\n        ...\n\n    def get_schema(self, schema_id) -> dict:\n        ...\n\n    def save_schema(self, schema: ExtractionSchema):\n        ...\n```\n\n### 6.3 Emplacement du fichier SQLite\n\n`data/extraction/extraction_db.sqlite` -- dans le repertoire data du projet, jamais commite (dans .gitignore).\n\n### 6.4 Extension vers PostgreSQL\n\nL'abstraction est simple : `DataStore` utilise un `connection_string` et soit `sqlite3` soit `psycopg2`. La difference principale est la gestion du JSON (TEXT en SQLite, JSONB en PostgreSQL). Prevoir une methode `_get_connection()` qui retourne l'objet connection appropriate.\n\n---\n\n## 7. Endpoints API\n\n### 7.1 Nouveaux endpoints dans `api_stream.py`\n\n| Methode | Endpoint | Description |\n|---------|----------|-------------|\n| POST | `/api/v1/extraction/schemas` | Creer/mettre a jour un schema d'extraction |\n| GET | `/api/v1/extraction/schemas` | Lister les schemas |\n| GET | `/api/v1/extraction/schemas/{schema_id}` | Recuperer un schema |\n| POST | `/api/v1/extraction/start` | Demarrer un run d'extraction |\n| GET | `/api/v1/extraction/runs` | Lister les runs |\n| GET | `/api/v1/extraction/runs/{run_id}` | Etat d'un run |\n| POST | `/api/v1/extraction/runs/{run_id}/pause` | Pause/resume |\n| POST | `/api/v1/extraction/runs/{run_id}/cancel` | Annuler |\n| GET | `/api/v1/extraction/runs/{run_id}/data` | Donnees extraites |\n| GET | `/api/v1/extraction/runs/{run_id}/export` | Export CSV/JSON |\n| POST | `/api/v1/extraction/extract_single` | Extraction ponctuelle (debug) |\n\n### 7.2 Modeles Pydantic pour les requetes\n\n```python\nclass StartExtractionRequest(BaseModel):\n    workflow_id: str\n    schema_id: str\n    session_id: Optional[str] = None  # Auto-detect si vide\n    params: Optional[Dict[str, Any]] = None\n    max_items: Optional[int] = None  # Limite optionnelle\n    extraction_mode: str = \"vlm\"  # fast, vlm, thorough\n\nclass ExtractionSchemaCreate(BaseModel):\n    schema_id: str\n    name: str\n    description: str\n    target_app: Optional[str] = None\n    fields: List[Dict[str, Any]]\n```\n\n---\n\n## 8. Integration dans le cycle Shadow - Copilot - Autonomous\n\n### 8.1 Phase Shadow (Observation)\n\nL'utilisateur ouvre le DPI et parcourt manuellement quelques dossiers pendant que l'Agent V1 capture tout. Le serveur analyse chaque screenshot.\n\n**Ce qui se passe en plus pour l'extraction :**\n\n1. L'utilisateur parcourt 3-5 dossiers patients manuellement.\n2. Le serveur detecte le pattern repetitif (meme ecran de detail, memes actions).\n3. Via le chat (port 5004), l'utilisateur dit : *\"Les ecrans que tu viens de voir, c'est les fiches patients. Je veux extraire le NIP, le nom, le prenom, la date de naissance et le diagnostic.\"*\n4. Le systeme genere un **brouillon de schema** en utilisant le VLM sur les screenshots captures :\n   - Envoie un screenshot de fiche patient au VLM avec le prompt : \"Identifie les champs suivants dans cette capture d'ecran : NIP, nom, prenom, date de naissance, diagnostic. Pour chaque champ, decris sa position et son apparence visuelle.\"\n   - Le VLM retourne les descriptions qui deviennent les `extraction_hint` du schema.\n5. Le systeme presente le schema a l'utilisateur pour validation dans le chat.\n6. Le systeme genere aussi le **workflow de navigation** (depuis les ScreenStates et le GraphBuilder) avec la boucle et les edges.\n\n### 8.2 Phase Copilot (Supervision)\n\nL'utilisateur lance l'extraction via le chat : *\"Extrais les patients du service Cardiologie\"*.\n\nLe systeme :\n1. Demande confirmation : \"Je vais extraire les fiches patients du service Cardiologie. Schema : Fiche Patient DPI (6 champs). Confirmer ?\"\n2. L'utilisateur confirme.\n3. Le systeme execute le workflow en mode **supervise** :\n   - Apres chaque extraction, affiche les resultats dans le chat.\n   - Si un champ echoue la validation, demande confirmation a l'utilisateur.\n   - L'utilisateur peut corriger en temps reel (\"Non, le NIP c'est 12345678, pas 1234567\").\n   - Les corrections sont stockees comme `learning_examples` dans le schema.\n4. Apres 5-10 extractions reussies sans correction, le systeme propose de passer en mode Autonomous.\n\n### 8.3 Phase Autonomous\n\nLe systeme fonctionne seul. Les corrections humaines precedentes ont raffine le schema et les prompts VLM. Le systeme :\n- Execute la boucle complete sans interruption.\n- Stocke les resultats en DB.\n- Envoie un rapport de synthese dans le chat a la fin.\n- En cas d'erreur (ecran inattendu, timeout), tente un retry, puis saute l'item et continue.\n- L'utilisateur peut surveiller en temps reel via le chat ou le dashboard (port 5001).\n\n### 8.4 Progression du learning_state\n\nLe `learning_state` du workflow d'extraction suit la meme progression que les workflows classiques :\n\n```\nOBSERVATION -> COACHING -> AUTO_CANDIDATE -> AUTO_CONFIRMED\n     |              |            |                |\n  Shadow mode   Copilot     Autonomous      Autonomous\n  (learning)   (supervised)   (testing)     (production)\n```\n\nCriteres de passage :\n- OBSERVATION -> COACHING : schema cree, workflow valide, au moins 3 extractions manuelles reussies\n- COACHING -> AUTO_CANDIDATE : 10+ extractions supervisees, 0 correction sur les 5 dernieres\n- AUTO_CANDIDATE -> AUTO_CONFIRMED : 50+ extractions autonomes, taux de validation > 95%\n\n---\n\n## 9. Edge cases et robustesse\n\n### 9.1 Ecrans variables\n\n- **Pop-ups inattendus** : Le ScreenAnalyzer detecte un ecran non reconnu. L'IterationController compare le ScreenState courant avec le node attendu. Si la similarite est < 0.7, il tente de fermer le pop-up (Echap, clic sur X) et re-evalue.\n- **Ecran de chargement** : Ajouter un wait adaptatif : si l'ecran est presque vide ou contient un spinner, attendre jusqu'a 10s en re-capturant toutes les 2s.\n- **Session expiree** : Le VLM detecte un ecran de login. Le systeme met en pause et notifie l'utilisateur via le chat.\n\n### 9.2 Donnees manquantes\n\n- Champs `required=true` manquants : l'item est marque `status=validation_warning` en DB. Le run continue.\n- Tous les champs manquants : `status=extraction_failed`. Probablement un ecran different de celui attendu.\n- Post-run : un rapport liste les items en erreur pour reprise manuelle.\n\n### 9.3 Erreurs de navigation\n\n- Le retour a la liste echoue : tenter le bouton \"Retour\" du navigateur, ou Alt+Left, ou cliquer sur le breadcrumb.\n- L'item suivant n'est plus visible (la liste a change apres retour) : re-analyser la liste et recalculer les positions.\n- Pagination cassee : le bouton \"Suivant\" est grise ou absent alors qu'il reste des items. Loguer et terminer proprement.\n\n### 9.4 Performance\n\n- Une extraction VLM prend 5-15s. Pour 500 patients, c'est 40min a 2h.\n- Optimisation : utiliser le mode `fast` (OCR seul) pour les champs bien structures, `vlm` seulement pour les champs ambigus.\n- Parallelisation future : si l'Agent V1 est sur une VM, on pourrait lancer plusieurs VMs (hors scope v1).\n\n---\n\n## 10. Plan d'implementation sequence\n\n### Phase 1 : Fondations (2-3 jours)\n1. Creer `core/extraction/__init__.py`\n2. Creer `core/extraction/schema.py` -- ExtractionSchema + validation\n3. Creer `core/extraction/datastore.py` -- DataStore SQLite\n4. Creer `core/extraction/engine.py` -- ExtractionEngine (niveau 1 OCR + niveau 2 VLM)\n5. Tests unitaires pour schema, datastore, engine\n\n### Phase 2 : Integration dans le pipeline (2-3 jours)\n6. Etendre `ActionType` dans `workflow_graph.py` avec `EXTRACT`\n7. Etendre `_edge_to_normalized_actions` dans `api_stream.py` pour gerer `extract`\n8. Modifier `report_action_result` ou `get_next_action` pour intercepter les actions extract\n9. Initialiser `ExtractionEngine` dans `StreamProcessor._ensure_initialized()`\n10. Ajouter les endpoints API d'extraction dans `api_stream.py`\n\n### Phase 3 : Iteration (2-3 jours)\n11. Etendre `LoopConfig` avec `visual_list` dans `composition_models.py`\n12. Creer `core/extraction/iteration_controller.py`\n13. Modifier `_workflow_to_actions` pour expandre les boucles `visual_list`\n14. Gerer la pagination et la detection d'items dans la liste\n15. Tests d'integration avec un workflow de test\n\n### Phase 4 : Integration Chat (1-2 jours)\n16. Ajouter une intention `EXTRACT_DATA` dans `IntentParser`\n17. Ajouter la commande de creation de schema via le chat\n18. Ajouter le feedback en temps reel des extractions via SocketIO\n19. Ajouter le rapport de synthese de fin d'extraction\n\n### Phase 5 : Shadow learning (2-3 jours)\n20. Detecter automatiquement les patterns de navigation repetitive dans le `StreamProcessor`\n21. Generer un brouillon de schema via VLM sur les screenshots captures\n22. Proposer le schema et le workflow via le chat\n23. Gerer les corrections utilisateur comme exemples d'apprentissage\n\n---\n\n## Diagramme d'architecture (texte)\n\n```\n +-------------------+          +----------------------------+\n |   Agent V1        |          |   Streaming Server :5005   |\n |   (Windows)       |  HTTP    |   (Linux, RTX 5070)        |\n |                   |<-------->|                            |\n | ActionExecutorV1  |          |  api_stream.py             |\n | poll_and_execute  |          |    |                       |\n | _capture_screen   |          |  StreamProcessor           |\n +-------------------+          |    |-- ScreenAnalyzer      |\n                                |    |-- CLIPEmbedder        |\n                                |    |-- FAISSManager        |\n                                |    |-- ExtractionEngine  * |\n                                |    |     |-- OllamaClient  |\n                                |    |     |-- schema.py     |\n                                |    |                       |\n                                |  IterationController     * |\n                                |    |-- LoopExecutor        |\n                                |    |-- _replay_queues      |\n                                |                            |\n                                |  DataStore               * |\n                                |    |-- SQLite              |\n                                +----------------------------+\n                                           |\n +-------------------+          +----------v---------+\n |   Agent Chat      |          |   Stockage         |\n |   :5004           |          |                    |\n |                   |          | data/extraction/   |\n | IntentParser      |--------->|   extraction_db    |\n | AutonomousPlanner |          |   schemas/*.json   |\n | ConversationMgr   |          |   runs/            |\n +-------------------+          | data/workflows/    |\n                                |   *.json           |\n                                +--------------------+\n\n * = nouveaux modules\n```\n\n---\n\n### Critical Files for Implementation\n\n- `/home/dom/ai/rpa_vision_v3/agent_v0/server_v1/api_stream.py` - Point d'integration principal : ajouter les endpoints extraction, modifier `_edge_to_normalized_actions` et `report_action_result` pour gerer le type \"extract\", intercepter les actions extract dans la boucle de replay\n- `/home/dom/ai/rpa_vision_v3/core/models/workflow_graph.py` - Etendre `ActionType` avec EXTRACT, le modele Action et TargetSpec pour supporter les parametres d'extraction (schema_id, extraction_mode)\n- `/home/dom/ai/rpa_vision_v3/core/workflow/composition_models.py` - Etendre `LoopConfig` avec le type `visual_list` et la configuration de pagination/detection d'items pour le for-each visuel\n- `/home/dom/ai/rpa_vision_v3/core/detection/ollama_client.py` - Pattern a suivre pour les appels VLM ; sera utilise directement par ExtractionEngine pour l'extraction structuree avec `force_json=True`\n- `/home/dom/ai/rpa_vision_v3/agent_v0/server_v1/stream_processor.py` - Initialiser lazily l'ExtractionEngine et le DataStore dans `_ensure_initialized()`, les rendre disponibles pour l'extraction pendant le replay"}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":545,"cache_read_input_tokens":128267,"output_tokens":10123,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":545},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZ3fawNu1LbakRQK6y78R","type":"assistant","uuid":"65247dfb-c2cd-4340-af2c-4db0eb3ff8fb","timestamp":"2026-03-14T21:19:00.393Z"}
diff --git a/services.conf b/services.conf
index 2bd32442a..63df2beeb 100644
--- a/services.conf
+++ b/services.conf
@@ -18,4 +18,5 @@ vwb-backend|5002|visual_workflow_builder/backend/app.py|required
 monitoring|5003|monitoring_server.py|optional
 agent-chat|5004|agent_chat/app.py|optional
 streaming|5005|agent_v0/server_v1/api_stream.py|optional
+worker|5099|agent_v0/server_v1/run_worker.py|optional
 vwb-frontend|3002|cd visual_workflow_builder/frontend_v4 && npm run dev|required
diff --git a/svc.sh b/svc.sh
index 34b4f5da3..5438582ec 100755
--- a/svc.sh
+++ b/svc.sh
@@ -54,6 +54,7 @@ declare -A PORTS=(
     [monitoring]=5003
     [agent-chat]=5004
     [streaming]=5005
+    [worker]=5099
     [vwb-frontend]=3002
 )
 
@@ -63,14 +64,15 @@ declare -A SYSTEMD_UNITS=(
     [vwb-backend]="rpa-vwb-backend.service"
     [agent-chat]="rpa-agent-chat.service"
     [streaming]="rpa-streaming.service"
+    [worker]="rpa-worker.service"
     [vwb-frontend]="rpa-vwb-frontend.service"
 )
 
 # Services gérés par systemd (ceux qui ont un .service)
-SYSTEMD_SERVICES="streaming agent-chat dashboard vwb-backend vwb-frontend"
+SYSTEMD_SERVICES="streaming worker agent-chat dashboard vwb-backend vwb-frontend"
 
 # Tous les services connus
-ALL_SERVICES="api dashboard vwb-backend monitoring agent-chat streaming vwb-frontend"
+ALL_SERVICES="api dashboard vwb-backend monitoring agent-chat streaming worker vwb-frontend"
 
 declare -A COMMANDS=(
     [api]="$VENV_DIR/bin/python3 server/api_upload.py"
@@ -79,6 +81,7 @@ declare -A COMMANDS=(
     [monitoring]="$VENV_DIR/bin/python3 monitoring_server.py"
     [agent-chat]="$VENV_DIR/bin/python3 -m agent_chat.app"
     [streaming]="$VENV_DIR/bin/python3 -m agent_v0.server_v1.api_stream"
+    [worker]="$VENV_DIR/bin/python3 -m agent_v0.server_v1.run_worker"
     [vwb-frontend]="cd $SCRIPT_DIR/visual_workflow_builder/frontend_v4 && npm run dev"
 )
 
@@ -86,8 +89,8 @@ declare -A COMMANDS=(
 declare -A SVC_GROUPS=(
     [vwb]="vwb-backend vwb-frontend"
     [all]="api dashboard vwb-backend vwb-frontend"
-    [full]="api dashboard vwb-backend vwb-frontend monitoring agent-chat streaming"
-    [boot]="streaming agent-chat dashboard vwb-backend vwb-frontend"
+    [full]="api dashboard vwb-backend vwb-frontend monitoring agent-chat streaming worker"
+    [boot]="streaming worker agent-chat dashboard vwb-backend vwb-frontend"
 )
 
 # =============================================================================
@@ -350,7 +353,7 @@ do_install() {
 
     # Vérifier que les fichiers existent
     local missing=false
-    for unit in rpa-streaming.service rpa-agent-chat.service rpa-dashboard.service rpa-vwb-backend.service rpa-vwb-frontend.service rpa-vision.target; do
+    for unit in rpa-streaming.service rpa-worker.service rpa-agent-chat.service rpa-dashboard.service rpa-vwb-backend.service rpa-vwb-frontend.service rpa-vision.target; do
         if [ -f "$SYSTEMD_DIR/$unit" ]; then
             echo -e "  ${GREEN}OK${NC}  $unit"
         else
@@ -394,7 +397,7 @@ do_enable() {
     echo -e "${CYAN}${BOLD}Activation du demarrage automatique au boot...${NC}"
     systemctl --user daemon-reload
     systemctl --user enable rpa-vision.target
-    for unit in rpa-streaming.service rpa-agent-chat.service rpa-dashboard.service rpa-vwb-backend.service rpa-vwb-frontend.service; do
+    for unit in rpa-streaming.service rpa-worker.service rpa-agent-chat.service rpa-dashboard.service rpa-vwb-backend.service rpa-vwb-frontend.service; do
         systemctl --user enable "$unit" 2>/dev/null
         echo -e "  ${GREEN}OK${NC}  $unit"
     done
@@ -405,7 +408,7 @@ do_enable() {
 do_disable() {
     echo -e "${YELLOW}${BOLD}Desactivation du demarrage automatique...${NC}"
     systemctl --user disable rpa-vision.target 2>/dev/null || true
-    for unit in rpa-streaming.service rpa-agent-chat.service rpa-dashboard.service rpa-vwb-backend.service rpa-vwb-frontend.service; do
+    for unit in rpa-streaming.service rpa-worker.service rpa-agent-chat.service rpa-dashboard.service rpa-vwb-backend.service rpa-vwb-frontend.service; do
         systemctl --user disable "$unit" 2>/dev/null || true
         echo -e "  ${GREEN}OK${NC}  $unit"
     done
@@ -429,7 +432,8 @@ show_help() {
     echo "  disable             Desactiver le demarrage auto au boot"
     echo ""
     echo -e "${BOLD}Services:${NC}"
-    echo "  streaming      Streaming Server GPU (port 5005)"
+    echo "  streaming      Streaming Server HTTP (port 5005)"
+    echo "  worker         VLM Worker GPU (process séparé)"
     echo "  agent-chat     Agent Chat (port 5004)"
     echo "  dashboard      Web Dashboard (port 5001)"
     echo "  vwb-backend    VWB Backend Flask (port 5002)"
@@ -438,7 +442,7 @@ show_help() {
     echo "  monitoring     Monitoring (port 5003) [legacy uniquement]"
     echo ""
     echo -e "${BOLD}Groupes:${NC}"
-    echo "  boot           Services systemd (streaming, chat, dashboard, vwb)"
+    echo "  boot           Services systemd (streaming, worker, chat, dashboard, vwb)"
     echo "  vwb            VWB backend + frontend"
     echo "  all            Core (api, dashboard, vwb)"
     echo "  full           Tous les services"
diff --git a/tests/integration/test_stream_processor.py b/tests/integration/test_stream_processor.py
index 4180c71ff..0a76eb697 100644
--- a/tests/integration/test_stream_processor.py
+++ b/tests/integration/test_stream_processor.py
@@ -220,7 +220,7 @@ class TestStreamWorker:
         event_file = session_dir / "live_events.jsonl"
         event_file.write_text(
             json.dumps({"type": "click", "timestamp": 100}) + "\n"
-            + json.dumps({"type": "key_press", "timestamp": 200}) + "\n"
+            + json.dumps({"type": "key_press", "keys": ["enter"], "timestamp": 200}) + "\n"
         )
 
         # Simuler un tour de polling
diff --git a/tests/unit/test_auth.py b/tests/unit/test_auth.py
new file mode 100644
index 000000000..2cc9ddd57
--- /dev/null
+++ b/tests/unit/test_auth.py
@@ -0,0 +1,576 @@
+"""
+Tests du module d'authentification automatique (core/auth).
+
+Couvre :
+- TOTPGenerator : génération, vérification, vecteurs de test RFC 6238
+- CredentialVault : CRUD, chiffrement, persistance
+- AuthHandler : détection d'écrans d'auth, génération d'actions
+"""
+
+import json
+import os
+import tempfile
+import time
+
+import pytest
+
+from core.auth.credential_vault import CredentialVault, _HAS_FERNET
+from core.auth.totp_generator import TOTPGenerator
+from core.auth.auth_handler import AuthHandler, AuthRequest
+
+
+# =========================================================================
+# Tests TOTP
+# =========================================================================
+
+
+class TestTOTPGenerator:
+    """Tests du générateur TOTP RFC 6238."""
+
+    def test_generate_returns_6_digits(self):
+        """Le code généré fait exactement 6 chiffres."""
+        totp = TOTPGenerator("JBSWY3DPEHPK3PXP")
+        code = totp.generate()
+        assert len(code) == 6
+        assert code.isdigit()
+
+    def test_generate_deterministic(self):
+        """Le même timestamp donne le même code."""
+        totp = TOTPGenerator("JBSWY3DPEHPK3PXP")
+        ts = 1700000000.0
+        code1 = totp.generate(timestamp=ts)
+        code2 = totp.generate(timestamp=ts)
+        assert code1 == code2
+
+    def test_verify_current_code(self):
+        """Le code généré est validé par verify()."""
+        totp = TOTPGenerator("JBSWY3DPEHPK3PXP")
+        ts = time.time()
+        code = totp.generate(timestamp=ts)
+        assert totp.verify(code, timestamp=ts)
+
+    def test_verify_rejects_wrong_code(self):
+        """Un code incorrect est rejeté."""
+        totp = TOTPGenerator("JBSWY3DPEHPK3PXP")
+        # Utiliser un timestamp suffisamment grand pour éviter les problèmes
+        # avec window=-1 (counter négatif)
+        assert not totp.verify("000000", timestamp=1700000000.0)
+
+    def test_verify_with_window(self):
+        """La fenêtre de tolérance accepte les codes adjacents."""
+        totp = TOTPGenerator("JBSWY3DPEHPK3PXP", interval=30)
+        ts = 1700000000.0
+        # Code de l'intervalle précédent
+        prev_code = totp.generate(timestamp=ts - 30)
+        assert totp.verify(prev_code, timestamp=ts, window=1)
+        # Code de l'intervalle suivant
+        next_code = totp.generate(timestamp=ts + 30)
+        assert totp.verify(next_code, timestamp=ts, window=1)
+
+    def test_verify_window_zero_strict(self):
+        """Window=0 n'accepte que le code exact de l'intervalle courant."""
+        totp = TOTPGenerator("JBSWY3DPEHPK3PXP", interval=30)
+        ts = 1700000000.0
+        code = totp.generate(timestamp=ts)
+        assert totp.verify(code, timestamp=ts, window=0)
+        prev_code = totp.generate(timestamp=ts - 30)
+        assert not totp.verify(prev_code, timestamp=ts, window=0)
+
+    def test_time_remaining_in_range(self):
+        """time_remaining() retourne entre 1 et interval."""
+        totp = TOTPGenerator("JBSWY3DPEHPK3PXP", interval=30)
+        remaining = totp.time_remaining()
+        assert 1 <= remaining <= 30
+
+    def test_8_digits(self):
+        """Support des codes à 8 chiffres."""
+        totp = TOTPGenerator("JBSWY3DPEHPK3PXP", digits=8)
+        code = totp.generate()
+        assert len(code) == 8
+        assert code.isdigit()
+
+    def test_rfc6238_sha1_vector(self):
+        """Vecteur de test RFC 6238 pour SHA1.
+
+        Secret de test : "12345678901234567890" (ASCII)
+        En base32 : "GEZDGNBVGY3TQOJQGEZDGNBVGY3TQOJQ"
+        Timestamp : 59 → T = 59 // 30 = 1 → code attendu 287082
+        """
+        # Le secret ASCII "12345678901234567890" encodé en base32
+        secret_b32 = "GEZDGNBVGY3TQOJQGEZDGNBVGY3TQOJQ"
+        totp = TOTPGenerator(secret_b32, digits=8, interval=30, algorithm="SHA1")
+        code = totp.generate(timestamp=59)
+        assert code == "94287082"
+
+    def test_rfc6238_sha1_vector_t1111111109(self):
+        """Vecteur de test RFC 6238 — T=1111111109."""
+        secret_b32 = "GEZDGNBVGY3TQOJQGEZDGNBVGY3TQOJQ"
+        totp = TOTPGenerator(secret_b32, digits=8, interval=30, algorithm="SHA1")
+        code = totp.generate(timestamp=1111111109)
+        assert code == "07081804"
+
+    def test_rfc6238_sha256_vector(self):
+        """Vecteur de test RFC 6238 pour SHA256.
+
+        Secret 32 bytes : "12345678901234567890123456789012"
+        En base32 : "GEZDGNBVGY3TQOJQGEZDGNBVGY3TQOJQGEZDGNBVGY3TQOJQGEZA"
+        Timestamp : 59 → code attendu 46119246
+        """
+        secret_b32 = "GEZDGNBVGY3TQOJQGEZDGNBVGY3TQOJQGEZDGNBVGY3TQOJQGEZA"
+        totp = TOTPGenerator(secret_b32, digits=8, interval=30, algorithm="SHA256")
+        code = totp.generate(timestamp=59)
+        assert code == "46119246"
+
+    def test_invalid_secret_raises(self):
+        """Un secret invalide lève ValueError."""
+        with pytest.raises(ValueError, match="base32 invalide"):
+            TOTPGenerator("!!! not base32 !!!")
+
+    def test_invalid_algorithm_raises(self):
+        """Un algorithme inconnu lève ValueError."""
+        with pytest.raises(ValueError, match="non supporté"):
+            TOTPGenerator("JBSWY3DPEHPK3PXP", algorithm="MD5")
+
+    def test_secret_with_spaces(self):
+        """Les espaces dans le secret sont tolérés."""
+        totp1 = TOTPGenerator("JBSWY3DPEHPK3PXP")
+        totp2 = TOTPGenerator("JBSW Y3DP EHPK 3PXP")
+        ts = 1700000000.0
+        assert totp1.generate(timestamp=ts) == totp2.generate(timestamp=ts)
+
+    def test_zero_padded_code(self):
+        """Les codes courts sont zero-padded (ex: 003271 et non 3271)."""
+        totp = TOTPGenerator("JBSWY3DPEHPK3PXP")
+        # Tester beaucoup de timestamps pour trouver un code qui commence par 0
+        for ts in range(1700000000, 1700001000, 30):
+            code = totp.generate(timestamp=float(ts))
+            assert len(code) == 6, f"Code {code!r} n'a pas 6 chiffres pour ts={ts}"
+
+
+# =========================================================================
+# Tests CredentialVault
+# =========================================================================
+
+
+class TestCredentialVault:
+    """Tests du coffre-fort chiffré."""
+
+    def test_create_add_get(self):
+        """Créer un vault, ajouter un credential, le récupérer."""
+        with tempfile.NamedTemporaryFile(suffix=".enc", delete=False) as f:
+            vault_path = f.name
+        try:
+            os.unlink(vault_path)  # Supprimer pour que le vault se crée
+            vault = CredentialVault(vault_path, "test_password")
+            vault.add_credential("TestApp", "login", {
+                "username": "user1",
+                "password": "pass1",
+            })
+            cred = vault.get_credential("TestApp", "login")
+            assert cred is not None
+            assert cred["username"] == "user1"
+            assert cred["password"] == "pass1"
+        finally:
+            if os.path.exists(vault_path):
+                os.unlink(vault_path)
+
+    def test_save_and_reload(self):
+        """Sauvegarder et recharger un vault préserve les données."""
+        with tempfile.NamedTemporaryFile(suffix=".enc", delete=False) as f:
+            vault_path = f.name
+        try:
+            os.unlink(vault_path)
+            vault = CredentialVault(vault_path, "master123")
+            vault.add_credential("MyApp", "login", {
+                "username": "admin",
+                "password": "secret",
+            })
+            vault.add_credential("MyApp", "totp_seed", {
+                "secret": "JBSWY3DPEHPK3PXP",
+                "digits": 6,
+                "interval": 30,
+                "algorithm": "SHA1",
+            })
+            vault.save()
+
+            # Recharger
+            vault2 = CredentialVault(vault_path, "master123")
+            assert vault2.list_apps() == ["MyApp"]
+            login = vault2.get_credential("MyApp", "login")
+            assert login["username"] == "admin"
+            totp = vault2.get_credential("MyApp", "totp_seed")
+            assert totp["secret"] == "JBSWY3DPEHPK3PXP"
+        finally:
+            if os.path.exists(vault_path):
+                os.unlink(vault_path)
+
+    def test_remove_credential(self):
+        """Supprimer un credential fonctionne."""
+        with tempfile.NamedTemporaryFile(suffix=".enc", delete=False) as f:
+            vault_path = f.name
+        try:
+            os.unlink(vault_path)
+            vault = CredentialVault(vault_path, "pw")
+            vault.add_credential("App1", "login", {"username": "u", "password": "p"})
+            assert vault.remove_credential("App1", "login") is True
+            assert vault.get_credential("App1", "login") is None
+            assert vault.list_apps() == []
+        finally:
+            if os.path.exists(vault_path):
+                os.unlink(vault_path)
+
+    def test_remove_nonexistent(self):
+        """Supprimer un credential inexistant retourne False."""
+        with tempfile.NamedTemporaryFile(suffix=".enc", delete=False) as f:
+            vault_path = f.name
+        try:
+            os.unlink(vault_path)
+            vault = CredentialVault(vault_path, "pw")
+            assert vault.remove_credential("NopApp", "login") is False
+        finally:
+            if os.path.exists(vault_path):
+                os.unlink(vault_path)
+
+    def test_list_apps_sorted(self):
+        """list_apps() retourne les apps triées."""
+        with tempfile.NamedTemporaryFile(suffix=".enc", delete=False) as f:
+            vault_path = f.name
+        try:
+            os.unlink(vault_path)
+            vault = CredentialVault(vault_path, "pw")
+            vault.add_credential("Zebra", "login", {"username": "z", "password": "z"})
+            vault.add_credential("Alpha", "login", {"username": "a", "password": "a"})
+            vault.add_credential("Middle", "login", {"username": "m", "password": "m"})
+            assert vault.list_apps() == ["Alpha", "Middle", "Zebra"]
+        finally:
+            if os.path.exists(vault_path):
+                os.unlink(vault_path)
+
+    def test_invalid_credential_type(self):
+        """Un type de credential invalide lève ValueError."""
+        with tempfile.NamedTemporaryFile(suffix=".enc", delete=False) as f:
+            vault_path = f.name
+        try:
+            os.unlink(vault_path)
+            vault = CredentialVault(vault_path, "pw")
+            with pytest.raises(ValueError, match="invalide"):
+                vault.add_credential("App1", "invalid_type", {})
+        finally:
+            if os.path.exists(vault_path):
+                os.unlink(vault_path)
+
+    def test_encryption_on_disk(self):
+        """Le fichier vault sur disque ne contient pas de texte en clair."""
+        with tempfile.NamedTemporaryFile(suffix=".enc", delete=False) as f:
+            vault_path = f.name
+        try:
+            os.unlink(vault_path)
+            vault = CredentialVault(vault_path, "strong_password_42")
+            vault.add_credential("SecretApp", "login", {
+                "username": "robot_lea",
+                "password": "super_secret_password_xyz",
+            })
+            vault.save()
+
+            # Lire le fichier brut
+            raw_bytes = open(vault_path, "rb").read()
+            raw_str = raw_bytes.decode("latin-1")  # Pour chercher du texte ASCII
+
+            # Les données sensibles ne doivent PAS apparaître en clair
+            assert "robot_lea" not in raw_str
+            assert "super_secret_password_xyz" not in raw_str
+            assert "SecretApp" not in raw_str
+        finally:
+            if os.path.exists(vault_path):
+                os.unlink(vault_path)
+
+    def test_wrong_password_raises(self):
+        """Un mauvais mot de passe empêche le déchiffrement."""
+        with tempfile.NamedTemporaryFile(suffix=".enc", delete=False) as f:
+            vault_path = f.name
+        try:
+            os.unlink(vault_path)
+            vault = CredentialVault(vault_path, "correct_password")
+            vault.add_credential("App", "login", {"username": "u", "password": "p"})
+            vault.save()
+
+            # Tenter de charger avec un mauvais mot de passe
+            with pytest.raises(ValueError, match="[Mm]ot de passe|corrompu"):
+                CredentialVault(vault_path, "wrong_password")
+        finally:
+            if os.path.exists(vault_path):
+                os.unlink(vault_path)
+
+    def test_multiple_credential_types_per_app(self):
+        """Une app peut avoir plusieurs types de credentials."""
+        with tempfile.NamedTemporaryFile(suffix=".enc", delete=False) as f:
+            vault_path = f.name
+        try:
+            os.unlink(vault_path)
+            vault = CredentialVault(vault_path, "pw")
+            vault.add_credential("DPI", "login", {
+                "username": "lea", "password": "p"
+            })
+            vault.add_credential("DPI", "totp_seed", {
+                "secret": "JBSWY3DPEHPK3PXP"
+            })
+            assert vault.list_credential_types("DPI") == ["login", "totp_seed"]
+            assert vault.get_credential("DPI", "login")["username"] == "lea"
+            assert vault.get_credential("DPI", "totp_seed")["secret"] == "JBSWY3DPEHPK3PXP"
+        finally:
+            if os.path.exists(vault_path):
+                os.unlink(vault_path)
+
+
+# =========================================================================
+# Tests AuthHandler
+# =========================================================================
+
+
+class TestAuthHandler:
+    """Tests du gestionnaire d'authentification."""
+
+    @pytest.fixture
+    def vault_with_creds(self, tmp_path):
+        """Vault avec des credentials de test."""
+        vault_path = str(tmp_path / "test_vault.enc")
+        vault = CredentialVault(vault_path, "test_pw")
+        vault.add_credential("DPI_Crossway", "login", {
+            "username": "robot_lea",
+            "password": "secret123",
+            "domain": "HOPITAL",
+        })
+        vault.add_credential("DPI_Crossway", "totp_seed", {
+            "secret": "JBSWY3DPEHPK3PXP",
+            "digits": 6,
+            "interval": 30,
+            "algorithm": "SHA1",
+        })
+        vault.add_credential("Outlook", "login", {
+            "username": "lea@hopital.fr",
+            "password": "outlook_pass",
+        })
+        return vault
+
+    @pytest.fixture
+    def handler(self, vault_with_creds):
+        return AuthHandler(vault_with_creds)
+
+    def test_detect_login_screen(self, handler):
+        """Détecter un écran de login classique."""
+        screen_state = {
+            "perception": {
+                "detected_text": [
+                    "Bienvenue sur DPI Crossway",
+                    "Identifiant",
+                    "Mot de passe",
+                    "Se connecter",
+                ],
+            },
+            "ui_elements": [
+                {"type": "text_input", "role": "text", "label": "Identifiant", "center": [500, 300], "element_id": "e1", "tags": []},
+                {"type": "text_input", "role": "password", "label": "Mot de passe", "center": [500, 350], "element_id": "e2", "tags": []},
+                {"type": "button", "role": "primary_action", "label": "Se connecter", "center": [500, 420], "element_id": "e3", "tags": []},
+            ],
+            "window": {"app_name": "DPI_Crossway", "window_title": "DPI Crossway - Connexion"},
+        }
+
+        auth_req = handler.detect_auth_screen(screen_state)
+        assert auth_req is not None
+        assert auth_req.auth_type == "login"
+        assert auth_req.app_name == "DPI_Crossway"
+        assert auth_req.confidence >= 0.6  # Plusieurs signaux
+
+    def test_detect_totp_screen(self, handler):
+        """Détecter un écran 2FA/TOTP (sans éléments de login)."""
+        screen_state = {
+            "perception": {
+                "detected_text": [
+                    "Entrez votre code 2FA",
+                    "Code à 6 chiffres",
+                ],
+            },
+            "ui_elements": [
+                {"type": "text_input", "role": "text", "label": "Code OTP", "center": [500, 350], "element_id": "e1", "tags": []},
+                {"type": "button", "role": "primary_action", "label": "Confirmer", "center": [500, 420], "element_id": "e2", "tags": []},
+            ],
+            "window": {"app_name": "DPI_Crossway"},
+        }
+
+        auth_req = handler.detect_auth_screen(screen_state)
+        assert auth_req is not None
+        assert auth_req.auth_type == "totp"
+        assert auth_req.confidence >= 0.3
+
+    def test_detect_login_and_totp(self, handler):
+        """Détecter un écran combiné login + TOTP."""
+        screen_state = {
+            "perception": {
+                "detected_text": [
+                    "Connexion sécurisée",
+                    "Identifiant",
+                    "Mot de passe",
+                    "Code OTP",
+                ],
+            },
+            "ui_elements": [
+                {"type": "text_input", "role": "text", "label": "Identifiant", "center": [500, 300], "element_id": "e1", "tags": []},
+                {"type": "text_input", "role": "password", "label": "Mot de passe", "center": [500, 350], "element_id": "e2", "tags": []},
+                {"type": "text_input", "role": "text", "label": "Code OTP", "center": [500, 400], "element_id": "e3", "tags": []},
+                {"type": "button", "role": "primary_action", "label": "Valider", "center": [500, 450], "element_id": "e4", "tags": []},
+            ],
+            "window": {"app_name": "DPI_Crossway"},
+        }
+
+        auth_req = handler.detect_auth_screen(screen_state)
+        assert auth_req is not None
+        assert auth_req.auth_type == "login_and_totp"
+        assert auth_req.confidence >= 0.85  # Beaucoup de signaux
+
+    def test_no_auth_on_normal_screen(self, handler):
+        """Un écran normal ne déclenche pas de détection."""
+        screen_state = {
+            "perception": {
+                "detected_text": ["Patient: Jean Dupont", "Dossier médical", "Résultats"],
+            },
+            "ui_elements": [
+                {"type": "button", "role": "navigation", "label": "Suivant", "center": [500, 500], "element_id": "e1", "tags": []},
+            ],
+            "window": {"app_name": "DPI_Crossway"},
+        }
+
+        auth_req = handler.detect_auth_screen(screen_state)
+        assert auth_req is None
+
+    def test_get_auth_actions_login(self, handler):
+        """Générer les actions pour un login classique."""
+        auth_req = AuthRequest(
+            auth_type="login",
+            app_name="DPI_Crossway",
+            detected_fields={
+                "username_field": {"type": "text_input", "label": "Identifiant", "center": [500, 300], "element_id": "e1"},
+                "password_field": {"type": "text_input", "label": "Mot de passe", "center": [500, 350], "element_id": "e2"},
+                "submit_button": {"type": "button", "label": "Se connecter", "center": [500, 420], "element_id": "e3"},
+            },
+            confidence=0.85,
+        )
+
+        actions = handler.get_auth_actions(auth_req)
+        assert len(actions) > 0
+
+        # Vérifier la séquence : click username, type username, click password, type password, click submit, wait
+        action_types = [(a["type"], a.get("text", "")) for a in actions]
+
+        # Il doit y avoir des clics et des saisies
+        has_click = any(a["type"] == "click" for a in actions)
+        has_type = any(a["type"] == "type_text" for a in actions)
+        has_wait = any(a["type"] == "wait" for a in actions)
+        assert has_click
+        assert has_type
+        assert has_wait
+
+        # Vérifier que le username et password sont ceux du vault
+        typed_texts = [a["text"] for a in actions if a["type"] == "type_text"]
+        assert "robot_lea" in typed_texts
+        assert "secret123" in typed_texts
+
+        # Toutes les actions ont le flag _auth_action
+        for action in actions:
+            assert action.get("_auth_action") is True
+
+    def test_get_auth_actions_totp(self, handler):
+        """Générer les actions pour une auth TOTP."""
+        auth_req = AuthRequest(
+            auth_type="totp",
+            app_name="DPI_Crossway",
+            detected_fields={
+                "otp_field": {"type": "text_input", "label": "Code", "center": [500, 350], "element_id": "e1"},
+                "submit_button": {"type": "button", "label": "Valider", "center": [500, 420], "element_id": "e2"},
+            },
+            confidence=0.85,
+        )
+
+        actions = handler.get_auth_actions(auth_req)
+        assert len(actions) > 0
+
+        # Vérifier qu'un code TOTP est tapé (6 chiffres)
+        typed_texts = [a["text"] for a in actions if a["type"] == "type_text"]
+        assert len(typed_texts) >= 1
+        totp_code = typed_texts[0]
+        assert len(totp_code) == 6
+        assert totp_code.isdigit()
+
+    def test_get_auth_actions_login_and_totp(self, handler):
+        """Générer les actions pour login + TOTP combiné."""
+        auth_req = AuthRequest(
+            auth_type="login_and_totp",
+            app_name="DPI_Crossway",
+            detected_fields={
+                "username_field": {"type": "text_input", "label": "Identifiant", "center": [500, 300], "element_id": "e1"},
+                "password_field": {"type": "text_input", "label": "Mot de passe", "center": [500, 350], "element_id": "e2"},
+                "otp_field": {"type": "text_input", "label": "Code OTP", "center": [500, 400], "element_id": "e3"},
+                "submit_button": {"type": "button", "label": "Valider", "center": [500, 450], "element_id": "e4"},
+            },
+            confidence=0.95,
+        )
+
+        actions = handler.get_auth_actions(auth_req)
+        assert len(actions) > 0
+
+        typed_texts = [a["text"] for a in actions if a["type"] == "type_text"]
+        # username + password + TOTP code
+        assert len(typed_texts) >= 3
+        assert "robot_lea" in typed_texts
+        assert "secret123" in typed_texts
+        # Le 3e est un code TOTP à 6 chiffres
+        totp_code = typed_texts[2]
+        assert len(totp_code) == 6
+        assert totp_code.isdigit()
+
+    def test_get_auth_actions_missing_credentials(self, handler):
+        """Si le vault n'a pas les credentials, retourne une liste vide."""
+        auth_req = AuthRequest(
+            auth_type="login",
+            app_name="AppInconnue",
+            detected_fields={
+                "username_field": {"type": "text_input", "label": "Login", "center": [500, 300], "element_id": "e1"},
+                "password_field": {"type": "text_input", "label": "Password", "center": [500, 350], "element_id": "e2"},
+            },
+            confidence=0.85,
+        )
+
+        actions = handler.get_auth_actions(auth_req)
+        assert actions == []
+
+    def test_detect_english_auth_screen(self, handler):
+        """Détecter un écran d'auth en anglais."""
+        screen_state = {
+            "perception": {
+                "detected_text": ["Sign in to your account", "Username", "Password"],
+            },
+            "ui_elements": [
+                {"type": "text_input", "role": "text", "label": "Username", "center": [500, 300], "element_id": "e1", "tags": []},
+                {"type": "text_input", "role": "password", "label": "Password", "center": [500, 350], "element_id": "e2", "tags": []},
+                {"type": "button", "role": "primary_action", "label": "Sign in", "center": [500, 420], "element_id": "e3", "tags": []},
+            ],
+            "window": {"app_name": "Outlook"},
+        }
+
+        auth_req = handler.detect_auth_screen(screen_state)
+        assert auth_req is not None
+        assert auth_req.auth_type == "login"
+        assert auth_req.app_name == "Outlook"
+
+    def test_detect_password_tag(self, handler):
+        """Détecter un champ password via les tags de l'élément UI."""
+        screen_state = {
+            "perception": {"detected_text": []},
+            "ui_elements": [
+                {"type": "text_input", "role": "text", "label": "", "center": [500, 300], "element_id": "e1", "tags": ["password"]},
+            ],
+            "window": {"app_name": "SomeApp"},
+        }
+
+        auth_req = handler.detect_auth_screen(screen_state)
+        assert auth_req is not None
+        assert "password_field" in auth_req.detected_fields
diff --git a/tests/unit/test_learning_pack.py b/tests/unit/test_learning_pack.py
new file mode 100644
index 000000000..ff7622f83
--- /dev/null
+++ b/tests/unit/test_learning_pack.py
@@ -0,0 +1,721 @@
+"""
+Tests unitaires pour core.federation.learning_pack
+
+Vérifie :
+- Export d'un workflow simple → pas de screenshots/OCR dans le pack
+- Merge de 2 packs → déduplication correcte des prototypes
+- Sérialisation / désérialisation JSON round-trip
+- Anonymisation du client_id (SHA-256, pas en clair)
+- Filtrage des données sensibles (textes OCR longs, métadonnées)
+- Index FAISS global (construction, recherche, persistance)
+"""
+
+import hashlib
+import json
+import tempfile
+from datetime import datetime
+from pathlib import Path
+from typing import List
+
+import numpy as np
+import pytest
+
+from core.federation.learning_pack import (
+    DEDUP_COSINE_THRESHOLD,
+    LEARNING_PACK_VERSION,
+    AppSignature,
+    EdgeStatistic,
+    ErrorPattern,
+    LearningPack,
+    LearningPackExporter,
+    LearningPackMerger,
+    ScreenPrototype,
+    UIPattern,
+    WorkflowSkeleton,
+    _hash_client_id,
+    _sanitize_text,
+)
+from core.models.workflow_graph import (
+    Action,
+    EdgeConstraints,
+    EdgeStats,
+    EmbeddingPrototype,
+    PostConditionCheck,
+    PostConditions,
+    ScreenTemplate,
+    TargetSpec,
+    TextConstraint,
+    UIConstraint,
+    WindowConstraint,
+    Workflow,
+    WorkflowEdge,
+    WorkflowNode,
+)
+
+
+# ============================================================================
+# Helpers — construction de workflows de test
+# ============================================================================
+
+def _make_node(
+    node_id: str,
+    name: str,
+    process_name: str = "Notepad.exe",
+    title_pattern: str = ".*Sans titre.*",
+    required_roles: List[str] = None,
+    prototype_vector: List[float] = None,
+) -> WorkflowNode:
+    """Créer un WorkflowNode minimal pour les tests."""
+    window = WindowConstraint(
+        title_pattern=title_pattern,
+        process_name=process_name,
+    )
+    text = TextConstraint(
+        required_texts=["Fichier", "Edition"],
+        forbidden_texts=["Erreur critique"],
+    )
+    ui = UIConstraint(
+        required_roles=required_roles or ["button", "textfield"],
+    )
+    embedding = EmbeddingPrototype(
+        provider="openclip_ViT-B-32",
+        vector_id="",
+        min_cosine_similarity=0.85,
+        sample_count=5,
+    )
+    template = ScreenTemplate(window=window, text=text, ui=ui, embedding=embedding)
+
+    metadata = {}
+    if prototype_vector is not None:
+        metadata["_prototype_vector"] = prototype_vector
+
+    return WorkflowNode(
+        node_id=node_id,
+        name=name,
+        description=f"Node de test : {name}",
+        template=template,
+        metadata=metadata,
+    )
+
+
+def _make_edge(
+    edge_id: str,
+    from_node: str,
+    to_node: str,
+    action_type: str = "mouse_click",
+    target_role: str = "button",
+    fail_fast_texts: List[str] = None,
+) -> WorkflowEdge:
+    """Créer un WorkflowEdge minimal pour les tests."""
+    target = TargetSpec(by_role=target_role)
+    action = Action(type=action_type, target=target)
+    constraints = EdgeConstraints()
+
+    fail_fast = []
+    for txt in (fail_fast_texts or []):
+        fail_fast.append(PostConditionCheck(kind="text_present", value=txt))
+
+    post_conditions = PostConditions(fail_fast=fail_fast)
+    stats = EdgeStats(execution_count=10, success_count=9, avg_execution_time_ms=150.0)
+
+    return WorkflowEdge(
+        edge_id=edge_id,
+        from_node=from_node,
+        to_node=to_node,
+        action=action,
+        constraints=constraints,
+        post_conditions=post_conditions,
+        stats=stats,
+    )
+
+
+def _make_workflow(
+    workflow_id: str = "wf_test_001",
+    name: str = "Workflow Test",
+    with_vectors: bool = True,
+) -> Workflow:
+    """Créer un Workflow complet minimal pour les tests."""
+    vec_a = np.random.randn(512).tolist() if with_vectors else None
+    vec_b = np.random.randn(512).tolist() if with_vectors else None
+
+    node_a = _make_node("node_a", "Écran principal", prototype_vector=vec_a)
+    node_b = _make_node(
+        "node_b", "Dialogue Enregistrer",
+        process_name="Notepad.exe",
+        title_pattern=".*Enregistrer.*",
+        prototype_vector=vec_b,
+    )
+
+    edge_ab = _make_edge(
+        "edge_ab", "node_a", "node_b",
+        fail_fast_texts=["Accès refusé", "Fichier introuvable"],
+    )
+
+    now = datetime.now()
+    return Workflow(
+        workflow_id=workflow_id,
+        name=name,
+        description="Workflow de test pour Learning Pack",
+        version=1,
+        learning_state="COACHING",
+        created_at=now,
+        updated_at=now,
+        entry_nodes=["node_a"],
+        end_nodes=["node_b"],
+        nodes=[node_a, node_b],
+        edges=[edge_ab],
+        safety_rules=Workflow.from_dict({
+            "workflow_id": "tmp", "name": "tmp", "nodes": [], "edges": [],
+            "safety_rules": {}, "stats": {}, "learning": {},
+            "entry_nodes": [], "end_nodes": [], "created_at": now.isoformat(),
+            "updated_at": now.isoformat(),
+        }).safety_rules,
+        stats=Workflow.from_dict({
+            "workflow_id": "tmp", "name": "tmp", "nodes": [], "edges": [],
+            "safety_rules": {}, "stats": {}, "learning": {},
+            "entry_nodes": [], "end_nodes": [], "created_at": now.isoformat(),
+            "updated_at": now.isoformat(),
+        }).stats,
+        learning=Workflow.from_dict({
+            "workflow_id": "tmp", "name": "tmp", "nodes": [], "edges": [],
+            "safety_rules": {}, "stats": {}, "learning": {},
+            "entry_nodes": [], "end_nodes": [], "created_at": now.isoformat(),
+            "updated_at": now.isoformat(),
+        }).learning,
+    )
+
+
+# ============================================================================
+# Tests — Anonymisation
+# ============================================================================
+
+class TestAnonymisation:
+    """Vérifier que l'anonymisation fonctionne correctement."""
+
+    def test_client_id_est_hashe(self):
+        """Le client_id ne doit PAS apparaître en clair dans le pack."""
+        wf = _make_workflow()
+        exporter = LearningPackExporter()
+        pack = exporter.export([wf], client_id="CHU-Lyon-001")
+
+        pack_json = json.dumps(pack.to_dict())
+        assert "CHU-Lyon-001" not in pack_json, \
+            "Le client_id apparaît en clair dans le pack !"
+
+    def test_source_hash_est_sha256(self):
+        """Le source_hash doit être un hash SHA-256 du client_id."""
+        wf = _make_workflow()
+        exporter = LearningPackExporter()
+        pack = exporter.export([wf], client_id="CHU-Lyon-001")
+
+        expected_hash = hashlib.sha256(b"CHU-Lyon-001").hexdigest()
+        assert pack.source_hash == expected_hash
+
+    def test_hash_client_id_deterministe(self):
+        """Le même client_id doit toujours donner le même hash."""
+        h1 = _hash_client_id("Clinique-Pasteur")
+        h2 = _hash_client_id("Clinique-Pasteur")
+        assert h1 == h2
+
+    def test_hash_client_id_differents(self):
+        """Deux client_id différents doivent donner des hash différents."""
+        h1 = _hash_client_id("CHU-Lyon")
+        h2 = _hash_client_id("CHU-Marseille")
+        assert h1 != h2
+
+    def test_pas_de_screenshots_dans_pack(self):
+        """Le pack ne doit contenir aucun chemin de screenshot."""
+        wf = _make_workflow()
+        # Ajouter un chemin screenshot dans les métadonnées du node
+        wf.nodes[0].metadata["screenshot_path"] = "/tmp/capture_001.png"
+        wf.nodes[0].metadata["ocr_text"] = "Texte OCR brut avec données patient"
+
+        exporter = LearningPackExporter()
+        pack = exporter.export([wf], client_id="test")
+
+        pack_json = json.dumps(pack.to_dict())
+        assert "/tmp/capture_001.png" not in pack_json
+        assert "données patient" not in pack_json
+
+    def test_texte_ocr_long_filtre(self):
+        """Les textes OCR longs (> 120 chars) doivent être filtrés."""
+        assert _sanitize_text("OK") == "OK"
+        assert _sanitize_text("x" * 200) is None
+        assert _sanitize_text("") is None
+
+    def test_texte_patient_filtre(self):
+        """Les textes contenant des identifiants patient doivent être filtrés."""
+        assert _sanitize_text("patient Dupont") is None
+        assert _sanitize_text("NIP: 123456") is None
+        assert _sanitize_text("Dossier n°789") is None
+
+    def test_texte_court_et_sur_passe(self):
+        """Les textes courts et non-sensibles doivent passer."""
+        assert _sanitize_text("Enregistrer") == "Enregistrer"
+        assert _sanitize_text("Fichier") == "Fichier"
+        assert _sanitize_text("Erreur de connexion") == "Erreur de connexion"
+
+
+# ============================================================================
+# Tests — Export
+# ============================================================================
+
+class TestExport:
+    """Vérifier l'export de workflows en Learning Pack."""
+
+    def test_export_basique(self):
+        """Export d'un workflow simple doit produire un pack valide."""
+        wf = _make_workflow()
+        exporter = LearningPackExporter()
+        pack = exporter.export([wf], client_id="test_client")
+
+        assert pack.version == LEARNING_PACK_VERSION
+        assert pack.pack_id.startswith("lp_")
+        assert pack.source_hash  # Non vide
+        assert pack.created_at  # Non vide
+
+    def test_export_stats(self):
+        """Les stats du pack doivent refléter le contenu."""
+        wf = _make_workflow()
+        exporter = LearningPackExporter()
+        pack = exporter.export([wf], client_id="test")
+
+        assert pack.stats["workflows_count"] == 1
+        assert pack.stats["total_nodes"] == 2
+        assert pack.stats["total_edges"] == 1
+        assert "Notepad.exe" in pack.stats["apps_seen"]
+
+    def test_export_prototypes_avec_vecteurs(self):
+        """Les prototypes doivent contenir les vecteurs 512d."""
+        wf = _make_workflow(with_vectors=True)
+        exporter = LearningPackExporter()
+        pack = exporter.export([wf], client_id="test")
+
+        assert len(pack.screen_prototypes) == 2
+        for proto in pack.screen_prototypes:
+            assert proto.vector is not None
+            assert len(proto.vector) == 512
+
+    def test_export_prototypes_sans_vecteurs(self):
+        """L'export doit fonctionner même sans vecteurs prototype."""
+        wf = _make_workflow(with_vectors=False)
+        exporter = LearningPackExporter()
+        pack = exporter.export([wf], client_id="test")
+
+        # Les prototypes sont exportés mais sans vecteur
+        assert len(pack.screen_prototypes) == 2
+        for proto in pack.screen_prototypes:
+            assert proto.vector is None
+
+    def test_export_app_signatures(self):
+        """Les signatures d'application doivent être collectées."""
+        wf = _make_workflow()
+        exporter = LearningPackExporter()
+        pack = exporter.export([wf], client_id="test")
+
+        app_names = [sig.app_name for sig in pack.app_signatures]
+        assert "Notepad.exe" in app_names
+
+    def test_export_error_patterns(self):
+        """Les patterns d'erreur des PostConditions doivent être extraits."""
+        wf = _make_workflow()
+        exporter = LearningPackExporter()
+        pack = exporter.export([wf], client_id="test")
+
+        error_texts = [ep.error_text for ep in pack.error_patterns]
+        assert "Accès refusé" in error_texts
+        assert "Fichier introuvable" in error_texts
+
+    def test_export_edge_statistics(self):
+        """Les statistiques d'edges doivent être exportées."""
+        wf = _make_workflow()
+        exporter = LearningPackExporter()
+        pack = exporter.export([wf], client_id="test")
+
+        assert len(pack.edge_statistics) == 1
+        stat = pack.edge_statistics[0]
+        assert stat.action_type == "mouse_click"
+        assert stat.execution_count == 10
+        assert stat.success_rate == 0.9
+
+    def test_export_workflow_skeleton(self):
+        """Le squelette du workflow doit refléter la structure."""
+        wf = _make_workflow()
+        exporter = LearningPackExporter()
+        pack = exporter.export([wf], client_id="test")
+
+        assert len(pack.workflow_skeletons) == 1
+        skel = pack.workflow_skeletons[0]
+        assert skel.node_count == 2
+        assert skel.edge_count == 1
+        assert "Écran principal" in skel.node_names
+        assert skel.learning_state == "COACHING"
+
+    def test_export_action_sans_texte_saisi(self):
+        """L'export ne doit PAS inclure le texte saisi (action text_input)."""
+        wf = _make_workflow()
+        # Ajouter un edge text_input avec un texte sensible
+        edge_text = _make_edge(
+            "edge_text", "node_a", "node_b",
+            action_type="text_input", target_role="textfield",
+        )
+        edge_text.action.parameters["text"] = "mot_de_passe_secret_123"
+        wf.edges.append(edge_text)
+
+        exporter = LearningPackExporter()
+        pack = exporter.export([wf], client_id="test")
+
+        pack_json = json.dumps(pack.to_dict())
+        assert "mot_de_passe_secret_123" not in pack_json
+
+
+# ============================================================================
+# Tests — Sérialisation
+# ============================================================================
+
+class TestSerialisation:
+    """Vérifier le round-trip JSON (to_dict → from_dict)."""
+
+    def test_round_trip_learning_pack(self):
+        """Sérialisation → désérialisation doit être idempotente."""
+        wf = _make_workflow()
+        exporter = LearningPackExporter()
+        pack = exporter.export([wf], client_id="round_trip_test")
+
+        # Sérialiser → désérialiser
+        data = pack.to_dict()
+        restored = LearningPack.from_dict(data)
+
+        assert restored.version == pack.version
+        assert restored.source_hash == pack.source_hash
+        assert restored.pack_id == pack.pack_id
+        assert len(restored.screen_prototypes) == len(pack.screen_prototypes)
+        assert len(restored.workflow_skeletons) == len(pack.workflow_skeletons)
+        assert len(restored.error_patterns) == len(pack.error_patterns)
+        assert len(restored.edge_statistics) == len(pack.edge_statistics)
+
+    def test_round_trip_json_string(self):
+        """Le JSON doit être parseable et reproductible."""
+        wf = _make_workflow()
+        exporter = LearningPackExporter()
+        pack = exporter.export([wf], client_id="json_test")
+
+        json_str = json.dumps(pack.to_dict(), sort_keys=True)
+        data = json.loads(json_str)
+        restored = LearningPack.from_dict(data)
+
+        assert json.dumps(restored.to_dict(), sort_keys=True) == json_str
+
+    def test_save_load_fichier(self, tmp_path):
+        """Sauvegarde → chargement fichier doit être idempotent."""
+        wf = _make_workflow()
+        exporter = LearningPackExporter()
+        pack = exporter.export([wf], client_id="file_test")
+
+        filepath = tmp_path / "test_pack.json"
+        pack.save(filepath)
+
+        loaded = LearningPack.load(filepath)
+        assert loaded.pack_id == pack.pack_id
+        assert loaded.source_hash == pack.source_hash
+        assert len(loaded.screen_prototypes) == len(pack.screen_prototypes)
+
+    def test_all_sub_dataclasses_round_trip(self):
+        """Chaque sous-structure doit supporter le round-trip."""
+        sig = AppSignature(app_name="Chrome.exe", version="120.0", observation_count=5)
+        assert AppSignature.from_dict(sig.to_dict()).app_name == "Chrome.exe"
+
+        proto = ScreenPrototype(
+            prototype_id="test",
+            vector=[1.0, 2.0, 3.0],
+            provider="test_provider",
+        )
+        restored = ScreenPrototype.from_dict(proto.to_dict())
+        assert restored.vector == [1.0, 2.0, 3.0]
+
+        skel = WorkflowSkeleton(
+            skeleton_id="sk1", name="Test", description="",
+            learning_state="OBSERVATION", node_names=["A", "B"],
+            edge_summaries=[], entry_nodes=["A"], end_nodes=["B"],
+        )
+        assert WorkflowSkeleton.from_dict(skel.to_dict()).name == "Test"
+
+        err = ErrorPattern(pattern_id="e1", error_text="Timeout")
+        assert ErrorPattern.from_dict(err.to_dict()).error_text == "Timeout"
+
+
+# ============================================================================
+# Tests — Merge
+# ============================================================================
+
+class TestMerge:
+    """Vérifier la fusion de plusieurs Learning Packs."""
+
+    def test_merge_deux_packs(self):
+        """Fusionner 2 packs doit produire un pack combiné."""
+        wf1 = _make_workflow("wf_1", "Workflow A")
+        wf2 = _make_workflow("wf_2", "Workflow B")
+
+        exporter = LearningPackExporter()
+        pack_a = exporter.export([wf1], client_id="Client-A")
+        pack_b = exporter.export([wf2], client_id="Client-B")
+
+        merger = LearningPackMerger()
+        merged = merger.merge([pack_a, pack_b])
+
+        assert merged.stats["workflows_count"] == 2
+        assert merged.stats["source_packs_count"] == 2
+        assert merged.pack_id.startswith("lp_merged_")
+
+    def test_merge_deduplication_prototypes_identiques(self):
+        """Deux prototypes avec le même vecteur doivent être fusionnés."""
+        # Créer un vecteur fixe pour les deux packs
+        fixed_vec = np.random.randn(512).tolist()
+
+        wf1 = _make_workflow("wf_same_1")
+        wf1.nodes[0].metadata["_prototype_vector"] = fixed_vec
+        wf2 = _make_workflow("wf_same_2")
+        wf2.nodes[0].metadata["_prototype_vector"] = fixed_vec
+
+        exporter = LearningPackExporter()
+        pack_a = exporter.export([wf1], client_id="A")
+        pack_b = exporter.export([wf2], client_id="B")
+
+        # Avant merge : 2 prototypes avec le même vecteur pour node_a
+        total_before = len(pack_a.screen_prototypes) + len(pack_b.screen_prototypes)
+        assert total_before == 4  # 2 nodes × 2 packs
+
+        merger = LearningPackMerger()
+        merged = merger.merge([pack_a, pack_b])
+
+        # Après merge : les prototypes identiques (node_a) doivent être dédupliqués
+        # node_b a des vecteurs différents (random), donc pas de dédup
+        # node_a est identique → fusionné en 1
+        # Résultat attendu : entre 2 et 3 prototypes (1 dédupliqué + 2 différents)
+        assert len(merged.screen_prototypes) < total_before
+
+    def test_merge_prototypes_differents_conserves(self):
+        """Deux prototypes très différents ne doivent PAS être fusionnés."""
+        # Créer deux vecteurs orthogonaux
+        vec_a = np.zeros(512, dtype=np.float32)
+        vec_a[0] = 1.0
+        vec_b = np.zeros(512, dtype=np.float32)
+        vec_b[1] = 1.0
+
+        wf1 = _make_workflow("wf_diff_1")
+        wf1.nodes[0].metadata["_prototype_vector"] = vec_a.tolist()
+        # Supprimer node_b pour simplifier
+        wf1.nodes = [wf1.nodes[0]]
+        wf1.edges = []
+
+        wf2 = _make_workflow("wf_diff_2")
+        wf2.nodes[0].metadata["_prototype_vector"] = vec_b.tolist()
+        wf2.nodes = [wf2.nodes[0]]
+        wf2.edges = []
+
+        exporter = LearningPackExporter()
+        pack_a = exporter.export([wf1], client_id="A")
+        pack_b = exporter.export([wf2], client_id="B")
+
+        merger = LearningPackMerger()
+        merged = merger.merge([pack_a, pack_b])
+
+        # Les deux prototypes sont très différents → pas de dédup
+        assert len(merged.screen_prototypes) == 2
+
+    def test_merge_error_patterns_cross_clients(self):
+        """Les patterns d'erreur vus par plusieurs clients ont un cross_client_count > 1."""
+        # Même erreur dans les deux packs
+        wf1 = _make_workflow("wf_err_1")
+        wf2 = _make_workflow("wf_err_2")
+
+        exporter = LearningPackExporter()
+        pack_a = exporter.export([wf1], client_id="Hôpital-A")
+        pack_b = exporter.export([wf2], client_id="Hôpital-B")
+
+        merger = LearningPackMerger()
+        merged = merger.merge([pack_a, pack_b])
+
+        # "Accès refusé" et "Fichier introuvable" sont dans les deux packs
+        for ep in merged.error_patterns:
+            if ep.error_text == "Accès refusé":
+                assert ep.cross_client_count == 2
+                assert ep.observation_count == 2  # 1 par pack
+                break
+        else:
+            pytest.fail("Pattern 'Accès refusé' non trouvé dans le merge")
+
+    def test_merge_app_signatures_union(self):
+        """Les signatures d'application doivent être l'union des packs."""
+        wf1 = _make_workflow("wf_app_1")
+        wf2 = _make_workflow("wf_app_2")
+        # Changer l'app du deuxième workflow
+        wf2.nodes[0].template.window.process_name = "Chrome.exe"
+
+        exporter = LearningPackExporter()
+        pack_a = exporter.export([wf1], client_id="A")
+        pack_b = exporter.export([wf2], client_id="B")
+
+        merger = LearningPackMerger()
+        merged = merger.merge([pack_a, pack_b])
+
+        app_names = {sig.app_name for sig in merged.app_signatures}
+        assert "Notepad.exe" in app_names
+        assert "Chrome.exe" in app_names
+
+    def test_merge_liste_vide(self):
+        """Merger une liste vide retourne un pack vide."""
+        merger = LearningPackMerger()
+        merged = merger.merge([])
+        assert merged.pack_id.startswith("lp_merged_")
+        assert len(merged.screen_prototypes) == 0
+
+    def test_merge_un_seul_pack(self):
+        """Merger un seul pack le retourne avec un nouveau pack_id."""
+        wf = _make_workflow()
+        exporter = LearningPackExporter()
+        pack = exporter.export([wf], client_id="solo")
+
+        merger = LearningPackMerger()
+        merged = merger.merge([pack])
+
+        assert merged.pack_id != pack.pack_id
+        assert merged.pack_id.startswith("lp_merged_")
+        assert len(merged.screen_prototypes) == len(pack.screen_prototypes)
+
+    def test_merge_edge_statistics_moyennes(self):
+        """Les statistiques d'edges doivent être combinées par moyenne pondérée."""
+        wf1 = _make_workflow("wf_stat_1")
+        wf2 = _make_workflow("wf_stat_2")
+
+        exporter = LearningPackExporter()
+        pack_a = exporter.export([wf1], client_id="A")
+        pack_b = exporter.export([wf2], client_id="B")
+
+        merger = LearningPackMerger()
+        merged = merger.merge([pack_a, pack_b])
+
+        # Les edges ont les mêmes noms de nodes → ils sont mergés
+        for stat in merged.edge_statistics:
+            if stat.from_node_name == "Écran principal":
+                # 10 exécutions par pack → 20 au total
+                assert stat.execution_count == 20
+                # success_rate = 0.9 pour les deux → moyenne = 0.9
+                assert abs(stat.success_rate - 0.9) < 0.01
+                break
+
+
+# ============================================================================
+# Tests — Index FAISS Global
+# ============================================================================
+
+class TestGlobalFAISSIndex:
+    """Tests de l'index FAISS global (nécessite faiss-cpu)."""
+
+    @pytest.fixture
+    def sample_packs(self):
+        """Créer deux packs de test avec des vecteurs."""
+        wf1 = _make_workflow("wf_faiss_1", "Workflow FAISS A")
+        wf2 = _make_workflow("wf_faiss_2", "Workflow FAISS B")
+
+        exporter = LearningPackExporter()
+        pack_a = exporter.export([wf1], client_id="Client-FAISS-A")
+        pack_b = exporter.export([wf2], client_id="Client-FAISS-B")
+        return [pack_a, pack_b]
+
+    def test_build_from_packs(self, sample_packs):
+        """Construction de l'index depuis les packs."""
+        try:
+            from core.federation.faiss_global import GlobalFAISSIndex
+        except ImportError:
+            pytest.skip("FAISS non installé")
+
+        index = GlobalFAISSIndex(dimensions=512)
+        count = index.build_from_packs(sample_packs)
+
+        assert count > 0
+        assert index.total_vectors == count
+
+    def test_search(self, sample_packs):
+        """Recherche dans l'index global."""
+        try:
+            from core.federation.faiss_global import GlobalFAISSIndex
+        except ImportError:
+            pytest.skip("FAISS non installé")
+
+        index = GlobalFAISSIndex(dimensions=512)
+        index.build_from_packs(sample_packs)
+
+        # Chercher avec un vecteur aléatoire
+        query = np.random.randn(512).astype(np.float32)
+        results = index.search(query, k=3)
+
+        assert len(results) > 0
+        assert len(results) <= 3
+        for r in results:
+            assert r.prototype_id
+            assert r.pack_source_hash
+            assert -1.0 <= r.similarity <= 1.0
+
+    def test_search_index_vide(self):
+        """Recherche dans un index vide retourne une liste vide."""
+        try:
+            from core.federation.faiss_global import GlobalFAISSIndex
+        except ImportError:
+            pytest.skip("FAISS non installé")
+
+        index = GlobalFAISSIndex(dimensions=512)
+        results = index.search(np.random.randn(512).astype(np.float32))
+        assert results == []
+
+    def test_add_pack_incremental(self, sample_packs):
+        """Ajout incrémental d'un pack à l'index."""
+        try:
+            from core.federation.faiss_global import GlobalFAISSIndex
+        except ImportError:
+            pytest.skip("FAISS non installé")
+
+        index = GlobalFAISSIndex(dimensions=512)
+        count1 = index.add_pack(sample_packs[0])
+        count2 = index.add_pack(sample_packs[1])
+
+        assert count1 > 0
+        assert count2 > 0
+        assert index.total_vectors == count1 + count2
+
+    def test_save_load(self, sample_packs, tmp_path):
+        """Sauvegarde et chargement de l'index."""
+        try:
+            from core.federation.faiss_global import GlobalFAISSIndex
+        except ImportError:
+            pytest.skip("FAISS non installé")
+
+        index = GlobalFAISSIndex(dimensions=512)
+        index.build_from_packs(sample_packs)
+
+        base_path = tmp_path / "global_index"
+        index.save(base_path)
+
+        loaded = GlobalFAISSIndex.load(base_path)
+        assert loaded.total_vectors == index.total_vectors
+        assert loaded.dimensions == index.dimensions
+
+        # Vérifier que la recherche fonctionne sur l'index chargé
+        query = np.random.randn(512).astype(np.float32)
+        results = loaded.search(query, k=2)
+        assert len(results) > 0
+
+    def test_get_stats(self, sample_packs):
+        """Statistiques de l'index global."""
+        try:
+            from core.federation.faiss_global import GlobalFAISSIndex
+        except ImportError:
+            pytest.skip("FAISS non installé")
+
+        index = GlobalFAISSIndex(dimensions=512)
+        index.build_from_packs(sample_packs)
+
+        stats = index.get_stats()
+        assert stats["dimensions"] == 512
+        assert stats["total_vectors"] > 0
+        assert stats["unique_sources"] >= 1
diff --git a/visual_workflow_builder/backend/api_v3/learned_workflows.py b/visual_workflow_builder/backend/api_v3/learned_workflows.py
index c1d48a9bf..85c96ac7c 100644
--- a/visual_workflow_builder/backend/api_v3/learned_workflows.py
+++ b/visual_workflow_builder/backend/api_v3/learned_workflows.py
@@ -55,6 +55,8 @@ def list_learned_workflows():
 
     Query params:
         machine_id: Filtrer par machine (optionnel)
+        os: Filtrer par OS — 'windows' ou 'linux' (optionnel).
+            Filtre sur la portion OS du machine_id (ex: DESKTOP-58D5CAC_windows).
 
     Response:
     {
@@ -76,6 +78,7 @@ def list_learned_workflows():
     }
     """
     machine_id = request.args.get('machine_id')
+    os_filter = request.args.get('os')  # 'windows' ou 'linux'
 
     from services.learned_workflow_bridge import list_learned_workflows_from_disk
 
@@ -132,6 +135,14 @@ def list_learned_workflows():
     if machine_id:
         merged = [w for w in merged if w.get("machine_id") == machine_id]
 
+    # Filtrer par OS si demandé (cherche 'windows' ou 'linux' dans le machine_id)
+    if os_filter:
+        os_filter_lower = os_filter.lower()
+        merged = [
+            w for w in merged
+            if os_filter_lower in (w.get("machine_id") or "").lower()
+        ]
+
     # Enrichir : vérifier si déjà importé dans le VWB
     for wf in merged:
         existing = Workflow.query.filter(
diff --git a/visual_workflow_builder/backend/app.py b/visual_workflow_builder/backend/app.py
index d4d275acf..712437b8c 100644
--- a/visual_workflow_builder/backend/app.py
+++ b/visual_workflow_builder/backend/app.py
@@ -58,9 +58,15 @@ db.init_app(app)
 migrate = Migrate(app, db)
 
 cache = Cache(app)
+_ALLOWED_ORIGINS = [
+    "http://localhost:3002",
+    "http://localhost:5002",
+    "https://vwb.labs.laurinebazin.design",
+    "https://lea.labs.laurinebazin.design",
+]
 socketio = SocketIO(
     app,
-    cors_allowed_origins="*",
+    cors_allowed_origins=_ALLOWED_ORIGINS,
     async_mode='threading',
     logger=True,
     engineio_logger=True
@@ -204,6 +210,16 @@ def set_security_headers(response):
     response.headers['X-Content-Type-Options'] = 'nosniff'
     response.headers['X-Frame-Options'] = 'SAMEORIGIN'
     response.headers['X-XSS-Protection'] = '1; mode=block'
+    response.headers['Referrer-Policy'] = 'strict-origin-when-cross-origin'
+    response.headers['Content-Security-Policy'] = (
+        "default-src 'self'; "
+        "script-src 'self' 'unsafe-inline' 'unsafe-eval'; "
+        "style-src 'self' 'unsafe-inline'; "
+        "img-src 'self' data: blob:; "
+        "connect-src 'self' ws: wss: http://localhost:* https://vwb.labs.laurinebazin.design https://lea.labs.laurinebazin.design; "
+        "font-src 'self' data:; "
+        "frame-ancestors 'self'"
+    )
     return response
 
 
diff --git a/visual_workflow_builder/backend/catalog_routes_v2_vlm.py b/visual_workflow_builder/backend/catalog_routes_v2_vlm.py
index 36a5e10a0..13f993319 100644
--- a/visual_workflow_builder/backend/catalog_routes_v2_vlm.py
+++ b/visual_workflow_builder/backend/catalog_routes_v2_vlm.py
@@ -67,6 +67,11 @@ import os
 import json
 import requests
 import re
+try:
+    from vlm_provider import vlm_hub
+except ImportError:
+    from visual_workflow_builder.backend.vlm_provider import vlm_hub
+
 try:
     import cv2
     import numpy as np
@@ -624,21 +629,33 @@ def find_anchor_with_vlm(
 ) -> Optional[Dict[str, Any]]:
     """
     Utilise un VLM (Vision Language Model) pour trouver l'ancre sur l'écran.
-
-    Le VLM comprend le contexte visuel et peut distinguer:
-    - Une icône dans le dock vs le même logo dans la fenêtre principale
-    - Un bouton actif vs sa copie dans une miniature
-
-    Args:
-        screenshot_base64: Capture d'écran complète en base64
-        anchor_image_base64: Image de l'ancre à trouver en base64
-        anchor_description: Description textuelle de l'élément (optionnel)
-        screen_width: Largeur de l'écran
-        screen_height: Hauteur de l'écran
-
-    Returns:
-        Dict avec coordonnées {x, y, confidence} ou None si non trouvé
+    En priorité via Gemini Cloud (vlm_hub), sinon via Ollama local.
     """
+    # 1. Essayer via le VLM Hub (Gemini Cloud)
+    if vlm_hub.use_cloud and vlm_hub.google_api_key:
+        print(f"🧠 [VLM Hub] Tentative via Gemini Cloud pour: '{anchor_description}'...")
+        coords = vlm_hub.detect_ui_element(
+            screenshot=screenshot_base64,
+            anchor_image=anchor_image_base64,
+            description=anchor_description or "l'élément UI spécifié"
+        )
+        if coords and coords.get('found'):
+            # Convertir bbox normalisée [ymin, xmin, ymax, xmax] (0-1000) en pixels [x, y]
+            bbox = coords.get('bbox')
+            if bbox and len(bbox) == 4:
+                # Gemini retourne souvent [ymin, xmin, ymax, xmax] sur une échelle 0-1000
+                y1, x1, y2, x2 = bbox
+                x = int((x1 + x2) / 2 * screen_width / 1000)
+                y = int((y1 + y2) / 2 * screen_height / 1000)
+                confidence = float(coords.get('confidence', 0.9))
+                
+                print(f"✅ [VLM Hub] Gemini a trouvé l'élément à ({x}, {y}) avec confiance {confidence:.0%}")
+                return {
+                    "found": True, "x": x, "y": y, "center_x": x, "center_y": y,
+                    "confidence": confidence, "method": "gemini_cloud"
+                }
+
+    # 2. Fallback sur Ollama Local
     if not check_ollama_available():
         print("⚠️ [VLM] Ollama/qwen2.5vl non disponible, fallback sur pyautogui")
         return None
@@ -976,7 +993,9 @@ def find_anchor_multiscale(anchor_image_base64: str, scales: List[float] = None,
         Dict avec les coordonnées trouvées ou None
     """
     if scales is None:
-        scales = [0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3]
+        # Plage étendue 0.5x-2.0x pour couvrir les écarts de résolution importants
+        # (ex: apprentissage 2560x1600 → replay 1280x720 = ratio ~0.5x)
+        scales = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.5, 1.75, 2.0]
 
     if not CV2_AVAILABLE:
         return None
diff --git a/visual_workflow_builder/backend/data/analytics/metrics.db/timeseries.db b/visual_workflow_builder/backend/data/analytics/metrics.db/timeseries.db
new file mode 100644
index 000000000..629643ad8
Binary files /dev/null and b/visual_workflow_builder/backend/data/analytics/metrics.db/timeseries.db differ
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_01a7b6e80168_1769095224/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_01a7b6e80168_1769095224/metadata.json
new file mode 100644
index 000000000..78a1f21f5
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_01a7b6e80168_1769095224/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_01a7b6e80168_1769095224",
+  "bounding_box": {
+    "x": 52.666666666666664,
+    "y": 23.866658528645832,
+    "width": 100,
+    "height": 24
+  },
+  "original_size": {
+    "width": 120,
+    "height": 44
+  },
+  "thumbnail_size": {
+    "width": 120,
+    "height": 44
+  },
+  "created_at": "2026-01-22T16:20:24.448773",
+  "original_file_size": 4172,
+  "thumbnail_file_size": 1342
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_07961dc0aa07_1769032466/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_07961dc0aa07_1769032466/metadata.json
new file mode 100644
index 000000000..f778dbfff
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_07961dc0aa07_1769032466/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_07961dc0aa07_1769032466",
+  "bounding_box": {
+    "x": 788.4444173177084,
+    "y": 89.7407145416744,
+    "width": 144,
+    "height": 141.33332055362771
+  },
+  "original_size": {
+    "width": 164,
+    "height": 161
+  },
+  "thumbnail_size": {
+    "width": 153,
+    "height": 150
+  },
+  "created_at": "2026-01-21T22:54:26.818654",
+  "original_file_size": 15929,
+  "thumbnail_file_size": 2733
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_0ad95fe4cd0d_1769094638/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_0ad95fe4cd0d_1769094638/metadata.json
new file mode 100644
index 000000000..1cd414f92
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_0ad95fe4cd0d_1769094638/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_0ad95fe4cd0d_1769094638",
+  "bounding_box": {
+    "x": 378,
+    "y": 95.86665852864583,
+    "width": 614.6666666666666,
+    "height": 68
+  },
+  "original_size": {
+    "width": 634,
+    "height": 88
+  },
+  "thumbnail_size": {
+    "width": 200,
+    "height": 28
+  },
+  "created_at": "2026-01-22T16:10:38.629806",
+  "original_file_size": 32381,
+  "thumbnail_file_size": 1690
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_0b04180c5773_1769093895/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_0b04180c5773_1769093895/metadata.json
new file mode 100644
index 000000000..c9592af36
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_0b04180c5773_1769093895/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_0b04180c5773_1769093895",
+  "bounding_box": {
+    "x": 702,
+    "y": 55.866658528645836,
+    "width": 240,
+    "height": 21.333333333333332
+  },
+  "original_size": {
+    "width": 260,
+    "height": 41
+  },
+  "thumbnail_size": {
+    "width": 200,
+    "height": 32
+  },
+  "created_at": "2026-01-22T15:58:15.431474",
+  "original_file_size": 7749,
+  "thumbnail_file_size": 1693
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_109e03d0bd6e_1769027528/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_109e03d0bd6e_1769027528/metadata.json
new file mode 100644
index 000000000..09e695de3
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_109e03d0bd6e_1769027528/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_109e03d0bd6e_1769027528",
+  "bounding_box": {
+    "x": 661.7777506510416,
+    "y": 37.648108574932316,
+    "width": 325.3333333333333,
+    "height": 49.33332887249268
+  },
+  "original_size": {
+    "width": 345,
+    "height": 69
+  },
+  "thumbnail_size": {
+    "width": 200,
+    "height": 40
+  },
+  "created_at": "2026-01-21T21:32:08.911570",
+  "original_file_size": 8040,
+  "thumbnail_file_size": 1021
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_154f2965ccd7_1769071042/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_154f2965ccd7_1769071042/metadata.json
new file mode 100644
index 000000000..2e0fee9fc
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_154f2965ccd7_1769071042/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_154f2965ccd7_1769071042",
+  "bounding_box": {
+    "x": 6,
+    "y": 837.1999918619791,
+    "width": 52,
+    "height": 57.333333333333336
+  },
+  "original_size": {
+    "width": 72,
+    "height": 73
+  },
+  "thumbnail_size": {
+    "width": 72,
+    "height": 73
+  },
+  "created_at": "2026-01-22T09:37:23.002573",
+  "original_file_size": 3336,
+  "thumbnail_file_size": 1081
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_1f14f3421523_1769019671/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_1f14f3421523_1769019671/metadata.json
new file mode 100644
index 000000000..2536dae67
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_1f14f3421523_1769019671/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_1f14f3421523_1769019671",
+  "bounding_box": {
+    "x": 7.511067708333333,
+    "y": 840,
+    "width": 41.333333333333336,
+    "height": 60
+  },
+  "original_size": {
+    "width": 61,
+    "height": 70
+  },
+  "thumbnail_size": {
+    "width": 61,
+    "height": 70
+  },
+  "created_at": "2026-01-21T19:21:11.133210",
+  "original_file_size": 3663,
+  "thumbnail_file_size": 1044
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_22071a702f14_1769019035/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_22071a702f14_1769019035/metadata.json
new file mode 100644
index 000000000..c12162400
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_22071a702f14_1769019035/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_22071a702f14_1769019035",
+  "bounding_box": {
+    "x": 0.4444173177083333,
+    "y": 840.4073133312253,
+    "width": 53.333333333333336,
+    "height": 55.99999493634308
+  },
+  "original_size": {
+    "width": 73,
+    "height": 70
+  },
+  "thumbnail_size": {
+    "width": 73,
+    "height": 70
+  },
+  "created_at": "2026-01-21T19:10:35.916823",
+  "original_file_size": 3804,
+  "thumbnail_file_size": 1105
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_2dcada499503_1769026880/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_2dcada499503_1769026880/metadata.json
new file mode 100644
index 000000000..03ce6510c
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_2dcada499503_1769026880/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_2dcada499503_1769026880",
+  "bounding_box": {
+    "x": 1.7777506510416667,
+    "y": 839.0739801184551,
+    "width": 53.333333333333336,
+    "height": 47.99999565972272
+  },
+  "original_size": {
+    "width": 73,
+    "height": 67
+  },
+  "thumbnail_size": {
+    "width": 73,
+    "height": 67
+  },
+  "created_at": "2026-01-21T21:21:20.684380",
+  "original_file_size": 3414,
+  "thumbnail_file_size": 1063
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_38b46eeb9aa7_1769108446/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_38b46eeb9aa7_1769108446/metadata.json
new file mode 100644
index 000000000..c983769f6
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_38b46eeb9aa7_1769108446/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_38b46eeb9aa7_1769108446",
+  "bounding_box": {
+    "x": 787.3333333333334,
+    "y": 86.5333251953125,
+    "width": 138.66666666666666,
+    "height": 142.66666666666666
+  },
+  "original_size": {
+    "width": 158,
+    "height": 162
+  },
+  "thumbnail_size": {
+    "width": 146,
+    "height": 150
+  },
+  "created_at": "2026-01-22T20:00:46.168811",
+  "original_file_size": 15894,
+  "thumbnail_file_size": 2792
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_3b097ff2f8e0_1769032447/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_3b097ff2f8e0_1769032447/metadata.json
new file mode 100644
index 000000000..9fd795b8a
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_3b097ff2f8e0_1769032447/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_3b097ff2f8e0_1769032447",
+  "bounding_box": {
+    "x": 57.777750651041664,
+    "y": 25.740720328710907,
+    "width": 96,
+    "height": 27.999997468171525
+  },
+  "original_size": {
+    "width": 116,
+    "height": 47
+  },
+  "thumbnail_size": {
+    "width": 116,
+    "height": 47
+  },
+  "created_at": "2026-01-21T22:54:07.560023",
+  "original_file_size": 3897,
+  "thumbnail_file_size": 1276
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_3bfad8fb87f6_1769071011/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_3bfad8fb87f6_1769071011/metadata.json
new file mode 100644
index 000000000..6ee9c5dae
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_3bfad8fb87f6_1769071011/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_3bfad8fb87f6_1769071011",
+  "bounding_box": {
+    "x": 10,
+    "y": 846.5111083984375,
+    "width": 40,
+    "height": 44
+  },
+  "original_size": {
+    "width": 60,
+    "height": 64
+  },
+  "thumbnail_size": {
+    "width": 60,
+    "height": 64
+  },
+  "created_at": "2026-01-22T09:36:51.077375",
+  "original_file_size": 3265,
+  "thumbnail_file_size": 1012
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_3fa725d0afac_1769027605/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_3fa725d0afac_1769027605/metadata.json
new file mode 100644
index 000000000..af25ca1b7
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_3fa725d0afac_1769027605/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_3fa725d0afac_1769027605",
+  "bounding_box": {
+    "x": 681.7777506510416,
+    "y": 52.40738458411236,
+    "width": 185.33333333333334,
+    "height": 21.33333140432116
+  },
+  "original_size": {
+    "width": 205,
+    "height": 41
+  },
+  "thumbnail_size": {
+    "width": 200,
+    "height": 40
+  },
+  "created_at": "2026-01-21T21:33:25.656268",
+  "original_file_size": 6955,
+  "thumbnail_file_size": 1693
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_411d198f7d8d_1769026971/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_411d198f7d8d_1769026971/metadata.json
new file mode 100644
index 000000000..ac0d3ad54
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_411d198f7d8d_1769026971/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_411d198f7d8d_1769026971",
+  "bounding_box": {
+    "x": 4.444417317708333,
+    "y": 825.7406479907545,
+    "width": 53.333333333333336,
+    "height": 59.99999457465325
+  },
+  "original_size": {
+    "width": 73,
+    "height": 79
+  },
+  "thumbnail_size": {
+    "width": 73,
+    "height": 79
+  },
+  "created_at": "2026-01-21T21:22:51.165280",
+  "original_file_size": 3422,
+  "thumbnail_file_size": 1059
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_41312810e9ab_1769018430/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_41312810e9ab_1769018430/metadata.json
new file mode 100644
index 000000000..9fe1b75f0
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_41312810e9ab_1769018430/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_41312810e9ab_1769018430",
+  "bounding_box": {
+    "x": 20,
+    "y": 20,
+    "width": 100,
+    "height": 80
+  },
+  "original_size": {
+    "width": 120,
+    "height": 100
+  },
+  "thumbnail_size": {
+    "width": 120,
+    "height": 100
+  },
+  "created_at": "2026-01-21T19:00:30.494847",
+  "original_file_size": 222,
+  "thumbnail_file_size": 798
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_41d6a1572597_1769026781/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_41d6a1572597_1769026781/metadata.json
new file mode 100644
index 000000000..971d93b6e
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_41d6a1572597_1769026781/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_41d6a1572597_1769026781",
+  "bounding_box": {
+    "x": 3.111083984375,
+    "y": 840.7591606301395,
+    "width": 50.666666666666664,
+    "height": 51.9999952980329
+  },
+  "original_size": {
+    "width": 70,
+    "height": 70
+  },
+  "thumbnail_size": {
+    "width": 70,
+    "height": 70
+  },
+  "created_at": "2026-01-21T21:19:41.943551",
+  "original_file_size": 3436,
+  "thumbnail_file_size": 1055
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_490e337ebcfc_1769086898/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_490e337ebcfc_1769086898/metadata.json
new file mode 100644
index 000000000..e478ba662
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_490e337ebcfc_1769086898/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_490e337ebcfc_1769086898",
+  "bounding_box": {
+    "x": 3.111083984375,
+    "y": 151.07404232909775,
+    "width": 50.666666666666664,
+    "height": 53.33332851080289
+  },
+  "original_size": {
+    "width": 70,
+    "height": 73
+  },
+  "thumbnail_size": {
+    "width": 70,
+    "height": 73
+  },
+  "created_at": "2026-01-22T14:01:38.851522",
+  "original_file_size": 7591,
+  "thumbnail_file_size": 1434
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_4e3067c7d77f_1769032292/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_4e3067c7d77f_1769032292/metadata.json
new file mode 100644
index 000000000..bdf0fd110
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_4e3067c7d77f_1769032292/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_4e3067c7d77f_1769032292",
+  "bounding_box": {
+    "x": 673.7777506510416,
+    "y": 43.07405209472185,
+    "width": 305.3333333333333,
+    "height": 37.333329957562036
+  },
+  "original_size": {
+    "width": 325,
+    "height": 57
+  },
+  "thumbnail_size": {
+    "width": 200,
+    "height": 35
+  },
+  "created_at": "2026-01-21T22:51:32.559892",
+  "original_file_size": 7844,
+  "thumbnail_file_size": 1108
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_55e73b435685_1769027422/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_55e73b435685_1769027422/metadata.json
new file mode 100644
index 000000000..e3bbef221
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_55e73b435685_1769027422/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_55e73b435685_1769027422",
+  "bounding_box": {
+    "x": 7.111083984375,
+    "y": 843.0739797567654,
+    "width": 45.333333333333336,
+    "height": 49.33332887249268
+  },
+  "original_size": {
+    "width": 65,
+    "height": 67
+  },
+  "thumbnail_size": {
+    "width": 65,
+    "height": 67
+  },
+  "created_at": "2026-01-21T21:30:22.544153",
+  "original_file_size": 3419,
+  "thumbnail_file_size": 1073
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_58b59356cf75_1769111447/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_58b59356cf75_1769111447/metadata.json
new file mode 100644
index 000000000..b1118298c
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_58b59356cf75_1769111447/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_58b59356cf75_1769111447",
+  "bounding_box": {
+    "x": 615.3333333333334,
+    "y": 81.19999186197917,
+    "width": 144,
+    "height": 150.66666666666666
+  },
+  "original_size": {
+    "width": 164,
+    "height": 170
+  },
+  "thumbnail_size": {
+    "width": 145,
+    "height": 150
+  },
+  "created_at": "2026-01-22T20:50:47.554442",
+  "original_file_size": 11685,
+  "thumbnail_file_size": 2441
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_68b5b0da9a64_1769097688/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_68b5b0da9a64_1769097688/metadata.json
new file mode 100644
index 000000000..a83090c14
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_68b5b0da9a64_1769097688/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_68b5b0da9a64_1769097688",
+  "bounding_box": {
+    "x": 67.33333333333333,
+    "y": 26.5333251953125,
+    "width": 22.666666666666668,
+    "height": 26.666666666666668
+  },
+  "original_size": {
+    "width": 42,
+    "height": 46
+  },
+  "thumbnail_size": {
+    "width": 42,
+    "height": 46
+  },
+  "created_at": "2026-01-22T17:01:28.364282",
+  "original_file_size": 1795,
+  "thumbnail_file_size": 707
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_6a859da4b8a2_1769091100/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_6a859da4b8a2_1769091100/metadata.json
new file mode 100644
index 000000000..6d4b18507
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_6a859da4b8a2_1769091100/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_6a859da4b8a2_1769091100",
+  "bounding_box": {
+    "x": -0.888916015625,
+    "y": 845.7406461823056,
+    "width": 57.333333333333336,
+    "height": 50.66666208526279
+  },
+  "original_size": {
+    "width": 77,
+    "height": 65
+  },
+  "thumbnail_size": {
+    "width": 77,
+    "height": 65
+  },
+  "created_at": "2026-01-22T15:11:40.315519",
+  "original_file_size": 3437,
+  "thumbnail_file_size": 1125
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_6ca93ca7659b_1769095422/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_6ca93ca7659b_1769095422/metadata.json
new file mode 100644
index 000000000..a0d0be2af
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_6ca93ca7659b_1769095422/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_6ca93ca7659b_1769095422",
+  "bounding_box": {
+    "x": 68.66666666666667,
+    "y": 31.866658528645832,
+    "width": 85.33333333333333,
+    "height": 14.666666666666666
+  },
+  "original_size": {
+    "width": 105,
+    "height": 34
+  },
+  "thumbnail_size": {
+    "width": 105,
+    "height": 34
+  },
+  "created_at": "2026-01-22T16:23:42.687417",
+  "original_file_size": 3309,
+  "thumbnail_file_size": 1132
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_72dc58cdbbe2_1769079328/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_72dc58cdbbe2_1769079328/metadata.json
new file mode 100644
index 000000000..31a7d33d3
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_72dc58cdbbe2_1769079328/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_72dc58cdbbe2_1769079328",
+  "bounding_box": {
+    "x": 5.777750651041667,
+    "y": 843.0739797567654,
+    "width": 46.666666666666664,
+    "height": 53.33332851080286
+  },
+  "original_size": {
+    "width": 66,
+    "height": 67
+  },
+  "thumbnail_size": {
+    "width": 66,
+    "height": 67
+  },
+  "created_at": "2026-01-22T11:55:28.209469",
+  "original_file_size": 4025,
+  "thumbnail_file_size": 1237
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_74a09f5b2603_1769107519/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_74a09f5b2603_1769107519/metadata.json
new file mode 100644
index 000000000..8d6fbd0f0
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_74a09f5b2603_1769107519/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_74a09f5b2603_1769107519",
+  "bounding_box": {
+    "x": 56.666666666666664,
+    "y": 27.866658528645832,
+    "width": 98.66666666666667,
+    "height": 21.333333333333332
+  },
+  "original_size": {
+    "width": 118,
+    "height": 41
+  },
+  "thumbnail_size": {
+    "width": 118,
+    "height": 41
+  },
+  "created_at": "2026-01-22T19:45:19.515074",
+  "original_file_size": 3850,
+  "thumbnail_file_size": 1288
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_7677c82a68bc_1769093993/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_7677c82a68bc_1769093993/metadata.json
new file mode 100644
index 000000000..1cddc3db8
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_7677c82a68bc_1769093993/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_7677c82a68bc_1769093993",
+  "bounding_box": {
+    "x": 415.3333333333333,
+    "y": 107.86665852864583,
+    "width": 98.66666666666667,
+    "height": 110.66666666666667
+  },
+  "original_size": {
+    "width": 118,
+    "height": 130
+  },
+  "thumbnail_size": {
+    "width": 118,
+    "height": 130
+  },
+  "created_at": "2026-01-22T15:59:53.309677",
+  "original_file_size": 17093,
+  "thumbnail_file_size": 3009
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_79d84dda4257_1769079491/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_79d84dda4257_1769079491/metadata.json
new file mode 100644
index 000000000..aa9989a53
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_79d84dda4257_1769079491/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_79d84dda4257_1769079491",
+  "bounding_box": {
+    "x": 5.777750651041667,
+    "y": 847.0739793950756,
+    "width": 46.666666666666664,
+    "height": 50.66666208526279
+  },
+  "original_size": {
+    "width": 66,
+    "height": 63
+  },
+  "thumbnail_size": {
+    "width": 66,
+    "height": 63
+  },
+  "created_at": "2026-01-22T11:58:11.312760",
+  "original_file_size": 3813,
+  "thumbnail_file_size": 1097
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_7de070f7b4c5_1769091889/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_7de070f7b4c5_1769091889/metadata.json
new file mode 100644
index 000000000..033902753
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_7de070f7b4c5_1769091889/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_7de070f7b4c5_1769091889",
+  "bounding_box": {
+    "x": 6,
+    "y": 847.8666585286459,
+    "width": 42.666666666666664,
+    "height": 41.333333333333336
+  },
+  "original_size": {
+    "width": 62,
+    "height": 61
+  },
+  "thumbnail_size": {
+    "width": 62,
+    "height": 61
+  },
+  "created_at": "2026-01-22T15:24:49.229883",
+  "original_file_size": 3265,
+  "thumbnail_file_size": 972
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_7f05480c1da2_1769086942/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_7f05480c1da2_1769086942/metadata.json
new file mode 100644
index 000000000..49ea22d72
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_7f05480c1da2_1769086942/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_7f05480c1da2_1769086942",
+  "bounding_box": {
+    "x": 0.4444173177083333,
+    "y": 95.07404739275468,
+    "width": 50.666666666666664,
+    "height": 58.6666613618832
+  },
+  "original_size": {
+    "width": 70,
+    "height": 78
+  },
+  "thumbnail_size": {
+    "width": 70,
+    "height": 78
+  },
+  "created_at": "2026-01-22T14:02:22.124508",
+  "original_file_size": 6806,
+  "thumbnail_file_size": 1469
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_851447edda6a_1769094102/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_851447edda6a_1769094102/metadata.json
new file mode 100644
index 000000000..f4268963b
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_851447edda6a_1769094102/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_851447edda6a_1769094102",
+  "bounding_box": {
+    "x": 615.3333333333334,
+    "y": 83.86665852864583,
+    "width": 145.33333333333334,
+    "height": 150.66666666666666
+  },
+  "original_size": {
+    "width": 165,
+    "height": 170
+  },
+  "thumbnail_size": {
+    "width": 146,
+    "height": 150
+  },
+  "created_at": "2026-01-22T16:01:42.039695",
+  "original_file_size": 11696,
+  "thumbnail_file_size": 2474
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_8676ea613f0d_1769031704/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_8676ea613f0d_1769031704/metadata.json
new file mode 100644
index 000000000..6d453ecc3
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_8676ea613f0d_1769031704/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_8676ea613f0d_1769031704",
+  "bounding_box": {
+    "x": 405.7777506510417,
+    "y": 100.40738024383496,
+    "width": 120,
+    "height": 117.33332272376639
+  },
+  "original_size": {
+    "width": 140,
+    "height": 137
+  },
+  "thumbnail_size": {
+    "width": 140,
+    "height": 137
+  },
+  "created_at": "2026-01-21T22:41:44.732783",
+  "original_file_size": 17773,
+  "thumbnail_file_size": 3255
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_9b8fb0066648_1769088263/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_9b8fb0066648_1769088263/metadata.json
new file mode 100644
index 000000000..3046c8c08
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_9b8fb0066648_1769088263/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_9b8fb0066648_1769088263",
+  "bounding_box": {
+    "x": 4.444417317708333,
+    "y": 512.4073429897875,
+    "width": 44,
+    "height": 46.666662446952536
+  },
+  "original_size": {
+    "width": 64,
+    "height": 66
+  },
+  "thumbnail_size": {
+    "width": 64,
+    "height": 66
+  },
+  "created_at": "2026-01-22T14:24:23.082162",
+  "original_file_size": 3870,
+  "thumbnail_file_size": 1015
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_a14d9a2ab6d5_1769031402/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_a14d9a2ab6d5_1769031402/metadata.json
new file mode 100644
index 000000000..2553823b1
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_a14d9a2ab6d5_1769031402/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_a14d9a2ab6d5_1769031402",
+  "bounding_box": {
+    "x": 619.111083984375,
+    "y": 85.59256224748968,
+    "width": 138.66666666666666,
+    "height": 146.666653404708
+  },
+  "original_size": {
+    "width": 158,
+    "height": 166
+  },
+  "thumbnail_size": {
+    "width": 143,
+    "height": 150
+  },
+  "created_at": "2026-01-21T22:36:42.812376",
+  "original_file_size": 11642,
+  "thumbnail_file_size": 2554
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_a80b7accc764_1769095298/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_a80b7accc764_1769095298/metadata.json
new file mode 100644
index 000000000..7b23dc83c
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_a80b7accc764_1769095298/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_a80b7accc764_1769095298",
+  "bounding_box": {
+    "x": 784.6666666666666,
+    "y": 89.17777506510417,
+    "width": 138.66666666666666,
+    "height": 142.66666666666666
+  },
+  "original_size": {
+    "width": 158,
+    "height": 162
+  },
+  "thumbnail_size": {
+    "width": 146,
+    "height": 150
+  },
+  "created_at": "2026-01-22T16:21:38.941443",
+  "original_file_size": 15842,
+  "thumbnail_file_size": 2829
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_af322c06f1ff_1769097662/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_af322c06f1ff_1769097662/metadata.json
new file mode 100644
index 000000000..9ad864a37
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_af322c06f1ff_1769097662/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_af322c06f1ff_1769097662",
+  "bounding_box": {
+    "x": 64.66666666666667,
+    "y": 31.866658528645832,
+    "width": 80,
+    "height": 13.333333333333334
+  },
+  "original_size": {
+    "width": 100,
+    "height": 33
+  },
+  "thumbnail_size": {
+    "width": 100,
+    "height": 33
+  },
+  "created_at": "2026-01-22T17:01:02.148524",
+  "original_file_size": 3358,
+  "thumbnail_file_size": 1226
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_b75fa9a8f1dd_1769090648/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_b75fa9a8f1dd_1769090648/metadata.json
new file mode 100644
index 000000000..dfdee404d
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_b75fa9a8f1dd_1769090648/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_b75fa9a8f1dd_1769090648",
+  "bounding_box": {
+    "x": 0.4444173177083333,
+    "y": 847.0739793950756,
+    "width": 53.333333333333336,
+    "height": 41.33332959587233
+  },
+  "original_size": {
+    "width": 73,
+    "height": 61
+  },
+  "thumbnail_size": {
+    "width": 73,
+    "height": 61
+  },
+  "created_at": "2026-01-22T15:04:08.862025",
+  "original_file_size": 3821,
+  "thumbnail_file_size": 1097
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_b98b50f27a10_1769031253/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_b98b50f27a10_1769031253/metadata.json
new file mode 100644
index 000000000..75db98a84
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_b98b50f27a10_1769031253/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_b98b50f27a10_1769031253",
+  "bounding_box": {
+    "x": 764.4444173177084,
+    "y": 99.0740470310649,
+    "width": 121.33333333333333,
+    "height": 119.99998914930653
+  },
+  "original_size": {
+    "width": 141,
+    "height": 139
+  },
+  "thumbnail_size": {
+    "width": 141,
+    "height": 139
+  },
+  "created_at": "2026-01-21T22:34:13.654029",
+  "original_file_size": 18201,
+  "thumbnail_file_size": 3211
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_b9bc7ea3369b_1769070632/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_b9bc7ea3369b_1769070632/metadata.json
new file mode 100644
index 000000000..c0be595ee
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_b9bc7ea3369b_1769070632/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_b9bc7ea3369b_1769070632",
+  "bounding_box": {
+    "x": 4.666666666666667,
+    "y": 841.1999918619791,
+    "width": 42.666666666666664,
+    "height": 49.333333333333336
+  },
+  "original_size": {
+    "width": 62,
+    "height": 69
+  },
+  "thumbnail_size": {
+    "width": 62,
+    "height": 69
+  },
+  "created_at": "2026-01-22T09:30:32.058188",
+  "original_file_size": 3302,
+  "thumbnail_file_size": 992
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_c5acc71c3066_1769032342/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_c5acc71c3066_1769032342/metadata.json
new file mode 100644
index 000000000..fb9d07c7c
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_c5acc71c3066_1769032342/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_c5acc71c3066_1769032342",
+  "bounding_box": {
+    "x": 771.111083984375,
+    "y": 100.40738024383496,
+    "width": 114.66666666666667,
+    "height": 115.99998951099631
+  },
+  "original_size": {
+    "width": 134,
+    "height": 135
+  },
+  "thumbnail_size": {
+    "width": 134,
+    "height": 135
+  },
+  "created_at": "2026-01-21T22:52:22.390957",
+  "original_file_size": 18134,
+  "thumbnail_file_size": 3249
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_cb7bb23c8a14_1769032268/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_cb7bb23c8a14_1769032268/metadata.json
new file mode 100644
index 000000000..299c85498
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_cb7bb23c8a14_1769032268/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_cb7bb23c8a14_1769032268",
+  "bounding_box": {
+    "x": 1.7777506510416667,
+    "y": 847.0739793950756,
+    "width": 46.666666666666664,
+    "height": 42.666662808642286
+  },
+  "original_size": {
+    "width": 66,
+    "height": 62
+  },
+  "thumbnail_size": {
+    "width": 66,
+    "height": 62
+  },
+  "created_at": "2026-01-21T22:51:08.672430",
+  "original_file_size": 3811,
+  "thumbnail_file_size": 1097
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_cbbf8da48554_1769087932/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_cbbf8da48554_1769087932/metadata.json
new file mode 100644
index 000000000..65bdb9fc4
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_cbbf8da48554_1769087932/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_cbbf8da48554_1769087932",
+  "bounding_box": {
+    "x": 3.111083984375,
+    "y": 513.7406762025574,
+    "width": 50.666666666666664,
+    "height": 50.66666208526279
+  },
+  "original_size": {
+    "width": 70,
+    "height": 70
+  },
+  "thumbnail_size": {
+    "width": 70,
+    "height": 70
+  },
+  "created_at": "2026-01-22T14:18:52.177268",
+  "original_file_size": 4500,
+  "thumbnail_file_size": 1161
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_ce32eeaee5f3_1769094302/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_ce32eeaee5f3_1769094302/metadata.json
new file mode 100644
index 000000000..c4792e839
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_ce32eeaee5f3_1769094302/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_ce32eeaee5f3_1769094302",
+  "bounding_box": {
+    "x": 554,
+    "y": 246.5111083984375,
+    "width": 397.3333333333333,
+    "height": 80
+  },
+  "original_size": {
+    "width": 417,
+    "height": 100
+  },
+  "thumbnail_size": {
+    "width": 200,
+    "height": 48
+  },
+  "created_at": "2026-01-22T16:05:02.298864",
+  "original_file_size": 722,
+  "thumbnail_file_size": 434
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_d17a52b107a3_1769107828/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_d17a52b107a3_1769107828/metadata.json
new file mode 100644
index 000000000..87213398f
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_d17a52b107a3_1769107828/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_d17a52b107a3_1769107828",
+  "bounding_box": {
+    "x": 67.33333333333333,
+    "y": 33.199991861979164,
+    "width": 86.66666666666667,
+    "height": 16
+  },
+  "original_size": {
+    "width": 106,
+    "height": 36
+  },
+  "thumbnail_size": {
+    "width": 106,
+    "height": 36
+  },
+  "created_at": "2026-01-22T19:50:28.013035",
+  "original_file_size": 3411,
+  "thumbnail_file_size": 1147
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_d56f40038e9e_1769032370/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_d56f40038e9e_1769032370/metadata.json
new file mode 100644
index 000000000..6f67e533f
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_d56f40038e9e_1769032370/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_d56f40038e9e_1769032370",
+  "bounding_box": {
+    "x": 615.111083984375,
+    "y": 83.07404847782404,
+    "width": 145.33333333333334,
+    "height": 143.99998697916786
+  },
+  "original_size": {
+    "width": 165,
+    "height": 163
+  },
+  "thumbnail_size": {
+    "width": 152,
+    "height": 150
+  },
+  "created_at": "2026-01-21T22:52:50.018519",
+  "original_file_size": 11671,
+  "thumbnail_file_size": 2573
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_e58aac648ced_1769107942/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_e58aac648ced_1769107942/metadata.json
new file mode 100644
index 000000000..ed2d0f501
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_e58aac648ced_1769107942/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_e58aac648ced_1769107942",
+  "bounding_box": {
+    "x": 66,
+    "y": 30.5333251953125,
+    "width": 36,
+    "height": 14.666666666666666
+  },
+  "original_size": {
+    "width": 56,
+    "height": 34
+  },
+  "thumbnail_size": {
+    "width": 56,
+    "height": 34
+  },
+  "created_at": "2026-01-22T19:52:22.111021",
+  "original_file_size": 2182,
+  "thumbnail_file_size": 764
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_eee366c56930_1769026720/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_eee366c56930_1769026720/metadata.json
new file mode 100644
index 000000000..f9c1f511a
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_eee366c56930_1769026720/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_eee366c56930_1769026720",
+  "bounding_box": {
+    "x": 667.111083984375,
+    "y": 460.40734769175464,
+    "width": 281.3333333333333,
+    "height": 166.66665159625904
+  },
+  "original_size": {
+    "width": 301,
+    "height": 186
+  },
+  "thumbnail_size": {
+    "width": 200,
+    "height": 124
+  },
+  "created_at": "2026-01-21T21:18:40.259323",
+  "original_file_size": 37477,
+  "thumbnail_file_size": 4521
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/anchor_images/anchor_f4a2ad879e23_1769088708/metadata.json b/visual_workflow_builder/backend/data/anchor_images/anchor_f4a2ad879e23_1769088708/metadata.json
new file mode 100644
index 000000000..2103b7b04
--- /dev/null
+++ b/visual_workflow_builder/backend/data/anchor_images/anchor_f4a2ad879e23_1769088708/metadata.json
@@ -0,0 +1,20 @@
+{
+  "anchor_id": "anchor_f4a2ad879e23_1769088708",
+  "bounding_box": {
+    "x": -0.888916015625,
+    "y": 847.0739793950756,
+    "width": 57.333333333333336,
+    "height": 51.9999952980329
+  },
+  "original_size": {
+    "width": 77,
+    "height": 63
+  },
+  "thumbnail_size": {
+    "width": 77,
+    "height": 63
+  },
+  "created_at": "2026-01-22T14:31:48.213019",
+  "original_file_size": 3431,
+  "thumbnail_file_size": 1026
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/learning/target_memory.db b/visual_workflow_builder/backend/data/learning/target_memory.db
new file mode 100644
index 000000000..a20f0826d
Binary files /dev/null and b/visual_workflow_builder/backend/data/learning/target_memory.db differ
diff --git a/visual_workflow_builder/backend/data/templates/templates.json b/visual_workflow_builder/backend/data/templates/templates.json
new file mode 100644
index 000000000..d0e6cab5c
--- /dev/null
+++ b/visual_workflow_builder/backend/data/templates/templates.json
@@ -0,0 +1,1575 @@
+{
+  "login_template": {
+    "id": "login_template",
+    "name": "Connexion à un site web",
+    "description": "Template pour automatiser la connexion à un site web avec nom d'utilisateur et mot de passe",
+    "category": "Web Automation",
+    "workflow": {
+      "id": "login_template_workflow",
+      "name": "Modèle de Connexion",
+      "description": "Workflow de base pour se connecter à un site web",
+      "version": "1.0.0",
+      "created_at": "2025-12-11T15:39:57.444253",
+      "updated_at": "2025-12-11T15:39:57.444262",
+      "created_by": "system",
+      "nodes": [
+        {
+          "id": "start",
+          "type": "start",
+          "position": {
+            "x": 50,
+            "y": 100
+          },
+          "size": {
+            "width": 100,
+            "height": 50
+          },
+          "parameters": {},
+          "input_ports": [],
+          "output_ports": [
+            {
+              "id": "out",
+              "name": "output",
+              "type": "output",
+              "data_type": null
+            }
+          ],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Début",
+          "description": null,
+          "color": null
+        },
+        {
+          "id": "navigate",
+          "type": "navigate",
+          "position": {
+            "x": 200,
+            "y": 100
+          },
+          "size": {
+            "width": 150,
+            "height": 80
+          },
+          "parameters": {
+            "url": "{{login_url}}",
+            "wait_for_load": true,
+            "timeout": 10000
+          },
+          "input_ports": [
+            {
+              "id": "in",
+              "name": "input",
+              "type": "input",
+              "data_type": null
+            }
+          ],
+          "output_ports": [
+            {
+              "id": "out",
+              "name": "output",
+              "type": "output",
+              "data_type": null
+            }
+          ],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Naviguer vers la page de connexion",
+          "description": null,
+          "color": null
+        },
+        {
+          "id": "username",
+          "type": "type",
+          "position": {
+            "x": 400,
+            "y": 50
+          },
+          "size": {
+            "width": 150,
+            "height": 80
+          },
+          "parameters": {
+            "target": "{{username_selector}}",
+            "text": "{{username}}",
+            "clear_first": true,
+            "timeout": 5000
+          },
+          "input_ports": [
+            {
+              "id": "in",
+              "name": "input",
+              "type": "input",
+              "data_type": null
+            }
+          ],
+          "output_ports": [
+            {
+              "id": "out",
+              "name": "output",
+              "type": "output",
+              "data_type": null
+            }
+          ],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Saisir nom d'utilisateur",
+          "description": null,
+          "color": null
+        },
+        {
+          "id": "password",
+          "type": "type",
+          "position": {
+            "x": 400,
+            "y": 150
+          },
+          "size": {
+            "width": 150,
+            "height": 80
+          },
+          "parameters": {
+            "target": "{{password_selector}}",
+            "text": "{{password}}",
+            "clear_first": true,
+            "timeout": 5000
+          },
+          "input_ports": [
+            {
+              "id": "in",
+              "name": "input",
+              "type": "input",
+              "data_type": null
+            }
+          ],
+          "output_ports": [
+            {
+              "id": "out",
+              "name": "output",
+              "type": "output",
+              "data_type": null
+            }
+          ],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Saisir mot de passe",
+          "description": null,
+          "color": null
+        },
+        {
+          "id": "login_button",
+          "type": "click",
+          "position": {
+            "x": 600,
+            "y": 100
+          },
+          "size": {
+            "width": 150,
+            "height": 80
+          },
+          "parameters": {
+            "target": "{{login_button_selector}}",
+            "timeout": 5000
+          },
+          "input_ports": [
+            {
+              "id": "in",
+              "name": "input",
+              "type": "input",
+              "data_type": null
+            }
+          ],
+          "output_ports": [
+            {
+              "id": "out",
+              "name": "output",
+              "type": "output",
+              "data_type": null
+            }
+          ],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Cliquer sur Se connecter",
+          "description": null,
+          "color": null
+        },
+        {
+          "id": "end",
+          "type": "end",
+          "position": {
+            "x": 800,
+            "y": 100
+          },
+          "size": {
+            "width": 100,
+            "height": 50
+          },
+          "parameters": {},
+          "input_ports": [
+            {
+              "id": "in",
+              "name": "input",
+              "type": "input",
+              "data_type": null
+            }
+          ],
+          "output_ports": [],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Fin",
+          "description": null,
+          "color": null
+        }
+      ],
+      "edges": [
+        {
+          "id": "e1",
+          "source": "start",
+          "target": "navigate",
+          "source_port": "out",
+          "target_port": "in",
+          "condition": null,
+          "style": null,
+          "selected": false,
+          "animated": false
+        },
+        {
+          "id": "e2",
+          "source": "navigate",
+          "target": "username",
+          "source_port": "out",
+          "target_port": "in",
+          "condition": null,
+          "style": null,
+          "selected": false,
+          "animated": false
+        },
+        {
+          "id": "e3",
+          "source": "username",
+          "target": "password",
+          "source_port": "out",
+          "target_port": "in",
+          "condition": null,
+          "style": null,
+          "selected": false,
+          "animated": false
+        },
+        {
+          "id": "e4",
+          "source": "password",
+          "target": "login_button",
+          "source_port": "out",
+          "target_port": "in",
+          "condition": null,
+          "style": null,
+          "selected": false,
+          "animated": false
+        },
+        {
+          "id": "e5",
+          "source": "login_button",
+          "target": "end",
+          "source_port": "out",
+          "target_port": "in",
+          "condition": null,
+          "style": null,
+          "selected": false,
+          "animated": false
+        }
+      ],
+      "variables": [],
+      "settings": {
+        "timeout": 300000,
+        "retry_on_failure": true,
+        "max_retries": 3,
+        "enable_self_healing": true,
+        "enable_analytics": true
+      },
+      "tags": [
+        "login",
+        "authentication",
+        "web"
+      ],
+      "category": "Web Automation",
+      "is_template": true
+    },
+    "parameters": [
+      {
+        "name": "login_url",
+        "type": "string",
+        "description": "URL de la page de connexion",
+        "default_value": null,
+        "node_id": "navigate",
+        "parameter_name": "url",
+        "label": "URL de connexion",
+        "placeholder": "https://example.com/login",
+        "required": true
+      },
+      {
+        "name": "username_selector",
+        "type": "target",
+        "description": "Sélecteur du champ nom d'utilisateur",
+        "default_value": null,
+        "node_id": "username",
+        "parameter_name": "target",
+        "label": "Champ nom d'utilisateur",
+        "placeholder": "input[name='username']",
+        "required": true
+      },
+      {
+        "name": "username",
+        "type": "string",
+        "description": "Nom d'utilisateur à saisir",
+        "default_value": null,
+        "node_id": "username",
+        "parameter_name": "text",
+        "label": "Nom d'utilisateur",
+        "placeholder": "votre_nom_utilisateur",
+        "required": true
+      },
+      {
+        "name": "password_selector",
+        "type": "target",
+        "description": "Sélecteur du champ mot de passe",
+        "default_value": null,
+        "node_id": "password",
+        "parameter_name": "target",
+        "label": "Champ mot de passe",
+        "placeholder": "input[name='password']",
+        "required": true
+      },
+      {
+        "name": "password",
+        "type": "string",
+        "description": "Mot de passe à saisir",
+        "default_value": null,
+        "node_id": "password",
+        "parameter_name": "text",
+        "label": "Mot de passe",
+        "placeholder": "votre_mot_de_passe",
+        "required": true
+      },
+      {
+        "name": "login_button_selector",
+        "type": "target",
+        "description": "Sélecteur du bouton de connexion",
+        "default_value": null,
+        "node_id": "login_button",
+        "parameter_name": "target",
+        "label": "Bouton de connexion",
+        "placeholder": "button[type='submit']",
+        "required": true
+      }
+    ],
+    "tags": [
+      "login",
+      "authentication",
+      "web",
+      "form"
+    ],
+    "difficulty": "beginner",
+    "estimated_time": 3,
+    "usage_count": 0,
+    "rating": 0.0,
+    "created_at": "2025-12-11T15:39:57.444287",
+    "updated_at": "2025-12-11T15:39:57.444288",
+    "created_by": "system"
+  },
+  "form_fill_template": {
+    "id": "form_fill_template",
+    "name": "Remplissage de formulaire",
+    "description": "Template pour remplir automatiquement un formulaire de contact ou d'inscription",
+    "category": "Web Automation",
+    "workflow": {
+      "id": "form_fill_template_workflow",
+      "name": "Modèle de Remplissage de Formulaire",
+      "description": "Workflow pour remplir automatiquement un formulaire web",
+      "version": "1.0.0",
+      "created_at": "2025-12-11T15:39:57.444299",
+      "updated_at": "2025-12-11T15:39:57.444299",
+      "created_by": "system",
+      "nodes": [
+        {
+          "id": "start",
+          "type": "start",
+          "position": {
+            "x": 50,
+            "y": 150
+          },
+          "size": {
+            "width": 100,
+            "height": 50
+          },
+          "parameters": {},
+          "input_ports": [],
+          "output_ports": [
+            {
+              "id": "out",
+              "name": "output",
+              "type": "output",
+              "data_type": null
+            }
+          ],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Début",
+          "description": null,
+          "color": null
+        },
+        {
+          "id": "navigate",
+          "type": "navigate",
+          "position": {
+            "x": 200,
+            "y": 150
+          },
+          "size": {
+            "width": 150,
+            "height": 80
+          },
+          "parameters": {
+            "url": "{{form_url}}",
+            "wait_for_load": true
+          },
+          "input_ports": [
+            {
+              "id": "in",
+              "name": "input",
+              "type": "input",
+              "data_type": null
+            }
+          ],
+          "output_ports": [
+            {
+              "id": "out",
+              "name": "output",
+              "type": "output",
+              "data_type": null
+            }
+          ],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Naviguer vers le formulaire",
+          "description": null,
+          "color": null
+        },
+        {
+          "id": "fill_name",
+          "type": "type",
+          "position": {
+            "x": 400,
+            "y": 50
+          },
+          "size": {
+            "width": 150,
+            "height": 80
+          },
+          "parameters": {
+            "target": "{{name_selector}}",
+            "text": "{{name_value}}",
+            "clear_first": true
+          },
+          "input_ports": [
+            {
+              "id": "in",
+              "name": "input",
+              "type": "input",
+              "data_type": null
+            }
+          ],
+          "output_ports": [
+            {
+              "id": "out",
+              "name": "output",
+              "type": "output",
+              "data_type": null
+            }
+          ],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Remplir le nom",
+          "description": null,
+          "color": null
+        },
+        {
+          "id": "fill_email",
+          "type": "type",
+          "position": {
+            "x": 400,
+            "y": 150
+          },
+          "size": {
+            "width": 150,
+            "height": 80
+          },
+          "parameters": {
+            "target": "{{email_selector}}",
+            "text": "{{email_value}}",
+            "clear_first": true
+          },
+          "input_ports": [
+            {
+              "id": "in",
+              "name": "input",
+              "type": "input",
+              "data_type": null
+            }
+          ],
+          "output_ports": [
+            {
+              "id": "out",
+              "name": "output",
+              "type": "output",
+              "data_type": null
+            }
+          ],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Remplir l'email",
+          "description": null,
+          "color": null
+        },
+        {
+          "id": "fill_message",
+          "type": "type",
+          "position": {
+            "x": 400,
+            "y": 250
+          },
+          "size": {
+            "width": 150,
+            "height": 80
+          },
+          "parameters": {
+            "target": "{{message_selector}}",
+            "text": "{{message_value}}",
+            "clear_first": true
+          },
+          "input_ports": [
+            {
+              "id": "in",
+              "name": "input",
+              "type": "input",
+              "data_type": null
+            }
+          ],
+          "output_ports": [
+            {
+              "id": "out",
+              "name": "output",
+              "type": "output",
+              "data_type": null
+            }
+          ],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Remplir le message",
+          "description": null,
+          "color": null
+        },
+        {
+          "id": "submit",
+          "type": "click",
+          "position": {
+            "x": 600,
+            "y": 150
+          },
+          "size": {
+            "width": 150,
+            "height": 80
+          },
+          "parameters": {
+            "target": "{{submit_selector}}"
+          },
+          "input_ports": [
+            {
+              "id": "in",
+              "name": "input",
+              "type": "input",
+              "data_type": null
+            }
+          ],
+          "output_ports": [
+            {
+              "id": "out",
+              "name": "output",
+              "type": "output",
+              "data_type": null
+            }
+          ],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Soumettre le formulaire",
+          "description": null,
+          "color": null
+        },
+        {
+          "id": "end",
+          "type": "end",
+          "position": {
+            "x": 800,
+            "y": 150
+          },
+          "size": {
+            "width": 100,
+            "height": 50
+          },
+          "parameters": {},
+          "input_ports": [
+            {
+              "id": "in",
+              "name": "input",
+              "type": "input",
+              "data_type": null
+            }
+          ],
+          "output_ports": [],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Fin",
+          "description": null,
+          "color": null
+        }
+      ],
+      "edges": [
+        {
+          "id": "e1",
+          "source": "start",
+          "target": "navigate",
+          "source_port": "out",
+          "target_port": "in",
+          "condition": null,
+          "style": null,
+          "selected": false,
+          "animated": false
+        },
+        {
+          "id": "e2",
+          "source": "navigate",
+          "target": "fill_name",
+          "source_port": "out",
+          "target_port": "in",
+          "condition": null,
+          "style": null,
+          "selected": false,
+          "animated": false
+        },
+        {
+          "id": "e3",
+          "source": "fill_name",
+          "target": "fill_email",
+          "source_port": "out",
+          "target_port": "in",
+          "condition": null,
+          "style": null,
+          "selected": false,
+          "animated": false
+        },
+        {
+          "id": "e4",
+          "source": "fill_email",
+          "target": "fill_message",
+          "source_port": "out",
+          "target_port": "in",
+          "condition": null,
+          "style": null,
+          "selected": false,
+          "animated": false
+        },
+        {
+          "id": "e5",
+          "source": "fill_message",
+          "target": "submit",
+          "source_port": "out",
+          "target_port": "in",
+          "condition": null,
+          "style": null,
+          "selected": false,
+          "animated": false
+        },
+        {
+          "id": "e6",
+          "source": "submit",
+          "target": "end",
+          "source_port": "out",
+          "target_port": "in",
+          "condition": null,
+          "style": null,
+          "selected": false,
+          "animated": false
+        }
+      ],
+      "variables": [],
+      "settings": {
+        "timeout": 300000,
+        "retry_on_failure": true,
+        "max_retries": 3,
+        "enable_self_healing": true,
+        "enable_analytics": true
+      },
+      "tags": [
+        "form",
+        "fill",
+        "web"
+      ],
+      "category": "Web Automation",
+      "is_template": true
+    },
+    "parameters": [
+      {
+        "name": "form_url",
+        "type": "string",
+        "description": "URL du formulaire à remplir",
+        "default_value": null,
+        "node_id": "navigate",
+        "parameter_name": "url",
+        "label": "URL du formulaire",
+        "placeholder": null,
+        "required": true
+      },
+      {
+        "name": "name_selector",
+        "type": "target",
+        "description": "Sélecteur du champ nom",
+        "default_value": null,
+        "node_id": "fill_name",
+        "parameter_name": "target",
+        "label": "Champ nom",
+        "placeholder": null,
+        "required": true
+      },
+      {
+        "name": "name_value",
+        "type": "string",
+        "description": "Valeur à saisir dans le champ nom",
+        "default_value": null,
+        "node_id": "fill_name",
+        "parameter_name": "text",
+        "label": "Nom",
+        "placeholder": null,
+        "required": true
+      },
+      {
+        "name": "email_selector",
+        "type": "target",
+        "description": "Sélecteur du champ email",
+        "default_value": null,
+        "node_id": "fill_email",
+        "parameter_name": "target",
+        "label": "Champ email",
+        "placeholder": null,
+        "required": true
+      },
+      {
+        "name": "email_value",
+        "type": "string",
+        "description": "Valeur à saisir dans le champ email",
+        "default_value": null,
+        "node_id": "fill_email",
+        "parameter_name": "text",
+        "label": "Email",
+        "placeholder": null,
+        "required": true
+      },
+      {
+        "name": "message_selector",
+        "type": "target",
+        "description": "Sélecteur du champ message",
+        "default_value": null,
+        "node_id": "fill_message",
+        "parameter_name": "target",
+        "label": "Champ message",
+        "placeholder": null,
+        "required": true
+      },
+      {
+        "name": "message_value",
+        "type": "string",
+        "description": "Valeur à saisir dans le champ message",
+        "default_value": null,
+        "node_id": "fill_message",
+        "parameter_name": "text",
+        "label": "Message",
+        "placeholder": null,
+        "required": true
+      },
+      {
+        "name": "submit_selector",
+        "type": "target",
+        "description": "Sélecteur du bouton de soumission",
+        "default_value": null,
+        "node_id": "submit",
+        "parameter_name": "target",
+        "label": "Bouton de soumission",
+        "placeholder": null,
+        "required": true
+      }
+    ],
+    "tags": [
+      "form",
+      "contact",
+      "web",
+      "automation"
+    ],
+    "difficulty": "beginner",
+    "estimated_time": 5,
+    "usage_count": 0,
+    "rating": 0.0,
+    "created_at": "2025-12-11T15:39:57.444308",
+    "updated_at": "2025-12-11T15:39:57.444308",
+    "created_by": "system"
+  },
+  "data_extraction_template": {
+    "id": "data_extraction_template",
+    "name": "Extraction de données web",
+    "description": "Template pour extraire et sauvegarder des données depuis une page web",
+    "category": "Data Processing",
+    "workflow": {
+      "id": "data_extraction_template_workflow",
+      "name": "Modèle d'Extraction de Données",
+      "description": "Workflow pour extraire des données d'une page web",
+      "version": "1.0.0",
+      "created_at": "2025-12-11T15:39:57.444317",
+      "updated_at": "2025-12-11T15:39:57.444318",
+      "created_by": "system",
+      "nodes": [
+        {
+          "id": "start",
+          "type": "start",
+          "position": {
+            "x": 50,
+            "y": 150
+          },
+          "size": {
+            "width": 100,
+            "height": 50
+          },
+          "parameters": {},
+          "input_ports": [],
+          "output_ports": [
+            {
+              "id": "out",
+              "name": "output",
+              "type": "output",
+              "data_type": null
+            }
+          ],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Début",
+          "description": null,
+          "color": null
+        },
+        {
+          "id": "navigate",
+          "type": "navigate",
+          "position": {
+            "x": 200,
+            "y": 150
+          },
+          "size": {
+            "width": 150,
+            "height": 80
+          },
+          "parameters": {
+            "url": "{{target_url}}",
+            "wait_for_load": true
+          },
+          "input_ports": [
+            {
+              "id": "in",
+              "name": "input",
+              "type": "input",
+              "data_type": null
+            }
+          ],
+          "output_ports": [
+            {
+              "id": "out",
+              "name": "output",
+              "type": "output",
+              "data_type": null
+            }
+          ],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Naviguer vers la page",
+          "description": null,
+          "color": null
+        },
+        {
+          "id": "extract_title",
+          "type": "extract",
+          "position": {
+            "x": 400,
+            "y": 100
+          },
+          "size": {
+            "width": 150,
+            "height": 80
+          },
+          "parameters": {
+            "target": "{{title_selector}}",
+            "attribute": "text",
+            "variable": "page_title"
+          },
+          "input_ports": [
+            {
+              "id": "in",
+              "name": "input",
+              "type": "input",
+              "data_type": null
+            }
+          ],
+          "output_ports": [
+            {
+              "id": "out",
+              "name": "output",
+              "type": "output",
+              "data_type": null
+            }
+          ],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Extraire le titre",
+          "description": null,
+          "color": null
+        },
+        {
+          "id": "extract_content",
+          "type": "extract",
+          "position": {
+            "x": 400,
+            "y": 200
+          },
+          "size": {
+            "width": 150,
+            "height": 80
+          },
+          "parameters": {
+            "target": "{{content_selector}}",
+            "attribute": "text",
+            "variable": "page_content"
+          },
+          "input_ports": [
+            {
+              "id": "in",
+              "name": "input",
+              "type": "input",
+              "data_type": null
+            }
+          ],
+          "output_ports": [
+            {
+              "id": "out",
+              "name": "output",
+              "type": "output",
+              "data_type": null
+            }
+          ],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Extraire le contenu",
+          "description": null,
+          "color": null
+        },
+        {
+          "id": "save_data",
+          "type": "save",
+          "position": {
+            "x": 600,
+            "y": 150
+          },
+          "size": {
+            "width": 150,
+            "height": 80
+          },
+          "parameters": {
+            "filename": "{{output_file}}",
+            "format": "json",
+            "data": {
+              "title": "${page_title}",
+              "content": "${page_content}",
+              "extracted_at": "${current_timestamp}"
+            }
+          },
+          "input_ports": [
+            {
+              "id": "in",
+              "name": "input",
+              "type": "input",
+              "data_type": null
+            }
+          ],
+          "output_ports": [
+            {
+              "id": "out",
+              "name": "output",
+              "type": "output",
+              "data_type": null
+            }
+          ],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Sauvegarder les données",
+          "description": null,
+          "color": null
+        },
+        {
+          "id": "end",
+          "type": "end",
+          "position": {
+            "x": 800,
+            "y": 150
+          },
+          "size": {
+            "width": 100,
+            "height": 50
+          },
+          "parameters": {},
+          "input_ports": [
+            {
+              "id": "in",
+              "name": "input",
+              "type": "input",
+              "data_type": null
+            }
+          ],
+          "output_ports": [],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Fin",
+          "description": null,
+          "color": null
+        }
+      ],
+      "edges": [
+        {
+          "id": "e1",
+          "source": "start",
+          "target": "navigate",
+          "source_port": "out",
+          "target_port": "in",
+          "condition": null,
+          "style": null,
+          "selected": false,
+          "animated": false
+        },
+        {
+          "id": "e2",
+          "source": "navigate",
+          "target": "extract_title",
+          "source_port": "out",
+          "target_port": "in",
+          "condition": null,
+          "style": null,
+          "selected": false,
+          "animated": false
+        },
+        {
+          "id": "e3",
+          "source": "extract_title",
+          "target": "extract_content",
+          "source_port": "out",
+          "target_port": "in",
+          "condition": null,
+          "style": null,
+          "selected": false,
+          "animated": false
+        },
+        {
+          "id": "e4",
+          "source": "extract_content",
+          "target": "save_data",
+          "source_port": "out",
+          "target_port": "in",
+          "condition": null,
+          "style": null,
+          "selected": false,
+          "animated": false
+        },
+        {
+          "id": "e5",
+          "source": "save_data",
+          "target": "end",
+          "source_port": "out",
+          "target_port": "in",
+          "condition": null,
+          "style": null,
+          "selected": false,
+          "animated": false
+        }
+      ],
+      "variables": [
+        {
+          "name": "page_title",
+          "type": "string",
+          "value": "",
+          "description": "Titre de la page extrait"
+        },
+        {
+          "name": "page_content",
+          "type": "string",
+          "value": "",
+          "description": "Contenu de la page extrait"
+        },
+        {
+          "name": "current_timestamp",
+          "type": "string",
+          "value": "",
+          "description": "Timestamp de l'extraction"
+        }
+      ],
+      "settings": {
+        "timeout": 300000,
+        "retry_on_failure": true,
+        "max_retries": 3,
+        "enable_self_healing": true,
+        "enable_analytics": true
+      },
+      "tags": [
+        "extraction",
+        "data",
+        "scraping"
+      ],
+      "category": "Data Processing",
+      "is_template": true
+    },
+    "parameters": [
+      {
+        "name": "target_url",
+        "type": "string",
+        "description": "URL de la page à analyser",
+        "default_value": null,
+        "node_id": "navigate",
+        "parameter_name": "url",
+        "label": "URL cible",
+        "placeholder": null,
+        "required": true
+      },
+      {
+        "name": "title_selector",
+        "type": "target",
+        "description": "Sélecteur de l'élément titre",
+        "default_value": null,
+        "node_id": "extract_title",
+        "parameter_name": "target",
+        "label": "Sélecteur du titre",
+        "placeholder": null,
+        "required": true
+      },
+      {
+        "name": "content_selector",
+        "type": "target",
+        "description": "Sélecteur de l'élément contenu",
+        "default_value": null,
+        "node_id": "extract_content",
+        "parameter_name": "target",
+        "label": "Sélecteur du contenu",
+        "placeholder": null,
+        "required": true
+      },
+      {
+        "name": "output_file",
+        "type": "string",
+        "description": "Nom du fichier de sortie",
+        "default_value": "extracted_data.json",
+        "node_id": "save_data",
+        "parameter_name": "filename",
+        "label": "Fichier de sortie",
+        "placeholder": null,
+        "required": true
+      }
+    ],
+    "tags": [
+      "extraction",
+      "scraping",
+      "data",
+      "web"
+    ],
+    "difficulty": "intermediate",
+    "estimated_time": 8,
+    "usage_count": 0,
+    "rating": 0.0,
+    "created_at": "2025-12-11T15:39:57.444325",
+    "updated_at": "2025-12-11T15:39:57.444325",
+    "created_by": "system"
+  },
+  "navigation_template": {
+    "id": "navigation_template",
+    "name": "Navigation avec menus",
+    "description": "Template pour naviguer dans un site web en utilisant les menus déroulants",
+    "category": "Web Automation",
+    "workflow": {
+      "id": "navigation_template_workflow",
+      "name": "Modèle de Navigation",
+      "description": "Workflow pour naviguer dans un site web avec menus",
+      "version": "1.0.0",
+      "created_at": "2025-12-11T15:39:57.444333",
+      "updated_at": "2025-12-11T15:39:57.444334",
+      "created_by": "system",
+      "nodes": [
+        {
+          "id": "start",
+          "type": "start",
+          "position": {
+            "x": 50,
+            "y": 200
+          },
+          "size": {
+            "width": 100,
+            "height": 50
+          },
+          "parameters": {},
+          "input_ports": [],
+          "output_ports": [
+            {
+              "id": "out",
+              "name": "output",
+              "type": "output",
+              "data_type": null
+            }
+          ],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Début",
+          "description": null,
+          "color": null
+        },
+        {
+          "id": "navigate_home",
+          "type": "navigate",
+          "position": {
+            "x": 200,
+            "y": 200
+          },
+          "size": {
+            "width": 150,
+            "height": 80
+          },
+          "parameters": {
+            "url": "{{home_url}}",
+            "wait_for_load": true
+          },
+          "input_ports": [
+            {
+              "id": "in",
+              "name": "input",
+              "type": "input",
+              "data_type": null
+            }
+          ],
+          "output_ports": [
+            {
+              "id": "out",
+              "name": "output",
+              "type": "output",
+              "data_type": null
+            }
+          ],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Page d'accueil",
+          "description": null,
+          "color": null
+        },
+        {
+          "id": "click_menu",
+          "type": "click",
+          "position": {
+            "x": 400,
+            "y": 150
+          },
+          "size": {
+            "width": 150,
+            "height": 80
+          },
+          "parameters": {
+            "target": "{{menu_selector}}",
+            "wait_after": 1000
+          },
+          "input_ports": [
+            {
+              "id": "in",
+              "name": "input",
+              "type": "input",
+              "data_type": null
+            }
+          ],
+          "output_ports": [
+            {
+              "id": "out",
+              "name": "output",
+              "type": "output",
+              "data_type": null
+            }
+          ],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Cliquer sur le menu",
+          "description": null,
+          "color": null
+        },
+        {
+          "id": "click_submenu",
+          "type": "click",
+          "position": {
+            "x": 400,
+            "y": 250
+          },
+          "size": {
+            "width": 150,
+            "height": 80
+          },
+          "parameters": {
+            "target": "{{submenu_selector}}",
+            "wait_after": 1000
+          },
+          "input_ports": [
+            {
+              "id": "in",
+              "name": "input",
+              "type": "input",
+              "data_type": null
+            }
+          ],
+          "output_ports": [
+            {
+              "id": "out",
+              "name": "output",
+              "type": "output",
+              "data_type": null
+            }
+          ],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Cliquer sur le sous-menu",
+          "description": null,
+          "color": null
+        },
+        {
+          "id": "wait_page_load",
+          "type": "wait",
+          "position": {
+            "x": 600,
+            "y": 200
+          },
+          "size": {
+            "width": 150,
+            "height": 80
+          },
+          "parameters": {
+            "condition": "element_visible",
+            "target": "{{target_element}}",
+            "timeout": 10000
+          },
+          "input_ports": [
+            {
+              "id": "in",
+              "name": "input",
+              "type": "input",
+              "data_type": null
+            }
+          ],
+          "output_ports": [
+            {
+              "id": "out",
+              "name": "output",
+              "type": "output",
+              "data_type": null
+            }
+          ],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Attendre le chargement",
+          "description": null,
+          "color": null
+        },
+        {
+          "id": "end",
+          "type": "end",
+          "position": {
+            "x": 800,
+            "y": 200
+          },
+          "size": {
+            "width": 100,
+            "height": 50
+          },
+          "parameters": {},
+          "input_ports": [
+            {
+              "id": "in",
+              "name": "input",
+              "type": "input",
+              "data_type": null
+            }
+          ],
+          "output_ports": [],
+          "selected": false,
+          "highlighted": false,
+          "status": null,
+          "label": "Fin",
+          "description": null,
+          "color": null
+        }
+      ],
+      "edges": [
+        {
+          "id": "e1",
+          "source": "start",
+          "target": "navigate_home",
+          "source_port": "out",
+          "target_port": "in",
+          "condition": null,
+          "style": null,
+          "selected": false,
+          "animated": false
+        },
+        {
+          "id": "e2",
+          "source": "navigate_home",
+          "target": "click_menu",
+          "source_port": "out",
+          "target_port": "in",
+          "condition": null,
+          "style": null,
+          "selected": false,
+          "animated": false
+        },
+        {
+          "id": "e3",
+          "source": "click_menu",
+          "target": "click_submenu",
+          "source_port": "out",
+          "target_port": "in",
+          "condition": null,
+          "style": null,
+          "selected": false,
+          "animated": false
+        },
+        {
+          "id": "e4",
+          "source": "click_submenu",
+          "target": "wait_page_load",
+          "source_port": "out",
+          "target_port": "in",
+          "condition": null,
+          "style": null,
+          "selected": false,
+          "animated": false
+        },
+        {
+          "id": "e5",
+          "source": "wait_page_load",
+          "target": "end",
+          "source_port": "out",
+          "target_port": "in",
+          "condition": null,
+          "style": null,
+          "selected": false,
+          "animated": false
+        }
+      ],
+      "variables": [],
+      "settings": {
+        "timeout": 300000,
+        "retry_on_failure": true,
+        "max_retries": 3,
+        "enable_self_healing": true,
+        "enable_analytics": true
+      },
+      "tags": [
+        "navigation",
+        "menu",
+        "web"
+      ],
+      "category": "Web Automation",
+      "is_template": true
+    },
+    "parameters": [
+      {
+        "name": "home_url",
+        "type": "string",
+        "description": "URL de la page d'accueil",
+        "default_value": null,
+        "node_id": "navigate_home",
+        "parameter_name": "url",
+        "label": "URL d'accueil",
+        "placeholder": null,
+        "required": true
+      },
+      {
+        "name": "menu_selector",
+        "type": "target",
+        "description": "Sélecteur de l'élément de menu principal",
+        "default_value": null,
+        "node_id": "click_menu",
+        "parameter_name": "target",
+        "label": "Menu principal",
+        "placeholder": null,
+        "required": true
+      },
+      {
+        "name": "submenu_selector",
+        "type": "target",
+        "description": "Sélecteur de l'élément de sous-menu",
+        "default_value": null,
+        "node_id": "click_submenu",
+        "parameter_name": "target",
+        "label": "Sous-menu",
+        "placeholder": null,
+        "required": true
+      },
+      {
+        "name": "target_element",
+        "type": "target",
+        "description": "Élément à attendre sur la page de destination",
+        "default_value": null,
+        "node_id": "wait_page_load",
+        "parameter_name": "target",
+        "label": "Élément de destination",
+        "placeholder": null,
+        "required": true
+      }
+    ],
+    "tags": [
+      "navigation",
+      "menu",
+      "web",
+      "click"
+    ],
+    "difficulty": "beginner",
+    "estimated_time": 4,
+    "usage_count": 0,
+    "rating": 0.0,
+    "created_at": "2025-12-11T15:39:57.444339",
+    "updated_at": "2025-12-11T15:39:57.444339",
+    "created_by": "system"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/vwb.db b/visual_workflow_builder/backend/data/vwb.db
new file mode 100644
index 000000000..e69de29bb
diff --git a/visual_workflow_builder/backend/data/workflows/2d5484c7-077d-4e1e-b789-33ebf63bc3c5.json b/visual_workflow_builder/backend/data/workflows/2d5484c7-077d-4e1e-b789-33ebf63bc3c5.json
new file mode 100644
index 000000000..e76ed9d29
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/2d5484c7-077d-4e1e-b789-33ebf63bc3c5.json
@@ -0,0 +1,749 @@
+{
+  "id": "2d5484c7-077d-4e1e-b789-33ebf63bc3c5",
+  "name": "nouveau_test_onlyoffice_v0",
+  "description": "Workflow créé avec le Visual Workflow Builder V2",
+  "version": "1.0.0",
+  "created_at": "2026-01-22T20:51:14.756283",
+  "updated_at": "2026-01-22T20:51:14.756283",
+  "created_by": "vwb_user",
+  "nodes": [
+    {
+      "id": "step_1769091866031",
+      "type": "click_anchor",
+      "position": {
+        "x": 141.99200000000005,
+        "y": 10.567331298828165
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "visual_anchor": {
+          "anchor_id": "anchor_7de070f7b4c5_1769091889",
+          "anchor_type": "generic",
+          "thumbnail_url": "/api/anchor-images/anchor_7de070f7b4c5_1769091889/thumbnail",
+          "reference_image_url": "/api/anchor-images/anchor_7de070f7b4c5_1769091889/original",
+          "bounding_box": {
+            "x": 6,
+            "y": 847.8666585286459,
+            "width": 42.666666666666664,
+            "height": 41.333333333333336
+          },
+          "confidence_threshold": 0.8,
+          "description": "Élément sélectionné pour l'étape vwb_action_1769091868111",
+          "metadata": {
+            "capture_method": "screen_capture",
+            "capture_timestamp": "2026-01-22T14:24:49.230Z",
+            "screen_resolution": {
+              "width": 1920,
+              "height": 1080
+            }
+          }
+        }
+      },
+      "input_ports": [],
+      "output_ports": [],
+      "self_healing": null,
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": "Cliquer sur Ancre",
+      "description": null,
+      "color": null,
+      "data": {
+        "label": "Cliquer sur Ancre",
+        "stepType": "click_anchor",
+        "parameters": {
+          "visual_anchor": {
+            "anchor_id": "anchor_7de070f7b4c5_1769091889",
+            "anchor_type": "generic",
+            "thumbnail_url": "/api/anchor-images/anchor_7de070f7b4c5_1769091889/thumbnail",
+            "reference_image_url": "/api/anchor-images/anchor_7de070f7b4c5_1769091889/original",
+            "bounding_box": {
+              "x": 6,
+              "y": 847.8666585286459,
+              "width": 42.666666666666664,
+              "height": 41.333333333333336
+            },
+            "confidence_threshold": 0.8,
+            "description": "Élément sélectionné pour l'étape vwb_action_1769091868111",
+            "metadata": {
+              "capture_method": "screen_capture",
+              "capture_timestamp": "2026-01-22T14:24:49.230Z",
+              "screen_resolution": {
+                "width": 1920,
+                "height": 1080
+              }
+            }
+          }
+        },
+        "isVWBCatalogAction": true,
+        "vwbActionId": "click_anchor"
+      }
+    },
+    {
+      "id": "step_1769093845389",
+      "type": "type_text",
+      "position": {
+        "x": 547.4300000000001,
+        "y": 24.02866870117188
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "visual_anchor": {
+          "anchor_id": "anchor_0b04180c5773_1769093895",
+          "anchor_type": "generic",
+          "thumbnail_url": "/api/anchor-images/anchor_0b04180c5773_1769093895/thumbnail",
+          "reference_image_url": "/api/anchor-images/anchor_0b04180c5773_1769093895/original",
+          "bounding_box": {
+            "x": 702,
+            "y": 55.866658528645836,
+            "width": 240,
+            "height": 21.333333333333332
+          },
+          "confidence_threshold": 0.8,
+          "description": "Élément sélectionné pour l'étape vwb_action_1769093848350",
+          "metadata": {
+            "capture_method": "screen_capture",
+            "capture_timestamp": "2026-01-22T14:58:15.433Z",
+            "screen_resolution": {
+              "width": 1920,
+              "height": 1080
+            }
+          }
+        },
+        "text": "onlyoffice"
+      },
+      "input_ports": [],
+      "output_ports": [],
+      "self_healing": null,
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": "Saisir Texte",
+      "description": null,
+      "color": null,
+      "data": {
+        "label": "Saisir Texte",
+        "stepType": "type_text",
+        "parameters": {
+          "visual_anchor": {
+            "anchor_id": "anchor_0b04180c5773_1769093895",
+            "anchor_type": "generic",
+            "thumbnail_url": "/api/anchor-images/anchor_0b04180c5773_1769093895/thumbnail",
+            "reference_image_url": "/api/anchor-images/anchor_0b04180c5773_1769093895/original",
+            "bounding_box": {
+              "x": 702,
+              "y": 55.866658528645836,
+              "width": 240,
+              "height": 21.333333333333332
+            },
+            "confidence_threshold": 0.8,
+            "description": "Élément sélectionné pour l'étape vwb_action_1769093848350",
+            "metadata": {
+              "capture_method": "screen_capture",
+              "capture_timestamp": "2026-01-22T14:58:15.433Z",
+              "screen_resolution": {
+                "width": 1920,
+                "height": 1080
+              }
+            }
+          },
+          "text": "onlyoffice"
+        },
+        "isVWBCatalogAction": true,
+        "vwbActionId": "type_text"
+      }
+    },
+    {
+      "id": "step_1769093958114",
+      "type": "click_anchor",
+      "position": {
+        "x": 68.87900000000002,
+        "y": 142.21
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "visual_anchor": {
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+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/31d2a6e7-2b3a-48ef-8123-ec134fea922f.json b/visual_workflow_builder/backend/data/workflows/31d2a6e7-2b3a-48ef-8123-ec134fea922f.json
new file mode 100644
index 000000000..c7aad6448
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/31d2a6e7-2b3a-48ef-8123-ec134fea922f.json
@@ -0,0 +1,749 @@
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+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/5a8191ca-b095-4dd4-9c89-0f009c59124e.json b/visual_workflow_builder/backend/data/workflows/5a8191ca-b095-4dd4-9c89-0f009c59124e.json
new file mode 100644
index 000000000..879386d95
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/5a8191ca-b095-4dd4-9c89-0f009c59124e.json
@@ -0,0 +1,663 @@
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diff --git a/visual_workflow_builder/backend/data/workflows/ae2ae57b-afe5-479f-8ee9-5858346322c7.json b/visual_workflow_builder/backend/data/workflows/ae2ae57b-afe5-479f-8ee9-5858346322c7.json
new file mode 100644
index 000000000..b72616a10
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/ae2ae57b-afe5-479f-8ee9-5858346322c7.json
@@ -0,0 +1,359 @@
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\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/b8f0a3af-ba54-4194-958c-9e412c80ec85.json b/visual_workflow_builder/backend/data/workflows/b8f0a3af-ba54-4194-958c-9e412c80ec85.json
new file mode 100644
index 000000000..fcaadb6e5
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/b8f0a3af-ba54-4194-958c-9e412c80ec85.json
@@ -0,0 +1,663 @@
+{
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\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/export_rapport.json b/visual_workflow_builder/backend/data/workflows/export_rapport.json
new file mode 100644
index 000000000..e074c0fea
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/export_rapport.json
@@ -0,0 +1,87 @@
+{
+  "name": "Export Rapport",
+  "description": "Exporter un rapport au format PDF ou Excel",
+  "tags": [
+    "export",
+    "rapport",
+    "report",
+    "pdf",
+    "excel"
+  ],
+  "version": "1.0",
+  "created_at": "2024-11-29",
+  "learning_state": "AUTO_CANDIDATE",
+  "variables": [
+    {
+      "name": "format",
+      "description": "Format d'export (pdf, excel)",
+      "required": false,
+      "default_value": "pdf",
+      "type": "string"
+    },
+    {
+      "name": "periode",
+      "description": "Période du rapport",
+      "required": false,
+      "type": "string"
+    }
+  ],
+  "param_patterns": [
+    "(?:format|en)\\s+(?P<format>pdf|excel|xlsx|csv)",
+    "(?:période|period)\\s+(?P<periode>[A-Za-z0-9_\\-]+)"
+  ],
+  "nodes": [
+    {
+      "node_id": "start",
+      "name": "Menu rapports",
+      "type": "entry"
+    },
+    {
+      "node_id": "export_dialog",
+      "name": "Dialogue export",
+      "type": "intermediate"
+    },
+    {
+      "node_id": "exported",
+      "name": "Rapport exporté",
+      "type": "end"
+    }
+  ],
+  "edges": [
+    {
+      "edge_id": "e1",
+      "source": "start",
+      "target": "export_dialog",
+      "action": {
+        "type": "mouse_click",
+        "target": {
+          "role": "primary_action",
+          "text": "Exporter"
+        }
+      }
+    },
+    {
+      "edge_id": "e2",
+      "source": "export_dialog",
+      "target": "exported",
+      "action": {
+        "type": "mouse_click",
+        "target": {
+          "role": "submit",
+          "text": "{{format}}"
+        }
+      }
+    }
+  ],
+  "entry_nodes": [
+    "start"
+  ],
+  "end_nodes": [
+    "exported"
+  ],
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2026-01-20T14:41:18.030292",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/f4d8a1fd-d6aa-4cbd-a589-35bdb8be1644.json b/visual_workflow_builder/backend/data/workflows/f4d8a1fd-d6aa-4cbd-a589-35bdb8be1644.json
new file mode 100644
index 000000000..b6d40a3ce
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/f4d8a1fd-d6aa-4cbd-a589-35bdb8be1644.json
@@ -0,0 +1,491 @@
+{
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+        "vwbActionId": "wait_for_anchor"
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+      },
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+      "output_ports": [],
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+      "color": null,
+      "data": {
+        "label": "Saisir Texte",
+        "stepType": "type_text",
+        "parameters": {
+          "text": "coucou, ceci est un texte"
+        },
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+        "vwbActionId": "type_text"
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+      "target_port": "in",
+      "condition": null,
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+      "animated": false
+    }
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+  "settings": {
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+    "retry_on_failure": true,
+    "max_retries": 3,
+    "enable_self_healing": true,
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+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/facturation_client.json b/visual_workflow_builder/backend/data/workflows/facturation_client.json
new file mode 100644
index 000000000..4e89287f6
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/facturation_client.json
@@ -0,0 +1,124 @@
+{
+  "name": "Facturation Client",
+  "description": "Créer une facture pour un client dans le système de facturation",
+  "tags": [
+    "facturer",
+    "facture",
+    "client",
+    "invoice",
+    "billing"
+  ],
+  "version": "1.0",
+  "created_at": "2024-11-29",
+  "learning_state": "AUTO_CANDIDATE",
+  "variables": [
+    {
+      "name": "client",
+      "description": "Nom du client à facturer",
+      "required": true,
+      "type": "string"
+    },
+    {
+      "name": "montant",
+      "description": "Montant de la facture",
+      "required": false,
+      "default_value": "0",
+      "type": "number"
+    }
+  ],
+  "param_patterns": [
+    "(?:client|customer)\\s+(?P<client>[A-Za-z0-9_\\-]+)",
+    "(?:facturer|invoice)\\s+(?P<client>[A-Za-z0-9_\\-]+)",
+    "(?P<montant>\\d+(?:\\.\\d+)?)\\s*(?:€|EUR|euros?)"
+  ],
+  "nodes": [
+    {
+      "node_id": "start",
+      "name": "Écran d'accueil",
+      "type": "entry"
+    },
+    {
+      "node_id": "client_search",
+      "name": "Recherche client",
+      "type": "intermediate"
+    },
+    {
+      "node_id": "client_selected",
+      "name": "Client sélectionné",
+      "type": "intermediate"
+    },
+    {
+      "node_id": "invoice_form",
+      "name": "Formulaire facture",
+      "type": "intermediate"
+    },
+    {
+      "node_id": "invoice_created",
+      "name": "Facture créée",
+      "type": "end"
+    }
+  ],
+  "edges": [
+    {
+      "edge_id": "e1",
+      "source": "start",
+      "target": "client_search",
+      "action": {
+        "type": "mouse_click",
+        "target": {
+          "role": "search_field",
+          "text": "Rechercher client"
+        }
+      }
+    },
+    {
+      "edge_id": "e2",
+      "source": "client_search",
+      "target": "client_selected",
+      "action": {
+        "type": "text_input",
+        "target": {
+          "role": "search_field"
+        },
+        "parameters": {
+          "text": "{{client}}"
+        }
+      }
+    },
+    {
+      "edge_id": "e3",
+      "source": "client_selected",
+      "target": "invoice_form",
+      "action": {
+        "type": "mouse_click",
+        "target": {
+          "role": "primary_action",
+          "text": "Nouvelle facture"
+        }
+      }
+    },
+    {
+      "edge_id": "e4",
+      "source": "invoice_form",
+      "target": "invoice_created",
+      "action": {
+        "type": "mouse_click",
+        "target": {
+          "role": "submit",
+          "text": "Créer"
+        }
+      }
+    }
+  ],
+  "entry_nodes": [
+    "start"
+  ],
+  "end_nodes": [
+    "invoice_created"
+  ],
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2026-01-20T14:41:18.027595",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_03f5cce5e4ff.json b/visual_workflow_builder/backend/data/workflows/wf_03f5cce5e4ff.json
new file mode 100644
index 000000000..596335bfd
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_03f5cce5e4ff.json
@@ -0,0 +1,484 @@
+{
+  "id": "wf_03f5cce5e4ff",
+  "name": "Long Workflow",
+  "description": "",
+  "version": "1.0.0",
+  "created_at": "2025-12-03T20:47:09.902749",
+  "updated_at": "2025-12-03T20:47:09.902891",
+  "created_by": "system",
+  "nodes": [
+    {
+      "id": "wait_0",
+      "type": "wait",
+      "position": {
+        "x": 100,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [],
+      "output_ports": [
+        {
+          "id": "out",
+          "name": "Output",
+          "type": "output",
+          "data_type": null
+        }
+      ],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "wait_1",
+      "type": "wait",
+      "position": {
+        "x": 150,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [
+        {
+          "id": "in",
+          "name": "Input",
+          "type": "input",
+          "data_type": null
+        }
+      ],
+      "output_ports": [
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+          "id": "out",
+          "name": "Output",
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+          "data_type": null
+        }
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+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "wait_2",
+      "type": "wait",
+      "position": {
+        "x": 200,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [
+        {
+          "id": "in",
+          "name": "Input",
+          "type": "input",
+          "data_type": null
+        }
+      ],
+      "output_ports": [
+        {
+          "id": "out",
+          "name": "Output",
+          "type": "output",
+          "data_type": null
+        }
+      ],
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+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "wait_3",
+      "type": "wait",
+      "position": {
+        "x": 250,
+        "y": 100
+      },
+      "size": {
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+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
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+      "highlighted": false,
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+      "color": null
+    },
+    {
+      "id": "wait_4",
+      "type": "wait",
+      "position": {
+        "x": 300,
+        "y": 100
+      },
+      "size": {
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+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [
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+        }
+      ],
+      "output_ports": [
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+          "name": "Output",
+          "type": "output",
+          "data_type": null
+        }
+      ],
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+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "wait_5",
+      "type": "wait",
+      "position": {
+        "x": 350,
+        "y": 100
+      },
+      "size": {
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+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [
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+        }
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+      "highlighted": false,
+      "status": null,
+      "label": null,
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+      "color": null
+    },
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+      "position": {
+        "x": 400,
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+      },
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diff --git a/visual_workflow_builder/backend/data/workflows/wf_04ee1c6d9d62.json b/visual_workflow_builder/backend/data/workflows/wf_04ee1c6d9d62.json
new file mode 100644
index 000000000..e76f07130
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_04ee1c6d9d62.json
@@ -0,0 +1,100 @@
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diff --git a/visual_workflow_builder/backend/data/workflows/wf_1e7cd99722b9.json b/visual_workflow_builder/backend/data/workflows/wf_1e7cd99722b9.json
new file mode 100644
index 000000000..47b3dfa85
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_1e7cd99722b9.json
@@ -0,0 +1,98 @@
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diff --git a/visual_workflow_builder/backend/data/workflows/wf_201335e7f498.json b/visual_workflow_builder/backend/data/workflows/wf_201335e7f498.json
new file mode 100644
index 000000000..5165bdbeb
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_201335e7f498.json
@@ -0,0 +1,100 @@
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diff --git a/visual_workflow_builder/backend/data/workflows/wf_20260113_234050.json b/visual_workflow_builder/backend/data/workflows/wf_20260113_234050.json
new file mode 100644
index 000000000..329c5c5f4
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_20260113_234050.json
@@ -0,0 +1,583 @@
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+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_20260114_000213.json b/visual_workflow_builder/backend/data/workflows/wf_20260114_000213.json
new file mode 100644
index 000000000..97cdccc8c
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_20260114_000213.json
@@ -0,0 +1,437 @@
+{
+  "id": "wf_20260114_000213",
+  "name": "Ouverture et saisie dans onlyoffice texte",
+  "description": "Workflow créé avec le Visual Workflow Builder V2 permet sous linux ubuntu d'ouvrir onlyoffice texte et d'écrire",
+  "version": "1.0.0",
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+  "updated_at": "2026-01-22T09:28:59.220859",
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+        }
+      },
+      "input_ports": [],
+      "output_ports": [],
+      "self_healing": null,
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null,
+      "data": {
+        "isVWBCatalogAction": true,
+        "label": "Cliquer sur Ancre",
+        "parameters": {
+          "visual_anchor": {
+            "anchor_id": "anchor_1768344583207",
+            "anchor_type": "generic",
+            "bounding_box": {
+              "height": 50.69955817378491,
+              "width": 42.857142857142854,
+              "x": 0.4983388704318937,
+              "y": 622.212806892676
+            },
+            "confidence_threshold": 0.8,
+            "description": "Élément sélectionné pour l'étape vwb_action_1768344569642",
+            "metadata": {
+              "capture_method": "screen_capture",
+              "capture_timestamp": "2026-01-13T22:49:43.207Z",
+              "screen_resolution": {
+                "height": 1080,
+                "width": 1920
+              }
+            },
+            "reference_image_base64": 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+          }
+        },
+        "stepType": "click_anchor",
+        "vwbActionId": "click_anchor"
+      }
+    },
+    {
+      "id": "step_1768344642508",
+      "type": "type_text",
+      "position": {
+        "x": 151.16062816354855,
+        "y": -94.44477319437412
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "text": "onlyoffice",
+        "visual_anchor": {
+          "anchor_id": "anchor_1768344670956",
+          "anchor_type": "generic",
+          "bounding_box": {
+            "height": 23.85861561119293,
+            "width": 208.30564784053155,
+            "x": 508.80398671096344,
+            "y": 36.68261543464941
+          },
+          "confidence_threshold": 0.8,
+          "description": "Élément sélectionné pour l'étape vwb_action_1768344652324",
+          "metadata": {
+            "capture_method": "screen_capture",
+            "capture_timestamp": "2026-01-13T22:51:10.956Z",
+            "screen_resolution": {
+              "height": 1080,
+              "width": 1920
+            }
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+          "reference_image_base64": 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+        }
+      },
+      "input_ports": [],
+      "output_ports": [],
+      "self_healing": null,
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null,
+      "data": {
+        "isVWBCatalogAction": true,
+        "label": "Saisir Texte",
+        "parameters": {
+          "text": "onlyoffice",
+          "visual_anchor": {
+            "anchor_id": "anchor_1768344670956",
+            "anchor_type": "generic",
+            "bounding_box": {
+              "height": 23.85861561119293,
+              "width": 208.30564784053155,
+              "x": 508.80398671096344,
+              "y": 36.68261543464941
+            },
+            "confidence_threshold": 0.8,
+            "description": "Élément sélectionné pour l'étape vwb_action_1768344652324",
+            "metadata": {
+              "capture_method": "screen_capture",
+              "capture_timestamp": "2026-01-13T22:51:10.956Z",
+              "screen_resolution": {
+                "height": 1080,
+                "width": 1920
+              }
+            },
+            "reference_image_base64": 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+          }
+        },
+        "stepType": "type_text",
+        "vwbActionId": "type_text"
+      }
+    },
+    {
+      "id": "step_1768344749439",
+      "type": "click_anchor",
+      "position": {
+        "x": 487.0496771091505,
+        "y": -86.20244456930321
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "visual_anchor": {
+          "anchor_id": "anchor_1768344821688",
+          "anchor_type": "generic",
+          "bounding_box": {
+            "height": 93.44624447717231,
+            "width": 98.67109634551491,
+            "x": 572.5913621262458,
+            "y": 72.47053885143882
+          },
+          "confidence_threshold": 0.8,
+          "description": "Élément sélectionné pour l'étape vwb_action_1768344797121",
+          "metadata": {
+            "capture_method": "screen_capture",
+            "capture_timestamp": "2026-01-13T22:53:41.688Z",
+            "screen_resolution": {
+              "height": 1080,
+              "width": 1920
+            }
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+        }
+      },
+      "input_ports": [],
+      "output_ports": [],
+      "self_healing": null,
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null,
+      "data": {
+        "isVWBCatalogAction": true,
+        "label": "Cliquer sur Ancre",
+        "parameters": {
+          "visual_anchor": {
+            "anchor_id": "anchor_1768344821688",
+            "anchor_type": "generic",
+            "bounding_box": {
+              "height": 93.44624447717231,
+              "width": 98.67109634551491,
+              "x": 572.5913621262458,
+              "y": 72.47053885143882
+            },
+            "confidence_threshold": 0.8,
+            "description": "Élément sélectionné pour l'étape vwb_action_1768344797121",
+            "metadata": {
+              "capture_method": "screen_capture",
+              "capture_timestamp": "2026-01-13T22:53:41.688Z",
+              "screen_resolution": {
+                "height": 1080,
+                "width": 1920
+              }
+            },
+            "reference_image_base64": 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+          }
+        },
+        "stepType": "click_anchor",
+        "vwbActionId": "click_anchor"
+      }
+    },
+    {
+      "id": "step_1768344883203",
+      "type": "wait_for_anchor",
+      "position": {
+        "x": -230.19595011018868,
+        "y": 29.872058799507542
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "visual_anchor": {
+          "anchor_id": "anchor_1768344954268",
+          "anchor_type": "generic",
+          "bounding_box": {
+            "height": 85.49337260677467,
+            "width": 618.9368770764119,
+            "x": 46.34551495016611,
+            "y": 39.66494238604853
+          },
+          "confidence_threshold": 0.8,
+          "description": "Élément sélectionné pour l'étape vwb_action_1768344887499",
+          "metadata": {
+            "capture_method": "screen_capture",
+            "capture_timestamp": "2026-01-13T22:55:54.268Z",
+            "screen_resolution": {
+              "height": 1080,
+              "width": 1920
+            }
+          },
+          "reference_image_base64": 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f/bajP7ISLGHk+IyRg920oAfrNX1gOteafH1IJPCejpciMwtq8QkEhwu3ZJnJ3Lgf8CH1FaUoe0mo9yKk+SLkeMp+yJA7BU+JULMegXTFJP/AJGqX/hj3/qoX/lI/wDttdH4MtNFt/iP4XbTI7FXN8Q5glVzjyn64nkx+Q+te0+IZtUtLyCX+0hbabLNHGxhtfMliG1yzElWGCQo6cfjV4iiqMkk73Io1XUTdtj5z/4Y9/6qF/5SP/ttH/DHv/VQv/KR/wDba96t/EmupCqXOlqHAAd3glGxcriZ1X+Ehj8i/MCD+Etx4l1cMYrfSFeRFkZ90cwUbQxXHA+8AMfX8KwNjwD/AIY9/wCqhf8AlI/+20f8Me/9VC/8pH/22vfZvEmvJcSQrokJMat8x8wCQhmGVGDwMLkHnng1Fe65r2bhdpt5YpkWGOK0dhP8xDcsp64yB8v+8O4B4P8A8Me/9VC/8pH/ANto/wCGPf8AqoX/AJSP/ttfUjXtqAW8xtuN3+rbpgn09AasUAfKf/DHv/VQv/KR/wDbaRv2PsY/4uF1OP8AkEf/AG2vq2myfw/7woA+UX/ZEgRir/EmFGHUNpig/rNSx/sgxyAmP4jI4HUrpQP8pq634iWuj3HxQ8RHVI7Jipt/L8+UJ/yxXOMzR57ev4d+w/Z/jhj0rxLHYeSkY1FRFsIZB+5GOjNkZ/2j9a66mFUKXtL9vxOaGIcqnJbueS/8Me/9VC/8pH/22j/hj3/qoX/lI/8Atte56jf+LrI3IaSSZoV2CWK2TyWYWxfhNpfJfABztzwalOuawIrB7KG9vUSXzr9mhjc+TlV2AqqAH5mb5QW+THeuQ6Twf/hj3/qoX/lI/wDttH/DHv8A1UL/AMpH/wBtr3CPXPEK21tPA9zfBSkl4rWW0rh23xJhRnKDI6kEDn5sVLous62+spDq8lxDGSWVUhREwZJMKcoWbChBwR696APCv+GPf+qhf+Uj/wC20f8ADHv/AFUL/wApH/22voHUPEOtQPcyW+kJcQRuwiCpLvdQxUHpjsG+la/hm7ur3TpJ7wqX+0yqhWJo1KBsKQG5xjuaFqB8z/8ADHv/AFUL/wApH/22j/hj3/qoX/lI/wDttfVlFAHyif2PwC2fiGAF5JOkYH/o2o/+GRrb/oplv/4LV/8Aj1fSnj3/AJEnxF/2DZ//AEU1fN1/Y+H18KyTRRad9s+yBsrMpfftGfl+0k59tn4CuvDYZVlduxzV67pOyVx3/DHv/VQ//KR/9tpf+GPf+qhf+Uj/AO219Eatf6lp9joc1jBJcQ7AbqJIt7OnkgAA9juIPvgisi28Q+KkshFPpYluYVjMjLGQ8x3ru2rjaFOWXPUYzXIdJ4d/wx7/ANVC/wDKR/8AbaP+GPf+qhf+Uj/7bX0FdeItRGhvPHaxi5MjosgglMYURkhwuN33/kwe/PSorXxJq7eUk+mpFkqskjwzERH5vlYKOWbaCCvA3DPuAeBf8Me/9VC/8pH/ANto/wCGPf8AqoX/AJSP/ttfTPh7ULrUbeaS7tPsrxzFFXaw3KOj/N2bqB2HB5rToA+U/wDhj3/qoX/lI/8AttH/AAx7/wBVC/8AKR/9tr6sooA+Uj+x7gE/8LC6DP8AyCP/ALbTH/ZCiQKZPiPHHnpu0oDP5zV9Xt9xvoa8j+PsNnPq3haO/WBrcrdbhMwVfux45Lp3/wBofj0rWjT9pNRuZ1Z8keY8sj/ZDhkbbH8SYnb0XSwT+k1Sf8Me/wDVQv8Aykf/AG2u1+EVvpdv8UrYaWloqnTLnf5EgcZymM4lk/mPpXpF3rOrW2p3QgM115d5JG9u9qRHDAFXbLuVQxwT0ycjOMYp16SpT5U76Co1PaRvY8C/4Y9/6qF/5SP/ALbR/wAMe/8AVQv/ACkf/ba+grfxLqJZWuNOWJAyBh5UxYoeso4xt9Afm9ahi8S+IGtluToKsGXd5KrKJCc4xyMDnn6Viangf/DHv/VQv/KR/wDbaP8Ahj3/AKqF/wCUj/7bXvMniPxE2nyTR6VbKwjyrKkrgsVkIIGBwNgBH+116VNp+vaottqEl3btJNHIPKQwt5aAyEEcKGOEAc8tweDk4AB4B/wx7/1UL/ykf/baP+GPf+qhf+Uj/wC219K6DqTzaNBNqDFbllZpMQsvGWwcY4+Vc+v4mtYcjIoA+VP+GPf+qh/+Uj/7bSH9j7DKP+Fhdc/8wj/7bX1bTW/1ifjQB8oN+yLbqxVviXACOCDpigj/AMjU9P2QEddyfEVXX1XSQR+k1bGpWehT+J/EL6lHp7Tf2vcgGeZUbbv44NxHx1/h/H09I+FU01n8GZptPJSSK5ujEY037R52MhfmzgZ9fxrrrYVU6fPc5qVdzny2PIf+GPf+qhf+Uj/7bR/wx7/1UL/ykf8A22vfP+Eg1i3mmihspNSgWRvsszwMkl0nPI2qFG0g5JA3DGB6ySeJdTUJHFp8VxLI6BHSGdUKsG3NyMgAgD8a5DpPn/8A4Y9/6qF/5SP/ALbR/wAMe/8AVQv/ACkf/ba99PiTXlmhibRISXYZf94qY6bRwTu4JHbFNfW9enV1e2NpizaVfIgZmaQiIooLggY3sDkfwkjGKAPBP+GPf+qhf+Uj/wC20f8ADHv/AFUL/wApH/22vom+1TUrXU9O01CJhuiW6uDbsTIckSYwNqgAAk/7QxW7DcwSsFjckkZHyMOwPcf7Q/zmgD5Z/wCGPf8AqoX/AJSP/ttH/DHv/VQv/KR/9tr6sooA+VU/Y7UxGR/iOsagkEtpAA/MzUg/Y+tiQB8T7ck9ANMX/wCPV718bQD8JNZDAEGSLOf+u6V4m1l4fg1DTJNPi04TjUrXBhmVmx5q54FxJ/6D+IrsoYVVYczdjmrYh05WsV/+GNf+qi/+Ub/7dR/wxr/1UX/yjf8A26vo/wAXahqVpq6xWU86E2rSQwpaiRbifzQAjHacDb6FcZz2qpF4n1h3CtpMcec8mKYjfjPk8D7+f4vu8VxnVY+e/wDhjX/qov8A5Rv/ALdR/wAMa/8AVRf/ACjf/bq+hF8Ta1cSsLTSIvKURjfLHMMllBOOB91sj8Kji8UeIJvNK6DFEF3ELJ5pcARs+CAP4toAx/e6UCPn/wD4Y1/6qL/5Rv8A7dR/wxr/ANVF/wDKN/8Abq9/0rXddl8QQpfIY7PfIsqJasMKGlwclSTjEYJ3Dr905yNLw3rV5eSXsmpx/Z0TZ5UYgkGw7WLqSR8xGBkjj0oC583f8Ma/9VF/8o3/ANuo/wCGNf8Aqov/AJRv/t1fWMciSKWQ5AYqeCOQcHrWXrl3qFtLGLCIzBkPnfui3kpn/Wj+8R/zz6nrxjkA+X5f2N/LjZ/+FiZx2/sf/wC3VH/wx7/1UL/ykf8A22vrS45tGIO75Rz69OarmgD5T/4Y9/6qF/5SP/ttH/DHv/VQv/KR/wDba+rKKAPlP/hj3/qoX/lI/wDttH/DHv8A1UL/AMpH/wBtr6sooA+U/wDhj3/qoX/lI/8AttH/AAx7/wBVC/8AKR/9tr6sooA+U/8Ahj3/AKqF/wCUj/7bR/wx7/1UL/ykf/ba+rKKAPlP/hj3/qoX/lI/+20f8Me/9VC/8pH/ANtr6sooA+U/+GPf+qhf+Uj/AO20f8Me/wDVQv8Aykf/AG2vqyigD5T/AOGPf+qhf+Uj/wC20f8ADHv/AFUL/wApH/22vqyigD5T/wCGPf8AqoX/AJSP/ttH/DHv/VQv/KR/9tr6sooA+U/+GPf+qhf+Uj/7bR/wx7/1UL/ykf8A22vqyigD5T/4Y9/6qF/5SP8A7bR/wx7/ANVC/wDKR/8Aba+rKKAPlP8A4Y9/6qF/5SP/ALbR/wAMe/8AVQv/ACkf/ba+rKKAPlP/AIY9/wCqhf8AlI/+20f8Me/9VC/8pH/22vqyigD5T/4Y9/6qF/5SP/ttH/DHv/VQv/KR/wDba+rKKAPlP/hj3/qoX/lI/wDttH/DHv8A1UL/AMpH/wBtr6sooA+U/wDhj3/qoX/lI/8AttH/AAx7/wBVC/8AKR/9tr6sooA+U/8Ahj3/AKqF/wCUj/7bR/wx7/1UL/ykf/ba+rKKAPlP/hj3/qoX/lI/+20f8Me/9VC/8pH/ANtr6sooA+Uv+GPf+qhf+Uj/AO201v2Pj/0UH/ykf/ba+r6SgD5OH7H7Z/5KAP8AwUf/AG2pF/Y+P/RQv/KR/wDba+rcCloA+U/+GPf+qhf+Uj/7bR/wx7/1UL/ykf8A22vqyigD5T/4Y9/6qF/5SP8A7bR/wx7/ANVC/wDKR/8Aba+rKKAPlP8A4Y9/6qF/5SP/ALbR/wAMe/8AVQv/ACkf/ba+rKbJnYcUAfKn/DHwzj/hYY/8FH/22l/4Y9/6qF/5SP8A7bX0nZyas13tuLSxjg5w8V07v7fKYwPrzx71s0AfKR/Y+AOD8RAP+4R/9tpf+GPf+qhf+Uj/AO217n4uHiCTULgabdatFIhhW3it4cQybvvs8mMDHueg967dPu+2ePpQB8qH9j4Dr8QwP+4R/wDbaP8Ahj30+If/AJSP/ttfSviU6kIY/wCygGut42CT/UkYOfMPUL9Oc4xVnQzMdOiM5nMm35/OAD7u+QOPpjjGKAPyzAJOAMnsB1NfR3hD4OaBdeHNKg1RJHukm+03zoDmUlMCHcOVQEjpycH1rwqPV7NdGsbD+y4Fnt7oTPdj77qGzg/y/AV9R6D4iuYbNptPmUmSItEG5QsV+Un8cV81xDLGypQ+rvkd3rf5L5bv/hj7jh3KqGJp1p6TcVHTXrdteulu33nOfBvw9d/CfxFfapqNtHe3N1F5CGM7Ujj37jsY9c4XOcdK9b+KngTQfiroNjLfwS2l/AQ1vPgLKiN95CeQV/iHUZAI6mvItR8X3+oRiPVbmaSWMnEbjG1j1wor1T4d6jPD4X02C7Yq0KMzBjyqkkqv5EV2cb5GstwmFzGMoxxLdpODk1Jcr1XM9tEnp9o5sJRo4yEoUoNKPc+JvE+jXfh3xLqOhX2PtNhdPbyEdGKnGR7EYP41+i3wBhdvgd4IIxg6Hbd/9ivgv45ahBqvxe8SX1sytG97tBHQlUVG/VTX33+z8cfArwQfTQrY/wDjlenhpzqUYSnu0m/W2p8vXjyTcezZ1N3plvd7PtVrbz7DlPMQNtPtmpxBIMYwMdMHpXnui32rXMtjLceNL9I5lgeQi0Z0jllhQrCzCHygPMkVuWBwwXjOa77w/evqWg6fqMkaxvdW0czIvRSygkD25rYwJfJl9f8Ax6gxSnq2fq1VvE1/NpulG4tlhaZ5ooIzMxEatI4QM2OcDOcDriuAh17XrK2ttROvNeKsZmKXNtdRw3gELOVUtAqoWCllO7jGOaAPSPJl7n2+9SeTL7fnViNt8aOBjcob8xmub8e6reWFtBa2N1DZS3IdmupFdjGqNGCEVFYs7eYFHBxnOD0oA3fKmORu69fmpr28pQgBcn3rjdH1bVbTWrNLvXLW4ju3EBsbyV4rjlh+9RZY4yQozkYO7scjnvaBJp7GHqvhjRdWuFuNU0bTr2ZV2LJPCrsFyTjJHTJP507SPDmk6Q8j6VpNhYtKAsht4VQuB0BwOavahLdxz26wRK0TMd7F9vPZehwD6/h3qW3nd5Whmi8qVVDYD7gy5xkHjv7VXPK1r6Byq97DfJl68fnR5Mvr/wCPVZqtJcSmWSO3gEnl8OzSbACRnA4OTgg/jUjDyZfX/wAeo8qb1/8AHqTS5LmW0D3UYR8kKc5LL2Y8DBNWqAKNxYpcIEuIIZ0ByFlUOM+uCKelu6IERVVVGAq8AD0AqudahEhT7DquRJOmRZPj90Mk59G6If4jwKvWkwubSG5WOWNZY1cJKhR1BGcMp5B9R2NAEXkSe350eRJ7fnVqigCp9nk35wuNuOtYX/CC+FM5/wCEX0X/AMBE/wAK6iiqUnHZicU90ULOwSytYrWzgit4IVCRxxDaqKOwA6CpvJl9f/Hqs0VIyt5Mvr/49UJ0+I3AuTbW5nHSUou8f8Cxmr9FAFbyZfX/AMeo8qb1/wDHqs0UAVvJl9vzpPIk9vzq1RQBVEEme351S1HRLLVLRLXVdPtL2FW3iOdA6hucHB74J/Oteimm1qgavuc7Y+D/AA9Y3cd5ZeH9KtriI7o5YrdVdD0yCBx1rZEMo5BA+hqzRQ5OW7EklsVhDKOh6f7VHky+v/j1WaKQyt5Mvr/49R5Mvr/49VmigCt5Mvr/AOPUnkSe351aooAq+RJ7fnSNbyHbgLwwPWrdFAHP6h4R0DUbx7y/0HS7q5kxvlmt1Z2wMDJI9ABVrSdC0/SIXh0rTrOxjkbe6W8YQM2MZIHU4rWoqnOTVri5UnexW8mUdMfnQYpj1Ofq1WaKkZWMMp6nP/AqBFMBgHj/AHqs0UAVvJl9R+dBhlJycH8as0UAVfIk9vzo8iT2/OrVFAFCexFxFNBcRRSwzLseN+VdSMEEdwaxv+EE8J4x/wAItouP+vRP8K6iiqUpR2YnFPdFRLd0UIgVVUAAA4AA6CneTLjGR/31VmipGVvKmznPP+9R5Mvr/wCPVZooAq+RJ7fnR5Ent+dWqKAKvkSe350eRJ7fnVqigCq0EhUjjketZ+q+HtM1dYRq2lWN95IPl/aIlfZnGcZ6ZwPyraopptO6E0nuYOl+FdD0u6+1abomm2dxtKebBAqNtPUZA6HFavky8c9OnzdKs0UNt7gklsVvKm9f/HqPJl9R+dWaKQyt5Muc55/3qPJl9f8Ax6rNZ/iXUJNK0C+1KKFJpLaEyLG7FVY8YBI5A5oAn8mX1H/fVJ5Ent+dYE/i2TTr6703VNOMl7ax+e4sX3IYAm5n+cqQRjBXknIxnNWYPFVpMsMqWN99luJJEtrjamyby1kZiBu3D/VMOQOo7UPQDW8iT2/OkNvLuU4HGe9YEXjmwkKsum6j5TDPmkR7QAYgxxvzwZk7c84zimW/j3S7of6HZX9wSx8sIqAOoWRt2SwA4ibgkH7vHND0As3Pgvw1c3ElzceHNIlmlcvJI9shZ2JySTjkmtLTNJtdMtFtNNs7ezt1JZYoFCKCTkkAetc0/j1nkga20lvs8kzLvlk5MYlSMP8AKCIwd+cyED5cZzWl4h8XW+j6g0EllcSQQT+TdXAxtRvIaYKozksQF7AfN1qnKTWrJUUnojc8mXnnr1+ajypvX/x6si18TpdX8mnwaPqL3luSLqEeVm35wuTvw27IxtJ98Vf8O6xa67pi6lYpKLZ3ZY2kUAvtOCQM9A2V57qe2DUlFjyZfX/x6jyZfUfnVmigCt5MuMZGP96jyZfb86s0UAVfIk9vzo8iT2/OrVFAFO5060vtPksdStILu2lbLwzIHRucjIPuAazYvBnhCGVJovC+jJJGwZGWzQFWByCDjqDW9RVKclomJxT3QuTzyeevPWjJ9T0x1pKKkYpJPUn86Mn1P50lFAC5P94/nRk/3j+dJRQAUUUUAMnUvCyr1IqDyJPb86tUUAVfIk9vzo8iT2/OrVFAFXyJPb86PIk9vzq1RQBV8iT2/OjyJPb86tUUAVfIk9vzo8iT2/OrVFAFXyJPb86PIk9vzq1RQBV8iT2/OjyJPb86tUUAVfIk9vzo8iT2/OrVFAFXyJPb86PIk9vzq1RQBV8iT2/OjyJPb86tUUAVfIk9vzo8iT2/OrVFAFXyJPb86PIk9vzq1RQBV8iT2/OjyJPb86tUUAVfIk9vzo8iT2/OrVFAFXyJPb86PIk9vzq1RQBV8iT2/OjyJPb86tUUAVfIk9vzo8iT2/OrVFAFXyJPb86PIk9vzq1RQBV8iT2/OjyJPb86tUUAVfIk9vzo8iT2/OrVFAFXyJPb86PIk9vzq1RQBV8iT2/OjyJPb86tUUAVfIk9vzo8iT2/OrVFAFQWzA5Crml8iT2/OrVFAFQ2zE5KqTS+RJ2AP41ar57/AGmvE+ryeNND8C2l5dWOnXawyXbW7lHn8yTaF3eigHjpk85oWoN2PeTbuw5CkfWlFu4GAFH418mfDTxBqvgn4iaBa2M2px6fq0qw3el3khdlDStGGxgYPAdTtBxnOR1+vSMEj0OKSdxtWPyVr0T4e+Oxp1smm6m5EScRSnoB6H0+ted0U5wjNcsldHsZVm2Iyuv7ag/Jp7NeZ9FweKNJdBcefA2BkPlT+ua5/wAW/Fk2llLZaJKJLh1KiQHKx++ehI7AfjXimB6D8qK4/wCzcLzKTje3fWx9HjuNq+IounSpKDe73fy0Vn944szyF3YszHJJOST61+l/7Pv/ACQvwP8A9gK2/wDQK/M8dRX6Yfs+/wDJDPA//YDtv/QK7WfDzNGHwVawWL2MGt65FbNLHL5azREBo9mzBMRPHlJ3/h5zk56HTrSGw0+2sbcMIbeJYo9xydqjAye54qeue8X6zf6XcaZb2EKyPeyyqxNpJcFQke7hI2UnJ4znikZm1f2lrf2ctne28VxbyrtkilQMrD3BrGTwpZNFZ297fahqFnZsjQ2l20TxAopVMgRgttB4yeuCcmqD+MJhDqiRaa082nJK07lhEibQ2z5WbccshyAcj61FJ48SCYQXWlzR3GVjMKsHzIxj24Zc/LiQH7pIxj6i1A7SqGuaTZaxaLb3keTHIssMqhfMhkVgyuhIO1gQOce3SqvhrXl1w3DRWFzbRwrFlpyFYs6B9u3qMAjk1s0AedeJvho+seJk1U6srCQQieW4h33IEeOI3XCruwM/LwckdTXoxOST6nNc74z8SDw+lptiEzyszyLsdisCYMjDYDg8gDPGetMfxUGfFrpc1ykt01paOLmNRcSLu3dTlANpIJznjpkUkktjKnQhTcpRW+50lQQQGOVpZJWmlYBdxAGB1wAOlc3e+N7K0iVpLG5LvNJAiBlbLpJFHjK54JmHIz0PXin6F4rOra/Fp8dk8Eb27ysJlKyIVCHGO4yx5wOMetM1OoqHyCtyZo5WQOQZEwCGIGM+oOPT0qasfxXrR0Ozt7vyBMjz7JFzhtoikc7e2fkxzxzQCVzYorlD40iSRFudKuYFEgSeVpVMcWVjZcsOCSJBgHHIIyTgGm3xBSTTGurTQ7x3NvLcxrK4jQxpF5oO5h3GAQAcHv3oBanb0VyK+No0e6WfTZsWkwjuWSRcRF3KIACcvllIJGMdcdq2PDWtrrcErizltHjETFJHV8rJGJFOV46HkdiO9AGtRRXN+JNb1PT7+8W0SwNvY6Z9vmFxvDSfO42KwOF4TgkHk0AdJRXIR+OoJvtJtdF1KYRMyqQhAba+x8/KduD6bjgE4GKS68c20WT9hlVP3bRuZFYTo23LJg4YDcBuBPPUDjIB2FFcc3j22SaO3k0i++0MBI0SESFYykbBhtBycSr8vHQ89M9FoGpf2tpy3y2z26PI6oruGLBWK7uOmSp4p2Av0UVxHijxrd6Pe65ZJYW8s1rDE2nbnYC4coHkV/TarBuO2aQHb0Vx1x47trNJ5b3T7hIIfP8A3wwBIY2kGxBzubCdMg85AxnF5/FcMfh/+1ZdPu43+2LZrAykFpGYAEHbkrznO314oA6Oiua0fxdDqV/b2y6ZeQJPIsQkmKqVkaEy7Sn3sbVIz649ahm8awRtIjabMrF5Ut906fvzHN5T4xkjnkDBJGeODRsB1dFcU/xF05bFNQGm3hsnX5ZQyZ3+SsuzZ16Ooz0yfTr02g6kNV08XYtprZhI8TRyqQQynBIyBkHqDgZoAv0UVx2p+KtQtPFU2mpaxPbx3UFuM28uXDxCRz527YrKu4hSMtjA5IoA7GiuAPxEc28F8ukyLYt5k0ruGDeQIfMTYCBucnAPZffrV1fHSx25N1pF0syBRJjKrvabykVQw3EE87sdM8dqPIDsqK49/HtnHA1zPpV9FboFEjuVDK5jdwuw4J4jIzx1B6dLE3jBbfVI9NudGvVutqtMkTLN5e9mVOV4OdpzkrjvQB1FFZnhvV11mwa5Fs9rIknlyQSNl4zgHDDAIOCOCPzGCdOgAorBvdV1Btcu7Gx/s2KKwihlne9dlMgkLfdKnCgBT8xDZPGKxdT8emK3SS000gyxNPD575EkW2Xa3y8qd0WNp7H16AHcUVxsnjjzdRSx0/SzcTG4VADcKFkjKzYZW+6DmE8cjB656C+P7OS3jubfS7yWGc+XbnzEUySfu8oQfu480fMeDg+2RagdlRXHXPjhF1B7G20xpZVmVC32hShQtIrOCBgkNGRgHn1ByAWvjqGaG0caXcv9rZYoCsiKJJN8SOME5UBpRyc5APtkWuwPTc7GiqWiagmqaXDfpE8Ik3AxsQSpVipGRweVPNXT0oAKK4m78Y31pqmqhrKO6s9OnnSWOGCVZUjjh3+YZWPlnJwu3g/MPep9Q8atbExpo0rSm4+zIGuUCtKrxrIpIGQF8zg98dsjItQeh19FcjY+OYL9o0s9IvZJJ2P2cOwjWVAHYtvYBQQIyduT1HPXEdv42kkvBatpDmeRpRBElwnziOSZSxc8D5YScY6nr6Fx2OyornfD/iu21q9jggsriKKZZDDM7L8+xY2b5RyOJF/I10VFhXuFFZHi3V20bRzcwokl1LIsNujq7K0jHuEBbAAJOB0FctfePdR+2xNY6XbPpz2qXDSySnzRmGSRl2ZHIKbcH0Oe1AHoFFcfd+NJkgtbu20WSW1nMzqWuUDyRRxyMWAz8pzGRhvUc5zh/wDwnNm+o/YYLGaaWVkW2KyrtmLOqcnouC3PJ6EcHijrYOlzraK5Tw943s9a1C3tLfTr2PzgAzsNyxuULhWKjGMD72epAx3rq6ACisfxjqt3o2hPf2Nql3Os0KLCxI3BpFVsY74Jx74rAi8fJLfagsGn/arSMebZyRShTNCkReWQluOCCFA6/rQtQO3orkbTxqlzrFlbx6fILK9JSCZpBvdhMIslf4Vzu4PPT3qrbfEO3klA/sy6kEmJI1hBdxFsiLMcAgtulGAMDA6+oB3FFc9D4qtzp+q3lxZXFt/Zw3PC7DzGUkhTjjAJHB5HucHFe/8AGK2EkqXmkXEbW21bnbPG/lu+/Yowfmz5Z54xkZHXBcDqaK5BvHCIwSXRLtJHlWFF+0Rnc58nAz2GJ0OfZh6ZqXPxA8uaaFNLlaVX2rEoLsNgbzs7c7iNuFxjOecUAtTuqhvrS2vrOazvIUnt50KSxuPldT1BrmG8cW6ysZNJu0tkY7pjKhIUTGEts653Dp1wfbFQw+P7eW0W4XRb/aYXuDuYIPKVFkLAsBuO1ug7jGeho3QbM3f+Ea0A2wtjpNs0YkMmGBJLEYJLE5bIABySCBill8N6DK0zSaTas0z+ZIdpGW554PGdzZAwDuOc5rBbx5HEkf2nTWjeSSaMbJ1k27XlWMkAA/N5RPJHtnBwo8e24WHdpN65kZwPL+Yssfl72CqCeDIoAPXnn1FqGx0K6FoyxiNdMtQgBXaE4wSpI/NE/wC+RSxaJpETMyafACXZ+hIBZWU4BOBkOwwMD5jUOga2urTXkJtJbSW2cAxysN5UlgG29gdpweQex641qAMlfDWgK6OukWgKPvXCnAPy9s4P3VODxkA9adL4d0KWcTy6TayShdm51LcbSvc4PyswyecHFUfGXiVNAezURrKZWaWcFHYpAmA7DYDg/MMbsDg1o6DqD6lDdyOkaiC+ntlKEkMsbYDfU0bhsRf8I1oG2JTpNsfK3bMg5+bk5OcnJ55zzV+ys7SyRo7O2it0dtzLGu0E4Azj6AD8KnooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK5bxB4luoLyG20e0F1mZoTIULLLMBnyEwQQcA7pOVTGDz01fEurro2npP9neeaaUQQRq6KDIwYjLOygL8pyc/SgDUorBl8V6TBJb29zI0d1KgZohtby/lDfMwJXoQcBj1HrTrPxZoF1FFKmoIiSkCPzFZSchTkjGVHzqMtjkj1FAG5RVPStU0/VYWm067juY0IDMgIxkBh1A4IIIPQ0sGpafPqE2nw3kMl3CMywKfnjHHLDsORj17ZoAt0UVmeKdSfSPD17qUaozwIGAcMV5YLkheSBnOBycYoA06K4uXxrJYA/bLaO8jV1DT20M8GAWC/6uVDyCf7+D7V2rDDEHscUAJXn3xi+F2m/EO2tpmvH07VLMFYLpE3goTko65GRnkEHIOfWux1y+ns4reKzhjmvLucQQLKxVAdrOzMRzgKjHA5OAO9Q6dfX66q2laolqZjb/aIZrbcqSIGCsCrElWUsvcghu2CKAPLvhZ8CbXwt4jh8Q65rI1i9tm320aRFY0foHYsSWI7dAOvPFez0UUAfkuis7qiKWZiAAOpJ7VuL4R11gD9miHsbhAR+tV/B9q194r0qzRgrTXcaA9cZPWvpO28CaMqBWlv3Pr5qjP4bab20O+h9WS/fN/I+ZdV0fUtMmWK7tXVmXcpT51I9mGRXqvgvwBZSR21odPj1DUJ1BbzSMZxnAyQABXpF94K05NOlFvLqAJB484Y/wDQa57SrptO1G2neNTNayK5jc4yVP8AI+tePmdSrGMVsr62PleLa3s40/q0pcjb5uj6W1XzPNPiJ4WsbGxGq6bEIAjhZoh90gnAI9Oa+8f2ff8Akhngf/sBW3/oFfEPxS1a1i0X+zFkV7i4dTsBztUHOT+Vfbv7P/8AyQvwP/2A7X/0GujLZ1JUE6n9IrIatepg06zb1dr9v6ud1UckMMkscskUbyRZMbMoJTIwcHtkcVqJGiLgKKdtX+6PyrvPZOfn0fSJ5hNPpdlLKC5DvApOX+8c4796WbSdKm8zztNs5PNGJN0KncPl4PH+yv8A3yPSt/av90flRtX+6PyoAx7W1tbVWW1t4YA2MiNAucAKOnoAB9BUwxnrWltX+6Pyo2r/AHR+VAHm1zrvjIXkrp8MmmO1oRMdXtwXjz06ZweuKzWm15klRvg1aFZlVZAdStfnC/dB+XtgYr1vav8AdH5UmE9FrX2kf5F+P+ZnyS/mf4f5HlDXfiJnkc/B22LSrskP9p2uWXjg8dPlX/vkelTW2q+LLWRZbb4SxwyImxWTVrYMFwBtBx0wAPwFeo4T0WjCei0e0j/Ivx/zDkl/M/w/yMq3d3t43li8mRkBeMsG2Ejlcjg4PGabd2trdxiO6t4Z0ByFkQMAcEZ59iR+JrXwnotGE9FrI0MCTSNJkuEuJNMs2mjYMjmFSykAAHOPRVH4D0pItH0iJGjj0uyRG37lEK4O8Ybt3HB9q6DCei0YT0WgDnl0TRleJ10mxDRZ8siBcpnrjjvVq2tra2z9nt4odwUHy0C5Cjao49AMD0Fa+E9FownotAGdVG90jSb67iu73TbO5uIRiOWWFWZBnOAT78/WugAQ9AtJhPRaAOfn0bR52nebS7ORrghpi0KkyEHIJ9TmkGh6KHZxpFgGYAMfs68gYx27YH5CuhwnotGE9FoAwZNJ0qRkaTTbNmRldSYVyGUBQRx2AA+gHpVmCKGCIRQRJFGucIigAZOTwPck/jWttX0H5UbV/uj8qAM2qs+nafcSNJPY2srtnczxKxOU2Hkjuvy/Titzav8AdH5UbV/uj8qAOeOjaOZ/POl2JlIYF/IXJDZ3du+5s/U+tPGmaaNPOnCwthZnrB5Y2dc9PrzW9tX+6Pyo2r/dH5UAYkGn6fAyNDZW0ZjYMhWIDaQuwEe4X5fpxVG08NaHbpMv9nW0zTzNNI8sSszMXL8nHYsceldTtX+6Pyo2r/dH5UAccnhLRF1ddQW1jASNo0thFGIVBTYeAuSNpPBJHJ4ratLa2s7dbe0gjghT7sca4Ud+la+1f7o/Kjav90flQBm1XksrKRnaS0gcySJI5aMHc6Y2MfUjAwe2BW1tX+6Pyo2r/dH5UAc/Fo+kRIyRaZZIjFiVEK4JYbW7dxwfahNH0lLdrZdMsxCyeWyCFcFd27H0yc/Xmug2r/dH5UbV/uj8qAMFdK0tYxGunWgQYwvkrgYUqO3ZSR9CRUcOh6LCIRFpNjH5AIi2wKNmeuOO+TXRbV/uj8qNq/3R+VAGLYWVlp8HkWNpBaxZ3bIkCjPrxVitLav90flRtX+6PyoAwb3TNMvbmG5vNPtLmaD/AFUksSsyc54JHrzTBoujjzMaVYjzXLyfuF+diCCTx1wzfmfWuh2r/dH5UbV/uj8qAOZk8O+H5N2/RNNbcSTm2Xkkknt6k/mfWphpGkidpxplkJWVVZ/IXJC4Kjp22rj/AHR6Cug2r/dH5UbV/uj8qAObbQdDaRpG0ewLu29mNuuS27dnp/e5+tSR6PpEUxmj0yySU7MusCg/IQV5x2Krj6D0roNq/wB0flRtX+6PyoAyYIYbeIRQRJFGCSFRQAMnJ4HuSfxqStLav90flRtX+6PyoAx2tbVoZ4GtoTFcFmnQoNshYYJYd8jrmq8+j6RPNPNNpllJJcKFmdoVJkAxgMcc9B+Q9K6Dav8AdH5UbV/uj8qAOck0PRJIpIpNIsHjlkEsitApDOM4YjHXk/mfWiXQ9ElWVZdIsHErb5AYF+dsk5PHXJJ/E+tdHtX+6Pyo2r/dH5UAYsVlZQyiWK0gjkBYhljAILY3dPXaufXA9KsVpbV/uj8qNq/3R+VAGU8UTyxSvGjSREmNyOUJGCQe2RxVVtJ0pi5bTbMmQsXzCvzbs5zxznc2f94+tb+1f7o/Kjav90flQBzyaNo6SSyJpdirzMzSMIFBcsCrE8c5BIP1PrVXUPDOi3dvJGthbW0khBM0NvHvHIP8SkEEqMgjnFdXtX+6Pyo2r/dH5UAc1pGg6TpcNqtrZxCS1hEMczKDJt54LY9z+dadaW1f7o/Kjav90flQBlSxRTKFljSQKyuAwzhlOQfqCARVKTRNFkhWB9JsGiTbtQ267V2ghcDHYE/ma6Lav90flRtX+6PyoA5x9D0R5ZpX0iwaSc5lcwLlzkNknHPIB+ozThoujBYlGlWIWJg8YECgIwAAI44wAB+A9K6Hav8AdH5UbV/uj8qAMGDS9Mt47iKDT7SKO5z56rEoEuc53Dv1P51EmhaInlbNIsF8pGjjxbr8qtnIHHQ5P5n1ro9q/wB0flRtX+6PyoA5nVNA0jUbb7PcWUIXzUkJSNQSVZDjp0PloD6gAVIdE0UwrCdJsfKTbtTyFwNuQvbtk/ma6Lav90flRtX+6PyoAw/7O07OfsNrnOf9UvXfv9P7/wA315qKLRtHiiaKLS7JI2DhlEK4IYAMOnQgAH6V0O1f7o/Kjav90flQBzb6BoMkjSPo2ns7sWZjbqSxJ3Enjrkk/UmpX0nSXSNH02zKxSeZGDCuFfj5hx14H5Ct/av90flRtX+6PyoAxLKwsLJpWs7O3tmmbdKYowu888nHXqfzNWa0tq/3R+VG1f7o/KgDIa3t2keRoY2eSPynYqMsnJ2n1HJ496S1tra0i8m1gigjyW2RqFGT1OBWxtX+6Pyo2r/dH5UAZtFaW1f7o/Kjav8AdH5UAZtFaW1f7o/Kjav90flQBm0VpbV/uj8qNq/3R+VAGbRWltX+6Pyo2r/dH5UAZtFaW1f7o/Kjav8AdH5UAZtFaW1f7o/Kjav90flQBm0VpbV/uj8qNq/3R+VAGbRWltX+6Pyo2r/dH5UAZtFaW1f7o/Kjav8AdH5UAZtFaW1f7o/Kjav90flQBm0VpbV/uj8qNq/3R+VAGbRWltX+6Pyo2r/dH5UAZtFaW1f7o/Kjav8AdH5UAZtFaW1f7o/Kjav90flQBm0VpbV/uj8qNq/3R+VAGbRWltX+6Pyo2r/dH5UAZtFaW1f7o/Kjav8AdH5UAZtFaW1f7o/Kjav90flQBm0VpbV/uj8qNq/3R+VAGbRWltX+6Pyo2r/dH5UAZtFaW1f7o/Kjav8AdH5UAZtFaW1f7o/Kjav90flQBm0VpbV/uj8qNq/3R+VAGbRWltX+6Pyo2r/dH5UAZtFaW1f7o/Kjav8AdH5UAZtFaW1f7o/Kjav90flQBm0VpbV/uj8qNq/3R+VAGbUN7Cbmynt1meEyxPGJEPzJuUjcPcZyPpWxtX+6Pyo2r/dH5UAcTY+Hb2xSB7TWIVuY4vI8x9PVlWMYwkabwEHGTySx6ngAaPiDR4Nas4Le4nkiMM6zo6JG3zKCOVkVlI+Y8EeldLtX+6Pyo2r/AHR+VAHEN4OsJJUklv75wrCQxjykQuFVN+1UAB2oowMDjgDNNTwTpaIUF3flXURygyJ+9jGzEZ+XoPLXkYPXnk13O1f7o/Kjav8AdH5UAc7oukWukq62zzNvjhjPmMDxEgRegHYc+9XgkYkaQIgdwAzADLAdMnvjJx9a1Nq/3R+VG1f7o/KgDNqlrlgNT0q4sDM0HmgYkVQxQhgwOD15UcVv7V/uj8qNq/3R+VAHK6taa9c2UUMGsxCYMvmObUBWIbJbbvPGONvOf5bGa0tq/wB0flRtX+6PyqnJtJdhJWdzC1XT7fUbdYZ2kQxyLLFLE+14nXoyn15PqCCQQQao2Giy2er/ANpHVru8lki8mf7UEOUGSoQIFEZDHJwPmzzyAR1e1f7o/Kjav90flUjM2itEohGCoI+lZ7jbI6j+E4oA/Mz4calay+IPC+lLpcKXMeqxu12D87gk8fr+gr6lih46V8d+B9Rt9J8ZaPqd2SLe1vI5JSBnChuT+Ar66s/FPhOeJZIvE2jMjDIP22McfQnNTGmoXt11N69WVRpy6JL7jqbXXNfitktotXuUhRQiIFjwAOg5XNYVx4c068kMk9tHIxOSWUHmnR+JfCw6+JdF/wDA+L/4qrKeKPCSLuk8UaGqj1v4v/iqqxg1fc+Xv2jrKCw+Jr29vGscYsbcgAYHINfc/wCz/wD8kK8D/wDYDtf/AEEV8I/tBa9pfiL4n3d9o90l3ZxwQ26zR8o5RfmKnuMnGfavu79n/wD5IV4H/wCwHa/+gimU9kem1kLqFzmORniZXkYeUkDswRW2k5BPTjtWvVOWyZboXFo0MD4YPmLIbJBzwRzxSJLNvNHPEssTblPQ4I9jwaZfT/ZrV5thfbjjOOpxk+gHU+1FjAbe3ETOHbczFgMAksSePxqSZDJE6B2QspAZeo9xQBmQazBcNB5bIiP99n7HpsHqc9fStWqumWn2K1EHmbwGJGAQBnsMkn9T1q1QBCxIsiQefL/pXP6r4n0fS9cl0y6SICGze5kdWVnG1S+0R/eJ2KzZAxxjqa6AqWsioGSY8D8qy7/Q9Dv0nW7sTIbiXzZGO8OW27PvDkDaMYBxigDNvfGGj2cLNPaSpJmVURotuWRc7ST0J6d8d66W2WCa3jmECASIGxtHGRmsO/8ACvhu+uJJ7mxlaSRtzlZJV3HJOeD7n8zW7E8MUaxoHCoAqjY3AH4ULbUOo/yYf+eUf/fIo8mH/nlH/wB8ik8+P/b/AO+D/hR58f8At/8AfB/woAXyYf8AnlH/AN8ijyYf+eUf/fIpPPj/ANv/AL4P+FHnx/7f/fB/woAXyYf+eUf/AHyKhu3sbSAz3TW8EQIBeTCqCTgcn1JAqXz4/wDb/wC+D/hUdyLS6t5La5hE8EqlJI5Iiyup6ggjBB9KAI4pbaVIrmzeJ4pELJJGQVYY4II6isvVNV+wXf2VdJNwf7OlvEcSIu9oygKc9Cd4OTx9a18KxRIUKoiEAbdoAxgCszUdE03UbpLu7bUVmW3a3/dXU0ahGxuGFIGTgc9eB6UnfoNW6mhpkkF9p1terbognhSUKQDjcoOP1pL+60vT0V76e0tlbO0ysqA4GTjPtRpkNrp2nwWNt5/kwIETzC8jADoCzZJ/E0+6SyulRbm3ScRyLIgkh3bXU5VhkcEHkHtVO19CUSxKqzuEAAKqcDp3rG17XprHWrPS7a1SU3CnzJ3YhLdm4h3YHIdgw/CtmI75ncBgu0DJGM9f8aw9W8IaVqd9c390ZzeSmMxTLIQYPLwU2jocNluQeWPakhm5aG4Nsn2oRCbHz+WTtz6jPNU/EGs2eh2kdze+aUkmSECNNxBY/eI/uqMsT2AJrRrJ8R+HdJ8QrCmrW7XEUQcLH5jKuXG0k7SOduR9GNDAu6dJeyRM17bxQOGIVY5N4IHfOB19PTHeotevzpmkXN6sRmkjT91EDgyyE4RAfVmIH41atYhBbRQB3cRoEDO2WOBjJPc1S17R7TWraG1vt7W8cyzNGrFRIVztBI5wGw3BHKim3dghNJ1i2vbDT55WS3nvVIWBm+YSKCXQepUqwP0NWr+/srCJZb67gto2OA0sgQE4Jxk+wJ/CsO28G6bbSwNbz3kcdrO09rEJTthZmDP7sGIP3s/eOK37u1truHybq3iuItytslQMuVIKnB7ggEe4pyUbKz9RK99SRWVlDKQVIyCO9Ni6v/vf0FPpkXV/97+gqRmXrWrS2Go2tsi2BWaCeVjcXohcGNQRtUgll5+Zv4RzzUPhvXJdVuJYpP7JwiBv9D1MXLdccgIuB71a1bTJ7y/trmK8jgWGGaNka1SQsXUAEM3K4xnA4bvUehaTdafNJJcagl0HUAKLSOLBz1yo5q5ctlb+tWSr3dy9NqFjDeJZy3lvHcyY2QtIA7ZzjA6nofyNJfXFxBNaLBamdJZtkzhseUu1juxjnkAdutSyWttJdRXUlvE88IZYpWQF0DY3AHqM4GcdcCq2q2Mt7JZNHdNALe5WZwN37wAEbeCPXvkcdKULX1G720MrQvEU2o6mLRxouCrH/RtWWeTj/YCD8eeK2r3ULGyeNLy8t7dpf9WJJApfkDjPXkj8xWZpGi3llfi4m1RLhACPLFjFH1/2lGa17i1trlomuLeKZoZBJEXQMY3AIDLnocEjI9TUjItUuLm2tRLa2pupPMRSgbHylwGboegJOPasuTWb2HX49NuINKiSWTEe7VAJnTJwwiMeSTjpn15rbmRnhdFcozKQGHbjrWNp2hT2VrpkP9pyXDWWRJNNCryTrk4BY8rjPUVWnL5i1ual9fWdhEst7dwW0bHaHlkCKTgnGT7An8KS9vra0sHvpZP3CqG3KM5z0x9cj86ku7W2vIDBd28VxESGKSoGUkEEHB44IBHuKfIiSxtHIiujgqysMgg9QRSVr6jfkcrq994ptJ18krLHKu9Vi0dpTGM8KzfaQCw9hiurQkopbrjnjFcnqvg5tSnElze2cyxgpCs+k28piTPCAsCcCusRdqKvHAxwMUgElYpGzqjOVBIVcZb2GeK5fU9U1xoor/SZ0uLO4z5ccektNJHjrvPnp3z2/wAa6iWNJYnikUMjqVYHuD1rm9e8LPqsqiW8tTaxf6i3l0yCZYRgDClwfSnpbzFrc3tMknl063luQRM0amQGLy/mxz8u5tv0yfrViq2l2osdOt7NShEMaoNkaxrwMcKvAHsOKs0hnO3fiC8gTUWksrGAWt2tvG11qAiSQFFbJbacH5h8vP161qaHfNqGmx3b/ZAWLD/RrkTx8HHDgDP5cVny6FeMNQ2au2bu6Wdd9rG4jAQLtwRz90cn0+pOnpFrLZWCW81wLh1Jy4iWPOTn7q8Crqct/dJje2o6w1Cxv1Z7G8t7lVxuMUgcDIyM49q5Dxx4l8QaRqBhsobaCI/6p57GWZZv3Tux3q6qmCm3aeTnI7CuztbW2tVdba3igEkjSOI0C7nY5ZjjqSeSe9Z2taTNqT4a8jEAHEUlqkgBwQT83sSPxqYq/Ww27FvRnupNLt5b2WGW4eMM7RRGNOeeFLMRx7mnW2oWN1cSW9teW800WfMjSQMyYJHIHTkEc+lQaFZXtjBNFe3/ANs3Slov3QQRJgAIAPoT+OO1W4bW2hnmnht4o5ZyGmdUAaQgYBYjrgADntRJJOydwTuVfEOqQaLot1qtwkjxWyb2WMZY+wzWT/wkuq/9CXrv/fVv/wDHaT4pf8k/1j/rh/7MK6bA9K6I8kKSk43bb79LdvUyfNKbinayX6nNf8JLqv8A0Jeu/wDfVv8A/HaP+El1X/oS9d/76t//AI7XS4HpRgelL2tP+Rfe/wDMfJL+Z/h/kc1/wkuq/wDQl67/AN9W/wD8do/4SXVf+hL13/vq3/8AjtdLgelGB6Ue1p/yL73/AJhyS/mf4f5HNf8ACS6r/wBCXrv/AH1b/wDx2j/hJdV/6EvXf++rf/47XS4HpRgelHtaf8i+9/5hyS/mf4f5HNf8JLqv/Ql67/31b/8Ax2tDw9rH9tacbyGzlgCyyQvFOwDo6MVYHGR1B6GtXA9K5r4df8ge9/7C19/6UPTlyTpOSjZprv1v39BLmjNJu+j/AEOh3Tf88k/77/8ArUbpv+eSf99//WqSiuY2I903/PJP++//AK1G6b/nkn/ff/1qkooAj3Tf88k/77/+tRum/wCeSf8Aff8A9apKKAI903/PJP8Avv8A+tTXkmVcmJeoHD+px6VNUdx/qx/vr/6EKADdN/zyT/vv/wCtRum/55J/33/9amX9w1rZTXK209y0aFhDCAXfHZQSBn6kVyaeP1fUptNTwj4na8hiWaSEW8O5UYkK3+txyVb8q0hSnNXijOdWMHaR1+6b/nkn/ff/ANajdN/zyT/vv/61ZE3iO0so3l1YLp6G8W1gMkqMZS23BwpO3lsEHpjJ45rWS5t3cIs8TMTgAOCScZ/kQalwkt0UpxezF3Tf88k/77/+tRum/wCeSf8Aff8A9aiO4gkleKOaN5I/vqrAlfqO1Vdb1W00exN5eNJs3BESONpHkc8KqqoJJJpKLbshuSSuy1um/wCeSf8Aff8A9ajdN/zyT/vv/wCtVLwzrNr4g0K01mySZLe6TeizKFcckYIBODx61o0Si4tp7hGSkrohWSZmZREvynB+f2+lO3Tf88k/77/+tRF/rZv94fyFSUhke6b/AJ5J/wB9/wD1qN03/PJP++//AK1O8xPM8veu/G7bnnHrinUAR7pv+eSf99//AFqN03/PJP8Avv8A+tUlFAEe6b/nkn/ff/1qN03/ADyT/vv/AOtUlFAEMkkyLuMS9uj/AP1qdum/55J/33/9akuv9SfqP5ipaAI903/PJP8Avv8A+tRum/55J/33/wDWqSigCPdN/wA8k/77/wDrUbpv+eSf99//AFqkooAj3Tf88k/77/8ArUbpv+eSf99//WqSigCPdN/zyT/vv/61NWSYuyiJflx/H/8AWqaoov8AXS/h/KgBd03/ADyT/vv/AOtRum/55J/33/8AWqSigCPdN/zyT/vv/wCtRum/55J/33/9apKKAI903/PJP++//rUbpv8Ankn/AH3/APWqSigCPdN/zyT/AL7/APrU2SSZELGJcD0f/wCtU1R3X/Hu9ABum/55J/33/wDWo3Tf88k/77/+tUlFAEe6b/nkn/ff/wBajdN/zyT/AL7/APrVJRQBHum/55J/33/9ajdN/wA8k/77/wDrVJRQBHum/wCeSf8Aff8A9ajdN/zyT/vv/wCtUlFAEIkmLsnlLkY/j9fwp26b/nkn/ff/ANakT/j4k+i/1qWgCPdN/wA8k/77/wDrUbpv+eSf99//AFqkooAj3Tf88k/77/8ArUbpv+eSf99//WqSigCPdN/zyT/vv/61G6b/AJ5J/wB9/wD1qkooAhkkmRGcxLgDPD//AFqdum/55J/33/8AWouv+PaT/dNSUAR7pv8Ankn/AH3/APWo3Tf88k/77/8ArVJRQBHum/55J/33/wDWo3Tf88k/77/+tUlFAEe6b/nkn/ff/wBajdN/zyT/AL7/APrVJRQBHum/55J/33/9amiSYuU8pcgA/f8AXPt7VNUa/wDHw/8AuL/M0AG6b/nkn/ff/wBagSOHVXjC7uAQ2eakqOX78X+//wCymgBt9dQWVlPeXL+XBBG0sjYztVRkn8hXHat4t8RwafNc2vhC9QrcmOJZlZ2lTyfMDbYQ5UlsJzwD1Pau0nijnheGaNZIpFKujDIYEYII9KwbjwtAfscdrdSxQ23CLKzTMg3Bv3bM2UPG3PPy8YqoJN2bsJtpaHQISyAldpIyR6UtFFSMKKKKACiiigAooooAKzpf+PiX/e/oK0azpf8Aj4l/3v6CgD8k6CAeoH5UV03hTwN4g8TWoudLggaNpfKTzJdpdvRQASetTVqwpR5puyOyhh6teXJSi2/I5jav90flS7V/uj8q7ofCvxUf+gb/AOBR/wDiara78NvFOjaZNqF5BamGGMSuI58tsP8AEAQMjHNc8cwws3aNRfedk8ox1NOUqUkvRnHjqPrX6X/s/wD/ACQrwP8A9gO1/wDQRX5ojqPrX6Xfs/8A/JCvA/8A2A7X/wBBFdbPLmem0ZHrQeATXluqTazqPiRol8WanYtc3kttbwWwtgsKxyFNwDurMcDcflf8vlpGZ6lRWX4RvLjUPC+mX104eee1jkkYDAZioycDpR4ukni8K6tJbNIs6WMzRtGTuDCNsEY5zmgDUory7w7cWjeNNDn0y411I7yOQvBcaotxA0Yif5wonc/fUDJGM55B4PqNADIf9Sn+6KfTIf8AUp/uiuC+LGs+JNN1DS7Tw62otLNZ3k5isrKK4aR4vJ8sMJCNqZcgkHPIoA9Aoqh4c1BdW0DT9UR43W7to5g0edp3KDxnnHPfmrGo3EVrp9xdTStFFDE0juoyVABJOKAJ6KyPCEWqJoVvJrF9Pd3kyCV/NjjQxZGdmEVRx6+ufpUviu7nsPDGq31qwWe3s5ZYyRkBlQkHHfkUAaVFZFtqE03iuewWRHtUsIp1AAPzNJIM59CFFa0jFUZgpYgZ2jqfYUALRXI6PqOuprCHV472FLueSKK2eK38sAElGRlffnYpLBt3fAGOeupuLW4k7hRXETeJ5rzxpf8Ah+z1S0iDQva2yqUMyXSIJGcgnO3a2BkYzGfWuxsVmWzhW5YNMI1EhHdsDP60W0uF9SaiuQ1bxYlp4/stFF7ZpbkLDcQsw81ppsmIqM5wNmDgf8tV9K6m1ura6Ehtp45hFIY32MDtcdVPuKLMZNRXMeOtUmt30/SrJybu7nDFBL5ReJCC6B/4Wb7q9MnjI61p6H4g0nWsrp10J3VN0qqDmI5I2v2Vsg/KeeD2pAalFUDqsI1tdJ+z3fntH5gfyG8rZ67/ALvXjHXJ6Y5q+TgZoAKK5DwB4qXxBd6lE17aXADC5tRA6kpbuWVFfBPzgoSc/wB8V0GtapDpUEc09vdzLJIIwLeBpTuP3QQvIycDPTJGSOtAF+imu6pGZJCEVRlixwAPeuL1nxVqMM0WoW8Ah0tuLXzIyTqD5+4Mcxlh/q+DvPXAxkA7amRdX/3v6CljYvGrFWQkA7W6j2NJF1f/AHv6CgCKW9tI3KPcRqw6gmkS+s3cKtxGSeAM15547uXt7qPZdyW+55Pu3k0G7kf884pM/jj8ad4YuLKaU/Z9Wv72YIpkjnmlkROf4S6L34z1x2FAHpVHSisTxnLBDpAea8ubZt/7swyvHvfBwrMisQv4GgDbyPWiuJ8EXcVxqZWbUbx5wp8uI3088bDHJPmQxgEdutdtQAUZHqKz/ET2yaRM13dXNpCNu6W3Zg68jGCoJ/SuN0m+RvEttDbaleXFuZgFM2o3O5hjvGYNv4F8e9AHoVFFVNacppF44YqVgcgh2Qjg/wAShiPqAT7GgC3keoory211Kc3MIN9cHMijH9s3ZzyOxtQD+Yr1KgAooNeZXureJhq9laTatqlhPNJ/pEP2GEQoDd28QEUjRneNkrHO4noTjpQB6bRUdxIYbeSby5JdiFtiDLNgdAO5qvpGow6pYJe28dwkLk7POhaNmGeu1sEA+4BoAuUV5ifGET6u+r21xcLdOqxppggYrNFk4DNjiYkkDHAPyHPJHpcEqzQJMnKuoYYIP8uKAH0VQ0bVYdVilkgt7uEROY2+0QNEdw+8AD1weMjj0JrAit0ula4nt9XnkfU54WaG8kRQiyuAcBwAoAA6Dp+dximm2yW3sjrqKxvDGUOpW+Z/LgvWjjWaQuyr5cZ6kk4JJI579ulWtP1WG9v7uyjt7uOS0bbK0sBRMnptY8NkYPGeCM4PFKceV2GndGR8Uv8Akn+sf9cP/ZhUnxL1C+0vwBrmoaZdfZL2CykeCfy1fynxw21uGwecHg1H8UyB8PtZJOALfn/voVcvNb8K3lrJa3er6NcQSrtkiluYmVx6EE4Irp9nKdCPKr6v8omPNGNV3fRfqcDp/wAQ9ffxvB4We1tZtXNsltPaySeTDHdJ57ySlwrNskijRkGDkMOmGqxpHxS1LWrW3utK8M2/k3d5FZW5udR2EyNYm7ZmCxthVA25GS3UcV02pn4e6nLPNqLeGbuW4WNZnmaB2kCFigYnkhSzY9Nx9TU8d74IjKmO78PIVlEy7ZYRiQR+UGHPXy/kz/d46Vj7Cr/K/uNPaw7o4mb4v3MWlLdSeHoFupbCHU4bYXryM9tJA8xJ2RHDLsw38A3Kd3aus8AeIrnxBqOuyO5+yRS2rWkbKoaOOWzhmIJHU7pCef5UXEfw4uEhSdfC0qwqiRBzAQiopVAPQKCQB2BIq7pmo+C9LiMWm3+g2aEKCsE8KAhVCL0PZVCj0AAo9hV/lf3B7WHdHQUVlf8ACSeHf+g9pf8A4GR/40f8JJ4d/wCg9pf/AIGR/wCNHsKv8r+4Paw7o1a5r4df8ge9/wCwtff+lD1of8JJ4d/6D2l/+Bkf+NZvw1kjl0K7lidZI31S9ZXQgqwNw+CCOorX2c4UJcytqv1M+eMqqs+j/Q5f4oeP7nw94z0rT7O6MdpZLHeawotGlEkEsohVdwU+XtXzZskjPlAdCadD8VJLt9FtbPRYGvNYgV7dZL7EaMZnjAdghO3EbEEAknCgd69CbTdOY3hawtmN6oW7JiH78BdoD8fMNvGD24rNTwb4RSCeBPDGjJFOAsqLZRgOA24AjHQMM/XnrXKbnMat8S30xLxbnRoTPaTWsLJHfBlZprdpjtbZyF2kZxz146VJp/xGk/4RnX9Y1XRDbPo+nw6g0Ftc+d5kcsJlVdxVcMMFTwRxkE11EnhXwxJNFNJ4d0lpIYlhiY2cZKRqCFQccAAkAdgT61ci0vTIkmSLTrRFniWGYLCoEkartVG45UAkAHgA4oA4GX4kaxbRwyal4Tk06ATMlzdXUsiQxqPLw3+qLqD5jDdIqIDGRu+ZTTLn4o3NtMUn0S1jW5ub2008i+Z2mltrtbYq6LESpcuGULuOflOODXYR+DvCcUdvHH4a0hEtnMkIFnGPLY4yRxweF/Iegqvp3gXwrZ297C2i2d2b6aWa6kubdHeUyTNMQx28gO2R6YHcZoA5bS/ipNqGmXupx6CiWuk2jXOpiS8KyrtkuI9sKlP3hzbt94p94DqCB2fhfVbrWvDdrqV5YGxlnKsYTv8AlG4Y++qk/XGD2yKWPwn4WiuILiLw5pMc0DM0LrZxgxljlivHBJJJ9yauWWm6fpWnR2Ol2VvZWqOCkMEYRFywJwBwKAL1cXpeP+Fxa7/2BrP/ANGTV2lQpaWqXkl6ttCtzIixvMEG9lUkhSepAycD3NaU58qku6t+KM5w5nF9mePaxbWUzaiby3tpIx48hVjMilQrJEGBzxgjAPrWja6dGn/CxdY021RtWtZ5UsJEUFoT9jj/ANX/AHSfbrgelekXGkaTcW1zbT6ZZSwXUnmXEbwKyzPx8zAjDHgcn0FTWdlZWXmfY7SC381g0nlRhdxACgnHU4AH0ArreM92yX9af5HKsH712/61/wAzyjwppWJfCN9aXvhSyy6NFJZ+Z9qvUMZ8yNyT85IySWzgrnivXJpYokDzSIi5wCzADP41SstE0axvZL2y0mwtrqTO+aK2RHbPXLAZNS6tpmm6tbC11XT7S/gDBxFcwrIoYdDhgRnk8+9Y16yrTTexrRoulFpbnM/BiWJvhvo0ayIzrC25QwJHztXY1m6RoOh6RLJLpOjafYSSALI1tbJGXA6AlQM1pVnWmp1HKPU0owcKai+hHF/rZv8AeH8hXG/FTRtKudPt7+4sYpLo31nCZTndsM6ArkHoQSPxrsov9bN/vD+QpLm3t7qMR3MEcyBlcLIgYBlOQee4IBFKlUdOakh1aaqQcWebeOVbR7s6Zokq6Tbx2UMitbxoHXdexqw3kEhSGJxnGas6rr3iCytPEV9HqBljs9Qi0+3j+zxnYJPIBlY8biu9iASF9eOndXum6de+Z9ssLW48yLyX82JW3JnO05HIzzj1pU0+wS3mt1srZYZ/9dGIhtk4C/MMc8ADnsBXQsTDlSlG/wDS/wAjB4eV24yt/TMPwVf6vc3epWmpfaHitzGYJLlYUm+ZTuV1iYgYwCDgZDe2ayLzTLO+8Yq2ktM97bX6T3+pSTnEIGD9lTnByuAUAwoYk8kCuz03TtP02Ew6dY21pEx3FIIggJ9SAOtVG8N+HmvjfHQ9NN0ZPNM32VN5fOd2cZznnNQq0VNyWl+xToycVF6mrRRRXMdJFdf6k/UfzFLdQpcW0kEu/ZIpVtrlTg+hBBH1FJc/6o/UfzFS0AeaLbw6fY+Ib1DcXL2mtRWsCXd3NNGsbG342s+DguxBPQ1PpmveIo5Y43vre7Uf2ncy7rf5ylvcBFiXDcEgkZIOOODjnu2sLF45o2s7dknkEsqmIYdxjDN6n5V59h6U2303Tra6e6t7C2huJCxeVIlVmLEFiSBnkqufXArseJi17yv/AMNb/gnIsPJP3Xb/AIf+kc14G17XNVuYTqNvGtvdWK3aOFRChJHyqBI5ZMHhiByvPXAp67P9n8T2V3aaleOjapHBdTi9zHAThRbeQD/ESp3YJG7dniuw0/S9N095HsNPtbVpTmQwwqhb64HPehtK0ttRGpNp1ob0dLgwr5g4x97GenFT7aCm5Jaf1/X6Fexm4KLepcooorlOkKii/wBdL+H8qlqKL/XS/h/KgDF8WPfrc6VDYancWj3V4IXWNI2ym1nc/Op5AQ/nWPp3ijVWls4oraO7S4jhCGaULKzyvKVJKoF2iOLccDPIrs5YIJZI5JYY3eMkozKCVyMHHpkEioI9N06KeOeOwtkljRUjdYgCqqCFAOOAASB7E1vGpBRs43MJU5uV1Kxk+HPEk2rs8zaVc29l5BminZWO5c4AxjkkfMNuePeq/g/XbvW9c1UyO0drDHB5Ns0DI0ZbcTvLAEsRsJHQZ/E9BZWFjZPK9nZ29u0p3SGKMKXPvjr1P51NHFFG8jpGqvI26RguCxwBk+vAA/Ck50/etHfYahPS72H0UUVibBUd1/x7vUlR3X+oagBZlZ4XRZGiZlIDqBlfcZBGfqK4Jtd1fTrS+me/mv5ozqIiWcRJGBb8ru2Rgk4969Aqq+nae4YPY2zBt5IMQOd/3+38Xf171tSqRj8SuZVISl8Lsc1P4vvopGt/7G86c3UtvGsUrPvMShnOAhIySAPxJwBU3ifUdUigsb63+1W1otpLc3kcTwCZAAhHEgYHAL5x3xz0rbk0fSZLQWkmmWb24feIzCpXdjGcY644zT7zTdOvFiW7sbadYeYhJEGCfTI46Cq9pTTTUSPZ1GmnIsQSLNCkqElXUMMjHBGafRRXOdAUUUUARJ/x8SfRf61h+NJdQj/stNP1K5s5bm+jtyIkjYMpyzn51bkIrYx+tbif8fEn0X+tLLDDLJFJLEjvE26NmUEo2CMj0OCR9CaAOGsvFmqiW2jgt0vopVRUNxMElkeW4kjjOUQKF2RO54zj9drwx4luNbvCq6TPDZsjtHctnadrbe4H3uSME8DnB4rXj0rS45opo9OtElhRUidYVBRVzgA44A3Nj6n1p1np2n2c001pY29vLOd0rxxBS565JA55z+dAHN+B9dvtXvWe+aeMz2ou4rciMxrEzkIQVG4NheQxOeoxggddVaysLGxaVrOzt7dpm3ymKMLvb1OOpqzQAUUUUAR3X/HtJ/umnSqWidVdoyVIDLjK+4zxTbr/AI95P901JQB502v6xpkd/LJqVxqDxXF9DCk6wpGBDCZFLbIwx6diK0G8Z6hGBC2jLLcNc/ZY/Jldw7rAJXbAQkDBwOCepOBXVtp2nsSWsbYku7nMS8swwx6dSOD6ioW0TR2svsLaVZG13B/JMC7NwGAcY64AGfSgDK8R3t9L4ctLy0ubzS7668uOGDbET5suAqvuVhhSSTjHANdFArpCiSSGV1UBnIALHHXA45pjWtsyQo1vEVgIaJSgxGQMAr6YHHFTUAFFFFABUa/8fD/7i/zNSVGv/Hw/+4v8zQBhXem6+1+9rb6qE0u4cyySkn7TBzzFGcY2t/ePKDIGcgruy/fi/wB//wBlNSVHL/rIv9//ANlNAElGR61T12SSLRL6WI3IkS2kZTboHlyFONingt6A9TiuU1fUrOxvdQsL3UNf09ILKFTqLSB4YgQdsp67TlCCzAKcEE80AdvRTIHSSFJEkWRGUFXUghgR1GKfQAUUUUAFFFFABRRRQAVnS/8AHxL/AL39BWjWdL/x8S/739BQB+SdfRHwMS5tPBWl39ttEiXDzIT/AHlkOP5V88DrX058G7WVvh3osUMTyu0LOFRSScux6D614XETtho2/mX5M+x4Rt9cm3tyv80ds2labdpNqaLqlvbbyZYo7PzVjJ5KiXcFxzxuAPTIrnfiI0mp6FrEnkLFH9geOKMNu2RpFhRnucDr65r36yjh0W30nwJNau8V9aO1/KIyQJZBwM9OxH4LXjXi/R7qxt9V065idXSKaLLKQG+UjI9e35189iKHsHBrur+T3/XofT4THfWvaRk3s+W73i9L7Lt1u7W11PjZeQp+lfpf+z//AMkK8D/9gO1/9BFfmgnRfoK/S/8AZ/GfgV4IHTOhW3/oFffs/K57Hpp6VgyeGYvtE8ttq2rWazytK0UFyAgdjliAVOMnJ/GtBL51XElu7MO6EYP5mnfbx/z7T/8Ajv8AjSMyTTLKDTtOt7C1Vlgt4lijDMSdqjAyT1qxVP7eP+faf/x3/Gj7eP8An2n/APHf8aAKuh6DbaTqF/eQTzyNevuKybcRjfJJtXABxvlkPOTyB0ArWqn9vH/PtP8A+O/40fbx/wA+0/8A47/jQBajBWNVPUACsnxH4Y0XxDJbyatavM9srrEyXEkTKHxvGUYZB2rkH0q59vH/AD7T/wDjv+NH28f8+0//AI7/AI0AT2dtb2dpDaWsKQ28KLHFGgwqKBgADsAKra5pNlrVg1jqCSvbscskc7xbvYlGBI9ulO+3j/n2n/8AHf8AGj7eP+faf/x3/GgC1EixRJGpYqqhRuYsePUnk/U0y7iae2khWVoi6ld6qrEfgwIP4ioPt4/59p//AB3/ABo+3j/n2n/8d/xoAp+HdAt9DMotHURy8tGltFEN3r8ignjj0rYqn9vH/PtP/wCO/wCNH28f8+0//jv+NNu7uJKxDaaFpVrqL6hBa7Z2Z2yZGKoX5YqpO1C3cqBnvWlVP7eP+faf/wAd/wAaPt4/59p//Hf8aQxF0nThFFELVNsVwblOTkSlixbPXOWP5mrtU/t4/wCfaf8A8d/xo+3j/n2n/wDHf8aAEl0nT5YriKS1RluZRNLknLONuGznII2rjHoKls7O3tDMbdNnnSmVwOm4gA/ToKj+3j/n2n/8d/xo+3j/AJ9p/wDx3/Gnd7BYytb8MWWqahJdXFrDKJVRJUeWQJMqZKh0B2sASSMjg1r2EM9uiw+XaxwIoVUhUjGOAAOmMU37eP8An2n/APHf8aPt4/59p/8Ax3/GvNWXWre19rPe9ubT0t2L59LWLlcd8ULf4i3VjbW/gBvCf7zzEv115bgqyEAKI/JIIP3s59sV0v28f8+0/wD47/jR9vH/AD7T/wDjv+NeiQeT6Zpv7Q0WqWU90PhIsMAELG3h1ASLAWXeq5OM4UYz3Ar2T61T+3j/AJ9p/wDx3/Gj7eP+faf/AMd/xoAtTRxzQvDMiyRupV0YZDA8EEdxXOHwfbC6gnTV9aT7NkW6C8JWIHjABB7cZOTjIzya2ft4/wCfaf8A8d/xo+3j/n2n/wDHf8aALlNRSC2e7ZFVft4/59p//Hf8aPt4/wCfaf8A8d/xoAxNY8OSX9wWeS4ChmKmC9lgzk99jDP45qeDSLtRFEW+RMAF5S5wPc8k+5rU+3j/AJ9p/wDx3/Gj7eP+faf/AMd/xoAuVHcwpcQPBIZArqVJjkZGAPoykEH3BzVf7eP+faf/AMd/xo+3j/n2n/8AHf8AGgA0zTbfT1dYJLxw5BP2i8lnxj08xmx+FXKp/bx/z7T/APjv+NH28f8APtP/AOO/40AXKzzpFqdR+3mbUPN3btov5xFnGP8AV79mPbGKk+3j/n2n/wDHf8aPt4/59p//AB3/ABoAuUy4iWeB4XLhXUqSjlGAPoykEH3BzVb7eP8An2n/APHf8aPt4/59p/8Ax3/GgCmnhzT0dWFzrGVIIzrF0Rx6gyc1sVT+3j/n2n/8d/xo+3j/AJ9p/wDx3/GgC5VDUtIsdQura5uo2aS2IMZDkY/eRyduvzRJ+XvT/t4/59p//Hf8aPt4/wCfaf8A8d/xoAuUVT+3j/n2n/8AHf8AGj7eP+faf/x3/GgCFNB0hNWfVVskF24OXycZIwWC52hiOCwGSO9W9Ps7aws4rOzhWG3iXbHGvRR6VF9vH/PtP/47/jR9vH/PtP8A+O/40AXKx5dAjaV2i1HUrdWlaby4rjaoZiScDHQkk46c1c+3j/n2n/8AHf8AGj7eP+faf/x3/GqjNx2E0nuLpdhFYRSIkk0rSyGSSSV9zM2AMk/QAfhVuqf28f8APtP/AOO/40fbx/z7T/8Ajv8AjSbbd2CVizNFFNE0U0aSRsMMrjII9wao/wBg6H/0BtO/8BU/wqX7eP8An2n/APHf8aPt4/59p/8Ax3/GmpSjswcU9yL+wdD/AOgNp3/gKn+FH9g6H/0BtO/8BU/wqX7eP+faf/x3/Gj7eP8An2n/APHf8aftJ92Lkj2Iv7B0P/oDad/4Cp/hR/YOh/8AQG07/wABU/wqX7eP+faf/wAd/wAaPt4/59p//Hf8aPaT7sOSPYi/sHQ/+gNp3/gKn+FH9g6H/wBAbTv/AAFT/Cpft4/59p//AB3/ABo+3j/n2n/8d/xo9pPuw5I9iL+wdD/6A2nf+Aqf4VahsrSCMRQW0MUa9FRAoH4Covt4/wCfaf8A8d/xo+3j/n2n/wDHf8aTnKW7GopbIsfZ4f8AnmtH2eH/AJ5rVf7eP+faf/x3/Gj7eP8An2n/APHf8akZY+zw/wDPNaPs8P8AzzWq/wBvH/PtP/47/jR9vH/PtP8A+O/40AWPs8P/ADzWj7PD/wA81qv9vH/PtP8A+O/40fbx/wA+0/8A47/jQBY+zw/881pDbwHrEp/CoPt4/wCfaf8A8d/xo+3j/n2n/wDHf8aALH2eH/nmtH2eH/nmtV/t4/59p/8Ax3/Gj7eP+faf/wAd/wAaALH2eH/nmtH2eH/nmtV/t4/59p//AB3/ABo+3j/n2n/8d/xoAsfZ4f8AnmtH2eH/AJ5rVf7eP+faf/x3/Gj7eP8An2n/APHf8aALH2eH/nmtH2eH/nmtV/t4/wCfaf8A8d/xo+3j/n2n/wDHf8aAJ/s8H/PJfypfs8P/ADzWq/28f8+0/wD47/jR9vH/AD7T/wDjv+NAFj7PD/zzWj7PD/zzWq/28f8APtP/AOO/40fbx/z7T/8Ajv8AjQBY+zw/881o+zw/881qv9vH/PtP/wCO/wCNH28f8+0//jv+NAFj7PD/AM81o+zw/wDPNar/AG8f8+0//jv+NH28f8+0/wD47/jQBObeA9YlP4Uv2eH/AJ5rVf7eP+faf/x3/Gj7eP8An2n/APHf8aALH2eH/nmtH2eH/nmtV/t4/wCfaf8A8d/xo+3j/n2n/wDHf8aALH2eH/nmtH2eH/nmtV/t4/59p/8Ax3/Gj7eP+faf/wAd/wAaALH2eH/nmtH2eH/nmtV/t4/59p//AB3/ABo+3j/n2n/8d/xoAsfZ4f8AnmtJ9ng/55L+VQfbx/z7T/8Ajv8AjR9vH/PtP/47/jQBY+zw/wDPNaPs8P8AzzWq/wBvH/PtP/47/jR9vH/PtP8A+O/40AWPs8P/ADzWj7PD/wA81qv9vH/PtP8A+O/40fbx/wA+0/8A47/jQBY+zw/881o+zw/881qv9vH/AD7T/wDjv+NH28f8+0//AI7/AI0AWPs8P/PNaQ28B6xKfwqD7eP+faf/AMd/xo+3j/n2n/8AHf8AGgCx9nh/55rR9nh/55rVf7eP+faf/wAd/wAaPt4/59p//Hf8aALH2eH/AJ5rR9nh/wCea1X+3j/n2n/8d/xo+3j/AJ9p/wDx3/GgCx9nh/55rR9nh/55rVf7eP8An2n/APHf8aPt4/59p/8Ax3/GgCx9nh/55rR9nh/55rVf7eP+faf/AMd/xo+3j/n2n/8AHf8AGgCf7PB18pfypfs8P/PNar/bx/z7T/8Ajv8AjR9vH/PtP/47/jQBY+zw/wDPNaPs8P8AzzWq/wBvH/PtP/47/jR9vH/PtP8A+O/40AWPs8P/ADzWj7PD/wA81qv9vH/PtP8A+O/40fbx/wA+0/8A47/jQBY+zw/881o+zw/881qv9vH/AD7T/wDjv+NH28f8+0//AI7/AI0ATm3gIwYlP4Uv2eH/AJ5rVf7eP+faf/x3/Gj7eP8An2n/APHf8aALH2eH/nmtH2eH/nmtV/t4/wCfaf8A8d/xo+3j/n2n/wDHf8aALH2eH/nmtH2eH/nmtV/t4/59p/8Ax3/Gj7eP+faf/wAd/wAaALH2eH/nmtH2eH/nmtV/t4/59p//AB3/ABo+3j/n2n/8d/xoAsfZ4f8AnmtJ9ngznyl/KoPt4/59p/8Ax3/Gj7eP+faf/wAd/wAaALH2eH/nmtKkUaHKooPriq328f8APtP/AOO/40fbx/z7T/8Ajv8AjQBPeW8d3aTWspcRzRtG2xyrYIwcEcg89RWVceGtNutQsL68866lsofKUTMGWXpteQYwzKckHsWJ9MXvt4/59p//AB3/ABo+3j/n2n/8d/xoAuUVT+3j/n2n/wDHf8aPt4/59p//AB3/ABoAuUVT+3j/AJ9p/wDx3/Gj7eP+faf/AMd/xoAuUVT+3j/n2n/8d/xo+3j/AJ9p/wDx3/GgC5RVP7eP+faf/wAd/wAaPt4/59p//Hf8aALlZ0v/AB8S/wC9/QVIb8kfLbS5/wBogD+dQDccs5yzHJoA/Jcda+wf2YvFejaLoGhy3Gs2ttbLZeTeLkM+QSduOoO7HPpXx9QVU9VU/UVy4vC/WOVqVnF3/rY9XBYxYbnTjdSVn/Vn+R+gF/8AFa+a/uDY6lpSWvmN5KuUJ2Z4yd3XFUvip460LV9OgkGtWYtILdpJo3ZVMchXBwSfmGOMCvgvYn9xf++RShUByEUfhXDLKatSMoTrNp+X5anoQzijTnGdOgk4+flbXQUYyMdO1fpf+z7/AMkM8D/9gK2/9Ar80B1Ffpf+z7/yQzwP/wBgK2/9Ar2WfPT2O5oqpdanptrdxWl1qNnBcy48uKSdVd89MAnJq3SMyN5QsgQJI7YzhFzxQkoZwmyRSQSNy4zj/wDXXO/EfS9S1vwjrOj6RqbaVqN7YGG1vFMi+TJuyG3J8w/DnmrnhiTXZbOFvEf9l/2iPM8z+zVlEAXcu3HmjdnHX9KANqiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAI7iaO3iMspIUEDgZ61W/tSz/vS/9+zTtXjkksisaM7b1OAOetYa6fdC6a48i6JKKm0/dGCTkD156+gHpQBtf2paesv/AH7NH9qWnrL/AN+zXPRaLcJp7Wfl3m1nZ96/K43SF8A/U4+lNv8AR726v7S4+zkLbuXKmI7ie2CDwPbHPFAHR/2pZ/3pf+/Zo/tS0/vS/wDfs1iw6fdRTyyiC5YyMrFW5VcADgdumT71Xg0W4i06OyCXhVG3bx8rH59/X07fTigDov7UtP70v/fs0f2pZ/3pf+/ZrnrrSr6XUbS7WBlEHmBlMRJYMAMA5wOnoasQafdRTSSiC5YyOHIbkLgAYHoOM/Uk96ANn+1LT1l/79mstta1R4Li6trTTnjiuTAsDTyCdzuwoxtxuYYYDpg8ng1RttFuINNt7IR3hWDYQ44ZtrbufY4wR3FVba8+wyzeIWtNQa4tHlgEQsWMJjRyjEyY+98pIbooJGDzkA7clwSBb3BGf+edGZP+fe4/74pJlt3t76Avff6WW3Eqx8vKBMLxwOM49SfWl/0c2dtbb75RAYyGRGUtsxweOQccjvQAZk/597j/AL4ozJ/z73H/AHxUSgLrz6lvfy2thAY/sz7shiQd2cY5PGPxpJhZXWn6jZJcXrC6MqSNgsYmZcELx8uM5x2zQBNmT/n3uP8AvijMn/Pvcf8AfunzSW8qW6lr1PIdXGxGXdgYw3HI56VVijjXX5tTYkq8IiRVtXDjpks2cN0444/E5AJ8yf8APvcf98UZk/597j/vioL8WR0XULSa4vY4blJTJNIDmIODkgkYAGePTFW5pYJLmCcm9UwlsKqMFbIx8wxzjtQBHmT/AJ97j/v3RmT/AJ97j/viq8MMcfiCbVS0mJIRFsW2YE4IILHOCRg9hwfanXMFtNocmlGbUNjxlPNZWaTk5zkjrQBNmT/n3uP++KMyf8+9x/3xTp7m0+220zy3UbjekceGCyEjJyv8RAUkenNVbOLyNdvNTe5uZVuYkjERtyPLCFiMH0+Y9qALGZP+fe4/74ozJ/z73H/fFQ3MFtNoq6WZr/aqovmsrNIdpBySRyTjmrbzwNdx3ObwGNHQIEbYdxU5IxyRt4PbJ9aAIsyf8+9x/wB+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+        }
+      },
+      "input_ports": [],
+      "output_ports": [],
+      "self_healing": null,
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null,
+      "data": {
+        "isVWBCatalogAction": true,
+        "label": "Attendre Ancre",
+        "parameters": {
+          "visual_anchor": {
+            "anchor_id": "anchor_1768344954268",
+            "anchor_type": "generic",
+            "bounding_box": {
+              "height": 85.49337260677467,
+              "width": 618.9368770764119,
+              "x": 46.34551495016611,
+              "y": 39.66494238604853
+            },
+            "confidence_threshold": 0.8,
+            "description": "Élément sélectionné pour l'étape vwb_action_1768344887499",
+            "metadata": {
+              "capture_method": "screen_capture",
+              "capture_timestamp": "2026-01-13T22:55:54.268Z",
+              "screen_resolution": {
+                "height": 1080,
+                "width": 1920
+              }
+            },
+            "reference_image_base64": 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+          }
+        },
+        "stepType": "wait_for_anchor",
+        "vwbActionId": "wait_for_anchor"
+      }
+    },
+    {
+      "id": "step_1768344978757",
+      "type": "type_text",
+      "position": {
+        "x": 189.4265495917023,
+        "y": 87.55595890675795
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "text": "Ceci est un texte",
+        "visual_anchor": {
+          "anchor_id": "anchor_1768345005655",
+          "anchor_type": "generic",
+          "bounding_box": {
+            "height": 493.0780559646539,
+            "width": 493.3554817275747,
+            "x": 374.25249169435216,
+            "y": 157.9639114582135
+          },
+          "confidence_threshold": 0.8,
+          "description": "Élément sélectionné pour l'étape vwb_action_1768344989070",
+          "metadata": {
+            "capture_method": "screen_capture",
+            "capture_timestamp": "2026-01-13T22:56:45.655Z",
+            "screen_resolution": {
+              "height": 1080,
+              "width": 1920
+            }
+          },
+          "reference_image_base64": 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+        }
+      },
+      "input_ports": [],
+      "output_ports": [],
+      "self_healing": null,
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null,
+      "data": {
+        "isVWBCatalogAction": true,
+        "label": "Saisir Texte",
+        "parameters": {
+          "text": "Ceci est un texte",
+          "visual_anchor": {
+            "anchor_id": "anchor_1768345005655",
+            "anchor_type": "generic",
+            "bounding_box": {
+              "height": 493.0780559646539,
+              "width": 493.3554817275747,
+              "x": 374.25249169435216,
+              "y": 157.9639114582135
+            },
+            "confidence_threshold": 0.8,
+            "description": "Élément sélectionné pour l'étape vwb_action_1768344989070",
+            "metadata": {
+              "capture_method": "screen_capture",
+              "capture_timestamp": "2026-01-13T22:56:45.655Z",
+              "screen_resolution": {
+                "height": 1080,
+                "width": 1920
+              }
+            },
+            "reference_image_base64": 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+        },
+        "stepType": "type_text",
+        "vwbActionId": "type_text"
+      }
+    }
+  ],
+  "edges": [
+    {
+      "id": "conn_1768344690845",
+      "source": "step_1768344559716",
+      "target": "step_1768344642508",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    },
+    {
+      "id": "conn_1768344834473",
+      "source": "step_1768344642508",
+      "target": "step_1768344749439",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    },
+    {
+      "id": "conn_1768344966317",
+      "source": "step_1768344749439",
+      "target": "step_1768344883203",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    },
+    {
+      "id": "conn_1768344985005",
+      "source": "step_1768344883203",
+      "target": "step_1768344978757",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    }
+  ],
+  "variables": [],
+  "settings": {
+    "timeout": 300000,
+    "retry_on_failure": true,
+    "max_retries": 3,
+    "enable_self_healing": true,
+    "enable_analytics": true
+  },
+  "tags": [],
+  "category": "default",
+  "is_template": false
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_20260114_004101.json b/visual_workflow_builder/backend/data/workflows/wf_20260114_004101.json
new file mode 100644
index 000000000..31fddc83d
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_20260114_004101.json
@@ -0,0 +1,366 @@
+{
+  "id": "wf_20260114_004101",
+  "name": "Ouverture et saisie dans onlyoffice texte v2",
+  "description": "Workflow créé avec le Visual Workflow Builder V2 permet sous linux ubuntu d'ouvrir onlyoffice texte et d'écrire",
+  "created_by": "unknown",
+  "created_at": "2026-01-14T00:41:01.445882",
+  "updated_at": "2026-01-14T00:41:01.445882",
+  "nodes": [
+    {
+      "id": "step_1768344559716",
+      "type": "click_anchor",
+      "name": "Cliquer sur Ancre",
+      "label": "Cliquer sur Ancre",
+      "position": {
+        "x": -193.03488507130817,
+        "y": -102.23548539592744
+      },
+      "data": {
+        "isVWBCatalogAction": true,
+        "label": "Cliquer sur Ancre",
+        "parameters": {
+          "visual_anchor": {
+            "anchor_id": "anchor_1768344583207",
+            "anchor_type": "generic",
+            "bounding_box": {
+              "height": 50.69955817378491,
+              "width": 42.857142857142854,
+              "x": 0.4983388704318937,
+              "y": 622.212806892676
+            },
+            "confidence_threshold": 0.8,
+            "description": "Élément sélectionné pour l'étape vwb_action_1768344569642",
+            "metadata": {
+              "capture_method": "screen_capture",
+              "capture_timestamp": "2026-01-13T22:49:43.207Z",
+              "screen_resolution": {
+                "height": 1080,
+                "width": 1920
+              }
+            },
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+          }
+        },
+        "stepType": "click_anchor",
+        "vwbActionId": "click_anchor"
+      },
+      "stepType": "click_anchor",
+      "parameters": {
+        "visual_anchor": {
+          "anchor_id": "anchor_1768344583207",
+          "anchor_type": "generic",
+          "bounding_box": {
+            "height": 50.69955817378491,
+            "width": 42.857142857142854,
+            "x": 0.4983388704318937,
+            "y": 622.212806892676
+          },
+          "confidence_threshold": 0.8,
+          "description": "Élément sélectionné pour l'étape vwb_action_1768344569642",
+          "metadata": {
+            "capture_method": "screen_capture",
+            "capture_timestamp": "2026-01-13T22:49:43.207Z",
+            "screen_resolution": {
+              "height": 1080,
+              "width": 1920
+            }
+          },
+          "reference_image_base64": 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+        }
+      },
+      "isVWBCatalogAction": true,
+      "vwbActionId": "click_anchor"
+    },
+    {
+      "id": "step_1768344642508",
+      "type": "type_text",
+      "name": "Saisir Texte",
+      "label": "Saisir Texte",
+      "position": {
+        "x": 151.16062816354855,
+        "y": -94.44477319437412
+      },
+      "data": {
+        "isVWBCatalogAction": true,
+        "label": "Saisir Texte",
+        "parameters": {
+          "text": "onlyoffice",
+          "visual_anchor": {
+            "anchor_id": "anchor_1768344670956",
+            "anchor_type": "generic",
+            "bounding_box": {
+              "height": 23.85861561119293,
+              "width": 208.30564784053155,
+              "x": 508.80398671096344,
+              "y": 36.68261543464941
+            },
+            "confidence_threshold": 0.8,
+            "description": "Élément sélectionné pour l'étape vwb_action_1768344652324",
+            "metadata": {
+              "capture_method": "screen_capture",
+              "capture_timestamp": "2026-01-13T22:51:10.956Z",
+              "screen_resolution": {
+                "height": 1080,
+                "width": 1920
+              }
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+            "reference_image_base64": 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+          }
+        },
+        "stepType": "type_text",
+        "vwbActionId": "type_text"
+      },
+      "stepType": "type_text",
+      "parameters": {
+        "text": "onlyoffice",
+        "visual_anchor": {
+          "anchor_id": "anchor_1768344670956",
+          "anchor_type": "generic",
+          "bounding_box": {
+            "height": 23.85861561119293,
+            "width": 208.30564784053155,
+            "x": 508.80398671096344,
+            "y": 36.68261543464941
+          },
+          "confidence_threshold": 0.8,
+          "description": "Élément sélectionné pour l'étape vwb_action_1768344652324",
+          "metadata": {
+            "capture_method": "screen_capture",
+            "capture_timestamp": "2026-01-13T22:51:10.956Z",
+            "screen_resolution": {
+              "height": 1080,
+              "width": 1920
+            }
+          },
+          "reference_image_base64": 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+        }
+      },
+      "isVWBCatalogAction": true,
+      "vwbActionId": "type_text"
+    },
+    {
+      "id": "step_1768344749439",
+      "type": "click_anchor",
+      "name": "Cliquer sur Ancre",
+      "label": "Cliquer sur Ancre",
+      "position": {
+        "x": 487.0496771091505,
+        "y": -86.20244456930321
+      },
+      "data": {
+        "isVWBCatalogAction": true,
+        "label": "Cliquer sur Ancre",
+        "parameters": {
+          "visual_anchor": {
+            "anchor_id": "anchor_1768344821688",
+            "anchor_type": "generic",
+            "bounding_box": {
+              "height": 93.44624447717231,
+              "width": 98.67109634551491,
+              "x": 572.5913621262458,
+              "y": 72.47053885143882
+            },
+            "confidence_threshold": 0.8,
+            "description": "Élément sélectionné pour l'étape vwb_action_1768344797121",
+            "metadata": {
+              "capture_method": "screen_capture",
+              "capture_timestamp": "2026-01-13T22:53:41.688Z",
+              "screen_resolution": {
+                "height": 1080,
+                "width": 1920
+              }
+            },
+            "reference_image_base64": 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+          }
+        },
+        "stepType": "click_anchor",
+        "vwbActionId": "click_anchor"
+      },
+      "stepType": "click_anchor",
+      "parameters": {
+        "visual_anchor": {
+          "anchor_id": "anchor_1768344821688",
+          "anchor_type": "generic",
+          "bounding_box": {
+            "height": 93.44624447717231,
+            "width": 98.67109634551491,
+            "x": 572.5913621262458,
+            "y": 72.47053885143882
+          },
+          "confidence_threshold": 0.8,
+          "description": "Élément sélectionné pour l'étape vwb_action_1768344797121",
+          "metadata": {
+            "capture_method": "screen_capture",
+            "capture_timestamp": "2026-01-13T22:53:41.688Z",
+            "screen_resolution": {
+              "height": 1080,
+              "width": 1920
+            }
+          },
+          "reference_image_base64": 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+        }
+      },
+      "isVWBCatalogAction": true,
+      "vwbActionId": "click_anchor"
+    },
+    {
+      "id": "step_1768344883203",
+      "type": "wait_for_anchor",
+      "name": "Attendre Ancre",
+      "label": "Attendre Ancre",
+      "position": {
+        "x": -230.19595011018868,
+        "y": 29.872058799507542
+      },
+      "data": {
+        "isVWBCatalogAction": true,
+        "label": "Attendre Ancre",
+        "parameters": {
+          "visual_anchor": {
+            "anchor_id": "anchor_1768344954268",
+            "anchor_type": "generic",
+            "bounding_box": {
+              "height": 85.49337260677467,
+              "width": 618.9368770764119,
+              "x": 46.34551495016611,
+              "y": 39.66494238604853
+            },
+            "confidence_threshold": 0.8,
+            "description": "Élément sélectionné pour l'étape vwb_action_1768344887499",
+            "metadata": {
+              "capture_method": "screen_capture",
+              "capture_timestamp": "2026-01-13T22:55:54.268Z",
+              "screen_resolution": {
+                "height": 1080,
+                "width": 1920
+              }
+            },
+            "reference_image_base64": 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+          }
+        },
+        "stepType": "wait_for_anchor",
+        "vwbActionId": "wait_for_anchor"
+      },
+      "stepType": "wait_for_anchor",
+      "parameters": {
+        "visual_anchor": {
+          "anchor_id": "anchor_1768344954268",
+          "anchor_type": "generic",
+          "bounding_box": {
+            "height": 85.49337260677467,
+            "width": 618.9368770764119,
+            "x": 46.34551495016611,
+            "y": 39.66494238604853
+          },
+          "confidence_threshold": 0.8,
+          "description": "Élément sélectionné pour l'étape vwb_action_1768344887499",
+          "metadata": {
+            "capture_method": "screen_capture",
+            "capture_timestamp": "2026-01-13T22:55:54.268Z",
+            "screen_resolution": {
+              "height": 1080,
+              "width": 1920
+            }
+          },
+          "reference_image_base64": 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+        }
+      },
+      "isVWBCatalogAction": true,
+      "vwbActionId": "wait_for_anchor"
+    },
+    {
+      "id": "step_1768344978757",
+      "type": "type_text",
+      "name": "Saisir Texte",
+      "label": "Saisir Texte",
+      "position": {
+        "x": 189.4265495917023,
+        "y": 87.55595890675795
+      },
+      "data": {
+        "isVWBCatalogAction": true,
+        "label": "Saisir Texte",
+        "parameters": {
+          "text": "Ceci est un texte",
+          "visual_anchor": {
+            "anchor_id": "anchor_1768345005655",
+            "anchor_type": "generic",
+            "bounding_box": {
+              "height": 493.0780559646539,
+              "width": 493.3554817275747,
+              "x": 374.25249169435216,
+              "y": 157.9639114582135
+            },
+            "confidence_threshold": 0.8,
+            "description": "Élément sélectionné pour l'étape vwb_action_1768344989070",
+            "metadata": {
+              "capture_method": "screen_capture",
+              "capture_timestamp": "2026-01-13T22:56:45.655Z",
+              "screen_resolution": {
+                "height": 1080,
+                "width": 1920
+              }
+            },
+            "reference_image_base64": 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+        },
+        "stepType": "type_text",
+        "vwbActionId": "type_text"
+      },
+      "stepType": "type_text",
+      "parameters": {
+        "text": "Ceci est un texte",
+        "visual_anchor": {
+          "anchor_id": "anchor_1768345005655",
+          "anchor_type": "generic",
+          "bounding_box": {
+            "height": 493.0780559646539,
+            "width": 493.3554817275747,
+            "x": 374.25249169435216,
+            "y": 157.9639114582135
+          },
+          "confidence_threshold": 0.8,
+          "description": "Élément sélectionné pour l'étape vwb_action_1768344989070",
+          "metadata": {
+            "capture_method": "screen_capture",
+            "capture_timestamp": "2026-01-13T22:56:45.655Z",
+            "screen_resolution": {
+              "height": 1080,
+              "width": 1920
+            }
+          },
+          "reference_image_base64": 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+        }
+      },
+      "isVWBCatalogAction": true,
+      "vwbActionId": "type_text"
+    }
+  ],
+  "edges": [
+    {
+      "id": "conn_1768344690845",
+      "source": "step_1768344559716",
+      "target": "step_1768344642508"
+    },
+    {
+      "id": "conn_1768344834473",
+      "source": "step_1768344642508",
+      "target": "step_1768344749439"
+    },
+    {
+      "id": "conn_1768344966317",
+      "source": "step_1768344749439",
+      "target": "step_1768344883203"
+    },
+    {
+      "id": "conn_1768344985005",
+      "source": "step_1768344883203",
+      "target": "step_1768344978757"
+    }
+  ],
+  "variables": [],
+  "settings": {},
+  "tags": [],
+  "category": "default",
+  "is_template": false
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_20260114_101425.json b/visual_workflow_builder/backend/data/workflows/wf_20260114_101425.json
new file mode 100644
index 000000000..85ed5a77b
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_20260114_101425.json
@@ -0,0 +1,260 @@
+{
+  "id": "wf_20260114_101425",
+  "name": "test_1",
+  "description": "Test 1 aprés modifs descoordonnées d'ancre",
+  "created_by": "unknown",
+  "created_at": "2026-01-14T10:14:25.235317",
+  "updated_at": "2026-01-14T10:14:25.235317",
+  "nodes": [
+    {
+      "id": "step_1768381548100",
+      "type": "click_anchor",
+      "name": "Cliquer sur Ancre",
+      "label": "Cliquer sur Ancre",
+      "position": {
+        "x": -35.176572323879796,
+        "y": 111.2144230575727
+      },
+      "data": {
+        "label": "Cliquer sur Ancre",
+        "stepType": "click_anchor",
+        "parameters": {
+          "visual_anchor": {
+            "anchor_id": "anchor_1768381616132",
+            "anchor_type": "generic",
+            "reference_image_base64": 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",
+            "bounding_box": {
+              "x": 3.488372093023256,
+              "y": 631.1597877468732,
+              "width": 35.880398671096344,
+              "height": 42.74668630338738
+            },
+            "confidence_threshold": 0.8,
+            "description": "Élément sélectionné pour l'étape vwb_action_1768381569956",
+            "metadata": {
+              "capture_method": "screen_capture",
+              "capture_timestamp": "2026-01-14T09:06:56.132Z",
+              "screen_resolution": {
+                "width": 1920,
+                "height": 1080
+              }
+            }
+          }
+        },
+        "isVWBCatalogAction": true,
+        "vwbActionId": "click_anchor"
+      },
+      "stepType": "click_anchor",
+      "parameters": {
+        "visual_anchor": {
+          "anchor_id": "anchor_1768381616132",
+          "anchor_type": "generic",
+          "reference_image_base64": 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",
+          "bounding_box": {
+            "x": 3.488372093023256,
+            "y": 631.1597877468732,
+            "width": 35.880398671096344,
+            "height": 42.74668630338738
+          },
+          "confidence_threshold": 0.8,
+          "description": "Élément sélectionné pour l'étape vwb_action_1768381569956",
+          "metadata": {
+            "capture_method": "screen_capture",
+            "capture_timestamp": "2026-01-14T09:06:56.132Z",
+            "screen_resolution": {
+              "width": 1920,
+              "height": 1080
+            }
+          }
+        }
+      },
+      "isVWBCatalogAction": true,
+      "vwbActionId": "click_anchor"
+    },
+    {
+      "id": "step_1768381657878",
+      "type": "wait_for_anchor",
+      "name": "Attendre Ancre",
+      "label": "Attendre Ancre",
+      "position": {
+        "x": 149.63597878931995,
+        "y": 117.69521623356172
+      },
+      "data": {
+        "label": "Attendre Ancre",
+        "stepType": "wait_for_anchor",
+        "parameters": {
+          "timeout_ms": 40000,
+          "visual_anchor": {
+            "anchor_id": "anchor_1768381722873",
+            "anchor_type": "generic",
+            "reference_image_base64": 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+            "bounding_box": {
+              "x": 473.92026578073086,
+              "y": 25.747416612852657,
+              "width": 280.0664451827243,
+              "height": 42.746686303387335
+            },
+            "confidence_threshold": 0.8,
+            "description": "Élément sélectionné pour l'étape vwb_action_1768381688015",
+            "metadata": {
+              "capture_method": "screen_capture",
+              "capture_timestamp": "2026-01-14T09:08:42.873Z",
+              "screen_resolution": {
+                "width": 1920,
+                "height": 1080
+              }
+            }
+          }
+        },
+        "isVWBCatalogAction": true,
+        "vwbActionId": "wait_for_anchor"
+      },
+      "stepType": "wait_for_anchor",
+      "parameters": {
+        "timeout_ms": 40000,
+        "visual_anchor": {
+          "anchor_id": "anchor_1768381722873",
+          "anchor_type": "generic",
+          "reference_image_base64": 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+          "bounding_box": {
+            "x": 473.92026578073086,
+            "y": 25.747416612852657,
+            "width": 280.0664451827243,
+            "height": 42.746686303387335
+          },
+          "confidence_threshold": 0.8,
+          "description": "Élément sélectionné pour l'étape vwb_action_1768381688015",
+          "metadata": {
+            "capture_method": "screen_capture",
+            "capture_timestamp": "2026-01-14T09:08:42.873Z",
+            "screen_resolution": {
+              "width": 1920,
+              "height": 1080
+            }
+          }
+        }
+      },
+      "isVWBCatalogAction": true,
+      "vwbActionId": "wait_for_anchor"
+    },
+    {
+      "id": "step_1768381862621",
+      "type": "click_anchor",
+      "name": "Cliquer sur Ancre",
+      "label": "Cliquer sur Ancre",
+      "position": {
+        "x": -136.71208025455445,
+        "y": 285.256260474885
+      },
+      "data": {
+        "label": "Cliquer sur Ancre",
+        "stepType": "click_anchor",
+        "parameters": {
+          "visual_anchor": {
+            "anchor_id": "anchor_1768381925307",
+            "anchor_type": "generic",
+            "reference_image_base64": 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+            "bounding_box": {
+              "x": 527.7408637873754,
+              "y": 36.6660509966259,
+              "width": 115.61461794019942,
+              "height": 21.87039764359352
+            },
+            "confidence_threshold": 0.8,
+            "description": "Élément sélectionné pour l'étape vwb_action_1768381868692",
+            "metadata": {
+              "capture_method": "screen_capture",
+              "capture_timestamp": "2026-01-14T09:12:05.307Z",
+              "screen_resolution": {
+                "width": 1920,
+                "height": 1080
+              }
+            }
+          }
+        },
+        "isVWBCatalogAction": true,
+        "vwbActionId": "click_anchor"
+      },
+      "stepType": "click_anchor",
+      "parameters": {
+        "visual_anchor": {
+          "anchor_id": "anchor_1768381925307",
+          "anchor_type": "generic",
+          "reference_image_base64": 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+          "bounding_box": {
+            "x": 527.7408637873754,
+            "y": 36.6660509966259,
+            "width": 115.61461794019942,
+            "height": 21.87039764359352
+          },
+          "confidence_threshold": 0.8,
+          "description": "Élément sélectionné pour l'étape vwb_action_1768381868692",
+          "metadata": {
+            "capture_method": "screen_capture",
+            "capture_timestamp": "2026-01-14T09:12:05.307Z",
+            "screen_resolution": {
+              "width": 1920,
+              "height": 1080
+            }
+          }
+        }
+      },
+      "isVWBCatalogAction": true,
+      "vwbActionId": "click_anchor"
+    },
+    {
+      "id": "step_1768381947511",
+      "type": "type_text",
+      "name": "Saisir Texte",
+      "label": "Saisir Texte",
+      "position": {
+        "x": 72.9614879738022,
+        "y": 312.1366364737105
+      },
+      "data": {
+        "label": "Saisir Texte",
+        "stepType": "type_text",
+        "parameters": {
+          "text": "onlyoffice"
+        },
+        "isVWBCatalogAction": true,
+        "vwbActionId": "type_text"
+      },
+      "stepType": "type_text",
+      "parameters": {
+        "text": "onlyoffice"
+      },
+      "isVWBCatalogAction": true,
+      "vwbActionId": "type_text"
+    }
+  ],
+  "edges": [
+    {
+      "id": "conn_1768381728937",
+      "source": "step_1768381548100",
+      "target": "step_1768381657878"
+    },
+    {
+      "id": "conn_1768381934574",
+      "source": "step_1768381657878",
+      "target": "step_1768381862621"
+    },
+    {
+      "id": "conn_1768381975623",
+      "source": "step_1768381862621",
+      "target": "step_1768381947511"
+    }
+  ],
+  "variables": [],
+  "settings": {},
+  "tags": [],
+  "category": "default",
+  "is_template": false,
+  "version": "1.0.0",
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2026-01-20T14:41:18.031095",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_20260114_101626.json b/visual_workflow_builder/backend/data/workflows/wf_20260114_101626.json
new file mode 100644
index 000000000..d9f125c85
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_20260114_101626.json
@@ -0,0 +1,302 @@
+{
+  "id": "wf_20260114_101626",
+  "name": "test_1",
+  "description": "Test 1 aprés modifs descoordonnées d'ancre",
+  "created_by": "unknown",
+  "created_at": "2026-01-14T10:16:26.653699",
+  "updated_at": "2026-01-14T10:16:26.653699",
+  "nodes": [
+    {
+      "id": "step_1768381548100",
+      "type": "click_anchor",
+      "name": "Cliquer sur Ancre",
+      "label": "Cliquer sur Ancre",
+      "position": {
+        "x": -35.176572323879796,
+        "y": 111.2144230575727
+      },
+      "data": {
+        "label": "Cliquer sur Ancre",
+        "stepType": "click_anchor",
+        "parameters": {
+          "visual_anchor": {
+            "anchor_id": "anchor_1768381616132",
+            "anchor_type": "generic",
+            "reference_image_base64": 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",
+            "bounding_box": {
+              "x": 3.488372093023256,
+              "y": 631.1597877468732,
+              "width": 35.880398671096344,
+              "height": 42.74668630338738
+            },
+            "confidence_threshold": 0.8,
+            "description": "Élément sélectionné pour l'étape vwb_action_1768381569956",
+            "metadata": {
+              "capture_method": "screen_capture",
+              "capture_timestamp": "2026-01-14T09:06:56.132Z",
+              "screen_resolution": {
+                "width": 1920,
+                "height": 1080
+              }
+            }
+          }
+        },
+        "isVWBCatalogAction": true,
+        "vwbActionId": "click_anchor"
+      },
+      "stepType": "click_anchor",
+      "parameters": {
+        "visual_anchor": {
+          "anchor_id": "anchor_1768381616132",
+          "anchor_type": "generic",
+          "reference_image_base64": 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9PH/AI5/9epLPVFbC3A2n+8On41pKQwDKQQe4q1GLOmOFoS2X5mV/Y3/AE8f+Of/AF6P7G/6eP8Axz/69a1FHIivqdHt+Zk/2N/08/8Ajn/16P7G/wCnn/xz/wCvWtRRyIPqdHt+Zk/2N/08/wDjn/16P7G/6ef/ABz/AOvWtS0ciD6nR7fmZH9jf9PP/jn/ANej+xf+nn/xz/69a9FHIg+p0e35mT/Yv/Tz/wCOf/Xo/sX/AKef/HP/AK9a9FHIh/U6Pb8zI/sX/p5/8c/+vR/Yv/Tz/wCOf/XrXop8iD6nR7fmZH9i/wDTz/45/wDXo/sX/p5/8c/+vWvRRyIPqdHt+Zkf2L/08/8Ajn/16P7E/wCnn/xz/wCvWxRRyIPqdHt+Zj/2J/08/wDjn/16P7E/6ef/ABz/AOvWxRRyIPqdHt+Zj/2J/wBPP/jn/wBej+xP+nn/AMc/+vWxS0ciD6nR/l/Mx/7E/wCnn/xz/wCvR/Yn/Tz/AOOf/XrYoo5Ih9To/wAv5mP/AGJ/08/+Of8A16P7E/6ef/HP/r1sUUckQ+p0f5fzMf8AsT/p5/8AHP8A69H9if8ATz/45/8AXrYoo5Ih9To/y/mY/wDYn/Tz/wCOf/Xo/sT/AKef/HP/AK9bFFHJEPqVH+X8zH/sT/p5/wDHP/r0h0P/AKef/HP/AK9bNFHJEPqVH+X8zF/sP/p4/wDHP/r0o0P/AKef/HP/AK9bNFHIg+pUe35mP/Yn/Tz/AOOf/Xo/sT/p5/8AHP8A69bFFHJEf1Kj/L+Zj/2J/wBPP/jn/wBej+xP+nn/AMc/+vWxRRyRD6lR/l/Mx/7D/wCnn/xz/wCvR/Yn/Tz/AOOf/XrYoo5EH1Kj/L+Zj/2J/wBPP/jn/wBej+w/+nn/AMc/+vWxS0uRB9So/wAv5mN/Yf8A08/+Of8A16P7D/6ef/If/wBetmijkQfUqH8v5mN/Yf8A08/+Of8A16X+w/8Ap6/8c/8Ar1sUtHIg+pUf5fzOKAJOAMnsB1NfR3hD4OaBdeHNKg1RJHukm+03zoDmUlMCHcOVQEjpycH1rwqPV7NdGsbD+y4Fnt7oTPdj77qGzg/y/AV9R6D4iuYbNptPmUmSItEG5QsV+Un8cV8pxDLGypQ+rvkd3rf5L5bv/hj2uHcqoYmnWnpNxUdNet2166W7fec58G/D138J/EV9qmo20d7c3UXkIYztSOPfuOxj1zhc5x0r1v4qeBNB+Kug2Mt/BLaX8BDW8+AsqI33kJ5BX+IdRkAjqa8i1Hxff6hGI9VuZpJYycRuMbWPXCivVPh3qM8PhfTYLtirQozMGPKqSSq/kRXZxvkay3CYXMYyjHEt2k4OTUlyvVcz20Sen2jmwlGjjIShSg0o9z4l8UaNd+HfEmo6FfY+02Fy9vIR0YqcZHsRg/jWU33jXcfHPUINV+LviW+tmVo3vdoI6Eqio36qa4dvvGvXwU51KMJz3aTfrbU8KnHkqyj2/wAxKKKK7DoCiiigYUUUtABRRS0AFLRRQAUUtFAwoopaACiiloGFFKKKAClFApaBhSigUtABRRS0DCiilFAAKWgUooGApRQKUUAFLRS0DClpKWgApRQKWgYUooFLQAUtFKKBhS0UtABS0lLQMKWgUooAKWigUAFLmkYnoBkngD1qylqn3WV5X74JA/QiplJRMK2IjS3IARQCKs/Y0/59pP8Avpv/AIqj7In/AD7yf99N/wDFVPtEc/8AaEOzK4IpcipRBCQCIZMH/ab/ABo+zxf88ZP++m/xo9oh/wBoQ7MiyKXIqX7PF/zxk/76b/GjyIv+eMn/AH03+NHtEH9oQ7MjBFLkU/yI/wDnjJ/303+NHkR/88ZP++m/xo9og/tGHZjMilyKd5Ef/PGT/vpv8acLZSARBLg8j5m/xo9og/tGHZjARS5FO+yj/nhL/wB9N/jQbeNcbonXPTLt/jR7RD/tGHZiZFGRS+RF/dP/AH23+NHkxf3W/wC+2/xo9og/tGHZhkUZFHkxf3W/77b/ABo8mP8Aut/323+NHtEH9ow7MXIpQRTfJj/ut/323+NHkx/3W/77b/Gj2iD+0YdmOBFLkUzyY/7rf99t/jR5Mfo3/fxv8aPaIP7Rh2Y/Ipcio/Kj9G/7+N/jR5Ufo3/fxv8AGj2iH/aUOzJNwpdwqPyk9G/7+N/jR5Sejf8Afxv8aPaIP7Sh2ZLuFG4VF5Sejf8Afxv8aPLT0b/v43+NHtEH9pQ7MmBFGRUPlp6N/wB/G/xoKIBk7v8Avtv8aPaoP7Sh2ZPkUZFQ+Sf+eU3/AH03+NHkn/nlN/323+NHtUP+0odmT5FGRVfy1BwVcEdi7f40qxbhlY5WHqHb/Gj2iD+0odmWMijcKg8k/wDPKb/vtv8AGkMYXG5JFz0y7f40e1Qf2nDsyxuFG4VW2L/tf9/G/wAaNi/7X/fxv8aPaIP7Sh2ZZ3Ck3Cq+xf8Aa/7+N/jR5a/7X/fxv8aPaIP7Sh2ZYLCmlhUPlp6N/wB/G/xo8tPRv+/jf40e0Qv7Sh2ZIWpjMKTyo/Rv+/jf40nkx/3W/wC+2/xo9ohf2jDsxrkVC+DU/kRf3T/323+NI1vERgbkPqGJ/Q0vaIn6/B9GUJFzVWVKuuCCysMMpwahkWqOl2krozpEqBlwavyLVd0qWjCUSOOXHD/nV60u5rc/u2+Xup6Gs9lpEdkOOo9KRCk4s6mzv4bjCk7H/unv9Kt1yiOGGQa0LPUZYcLJ+8T36iqUjqhWvubdFR288U67onB9R3FS1Rve+wUUUUDCloopgFFFFABRRS0AFFFFABRRRQAUUUtACUtFFMAooooGFFFFABRRRQAUUUUAFFFFIAooopgFFFFIYUUUUAFFFFIApaKKACiiloGJS0UUAcTXonw98djTrZNN1NyIk4ilPQD0Pp9a87ornnCM1yyV0eflWbYjK6/tqD8mns15n0XB4o0l0Fx58DYGQ+VP65rn/FvxZNpZS2WiSiS4dSokBysfvnoSOwH414pgeg/KiuP+zcLzKTje3fWx9HjuNq+IounSpKDe73fy0Vn9452Z3LuxZmJJJOST61E33jT6Y33jXo09z5LD/ExKKKK1OwKKKWgAoopaACloooAKKKWgYUUUtABRRS0DCiiloAKUUCloGFLRRQAooopRQMKKKUUAApaKUUDAUooFLQAUooFLQMKKKUUAFLQKWgYUooxS0AApaKWgYClopaAAUUUooAKWgUooGFLRRigAxS0CloGIn+vi/wCui/zrUs/+Wn1H8qy1/wBfF/10X+daln/y0+o/kKxqbnkY/wDiL0N6HS7ZbWOS8nvFeSMSn7Pa+asKH7rSHIxkDOB2qhfW0lley2spUvG2CVOQw6gj2IIP41dh1O2a2ijvLW5keKMRZhujEsqD7qyDBzgHGRg44qvrN1HfapNeRI8azENsYg7DgDaD/dGMDvgDNZnCZEX+qX6VZsbZ7y8itY3jR5WChpH2qCfU1Wi/1S/Sp7WU29zFOoDGKRXAPQkEH+lCAuw6NdzQlo2hMhLhIdx3yBWCsRxjAJ7kHr6UPo9wt5BbpNby+cXCyRliuUzvHTORjsDnjGc0+DW54I/3cEQnUv5U2TmNXcMwx0PI6npk1BqGofa3i226W8cTM4SN2+8zbmOTyM9vQCgBupadc6eyi4CgsWAxnoDjPTv271WkjeMKZEZA67l3DG4eo9q0NS1me+sxbSxooDhgVJwAM7VA6DG4jPUgDPSqttfXVvH5ccg2ZyFdA4U+oBBwfcUAQSxyR4EkbIWXcNwxkHoas6bby3c1tawgGWYqiAnAyfX2qtK7yO8kjs7tkszHJJ+tWdOuJbSW2uoGCyxFXQkZGRQBsTaLCbUS2s927NG8kLzWvlxXKp9/y2yTwASMgZArn7v/AFI/3h/Ouki1qwVPL+x3qp5UkUY+170tRIDuMSkD1x8xPBI965u7/wBSP95f50AQVrWugzy6MNVuL7T7KCQututzKVe4KfeCAA9CQMnAzxWTW3aa+iaLDpd9pFnqCWzSNaSyvIjw7yCw+UgOuRnDUANi8K+IpZ44I9KmZ5YTOmHTaUBALbt23gkDrnJFSDwjrzWMN0lkXMlzLamHcFlSSPG4FSQe/bPQ5xW6/j2C/ju01bR4Ghaxlhit45JNkjySxOQTnKIPL4C9OlUW8c3sl9HfXGm2U11DfSXcMhLjZ5gAePAOCpCgZPIoAxovD+tS28lxHp0phjgS4d8qAI3BKNye+DgDnjpRd+HtbtLu1tLjS7mO4uztt49oLSHOMDB68jIPI71e1PxVNd2r2sOn21pDi0ESo7P5Qt92zBY853nOfSrGp+N9SvfEFjrfkRxXFpK02wzSSRO7feO1j8oPTC4o6gNvfCFxZm0s5rg/2reRwyQ2aR7zh3cPllJGFCA5HXPtVO98LazBcXqxWFzJBaSBGmZAgbdypAJ5yCCACeDViPxbPb67pup2GnWtmmnWxtYbZXdkMZ35BYndzvbnPHFSXfjCS8Ty7vS4bhYrlbm0Mt1K7WzhVXhicsCFHDdO1AGfceGdetzdrNpsitZpvuVEiFo1xkkgMTwBzjp3xUOr6FrGkLA2padNbCfiItg7jgHHBODgjg88ite18aXVvb6vEum2m7U5biV3DMpTzlKsOPvgA8Buh5qrrfii71QNutoIGN/9uDRknD+WiAc9vkB+tAD9T8H6zp+lQXlzayJK4neS3IG6KOIIWduf9vp1GOlU7HQLu41ttHuZrXTrtWVNt3IVy7EBUG0NkncK2LnxxJPNubRLBYpftJuoVkk23DXAXzDnOU5QEbelVrbxY8Xii/1+XSraae7QqqCV08jIA3IwO4NtGM9eTQg6EF94U1Sy0qXUJ3tB5ILSQLNmVYxIYvMxjGzeCM5z3xisKul/4S1/7NWwOj2TRBVgYtJIWa2WXzRATnpu/i+9jvWR9ttPK2/2Ra7vLlTf5kmdzNlX69UHA7Edc0AUaB99P98fzooX76f74/nQBcrUXT7MeH3vpb5orzIMNsVUiVN4UtnORjPQjnB7DNJoFxpVtLO2qWktwDGPI2AMEkDAgsCRlcZBGeaznYu7OcZYknAA5PsKAKt1/rk/3T/MVLBxbx/7g/lUV1/rk/3T/MVLB/x7x/7g/lQBsXGjPDaPcG6DBU37fslwufbLRhR9Sce9Yt3/AKk/UfzFbE+rtLavb/Y4VDJt3Cacke+DIV/MY9qx7v8A1J+o/mKAIKKKdE/lyLIFRtpB2uu5T7EHqPagBuDRVi9u2utm62s4duf+Pe3WLOfXHWq9AAoJYKOpOBWjqGj3NlFPJJPZS+RMIZlguBI0bndgED3Rh+FZykqwYdQcitPUtYe9huY/sNnbm5nE8zwiTLsCx/icgDLt0FAGZRRRQBfm0fUIbD7fJABalEdZt42vuOAqnu3Byo5GDnFUK0Z9avZtKXSnEP2OMDyohGAI2HWRe4dv4j3H0GM6gChP/wAfMv8AvD/0EVE4qeX/AI+Zf94f+gimOK2Wx7lH+HH0Kki1BItXHFQOtASRTdaiZatutQutJoylErglTkHFWIpQ3HQ+lRMtMIpGeqL0bsjB0Yqw6EVqWepg4S4GP9sf1FYUc2OH/Opwc00zWFRrY6lGV1DKQynoRTq5y2uZbdsxtgdwehrXs9Qimwr/ALt/Q9D+NUmdUKikXKKKKo0CiiigBaKKKACiiigApaKKACiiimAUUUUDCiiigAooooAKKKKQBRRRQAUUUUAFFFFAwooooAKKKKQBS0UUAFFFLQAUUUCgYUtFFIDikVndURSzMQAB1JPatxfCOusAfs0Q9jcICP1qv4PtWvvFelWaMFaa7jQHrjJ619J23gTRlQK0t+59fNUZ/DbWL20PFofVkv3zfyPmXVdH1LTJliu7V1Zl3KU+dSPZhkV6r4L8AWUkdtaHT49Q1CdQW80jGcZwMkAAV6RfeCtOTTpRby6gCQePOGP/AEGue0q6bTtRtp3jUzWsiuY3OMlT/I+tePmdSrGMVsr62PleLa3s40/q0pcjb5uj6W1XzPM/iL4WsbHT/wC1dNiEARgs0QPykHgEenNeet9416r8U9WtYtEOmLIr3Fw6nYDkqoOcn8q8qb7xrsymdSdC9T+kd3CtSvUwt6zb1dr9tP8AgiUUUteqfUBRRS0AFFApaACiiloGFFFLQAlKKKWgYUUUooABSiiloGFLSUtAC0UUUDFFFFKKAAUtFKKBgKWilFABSiiloGFFApaAClopaBhSiiloABSijFFAC0tFFAxRRRS0AFKKKUUDAUUUuKAAUtFKKBhiloooAYWVJY3dgqiRSSTwOanW/tF5W+hUnr84rP1f/jwl+n9a5Gsam55GP/iL0PQP7St/+ghD/wB9LR/aVv8A9BCH/vpa8/orM4TvRfWgAAv4cD/bWl+32v8Az/w/99rXA0UAd99vtf8An/h/77Wj7fa/8/8AD/32tcDRQB332+1/5/4f++1o+32v/P8Aw/8Afa1wNFAHe/b7X/n/AIf++1py6jbKoUahDgDA+Za4CigD0D+0rf8A6CEP/fS0jahaNjffQtjp84rgKKAO++32X/P5B/38FH2+y/5/IP8Av4K4GigDvvt9l/z+Qf8AfwUfb7L/AJ/IP+/grgaKAO++32X/AD+Qf9/BR9vsv+fyD/v4K4GigDvvt9l/z+Qf9/BR9vsv+fyD/v4K4GigDvvt9l/z+Qf9/BR9vsv+fyD/AL+CuBooA777fZf8/kH/AH8FH2+y/wCfyD/v4K4GigDvvt9l/wA/kH/fwUfb7L/n8g/7+CuBooA777fZf8/kH/fwUG+sT/y+Qf8AfwVwNFAHoH9pW/8A0EIf++lo/tK3/wCghD/30tef0UAd8b+zJ3NewsfUyClXUbVRhb+EDsN61wFFAHoH9pW//QQh/wC+lpG1C0bG+/hYDnG9a4CigDvvt9l/z+Qf9/BR9vsv+fyD/v4K4GigDvvt9l/z+Qf9/BR9vsv+fyD/AL+CuBooA777fZf8/kH/AH8FH2+y/wCfyD/v4K4GigDvvt9l/wA/kH/fwUfb7L/n8g/7+CuBooA777fZf8/kH/fwUfb7L/n8g/7+CuBooA7nekk0jxsrqWGCpyD8ooYVS8P/APINj+pq+wreOx7tD+FH0IXFQutWGFRMKC2is61Ey1ZYVEwpGTRVdajIqyy1Ey0jNogIpY5Ch9R6U5hTCKRm0WkcMMg08GqIJU5BxViKUNweDQUpGnaX80OFb94noeo+hrXtrmK4XMbc91PUVzYNPR2VgysQR0IqkzeFVo6eisq01MjCXAyP74/rWnG6ugZGDKe4qk7nTGSlsOooopjCiiloAKKKKYBRRRQMKKKKACiiigAooopAFFFFABRRRQAUUUUAFFFFAwooopAFLRRQAUUUUALRRRQMKKKWgAoopaQw+HGpWsviDwvpS6XClzHqsbtdg/O4JPH6/oK+pYoeOlfHfgfUbfSfGWj6ndki3tbyOSUgZwobk/gK+urPxT4TniWSLxNozIwyD9tjHH0JzXNGmoXt11PnK9WVRpy6JL7jqbXXNfitktotXuUhRQiIFjwAOg5XNYVx4c068kMk9tHIxOSWUHmnR+JfCw6+JdF/8D4v/iqsp4o8JIu6TxRoaqPW/i/+KqrGDV9z5e/aPsoLD4mSW9vGscYsbcgAYHKmvM2+8a9F/aD17S/EXxOvL7R7pLuzight1mj5Ryi/MVPcZOM+1edt941rTO7C7iUUUVqdoUtFLQAUUUtAwooooAKWiloGFFFKKAClopaBgKKKWgApaKBQMBS0UooAKWiloGGKWiloAKWiigBaBRSigYUoopaBhQKBS0ALSikFKKAClFFLQMBS0UtABS0lOxQMKKKUUAFLQKUUDAUtFAoAKWiloGUtX/48Jfp/WuQrr9Y/48Jfp/WuQrGpuePj/wCIvQkt4ZbieOCCJ5ZZGCIiKWZmJwAAOSa1/wDhEvFP/Qt6z/4AS/8AxNbPwJ/5LZ4Gxx/xUVh/6UJX6x4Hv+dZnCfkH/wiXin/AKFvWf8AwAl/+Jo/4RLxT/0Les/+AEv/AMTX6+EADJJ/Os6z13Qr2+axs9a0+4u1+9BFdo8g+qg5oA/JT/hEvFP/AELes/8AgBL/APE0f8Il4p/6FvWf/ACX/wCJr9fMD3/OjA9/zoA/IP8A4RLxT/0Les/+AEv/AMTR/wAIl4p/6FvWf/ACX/4mv18wPf8AOjA9/wA6APyD/wCES8U/9C3rP/gBL/8AE0f8Il4p/wChb1n/AMAJf/ia/XzA9/zowPf86APyD/4RLxT/ANC3rP8A4AS//E0f8Il4p/6FvWf/AAAl/wDia/XzA9/zowPf86APyD/4RLxT/wBC3rP/AIAS/wDxNH/CJeKf+hb1n/wAl/8Aia/XzA9/zowPf86APyD/AOES8U/9C3rP/gBL/wDE0f8ACJeKf+hb1n/wAl/+Jr9cbfUtMuLprWDUbWW4X70STqzj8Ac1bwPf86bi1uJST2PyD/4RLxT/ANC3rP8A4AS//E0f8Il4p/6FvWf/AAAl/wDia/XzA9/zowPf86Qz8g/+ES8U/wDQt6z/AOAEv/xNH/CJeKf+hb1n/wAAJf8A4mv1j8WeJ9A8K2C3uvalHZxOdsYYlnkPoqjJP4Cqfg3xz4W8XGVNC1VLiaIbnhZWjkUeu1gDj3FbLDVnT9qovl720+8xeIpKp7JyXN2vr9x+VH/CJeKf+hb1n/wAl/8AiaP+ES8U/wDQt6z/AOAEv/xNfr5ge/50YHv+dYmx+Qf/AAiXin/oW9Z/8AJf/iaP+ES8U/8AQt6z/wCAEv8A8TX6+YHv+dGB7/nQB+Qf/CJeKf8AoW9Z/wDACX/4mj/hEvFP/Qt6z/4AS/8AxNfr5ge/50YHv+dAH5B/8Il4p/6FvWf/AAAl/wDiaP8AhEvFP/Qt6z/4AS//ABNfr5ge/wCdGB7/AJ0AfkH/AMIl4p/6FvWf/ACX/wCJo/4RLxT/ANC3rP8A4AS//E1+vmB7/nRge/50AfkH/wAIl4p/6FvWf/ACX/4mj/hEvFP/AELes/8AgBL/APE1+vmB7/nRge/50AfkH/wiXin/AKFvWf8AwAl/+Jo/4RLxT/0Les/+AEv/AMTX6+YHv+dGB7/nQB+Qf/CJeKf+hb1n/wAAJf8A4mj/AIRLxT/0Les/+AEv/wATX6+YHv8AnRge/wCdAH5B/wDCJeKf+hb1n/wAl/8AiaP+ES8U/wDQt6z/AOAEv/xNfr5ge/50YHv+dAH5B/8ACJeKf+hb1n/wAl/+Jo/4RLxT/wBC3rP/AIAS/wDxNfr5ge/50YHv+dAH5B/8Il4p/wChb1n/AMAJf/iaP+ES8U/9C3rP/gBL/wDE1+vmB7/nQwG08np60AfjdeW1zZ3L213BLbzxnDxyoVZT7g8ioa9V/a2/5OK8ZZJP+nL/AOio68qoA63w9/yDY/qa0GFZ/h3/AJBkf1NaJreOx72H/hR9CNhUTCpmFMYUzRkDComFWGFRMKRm0V2FRstWGFRsKRDRXYVGRVhhUbCkZtEBFNxUrCmkUiGh0U2OH/OrAPcdKpEU6ORkPqPSgE7F0GpreeWBt0bEeo7Gq0bhhkGng0zVSNy01CKXCyYjf9DV2uYFW7S+lgwud6f3T2+lUpG8avc3KWoLa5huB8jfN3U9anqjZO4UUUUwCiiigAooooGFFFFIAooooAKKKKACiiigAooooGFFFFIApaKKACiiigApaBRQMKKKWgAoopRSGFFFFIDh6CAeoH5UV03hTwN4g8TWoudLggaNpfKTzJdpdvRQASetc9WrClHmm7I8Ghh6teXJSi2/I5jav90flS7V/uj8q7ofCvxUf+gb/wCBR/8Aiara78NvFOjaZNqF5BamGGMSuI58tsP8QBAyMc1zxzDCzdo1F952TyjHU05SpSS9GcfTG6mn0xvvGu+G5z4bdiUoopa1OsKKKWgAooooGFKKKKBhS0UooABS0UCgYtFFLQACloooAKWiigYtLQKKBgKdQKBQAoooFKKBgKUUClFAAKUUlKKBgKWiloAKUUYpaACloFLQMBS0UtABRRS0DAUtFLQAUooFLQMBS0lLQAUtFLQMBRRS0AUtY/48Jfp/WuPrsNY/48Jfp/WuPrGpuePj/wCIvQ7P4Ff8ls8Df9jDYf8ApQlfrLX5NfAr/ktngb/sYbD/ANKEr9ZazOE5P4jMsyaNpNzO0Gn6jqKwXrh9m6MRu4jLdg7Iqn1BI71jOLkeKZbe0sdJ0Dw9pEBeG8RYt905TOEx9yNf4gOTjGcE13mpWFjqdo1pqNlb3ls5BaKeISIcdMgjFZA8EeDAcjwloOf+wfF/8TQBY8G393qXhDSdRvkxdXNlFLKAMZZkBPHvXG6UxubfULWW7u7xxYyzNPFdzKwkU/L5sZ5il68K2Dg8YAr0lFVFCqoVQMAAYAFLSaHc841efUbPT9ITSYtTlFhapqd35cxbeTtGxy7ZYFfNO0Z5A4qO5v8AVYAdt1ezWt/qVzJbSAsTFtWX92T2QgKy9sgj0r0uim9RLQ80g1B4tLMeg3V3efadOhhfZcNLtupGVQVZ2+V9pdiMgDaCcVGv9s30cFiY7mS50+xuopEub6WF9yOgjctHkO5Qqc57nmvT6KHqC0PN9OvFkuTJFqV5NrBu7MQRmd8yQmGEyFo87QuDISccHPOa9Ioop3ElYK8y/aQ/4Sj/AIQBv+Ed83yPN/4mPkZ83ycdsc7c/exzj2zXpuRRketb4Wv9XrRq2vZ3szDFUPrFGVK9rrdHyl4h/sj+ztQ/sf8A4R/7Ptg/4Rn+y9v9p+duXPmbP3mdu/fv/i27a+kvAv8Abh8HaZ/wkm3+1vs6/atv973x/FjGccZzV+30zTLe6a6t9PtIp2+9KkKq5+pAzVzI9a7cfmKxVOMFG1ne71e1v+C+71OPAZc8LUlNyvdWstFvf/gLstDzWe41dLe/DXF+f+EXXLkO+bnEolGf+en7hQO/LnvWZfXWtrZX0NzeXy+Tbf2iZHmkRQLqSPbGWXkBMTDj7oxXruR60ZHrWcMeov4P66/r95c8A5fbf9bfhb1PkH4sSTy/Eq2F9JHdaWjxi2C3by2/l5BkVZZO27OT0BPoKk+H8dwnxs0A6KER3lRp4oAmIYyD5qEx/KcJkkj1HevqDxb4X0DxVYLZa9p0N5Eh3RliQ0Z9VYYI/A1U8GeBfCvhAyPoOlR20so2vMztJIR6bmJIHsK9tcQUfqns+R83K4+WvW/56fM8V5BV+t+051y3T89OiX/B+R0o6UUUV8mfVhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABSN90/SlpG+6fpQB+Xn7W3/ACcV4y/6/h/6KjryuvVP2tv+TivGX/X8P/RUdeV0Add4d/5Bkf1NaRrN8O/8gyP6mtIit47Hv4f+FH0GGmEVIaYRTNGRMKjYVOwqNhSJaIGFRsKnYVGwpGbRAwqNhVhhUbCghogYVGwqdhTGFIhohIppFSsKYRSsQ0MBKnKnBqzFKG4PDVARTSKQloXwacDVSKbHD/nVhSDVGikSqSCCCQR0IrRtNSZcLOCw/vDrWWDTs0XNYya2OljdJE3owZfUU6udgmkhfdGxU/zrUtNRjkwsuI29ex/wq1I3jUT3L1FFFM0CiiimAUUUUgCiiigAooooAKKKKBhR3oopAFLRRQAUUUUAFLRRQMKKKWgAoopaQxKWiikAUUUtIZw1fRHwMS5tPBWl39ttEiXDzIT/AHlkOP5V88DrX058G7WVvh3osUMTyu0LOFRSScux6D6187xE7YaNv5l+TNuEbfXJt7cr/NHbNpWm3aTami6pb228mWKOz81YyeSol3Bcc8bgD0yK534iNJqehaxJ5CxR/YHjijDbtkaRYUZ7nA6+ua9+so4dFt9J8CTWrvFfWjtfyiMkCWQcDPTsR+C1414v0e6sbfVdOuYnV0imiyykBvlIyPXt+dfPYih7Bwa7q/k9/wBeh9PhMd9a9pGTez5bveL0vsu3W7tbXU+Nl5UH2pp+8acv3R9Kaepr9Dhufm+G3YUUUtanWFFFFAwpaKBQMKWigUAKKWigUDClopRQACloooGFLRRQAUooFLQMKWilFABS0UtABS0UtAwoopRQMKWiloABSiiloAKXFApaBhS0UUALRQKWgYCloxS0AFKBSCloGFLRSigApaKWgAoopaBgKKKWgClrP/IPm+n9a46ux1n/AJB830/rXHVjU3PHx/8AEXodn8Cv+S2eBv8AsYbD/wBKEr9Za/Jr4Ff8ls8Df9jDYf8ApQlfrB5pyQkbOAcZBA5/E1mcJLRUXmSf88H/AO+l/wAaPMk/54P/AN9L/jQBLRUXmSf88H/76X/GjzJP+eD/APfS/wCNAEtFReZJ/wA8H/76X/GjzJP+eD/99L/jQBLRUXmSf88H/wC+l/xo8yT/AJ4P/wB9L/jQBLRUXmSf88H/AO+l/wAaPMk/54P/AN9L/jQBhan4bv7y/muYvGPiCySRsiC3Nv5cfHRd0JOPqTVf/hEtS/6H3xR+dp/8YrpfMk/54P8A99L/AI0eZJ/zwf8A76X/ABroWJqJWVvuX+Rg8NTbvr97/wAzmv8AhEtS/wCh98Ufnaf/ABij/hEtS/6H3xR+dp/8YrpfMk/54P8A99L/AI0eZJ/zwf8A76X/ABp/Wqnl9y/yF9Vp+f3v/M5r/hEtS/6H3xR+dp/8Yo/4RLUv+h98Ufnaf/GK6XzJP+eD/wDfS/40eZJ/zwf/AL6X/Gj61U8vuX+QfVafn97/AMzmv+ES1L/offFH52n/AMYo/wCES1L/AKH3xR+dp/8AGK6XzJP+eD/99L/jR5kn/PB/++l/xo+tVPL7l/kH1Wn5/e/8ytolhNp1l9nn1W+1N95bz7vy9+D2+RVGB9KvVF5kn/PB/wDvpf8AGjzJP+eD/wDfS/41zyk5O7N4xUVZEtFReZJ/zwf/AL6X/GjzJP8Ang//AH0v+NIZLRUXmSf88H/76X/GjzJP+eD/APfS/wCNAEtFReZJ/wA8H/76X/GjzJP+eD/99L/jQBLRUXmSf88H/wC+l/xo8yT/AJ4P/wB9L/jQBLRUXmSf88H/AO+l/wAaPMk/54P/AN9L/jQBLRUXmSf88H/76X/GjzJP+eD/APfS/wCNAEtFReZJ/wA8H/76X/GjzJP+eD/99L/jQBLRUXmSf88H/wC+l/xo8yT/AJ4P/wB9L/jQBLRUXmSf88H/AO+l/wAaPMk/54P/AN9L/jQBLSN90/So/Mk/54P/AN9L/jSiTcSrIyNjIzjkfhQB+X/7W3/JxXjL/r+H/oqOvK69U/a2/wCTivGX/X8P/RUdeV0Adf4c/wCQZH9TWkazfDn/ACC4/qa0zW8dj38P/Cj6DDTWFPNNNM1ZGaYwqQimkUEshIpjCpmFRkUiWiFhTGFTsKjIpENELCo2FTsKjIoIaISKYRUxFMYUiGiIimkVKRTCKRLRGRT45GjPqPSgimkUiS3HIrjINSA1QBKnKnFWIZg3B4NMtSLINKDTAaUGg0TLdreTQYAO5P7prWtbqK4HynDf3T1rAFKDg5BwfamnY0jUaOlorKtNRdMLPl1/vdx/jWlFJHKu+Ngw9qtNM3jJMfRRRTKCiiigAooooGFFFFIApaKKACiiigApaKKBhRRS0AFFFFIYUtFFIAoopaQwpaQUtAHDDrX2D+zF4r0bRdA0OW41m1trZbLybxchnyCTtx1B3Y59K+PqCqnqqn6iuDF4X6xytSs4u/8AWx5OCxiw3OnG6krP+rP8j9AL/wCK181/cGx1LSktfMbyVcoTszxk7uuKpfFTx1oWr6dBINasxaQW7STRuyqY5CuDgk/MMcYFfBexP7i/98ilCoDkIo/CuGWU1akZQnWbT8vy1PQhnFGnOM6dBJx8/K2ugvHbp2ph6mn03ua96B5WG+JhiiiitDsCloooGFLRRQACnUlLQAClopcUDAUtJS0AFLQKKBhS0UtAwpaBSigAFLRS4oABS0CgUDFoFFLQMBS0UooABS0UooAKWiloGFKKQUtABS0ClFAwpRRS0AFGKBSigYUtFLQAUtFFAC0UCloGFFFLQAUtFKKAKOs/8g+b6f1rja7LWf8AkHzfT+tcbWNTc8jMP4i9Ds/gV/yWvwP/ANjDYf8ApQlfrBbfdf8A32/nX5P/AAK/5LX4H/7GGw/9KEr9YLb7r/77fzrM4CWiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAqKT/AI+B/wBc2/mKlqKT/j4H/XNv5igD8wP2tv8Ak4rxl/1/D/0VHXldeqftbf8AJxXjL/r+H/oqOvK6AOw8N/8AILj+prTNZnhv/kFx/U1qGt47H0GH/hR9Bhppp5pppmowimGpDTSKCWiNhTGFSGmsKCWRGo2FTMKYRSJaISKYwqZhTCKRDRCRTGFTEUwigloiIphFSkU0ikS0REU0ipCKaRQQ0MIppFPNJipFYkimI+V+R61ZVgRkdDVEinRyMh45HpTuNSsXgacDUMciuMg/hUgNM0TH0+GV4m3RsVPtUQNOpFpmta6ij4WYBG/vdj/hV8HIyORXNA1YtbqWA4U5Xup6VSl3NY1O5u0VXtruKfgHa/8AdNWKu9zZO+wUUUUhhRS0UAFFFFABS0UUDCiiloAKKKKQxaKKKQBRRS0hhRS0UAFFFKKQzhq3PDXhfU9dPmW6rDbA4M8v3foB1Jqr4W0s614hstLDFVnkAdh1VByx/IGvoHwp4cuNR1e18PaXFFGT8qbjtjijUZLseyhQSTXm47FTo2hTV5M5+H8Fg8diZwxNSyhHma2bXe/ZdfVHnEXwxtjF8+sT+Z6rCu3+ea57xP4F1XRYGu42W+tF5d4lIZB6svp7jNfSSRfDK0nFnPf+JtQKna99axxRwk+qRtliv1IJrqZfh3oHkW91Z6ldXlldxCW3nVl2yIeOhXgjoQelcGcVszyClHE4+m1CXWy+522Z6tOvwtmnNQwcrTXVX+/XdHwzTe5rvvjr4Nh8FeO5bGy3/wBn3cS3Vru6qCSGX8GB/AiuB717WAxdPGUI16TvGSuj5qFJ0qsoPdBS0UV2nSApaKKAClopRQAUtFKKBgKKKWgApaBRQMKUUUtAwApRQKUUAFLRS0AApaKKBhS0UooGApaKWgAFLRS4oABSigUtAwooxS0AFLRS0DClFFFABSiiloGFKBQBS0AFLQKKAClopaBhRRS0AApaKUUAFFFLQMo61/yDpvp/WuMrs9a/5B030/rXGVjU3PHzD+IvQ7P4Ff8AJa/A/wD2MNh/6UJX6wW33X/32/nX5P8AwK/5LX4H/wCxhsP/AEoSv1gtvuv/AL7fzrM4CWiikYkKSMZx36UALmivne1tvGHxBvNWv73xdp1tJ4YupDa/Z1BjZ87t24EZj+UAMc9PrXpXwL8Q634m8HPqeuXlncztcuE8gAMij+F1HAOc49iDQB31FYF/qOqyape22nPYQx2EUckpulb96WycbgRsAA+8Qee3FU/7e1T7KmsEWZ06S7NssARvNC+YYw+7OCd3O3HTvmlcDq6K4jSvFGr6lZ2sOLO01D7FJcXIMTSICojZCo3A7GVz34II7Go7rxLrdvZ2Ektzbb7jTHv2MWmTSjI2YTCOSo+Y/MT+VPb+v67AtTu6K4k6zq88EviBWgittO2xzWgZm87Ko0h3A4yN3y8HOOo3VcvfEF+nhiDUI1iW4lv/ALMSLd5QF89o8hFO5jgdM9aHoC1OqorjtR1/WbOKCQmMQeS0ktzNp0yKGDEBXQMWhXA++QR+VaWsX+qnWIrLS5rKMGxkuiZomkDlWUBchhgHd15o2A36K4uTxNq1xcaeIo1tIry0a6TZZS3jlf3e3IQjb99uvoKmvPEmpQa1f6Ssdm0lnbPemQggNDs+VMZyH39e23B/iGB6AtTrqK4zSNX1Z7O0RbjzLrUp1CPc6fNCkOY2kbAdvnGFwACB61eTWtThnvLW5+wyy2thJcF4A21nV2AyM8cAZXqDnmh6AtTpaK42TW9dR55vtFgYY9IGoiP7K24khvk3eZ0yOuKl/wCEg1U2c2rhbP7DbXCwPAUbzXG5Vdg27AOScLg5A688OwrnW0VyHhrxRfajc6dZ3tvbw3M3mtMEBwyAbo3TJ6EZBznBBFdfSGFFFYHxF1G70jwLreqWEoiurSylmhcqG2sq5BweDV04OpNQW7diKk1Tg5vpqb9Fea/BX4lz+OreW2vdGuLa7tl/e3MKE2rn0DH7rf7Jz9a7fxVfzaX4b1HUbZUaa2tnlQOpYEgZGQCCfoK2r4SrQrexqK0jGhi6Vej7am7xNOiuM1LxNqGj6F/bF0RdwRTiOaNrCSzkKsMLsEjHOGwSem3d6U201fWLjxOLO6uUT7IsPmC1sJpYpGkTc2ZA21QOg3dOtNYSdnLovX/h+q+8l4yF1Hq/69Oj+47WiuMk8Q65F4fXXWbSzb3Vv50FvtYSR5K7QTu/efKTnAXB9RUviXXNZtrjVUsJLKJbD7KR51u0hfzmKnOHXGOtCwk27af00vzY3i6dr6/0m/yR11FclDrGrnxNcaXLcIY7VoVZotKldZNyBmJkDlY+vfOPeqmqeLdTt/D+l30cVsJbzTWunzC7hX3Qj5VByR+8bjqcChYSpJpLrb8VcTxlOKbfT9Gl+bO4orltJ1nWL3Tdb8pYJ7qxYrbM1rJAZG8sPtaJzuXk4znnqKp3PjOZSNShigOjhkiLsCXMjW7S9c46+WmMdSaSwlRtpdP6X3lfW6aSb6/pv9x2tFeeL4x1ySO1iaG1guA8Nte+XayTmO4bzt6qitkgCIEd8Nmuu8JalPqugwXt0qJOzOkiqpTBV2XlTyp45U8g8dqK2EqUY80go4unVlyx7XNWiiiuY6QooooAKik/4+B/1zb+YqWopP8Aj4H/AFzb+YoA/MD9rb/k4rxl/wBfw/8ARUdeV16p+1t/ycV4y/6/h/6KjryugDsPDf8AyC4/qa1TWX4b/wCQXH9TWpW8dj6DD/wo+g00008000zZjDTSKeaaaCWMIphFSEU00EsiIppFSEU0ikJkRFMIqUimEUiGiJhTSKkIppFBLIiKYRUpFNIpEkRFMIqUimkUEtERFJUhFNIoJsMIppFPIpMUibDQSpyDirMMwbhuG/nVYikNIE7GgDTgapwzFeH5HrVlWBAIOaZpGVyQGlFMBpwNBaY8HnNXrW/ePCy5dfXuKzxTgaE7FqTWx0EMqSrujYMP5VJXPRu8bbkYqfUVpWuoK2FnG0/3h0qlI2jUT3L9FIpBGQQQfSlqjQKWiigYCiiloAKBRRSGFLRRSAKKKWkAUUUtAwoopaQwFLSUtIZJ8L9asU1XStMbTII7gPKPtmfncsrYH9PwFfQfw2MU2u3+nGVIbjVdKubC2kcgASuoKAk9M7Suf9qvkK2mltrmK4gcpLE4dGHYg5Br27wn4qs9fs0ZHWK9UDzYM4IPqvqPp0rzatN4arGvTWzv31vc/Oc8qYmlXji4bcvI7ae7qrO3dPfv52Nu7s7u0vnsLu2mgu432PA6EOremOte9+FNNvNH8AaLpWpK0d4pmuHhb70KyMCqEdjgZI7Zrzfw7428d395a6NZa5KZH/dpNJDG8sSdyJGUuAB712fjXxX4f8BeH/tmuagQQp8qJn3XF0/sDyxJ6seB3Nc3iLxpPOcBDKqFH35tN9Xp29X+p0cEYCnHETxt3yxVtdNX83ey9Oh8/wD7YtzA/jDQ7RCpmg092kx1AeT5c/8AfJrwutzxz4lv/F3iq+8QajhZrp8rGpysSDhEHsBx78nvWHXVkOBlgMBSw0t4rX1er/Fn1LrKtiJzXUKWiivYNQpRQKWgApaKKBi0UCloAKWkpaBhS4oFLQMKUCgUtABS0ClFABSikpaBhS0UtAAKWilFAxKcKSlFACilFIKdQMKBQKWgAFLRS0DClpBS0AFLRS0DClFFLQAUopBS0AFLRS0DCiiloAKWiigApaKUUDCloooAo61/yDpvp/WuLrtNb/5B030/rXF1jU3PHzD+IvQ7P4Ff8lr8D/8AYw2H/pQlfrBbfdf/AH2/nX5P/Ar/AJLX4H/7GGw/9KEr9YLb7r/77fzrM4CWggEYPIoooA8q8SfBLw7f39pPpLtpUIlZr2FNzC5QsG2/e+UdQMdj7V33hTw1ovhewksdDsltIJJWlZQxYlj7nJ6AAela9FAGbqehaVqVwJ7y18x9gRsSMokUHIVwCA657Nkcn1pv/CP6R9uN59k/emQy48xtgcjBcJnaGwT82M1qUUAZsWhaTFcRXEdkiyw2hskcE5EPHydeRwOvP51LFpWnxeVstgPKtvsqfMeIuPl6/wCyOevFXaKAMgeGtFEkTizx5YjAXzX2N5YAQsucMQAMFgTwPSmnwxo5jeLy7oRtL52wXswCvv37lG/5TuJPGK2aKAMibw3pEyqJobiUqhjLPdSlnQnJRzuy65/hbI5NS6noemalKkl1DIWSJoR5c7xgxkglSFIBBwODWlRQBm3uh6bdzQyvHNFJBEYY2t7iSHahIO35GHHA/KkfQNIcEPZhiZXlLF2LMzpsYls5OV4x0wB6CtOigDITw3pS2n2Qx3TxAoUEl5M5jKfdKEsShHqpFNk8L6HJBHAbIhEV1+WZ1Lhzlw5DZcE8kNnNbNFAFOfS9PmaVpLZSZbb7K+CQDFz8uB0HJ6VXPh7SDeLdG0/eB1fHmPsLKAFYpnaWGBgkZ4HpWpRQBnW+h6VBc2dzDZok1lE8Nu+TlEbG5evI4HXNaNFFABVTWdNtNY0q60u/jMtrdRNFMgYruUjBGRyKt0U4ycXdbiklJWexW0vT7HS7GKx060htLWIbY4okCqo+gp2oWdvqFjPY3cfmW86GORNxG5SMEZHI/Cp6KfNK/NfUFFJcttDMg0HTIpIpGjuJ2hZmj+03Us20su0kb2P8JI/E+tQ2vhnR7SdJrWK5gZFjUCO8mVSEGEBUNhsDjkHjitmir9tU/mf3mfsaf8AKvuMZPC+gq8zf2erCVJIyjyMyKrnLhFJwmT12gVLD4f0mKzntRbM8dw6vMZJnd3K425diWONowM1qUUOtUf2n94exp/yr7jNfRNPbVH1IC5juZGRpDHdSorlRhdyBgp445HNQReFtCj3hbH5WXaFMzlUXeH2oCcINwBwuBwK2aKSrVFtJ/eN0ab3ivuIILO2hu7i6ij2zXJUytuPzFRgcdBx6VQHhvQxpJ0kadF9iNx9p8nJx5nmeZu65+9z+nTitaikqk1s/wClt9w3Tg9Gl1/Hf7+pkT+G9GladjaNG892LyR4pnjYzBNm/cpBB28ccVe02xtdOtFtLOLy4lJbBYsSSSSSSSSSSSSTmrNFEqk5KzbsCpQi+ZJXCiiioLCiiigAqKT/AI+B/wBc2/mKlqKT/j4H/XNv5igD8wP2tv8Ak4rxl/1/D/0VHXldeqftbf8AJxXjL/r+H/oqOvK6AOx8Nf8AIKj+prUrL8Nf8gqP6mtU1vHY+hw38KPoNNIadTTVGo002nGkNIQwimmnmmmgkYaYRUhppFAiMimGpCKaRSJZGwphFSkUwikSRkUwipSKYwoIZGRTCKlIppFIRERTSKkIppFBLRGRTSKkIppFBNhmKQ04ikIpWJsNIp0cjRng5HpSGkIpAXI5FccflUgNZ4JU5U4NWYZg3DcGmXGRZBpRTAacDQaJjwaUUynA0ikyxbXMsB+Rvl7qelalreRTfLnY/wDdNYgpQaadjSM2jo6KyLW+kj+WTMifqK1IJo5l3RsD6juKtNM2jJMkooooLCloopAFFFLSABUggm/54y/98GrvhtFfWYAwBHzHn1ANfVPw5+CmheJ/BOl69dazq0E95D5jxxGPYp3EcZUntXHWxE41PZwjfS+9je1GnR9rWlZXttfzPknyZv8AnjL/AN8Gl8ib/njL/wB8Gvs+5/Z/8MQMgbW/EL7zgeXHG2PrhOKn/wCGdvDX/Qf1v84v/iKn22J/kX3mP1vAf8/H/wCAnxO0bp99GX6gikr1T48eG7Twr4i1PQbSea5hs2gMcs+N53orHOAB/ERXlla4es6sW2rNOxvVpxhyuLumk18wpRRRWxmcJSo7o4dGZGU5DKcEfjSVbTTNSdQyadeMp5BEDYP6Uz5c1NL8a+LdLZm07xFqNszJsLRy/Nt9MkZ7VkajfXupXb3moXlxeXL/AHpZ5Wkc/iTmpP7J1T/oGXv/AH4b/Cj+ytUAydNvAP8Arg3+FZxpU4yc1FJ97aiglCPLHRFOinMpVirAgjggjBFN71vDc7MN8TClFApa0OwKXFFFAxRRRS0AFFFLQMKUUUtAwpQKAKWgApRQKKAFFLSClFAwpaKWgAFLSUtAwpaKUUAApaKUUDClopaACloooGKKWkpaAClopaAAUtJS0DAUtFLQAUtFFAwFLRS0AFLSUtABS0UtAwpaKKACloooGUNb/wCQbN9P61xddrrf/INm/wB3+tcVWNTc8bMP4i9Ds/gV/wAlr8D/APYw2H/pQlfrBbfdf/fb+dfk/wDAr/ktfgf/ALGGw/8AShK/WC2+6/8Avt/OszgJaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKR92xtmN2OM9M0tFAHIf8XL/AOpS/wDJmj/i5f8A1KX/AJM119FdP1n+5H7jl+rf35fech/xcv8A6lL/AMmaP+Ll/wDUpf8AkzXX0UfWf7kfuD6t/fl95yH/ABcv/qUv/Jmj/i5f/Upf+TNdfRR9Z/uR+4Pq39+X3nIf8XL/AOpS/wDJmj/i5f8A1KX/AJM119FH1n+5H7g+rf35fech/wAXL/6lL/yZo/4uX/1KX/kzXX0UfWf7kfuD6t/fl95yH/Fy/wDqUv8AyZo/4uX/ANSl/wCTNdfRR9Z/uR+4Pq39+X3nIf8AFy/+pS/8maP+Ll/9Sl/5M119FH1n+5H7g+rf35feY/hv/hJf3/8AwkX9kfw+R9g8z33bt/4Yx71sUUVhOXNK9rHRCPLG17hRRRUlBUUn/HwP+ubfzFS1FJ/x8D/rm38xQB+YH7W3/JxXjL/r+H/oqOvK69U/a2/5OK8Zf9fw/wDRUdeV0Adl4Z/5BUf1NalZfhn/AJBUf1NatdEdj6HDfwo+g00hpxpDTNhhpDTjSGkIYaQinU00EsYRTTTyKaaBMYRTSKkNMIpEkZFNIqQim0EsiIppFSkUwikSRkU0ipDTSKCWiMimEVIaaRSERkU0ipCKaRQTYYRTSKkIppoJsMIpKcRSUhDSKQinYpKQiSGYr8rZI9atKwIyDkVQIp0btGeOncUDUrGgDSioYpFccde4qUGg1TH0opgNOFBaY6nxuyMGVipHcVGKcKRSNO11AHCzjB/vD+taCkMoZSCD0Irnamt7iWE5jbjuOxqlI1jU7m7RVa1vYpsK3yP6Hoas07myaYtFFLQM0vDH/Ibg+jf+gmvu/wCDEjp8I/DG2RkBtTkh1X+M/wB4HNfCHhj/AJDUH0b/ANBNffPwJVW+EfhzcAf9E7j/AG2rgl/vf/bv6meZ/wC4L/H+h0kFvpqks7RPkYKukfrnPAq7aC0UMlqIlHUiMAfyqXy0/uL+VKFVeigfQV1HzB8W/tXf8lI8Qf71r/6KjrxGvb/2rf8AkpHiD/etf/RUdeI1y4Paf+Jn2c/4VH/BH8goopRXYZHN6Jpd79p03UJbSQWU10qLKR8rEHp+n6V7Lp1toM3hq5uNRv5LfUlZvsqQyGV524wrxbcIv+3v/wCAmvLtIeI6ToqDXZJZBfgnTiPlj5Pzf19Oa7a3mlt5o54JXiljYOjo2GVhyCCOhrGlOU079G11/X/hj5zF0o05RUeqT3T1fp+T17jWDr94OPrkUmT6n86t6pquqao6Pqeo3d80YIQ3EzSFQeuMniqdaHIefeOFA8RSkDlo0Y+5xWH3rd8df8jC/wD1yT+VYda0z0MJuFLRiitTuClopaACigUtAwFLRS0DClFJinUAFKKQUtABS0UtAwpaBS0AFAopaBhSigUooABS0UooGApaKWgApaQUooABS0UtAwpaKKAFFFFLQMBSiiloAKWgUUDClooFABS0ClFABS0UtAwpaSloAKWiigYUtFLQBn63/wAg2f8A3f61xVdtrn/INn/3f61xNY1Nzxsw/iL0Oz+BX/JbPA3/AGMNh/6UJX6wBJVLbGTaSTyDmvyf+BX/ACWzwN/2MNh/6UJX6wmVAxX5iR1wpP8AKszgExcesX60YuPWL9aXzk9JP+/bf4UecnpJ/wB+2/woATFx6xfrRi49Yv1pfOT0k/79t/hR5yekn/ftv8KAExcesX60YuPWL9aXzk9JP+/bf4UecnpJ/wB+2/woATFx6xfrRi49Yv1pfOT0k/79t/hR5yekn/ftv8KAExcesX60YuPWL9aXzk9JP+/bf4UecnpJ/wB+2/woATFx6xfrRi49Yv1pfOT0k/79t/hR5yekn/ftv8KAExcesX60YuPWL9aXzk9JP+/bf4UecnpJ/wB+2/woATFx6xfrRi49Yv1pfOT0k/79t/hR5yekn/ftv8KAExcesX60YuPWL9aXzk9JP+/bf4UecnpJ/wB+2/woATFx6xfrRi49Yv1pfOT0k/79t/hR5yekn/ftv8KAExcesX60YuPWL9aXzk9JP+/bf4UecnpJ/wB+2/woATFx6xfrRi49Yv1pfOT0k/79t/hR5yekn/ftv8KAExcesX60YuPWL9aXzk9JP+/bf4UecnpJ/wB+2/woATFx6xfrRi49Yv1pfOT0k/79t/hR5yekn/ftv8KAExcesX60YuPWL9aXzk9JP+/bf4UecnpJ/wB+2/woATFx6xfrRi49Yv1pfOT0k/79t/hR5yekn/ftv8KAExcesX60YuPWL9aXzk9JP+/bf4UecnpJ/wB+2/woATFx6xfrRi49Yv1pfOT0k/79t/hR5yekn/ftv8KAExcesX60YuPWL9aXzk9JP+/bf4UecnpJ/wB+2/woATFx6xfrQEfcXkK5CkAKKXzk9JP+/bf4UokRwwBOQOhBB/WgD8vv2tv+TivGX/X8P/RUdeV16p+1t/ycV4y/6/h/6KjryugDsvDP/IKj+prVrK8M/wDIKj+prWrojsfQ4b+FH0GmkNONNNM2ENNNONIaBDDSGnGkpCGmmGpKaRQSxhppp5ppoERkU0ipDTSKQmRkU0ipDTSKRJERSEU8imkUEjCKYRUpFMIoJaGEUwipCKaRSERmkIp5FIRQTYjNIRTyKaaQrDCKSnkUhFImwyinEUmKBDQSpyDg1ahnDcNwf51WpDQNaGkDSg1ShnK8NyP5VbVgQCDkUGkZEgpwqMGnA0maJjxThTAaUGkykPq3a3ssWFb509D1FUwaWi5adjet54p1zG2T3B6ipa55GZSGUkEdCK0LXUOizj/gQ/rVKRtGd9zpPDH/ACGoPo3/AKCa++fgP/ySPw5/16f+ztXwL4WZW1iBlYFSG5H+6a+zPhH8SvA+i/DjRNL1TxBb215b2+yaJo5CUO5jjhcd64JzjHFXk7e7+peOo1KuBSpxbfN0V+h7LRXC/wDC3vhx/wBDTa/9+pP/AImj/hb3w4/6Gm1/79Sf/E1t9YpfzL7z5/6hiv8An3L7mfM/7Vv/ACUjxB/vWv8A6KjrxGvYP2jtX03XvGWtarpF2l3ZTtb+XKoIDYjRT1APUEV5BWWCaam1/Mz6qrFxp0lJWahEKKKWuwxOK064+yX9vclSwikVyB3ANd6nifRGUH7W6+zQtkfpXndLTZ8s4pnov/CTaJ/z+n/v0/8AhSHxNogH/H4x+kLf4V53RSsLkRo+I7+PUtXluoQwjIVV3DBIA61nCilFa0zrwm7Clopa0O4KMUUtAwpaKWgBBS0UtAwpaBSigApaKWgYUtAooAKUUYpaBgKXFFLQAUtFKKBhilopaACiiloAKWgUtAwpaSlFABS0UtAwFLRS0AFFFLQMKWiigApaKWgApaKKBi0UCloAKWiigYUtFFABS0UooAz9c/5Bs3+7/WuJrttc/wCQZP8A7v8AWuJrGpueNmP8Reh2fwK/5LX4H/7GGw/9KEr9YLb7r/77fzr8n/gV/wAlr8D/APYw2H/pQlfrBbfdf/fb+dZnAS0Vl+JvEOjeGrBL7XL+Kyt5JVhR5Mnc7ZwoABJJwfyqPwz4m0TxLFPLot8t2kDBZCEddpIyPvAUAbFFVNW1Kx0qz+16hcpbw71jDN3ZmCqoA5JJIAAq3QAUUjEKpZiAAMkntTLaeG5t47i3mjmhlUPHJGwZXUjIII4IPrQBJRRRQAUVkDxLoP8Awjp8Q/2pbf2WpIN1u+QEPsI+u/5cevFMfxZ4cTToNRk1i0S1uL7+z4pWfAa58wx+UM/xb1K49RQBtUVzuveN/CuhzzW+q61bW00EscUkZ3FleRWdFwAeSqscegq/4b8QaN4jsGvtE1GC+gSQxu0TfccdVYHlTyOCO4oA06KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoorw/wCIXx7g0jWZ9K8N6XFqJtnKS3U0hWMsOoULyQP72QP5114PA18ZPkoxu0cmMx1DBw560rXPcKK+Z/8AhozxH/0L+k/9/JP8afL+0N4piCmXw1psYcZUs0o3D1Geten/AKt4/wDlX3o83/WPAfzP7mfStFfM/wDw0X4k/wChf0n/AL+Sf40p/aK8SgAnw9pQBGR+8l5H50f6t4/+Vfeg/wBZMB/M/uZ9L0V8z/8ADRfiT/oX9J/7+Sf40o/aJ8SnGPDulnccDDy8n86P9W8f/KvvQf6yYD+Z/cz6Xor5p/4aI8T5x/wjemZzjG6Xr6VpaL+0LfxahEniPwykNnIRmS2Zw6j+8Ff7w+hFTLh3HxV+Vfeio8Q4CTtzfgz6EqKT/j4H/XNv5im6deWuo2EF/ZTJPbXEayxSL0ZSMg06T/j4H/XNv5ivEaadme0mmro/MD9rb/k4rxl/1/D/ANFR15XXqn7W3/JxXjL/AK/h/wCio68rpDOz8Mf8gqP6mtWsrwx/yCo/qa1a6I7H0OG/hR9BDSGnU00zcSkpTSGgQ0000+mmkJjTSGnUlBLGGm08immgQ00w1IaaaBEZFNNPIppFIkaRTGFSGmkUiSM001IRTSKBEZFNIqQimkUEjCKaRTyKQikIjIppFSEUhFArEZFJTyKaRSFYaRTSKfSYpE2G0mKcRSUCsNIp0btGcr07ikoNIC5FKrjjr3FSg1mjIOQcGrUM4PD8H1oNIy7loGnA1GDTgaDRMkFXtDtY77Vre1lJCOx3Y64AJ/pVAGtbwl/yMNp9W/8AQTWNduNOTXZnRh0pVYp7No64eHdG/wCfP/yI3+NL/wAI7o3/AD5/+RG/xrTDoTgMCfTNK/KEZwcdfSvmPrFb+Z/ez636tQ/kX3IZYeFFt1069tLExi/uDBat9oPzSA4wRn5QSeCeDg+hrWMF3GzpLGimOz+2s3mAr5P97P14x1zx1qxH4kCTxqdLs/s0D2r2yIqpKht2BXdIBl8jeDn+/ntUFtrcaabDZT2KTrHd+Y8nm4L24k837OeOnmHdn04xWs5Qn8Um/X+vn6eZy03iKfwwS9P6+Xr5FsaNq7aiNOWz3XbWhvFiEiktGBk455Ycjb1yMdait7C8mtkus2sFs8SSie4uVijwxYKMk/eJRuOuBnpSN4juLiFzcWlsLopcok9qog2ibaxOFHJDgsD7mrOo6/a6tapa6pppMIjjLfZrgROZl37pBlSAHEhyuODyCOlLlo9GWquL6xX9fMp33h+8nurawvbPEs7SCNDMAMx537iDgYHOT2IPQ1kpo+mMoYW3BGfvt/jXRS6+80WoRNbQo90QLZxIf9DQosTqOMtujRFJ68E96pzwQwBBFqNneZyD5AkG3670X9M0SqSgrU5O3qa0oub/AH8Vf0Mv+xdN/wCfb/x9v8apa1pVnBp8k8EZjdMH7xOecd63aoeIP+QPcfQf+hCnQxFV1Ypye66mlfD0vZSaitn0PEq6b4feBPFXj3VzpnhbSZb6VAGmkyEigU9C7nhR+p7A1iaPp9zq2rWel2Sb7q8nS3hX1d2Cj9TX0n4o02L4Y6jf+DPCWtX0do9tbrquwhPOnVPmO4fMepJ5H3sdBX05+YylY55f2YtYSMx3nxE8E2t6OGtmumJVv7pOBz+Fed/E74U+Nfh28b+IdMU2MzbYb+1k822kPYbx90+zAe2a+qdB+HXhRfB+m6Jq9pCPFms2M15bzMWDQ4AKjGccAjqOfm9K4v4deJG0++m8D+LYft3hvUXNle2Vx8ywMTt3Jn7uG64+vBFFyOdnylTh0rp/i14Rl8C/EXWfC0jtIllPiCRuskLANGx99pGfcGuYHStYHdhN2LRRS1odwUtJSigAFLQKWgYUooxS0AApaBSigYCiinUAAoFFLQMKUUClFABS0UooGGKWiloAKKKWgAFKKKUUDCiilFABS0UooGApaKWgAoopaBhS0lLQAUooFLQAUopKWgApaKWgYUCiloGFLRRQAUtFFABS0UtAGfrv/IMn/wB3+tcRXb67/wAgyf8A3f61xFY1Nzxsx/iL0Oz+BX/Ja/A//Yw2H/pQlfrBbfdf/fb+dfk/8Cv+S1+B/wDsYbD/ANKEr9YLb7r/AO+386zOA88+Pc/2XSfDdydVTSVj8RWzG+eMOtuNknzENwR259ayNW8Q3Nz4Pmk8PePk8UeIY7+EaWlnGkSvOQcQSqhAaJlDlix+UAt1Ar1+ijpb+un+Qdb/ANdf8z571/Wb17XQxN4gvodMbSpZvtV9qFzaStqYlYTKTEjkyR9Fh+7gkAMBxrLq96NVVPF/iXXrLWo4NN/sqCwWSMXhZEMrC3ICylpC6urj5FHRete3UU0xNHidr4l/tCbQ/Cs2sX0mqtreqxajarNKJVgC3flhyOQuPL2ZPOF29K5WDU7yz8J+GbODXZtO0pPDETWch1O6gLX2WEygxI5lkjIQCFuOSAp7fS1FSlb+vIb3v/W9/wDgHhuteLtQ0yw1zSdX1y6h1+4u9INpEgkjd1dbYTNEuMqhYS7uw+YGu9+FkNzLFrep3uoahdTS6xfQItxcM0cMUdzIqKiE4UAd8ZPHOAAO1oqr/wBfd/kSo2VjxqaxuV+JLfDwQzf2bcawvibdtPli2A3vHnp/x9qpx6NWNrvhy81vxv4n8DxRSiC0N14hs5CCE8+5iVYMH1WYTt+Rr36ilsv6+X3WX3Fdb/15/fd/efOY1OS/0LRPHlzqX/CONrni1rn7XcQhvs0UdnLAgZW4wSjDn+/XoHwWuTda14tuE1NNfimvIJBrccPlJdN5IUxhR8n7sKoyvXdzyDXplFNO1/67f5Ct/Xzf+YUUUUhhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBDexyS2c0UT7JHjZVb0JBANfGnw2v7Twb8SUuPFEEghsVuYbuExb2ZjG6bNp65Jxzxz6V9o1yPjL4b+D/Fl2LzWNJVrvABuIZGikYDoGKn5vxr2sozGlhY1KVZNxmrO263/zPFzbLqmKlTq0WlKDur7Pb/I+QPGOo6TqviG5vdF0ZNHsZD8lqshcD1PoM+g4HatjxDqel6pqWlyXt7BOROftEkMUojWD5NoKMc7uGyFPTHJr6H/4UV8PP+fC+/8AA6T/ABo/4UV8PP8Anwvv/A6T/Gvo/wDWLAe7bm930/Ns+d/1dx75ruPvev5JHgF3c+EY38+zGm+ZiN5VeCVhkDkRAAAHcMnOAQR7in3t74SuDcSSGzdz5pfEMoypaUoIOMI2TGTnAx+IPvn/AAor4ef8+F9/4HSf40f8KK+Hn/Phff8AgdJ/jWX9vYHvP8P8zVZFju0PxPAobvwZNeTGS30+CMNLHGRDLgR7l2Nt53Pjd1IPuDisrwzq9nZ6Rdx3UzLc2jNc6bhCczOhjYZH3cZV8n/nn6mvpL/hRXw8/wCfC+/8DpP8aP8AhRXw8/58L7/wOk/xqln+A5XF87v6f59epP8AYGP5lJcmnr/keGzeK9IbTpAty/2n7H9sU+W3/IQdPJkXOOmzDbumRVD4ha1p+pWRjtb2Cc/b2miETztiMpjL+b91sgYVOOvHSvoH/hRXw8/58L7/AMDpP8a0NA+D/gHRtQjv7fRjPPEQ0ZupnlVSOhCscZ+orNZ5l9N88FK69DX+w8fNcknGz9S58EbC90z4WaDZ6grpcC3LlHGCqu7MoPphWHFdbJ/x8D/rm38xUtRSf8fA/wCubfzFfHV6rrVZVH1bf3n19CkqNKNNdEl9x+YH7W3/ACcV4y/6/h/6KjryuvVP2tv+TivGX/X8P/RUdeV1kanZ+GP+QVH9TWtWV4Y/5BMf1Na1dEdj6LDfwo+g2kNONIaZsNNIaWkNAmJTTTjSUhDTTTTjSGgQ0000+mmgQw0hpxpDQSMIppFPNNNIQwim08000CGmmEVIaaRSJGEU0ipCKaRQKxGRSEU80hFIkjIppFSEU0igViM0mKeRSEUhDCKQinkUmKQhlIRTiKSgVhppKdikIpANoxS4ooESQzFOG5X+VW0YMMg5FZ9Ojdo2yp/CkUpWNIGtbwlz4htB7t/6Caw4ZVkHHB9KvaXePY38N3GoZo2zg9D2I/Ks60XOnKK6pnVh6ijUjJ7Jo950uS11HTtLs9Qa0OUvGMccEEck0kfl+RGzjY2DlurruxjNS6ZaeHo71LoxCCSHU02W15dKf9GEiCR3AYrlWJULuO5SW525PlP/AAmEDD5tNc56/vR/hQPF1vjH9mNj/roP8K8ZUK6s3T/I9mU8O78tWyfkz0uK1u7fR9Tln0nTWiMxiSzR4mMTgoxkaUuZAoHAAY53N2FXbuBnvppoIdPXVZ7YnT9Omjtv3DecoKjZ+7kYoT5e/wCYgHgnGfJx4rtcg/2Ucjp+8Xj9KX/hK7UoV/sxtp7eYP8ACpWHrqy5PxRTqUG7+0/B/wBevc9R1fTjLpcqQ2dkurW8sB1KOFoo/KfypPMAGQB0TcF4DZqj4RutMWLUJbu9htxutUSb7JFdMitKd+Ec9MdSOQO1cFL4z864W5mtJ5Z027ZZJwzDb05I7YFT3/j661Aob9Lq6MednmTA7c9ccUfV6ynzqH5f5jVak6XsnU0fXW/5HptzJpRS30h4raA3guIzHFHA8dqTcvtlNznedkYGFAwwx61Q8RzaVKbbUtNntRBKGheOOIwbGQ/Idjc5MZTLd2DHrXnI8WwDj+zXx/10H+FWdP8AHlxp8rS6fFc2sjjazRzAFh1x0pSw9ea5XD8v8y6VWhSlzRqX+/8Ay/qx2IhlaATqIjGYvOBEq527tvTOc57dcc4xzVDxNE8GnXkEoUPGQrbWDDO4dCMg/hWGnjOG6neS6tZDNI25neQHcT3Jx1pdS1n7VaNbxW/lq2Mktnj2qaOCqqpF8uia6o7KmNpSpyXNe6fRnLfB2+ttM+LHhS/vGC28Gr27SMeijeBn8M19b/8ACI6Vqnxr8S2GsWN1d30l0LyzRSwh8vhiXK8nPygduo618O/n+FfVPw1+JWhfEfw/YaL4j8RHwz43sIRa2+pvM0UOpRD7qu4Iw3TgnryM5Ir6Jq6PzeW9z0TXvFXgibx1FrupS3J1PTX8lWt1uPLQISCFX7pHLfWsz4weGvDt1qOm3ekWd+ms65dxyRuM+RKpwDwejcqePcmsxvhB45ZyYrOyuIScieO9Qow/vAnnFc98Stbsvhbp9jPD42TVfGVrco1vplpOZoLWPGG8xjnbxxgbSQcY71MY2Ik+Z7Hnf7ZN9bXnx81dLZlb7LbW1tKR/wA9Fjyw/DcBXkC9Kn1fULzVtVu9U1Cdri8vJnnnlbq7sck/magXoK3gd+EVmxaKKWtDvCloFLQAUooxS0AFKKBS0DAUtJS0AFLQKWgYUtApaAAUooFLQMBSiiloAKKKWgApaBS0DAUtJS0AApaKUUDAUooFLQAUUUtAwpRSUtABSigUooAKKKWgApaKWgYUUUtAwpaKKACloooAKWiloGFFApaAM7Xf+QZP/u/1riK7jXv+QZP/ALv9a4esam54uY/xF6HZ/Ar/AJLX4H/7GGw/9KEr9YLb7r/77fzr8n/gV/yWvwP/ANjDYf8ApQlfrBbfdf8A32/nWZwFXWtZ0nRLUXWr6laWEBbaJLiZY1J9AT3rG/4WJ4F/6G/Q/wDwOT/GvIv2xGbZ4aXJ277k498R18+IrO6ogJZiAAO5NcVbFOnNxSPFxmaToVnTUb2PuD/hYngX/ob9D/8AA5P8aP8AhYngX/ob9D/8Dk/xr4kvba5sruW0vIJbe4ibbJFKhV0PoQeRS2Fnd390trY201zOwJWOJCzEAZPA9BWf1yXY5v7aq3tyI+2v+FieBf8Aob9D/wDA5P8AGj/hYngX/ob9D/8AA5P8a+LLjR9Wt5lhn0y8jkaRYlVoWBLsMqv1I6DvUsmgavFo51WWwuI7UNje0TAEdN2cY254znrT+uT7D/tit/J+Z9nf8LE8C/8AQ36H/wCByf40f8LE8C/9Dfof/gcn+NfFl3o+r2m37Vpl7BujMo8yBlyg6tyOgyMntmn2Gg65fx+ZY6Rf3KbQ+6KBmG05weB04PPtR9cn/KH9s1r25PzPtD/hYngX/ob9D/8AA5P8aP8AhYngX/ob9D/8Dk/xr4wbw/rqwJcNo+oCKQqqOYGwxY4AHHOTwKiv9I1WwmihvtNvLaSY4iWWFlLnOMDPXkgUfXJ/yg85rLVw/M+1P+FieBf+hv0P/wADk/xo/wCFieBf+hv0P/wOT/GvidbC/aK4lFncGO2JE7CM4iI6hj2x71NDo2sTXLW0OlX0k67d0awMWG5Sy5GO6gkeoGaFjJvoH9s1f5F+J9pf8LE8C/8AQ36H/wCByf41p6F4k0DXTING1mw1AxYMgtrhZCuemcHivhK+tLuxuWtr22mtp1wWjlQqwzyODXqn7KTMPidMASA2mS5Hr88dVTxcpSUWjXD5tUqVY05Rtc+riQASTgCqf9qaf/z+Qn6NTtY/5BF5/wBcH/8AQTVkAAYHAFegkrXZ7bbvZFT+1NP/AOfuL86P7U0//n7i/OmNrOkLqX9mtqlmt7uC/ZzOvmbiMgbc5zjnFQaf4j0W+mnjttRtZBCyguJ0KtuIAIwem4hfrxWns3a/K/6+Rm6qTtzL+vmWv7U0/wD5+4vzo/tTT/8An7i/OlGp6abdLgahamFwzJJ5y7WC/eIOcHGDn0qsniLQHuJLdda08zRZ8xBcLuTHXIzxQqbe0X/XyG6iW8l/XzLH9qaf/wA/cX50f2pp/wDz9xfnTpNQ0+OS4jkvrZHtlV51aUAxK2dpYZ4BwcZ9KddX1labvtV3BBsjMreZIFwgIBY57ZIGfep5V2f9fIfM+6/r5kf9qaf/AM/cX50f2pp//P3F+dV5vEOgwwQzy6zYJFPu8pzcLh9vDY55xkZ9KkGt6Mb8aeNVsvtbEAQeeu8kjcBjOckHP0qvZv8Alf8AXyJ9qv5l/XzJP7U0/wD5+4vzq2jq6B0YMrDIIOQRS1T0fiywOgllA/7+NUNJq6NE3ezLlFFFQUFFFFABRRRQAUUUUAFFFFABRRRQAVFJ/wAfA/65t/MVLUUn/HwP+ubfzFAH5gftbf8AJxXjL/r+H/oqOvK69U/a2/5OK8Zf9fw/9FR15XQB2nhj/kEx/U1rVk+GP+QTH9TWtXRHY+iw38KPoIaSnU2mbCGkNONNNACUlKaSgQhppp1IaQmNNNp1IaCRpFNNPNNIoEMNIacaQ0CGEU0inmmmkIYRSGnkU0ikKw0imEVIRSEUEkZFIRTyKaRSEMIppqQimkUCsMIppFPxSYpCsMIpuKkIpCKQrEdIRT8UmKBWGEUlPIpCKQhtIRS4ooAbikxT8UmKQrDRkHIOCKtQXAOFfg+tVqQikNXRpqacDVCGcpw3K/yq4jBhlTkUGsZXJV64r3v4efs26l4v8B6T4tXxlpmnW+pReYkM1m7FPmIwWDgE8V4Ghyw+tfoR+zpPNF8AfA6wojb7Fsl0LAYdvQikY4mrKnFOJ4gP2TdRIJHxE0TAOCfsT9f+/lLH+ybqMkoij+ImiNITgKLJySfp5lfWNvcSbv38KOMdEiZefzNaVpIWVP8ARzGvupyKLHF9crdz80fin4PuPAPjvUfCd1fxX8tj5W6eKMor741k4UkkY3Y69q5gV6v+13/ycN4m/wC3X/0miryioZ7NKTlBN9hwqza3csHAO5P7pqqKUUrmqdtjGjR5G2xozt6KMmpDZ3RGDazEf9czUFGB7V0Hz1jRjl1yO3+zxz6mkOMeWssgX8gcVV+x3Xa1m9fuGtuz8DeL7zSzqlt4a1KW0xuEghPzD1Cnkj6CufdGR2R0KupwysMEH0I7VUoSjurEQqwm2otOw6WKWIgSxuhPTcMZpV6CoqlXoKcNzuwvxMUUoopa0O4KWgUtABS0CloGFFFLQAUtFLQMKUUCloAKWilFAwFLQKKAFFFFLQAUtFKKBhRRSigApRRSigYClopaAAUUUtAwFAopaAClFFFAC0CiloAKWiloGFFFLQMKKKWgApaQUtABS0UtAwoopaACiiloAzte/wCQZP8AT+tcPXca9/yC5/p/WuHrGpueLmP8Reh2fwK/5LX4H/7GGw/9KEr9YLb7r/77fzr8n/gV/wAlr8D/APYw2H/pQlfrBbfdf/fb+dZnAfP37YfTw1/vXP8AKOvAbN1ju4ZHOFSRWP0BBNfTv7TnhDXPEtlo9xotp9qazklEsasA2HC4Iz/u/rXhf/Cs/HX/AELtz/30n+NeXiac3UbSPl8yw9aWJlKMW1p08kbXxt+Iun+OtQjXTtCt7aC3bCXsqf6VKPQkcKv+zyfpXJeCryzstbaW/eBIHtLiHM6u0ZZ4mVQwT5sEkZxWn/wrPx1/0Ltz/wB9J/jR/wAKz8df9C7c/wDfSf41m41ZS5nH8DmnTxVSp7SUHf0ZZ8P6xo+jXV8Wu7YJfKluq6dFN5drwc3K+aN29M4AHPzNjtTTqeixWuhXIfSbmXTYbdZVMVwLljHKWKoSPLwR0z6881B/wrPx1/0Ltz/30n+NH/Cs/HX/AELtz/30n+NO1T+X8A9liLW9m/ufUfb3ek2r3cD+JJrxdQF0u/yJPKh8xMK7hhuLkjB25wPWq8up6bDM0Ed358cOgPYLMsTBZZSS3ygjIX5sZIHTtUv/AArPx1/0Ltz/AN9J/jR/wrPx1/0Ltz/30n+NTyVLW5f6s1+oeyxF7+zf3Pun+haj1TRPN8PXpn0oPYx2SzkxXP2oeWw3gceWQB6dR71j6/q1lfaJbQWcENjJDf3Ephg8wqwcJtly5J3fKRgHsOKvf8Kz8df9C7c/99J/jR/wrPx1/wBC7c/99J/jTcajv7v4en+QnSxDVvZv7maB8R6HNrWmiWaRLC/EsutkRH5JZ4xHIAP4gu3cMf3varaeL9JvJLSe6mRLi4Nyt8J4pDGFWGSK2DbPmI2yYO3kYrE/4Vn46/6F25/76T/Gj/hWfjr/AKF25/76T/Gnar/L+Bajik78j+5/1qZvjK8sry8svsUkDrb2McD/AGdZFhVlLcRiT59uCPvdye1d7+yn/wAlOl/7Bs3/AKHHXKf8Kz8df9C7c/8AfSf416l+zd4F8S6H4zudX1jT2s7dbN4VDsCzszKeMdhtqqNOftE2upphKFb6zGUoNL0Z79rH/IIvP+uD/wDoJq3UF/E09hcQJjdJEyjPTJGKiF5PjnTbsHvjYf8A2avYSvHQ+obtI5i98P6pLqd7drLM0L6zbXS2mY/LkjRYgzk43AgqSBuH3Bwc81NG0fXE8Lw6TcW+p+bD9m+W4ltjCuydGbYUO77oJ+bsPWuy+2S/9A67/wDHP/iqPtkv/QOu/wDxz/4qutYqooqNlpb8Dklhabk5Xet/xdzz4eEddt9Pis7a0jaCSwuzJH5ygw3MiFcDnBV8gnHRgT3roNc0XUbpdfEMAb7Zb20cHzgbihbd34xnvXQ/bJf+gdd/+Of/ABVH2yX/AKB13/45/wDFU5YurJpu39O/cUMJSimk3/St2OB1Pwz4lkn1TUlt4JpNTS5ilgVgsiplTBucttbAjAwMY8w+9a/iDTtS1i7+2f2Ncoi2hj8p7mOOXeJ4nBVlZgGAUsMnGVwcZrp/tkv/AEDrv/xz/wCKo+2S/wDQOu//ABz/AOKoeLqNxdlpt/Vx/VadpK797f8AqxxUek+IotRt71oNTVQLld9qbRbkh2hKmXP7tidjcrzgLnnNXJ/D+rS3094ZLgxPrFtdCzYxbHRViDOSBuDKVJwGxlRwQeep+2S/9A67/wDHP/iqPtkv/QOu/wDxz/4ql9aqXTSX9O4fVadmm3r/AJWLlU9H/wCPL/trL/6Maj7ZL/0Drv8A8c/+Kp+mxSQ2irKoVyzOVznG5i2P1rktaOp1p3kWaKKKgsKKKKACiiigAooooAKKKKACiiigAqKT/j4H/XNv5ipaik/4+B/1zb+YoA/MD9rb/k4rxl/1/D/0VHXldeqftbf8nFeMv+v4f+io68roA7Twv/yCY/qa1qyfC/8AyCY/qa166I7H0WG/hR9BKQ0tJTNxKQ0tBoENNIaU0lAmIaaacaQ0hDTSGnGm0CG0hp1IaBMYaQ08000CGEUhp5ppFIQw0hFONIRSEMIpCKeRTSKBWG4ppFPxSEUhEZFIRTyKQigmwwimkU80hFIBhpCKeRTSKQrDCKQinkUhFArDMUmKcRSEUhWG4pCKdQRQIZikp+KQipAbSYp2KSgLDcU6N2jbKn8KKTFAi9bzLIRjg9xX6Efs9At8BPAeIy/+gSZO7GPnb86/OlchgRwQa/RP9neIzfs+eBGKxMVsXwZJAoB8xvcUjnxcrwSPR7OCJ51DjZzn1/rXQgAKABgAcVztlHGbhVdLdsnGFmBP5A10YGFwOgFM88/PX9rv/k4bxN/26/8ApNFXlAr1b9rw/wDGQ3ib/t1/9JYq8oFQz6Ch/Dj6IeKWmg0oqTdFbw1Y2Gpa3bWWp6tFpNpIxEl3LGXWP8B69Mngd6+ofAHw48F6Fbwahp0EWrTsNyX87LNn3QD5V/Dn3r5y0C38Fvb6e2t3+pxSNMReLbRhiqc4xnt05GT14r6E8I+H/Cc2kx3Hw91yWwMY+aW0uDKGP/TeF8gn6hT6Gms/w2XTTr0pNN/Fbb+vI+KzvA1qkVGFW11ey217ta37rX0PQ++e9cx4z8DeFvFMTyazp0QnC/8AH5EfLlQepcdR/vZFR/bfHsP+iHR9DvH6LfretDF9WhKlgfZSR7iqmr6BYyafLe+P9eF9bAHdFJJ9lsY/91Acsfdixr0cdxhlUKS5X7Rvol+d9vzPlsLlGKhU5ubkt1T1+Vv1sfNnxC0PRdA142Oh+IYdagwSzovMRz9xiPlY+61gr90VvfECHwjBrpTwbc3s9jtO8zr8qtnohPzMuO5H51hJ0Fc1Gqqy9oo8qfR9PvP0XL4uMbSd3bdiiloFLWx6QUtFFAxaBQKUUAFKKKWgYUoFApaACloxS0DAUtFFAAKWiloAKWiloGAoopaAClopcUDAUtFFABS0CloGFFFLQAUUtFABSigUtABS0UUDFooFLQMKKKWgAoopaACloooAKWiloGFFFLQAUUUtAzO17/kFz/7v9RXDV3Ovf8guf/d/qK4asam54mY/xF6HZ/Ar/ktfgf8A7GGw/wDShK/WC2+6/wDvt/Ovyf8AgV/yWvwP/wBjDYf+lCV+sFt91/8Afb+dZnASkA9aTavoKr35O1BnjmqlS5WNY0uZXuae1fQUbV9BWZmijmK9h5mntX0FG1fQVmUUcwew8zT2r6CjavoKzKKOYPYeZp7V9BRtX0FZlFHMHsPM09q+go2r6CsyijmD2Hmae1fQUAAdKzKKOYPYeZqUVl0Ucwew8zUorLoo5g9h5mpRWXRRzB7DzNSisuijmD2HmalFZdFHMHsPM1KKy6KOYPYeZqUVl0Ucwew8zUorLoo5g9h5mpRWXRRzB7DzNSisuijmD2HmalFZdFHMHsPM1KKy6KOYPYeZqVFJ/wAfA/65t/MUWpJgXJzRJ/x8D/rm38xVGLVnY/MD9rb/AJOK8Zf9fw/9FR15XXqn7W3/ACcV4y/6/h/6KjryugR2vhf/AJBMf1Na9ZHhf/kEx/U1r10R2PosN/Cj6CGkpTSVRuIaSlNJSEIaQ0pooAbSGlpDQIQ00inUhpCG0lOpKBDTTSKfSGgQw0008ikNIQwikp5ppFIBhpDT8UhoJGEUmKfTSKQhhFIRT6TFIViMikxTyKTFIBhFNIp5FIRQIYRSU8imkUhWGkU0in4ooFYjIpMVIRTcUhDKKdikxSCw0ikxTsUYoCwzFJTyKTFIVhF+8PrX6Efs3+IPDcPwC8H2V54l0ezuIbIiSKa9jV1O9+CCwI61+ftvaXVzHcSW1tNMltF507RoWESbgu5sdBlgMnuRTbmznt47eW5tHiS6j82B5IsCVNxXcpI+YZVhkdwaRjVpqorXP1CHiDwmCMeL/DnH/T7F/wDF1dtvFHg1DuPijQiw6FdTj/8Ai6/LKwsJb77R9lhjf7PA9xL0G2Nep/XpS2NlNcQ3dxbwxNHZwiacnA2oXVM+/wAzLTuYfVY9z2D9q69tL/4++I7uxuoLq3kFtslhkWRGxbRA4YEg8givLgarW80brtUBSP4RVhTUs9OnaMUuw8GnA0wGnA1JsmVvD02lQaxBLrdnPeWCn97DDJsZvTn+nGfUV9BeF/8AhX/iK2gHht49PvLdf3ZtGNtdw/lyw+u4Gvm6nRO8UiyRuyOhyrKcFT6gjpXPjsB9aV1Nxa+77jly7MlhE4TpxnF73Wv3n1iP+E5hX7JFrWkTw9BeXNk32hR7qrCNj74H0rK1u28K6JGdS8X6kuo3TKQJdSYSsQeojiA2qP8AdX8a8Stfib41t9O+xJrLOuMCWSJXlUezEZ/E81yt7d3V9cvdXlzNczuctJK5Zj+Jrx6GRVVJ88lFf3VZv8FY9FZjlWE9/CYf33/Nql6av9DY8c3fhq81oy+F9OuLG0wdyyNw7Z6qvO0e2fyrHT7oqGp0+6PpX1FCCpxUU7276nkUqjq1ZTaSv2Vl9wtLRS1udYUUCloAKWiloGFLigUtABS4oFLQMBS0CigBaKKWgAooxSigYUtFLQAUtFLQMMUtFFACigUUtAwoopaAClxRRQAUooFLQAUtIKdQMSlopaACiiloGFFFLQAUooooAKWiloGAoopRQAUtJWnplrgCeQc/wg9vegAsbEDEk456hfT61RuE2TyJ6Ma3qx9TC/bGIIOQM47GgSZi69/yC5/93+tcLXda/wD8guf/AHf6iuFrGpuePmP8Reh2fwK/5LX4H/7GGw/9KEr9YLb7r/77fzr8n/gV/wAlr8D/APYw2H/pQlfrBbfdf/fb+dZnnkWodE/Gs7ULlLKwubyRSyW8LysB1IVSSB+VaOodE/GqciJLG8UiB0dSrKejAjBB/Cs5HZRtZXOLittWvJdJlv8AW9Xhu9UjkmX7DKEt7MCMOq7Cp38EDLHkg1v+Fr67vdPmj1Dyze2dzJaTvGMLIyEfOB2yCDjsc1nQ6P4jsFgtNOv9OmtbYFLWW7WXzYUIxtZVO2XA4BbHT8a2tD02PStPW1SWSdy7SzTSfflkY5ZzjuT27DA7VhBNM9PFVKcoWTT7WXm/uVrK3fXzeD468SXmianpNlbS2Vul6s7PNc2s1xt8sIQAsRB53Hk8cVNF4tsoJrKxvzI93c24uFkitmjikiAJklAc7lVAPmDcjcvB3Cr+uaFHql9ZXy6jqGn3VmsixS2kiKSsm3cDuVgfuiqs3hLT7m+gv766v727gjESzTSruMeGDoQqgbXDHcMc4XptFa6nEuW2plaT8QtPvmkjS2uZ7mWRDY2duiGWaF1Yq+fMKjhHJ3FduACM9dVfFtg8DOtpfrILmK18mSERv5skfmKCGIxwcHPQ/nVa28DaXbW8ccF9qkcsDo1tOs6iS3CKyhVwm0jazA7gxIPJOBT4/BOmR39vdLfartglinMDXW5JZY02LI+QWY7evOCcHGaHcHyFDRvHYvLa2abSbxry7t7eWGwtkV5cyRNI3zFwpUKuc5HUDkmrlj460m+voILW01KSCaSCIXXkgRI8y7o1bLbs9j8pwetEHgXS7eOD7NfarBcW6RpBdR3CiWNURowAduMFGIOQc4B6jNWrHwho9jFHFbLcJHHcW1wq+bnDQKFTqMkHGT6kk0+oPk6dvxMPxf4ivIPF66PHf2ljDbW8F4pks7mZpmdpF2t5JACjaDg9Se4FXdf8arZafqM9npl9cfYmaMXJtybeSRHVZEBB3ZG4gZABIIBOK09R8PC51mXVrbWtX064lt44JRaSxhXVCxXIdG5G9ulVp/BmmTvch7zVPIuHaVrYXOIlkZlZ3C46sVzzkDLYAzSVwvG6+X/BIbjxzp8JnQ6XrDXFsZjdWywI0lvHFtLSNh8FcOpG0knPA4NWLnxnodtaNeTSTLarPPCZdgK/uojKzdfulRx3JI4FN1PwZpd9d3d0bnUbaa8aX7Q1vcBPMjkVA8R4PyHy16cg5wRmorrwHolzdySTSX7WrmVvsInAt1aWLynYDG7O3p82AeRilrb+twXIUrP4iaZ9jJu45DffaWiFpCYgwXZ5incZNh+TGTuHPGM8V1uk39rqml2upWUnmW11Es0T4xlWGRxWA3gfS3to0a81FriOUypdF4/MXKbCoGzZgr/s9ec55ro7K3is7OG0h3eVDGsabmLHAGBknkn3qkTLl6EtFFFBIUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAaFp/x7r+NEn/HwP+ubfzFFp/x7r+NEn/HwP+ubfzFaLY45fEz8wP2tv+TivGX/AF/D/wBFR15XXqn7W3/JxXjL/r+H/oqOvK6ZJ2vhf/kEx/U1r9qyPC//ACCY/qa2K6I7H0eG/hR9BKQ0tIao2EpDS0hpCEpDS0GgBppKU0lIQhpKdSGgQ0ikNOpCKAG0lLQaBDTTSKfSGkKww0lONIaQDSKaRT6TFIQykIp5ppFAhhFJinmkIpCsMpDTyKSkIYaaRUhFNIoAYRSYp+KSkKxGRSEVJSYpCsR4pMVIRSYpCsMIppFPxSYoAYRSU/FIRxSAbSV6NoGhaNr+g6SraVZ6fNLHfS3FzA1w00wthGQqKWkUM285IjbgEgcVNongbQZ72O8S9vr+yj1iK08iSHyWnhaSMNLhgrBE3mNzgfOyYwCQC2tjB1oq5y3gjxFB4eTU2lgknN7FFbvCuAssPmAzRse25OAcHnB7V0SeO9OnWJfO1LRpYrRrS1ubWISvZQrcCSOJPmUkGMbWII59QTWdb6PYR6Lq13feGNRitEnMFnLI0pvDMGQmPAAjCqpJYsn8agc1rah4Z05ZL3V7Tw4s1rHZPNp2nD7VHLNtnWN2nRm8wmNXyfLIVuoIAYBJ7P8Ar+v66kTUG3f+v6/rYguvG+kzaPq0Nob/AExbqO9RdNhhU287TSh0kdgwAZVwuNpwFG3GTT73xh4YAv8Ayor27S/u5r6SGW0VFUvNayeQTvO5cQOpYYHzDjrVPxV4XtYfDC6np+jXVveySWr3FqvmS/YxJFKzIQckA7EcbvmAbGTVb4a6PpWpw6rLqaaXmFrSOF9SluI4EMspVsmHkHHQn5R1NNX5rLdaEtQ5OfpuTeNbyfxxq63OjvaSW9qrBWnaOykUO7MEJmnYuF6Dado6AAVyEcxSRopsBlJUkEEZHuOPxFdtqvgKxMF2bGLV45sSz2xukRYUAu/s62sjEZE3Qk5xkgYx81Wp/AGmWOpLKx1W8gjto5nsrdd05f7U0D8tEuY1K5JCfxAA4+apjG9kupoqqgn5HEKfxp4NW/EtldW/inWEtdOnFml9diIRwNsWOOQ7tuBjCjGew74qpJHLC/lzRSRPgEpIhVgCAQcHnkEEexqVqrnZGaehk0ZA6kfnXTfCrRbTxH8S/DWg34LWl/qcME6qcFkLDcM9sjj8a++NX8E/D3SVstK0r4b+GrzULrK2lsbCIAKoG6SRypIRcjJ5JJAGSa2crHiOVj84cqP4h+dG5f7y/nX6H6j8PIdPjNz/AMIL4BvlQbpLay0kRTY7hGfIc+xC5p8XgPwpqXh9Nes/DHht7OeMSIg0yINsPHPy8H1Hapc7dBKZ+d1Tp9wfSvRf2lvDGl+FPipc6fo9rHZ2k1rDci3iGEjZwdwUdhkZx2zXncf3F+lbU3fU7cJrJjqKKWtTvClopRQMBSikxS0ALS4oApaBhS0UUAApRRSigYUCiloAKUUUtABS0UooGApaKBQAUtFLQMKKKWgApRSUuKAClopaBhS0lKKAClopaBhRRRQAooopaACilooAKWigUDClopaACiiloGWdPt/Pmyw+ReT7+1bNQWUPk26qR8x5b6065dlVUj/1jnC+3qaCHqNkdpZDDEdoH33Hb2HvVTVIUjjiKLgDIrQhjWKMIvQfr71S1aZdogHLZyT6UDRzmv8A/ILn/wB3+orha7vX/wDkFz/7v9RXCVjU3PHzH+IvQ7P4Ff8AJa/A/wD2MNh/6UJX6wW33X/32/nX5P8AwK/5LX4H/wCxhsP/AEoSv1gtvuv/AL7fzrM88i1Don41Uq3qHRPxqpWctzrpfCFFFFIs5n4i+NNK8EaKuoaiHmllYpbW0ZG+ZsZPXoB3PavHpv2htZ3kx+F9PWNuU33MmSPrjBqf9rSyvTe6FqIDGyEMsGR0WUsG59CR/wCg15xr+qadeafYQfbFni+0wOsOG/0SJYUSRDkcZYE4XOcZ6muOrVmpNJ22PlczzHEQryhCXKl+Oh33/DQuu4J/4RjTMDr/AKRL/hSf8NDa5/0LOmf+BMn+Fchb3ngtrF4pY4o0kvyzwoZAAolxGwHOUEROcnOSe9V4YvCEGmvBNcafPcrDHumQTHe/JYJwPYFh+RrP2tS1+Y43j8Xe3tfy/wAjuP8AhoXXcZ/4RjTMf9fEv+FXdG/aFma9RdZ8NxR2rHDPaTszoPXawwfpkVw3m+B/tcqF7Qxk74o1EnkK+0j5s/eGO+ByRx1rG8SN4YbRVbSBBFcC5bCjzGkMe5/vMwAAA2Y659uaTrVF9oP7Qxcfe9rt6f5H2Npl9aalp1vqFjOs9rcxiWKRejKRkGqHi/xFpvhbQptX1R2EKEKiIMvK56Ko9T+nWuf+BNle2Hwr0aC+V0lZHlVGGCqO7Mg/Ig/jXNftPWd3N4Y0q8iVmtra7bz8dFLqApP4gj8a6qtSUaTmlrY/QMkoxx1ejCropWv917fPYxLn486pvaS28L2ot84VpbiQn8SBjNRf8L61nAP/AAjWm4PT/SJa8/u9Rgm8Fw2El1C0kKoLeGLzVdH8xy5kBOwjB4I5zj0Nal1ceEZIraOBrVmih2wfazKYlBKFhIFGd3MpBHcj2ry/rFZ/b/I/Rv7Hy+Ojw73a+10679f8zrP+F9ayOvhrTf8AwIlpP+F96x/0Lemf+BElcfLe+G7s/vEsBPDBFHbSTJLsdhCR+9xyVDBR/iM0qJ4JN557yxC0DBDCBJ5hPnZLDj7mzjk5xxjNP29a9uf8hf2TlqWuGf4/5nYH49ayDg+GtNB97iWtvwh8b7bUNUisdf0qPTkmYIlzDKXRCem8MMge46V5xfXPg+dL6aUJLduBube5DN5YwYzjj585HoOOKw/FMWm3GrQxaAkLrMgQRW6sRvLEBQW5YkbecD35zS+s1oO/NcSyTLq65PYOF1vrp+P5n2JUN1cJAoyCzHooqPRoZ7bR7G2uW3TxW0UcpznLBAD+oNYXxBiu5NFuVtFnZ3jVcQ/6wpuHmBP9rZuxXtrU/MprlbS1L0Gv2s9w9vBPZyzR/fjScMy/UA5FTnU2BCmOME9AW5NecSNeDUZm0rT7y0ijSOHTYYtJaEJll3tM7DpgEAZxjJ64qfxhpGpXfiVLyysWmcJbfZboMv8Aohjld5OpyN6lRwDu6HgUEJ3PQP7Sf/nkn5mrFpeLO2xl2t254NeU/Y/HkFoSuo6rNIYwpDGByubeNmIHy/MJd6jngdOxrtfBw1J9MsP7TjlS9A/fCRwzcE8kgAZxj/65ptWBSudXRRRSKCiiigAooooAKKKKACiiigAooooAKKKKANC0/wCPdfxok/4+B/1zb+YotP8Aj3X8aJP+Pgf9c2/mK0Wxxy+Jn5gftbf8nFeMv+v4f+io68rr1T9rb/k4rxl/1/D/ANFR15XTJO28Lf8AIJj+prXrI8Lf8gmP6mteuiOx9Hhv4UfQQ0GlpKo3GmilpKBCUlLRSENNIadSGkA2kp1JQISkp1IaBDTSEU6kIpANpKdikNIQ3FNIp5FJigBlJTyKTFIVhhpCKeaQigQw0hFPIppFILDaQinEUYpCsRkUmKkIppFIVhhFIRT8UhFAWGEUmKeRSEUhDKTFPIpCKQrEZFJin4oIoCxGRSVIRTcUhCxyzRPG8U0sbRNujZHKlD6gjofcUST3EjmSS4nd2zlmlYk5OTznueTTcUUhWRYfU9TaOWNtTvykxzKpupMOfVhnnoOtMOoagb6K/OoXn2yIgx3BuHMqY6bWzkY9jUJrq9P8Bard2kVw11Z2/mKGCOXLAHkZwCK6MPha2IdqMW2uxzYjEUMOr1ZJJ9zEufEGs3GqXGpS30oublg1yYz5a3B7mRUwGJ5znrk+tQ6tqb6iqr9h02yVc5WythCH/wB4A/NjtnpXVx/DbU3cINT07J9RJ/8AE1jeL/COr+Gfs0l8iSW10pMNzFkxuR1XkAhhjofrV18uxOHjzVINIyoY7CV58tOabMN7i5eFoXuZ2iZ/MZGlYqXxjcRnBOO/WnnUNRN2t2dQvDcou1ZvtD71X0DZyB7ZqDFGK4zt5UO86fGPtE2PmGPMP8X3u/fv696TzpvM8xpHZsAEsxJIAwBz7U00YpBY9d+Bg1L/AIS/4emSys1sP+Eih2Tqf3rHc3UenX8hX3BdzxaZ8RNPv71xFa3tg1hFM3CxziXzApPbeOB6lMelfnR8LdaGhfETw5qtxceXb2OpRTkuSUTB+8R0x6+1fcUvjS/1Sxe3kOj3tpOuGV4UkjkX/vrBFZRj7Fu/Vt6eZwYqr7Zxa6JLXy+S0/q5614gktLKxn1G8uo7W3gjLySyHCqB6mvN/AtxfyaDsd5IbOa4muo7cgDaskjOinvwCDiuftWtre4jnFpYzNEd0SXDPNHEexVHkKr+ArM8OHWPDut3xtNStX0K7Z7j7NO2WtpyckRkniM8nBPHaqlNPY5VFnz/APtl5HxnYEEEaVan8wxryCP7g+lejftPeIYPEvxau72C4t7jybSC2eSAgx7kU5AIJBxnH1rzmP7i/Suujsd+D3Y6loFFbHoC0oopaAClFIKcKBiClopaAClopaACgUClFAwpaBS0AFLRS0DAUUtAoAKWiloGFFApRQAUUUtABS0CloGFKKKKAFoopaBhRRS0AFFFLQAUUUtABS0UCgYUtFLQAUUUtAwqaxj8y6RSOM5P0FQ1f0ZcyyP6DH50CZp1DB+8mkmPQHYn0HX9akmbZE7/AN1SaS2TZbxr3C8/WgkWeQRQtIew/OsJ2Z2LscknJrR1h8IkY7nJrNoKiZ2v/wDIKn/3f6iuEru/EH/IKn/3f61wlY1Nzxsx/iL0Oz+BX/Ja/A//AGMNh/6UJX6wW33X/wB9v51+T/wK/wCS1+B/+xhsP/ShK/WC2+6/++386zPPItQ6J+NVK0pollADEjHpUX2RP7zVDTubwqRUbMpUVd+yJ/eaj7In95qOVl+1iY+s6Xp+s6dLp2qWcN5aTDDxSrkH/A+45rz+X4HfD95GcWmooCc7UvnAH0r1j7In95qPsif3mqZUlLdGFWlh6zvUin8jyX/hRngD/n31T/wPb/Cj/hRngD/n31T/AMD2/wAK9a+yJ/eaj7In95qn2EeyMfqeC/59r7jyX/hRngD/AJ99U/8AA9v8KvaL8HfAWl3yXkelzXUkZDIt3cNKgI77TwfxzXpn2RP7zUfZE/vNR7GPYccJg4u6gvuKQ4GBUV5bW95ay2l3BHPbyqUkikUMrqexBrS+yJ/eaj7In95qvlO1Vop3TPMbj4N+BJpmkFjewhjnZFeuqj6A5qP/AIUv4F/59tT/APA5v8K9S+yJ/eaj7In95qx+q0/5Ueis9xq/5fS+9nlv/Cl/Av8Az7an/wCBzf4Uf8KX8C/8+2p/+Bzf4V6l9kT+81H2RP7zUfVaf8qD+3sb/wA/pfezy3/hS/gX/n21P/wOb/Ctnwt8OfCPhu+F/p2ms12v3JriUytH/u54B98Zrufsif3mo+yJ/eamsNTi7qKIqZzi6sXCdWTT82UqbLGkibHUMPer/wBkT+81H2RP7zVtys4PaxMj7Bbf3W/76o+wW3o//fVa/wBkT+81H2RP7zUcrD2sTI+wW3o//fVTQQRQg+WuM9T3rR+yJ/eaj7In95qOVh7WJSoq79kT+81H2RP7zUcrD2sSlRV37In95qPsif3mo5WHtYlKirv2RP7zUfZE/vNRysPaxKVFXfsif3mo+yJ/eajlYe1iUqKu/ZE/vNR9kT+81HKw9rEpUVd+yJ/eaj7In95qOVh7WJSoq79kT+81H2RP7zUcrD2sR9p/x7r+NEn/AB8D/rm38xUkahECjoKjk/4+B/1zb+Yq0czd3c/MD9rb/k4rxl/1/D/0VHXldeqftbf8nFeMv+v4f+io68roEdt4WH/Eoi+prXrO8NL/AMSC1f1Zx/KtKuiOx9Hhv4UfQSg0UVRuNoNKaSkISkpaKAG0UtFIQ0ikNOpMUANpKdikxQAlJTqSkIaaQ06kxSENoNKaKAG00in0mKQWGEUlPxSYpCGmkp2KSkKwwikxT8UEUARkUmKeRSYpCGEUhFPxSEUgsMxSYp+KTFIVhhFNIqQ0hFArEeKTFSEUhFILDKTFPIpCKQWGYpCKfikIpXFYjcfK30Ne72a/6JB/1yT/ANBFeFOPkb6GvqX4e/D/AFnxdpYm0+5022EZjgAu5yhkcx7sKApzwCfwr6vhitCj7adR2Xu/qfJ8UU5VFSjBXfvfoc1aL+/Xiuyhg8Pa34PbQ9bMJjIZXSQ7SOSQ6nsRngiuzb4eLp9tbSzWmh3QYIN1tdeYzBsqHxtHylgRnNPbwhbhGP8AZNqOD6f417eIx2HxEeV7fI+eo4atRlzLc+N/iF4Ul8J62bQXUV7Zy5e1uEYHeoPRgOjDIz+YrmsV6b8dY2SfSAykfLN/NK80xXw2Z4eGGxU6UNlb8Umfe5ZiJ4jCwqT3f6OwwikxT8UmK4DuI6VSVGFJA9AcUEEVtaN4T8Saxai603R7m4gJIEgAVWx1wWIz+FdcISm7RV2ePOcYK8nYxtzf32/76NIWY9WY/ia6j/hXnjT/AKAFx/38j/8Aiqr6h4I8WWFq91daFdJDGNzsu19o7khSTireFrpXcH9zM1iaLdlNfejngMDAGBVqP/Vr9Kq1bj/1a/Ss6Z6OE+JjqBQKWtTvFoFKKKBhS0UtABSiiloAKKKUUDClFApaACloxRQMUUtIKWgAFLRRQMBS0UtABRSiigApRRS0DCloooAKWilFAwoopaACiiloAKBRS0AFLRQKBhS0UUALRRS0DCiiloAK09GH7qQ/7Q/lWZWpo3+pkH+1/SgT2LF7/wAejj1AH61MBUN6P9Ek9hn9anHTNBJk6sc3QHooqpVvVhi7+qiqlBa2M7xB/wAgqf8A3f6iuDrv9dUHSLpmzwnHHfIrgKxqbni5j/EXodn8Cv8Aktfgf/sYbD/0oSv1gtvuv/vt/Ovyf+BX/Ja/A/8A2MNh/wClCV+sFt91/wDfb+dZnnktFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABUUn/HwP+ubfzFS1FJ/x8D/rm38xQB+YH7W3/JxXjL/r+H/oqOvK69U/a2/5OK8Zf9fw/wDRUdeV0AegeGR/xS9sfSVqvVR8M/8AIrW4/wCmrVerojsfR4b+FH0A0lLSVRuBpKWkNIBKSnUhoASkpaKQhKSnUhoASkIp1JSENIpKdSGkAhpMUtFADcUhFOopCsMIpKeaQ0BYaaQinYpKkBuKQin0mKBDMUhFPIpDSFYZikxT8UlILDCKTFPIpCKQWGYpCKfikoFYZikxT6MUhWI8UhqQikIpBYYRTSKkIpuKQWGEUhFSYpCtArETj5G+hr7Y+Dnky+AL62j1rTtM1JLy1uLVru4WIcRhWYZ6/KW/HFfFLLlWHqDX0Hot5Z32kWs0E8MiNAo4cZHygEH0NfRZBSVZVabdvhf3NnzPEcnT9lNK/wAX4pH0xpOjWVpNPY2WoWN9bSXdvHp8NtOJHjtk3uQcdMM78+mK6KTQlETn7JJ90/xH0rx3S/jL4otrW3skj0cxwxrGpaJskKMDPz+1aK/GHxPMrKU0ZVIwT5Tf/F10VcuxrlfT7zyaeLwyVtfuPmH9oJSLjRuP4Z//AEJK8sxXqX7QN9aXGp6VaW88UslvFK0ojYNt3MuAcd/lPFeXkV42cSUsbNry/JH0+TRccFBPz/NjKTFPxSEV5Z6ZFX2B8Nvhhquo/Drw7qMOo6VBBdadFLEkzsGAIz/dx19K+Up4tFHh2CWG4nOqmUiWIj5AnPT9P1r7/wDhGVX4L+B9zoudFh+88a/wj++D+lehgszq4RynR0e2qPn8yy+nXjGFV3W+j79/PyONX4R6wxAGsaGSegErf/E0k3wvvdPZzqGpWDAIcJDuJY+jEgYGM16pc3UdrH5u8Bh8y48pgR65VeKTSdKl1DdqWoqfJ2loom6uccMw9PQfia8nNOOM1xFb+z8A06j+KVtIrr8/6Wu0YThzBUUsRWTstlfd/wBf1Y/P/wCMVv4btfFrW3h2OOPyk2XaQ/6pZQei+4HXHGfxrlYv9Wv0qK6/4+pv+ujfzNTRf6tfpXfQjyRUb3Pcp1nWrSqNJX7aIdS0Clrc6gpaKWgApaKWgYCigUtABS0ClFAAKWiloGFFFLQAUtAooGFLQKWgAoxQKWgApaKWgYUUtFABS0CloGFFFLQAUUUooAKWkFLQMKWiigApaKWgAoopaBhRRS0AJS0UUAFaOjN80ieoBrPqzpz+Xdpno3ymgGtDXkXfGyf3gRTbVt9tGT1xg/UcVJUMP7u4eI8B/nT+ooIKusJzHJ9VNZ9bl5D51uyD73UfWsTpQXHYh1sKPC15gjcRyM+4rzavS/EEcZ8LTvsG4J17/eFeaVjU3PEzH+IvQ7P4Ff8AJbPA3/Yw2H/pQlfrAqyoWCqjAsSMtg8/hX5P/Ar/AJLZ4G/7GGw/9KEr9YmmiRtrSKD6ZrM4BN0//POP/vs/4Ubp/wDnnH/32f8ACk+0Qf8APVfzo+0Qf89V/OgBd0//ADzj/wC+z/hRun/55x/99n/Ck+0Qf89V/Oj7RB/z1X86AF3T/wDPOP8A77P+FG6f/nnH/wB9n/Ck+0Qf89V/Oj7RB/z1X86AF3T/APPOP/vs/wCFG6f/AJ5x/wDfZ/wpPtEH/PVfzo+0Qf8APVfzoAXdP/zzj/77P+FG6f8A55x/99n/AApPtEH/AD1X86PtEH/PVfzoAXdP/wA84/8Avs/4Ubp/+ecf/fZ/wpPtEH/PVfzo+0Qf89V/OgBd0/8Azzj/AO+z/hRun/55x/8AfZ/wpPtEH/PVfzo+0Qf89V/OgBd0/wDzzj/77P8AhRun/wCecf8A32f8KT7RB/z1X86PtEH/AD1X86AF3T/884/++z/hRun/AOecf/fZ/wAKT7RB/wA9V/Oj7RB/z1X86AF3T/8APOP/AL7P+FG6f/nnH/32f8KT7RB/z1X86PtEH/PVfzoAXdP/AM84/wDvs/4Ubp/+ecf/AH2f8KT7RB/z1X86PtEH/PVfzoAXdP8A884/++z/AIUbp/8AnnH/AN9n/Ck+0Qf89V/Oj7RB/wA9V/OgBd0//POP/vs/4Ubp/wDnnH/32f8ACk+0Qf8APVfzo+0Qf89V/OgBd0//ADzj/wC+z/hRun/55x/99n/Ck+0Qf89V/Oj7RB/z1X86AF3T/wDPOP8A77P+FG6f/nnH/wB9n/Ck+0Qf89V/Oj7RB/z1X86AF3T/APPOP/vs/wCFG6f/AJ5x/wDfZ/wpPtEH/PVfzo+0Qf8APVfzoAXdP/zzj/77P+FG6f8A55x/99n/AApPtEH/AD1X86PtEH/PVfzoAXdP/wA84/8Avs/4Ubp/+ecf/fZ/wpPtEH/PVfzo+0Qf89V/OgBd0/8Azzj/AO+z/hRun/55x/8AfZ/wpPtEH/PVfzo+0Qf89V/OgBd0/wDzzj/77P8AhSBXLl3CrhSAAc/56UfaIP8Anqv505ZEkVtjhsDnBoA/L79rb/k4rxl/1/D/ANFR15XXqn7W3/JxXjL/AK/h/wCio68roA73wy3/ABTtqv8Atuf5VpVkeFiTo8I9CcVr10R2Po8N/Cj6CUUtJVG4lFLSUgENJTqMUgG0UtJQAlFLSUAJRS0UgG0hFOxRikIbSYp2KMUBYbikp2KSkFhpFJin0lILDcUmKfikxSEMxSYp+KTFIBuKQinEUEUCsMIpMU8ikxUhYZikIp+KSgLDCKQipMUmKQhmKbipMUmKQWGYpMU8ikxSFYYRSEU/FJigLEbEKpZiAB1JqS9t5rK5+z3SeVLtVwrEZIZQyn8QQfxrX8I3lnYatJNdyLAWtZY7e5aDzhbTMuElKYOccjgEjOQCRXW6r4s0Oa01wxfYru+uiFaa6tJgt2gtEiAUIBsKyK7gOFGSD2xQ9jGU5KVkjzQyR/8APROuPvDrT7mE28ipKYgzKrjbIrZDDI5BPODyOo6GvQY/GGnprt9P5oNnLqVokatYoQdPQOJY9u3gHcOOp9eKbB4k8N2+k6lDZ2dj9qkghjH2u1l2TotoI2jHl9GEmWy2ASQ2cik1pcn2kr2sedjY2cFTjrjnFS29rLcx3MkEYkS1j82cgj92u4Lk/iyj8a6bX7fVfFmq3E+gWF1e2NtHHBB5UCoIlEa8Y4/iDGtS91vQpZbqCTUZItLnsI7eGyhsSklltlgaSPO3azHy3w+WBOCcZosN1HZWR58ChGVZcexFTQ2001vcXEMZeK2VWmYEfIGbapP1JAr0fUtd8J3+phJLuCMGG2L3n2KaRXaG6d9rZTeWaFlUnbjK46c0HxX4Z+xGNm8zTJLi1aXSBY7WYx3XmSuXA2sHTtuzztwAAaS6EOrL+U8yBVhlSCPUGjFdn4pg1DxZq32vQtMi1CG3iET3FjbyJvO5mG8S4O4KQOBjAAycZrjiCCQRgjrUm0XdFYV+ivwgN03wK8G3FhBLdNHokOIobgRs5xjAJBH4kjFfnTXo/gX42/ErwXoUWh6Fr6Jp0GfJguLWOYRAnJClhkDJJxnFdMldHiyjc/QLUdMN3dW87WMT+UAcST43HHG4BTnH1q6XuxFK1zBbxxrExLLMWPA9CBXwh/w058YP+g3p3/grh/wqh4g/aG+LWt6TcaZdeJIobe5jMcv2WyiidlIwRuAyMj0rloYOlh3J042ctX5vzCbqTspPY8rucNcSspyC7EfmamiH7tfpVcDAwOgrQiQNAmeu0V3QWp3YNe8yKlpzIV69PWkrQ7gpaBRQAUtFLQMKUCiloGFLiiloAKWkFLQAUtFFAwpaKWgAoxRS0AFLiiloGApaKKAClopaBiUtFLQAUUClFABSiiigYUtFLQAgpaKWgBKWiloGFFFLQAlLRRQAUtFLTGJSgkHI4NFFIDetpBNCsg7jn60XEZdQU4kU5U+/pVDSZtshhY8PyPrWpQQ1ZjIJBKm4DB6MD1B9Kz9TtirGdB8p+8PQ+tXZYmD+dDgP/ED0Yf40+KRJVK4wRwysORQCOZ1wE+GrwiRhtHK9jyK84r0zxZbmDR7tov8AVsgDL6civM6xqbnjZj/EXodn8Cv+S1+B/wDsYbD/ANKEr9YLf7r/AO+386/J/wCBX/Ja/A//AGMNh/6UJX6wW33X/wB9v51mcBLRRRQAUUUUAFFFFABRRRkZx3oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKif/AI+B/wBc2/mKlqKT/j4H/XNv5igD8wP2tv8Ak4rxl/1/D/0VHXldeqftbf8AJxXjL/r+H/oqOvK6AO38K/8AIIi+prYrI8Kf8giL6mtiuiOx9Jhv4UfQSilpMVRuFJS0UgEpKWikAlFLRQA0iilopANopaKQrDaKWjFAWEpCKXFFIBppMU+kxSCw2kp+KTFIBuKTFPxSYoCw3FJinUYpCGEUmKfigikFhhFJin4pCKQWGYoxTsUGkFhmKTFPxSEUhDDSYp5FIRSCwwigin4pMUCsR4pMVJikIpBY6NfDukxwmW71HUwINIi1O6EFpGxAlaMKkeXGceZyTjpx1qfU/BD6dp2tTXF+BPpzSNCpChbqJJERmVd29SDIM5GAQRnNY+la5q+lPcS2F9PDNParamZZGEkcYZWARgcr9wD6ZFQwatqcVu9o19dTWUsvmz2klxJ5M7ZBO9QwzkgZOc+9Dt0/rUw5Knft/wAEteHfD66zY3d19saFbJw93iPd5Vv5bsZevZkC49XXnmuht/B8E+vTJDeRS3VveiO4t47KPyreIouJHiLglWLYG3IBHJBIrk73UPO3izsbfS0liMU8dnJMFmQkHD75GyMgcdOBxQNZ1kRGEaxqIjaXzigunwZMY39fvY70rhKE3qmbth4Ftb42UB1GYXLGxN1uswYkS6UsnlNu+dlHUEAHnH3TWFr+nWenSWEtrdXE1je2qXUbywhJUUuyMGUEjIKNjBwRjpUY1LVFtrW2XU70QWjb7aIXD7YW9UGcKeT09aktNRjhtRBdaTY6jtG2KS6ectEv91QkqqBkk4x1JougUJrd/wBdzpLTwnpmneLNP0fXJjPHqMzxxNukixEXCwyqUDZaTnaGG3pk4zil4J8OWdzqsc2uG7jtYdXtrBreKASNI8jsPnJYAJhGBIJyTgVi2usazaSGW11jUIJDCtuXjuXVvKXhUyD90dh0FJZ6xrFlKJbLV9QtpFhEAeK5dCIxyEyD90ZOB0FCaTRLpzaavuUbu2H2ibNvlVd8Ex5AUMR1x07ZqN0ZGKsrKw4KkYI/CrX2y98nyfttz5XlGHZ5rbfLLbymM/d3fNjpnnrUdzNPc3ElxczSTzSNukkkcszn1JPJNQtFY2tqZtb+heDPFWu2n2zSPD9/eWxJUSxx4QkdcEkZ/CsGvrr4ZWso+HfhkQbFiGnxlgR6jP8AMn0rqbseFOXKfOf/AArLx/8A9CnqX5L/APFVW1PwD4102zkvL7wzqUNvEpaSTywwQDqTgnA96+vIrZwD5vlseMbUx25/Wia2VoZFKDDIwPHUEEGlcj2jPh6tK3/1Cf7orPkAWRlAwASB+daNv/qI/wDdFa09z0sF8TH4pjxd1/KpaXFano2KuKKsugb6+tQshU89PWkS1YbSigUtAgpRQKKBiiiiloAKKWigYUtFLQAUCgUtABS0AUtAwpaKKAClopaBiClopaAEpRRS0AApRRiigYUtFLQAlLRS0DCiiloAKKKWgBKWiigApaKKBhS0UUAFLRiloAFJBBBwRyK3bWUTQLIOp4P1rCq7pU2yby2Pyv8AzoE0atMmhSTBOVcdGXgipKKCTnvFxnXQbtZAHXYPnHHcdRXl1er+Mf8AkXrz/cH8xXlFY1Nzxsw/iL0Oz+BX/Ja/A/8A2MNh/wClCV+sFt91/wDfb+dfk/8AAr/ktfgf/sYbD/0oSv1gtvuv/vt/OszgOb8eXWqJeeHtP0vU5NOOoakYJpo4Y5HCC3lkwA4IHKDnFcdf/EHV9KW50OYnUNTsbuffdRRbDcW8IicgKEdfNbzQmMBcgnK8V6P4h0DS9fht4tUgklFtN50LRzyRPG+0rkMjAjhiOveqEngjwu+n21gdKRYLfzNgSWRWPmHMgZgwZw55YMSGxzmhf1+AP+vxOVuPiLqdhbapdXGl2cyR38kdnEbpopGhS3jm5Xy2Yv8APjABAJ+YjGS0+P8AUf7UmubSyur21S2nnWyCL5jkJZMmCqkgKLhyeGPXGcAV1t94K8M3txNPcaYrSTSM8hWaRNxaNY2GFYDayqoZejbRkHFNHgbwqLU239kRmMxmLmVyQpEQ4O7IP7mLBzkbBgihfoJ9ConjQv4Pg1uGwhuLq5vFsobaC7DRtM0vlqDIVBUdzuUMOhXPFctF8T9TsHvbbUtFNxfQ3N2728UpPlwRSKgVCkZ8xySSoIXI6kHiu+/4RbQToEmhHT0awlcyyIzuWaQtv8wuTvL7sHdndkA5qkPAXhNbeOBdJVUR5H4nkBcyENIHO7LhiASrZBPJFC3/AK8h9P68yHw94xfVPFV3ok2mfYREJGhM8xWaVVKjeIyoyjBsgqzY6NtPFcroOo69e+LNV1NzrErWt5e2kLfYrZbPyonfZGZeZQMgZIGSR3ArvtP8MaFYaxLq1pYLHeSGQl/McqpkILlVJ2oWIBO0DOOar23g3w/barJqUFrcRzyzvcSBb2cRNI+dxMe/Yc5ORjFH+X4h/mcppvxKvI7fQYtT0u1knvrW0a6e3uiTHJOhYEJs4XgffZT83yhsconxTni02yn1Pw59judTt7a406IXvmpIkzY/eMseUK8E4VuDxk5FdFJ8PvChgjji00xGJYxEVuJfkMQxG2N+CyjgE844zjio9B+HXhfStCj0sWAmIggiknMjrITFyjIQ2YsNlgEIAJOKfV+v4C6GbafEeWe60qOTQJLSK9YRyT3U7RIJPOaIrGWjw5+XcAxQsrLtBPAt+NdY8Q/adW07w4ubu006KSNVWMu0s8xRSPMIX5Vjc4PBJGelan/CE+GPOtZf7LXdahQg82TDbXMi7xuxIQ5LAvuIJJ61NrHhnTdVv5ri+jE0NzaC1uYGHyyqr742yOQysWII/vewpBr+RzvhbxHqH9p2GnX1xfTSPd3NhdpexQpLFOkazJzCNhXy88jruXPOa72sHTvCmladf2dxZReVFaCZo4sli0suN8rOxLM2ARkk8E1vUAFFFFAwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA5rXfFMljqUlrYaRdalHZASalJFx5CEZAUf8tHx820fw+5APQWdxBd2kV1bSCSGZBJG46MpGQalooAKik/4+B/1zb+YqWopP+Pgf9c2/mKAPzA/a2/5OK8Zf9fw/9FR15XXqn7W3/JxXjL/r+H/oqOvK6AO48Kf8geP6mtmsbwp/yB4/qa2a6I7H0mF/hR9BKKWkpm4UlLRSASilxRigBKSlooGJQaWikA2jFLRikIbijFOxQRSAbikp1JigBKMUuKKkdhuKKWigBMUmKdikxSENIpMU/FBFIBmKQin4oxSAZikxT8UmKQDcUmKfSUgsMxSYp5FBFILDMUmKfikxQKwwijFOxRikFhhFJinkUmKQWGYpMU/FBFK4WIyK17TwxrV1bpPFZjy3G5S8qISOxwTmsph8p+hr2G0XFrCB/wA81/kK93I8qp5hKaqNpRtt53/yPn89zWrl0YOmk3K+/lb/ADPOV8HeIWYKtnESf+nmP/4qs7WdH1LR544dStWgaRN8ZJBV1zjII4PPFexWg/0ha6+fw5pHi7wTHpl8V82It5cikeZA+Thh7c8joRXp5hw3So070ZO/na35I8fA8T1qlVKtFcvle/5s+X8UmK2vF3hzUvDOsSabqUeGHzRSr9yVOzKf6dqx8V8fOMoScZKzR9nCcakVKLumMIppFS4puKm5djNr7L+GCzD4c+FhHB5iNpsW9t2Nvy18aCvt74Tc/DLwiEt5Jll0yPLoMhcL3rpkfOVNjVijkYHzIvL6Y+YHPHP5Hilkh/dt/un+VbENuZASYJI8Y++AM5Gf06H3pZ7QLDIxGAEYk/hSMj89Zv8AXv8A75/nWlbD/R4/90VmzYMzkcgscfnWna/8e8f+6K2pnr4H4mPpaXFFanohQRnjFLiigZE8XdfyqPFWqa6BvY+tFiXEr0opzKVPIpKRIUtFFAwpaBS0AFFApaAClxQKWgYUooFAoAKWiloGFAoFFABSiiloAKWgUUDCloFLQAUUUtAwooFFAC0UUtABRRRQAUtFFMYtFFFIApaKWgAoopaACgcHjrRRQM3LObz4Ff8Ai6N9amrI0ybyp9h+6/H49q2KCGrGH4y/5F68/wBwfzFeUV6v4y/5F68/3B/MV5RWNTc8XMP4i9Ds/gV/yWvwP/2MNh/6UJX6wW33X/32/nX5P/Ar/ktfgf8A7GGw/wDShK/WC2+6/wDvt/OszgJaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoqvqV9Z6bYzX2oXUNrawrukllcKqj3JrgJvjd8OY5WjGtTSbTjcllKVP0O2uijhK9e7pQcvRNnPWxdCg0qs1G/d2PR6K81/wCF4/Dr/oLXP/gDN/8AE10vg7x34V8Ws8ehatFcTRjc8LK0cgHrtYAke4q6uAxVKPNOm0vNMinj8NVlywqJv1R0tFFFch1hRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFRSf8AHwP+ubfzFS1FJ/x8D/rm38xQB+YH7W3/ACcV4y/6/h/6KjryuvVP2tv+TivGX/X8P/RUdeV0Adz4T/5A8X+8a2axvCf/ACB4vqa2a6I7H0uF/gx9BKKWimbiUUtFIBtFOpKBiUUuKMUgEopaSkAlFLRSASilooAbRinUYpANxRilxRikA3FGKdSYpANop2KMUgG4pKdijFIBtJin4pMUrgNxSYp+KSkFhuKTFPxSYpBYZijFPxSYoCwzFGKeRSYpCsMxSYp+KCKQWGEUmKfijFILEeKTFSYpMUgsRuPlb6GvfvCHg/xL4k08zaHpE16kCokjIyKFYqCB8xHavA3Hyt9DX2X8E7bVZvAN7c6JCZtQs9RtZoY8gZzDsbOewVyfwr6PIMTLDwrTja/u77atnynE1BVpUYyvb3tvRGX/AMK3msIYJtR0TVLfO1GdpFK7yOnyk96kbwVZBSfsd5kA/wAZ/wAK9Ms9A1HThPZ6h5zQQXNrZWM7vuaaFWeQt9cvtP8AuVrSaMnlMd833T/D7V6Ms1qX1lf0ufPxwELbHw38YVKzaYMH7sv81rgMV6V8bx/pGlfSb+a15xivn89d8fU+X5I+xyBf8J9P5/mxmKTFPxSYryD2bGPXdeDvi34+8J6PHpGj60q2ERPlQz20cwjyckKWGQMnOOlcLXRaB4H8X6/Y/b9G8Oahe2hYqJ448ISOoBJAP4V2nzrt1Oz/AOGgvih/0FdP/wDBbF/hVLWvjh8StW02fT7jXYoYJ0Mcn2azjicqRgjcBkZHpWUPhZ8Qz08I6kfwT/4qq+p/Drx1pljLfX3hXVIbaFd0snlBgg9TtJwPeloTaJyoAHArWtv+PeP/AHRWVWtaj/Ro/wDdFaQ3PQwPxMkFFLQK1PTCloFLigBKXFLRQAhAI5qJ4scrz7VPijFANXKlLVh4w3sfWoWQqeRSIasJSgUUUCClFFLQMKWiigApaKKBhS0UtAAKBRS0AFLigUtAwoopaACiiloGFFFLQAUUUtABRRS0DEpaKWgAoopaACgUUtABRRS0DCiiigApaKKAAVt2M3nQBifmHDfWsSrWnTeVOAT8r8GgTQzxl/yL15/uD+YryevV/GjKPD92CQCVAA9eRXlFY1Nzw8w/iL0Oz+BX/Ja/A/8A2MNh/wClCV+sFt91/wDfb+dfk/8AAr/ktfgf/sYbD/0oSv1gtvuv/vt/OszgJaKKrarcmy0y6vFTzDBC8gT+9tUnH6UN2HGLk0kTSSxxkB5EUt0BYDNPr4uuNUk8UXOt6z4j1OZr5bMz2YMmAZfMQCNR2UIz4Ax0r3f9mbxJqet+Gb/T9Snkuf7NmRIJpCSxjZSQpJ642n8CK4aGNVWfLbfY+ozThipgMK6/PfltzK1t7bPrv5HrVFFFdx8sc0uq3sHj6/sb2+tk0uPTIrmJCgQxsZHVmdyec4GOgHv1rJ8c6gq+JfsGo+Jbjw9p8WmNdQzQyrEZZg5DcsDu2KFOzvv5Brsr82MUEk98baOLbtkebaF2+hJ4xWXda94Rugi3Os6HMEYOokuom2sOhGTwfepckrXZpClUndxi3/SODh8a6na6xPB9rjm8+4SKKe+LRRQBxB8zxjBX7xwCQdzhcipLH4qXLaTFcz2mnS3TugS3t5yTOpjmcmPPJGYuDjHJrt5dc8HzLIsusaE4kGHDXMJ3dOuTz0H5Uvh6PwsRHFosmmXDWwbYYZUleMMxY8gkgZJNNTi0lccqFWN5Si7ejOY8HeNtd1vVrCwltNKRZTK8s0VwJAY1jifChHYBsy45boAcDOKS5+IF1a+J7zTblNNjgivI4Iz5pJEfnxxu7OCVBHmfdYKQcfeByO/t7S1t1VYLeGILnaEQLjJyenrUF1BpSvMbmKyVrlQs3mKmZQOgbP3h9aptXuYpOxzPgPxZe+JtRuzs09dPt4Q6yROzNIWllUEHpt2xZz3J9q4/RPHmunVdRlBmlj1qeGXRVv4WjgSP7StuRGw5dTG8U3HdzXq0M2lQpshmso1wFwrKBgdBx9aPN0pvKXzLI+X/AKsbl+X6enQdKXUHqjzxviLqscd40qaHFJp8MgkhlmdHu5F84ZhHPy5h5Bz1bkbcnQ0fxprdx4yt9CurHTwm5IpmSdVcs1v53mIrPvZBnb9znBO4Y213LWdm7rI1tAzBWVWMYJAb7wzjoe/rSTrZW7G+nFvE0cewzuFXamem49BQnbcdm3ZHhH7Xuo3yRaFpSM6WUxlnkA6O67QufoGJ/GvL9P0XRXttMv7i1ZrfVWhtIF81h5cwDLM2c5OGEZweP3lfR/xLsPA3jrQf7M1DxHpcEsT+ZbXMd5EWhfGOhbkEdR3/AAFeIzfB51fZD8Q/CbRIxMZa8Kn64GQDx2NfZZRm2Dhg40Z1OSSvfzv/AEj5HN8jzCeLlWhRc00rabFWDwnocU0Pm2TS4drGVS8jf6VBFI87bUIJGRH8oPfisYSP4b8e6DqGgxvBIwt5o0Cum5mba67HJZQ2CNpJ4Oc4Iro0+El4jh0+IvhNXDFwy6gwIY9Tn1967P4Z/DnwzoniGHXvEnjbR9WvoHDwRR3ilA46OzM25iOw459a7KmcYKnGUnX59LW11OWlkOYVJRisO4a722PexRSIyugdGDKRkEHIIpa+BPuAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACopP8Aj4H/AFzb+YqWopP+Pgf9c2/mKAPzA/a2/wCTivGX/X8P/RUdeV16p+1t/wAnFeMv+v4f+io68roA7nwn/wAgeL/eNbVY3hP/AJA8X+8a2a6I7H0uF/gx9AopaSg6AoxRRQAYoxRRSCwlFLRSCwlJTsUUDsJSYp2KMUgG4op1JSAQ0UuKMUgEpKdiikMbRTqTFIBKMUuKMUgG4oxTsUYpAMxRinYoxSCw3FJin4pMUMLDaMU7FJikFhuKMU7FGKkLDMUEU7FGKAsMIpMU/FGKQWGEUmKfikxSCw3FJin4pjPGrbS6hvQnmkFhrj5W+hr3bTJpDp0D280iLJChBRyM/KPSvD5I3jkaORGR1OGVhgg+4NWrW91WC3cWl1exwxAF/KkcKgJwCccDJOPrXsZRmkcBKTlG6lb8Dw86ymWYxhyys43/ABsfYug/FnTrCK1SPwVa/aYYlQ3CzqrMQuC2fLyM/XvW5/wul5lZF8NYyCMm94H/AI5XxHLquvwSbJdQ1SFx2eWRT+RpP7f11Rga3qQH/X0/+NdFTMcum7ui7/4n/mebDI8dFWVZfcdp8digvNJhGN4jldh3wWXB/Q15oRVu9a8mdbm8a4kaZdySTFiXUEjIJ6jII/A1XxXj47E/Wq8qtrX/AOGPoMvwn1TDxo3vb/O5GRQVp5FGK5DtsYIr7l+AsVtp/gjwrqMgmdE0VI1hjKgZchmbkH3HHqa+G6+6fhGEj+E3hiWRgiLpMLMx6ABeTXbI+Xq7HpZ8R6eeum3B/wC2qj+QrD8VTabq8KslhLFNGrBXaXd8jKQyH1B469MVwl3rniu4ivdR0jT7CXTYJ1EDmQH7RDzucPu2gDHXtnHauusp7fUNMW8tZY5oZYyyvG25Twc4PfmsKdaNR2Rx068ajsv69D89pQFldQMAMQPzrVtf+PaP/dFZc4/fyf77fzrVtP8Aj2j/AN0V109z3cD8TJMUuKBS1qemAopcUUDCloxS0AJilopcUAJQQCMEUuKWgCB4iOV5HpUeKt014w3PQ0WJcSvS0rKVODSUhBS0UUAFLRS0AFFFLQAUtFLQMKKKWgAoopaBhRRS0AFFFKKACiiimMKWilpAFFFLigAoopaACiilFAwooooAKWiimAUtFFAwoopaAM3xM7yaVcM7FjtH8xXn1ek62i/8I3fyY+baB+orzasKm54WZfxV6HZ/Ar/ktfgf/sYbD/0oSv1gtvuv/vt/Ovyf+BX/ACWvwP8A9jDYf+lCV+sFt91/99v51meeS0MAwIIBB6g0UUAeM698CoJtVu59A8QzaTZXoK3Fr5O9dhYMUBDDK5AIBzjAr0XwD4R0vwboK6Vpgd8t5k88mN8zkYLHH0wB2FdDRWMMPTpy5orU9LFZxjcVSVGtO8V6fi938wooorY80+WfivrF74x+LMnh+7u5YdOtr02UMSkbUxwz4PBYkHk9BiuO1PRIZJ5Z7EWtvaoqj99dwsC5BOxWQkFiFJwT/MV7N8Wvhbr0vi0+LvBwiluJHE01szKGWUDG9d3ysD3B7+ua4MeB/iookEfhowrIoDLFFaouQCAwAOA2GI3DnBxmvBr0Z875ot6n63leY4VYan7GrCKUUrN2afXT9Tnp/BOotqU9tY/ZbmGORkEofoQ4UK2AcMSRx09+tZ/2fVPDd5bX0F1HZ30bBlEEw86E43DcB0yPqOx9K7OLwX8WonkZdDuj5js7hjbspZiGJwWxnIBHp2q1pPwr+Imv3FtZazbLptlERvuJ2iLgAYz8mWcgcDcePWsfYSfwxdzt/tSjBN1q9Nx66pv7uvoe/eDdfm1vwDpuvOipPdWquwxx5n3T+G7n6VV8ZatfeG47OSx0/wA+BzJJf3skLyCCNACWbZzzk/QA8HFbuk6RZ6ZoFtotqhW1t4FgQZ5wBjP17/WqOs6RZ6qsSa1p0t2YQyq8MjKHVsblYKwypwMqcqcd6+igmoq+5+O4iUJVZOmrRbdvToUp/GlvavqC3el3q/Zb4WcKwKJnnzAJt4CnAXbuPJzx74psHjzQrqS0txBfB7yYQoklqVPKoyscnoVkQgctgnjg40J9N0qeaSaXR7hnkm89zgjL+UYs/e/uEr/9eqc3hrw9MY9+h3TBGRgnmOFYoqKm4b8MAI04bI+UHrVrz/ruYam7bqLa8a3TiJ0MiL2Qg4IHtyOPrXi3iyG4+I/xrm8H3t7Pb6HpMXmSQxtgykBST9SXAz2AOOTXtltFIZXuJwFdgFVAc7F9M9ye9eZfEXwR4lt/G0XjzwNJC2pbAl1aSkATADGRng5AAIJHQEHNcuKi5RVldX1Xke7kNanSrz5pKMnFqMnspdH5ddehyHxN+F/hyz8Eahr+h2Fzpk+mTukkU0hdbiNX2Fhu6Z+8COteXW+gWcnh62vzIxublpEjj86BFBVwo+VjvbJP8Ir1Pxynxg8U6M2jv4Nj06ykk824S1ljzO2c/MTJ0zzj161ydv4C+KEFlHZp4ZZoomZot8dq7xknJ2sSWHPPBryq1OMp3jB2t26n3uV4udLD8uIxMHLmvrNP3bbXv31/qxzMPg/U5pvKi+wuQZASku4LscIxOFJxvIXPr7ZNQp4X1Nrl7dorWKSNQz+ZKoCA7zyfby2z9Peutg8DfFaCUSR6DdZw4IZrdlId97ZBbB+bB56EcVIvgr4tG2Fp/YdwIzwSTb7iMMMFs5IAd8DPG44rH2Lt8D+49F5nBX/f0/8AwJf5nSfsv+J9QXXLnwtPcPPYvbtPbgkkRMpAO3PRSG6eo+tfQ1eV/Av4Z3Pg/wC0avrTxHVLiPykijbcsEeckFu7EgdOBjvXqle1g4ThSSmfmfEmIw2IzCc8PqtLtbN9X/W+4UUUV1HghRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVFJ/x8D/rm38xUtRSf8fA/65t/MUAfmB+1t/ycV4y/6/h/6KjryuvVP2tv+TivGX/X8P8A0VHXldAHdeEv+QPF/vGtqsXwj/yB4v8AeNbdbx2PpsL/AAY+iEopaKZuJRS0UDEopaKQCUYpaKVwEopaKQxKMUuKMUgExRiloxSCwmKKXFFIY2jFOxRikA3FGKdijFIBuKMU6jFIBmKKdijFILDaKdijFIdhlGKdijFAWG4oxTsUYqQG4pMU/FGKQWGYpMU/FGKAsMxSYqTFJipHYZijFPxSEYBoCwzFdXYa7Yab4EgsmSW7uJmvlls1MfktvVFjebKl8jll2kcr1HNMt/B899d21vpt/DKzaZDf3QeKTNsJAuAdqndlnAG3PqcUuneExHr1lp2tajaQPcvMPJid3fy0Eo83cqldu6M4GdxAyBT1WhyznTmlrtr+Bqa1B4Zu08QX9xf6ddT3FwZbd1nRZYyHhCj729wyGQkBcLg5IIpbeTwkNTF5EulWTQaoI41hlZUaCO9hKSFSxzmMyZPQhc4yM1hL4RvGtRMmqaa4lQNZkeb/AKXmAzfJlPlO0Hh9vIxTr7wtOkt25ubCz2eebe3aSSRphDEJJNrbeAAer7cngdKLtO/Yx5IONubcvx3OmaraTXdy2lTas0l29vbX124tsmWEAne+ASnmFcsAxHsBVrRo/DWnwaJcfaNGkvvMZXkaSMRuJLWUFZU3FgqSlF3OF9RxzWNZ+CtXu7y5toZLY/ZZCk7guwUCJZCwAXJGGUYAySenes6XQr5PE3/COjyHvftAtwVkBjLeu70xz7c96nZKJfLCV2paa/18jb0230IwQLNH4fk1JbVA8NzdlbRT9qkEuGD43CPYVAPQkjJxUOoL4W/su6tNOhs3P2a4ntrli32oyLebYkPPOYedu3PfrVC38N3F7p+qX2mXtve2+nQtM0qQzKkqqu5trMgAIHZsE9q008IXw8VahYeHtVaWbTcus6RTRFWCk7C4XCtgHnO09j1of+YrRTvzf0v61OQZGU4ZGU+jKRTcVau7+81F0nvr+6vJAuFe4naRgOuMsSQPaoMelQdiWhzlfeHwitYL/wCDXhq3lUSQzaPHG49QVII/nXwfXd+Dfi74/wDCejx6Po2tqthCT5UM9tHMI8nJClhkDJ6V3SVz5aceZWPqix8N+N9M0K50ixn0LyFdUtVMZwYyTv8AMyOSQRnr3rqfDmiQ6B4Zh05BGPJiZpSgIUuQSxGe2a+Sv+GhPil/0F9P/wDBbF/hVLW/jl8TNX02fT7jX44oJ0Mcv2azjicqRgjcBkZHpXPTw0abuvQ5aWDVN3Xax5zMQZpCOQXJH51q2n/HtF/uisccDArZs/8Aj1i/3RXbT3PbwPxMlxRS0VoemGKUCilxQAlLRS0DEpaKWgLCUuKWjFACUtLiigYhUEYIqF4iOV5FWMUYoE1cp0tWHiDcjg1CylTgjFIhqwlFKKKAAUtFLQAUUUtABRRS0DCiiloAKKKUUDCiilpgFApaKACiloFIAopaKACiiloGFFFFAC0UUUwCloooGFFFLQAUUUUAQa5/yK1/9B/MV5nXpmuf8itf/h/MV5nWFTc8LMv4q9Ds/gV/yWvwP/2MNh/6UJX6wW33X/32/nX5P/Ar/ktfgf8A7GGw/wDShK/WC2+6/wDvt/Oszzx8hcY2IG9ctimbp/8Ankv/AH3/APWqWigCLdP/AM8l/wC+/wD61G6f/nkv/ff/ANapaKAIt0//ADyX/vv/AOtRun/55L/33/8AWqWg9KAMjU/Eej6ZcfZ9S1bS7KYjd5dxepG2PXB5xVT/AITPwt/0Mmg/+DKL/GvnL4t2/wBu+M+qWbSbTPewQ7iM7QyRrnHtnpXPz6DEzytZalaSQRsyl5JF5Koztjyyw4VT3z0GKAPq3/hM/C3/AEMmg/8Agyi/xo/4TTwv/wBDJoX/AIMov8a+VLvwtqVsyq72bMSVISYMVYKzFTgcEBDTbLw5PcX1xam6tU+zyNEWBJ3yCN32JxyfkPJwPegD6u/4TTwv/wBDJoX/AIMov8aP+E08L/8AQyaF/wCDKL/Gvk248PX0OqJpvmWkty8TSKsUwbO0kMv+8CDx7Vct/CV5JAJmntyrD935ZLbjkccgYBDAg8gigD6l/wCE08L/APQyaF/4Mov8aP8AhNPC/wD0Mmhf+DKL/GvlpvCN4IVkW6tHwrmXBbClHZWUHbyRsJPTpxmkk8I3v2oQQXNo+5iqFmK7wGAZhxwo3LnODzwDQB9T/wDCaeF/+hk0L/wZRf40f8Jp4X/6GTQv/BlF/jXx61jOdSisYZLWR5RvVgzbfL2eZvJ2/d25PTPB46ZuQ+H9QltBdi404W+WzI0jgBQGIb7uSCEOMAn2FVysnmR9a/8ACZ+Fv+hk0H/wZRf40f8ACZ+Fv+hk0H/wZRf418i3GlyRPZRLPFLPcXEls8QVgYpE2hucHcvzdR+VXpvC97DJslubFPkaXJkbHlAZMnC/d7evtSasNO59Wf8ACZ+Fv+hk0H/wZRf40f8ACZ+Fv+hk0H/wZRf418rL4U1EiL99Y5lUsq+flsYyvygZ+YcjA/KqzeH7tLqCCSa1Tzrd7jzC7bEVF3NkheoHYZ54pDPrP/hNPC//AEMmhf8Agyi/xo/4TTwv/wBDJoX/AIMov8a+VX8J6ks3kmWz804Cp5hyxYOVA4xkiNyM44HOMgU4+ENS/eFZ9PZFcxq/n7Q7qzKyruAJIKN/TOaAPqj/AITTwv8A9DJoX/gyi/xo/wCE08L/APQyaF/4Mov8a+UL3w5cW9m10lzazxxRl5yhYeXhmUDkDOShAPc0tnoH9oQLJp15HKcYkEqFMPtyVHUsenQUAfV3/CaeF/8AoZNC/wDBlF/jR/wmnhf/AKGTQv8AwZRf418iaZpdzqCTPb+WBAAZN5xtUg89OnGPqR61rjwdfbHDTQeaF3gAnbt7kkgEYw2RjPFAH1J/wmnhf/oZNC/8GUX+NH/CaeF/+hk0L/wZRf418ka3p1rpzJEl+tzOQjOqxEKFaNXBDHr970FZuB6UAfZP/CaeF/8AoZNC/wDBlF/jV3Std03VmddL1HT78xjLi2u0kK/XbnFfFOB6V6h+zMSPiPIASAdOmzjv88dAH0run/55L/33/wDWo3T/APPJf++//rVLRQBFun/55L/33/8AWo3T/wDPJf8Avv8A+tUtFAEW6f8A55L/AN9//Wo3T/8APJf++/8A61S0UARbp/8Ankv/AH3/APWo3T/88l/77/8ArVLRQAiFioLDB9M5qOT/AI+B/wBc2/mKlqKT/j4H/XNv5igD8wP2tv8Ak4rxl/1/D/0VHXldeqftbf8AJxXjL/r+H/oqOvK6AO78I/8AIGi/3jW1WN4R/wCQNF/vGtqt47H02F/gx9AooxS0HQJiilopDEoopaAsJiilopDEopcUYpAJRS0UgEpaMUYpMApKWigdgpKXFGKkBKKXFFACUU7FJipHYSjFLilxSCw3FGKXFGKQ7CYoxS4oxSCw3FFOxRikFhuKMU7FGKQWGYoxT8UYpDsMxRinYoxSCw3FIRT8UmKQWNGLXdUjSKPdaSpHbfZSs1lFIJYQQVSTcp8wKQNu7O3Axim2+uapBDbxRSW6i2Lm3b7LHvhD7tyK23IQ72+XoM8CqGKKG2yPZQ7F2PWtWjhtIUu8JZgC3Hlr8mIzH6c/ISOf51bsvE+qQvdfafIvY7mOdWWWFNyNLD5TMjbcpwFyFxnaM1j4rQtdC1e6hWa306eSNhlWAABH4mmlKV7a3MqroUknUaivOyJP+En10xxI9zbuqLtcNZxETjyxHibK/vfkAHzZ6DvzUFte2Ntsvbe1vrbV45PNint7iKOCOQNkFYhFwB/dDfpxV+18HeKLpZmttDu5RCnmS7dp2rkDJ59SKZ/wiXiT/oDXX/jv+NNwqXvZnP8AWsClZVI2/wAS/wAyq/iDVGtri2H2GOGcSApHYQoI/MQJJ5eF/dllADbcZxUyeK9eWWSUXFoZJNrOzWUJJdVKiX7v+s2sRv8AvYPWqGoWF5p8/wBnvraW3lxnbIuCR6+9VsVm21ozrjTpTipRs0TJe3KRrGottqpEg/0aPpGcr26+p/i6Nmq8zNLM8r7d0jF22qFGScnAHAHsOKcRSYqW7miilscriloor0T5cKKKKACtqz/49Yv90Vi1t2f/AB6xf7grSnud2B+Jkooopa0PTCgUtGKAClxRS4pjCjFLS4pAJS4opaAExS4opaAEoxS0UDDFIVBGCKdRQBXeIryORTKt0x4g3I4NFiXHsQUUpUqcEUUiQoopaACiiloAKKKWmMKKKWgApaKKQBRS0UAFLRRQMKKKWgAooooGLRRRTAKWiigAoopaACiiigAopaO1AFHXptugXsJHDKCD75Fec16D4j/5BFx/uj+Yrz6sKm54eZfxV6HZ/Ar/AJLX4H/7GGw/9KEr9YLb7r/77fzr8n/gV/yWvwP/ANjDYf8ApQlfrBbfdf8A32/nWZ5xLRRVXVluX0u7SzbbctC4hOej7Tt/XFDHFXaRx3iX4s+CdB1ZtLu9RkmuI22Si3hMixt6EjjI7gZxXXaHquna3pkOp6Vdx3dpMMpLGeD7eoPseRXxno8r2Vt4htb2CL7TJYNAwuJAjxyCaMsVBGWf5T8owcEntXuX7KMN+nhrWJpg4sZbtfs2ehYLiQj/AMdH1BrzcNjJ1anK1o/wPtM64bw2BwTrU5O8Wlq1aV7bLpv56I9noPSig9K9I+KPkP4xtLH8XddmjSWKRLpWRlPzZEaYYEH2yPSuduLrW9Rmwbi+unZWHLkg/Kc5zx93d+Ga6r40XUmnfF/V7kBklS4iljyOuI0wfocGstfHWyCaFdKVVkclFEx2xrjCqAR/Cvy54yKWw7NmUbvxEokkF1qIjI+zORK2CBk7OvIwG4+tQiTW7RZGE19B9qUtIRIwMqkZJbnkYbr7+9b8XxAZJI5DppJSQSAC5YKCBgbRjCgjr1ySTxUQ8cM+V+w7flAGZ27DAB45T1TofancOVlG2i1/UrtLm0N7PcW+wCTzMOgOQoBJB55AA681UN9raxfZvtt+I0BCJ5jBVA44HoAMe2MVtv4yummMrQ2ruZ1lBmIl2hXLBBuBxjOARgjtUg8bSeUkRtY2jWExbPtGB0AHQZ6KM88nJyKBGMl34kuIJpIrzVJYo0CzETMQq4bg89PvfrTLrUNaW2Ytf3vz7JGPmt8xHKk89eP0rXm8Xb7We3js44hcRssrLdHJLbskccD5unt1qpN4h83w+2kNBHtKook8/ptxzt9Tj1x7d6EBgQ3d6jRuJ7gG3IeJgT+7IAAwe3AA/AVcm1HW2tlW4vNRaKcF8O7kOc4z75JP51uv4rHlhYLSKIM7PMPtJIlZgwIIx935s7eelPufGzi0eIW+0OG+b7SXOSzEjJX7g3HCjGMDmrckQos5u9k1azn2XEl3DPDL5vLtuSQ8FvZjjHrxV6z1HX2gaQX+onyT5jnzW+Qj5QT6cECryeOZBOki2qsFZnZGmLK7FgwLA9SMcZ9aTUvGcl9Y3VrJayATrs3GfPy5Q5YAAM3yD5uOCRSck0WqckUV1bVFKldSuwV3BT5zcbuW796dDq+pxXMdyt9O00cbRRuzliisMEDPTisn7VH/AHWo+1R/3WqLlcrNNNT1FZHkF/c7pBtcmU/OM5wfUZJ/M1Jc6zq1xcTTy6jdGSc/vCJSARkkD6Ak4FZqSxsu4MAPc4pd6f30/wC+hTJLh1G8NlJZtO7QyEFgxyThiwGfTcS2PXmm2l9e2mRa3c8APXy5CufyqrvT++n/AH0KN6f30/76FAFo3s4Vli2W4ddr+QuzeuQcNg8jIB+oqV9X1Z3Dvqd4zDoTM2Rzn19eaob0/vp/30KN6f30/wC+hQBZlvLqaSaWa4klknULK7tuZgMYBJ+g/KoKbvT++n/fQo3p/fT/AL6FADq9O/Zn/wCSkP8A9g6b/wBCjry/en99P++hXqP7Mil/iNK6DcqadLuK8gZePGaAPpqiiigAooooAKKKKACiiigAqKT/AI+B/wBc2/mKlqKT/j4H/XNv5igD8wP2tv8Ak4rxl/1/D/0VHXldeqftbf8AJxXjL/r+H/oqOvK6AO88If8AIGi/3jW2KxfCH/IFi+prbFbrY+nwv8GPogooooOkSjFLRSATFLilxRikAlFLRQAlFLRSHYTFLilopANop1GKQxuKXFLijFSOwmKMU7FJigBMUYp2KMUgG4oxTsUYpANxRinYoxUjsNxRinYoxSGNxRinYoxSuA3FGKdijFIBuKMU7FGKQWG4oxTsUYpDsNxRinYoxSCwzFGKfijFILDMUYp2KMUh2GMPlP0Neu2UQFrCMcCNcfkK8kYfKfoa9ns4/wDRYeP+Wa/yFeplm8vkfnnH8uWFD1l/7adD8PowNRvWA+cWUuwj7wOOcFf3g4z/AKsE4yG+TeaoxxjaOB09K6H4Z2SXOoaiJbkwIlg52iEuJSSAFJGCozzuBHTB4JByIkyinHUCvS+0z89nJqjB+v6Hm3xhjAn0tsclJR+q/wCNcDivRfjOuJtK/wByX+aV57ivAxv8eX9dD9j4TfNlFF/4v/SmR4oxT8UYrkPorHH0UUV6Z8mFFFFFwAVuWf8Ax6xf7orExW5Z/wDHpF/uCrp7ndgfjZLRSgUorY9QQClopaAClxRS0AJS0UtIYmKWiloASlopaYCUtFLSASjFLRigBKXFLRigBrKGGCOKheIryORVjFLigGrlOirEkQPI4NQsCpwRigi1hKKKWgApaKKADFLRRSAKXFFFAwpaBRQAUtFFABRRS0xhRRS0AJS0UUAFLRRQAUUUUAFLRRQAUUtFAzL8R/8AIHuP90fzFefV6D4j/wCQPcf7o/mK8+rCpueFmX8Veh2fwK/5LX4H/wCxhsP/AEoSv1gtvuv/AL7fzr8n/gV/yWvwP/2MNh/6UJX6wW33X/32/nWZ5xLRRRQBzOveAfB2u6idR1XQLS4ujjdLgqX/AN7aRu/HNdBY2ltY2kVpZ28VvbxKFjijUKqj0AHSpqKlQindI2qYitUgoTm2lsm3ZegUGiiqMT5N8ZCS8+PN7HrEYlQ6p5ZSUAqYgPkGG4xt28Hjmsy80eG6a4nurO6gljRdsJjtrVlUhyZWVPlZQVC8YPPXpX0R8S/hboXjadb6WWWw1FUCG5hUMJFHQOp4bHY8GuEH7OduOnitx3/5By//ABdePUwtZSdldXvufpGCz7LJUYOpLkkopNcre3ZruefHw54d1DVbqSK4kto1mlQQJIhJCyAF0wpwoVs4Pp94DOOe8QaTZWFnbS2rSzLJjFwZEKTAqCSqj5l2n5TnP4dK9iP7OkB6+LJP/Bev/wAXWn4c+AGhWGopdatq9xqkSMG+ziAQo+OzEEkj2yKh4SvLTlt8zpjn+VUfe9rzW6cr/Npfj/lbo/h7pFnP8K9HudR0exN21ghkd7VNxXsx467cHNHxMt10mws/7D8MRSb7iMz3FtpkUxjjEiAptPdwSN2DgA98V6AkaJEIkRVRRtCgYAHpiqyW08I2W9wojH3UkTdtHoCCDivZguVJdj8zxFRVqs5pWu27drnj+mi/ivpFtdLs7iaUst4t/pxkjtZw1w2xFG3GUjQYBxgqeSeb66rcQ3GnxXnhfQQJ7iNZPK0pizLJHC4VFJ5K+a4JyT8mdmN2PU9l9/z8W/8A35b/AOLoMd8ePtMA9xCf/iqpaGLVzM/sHQ/7S2Lo2m48nLj7JH13cdv978q8e07TdIl/aYv7LWbK0SGOHNhbtEqxs3loV+XGCcFyPf6V7xbQLCGO5nkc5d26sf8APauQ+I3w50bxm8F3NNPYanbjEN5bnDgA5AI7gHkdCOxrDEQlJJx6O/qevlOKo0JzjW0U4uN0r2vbW3y1OU+MWjaNB8LtZuNRs7GG7jvG+wSJarC+fNwiDABb5Mg9cjJrxrTrGCfwhZGOwLtLJMtxcR2sD7B5gALO/wA64XONvavW9U+Buoar5f8AafxB1K98sYj+0W5k2/TMnFUD+znbk5PixyfU6cv/AMXXBWpV6k7qPS26PrMtx+VYTD+ylXTfNzfBK21rLT5+vQ80sfDvh+6vBCtzdD/XlVMyFnEcwjAGEPJGXPHQcetNTwvpSo91JdzSWQQGOVZI1MrASllAOcf6tfpu75Femn9nSAjB8WSEf9g9f/i6Vf2dLfdlvFkp9caeoJ/HfWP1Wvb4fxR6DzzKtbV//JH/AJf1+XKfs0WqXvj25hlsobizNg7SrNEsgX512HkdeSM19H/8I9oP/QF03/wFj/wrL+HngfRfBOmyWulrJJNMQbi5mIMkpHTpwAOcAetdPXp4WlKlTUZbnw2fY+jjsZKrRVo6Jedupmf8I9oP/QF03/wFj/wo/wCEe0H/AKAum/8AgLH/AIVp0V0njGZ/wj2g/wDQF03/AMBY/wDCj/hHtB/6Aum/+Asf+FadFAGZ/wAI9oP/AEBdN/8AAWP/AAo/4R7Qf+gLpv8A4Cx/4Vp0UAZn/CPaD/0BdN/8BY/8KtWOn2Fju+xWVtbb/veTEqbvrgVZooAKKKKACiiigAooooAKKKKACopP+Pgf9c2/mKlqKT/j4H/XNv5igD8wP2tv+TivGX/X8P8A0VHXldeqftbf8nFeMv8Ar+H/AKKjryugDvfB/wDyBovqa26xfB//ACBYv941t1stj6jCfwY+iCiiloOgTFLRRRcYUUUtIBKKXFFIYlFLRSAKKWikMSilxRSASlpcUUh2EoxS0UgsJijFLilxUjG4paXFGKAsJRS4oqR2EoxS0UgExRinYoxSHYbijFOxRikFhuKXFLijFIdhMUYpcUuKVwsMxRin4oxSHYZijFPxRikFhmKMU/FGKQWI3HyN9DXv+mQh4LVCyoGjQFm6LwOTXgbD5W+hr6L8NvaKtjcXMbTW4iRiq/xfKMfhXpZa9ZfI/OvEHSOH9Zf+2msfDk1vNb7bqKbzgWHlKxwgGSx9qtz6BPDZtc+fCxSNZHjGdyqelT/2xZPqM90kd0XmgMQ3kYXnt6DFP1XVJ9QkeGEmO1OMJgA4A7nvXoXkfn0nh1F217HiXx2XE2j/AO5N/NK8zxXqn7QARLrRYgRuEUzEd8Fkwf0NeXYrw8Y/30v66H7Jwin/AGPR/wC3v/SmMxSYqTFJtrlufR2OJoopa9M+QCilopgFblkP9Ei/3BWHW7Zf8ekX+4K0p7ndgPjZLilFFLWp6gYopaKACilooGFFLiigApaKUCgBMUtFKKAEpcUtFAwxRS4ooASlxRS0AJijFLRikMTFIyhhginUtAFaSIryORTKuVG8QPK8Gghx7FelpSCDgjFFIkSlopaBiUtFApgFLRRSGFFFLTAKKKWgAooooAKWiigAoopaAEpaKWgYlLRRQAUUUtAGX4j/AOQPcf7o/nXnteheI/8AkD3H+7/UV57WFTc8LMv4q9Ds/gV/yWvwP/2MNh/6UJX6wW33X/32/nX5P/Ar/ktfgf8A7GGw/wDShK/WC2+6/wDvt/Oszzh7qWxh2T6Y/qKb5b/895PyX/CpKKAI/Lf/AJ7yfkv+FHlv/wA95PyX/CpKKAI/Lf8A57yfkv8AhR5b/wDPeT8l/wAKkooAj8t/+e8n5L/hR5b/APPeT8l/wrIg8TWMviVtC8i9jn+cRyyQFYpWQKXCnqcBhzgA8gEkGo/EHiNtO1BdOstIvtWuxbm6litigMcQOM5dgCxIICjk4PSgDb8t/wDnvJ+S/wCFHlv/AM95PyX/AArnYfGuj+ddrdyGxjtW2u1ywVif3eAE+9yZAMYznIq9Z+KPDt5bJc22s2UsMjKqSLKCpLBmXn3CsfwNAGp5b/8APeT8l/wo8t/+e8n5L/hWTZ+K/Dt5JbR22r2skl1I0UMYb5nYAEgDr0ZT9CD0NSDxJoX9ozaf/atr9qhkWJ49/IdmChfc7mUYHQkA4zQBpeW//PeT8l/wo8t/+e8n5L/hVGw1/Rr+/axstRt7i5WMyNFG25goYrk+g3Aj6g+lZFn470KW91yC5kexi0edIZLi4wsc247N0ZByR5gaP/eUj0oA6Xy3/wCe8n5L/hR5b/8APeT8l/wrFPjLwuPIJ12xUTwtNHmXGUG7J9vuPwcH5W9DU9j4m0G+vILO01S2lnnj8yJFb76kZGPcgZx1I56c0Aaflv8A895PyX/Cjy3/AOe8n5L/AIV5p+0D8QbzwVolraaPsXVNQLCOVl3CGNcbmAPBOSAM8da8Hj1X4k313JjxdqgYxRTBv7SkRHEilkC44yQG4wOVNe5gcjq4qj7ZyUYva/U8THZ5Swtb2MYuUlvbofYflv8A895PyX/Cjy2/57yfkv8AhXyDbzfEyV4lPi/VIhJbxXIZ9VlxskV2HQk5ARiRjIxXRfD74l+K/C3i6x0rxJqz6vpV75e55ZDI0ayfdkVmAcY7q3bPHSt6vDlWMW6dRSa1sc9LiKnKSVSm4p9T6c8t/wDnvJ+S/wCFHlv/AM95PyX/AAqSivnD6Mj8t/8AnvJ+S/4UeW//AD3k/Jf8KkooAj8t/wDnvJ+S/wCFHlv/AM95PyX/AAqSigCPy3/57yfkv+FHlv8A895PyX/CpKKAI/Lf/nvJ+S/4UeW//PeT8l/wqSigCPy3/wCe8n5L/hR5b/8APeT8l/wqSigCPy3/AOe8n5L/AIUeW/8Az3k/Jf8ACpKKAI/Lf/nvJ+S/4UeW/wDz3k/Jf8KkooAj8t/+e8n5L/hR5b/895PyX/CpKKAEUELgsW9zUcn/AB8D/rm38xUtRSf8fA/65t/MUAfmB+1t/wAnFeMv+v4f+io68rr1T9rb/k4rxl/1/D/0VHXldAHfeD/+QLF/vGtysPwd/wAgWL/eNbtarY+pwn8GPohKKWimdIUUUtIBKWjFFILCUtGKWkMSjFLRSAKKXFAFIdhMUuKWikMSjFLilxSAbS4paMUh2ExRilpcUgG4pcUuKKkdhKKXFLikMaBS4pcUuKQDcUYpcUuKQ7DcUYp1FILDcUYp2KXFK47DMUuKdijFILDcUYp2KMUh2G4oxTsUuKQWGYoxT8UY9qkdhmK0rLXtbsoFgtdVu4Yl4VFk4H0qbw5aQXSaizWkN9eQwo1raS3PkrLlsO2QyklV5C7hnOecYroLPw3pL2+kARW95dyLMzLHclI7kC0aVcnzSw2yAITiMZyMd6uPMlzJ2OLE+wqfu60FJeaTW3mYC+LPEoPGu3wP/XQf4VIPGPitRgeIdQH/AG0H+Fasllo8VhNbixsSFvrKW/X7YWexRov3wjcPl1DZH8eM+ozVbVtI0zSNDupJ7e2mv/mEafbS4VGuHVJAI35IjCkZyO5HNU51E7c34nJHC4Bq/sI/+Ax/yOf1G+vdRuTc393NdTEY3yuWOPSq2K6Px5Z6Bpk1tFpKTLGWfbcvOjR3MWF2OMSOQ3XPCDkDaCDXPLhhkEEHuKwle+p6lDkdNcisuw3FGKfijFTc1scHS1CCR0NOD+or1T4y5JRTQwPenUxhW9Zf8ekP+4Kwa37L/jzh/wBwVpT3O/AfGyWlopa1PUEFLRSgUAJSilFAoAKWgUuKBiAUuKWigAxRS0tACUUtFAwoxRS4oASlpaKQCYoxS0UAFFLRQAlFLiigBrKGGCKgeMrz1FWcUUCauU6Wp3iB5Xg+lQkEHBGKCWhKWiigAopaKACilooAKKKWgAooooAKKWigYUUtFABRRRQAUUUtABRRRQBmeJP+QPcf7v8AUV55XofiT/kD3P8Au/1FeeVhU3PCzP8Air0Oz+BX/Ja/A/8A2MNh/wClCV+sFt91/wDfb+dfk/8AAr/ktfgf/sYbD/0oSv1gtvuv/vt/OszziWmyukUbSSMFRAWZieAB1NOqvqVql7p9xZuxVJ4miYjqAwI/rQxxtdX2PDtZ+OWs3OpX7eFtDs59M0+MzSzXTNvaIOqb8AjAJZcDk816d8MPGtn448PnUIITbXML+VdW5bd5b4zwe6kcg/4V81ah4T8UeE73VtGuNK1SQ3dsbWKWzh3xXC+YjAk7TlTszgYYHHOMg+4/s8eD9T8L+HLy61iJre71KVJPs7feiRQQu70Y5Jx24ry8LWryq2l8/I+6z3LssoYD2lCyd1ytO7ktL319T0+iiivUPhDlrjwrdy+KDro1pvOijlW0DWqFohIBlGcEF4wQSEOME5JOBU/iLw7eX2qDU9K1qXSrt7U2k7rAsoeLJZSASNrqSxDcj5jkGuB+K/xZvtG8Q/8ACL+FLGO81JSElkdS4V2GRGiAjc2MZzxziuA1b4vfEnS7v7LeTadHIVDDNiBuB9MnnuPwrjqY2lB2100Po8JwvjsVTjUVlzapN6272sz2ab4eKNQ/tG11u6S7jmWaCSWMS7XGwZfJy+QhB5B+YnIIFZemfDG5fToLTVNafyGJa8gjgXMzATICH42grLkgDqODjIPkk/xr+IMErwzXGnRyRsVdWsQCrDsRu4NaPhv48+JbbUUOuW1lf2ZIEqwxeVIo9V5IJ9j19qhZjRdlqdE+DcxjFzXK/R/8BHs3hPwLBoWoW9/9rjlniSRG8q0SFXDLGoyBk5Ai6knO49BgVDe+A5ZtYvtSg1yS2aedLmKNLYeWJVkSQNIoYCTBTGcK2GOSTgjrLHUbS90mHVLaYSWk0ImjkHdCMg/lVHU9Vt7BIZdV1W10pbh9kKSsoLH0y3U/TpXfzXsz5Rw5bxaKXgzwofDgunOpSXs9zGiySPCqfMryvnA9TKePasex+GOn2U+n3Ftqd8J4Ahu2kdpBdstwlxu2sxEf7xWPyAD529q6HS9X07VL2ey0/wARQ3VzbjM0UewugyVyRjpkEfhWn5F0Ol85PvEuP5UdbiaurHIP8PmA1KK312eG21MS/bI/syMXZjKVwx+6B5uCOp2jlcnNu18D2lv4uXX1nR/nSZo3tkZ/NWEQgiQ8qu0DgDOe+CRXT20ztI8E6qsqAH5ejA9CP8K86+JHxD1XT/E1v4N8HaYmo67MoaQycpCCMjjIycckkgAY65qJ1I0o3Z14TBVcbU9nTWtm3fRJdW30RlftM+CNT8SaRY6xo1u91dabvWW3QZd4mwcqO5BHTuD7V8+Lr9/Z2ml2D2PlyaXdecWcMHkCklUYHoF3OB/vmvZ/GHiv4z+FdPTUNUGjtaF/Laa3gWRY3/utg8c8ema52P4sfE+awbUUhtHtBuzP/Z42fL153dq9bA8WxwdFUKtJyUdV0t+fczxXhvicwm8VQrwXNo7O6f5anFJ44uxMjtpqhVvZ7kCKVkZVkRkWNWwcBA7FT15ra8C+F9Z+InjHTZ1066g0qzES3N3O7SFo0YtgyMBvc5wMDgY7Cr3/AAu7x1/z9aX/AOAQ/wDiqcvxw8eKRm50wgdvsYH/ALNWtTjWhyv2NHlla173t8iqfhRmXMvbVoyje9tr/Ox9VUV5z8GviXH44gnsr22jtNWtUDukZJjlQnG9c8jB4I9xXo1eJTqRqR5o7DxmDrYOs6NZWkgoooqzmCiiigAooooAKKKKACiiigAooooAKKKKACiiigAqKT/j4H/XNv5ipaik/wCPgf8AXNv5igD8wP2tv+TivGX/AF/D/wBFR15XXqn7W3/JxXjL/r+H/oqOvK6AO/8ABv8AyBYv94/0rcrD8G/8gWL/AHmrdFarY+pwn8GPogoxS4ooOkKKWjFIYlFLilpAJiilopDEpcUYpaQxMUtFLSHYSjFLiikAYoxS0YpXGJilopaQWEoxS0VI7CYpcUtFIdhKKXFLSATFGKWjFIYmKXFGKXFILCUYpcUtIdhuKKdijFIdhMUYpcUuKkLDcUYp2KXFIY3FB4FOxSEZGKVwsdBYeEtTeC1v7+FoLC4iaTepO9QYZJIycjGG8vsScdcZFQnwjr0Vpb3V1Db2sM0RlaSeRkWFRF5vznb/AHOfl3enB4qBdavt9lI0Vk81nH5KTPDl3j2GMI/OGXaxHQHGOeKbcaxfT4eWO08/7K1pJOIcSSxtH5fznOCQoABAB4ycmqbjrb5HKo4i61RZl8IawbyOzRbG7kd2R/s8/miIrGJSXwuceWQwwDke/FD+Fta0+Wdma1tbhPMQxifbNMgjVpCi4G5fLkBIJBIJGOoqOPxDqqXDzB7Zt77pEaEFJB5IgKsM8qUGCPXnINQpq17FtFuttAiGcokcXyp5yBHxkk9AMZJweaV4XDlxDWtiDXdLj0nWbrTEuLa9+zTNEskAJBIJGMEDDcdB+ZrU0zwtu1hdNuntdwu7e3uGtblSbZpJChVxtwW47HAx1NVrq8sNSuJr7U0vIb6RixOnwxJGzdd7bmzuJ5OOPQVJ/wAJFqf21r5ItPiuZJoriZ47bHnSxsXDsM4JJJzjAOelKLSauOaqyjaO9vxLOi6TrNilzJpF8ftVyLZbN9PlDR3XmTtERkgHIYEYIGD7Vn+ItM1TTNQC6t809ynniUEnzAWILfMAeqkcgdPTBqZPEOqRSI1r9jtFiWMQRQW+1IdkvmqUBJ53kkkk5yQahM2l3k0s+oW9zayNgKmmRRrFj1IkYkHPpxUtppDhGpGblJaGbilxVxb1hKH+w6ccSRybTbDB2Lt2kZ+6erDueaq4qGdKv1POKKKK9Y+KClBI6GkooEPDnuK3rGeI2sSltpCgc1z9aMH+pT6CtKb1O7AyakzaGCMg5FLWUjun3WI+lWI7yQffAb9K1TPVVRdS8KWoI7qJupKn3qwpBGQQR7U9y009gpaBS0DExS0UuKACiiloGFFGKWgAopaKACilooAKKMUuKAEoxS4paB2ExRS0YoASjFOoxQAmKKWigBKa6Bhgin4ooCxVeMr7j1ptXMVFJD3Xj2pEuJAKWggg4NFBIUtAooAKKKWgYUUUtACUtFFABRRRQAUtFFABRRS0AFFFFAGX4k/5A9x/u/1FeeV6H4l/5A9z/u/1FeeVhU3PCzP+KvQ7P4Ff8lr8D/8AYw2H/pQlfrBbfdf/AH2/nX5P/Ar/AJLX4H/7GGw/9KEr9YLb7r/77fzrM84lopGZV+8QPqaTen99fzoAdRTd6f31/Ojen99fzoAdRTd6f31/Ojen99fzoA+VPiTYz+FvjTPqOpxSfZJ743sUgj3CSNuTgHAbaSQRnt71z8WpaRDFcojR75FX95Bo8USuoVgYtmcLklW3jnj2FfXmt6Vo+t2n2TV7G0voM5CToGAPqM9D9K53/hWfw9/6FfS/++P/AK9eXUwE+ZuDVtz7zCcWYdUYxrwlzJJaWs7bbnzxF4q0aW/nur2wMkjSSGOQ2qHEZkDBCARnI3Ak9M9eawtavLXUorO10y3kWQsqx2iWqgodoBVXGWkLNzz619Sf8Kz+Hv8A0K+l/wDfH/160tB8HeEdCuvtWk6HptpcdBKkY3D6E8j8Kh4CrLSTVjZcWYGl71KnK/RO1v1t8v1ZX8DaJeaP8ONL0a55u7ezUSLno/3iufYnFQ+OdP1LXobOPTLS1uLN1livt04hnMbBcxKxRtqtjD4wcKACOo63en99fzqCa3s5XMjBQ56srFSfrg816sYqKSXQ+BrVZVqkqkt5Nv7zk9Y8KalqTXEk1wqtNflysd1JHi2SF0hjyuDw7ByOmSeuKyk8G+KLafT2t7/zVguY5WEuozFQfLhEjEYyxLJKR8w+/wBMMRXffY7L1b/v83+NH2Oy78j0MrEfzqloZNXFiYT6g0kZBSKMxlh0LEgkfhj9a8T1m9TwB+0Dc69rscg0rWICsd0ELCPKoD+RTkdcEGvdEMKIEQoqjoBgAVU1fT9K1ezaz1S0tb23bkxzIHXPrz396xrUnUSs7NO56eWY6OEnNVI3hOLi7aOz6rzVjxn4qeOfCa/D/VNC0e/t9SutVuHkAtyzLGHkDl2J6H2HevI7TUtM/sGwtLjyxNaySM6vpqTM4aQMAspOU4GOlfT3/Cs/h7/0K+l/98f/AF6T/hWfw9/6FfS/++P/AK9cVTCVqkuZtdup9PgeIctwdL2cYzevNd8t72t+R842XiXSluw9zZDZ+/YMtonyM0wZOBjIEYKY7Z/Go49b0CJXuorIpcMgVIPsaGOIqJcMCSc5Lpxj+HnOBX0l/wAKz+Hv/Qr6X/3x/wDXpV+Gnw9Vgw8L6Vkc/c/+vWf1Gta10dX+tWXa2hPX0/z/AK+88h/Zb0a/uPFd54gZHWygtngMpHyySuVO0euACT6ZFfSFV7G3srG1jtLKG3treMYSKJQqqPYDipt6f31/OvQw9H2MOU+RznM3mWKde1lsl5IdRTd6f31/Ojen99fzrc8odRTd6f31/Ojen99fzoAdRTd6f31/Ojen99fzoAdRTd6f31/Ojen99fzoAdRTd6f31/Ojen99fzoAdRTd6f31/Ojen99fzoAdRTd6f31/Ojen99fzoAdRTd6f31/Ojen99fzoAdUUn/HwP+ubfzFSggjIIIqKT/j4H/XNv5igD8wP2tv+TivGX/X8P/RUdeV16p+1t/ycV4y/6/h/6KjryugD0Dwb/wAgWL/eat0Vh+DP+QJF/vNW6K0Wx9VhP4MfRBiilooOkKMUYpaQxKKWikAUUtFIYUUtFIYlLRijFIYUtFLikFhMUUtGKQwopcUVIxMUtGKXFIBKMU4CjFIdhMUYpcUoFIY3FLilApcUgG4paXFGKQ7CYoxTqKkdhMUYpcUuKQ7DcUoFLilxSATFGKWjFIdhMUYp1FTcdhtLilxS4pXGNxT0ikcZSKRh6qhNNPQ/SuzttyQRqjMoCAAA4HSvayXJ/wC05TXPyqNul9/mux4GfZ3/AGTCD5OZyv1ttbyfc5AW8/8Azwm/79n/AApjIyMVdWU+jDBrvLRpPPT94/8A30a6TU/CUHiPwrbXEJWPUYlYRyno+GPyN7eh7V6WP4VlhqXNTqcz7Wt+rPHwHGUMRVUKtPlj3ve34I8exRirF5a3FndSWt1E0M0TbXRhyDUOK+QkmnZn28WpK62ExRinYoxU3HY8yooor2T4ZhRRRQIUVowf6lP92s6tKD/Up9BVw3O3B/Ex9LRRWh6AopyMynKsR9KbS0AWY7uVfvYYe/WrEd3E33sqfes+gU7lqbRsKysMqQR7U6sdSVOVJB9jU8d3KvXDD3p3NFUXU0RS1WjvI2+8Cp/MVYRlcZVgfpTLTT2FpaBS0DCilxRQMMUUuKKYBRS0UDCiilxSASilooAMUUtFACUUuKKAEopaKAEopaKAGOgccj8ageMp9PWrVFAmrlOlqZ4e6/lUOMHmkTawUUUtAgooooAKKWigAoopaAEpaKKACiiloGJS0UUAZfiX/kD3P+7/AFFed16J4l/5A9z/ALv9RXndYVNzwcz/AIq9Ds/gV/yWvwP/ANjDYf8ApQlfrBbfdf8A32/nX5P/AAK/5LX4H/7GGw/9KEr9YLb7r/77fzrM84kZVb7yg/UU3yo/+eaf98in0UAM8qP/AJ5p/wB8ijyo/wDnmn/fIp9FADPKj/55p/3yKPKj/wCeaf8AfIp9FADPKj/55p/3yKPKj/55p/3yK46y8Zm8+KT+GI3gS1itJsq6MJpJ0aPJGRgIFcgHuQewGaXxY8bTeFr/AEiG1uIYlDG81BXiLl7RGVGRcfdYmTcCe0bULW3mD6+R33lR/wDPNP8AvkUeVH/zzT/vkV5zP8QNasdSvFvNGtZrOKW4igFtK7SyutzFBEMbDgHzecZ9gehsr8R51zLceG7m3trdYjfNJPteIvLJENiFQXGY887TgjjPFHRPuHVo73yo/wDnmn/fIo8qP/nmn/fIrzez+IeqDWnS+0UJbTW8NxHGl0rmKIwyzPIWC8nagGz1HBIOa3vDHjRdU0TUNV1DSrrSobKFbljMDh4jGXDDcqnIAIPGM9CRzQ9L36AtbeZ1XlR/880/75FHlR/880/75FeVeEPiTq9zomy5s7fVdVN24KxubVI4WtzcpncpOVUNETjlkJ9a3F+INxPaC+svD7TWs08dtbM97GjyTM8aEMhGVUGT7w3fdOQMjLtrYV9Lnc+VH/zzT/vkVBqFxp+n2j3d9NbWtvGMvLMyoq/Uniue8J+MX17WrixGi3dtbp53k3TBtkhil8txyoH3uRtLcDnaeK8H/ak1+8u/HsegzSSDTtPhjcQqcB3cbmf3OCAPTn1r0Mry94+uqSdla7fkefmmYLAUHVau72t5nuR+I/w8BwfFWjf9/lo/4WR8O/8AoatG/wC/wr5hHhC1e5ewhnuHu7oyy6b8w2yQLsKswxzuVn6Y+4aszeFfD1uLm6e8uGszG9zZvPciFXt98KKzMI25LSOOB1XtX0H9gYLRc8tfT+v+GZ4Cz/Gv/l3H73/Xb70fWui6no2tWn2vSL2yvoM43wOrgH0OOhq95Uf/ADzT/vkV8jfDK/uvCHxotdP02edrS5vks5UY/wCuikICsccHG4MD/LJFfXleFmuXfUakVGV4yV0e5lWY/XacnJWlF2Yzyo/+eaf98ijyo/8Anmn/AHyKfRXlnqDPKj/55p/3yKPKj/55p/3yKfRQAzyo/wDnmn/fIo8qP/nmn/fIp9FADPKj/wCeaf8AfIo8qP8A55p/3yKfRQAzyo/+eaf98ijyo/8Anmn/AHyKfRQAzyo/+eaf98ijyo/+eaf98in0UAM8qP8A55p/3yKPKj/55p/3yKfRQAzyo/8Anmn/AHyKPKj/AOeaf98in0UAM8qP/nmn/fIo8qP/AJ5p/wB8in0UAIoCjAAA9qjk/wCPgf8AXNv5ipaik/4+B/1zb+YoA/MD9rb/AJOK8Zf9fw/9FR15XXqn7W3/ACcV4y/6/h/6KjryugD0HwZ/yBIv95q3qwvBf/IDi/3mreq1sfV4T+DH0QlLRS0HSJRS0UhhRS0UgCilxRikMSlopcUh2EpcUuKMUhhiiloxSASlxS0VI7CUYpcUtIdhMUAUtKKQxKMUuKUUgEApaKWkOwlFLRU3GGKKWjFIdhMUuKXFFIYmKKXFLilcLCUYpcUuKm4xMUYpcUuKQ7DcUtLRilcYmKMU7FGKQWGkcH6V21vG7QoUR2wo+6pPauLI+U/Svp34Kalc6F4G1DVrNGZINTtvtEca5MqNFsC/99Mpr6vhfEvDxrTirv3dL23bPiOM6Kq+wi3Ze9+SOD07wmkphcatCHdQ2zy8kZGcda27Xw7qFlGy2uuPEpySqx8E/TNesxXOt6pDcaZrcwln064tbWUmMBJJyzuZBx0KGMfnVt9Dby2O23+6f4fb6V7lTNqjdp2/B/oj5GGAja8f8v1Pkf4hTTXN3aTXDmSUoylyBkgEYH61y+K674jIqzWW1QOJOn1WuUAr43iCyzGpZdvyR+k8M3eV0r+f/pTG4oxTsUYrxbnvnl1FFFe0fBBRRS0AArSt/wDUp/uis0VpW/8AqE/3auG524P4mSUUUorQ9ABS0lLQAtFFLQMKWkpaAFpQSDkEg+1JRQMsR3Uy9WDD3qzHexnh1Kn8xWfS07lqbRsRukgyjg/Q04VjAkHI61PHdTJ/FuHo1PmLVTuadFVI71Dw6lfcc1Zjkjk+46mnctST2H0YopaChKWilxQAlFLiigBKWilxQMSilxRQAlFLRigBKKWigBKKWigBKa6Kw5HPrT8UYoFYqujL16etNq4QOlQyQ91/KkS4kNLRjmigQUUtFABRRS0AJRS0UAFFFFABRRRQBl+Jf+QPc/7v9RXndeieJf8AkD3P+7/UV53WFTc8HM/4q9Ds/gV/yWvwP/2MNh/6UJX6wW33X/32/nX5P/Ar/ktfgf8A7GGw/wDShK/WC2+6/wDvt/OszziWoNSukstPuLyRSyQRNKwHUhQSf5VPTZY0liaKRQyOCrKRwQeooY42ur7HyPe+MPE3iy71nWbrxJeaebO0N3a21vOY0/1iKI1AI7OTnknbXtn7PXjLUvFXhy7ttXkNxeabKsZuCOZUYEqW/wBoYIJ78Vxuu/AnWIdVux4b1TTf7NuwU2XqEyQoWDYB2t0IGGG0447nPqfwt8E2ngfw+bCKc3N1O/m3VwV273xgADsoHA/E968vC0q8at57dfM+6z3H5XWwHJQs5XXKkrOK0vfT1+Z1tFFFeofCFS+02wvWLXdpFMxiaHLrk7GKll+hKqfwFE+m6fcS3Es9lBI9zALadmQEyRfN8jeq/M3Hua+dPi74s1vxT8RpPCFjqM1jp0F19jCRlgJJB953CkFucgL7e9cDqmiagl5KlhFqTRRxq7+ehhZcg9mb5idrHj0PHFedUx6i2oxutj7HCcJOrTjKtWUHJJ2teye13dan103hTw2wkDaJYkSKyODCMMGChgfrsT/vkHqKmtvD2h20CwQaTZxxqIwFWEAfu2Lp+TMzD3JPevj288Oa/b6jJZpFNMVZgrpMAr4bb3bgkkAKfm5HFP0jUfFXhO5ttWsrm8sWZgU3SZWTvtdM9COzAZHIqP7Sf2oaf15HS+Coyj+6xCb6K1v1f5H13pnhfw7pk3nafolhbSbi26OAA5Klf/QSR9DinWnhvQLTTLnTbbR7GGyuifPgWEBJO2GHcYGMenFQeF/ECa34MsPEEcWw3Vssvl54Dngr9N2RVTxbqSeH7JLyfSpNUGGkuZmdFWNVxwN/G4lgFQcse9enGSauup8RVpypTcJrVPX1Rs3Gj6TcagNQn0+1kuxH5XnNGC+zDDbn0w7j/gR9arReF/DkV39qj0WwWfEY3iFcjYVKflsT/vkegrm9M8b+FbqO5ubi1tba0jl8qKQbJWkOXGDGgLo37tmwR057EDUt/Engy5MCW9zZu1xJ5cYSBtxPy8/dyF+dMMcKd64PIpryIfmbFjoukWOo3Oo2em2sF3dHM80cQDvzk5PueT6nmvOvjh8KT42kh1fSLmG21eCPymWbIjnQEkAkDIYZODg9cGvSrfdBctalmaMpvjLHJAzgjPfqPzrx3x1e6748+KkngHTNUm0vSrGPfeyQkhpOFLZx1+8oA6dSc1dLH1MBONWl8Wy/y9Dejk1PNlOjVdoJc0n2S66dexwCfCH4rx3NvcRiMTWsXk28g1Jd0ceCNqnsMM3HuaktPhP8W7Qx/Z/Lj8qD7PGBqCELFu37ADxt3c49ea1PiH8MLHS/CV54h8Patqcy6dO8N3DdsMttfYzIVx0PPPUelef22hXk3h+PVlur1xKZFSOK3d1BVguGfcAuSfQ13VOM8bF8sqUTpwnhllmIp+0p4uVr2+Hr237JHtHwi+D+paR4lXxV4wvIri/jYyQQRuZMSEY3u56kdgPrnjFe2V8Sf2D4h8wIbW4DFmXm4UAFSAwJLYGCQPqQOvFRwaTrz3HlRW14JgA23zNpXO7BOTx9xv8Avk14mMz+vjKntKsPJdkvLQ+iwfh9hsJT5KWJVt3p+L94+36K8K/Zt8carf6lceFdYu5LxUgM1pLK+902kBkLdxyCPxr3WtaFaNaHMj53M8uqZdiHQqO9uvdBRRRWp54UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFRSf8fA/65t/MVLUUn/HwP8Arm38xQB+YH7W3/JxXjL/AK/h/wCio68rr1T9rb/k4rxl/wBfw/8ARUdeV0AeheC/+QHF/vNW9WF4K/5AcX+81b1Wj6zB/wAGPogopaKDpCilxRUjCilxS0hiYopaMUhhiilxS0gExRS0YpDsFGKWikMKKWipHYMUUtFK47BRS4opXASlpcUAUhhRilopDsJilApcUVNx2EoxTsUYpDExSgUuKKQCUuKXFAFTcdhMUuKWjFIdgopaKQ7CAUuKWjFSAmKMU4CgUhjWHyn6V7H4T8WeIvDdi8Wh6m1ok4V5AI0YMwXAPzA14/itS213UreFYlljZUGF3x5IFe7keZYbBupHERupW6J7X7+p81xHlOJzCNN4dpON+tt7dvQ+s9M+JHg6aGyfVNZ1i6nRVd4pLYmPzQvJ+VRnBzjnFa7/ABN8CvGyI9wWKkAfYn9PpXx2nibVUYMGt8j/AKZf/XqePxhrSZ2m1+vkf/Xrtnjcpk7pz/A8OOQ5sl9j72aHxOQJNYAdSshP5rXHYq7q2p3uq3AnvZQ7Ku1QFwFHsKp14GaYqOLxU60Nnb8EkfY5Pgp4LBwoTeqvt5tv9RMUYp2KMV556Z5VRRRXuHwAUtJS0ALWjb/6hP8AdrNFaVv/AKhPoKuB24P4mSUtJS1oegFKKSloAWlpKUUDCloooAWiiigYopaQUtAxRRSUtACil/GkoFAE8dzMnR8j0PNWI75TxIhHuKo0tO5Sk0a0c0Un3HB9u9SVi1NHcTR9HOPQ807miqdzVoqnHff89E/EVZjnik+64z6Hinc0UkySilopjExRilpaAG4paWigBKKWigBMUUtGKAExSU6igBuKKdikpAMdFb6+tQOhXr09atYoIB4xQJop0VNJD3X8qixSJtYKKKKBBRRRQAUUUtACUtFFAGV4l/5A9z/u/wBRXndeieJf+QPc/wC6P5ivO6wqbng5n/FXodn8Cv8Aktfgf/sYbD/0oSv1gtvuv/vt/Ovyf+BX/Ja/A/8A2MNh/wClCV+sFt91/wDfb+dZnnEtFNkYrjCM30xTPNb/AJ4Sfp/jQBLRUXmt/wA8JP0/xo81v+eEn6f40AS0VF5rf88JP0/xo81v+eEn6f40AfP/AMXfh74i03xy/i/w3pzanbzzC5eFELtFLj5soCCynk5HqfavP10vxXHDJBB4DuIoXIcRjTLhlWQAgSDcT82GI5yOnGRX2B5rf88JP0/xo81v+eEv6f41wVMBGUm07H1uE4tr0aMadSmpWSV7taLa/ofJVofiDbPM0fhjU8TyvK6/2dOoLMwbPy4PBHAz65zTrDwb468WX0Fo3huW0k3KJr65tWgyAu0M7N1wB0Uc+55r6z81v+eEv6f40ea3/PCT9P8AGp/s5PRydjZ8Y1I3dOjFS76mboGhW2j+FbPQLdiYrW3WFXI5JA+99c81Q8Q6TFq91p9xfXmoWFzp7s8X2dEeNnIA34dHGRjg4BGT610Pmt/zwk/T/GjzW/54Sfp/jXoJJJJHx1ScqknKTu3ucfa+EvDlvdC4SbUSULCFTnbCjeZlFwn3cyuecnOOcDFE3hLw/LLbuLnVFMEkUihM5YxpGi87Ny8RLkqVJywzgkV2Hmt/zwk/T/GjzW/54Sfp/jTWhLVyK2SSSdrmVDGCuyND1Azkk+5449q8n8f+HPFHhr4jj4g+EdPOqR3Eey+sl++eADgdSCFU5GSCOhFeu+a3/PCT9P8AGjzW/wCeEn6f41lVpKorbHbgMdPB1HJJNNNNPZp9D58+InjDxl4l8OTaFp3gLXdOtrqTfdPJBNM7/Nu2r8g2rn9OOK4CDSfFsdjbWr+CL2c2rs8E0mnXHmRlmDHoQDyO4NfYXmt/zwl/T/GjzW/54S/p/jXLPBOcuaU/wPocLxTHC0/ZUcOkr3+J7+u58jWUPj60n82LwvqnKyqy/wBnTgMJJfNOSuD94AjBHpzTvJ8eSQNCfCV+0sg2PMdMm8xxhwASeOPMbt6ZzivrfzW/54S/p/jR5rf88Jf0/wAaj+ztLczNnxg27+wj97PHf2efh1qfh64uPEWvQG1uZYfItrZiC6ISCzNjoTgAD65r2eovNb/nhJ+n+NHmt/zwk/T/ABrto0o0ocsT5nMcwq5hiHXq7vtsl2JaKi81v+eEn6f40ea3/PCT9P8AGtThJaKi81v+eEn6f40ea3/PCT9P8aAJaKi81v8AnhJ+n+NHmt/zwk/T/GgCWiovNb/nhJ+n+NHmt/zwk/T/ABoAloqLzW/54Sfp/jR5rf8APCT9P8aAJaKi81v+eEn6f40ea3/PCT9P8aAJaKi81v8AnhJ+n+NHmt/zwk/T/GgCWiovNb/nhJ+n+NHmt/zwk/T/ABoAlqKT/j4H/XNv5ipEJZQSpX2NRyf8fA/65t/MUAfmB+1t/wAnFeMv+v4f+io68rr1T9rb/k4rxl/1/D/0VHXldAHofgn/AJAcX+81b4rB8E/8gKL/AHmrfqj6zB/wI+iCiloApHUAFLRS0h2EopcUtIYmKWiikOwUuKKKQwopaKQBiilopDCilxS4qRiYopaXFIYgpcUYpcUhiYpcUtGKkBMUtLiilcdhMUoFLijFK4xMUuKWipHYTFLS4opDCjFLRikFhKWlopXGJS0uKKm47CUuKWg8DNIZc0KxGpavBYs8qq4dj5ShpG2Iz7UB4LNtwPc1fj0O3m0iS9SPVoGeK6mjMqKY7YQKDsnO0Hc+eMbcZXg54pQ6Tft5byW9xAZESS3faMHc6KrNg5RfnBDY7jHXNO1DTNagu7izuoruSRp3jlAmMiyvGu45IYhiF+bnkCr2WqOSb55+7M1tS8PaVBHdRQTat9pha7iVpDEYy8ECzEkBQdrBsYzlcZyelWY/DGnwamUlt9WliiaaBopQqtcsLRpleAhexAGCG6ryc4rAg0TV7wOqQPk2j3alp1AljBVW2tuxn5hkEg+tT3ltr2p6hLfrFqV1LDFHM8gQoYd0YYbAG4GBkbeSBuwKba7GKjJ2Sqd/+ASX2i2Np4YttSa8u1u7mNZo4HhcoAZGQxswjCh1C5J3deNo603RNHttRg05ZbieGfUNT+wxyZXyolCoxZhglid+AARzjrUdpp2vqtpKmmXF1Bdv5kMU/wC8hmYqW3MhbGSoLZbGQM54qtqsd39vNrdadDZXClV+zQQ+Wu44wQoJBJyOR14qHZO9v6/rQ1jzNcqn53/rz1/A3I/D2jyXQiS71VxLcW9oiiLY0Esok+/5kalwNi9FX7x545ntfDlo1mmntHffaJ7qzBvTGvlyeZbyybYOORuCqck5I7dKxZ9H8Ql7mWey1CR7Zv37tLvZCq7uu4kkLzxkqPSqcsF7HY2skv2hbSQlrY+dujzwTtAJCtyD2POaHJJbE+znJ6VP+HH6jZPZCxWRZkkuLGK5kSVNrIz5yuOuOO9VsVbivpVRxLbWN5I5JM95B503THDsc8dvSqyjAxWMmr6HdTUrWkeTUUUV7h8EFLSUtAC1o2/+oT/drOrRt/8AUJ/uirhuduD+JklKKSlrQ9AKWiigBaWkpRQMWikpaAFooooGLS0lLQAUtJS0DFopKUUALQKKKYC0tJS0DClpKWkMkjnlj+65x6HkVajvj0kTPuKo0tO41Jo1Y7iF+jgH0PFT1h1JHNJH9xyB6dqdzRVO5sUVQjvmH+sQEeo4qzFdQP8Ax7T6NxTuWppk1FKMdR0oplCUUtFACUUuKKAEopaKAExSU6igBtMkjDD0PrUlFILFR0ZTyKbVwgEYNQyREcrz7UENEVFFFIQUUUtACUUtFAGT4l/5A1z/ALv9RXndei+Jf+QNc/7o/mK86rCpueBmf8Veh2fwK/5LX4H/AOxhsP8A0oSv1gtvuv8A77fzr8n/AIFf8lr8D/8AYw2H/pQlfrBbfdf/AH2/nWZ5xLRRRQAUVzmu+OvCOh6h/Z+q6/ZW11xmJnJZc/3sA7fxxW9Z3NveWsd1aTxzwSqGjkjYMrA9wR1qVOLdkzaeHq04qc4tJ7NrR+hLRRQaoxDvSMyrjJAycDPevOrWC80/4mX2qfZtUuopXlNyZbCTbBEIkEfkurESZYY2gZ5YkLjmL4tabNc6tDeRaZdX8kdg6QQvp73VvK5bOwNGQ9tLwuJcgY/3aOwdz0skDqaMjOMivJ73UPGk3iKe3+w6obJpRiGWzEqRNHcwbSreWFwUMpB3tnGSVIwK9veePLiz3eXqzXkfmKl5Jp6ggt5OdiNGpUD5/lO4dcMwGQLUVz155I0OHdV4zycU4EHkGuL+IHhy01RvDpn0mLVZLfU4FmlmtllcQhX3Fjj7ucE9s1zrXvxCim1hbYXHnxGRbG0WwIgDidRbqJCoXy2jzvILYBJyhABBnq1RXdxBaWst1cypDBCheSRzhVUDJJPoBXkd3N42tzcXFlca+oumtnnlmsmd0H2U8JGsZC5mGGABx8ucA7q2fjYusS/A/UN2Te/ZYGvNibcgMhl+XJwOvGTx3rfDUVWrRpt2u0r+phiazo0ZVEr2Tf3HNa3+0ToNrfvBpeiXuoQIcCdpVhD+4BBOPrj6VQ/4aSsv+hTuP/A5f/iK8c8HTaZ5FzHqbW6CzkXUIfNAzMUBDQj13ZU4/wBk10Wnah4dewtbi7k077TNbrfzgooKzWy7Fhxjjzfmbb3yK+5nkuApe77Ju3W71/rU+Hp51j6vvKqlfpZadP8AL+kem6P+0ZoVxfJDqWhXtjAxwZkmWbZ7lQAcfTJ9q9qsLu2v7KG9s50nt50EkUiHKupGQQa+O/F91Y3HhkIstqZF+xtBsuIZCx8nEoVEAaIbuW3E5Ir6D/ZvS9T4S6Z9s3BWeVoA3Xyi52/h1xXjZzlmHoUFWpLl1ta9+lz2MmzTEYiu6NV82l72tbWx6PRRRXzB9OFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFRSf8AHwP+ubfzFS1FJ/x8D/rm38xQB+YH7W3/ACcV4y/6/h/6KjryuvVP2tv+TivGX/X8P/RUdeV0AeieCf8AkBRf7zV0ArA8Ef8AICi/3mroBTPrcH/Ah6IKKWig6gopaKm4wopaKQwFFKKKQwopaXFIYmKKWlqQEpcUAUtIoMUUuKMUgExS0uKKQwoxS0tSxiUUuKWlcdhMUYpaWkOwlFLS4qRiUtGKUUgsJSgUuKKVxiYpaXFGKm47CUuKWjFIYlKBSgUuKQ7CYpCMjFOoxUga66+8enRWsWno00cSR/appt8mFkjdVHyg7AY+FJONxwQOKdFrltDJcNDoiqHuJrqAG8YiKWaIxyZ+X51IOQvBGOprHpap1JMwWFpdjcv/ABIt7GtvPpC/YzbzwvAt1gASmNsRkJ8iqYlwuG7gk0yDxEySQSSaZFK9pJFNZnz2XypY4REGIA+cEKp28cjrgkHGxS4pOpJgsJSSskadprktuJgLGF/NtYLdsuekUEkIPTqRIT+GKYkttM1lfS38FvPYwwRJamCZjJ5AUL86rtG7b+FZ+KMUvaPcr6tDpp0+RsWWs2trZtcCw36mNUnvYD5pWOAyRBc4A+fBJ4yOgzkcUXeqxapptnpUwi0qC2YSeZh5Y9wiWPCxomUBC7jy3JPNZGKMVPtHa3TT8AWFgnzLfXX1J5PscRMSILzarj7QrvGHLAbG2MMjac8fxd6jmaJ5d0MJhTao2eYX5AGTk+pycds4pooxUNm8YWPI6KKK94/PwpaSloAUVo2/+oT/AHazRWlb/wCoT/dq4bnbg/iZJS0lKK0PQFoFJS0ALSikpRQMBS0lLQAtFFFAC0tIKWgYUtJS0DClpKWgBaKSloAUUUlLTGLRRRQAoooopDFpaSigBaKKKYD45JIz8jkVZjvpBxIob3HBqpRQNSaNSO7gf+Laf9qpwQRkHI9qxKejuhyjFfoadzRVO5s0VnRX0q/fAf8AQ1ZjvIX6kofenc0U0yxRQpDDKkEe1LimUJSU6jFADaKXFFACUlOpKQEckYbnofWoHUqcEVboIBGDzQS1cp0VLJCRyvI9KipEtWCg0UUCMrxN/wAga5/3R/MV51Xovib/AJA1z/uj+YrzqsKu54OZ/wAVeh2fwK/5LX4H/wCxhsP/AEoSv1gtvuv/AL7fzr8n/gV/yWvwP/2MNh/6UJX6wW33X/32/nWZ5pLVXVnuY9Lu3s13XKwuYRjOX2nb+uKtUUMcXZpnxNo8i3Nv4iuNStZLy7awaRZmwWhmM0eZGLEHuy8ZPzdMZx7l+ylcX8nhrV7eYubGG7X7NnorFcuB/wCOnHqa6jxL8IfBWvau+qXNlPbzyNvlFtMY1kbuSOxPcjFdjoOkaboWlw6ZpNpHa2kIwkafqSepJ7k815uGwc6VTmk9F+J9rnXEeFx2CdGnF80mnrtG1ttfLstGXqKKK9I+JMTxZ4s8PeFrdJ9d1OG0EmfLQ5Z3x12qMk1yn/C6/h6f+Yrc/wDgDL/8TXivjuWXX/jpd2mrK8sI1H7IIiW4hXgAYwcHrxyc1jX2lR6nLPfL5sQQJGEt9PmUsxViGZZHJVAExuHHtwa8mpjqvM+RK17H6Dg+FsF7KDxEpczSk7Wsr7LZn0H/AMLr+H3/AEFLn/wBm/8Aia0/DvxQ8Ea9qCWFjrSLcyHbHHPE8O8+ilgAT7V86yeDbO61K6NnqDwWsc7xbXhyysJAmBlvmUbhk9fbkVga7o39lQQv580rsV3OICsRLKHBjfPzYHXgYNQ8dXjrJK39eZ0LhXK6y5adSSl59PX3V+Z9uVWe8iDssaySlThvLQsAfTPTNc38P9Tv9R+F+kajes32uWxQyOep/h3/AFI5qL4haldaVHp1lpOrR2F1OJFt7fZGTMyhcFmk4WJc5cj5jkBTk4PrxlzJNdT87r0nRqypy3i2vuOo+2j/AJ9br/v0abLcxSxPFNZ3DxOpV1aAkEHqCO4rzPQ/H+tmSYTwJd3c8jbLWWWOKK3VTPnDIrOpxDjY4LHrkDIGrF8R5hLZpdaRbW4mmjWRvt24RpIkLocBMk4nAPG0FfvcirWuxi2lozC1X4CeCdUumvdPvNR0+KQk+TbyK0an0AdSR9M8Vzvij4M/DzwzZC81zxfqllEx2pvMRZz6KoTJ/AV7uoEepsE6SRbnHuCAD+IP6V4nPZW3iz9pO90/xGomtNOt/wDRLWQ/I+1UYDHcZdmI749BXZVz7MKUVGNR3eiuPLeGMtxlSc61O0YRcnbd26Lp1OR0bSfgXZ3yXF34h13Uo0OfIltXVG/3tiAke2a9bt/jL8OLeCO3t9QmiijUIiJp8oVVAwABt4FYnxh8N6JcfDbVtWm03TLG9026dbSW0jWPKCUIqNjqSvbseleNWmmWMnhawl+yqbu7klQzG2nkIxIFBDKwRcA/xA15GOzbH1ppVpqVldf8MfZ5PwtkcqDqUYTh73K7NPW19+Xax9Cf8Lr+H3/QVuf/AABm/wDiaVfjV8PiwH9q3Az3NjLj/wBBr5+tvCNrcXBjTVZcL5xObcAlY5hFkAvySxzjsB68VGng9g8jS3ri3jRXM6W5ZWyJSQuSMkeUfz5xiuD69iLXsv6+Z6/+rGT6+/PT+v5T670HWdL13Tk1DR76C9tXOBJE2RnuD3B9jzV+vmn9lvUbyLxve6dC0jWVzZtLIp6BkZdrEeuGIr6Wr0cNW9tT5mj4vO8sWW4t0E7rdej7hRRRXQeSFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABUUn/HwP+ubfzFS1FJ/x8D/AK5t/MUAfmB+1t/ycV4y/wCv4f8AoqOvK69U/a2/5OK8Zf8AX8P/AEVHXldAHovgj/kAxf7zV0Irn/A//IBh/wB5q6Gg+uwf8CHogooFLSOoKKKXFIoKMUtFILBRilApaQxKKWlxUjExS4pcUUh2CiloApDClxRRSGFFLiipAKKWlxSuMSlopcVNxiUtLiikOwUClxS4pDExSgUoFFIBKMUtKBUlCUtGKXFJjsJSgUtFIAAoxS4oxUjDFGKWikMSlpaKQ7EU7FQMdzToyrD3q9pVtFcXf71A4RSQD0zW2lnbDpbxf98CvQw8YuktNTxsbmscNWdNxuYNtbNck+UG464GcVcj0iRvvSOv/bP/AOvXSafp6vCX3w2sRbajOMB39Bj9T0HenSWtxACZoJIwHKEsuBuHUZ9ameDjLrY4XxDUUvdgred/0aOdl0QrA8iXO5lBO1kIz+NZIrtZF/cv/uH+VcWv3R9K4quH9i/ivf8AroevluYvGxk5RUWu1+vrcTFLS0YrC56Z5BRRRX0J+dhS0lLQAtaNv/qE/wB2s6tG3/1Cf7oq4bnbg/iZJS0lLWh3hS0lKKBi0UUCgBaWkpaBhS0lLQAoopKUUDFopKWgBaKKKBi0tIKBQAtLSUUALS0lFAxaWkooAWlpKKYxaWkoFIBaKKKAFopKWmAtFJRQMejshyrFT7GrEd7KvDYce/WqtFA02tjTjvYW4bKH36VYVlcZVgw9jWLSqxU5UkH2p3LVR9TaorNjvJk+8Q496sx30TffBT9RTuaKaZZpMUI6OMowb6GnUyhuKMUtGKAEqOSMNz0NS0lILFRlKnBFJVsgEYIqCSIjleRQQ4mL4m/5A1z/ALo/mK86r0bxN/yBrn/dH8xXnNc9Tc+fzT+KvQ7P4Ff8lr8D/wDYw2H/AKUJX6wW33X/AN9v51+OmnXt3p2oW+oWFzNa3dtKs0E8LlXidTlWUjkEEAgiuyHxi+K46fEjxZ/4Npv/AIqszzT9XqK/KH/hcfxX/wCikeLf/BtN/wDFUf8AC4/iv/0Ujxb/AODab/4qgD9XqK/KH/hcfxX/AOikeLf/AAbTf/FUf8Lj+K//AEUjxb/4Npv/AIqgD9XqK/KH/hcfxX/6KR4t/wDBtN/8VR/wuP4r/wDRSPFv/g2m/wDiqAP0O+Knwlt/FeqDXNJ1AaZqpAEpKkxykcBjjlWA4yPQcVwcnwJ8ZysWl8T6fIWXYS8s7Er6HI5Ht0r4t/4XH8V/+ikeLf8AwbTf/FUf8Lj+K/8A0Ujxb/4Npv8A4quWpg6U5czR72F4lzDC0lShJNLa6Tsu1z7QT4C+L0YsniLTFY5yVeYHnrzjv39a0fD3wBvTew/8JHr8MtjEc+RaB8sM8qC2AgPfAr4e/wCFx/Ff/opHi3/wbTf/ABVH/C4/iv8A9FI8W/8Ag2m/+KqVgKKexvPizM5xa50vRK5+rFva29vZR2UMSR28cYiSMD5VQDAH0xVG601Z1SO5tLLUI4/9V9pQFk/Eg5+vFflr/wALj+K//RSPFv8A4Npv/iqP+Fx/Ff8A6KR4t/8ABtN/8VXYfNttu7P1OW3kV3ddOsFaRt7kPyzYxk/JyccZpHtXdo2bT9P3xtujYtko2MZHycHHFfll/wALj+K//RSPFv8A4Npv/iqP+Fx/Ff8A6KR4t/8ABtN/8VQI/Ve2gMZeSSTzJn+82MDA6ADsK4T4lfDZfEuq2/iHRtUk0bXrYAJcoCVkA6bscgjJGR24IIr84/8AhcfxX/6KR4t/8G03/wAVR/wuP4r/APRSPFv/AINpv/iqipTjUVpHThMXWwlT2lF2f6dmuqPurxN8L/id4lgit9b8aadeQxHckbB1XPqQqgE+5rGj+BHjKOEwx+JdOjiPVElnVT+AGK+Lv+Fx/Ff/AKKR4t/8G03/AMVR/wALj+K//RSPFv8A4Npv/iq5ngaUnd3+89qnxTj6UeSHKl2UUkfZ8fwE8XRuHj8RaWjjOGV5gRnrggd6VfgL4u2LE3iPTRECcKGmwM9cDGK+L/8AhcfxX/6KR4t/8G03/wAVR/wuP4r/APRSPFv/AINpv/iqX9n0e34mn+t2Z/zL/wABR+k/wp+HeneBbKYpcG91G5AE9yybRtHRFHZc/ifyrt6/KH/hcfxX/wCikeLf/BtN/wDFUf8AC4/iv/0Ujxb/AODab/4quqEI048sVoeBisVVxVV1a0ryfU/V6ivyh/4XH8V/+ikeLf8AwbTf/FUf8Lj+K/8A0Ujxb/4Npv8A4qrOc/V6ivyh/wCFx/Ff/opHi3/wbTf/ABVH/C4/iv8A9FI8W/8Ag2m/+KoA/V6ivyh/4XH8V/8AopHi3/wbTf8AxVH/AAuP4r/9FI8W/wDg2m/+KoA/V6ivyh/4XH8V/wDopHi3/wAG03/xVH/C4/iv/wBFI8W/+Dab/wCKoA/V6ivyh/4XH8V/+ikeLf8AwbTf/FUf8Lj+K/8A0Ujxb/4Npv8A4qgD9XqK/KH/AIXH8V/+ikeLf/BtN/8AFUf8Lj+K/wD0Ujxb/wCDab/4qgD9XqK/KH/hcfxX/wCikeLf/BtN/wDFUf8AC4/iv/0Ujxb/AODab/4qgD9XqK/KH/hcfxX/AOikeLf/AAbTf/FUf8Lj+K//AEUjxb/4Npv/AIqgD9Xqhk/4+B/1zb+Yr8p/+Fx/Ff8A6KR4t/8ABtN/8VR/wuL4r5z/AMLI8WZ/7C03/wAVQBtfta/8nFeMv+v4f+ikryurmtarqWt6pPqmsX9zqF9cNumuLmUySSHAGWY8k4AqnQB6P4G/5AMP+81dCK57wN/yAYf95q6GkfX4L+BD0QUtGKWkdYgFKBS0UgCjFLilpDEpaKUCkMSlxRS1I7BRS0UrjAUUtFIYUUtLUjEpcUYpaVxiUtGKUUhiYpcUtLUjEpcUYpaVwExS0UuKQ7CUuKXFFSOwUUtFIYUUtFSMKMUtFIAoxS4pcUhiYpaKXFSMSlxRS0hmhoA/0mX/AK5/1robJIWuoluN/lFwGCY3EZ7Z6fWsDw//AMfUg9Y/61vxHa6sOqkH8jXp4X+Gj4vOv97l8vyNi20291vX5NNsli3xmRYkZtqKiE8D/PJpJgTpLx3fmefb3PkoQeAApyreuMcHt06dLWh6/DpOtNqtvppeZt+VkuSV+c5PRc1n3F004mBQL5ty05wehOeP1roPJKkg/cv/ALp/lXEqPlH0rvmQLbyO3ZGP04NcGo+UfSvPx28T6jhzap8v1CilxS4rzz6Wx45XZfCT4eax8R/Ex0jTJYLS3giNxfX1wcRWsI6u3qfQfyAJrjh1r6r8GaF8C2/ZkbUtQn0mPWW02U3d01yFv0vdrYjUbt3XACgbSp7gmvoT85bscMPBvwNvfFOgeCvD3irW9U1G91WK3vdXk2xWixYYuIhjBZiFVTyBuzk8Vb/ay+EnhL4cR6HdeFpLuF73zVuLK4nMzBUC4mBIyBltpzxkjFeAruwpPDYHT1r3b9nNn13wt8VoNeLXtt/wirM11csZJImQsY0DtkgEjOM4yoPagW2p4WK0bf8A1Cf7orNUkqCepAzWlb/6hP8AdrSG534P4mSUtJSitD0ApRSUtAC0UlLQAtFAooGLS0lFAC0tJRQMWlpKWgApaSgUDFpaSigBaWkpaAClpKKAFpaSigYtLSUUwFpaSigYtLTaWkAtFFFABS0lFAC0tJmimAtFJS0DFopKKAHAkHIOD7VPHeTJ1bcP9qq9FA02tjRjvo2++pX3HIqzHJHJ9xw30NYtKCQcg07lqo+pt0mKzI7qZP4tw9G5qzHfIf8AWIV9xzTuWpplqkxSRyxyfccE0+mXuZWv2ZutNnijHzsnHueteWyI0blHUhgcEGvZXXNZt7o9hdSF57VGY9T0J/Kspw5jzcdgXXalF6nlVFejyeHNOXkWikfU1H/YOm/8+i/maz9kzznldZdUeeUV6H/YOm/8+q/maP7B03/n1X8zR7Ni/syr3R55RXof9g6b/wA+q/maX+wdN/59V/M0ezYf2ZV7o87or0T+wdN/59F/M0f2Dpn/AD6L+Zo9mw/syr3R53RXon9g6Z/z6r+Zo/sDTP8An1X8zR7Nh/ZlXujzuivRP7B0z/n0X8zR/YOmf8+i/maPZsP7Mq90ed0V6J/YGmf8+i/maP7A0z/n1X8zR7Nh/ZlXujzuivRP7A0z/n1X8zR/YGmf8+q/maPZsP7Mq90ed0V6J/YGmf8APqv5mj+wNM/59F/76NHs2H9mVe6PO6K9F/sDTP8An0X/AL6NH9gaZ/z6L+Zo9mw/syr3R51RXov9gaZ/z6L+Zo/sDTP+fRf++jS9mw/syr3X9fI86or0X+wNM/59F/M0f2Bpn/Pov5mjkY/7Mq91/XyPOqK9G/4R/TP+fRfzNH/CP6Z/z6L/AN9GjkYf2XV7o85or0b/AIR/TP8An0X8zS/8I/pf/Pov5mjkYf2XV7o84or0f/hH9L/59F/76NH/AAj+l/8APov/AH0aXKw/sut3R5xRXpH/AAj2l/8APov/AH0aP+Ee0v8A59F/76NHKx/2VW7r+vkeb0V6T/wj2l/8+i/99Gj/AIR7S/8An0X/AL6P+NHKw/sqt3X9fI82or0n/hHdL/59F/76NH/CPaX/AM+a/wDfR/xpWD+yq3df18jzaivSv+Ed0r/n0X/vo0f8I7pX/Pmv/fR/xosH9lVu6/r5HmtFel/8I7pX/Pmv/fR/xo/4R3Sv+fNf++j/AI0WH/ZNbuv6+R5pRXpg8O6V/wA+a/8AfR/xo/4RzSv+fNf++j/jSD+ya3df18jzOlVSzBVGSa9L/wCEc0n/AJ81/wC+j/jU9pomnW0gkitI1YdCecfnQNZRVb1aGeFLSS00aCKQEOcsQe2a2cU1FxUgqT6GlTVOCguggFLRS0jUKKBS0hhRSigUgClopakYlLRSikUJSiilApAFLiiipuMKXFAFLSGJS4pcUoFIdhAKUUYpakYlLiilxSHYKKKWkAUCilqblWCjFLRSuMKKWlxUhYSlxRilxSGJS4pcUUhhiilxSgVI7CYoxS0dqQwoxW5Z6bp9zb6fa/Zbhbq702e8a9FwdkTRtLjchG0R/uwCcg5br2p8nh+zivBDNqs0Sx3UtlMZbdY906IGCxlm2lWzgM5XHGfvCrdKRy/XKadmYcTvE4eNyjDuDVgahe/8/L/kP8K0hoJWymlhmaSRYHS486FNtvOJEXyT825JBuyWIxjO3Ip+paDp1jLfqdca6+wRsLiO3iVnWUSrEActgKSc9dwAOR0otUhszKVTC1pe9FN7bXMwajfDpdP+Q/wpw1PUAci7k/If4VLbaVFcaPHcrdzLeS21xcpGYR5OyFgrBnzkE9RxjoO9XdU0DT9Onv4n1w3L6fFIbqK2jVpFdXRABlsBWL9zn5TxyKG6qV7/AIk8uC5uXkV72+H/AIBmTajfzRmKW7lZG4IyAD+VVcVt/wBhWgtVvzqF3Np+2JfPt7ZJC8sjBdqrvyAhOG3YbpgHcKlh8MK86WLajKL4u5O2AGHYl0LdiG3bt2TuAxjHGc1Lp1JPU1p4jDUl7isvJGBRW/B4dsJrQ6i+umz01tqLLdRKjrIZJI/mAYjaDETwSTkADNZuswxQXdukKIitYW0h29GZolLN+J5rKUHFXZ0U8TCpPkjueHV6J8EPGXhrwxq1/p3jTw/b6x4d1m3+zXjeQrXNr1xJE2Nw68gEZ4I5ArzuivoT4Bn0F4b+HPwhsvib4cvW+IGl+I/CGo3EsZt55BDNBIIy0SXAyDsLcFsKMgA4zVv9p3VfDXge9vPCvwyudPs7LxFYKuv2dgweFTHJmMqQSEZl3BgDyvYZr5yIB6gGgAAYAAHtSFYd3rRt/wDUJ/u1m1pW/wDqE+lXA7cH8TJKWkorQ9AWlpKKBiilpKWmAUopKBSAdQKSlpjFooooAWikpaBi0UlLQAtFJRQMWlpKKAFpaSgUAOopKKAFpaSigYtFFFMBaKSloGLRSUUgHUUlFAC0UUUALRSUUALS0lFMBaKSigY6ikooAWikzS0ALnmpo7mZOjkj0PNQUUDTsX474H/WIR7irMc0Mn3XBPp0NY9FO5aqNG3j2qOSJW5HBrNjuJo+Fc49DzVmO+/56J+K0XK509xWUqcEYpMVYWaCYYDj6Hg0ySIryORQO3YixS4FFLQSJijApaKAExRgUtFACYowKWigBMCjApaKADAowKKKQwwKMUtFACYpcUUUAGKMUtFIYYFGBS0UgExS4opaQ7CYpcCilpAJilxRS4oGJgUuBSiikMMCjAopaQwwKABRSikMMUYFLQKQABRiloqRhilwKMUtAxMUoFApRUjsApaKWkMKKKWkMSlFFLSGFFKKKkdgopcUUhhS0UoFIYAUUtAFSAUtFLSGJSgUooxSuMKUUUVIwpaBRSGFLRS4qRiYpaKWlcYUUtFIYUAUuKWkMSloxSgVNwExS4pcUtK4xMUtFLUjEpaKXFIYlLiilpXGStd3zWH9nNf3Zsv+fbzm8rrn7ucdeakXUdUWaCZdVvxLboY4X+0PujU9VU54HsKrYpRS5mR7KHYlmur6eBbefULyWFVKLG87MoUnJGCehIFOuby+uh/pV/d3H7sR/vZmb5AcheT0B5xUNApOTKVOK6FiW/vpNIi0k3Uq2MW8+QjsEcs24lhnDHPTim3F3fXKBLm/u51WPygJJmYBMg7eT0yAcewqKik5MFSgtkWRqWqieOcatqCyxx+Uji5cFU/ug54HTioYpbiLYYrq4Qou1SsrDaN27A56bufrzTRRScn3GqUF0Jra7vrVt1rf3cB2GP8Adzsvyk5K8HoSc49ajZpHCB5HcRoETcxO1R0Ueg9qSjFS2ylCKd0jxiiiivpD85CiiigBRWlb/wCoT/dooqobnZg/iZJRRRWh6AtHeiigYvajtRRQAtFFFMBRS0UUDQUUUUALQKKKBi9qKKKAFooooABS0UUhhS0UUwAUtFFABS0UUAFLRRQNBRRRTAKXvRRSAKKKKBi0CiimIWiiikMKO9FFABS0UUwCiiigApaKKBhR2oooAO9LRRQAUUUUAFSwzSowCuQPSiigZdlA+U45I5ptFFM1e4lHeiigQtHeiigAooooAKKKKBhS9qKKQBRRRQMWiiikAUtFFIAFFFFAxaWiipAKKKKBoWloopDCloopDCiiikMUUtFFJgA6UtFFIYUooopDQtFFFJjFFKKKKTGLRRRUgKKKKKGUKKKKKlgLS96KKllBSiiikMWloopMBaKKKQxaUUUVLGgFLRRSGFLRRUjFpaKKQwFFFFJjQopaKKljQU7vRRSGApaKKQBS0UVIxaKKKQxaKKKljFHWloopMaAUoooqRhS0UUgFoFFFIYooFFFIYtFFFSMBS0UUho//2Q==",
+          "bounding_box": {
+            "x": 3.488372093023256,
+            "y": 631.1597877468732,
+            "width": 35.880398671096344,
+            "height": 42.74668630338738
+          },
+          "confidence_threshold": 0.8,
+          "description": "Élément sélectionné pour l'étape vwb_action_1768381569956",
+          "metadata": {
+            "capture_method": "screen_capture",
+            "capture_timestamp": "2026-01-14T09:06:56.132Z",
+            "screen_resolution": {
+              "width": 1920,
+              "height": 1080
+            }
+          }
+        }
+      },
+      "isVWBCatalogAction": true,
+      "vwbActionId": "click_anchor"
+    },
+    {
+      "id": "step_1768381657878",
+      "type": "wait_for_anchor",
+      "name": "Attendre Ancre",
+      "label": "Attendre Ancre",
+      "position": {
+        "x": 149.63597878931995,
+        "y": 117.69521623356172
+      },
+      "data": {
+        "label": "Attendre Ancre",
+        "stepType": "wait_for_anchor",
+        "parameters": {
+          "timeout_ms": 40000,
+          "visual_anchor": {
+            "anchor_id": "anchor_1768381722873",
+            "anchor_type": "generic",
+            "reference_image_base64": 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+            "bounding_box": {
+              "x": 473.92026578073086,
+              "y": 25.747416612852657,
+              "width": 280.0664451827243,
+              "height": 42.746686303387335
+            },
+            "confidence_threshold": 0.8,
+            "description": "Élément sélectionné pour l'étape vwb_action_1768381688015",
+            "metadata": {
+              "capture_method": "screen_capture",
+              "capture_timestamp": "2026-01-14T09:08:42.873Z",
+              "screen_resolution": {
+                "width": 1920,
+                "height": 1080
+              }
+            }
+          }
+        },
+        "isVWBCatalogAction": true,
+        "vwbActionId": "wait_for_anchor"
+      },
+      "stepType": "wait_for_anchor",
+      "parameters": {
+        "timeout_ms": 40000,
+        "visual_anchor": {
+          "anchor_id": "anchor_1768381722873",
+          "anchor_type": "generic",
+          "reference_image_base64": 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+          "bounding_box": {
+            "x": 473.92026578073086,
+            "y": 25.747416612852657,
+            "width": 280.0664451827243,
+            "height": 42.746686303387335
+          },
+          "confidence_threshold": 0.8,
+          "description": "Élément sélectionné pour l'étape vwb_action_1768381688015",
+          "metadata": {
+            "capture_method": "screen_capture",
+            "capture_timestamp": "2026-01-14T09:08:42.873Z",
+            "screen_resolution": {
+              "width": 1920,
+              "height": 1080
+            }
+          }
+        }
+      },
+      "isVWBCatalogAction": true,
+      "vwbActionId": "wait_for_anchor"
+    },
+    {
+      "id": "step_1768381862621",
+      "type": "click_anchor",
+      "name": "Cliquer sur Ancre",
+      "label": "Cliquer sur Ancre",
+      "position": {
+        "x": -136.71208025455445,
+        "y": 285.256260474885
+      },
+      "data": {
+        "label": "Cliquer sur Ancre",
+        "stepType": "click_anchor",
+        "parameters": {
+          "visual_anchor": {
+            "anchor_id": "anchor_1768381925307",
+            "anchor_type": "generic",
+            "reference_image_base64": 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+            "bounding_box": {
+              "x": 527.7408637873754,
+              "y": 36.6660509966259,
+              "width": 115.61461794019942,
+              "height": 21.87039764359352
+            },
+            "confidence_threshold": 0.8,
+            "description": "Élément sélectionné pour l'étape vwb_action_1768381868692",
+            "metadata": {
+              "capture_method": "screen_capture",
+              "capture_timestamp": "2026-01-14T09:12:05.307Z",
+              "screen_resolution": {
+                "width": 1920,
+                "height": 1080
+              }
+            }
+          }
+        },
+        "isVWBCatalogAction": true,
+        "vwbActionId": "click_anchor"
+      },
+      "stepType": "click_anchor",
+      "parameters": {
+        "visual_anchor": {
+          "anchor_id": "anchor_1768381925307",
+          "anchor_type": "generic",
+          "reference_image_base64": 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+            "x": 527.7408637873754,
+            "y": 36.6660509966259,
+            "width": 115.61461794019942,
+            "height": 21.87039764359352
+          },
+          "confidence_threshold": 0.8,
+          "description": "Élément sélectionné pour l'étape vwb_action_1768381868692",
+          "metadata": {
+            "capture_method": "screen_capture",
+            "capture_timestamp": "2026-01-14T09:12:05.307Z",
+            "screen_resolution": {
+              "width": 1920,
+              "height": 1080
+            }
+          }
+        }
+      },
+      "isVWBCatalogAction": true,
+      "vwbActionId": "click_anchor"
+    },
+    {
+      "id": "step_1768381947511",
+      "type": "type_text",
+      "name": "Saisir Texte",
+      "label": "Saisir Texte",
+      "position": {
+        "x": 72.9614879738022,
+        "y": 312.1366364737105
+      },
+      "data": {
+        "label": "Saisir Texte",
+        "stepType": "type_text",
+        "parameters": {
+          "text": "onlyoffice",
+          "visual_anchor": {
+            "anchor_id": "anchor_1768382163792",
+            "anchor_type": "generic",
+            "reference_image_base64": 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+            "bounding_box": {
+              "x": 530.7308970099667,
+              "y": 43.64137832124735,
+              "width": 119.60132890365458,
+              "height": 10.93519882179676
+            },
+            "confidence_threshold": 0.8,
+            "description": "Élément sélectionné pour l'étape vwb_action_1768382129364",
+            "metadata": {
+              "capture_method": "screen_capture",
+              "capture_timestamp": "2026-01-14T09:16:03.792Z",
+              "screen_resolution": {
+                "width": 1920,
+                "height": 1080
+              }
+            }
+          }
+        },
+        "isVWBCatalogAction": true,
+        "vwbActionId": "type_text"
+      },
+      "stepType": "type_text",
+      "parameters": {
+        "text": "onlyoffice",
+        "visual_anchor": {
+          "anchor_id": "anchor_1768382163792",
+          "anchor_type": "generic",
+          "reference_image_base64": 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+          "bounding_box": {
+            "x": 530.7308970099667,
+            "y": 43.64137832124735,
+            "width": 119.60132890365458,
+            "height": 10.93519882179676
+          },
+          "confidence_threshold": 0.8,
+          "description": "Élément sélectionné pour l'étape vwb_action_1768382129364",
+          "metadata": {
+            "capture_method": "screen_capture",
+            "capture_timestamp": "2026-01-14T09:16:03.792Z",
+            "screen_resolution": {
+              "width": 1920,
+              "height": 1080
+            }
+          }
+        }
+      },
+      "isVWBCatalogAction": true,
+      "vwbActionId": "type_text"
+    }
+  ],
+  "edges": [
+    {
+      "id": "conn_1768381728937",
+      "source": "step_1768381548100",
+      "target": "step_1768381657878"
+    },
+    {
+      "id": "conn_1768381934574",
+      "source": "step_1768381657878",
+      "target": "step_1768381862621"
+    },
+    {
+      "id": "conn_1768381975623",
+      "source": "step_1768381862621",
+      "target": "step_1768381947511"
+    }
+  ],
+  "variables": [],
+  "settings": {},
+  "tags": [],
+  "category": "default",
+  "is_template": false,
+  "version": "1.0.0",
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2026-01-20T14:41:18.034964",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_20260114_130454.json b/visual_workflow_builder/backend/data/workflows/wf_20260114_130454.json
new file mode 100644
index 000000000..b1578fee3
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_20260114_130454.json
@@ -0,0 +1,87 @@
+{
+  "id": "wf_20260114_130454",
+  "name": "test_2",
+  "description": "Test_2 amélioration des coordonnées",
+  "created_by": "unknown",
+  "created_at": "2026-01-14T13:04:54.535540",
+  "updated_at": "2026-01-14T13:04:54.535540",
+  "nodes": [
+    {
+      "id": "step_1768392174447",
+      "type": "click_anchor",
+      "name": "Cliquer sur Ancre",
+      "label": "Cliquer sur Ancre",
+      "position": {
+        "x": 390,
+        "y": 161.38333129882812
+      },
+      "data": {
+        "label": "Cliquer sur Ancre",
+        "stepType": "click_anchor",
+        "parameters": {
+          "visual_anchor": {
+            "anchor_id": "anchor_1768392196921",
+            "anchor_type": "generic",
+            "reference_image_base64": 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",
+            "bounding_box": {
+              "x": 1.495016611295681,
+              "y": 637.1244416496716,
+              "width": 33.88704318936877,
+              "height": 32.805596465390295
+            },
+            "confidence_threshold": 0.8,
+            "description": "Élément sélectionné pour l'étape vwb_action_1768392179273",
+            "metadata": {
+              "capture_method": "screen_capture",
+              "capture_timestamp": "2026-01-14T12:03:16.921Z",
+              "screen_resolution": {
+                "width": 1920,
+                "height": 1080
+              }
+            }
+          }
+        },
+        "isVWBCatalogAction": true,
+        "vwbActionId": "click_anchor"
+      },
+      "stepType": "click_anchor",
+      "parameters": {
+        "visual_anchor": {
+          "anchor_id": "anchor_1768392196921",
+          "anchor_type": "generic",
+          "reference_image_base64": 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UL+wbxFpoutPjkluovPG6JYxlyf90dfTviuRufDPivVbm41O/s7OC7u30OWWKO53Ir2tw0k4U46YOV9c44qvpHgjX3OjeG9U07Tl0fR727uW1FLndJepMk6hPL25RmE53kkg7TjOeFrZrzf6f15h5+h30/ivw5BLexS63Yo9ikElyplGYlmOIi3oHPA9a2a8O8M/DDxXb6poF3qslk5a6xrjLNky29qyGx28fMf3K7h23tXuNU7WEr31CiiikMKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAIb0lbaQg4IU4/KvhB/nkZm+ZmYkk8kkmvu6+/49Jf9w/yr4SR3imEkbsjo25WU4KkHIIr7HhL/l9/27+p8pxP/wAuvn+hYvdNuLS0srqaNfLvImljwDkBXZDnjg5U07RNLudY1OLTrJYjcS7tgkYIpwpbqenT860tc8W65q+lWenXmo3skUERSUSXDMJyZGcMwPcZA/AVl6XetYXf2hYxIfKkjwTj76Mmc+27NfWRdV03dLm1t+nY+Zap86ttpf8AUtw+HrxrVLu4ks7K3cf6y6k2ANuddh4J3ZRuMcAZOKJfDuow3cVrNFDFLKJiAzjAEWd+SP8AdJHXPFX77xT/AGnZw2mq6XFcxRxoHKTtE0kql8ykgHBYOQwxzjPBps3iqScme40+F7tROsEqysqxLKMFdn8WATjke+ay5sT27/8AA6/ea8tC2/b/AIPT7ivB4W1GS4uIWNpD9nkeN3kc7TsTexXapLDbg5A/iHrVS50eeHySJLaZJrk20bxlsFwEJ4ZQQPnXkj1rVg8WTpJYs9qcW1lJasYbhopJS6hPN3gHa4VUXgfw+9Qya9aywokunXMrRXRuoZJdQLuGIQYclPnH7senXFEZYjm1Wny/rzE40eXR6/P+vIq2mgXFw98rTWkAsZRDM0pcjcSwAGxWJ5U84qGy0e6vVDWqxS5u0tODzvfO04x904PPtWvH4mtoZdQa3069tlv5Vll8nVGRwwZm4YJ935zxjsOaz9E1uXSZr54IVkF1CyKJHJMT5ykgPdlyfrk01KvZu2ultvmJxo3Svpr3+X6D4PDOozR6fKn2Tyr+5e3hcy8blz8zcZCnDYPfaaZP4fu0tBeQSWd5bNGrpJbuWD5k8vABUHO4gYIFaVj4yu7UW8SWNsbe1+zm2jACtG0QIDbwu5s7nyD/AHuKbZ+MLyLyJLq3S+uIURfNmkOW2S+ahIxjhu3cVPNib7L/AIH9aFcuHa3d7fiRf8IfqY1G6sJJLCOW2kijLGQsjtLnZtKqcg4PJwB61Ti8P3r6pBpzfZo5poDOCz5VUCsxyQDzhTwAa17TxrcpCgubV3mVYg01rcm1ZxG7soIRcY+cggYzgVEPFMJ1O31Q6UyXkULQlobwxqVZXHyqEyhG/ggnGKlSxSvdflvb17lOOGdrP+r/AORz99ara3JgMsE+ADvjDbTn/eVT+lfZPwqd5Phx4eZ2LMdOhySck/IK+OtUulvLw3Ci5GQB/pFyZ3OPVyAT9McV9ifCb/km3h3/ALBsP/oIrweKb/V6d97/AKHtcOW9vUttb9TpF/4+H/3F/makqNf+Ph/9xf5mpK+IPsANfP37WJJk8Pj3uP5JX0Ca+fv2sf8AW6B9Z/5JXsZB/wAjCn8/yZ5Wd/7jU+X5o8KooAJIABJPAAHJr1CP4V2unWVsfFOvXOnX08IuHt7Wwa4W0iJxvncHCjPBPbn0Jr9DxGKpYe3O99t2/uR8LQw1SvfkW3y/M8vorofH/hLU/BmvNpWolJAyeZBPH9yZDxuHoexHasm002+urC9v7e2eS2sVVrmQEYjDHaue559K0hWpzgqkXo+pnOlOE3CS1XQqUVYWyuWknjSMMbeMyS7WBCqMZOc4PUdKrkEHBGD71aaexDTQUUYPPB468dKkjgnkjlkjhkZIVDyMF4RSQAT6DJAp3SC1yOiggjqCPqKkuYJ7aZobiGSKRcEo64IyAR+hBouFiOirenabfagXFnbtKU27uQMbs46nvg1UHIB7EZHuKSkm7IbTSuFFaWj6Wb0hmDkM21EQfM5q/rHh2SzZomhmt7hV3eXJ3FZuvBS5W9S1Rm48yRz1FFFamYUUUUAFFFFABRRSsrKFLKQGGVJHUdMj1oASiiigD2r9lUkapro/6ZQfzevoQdK+ev2Vf+Qrrv8A1yg/m9fQo6V+bcQf8jCp8vyR+gZH/uMPn+bPySrsvhJ8PNY+I/iY6RpksFpbwRG4vr64OIrWEdXb1PoP5AE1xw619V+DNC+BbfsyNqWoT6THrLabKbu6a5C36Xu1sRqN27rgBQNpU9wTXjHut2OGHg34G3vinQPBXh7xVreqaje6rFb3urybYrRYsMXEQxgsxCqp5A3ZyeKt/tZfCTwl8OI9DuvC0l3C975q3FlcTmZgqBcTAkZAy2054yRivAV3YUnhsDp617t+zmz674W+K0GvFr22/wCEVZmurljJJEyFjGgdskAkZxnGVB7UC21PC16j61+m37O3/JCvBP8A2A7X/wBFivzIjJIUnqQM1+m/7O3/ACQrwT/2A7X/ANFihiqbHe0UVVtNS0+8u7m0tL62nuLVgtxFHKGaInoGA5XoevpSMi1UK/8AH43+4P5mpqrsxW7bAB+QdfqaALFFReY391fzo8xv7q/nQBLRUXmN/dX86PMb+6v50AS0VF5jf3V/OjzG/ur+dAEtFReY391fzo8xv7q/nQBLRUXmN/dX86PMb+6v50AS0VF5jf3V/OjzG/ur+dAEtFReY391fzo8xv7q/nQBLRUXmN/dX86PMb+6v50AS0VF5jf3V/OjzG/ur+dAEtFReY391fzo8xv7q/nQBLVY/wCvl/4D/KpPMb+6v51EpJmlJx/D0+lADqKKKAJIe9SVHD3p8jBELHoKAFoqt9sX+41H2xf7jVXJIj2ke5Zoqt9sX+41H2xf7jUckg9pHuWaKrfbF/uNR9sX+41HJIPaR7lmiq32xf7jUfbF/uNRySD2ke5Zoqt9sX+41H2xf7jUckg9pHuWaKrfbF/uNR9sX+41HJIPaR7lmiq32xf7jUfbF/uNRySD2ke5Zoqt9sX+41H2xf7jUckg9pHuWaKrfbF/uNR9sX+41HJIPaR7lmiq32xf7jUfbF/uNRySD2ke5Zoqt9sX+41H2xf7jUckg9pHuWaKrfbF/uNR9sX+41HJIPaR7lmiq32xf7jUfbF/uNRySD2ke5Zoqt9sX+41H2xf7jUckg9pHuWaKrfbF/uNR9sX+41HJIPaR7lmiq32xf7jUfbF/uNRySD2ke5Zoqt9sX+41H2xf7jUckg9pHuWaKrfbF/uNR9sX+41HJIPaR7lmiq32xf7jUfbF/uNRySD2ke5Zoqt9sX+41H2xf7jUckg9pHuWaKrfbF/uNR9sX+41HJIPaR7lmiq32xf7jUfbF/uNRySD2ke5Zoqt9sX+41H2xf7jUckg9pHuWaKrfbF/uNR9sX+41HJIPaR7lmiq32xf7jUfbF/uNRySD2ke5Zoqt9sX+41H2xf7jUckg9pHuWaKrfbF/uNR9sX+41HJIPaR7lmiq32xf7jUfbF/uNRySD2ke5Zoqt9sX+41H2xf7jUckg9pHuWaKrfbF/uNR9sX+41HJIPaR7lmiq32xf7jUfbF/uNRySD2ke5Zoqt9sX+41H2xf7jUckg9pHuWaKrfbF/uNR9sX+41HJIPaR7lmiq32xf7jUfbF/uNRySD2ke5YdQylT0NecX/wAGfBd3eS3LWLo0rl2VJGVcnrgA8V3/ANsX+41H2xf7jVtSq16N3Tk4+jsZVI0Kv8RJ+quedf8ACkfBP/PpN/3+b/Gj/hSPgn/n0m/7/N/jXov2xf7jUfbF/uNW313Gf8/Zf+BP/Mx+q4P/AJ9x+5f5HnX/AApHwT/z6Tf9/m/xo/4Uj4J/59Jv+/zf416L9sX+41H2xf7jUfXcZ/z9l/4E/wDMPquD/wCfcfuX+R51/wAKR8E/8+k3/f5v8aP+FI+Cf+fSb/v83+Nei/bF/uNR9sX+41H13Gf8/Zf+BP8AzD6rg/8An3H7l/kedf8ACkfBP/PpN/3+b/Gj/hSPgn/n0m/7/N/jXov2xf7jUfbF/uNR9dxn/P2X/gT/AMw+q4P/AJ9x+5f5HnX/AApHwT/z6Tf9/m/xo/4Uj4J/59Jv+/zf416L9sX+41H2xf7jUfXcZ/z9l/4E/wDMPquD/wCfcfuX+R51/wAKR8E/8+k3/f5v8aP+FI+Cf+fSb/v83+Nei/bF/uNR9sX+41H13Gf8/Zf+BP8AzD6rg/8An3H7l/kedp8EvBIcE2crAHODM2D+tejWFpBY2cVpbRrHDCgREUYCgDAApv2tf7jVYUhlDDoRmsK1evVt7WTfq2zejSo07+yil6KxEWZbl9sZb5F6Eep9ad5j/wDPB/8Avof40L/x8P8A7i/zNSVgbkXmP/zwf/vof414D+1czNLoG5CvM/Ug9k9K+g+1fP37WP8ArdA+s/8AJK9jIP8AkYU/n+TPKzv/AHGp8vzR4jp1z9j1G1vNm/7POku3+9tYNj8cV9fXOoaP448PadLp95JcaXdXCNepCU/1QVmMc2WBRdwUNjJPTGCSPjqgEjOCRnrg4zX22ZZWsa4yUuWUdnvv/Wh8hl+YvCKUXG6l8j1b9pTxNpWveJ7Gx0qaO4TTYnSWeMgqXYjKgjrgKPxJrkPDviHTdN0YaXPYSTpdNP8AbpAxDBXj8tAgBAbaMt83c8VzFFdFDA06WHjQWyMK2MnVruv1Z3sfjTTEMj7L4xlHRrPy08m4JdG3uc8HCFeh4xzjIrO1PxNaXPinTtTKyzw2u7czQKkh3MxGAWbJXcCMnqOMDFcnRVRwVKLuv6uS8XUasdmvimxSyeCS71W7YJIshmjQC+LRKimX5sjYRkZ3H3B5qwvje3e7nnu/ttwrXMkio4BDRfaIpEiIzwMI4xyAT0OTXCUUPBUnug+t1ErHbXHi6xkmuy73cpmtPs0cwgVGjbLkTYLsSy7to+bozcjgUs/inSJ3N1cC/nnSGVEikjUrIZI4lKsxbIUGNh0OQR05FcRRQsFSWqB4uo9zvrvxrp0140pjuZELblPkKjKN7Ns++xIAYAc464AFY3jHxBbazaWkNtG0YibfsMIUQjYq7FbccjjPAUdOM5Nc1RTp4OlTkpLoE8XUmmn1Ol8L3EIjhDtKBDJl/KbDgZzlT2NbPiPULSUwSQlvKtrZYi7RiPdgschASB19eTk964KN3jYMjMrDuDinzTzTY82V3x03GlPCqU+a4QxLjDlsRscsT6kmiiius5gooooAKKKKAJbN4I7uGS5gNxAsitJEHKeYoPK7hyMjjNdBrni6fW9OnsdR06zaNXU6b5K+X/Z6jAMaY+9GVHKt3+bOc55qis50YTkpSWq2/r+uxcas4xcU9GFFFFaEHtP7Kv8AyFdd/wCuUH83r6FHSvnr9lX/AJCuu/8AXKD+b19CjpX5txD/AMjCp8vyR+gZH/uMPn+bPySr0T4IeMvDXhjVr/TvGnh+31jw7rNv9mvG8hWubXriSJsbh15AIzwRyBXndFeOe8z6C8N/Dn4Q2XxN8OXrfEDS/EfhDUbiWM288ghmgkEZaJLgZB2FuC2FGQAcZq3+07qvhrwPe3nhX4ZXOn2dl4isFXX7OwYPCpjkzGVIJCMy7gwB5XsM185EA9QDQAAMAAD2pCsOX7341+m37O3/ACQrwT/2A7X/ANFivzJXqPrX6bfs7f8AJCvBP/YDtf8A0WKGTU2Nz4jfaP8AhDb/AOy/aPNwn+o3Zx5i5zt+bZjO7b823djmvMYc/b7c2n/Pyv8AZ32Ld5/lfbP3m3b82Nud/wBp/wCWm/Hevb6q2unWFpdXN1a2VvBPdMGuJY4wrSkdCxHJ/GkZFoVWf/j7b/c/rVmqz/8AH23+5/WgBaKKKACgdcUUDrmgDFt/EthIN00dzaRsrNE8yDEwV9h27SxJyRwQDyOKlfxHoaMFOpwckAYyRyA3UD0IPsDzVOTwlYSwrFPd3kyxEm3DlCICX3naNuDzj72eOKsReGtOigMEbTqhV1wGA4aNUPQeij8c0dAH/wDCR6OGYS3axBIy5Z/ugBiCMjPPGcdcHNWYtW02X7SUvIyLZC8xORsUZyeR0GCDjoQR1rLj8H6Sk7TKZwzcnlT82c7s7c9BjGce1Wbbw5p1udQ8vzAL9XST7uVDks204z1YnkmgBU8S6JIMx6hEwBwxOV29ck7scDHPpx61Yg1nTJ5YIorxDJOP3akMCevHI4PBwDgnBxVW48OWM0nmPLchg7uMOOrYz1HtUWn+E9Msr23u43neWHBG/YQSCxX+H5QNxwFwPagDQXVrFpr2FZT5lmpeUYx8oHJGeCAQR7EYpkGuaTO5SO+hLjgrnnoT+P3W6Z6EVUfw9FFLqV3azym6vYJIsTMNg3knqF3YBPHJwOPSoU8J2iwIi3t7G8abYmRl/dA53BQVPBLHrkjjGMUAa9tqdhcxSy29ykqROEdkBIDHGB09x+dR3mq21tdS2my4luIoRMY44icqW2jBOBnPvwOTinadpsWn2JtLWaZFL79xILZ4z1GO1F9psd1cNcefcQSmHyQ0TAbRvDZ5BB5HfII4IoYIpSeJtNWyt7oLcOs6GQKkeSiDO5zzjAx2J9s5FWLTWrS4upLYpcQOgDAzR7Q6ksAw64HyN97HAz0NV5PDdm9nDaG4uhFCrIAGUZV8+Yp+Xo2ecYxgbcU628P28UN3BJd3VxDeOWmWTYC4OcqWVQxGDjk5AAAxQA238T6XcXFnDCZ3+2E+U3l4H3mUcE55KN0BwME4BFbVYNr4U0y1ubeeF7rMD71Dy78nezj5mBYDLHgEZGAc1vUAFFFFABTE/wBbL/wH+VPpif62X/gP8qAH0UUUASQ96S6/493+lLD3pLr/AI93+lOO6Jl8LM+szVte03TLhLW4kmkunXetvbQPNLt6biqAkD3NaZrjGZ4m8V2cMSSa1NcCeKJ2KtPAQgj2kEEqoDDg8EH1r0KFOM2+b+tbHlYirKmly9f8r/edPpGqWGrW7T2FwJVRykilSjxt/dZWAKn2Iq5XPWUkF14/vrnT2WSKKxWC8lQ5VpvMyikjgsqbs+gYCrHjcak/he9ttHhMt/coLaHBwEMhCl2PZVUlifaoqwUZJLrbfzLo1HKLcul9vI2FZWUMrBlIyCDkGlyK8vsI/Fnh3SH0O10vUEgtI57awFgpmiAMsbwkMVBwsbOm4gD5COuM6NveeKLWWO5uYfEU5S4f7dEturxcrMFEAHzMmfKORkDgk53UnCyumNVL6NHf5FFea7fiB/ZhvIZ9UOoNGsa28yp5a/6CGZiuPvefkZz97jpWvYt4ni8F3l15moXV9b3Pn2sUsHlzzwoVJhIYkncA4BbB5HsaHC3UFUv0OzyKQMpJAZSRwQD0rze6m8dQX9qqjV5G8j98VTfEWeGVuAFwNkpjTkk/KOAvJi1ZfGtnYzJYjWprlgki3CIpaSUW0Z2uFXkeZvHJCjbgnoKap+YnV8j03emM71xnGcjr6UteUw2Xihx9nt7PWNh1GS5mhu4AsCn7YskLR8Z6Fi3tnODitzw03jT/AIRnWXu5buXU/soNotzbiMrc7G3hNxO5d2MdFz0JBNJwsr3Gql3ax3BkjGcugwQDlhwT0FCyRszKsiFl+8AwJH1HavJbiy8VteXMlimui0muxNFLe2QkuHmSGFYy6/L8gIk5bAyOccGtPT9G8RW+qJbQvqtpbXN9M91cR7d+xrmdid5BIBXyz9CKfIu4lVfY9IDKSQGBIODg9KUkAEkgAckntXlsn/CwPs0bKdQt5JOZJI7MPI8wghCbgpHy7hJkn5cjBwMV2Hi+G51Hw20cdlcXIju4TdWuza1zFHIDIqgn5gwBIGcMOO9JwtbUpVLp6HQRyRyxrJFIkiN91kYMD9CKVWVhlWVhnGQc81wE8Gtfv38J6Te6HblLuaKIxiJbicRRiNniI/dgtuwvG4rk4zWXDD45t7S2gsZ9SitfMlMJ/s8iTzCYyBIpbOwkynL4B5z/AAkihfqJ1LdD1TIpAykkBhkdRnpXI6JJrMs3iO4jOsJMRImn2uoRuIlZdwV1ZgAd7Y+UEgIFzyTXJGHxpHcXVzYN4iijuBF5s1xaBrlpVgwoCrj92JC2Sfl6ZO3mhQv1G6luh65RkVyHhN/EbeJNRh1c6i9qUbbLJGIoo23AARrgg8EkMrEYHzAGsHUZfH/9iSEJq63iO9vEYAPnMMZCzEKpJErnPVV+UZwOCuTzD2nkemFlDBSwBPQZ5NG5c43DIGSM153dHxd5Jv8Ay9XllkkuQEWNVa3jPlBNg2ll43ngFjggdBWbp8fjFtQknlh15b54oYI3a3AgmVLmbd5xIzjyWUg8HJH8WRTdPzJ9r5Hq4ZSAQQQRkEHrRkda8r06Hx3a2y2sUmpWscGnqkEUdkJFVBbKByWC+aJN3H3uMdMGte9vtch8E6a10+s2tzJq0dvK0XzXUkBkb7oZQ3KjoV3gepodOz0Y1UutUd7kUV5nFJ4/dVBOsi6KYtw0cYhMGyTLTHGBPnZxxzjAxurW0G48S6l4khmuV1mz0xMkpPCIhIywQ8MCM7TIZfTJB7Yo9nvqHtNtDtsijI9a898Rp41WS5ksbvVFifULlUEMHmNHGqL5GxRg7C27JJweM4FNu08bwlp/tWsSia4mS5SKNP3UCyxbWhXHDlDLt5OfcgUKF+oOpboehhlPRge3Bpa8u0608ZW+mai1pJrUCCXzIkmhHmOsl5KZZNq4LS+VsIAIPOcbq6CaXxXbeCLO9gF5farbzFntzEEkuImZlVWUk8gMjckH5Oecik4eYKpfodiOeBzRketeY61Z+MZYL/TpJ9dumWORJHjULFJEqIY2iZRkys4bIHPLAjGKm1BfHSrbPbXurASzXLc2/mMjCfEKsoK4j8rn5uDzk5xT5NtQ9rvoekUZHrXDfECbxpa6tHL4ciu7m1W3Fw8USqVZ4yVMPP8AfEit/wBsTWfMnjqOSW0ju9bknVjHNP5aeT5YkiEckJxy5XzC3X+LIGFojDm6hKpyvZno7SRqpZpECg4JLAAfjShlIBDAg9CDmvMl0bxE13Pp95DfPpzaylzaeTwBGt4Wdpj/ABMwKuD0Kg8ZBzFpUHja20y3s7dNXtriC3ASLy1FssK24ZRkj/XGYbSM5wT2waFBWvcPaO9rHqdJuXIG4ZPQZ61wfhq48YLpmu3evNdwGCyeW08xVHzyK03GO8WVi/4CevWsW1/4S65htNShTXbkQWvmRTXUS+f5jxRmYKABlTghfU7gOMU1T1abD2uidtz1em+ZHv8AL8xN/XbuGfyrgLe98WT+I7S4+z62thJeygW7weUqwGYgM5wcAJg7X2nGNp3ZFVvE2k+IofEep6rolvdm5edmimK7wqfZolJQHHP+sAH94UlDuwdTsj0okAZJGKaJIyu8SIV9Qwx+defRN4yxpyGTVp7VmmF2z2wR/L3HyOGJbdn7+efLwSAaw7XTfFJ0+5sjZayDPP5l1BJAq2zR7ISGTA/1nmA8DsG4wBTVO/UTq26Hr9FcZ4Nn8UJql42vtfyo7bPLNoRGkhlbBjbPMezGSBt6EnORXZ1DVjSMrhRRRSKCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK0oP8AUp/uis2tKD/Up/uis6mxtR3EX/j4f/cX+Zrzz4/6zqOkeE7b+z7trMXN3HFNOnDRJkHcGz8pBA5PHrXoa/8AHw/+4v8AM0XEENzA8FxFHNE4w6OoZWHoQetVha0aNaNSSul0KxNKVWlKEXZvqeafs/61qOrabrMN5qD6jDa3pW3unIZ5VYsSxcHDc8ccDGK479qSyvbvVNI+zqZEigkbygeSzOBlR3PA4HNe92ltb2lulvaQRW8KDCxxIFVfoBwKdLFHKMOoIrrhmKpY361Th8vlY5JYB1MJ9WnL5/O58NS6RqkUs0UljMkkOfNQgblwMnIzngUh0nUxB9o+wT+TvKeYFyu7GcZ6ZxX2/wDYLX/niv5Uv2C1/wCeK/lXsf611v8An2vxPL/1Zo/zv8D4ik0TWI5lhk0u7WVgrKhiO4g9CB/nHfFWP+EW8R+ZJH/Yl7uiAaQeX90HJB/Q19qfYLX/AJ4r+VH9n2n/ADxT8qT4rxHSC/Ea4aodZv8AA+Jh4e13fCn9kXm6dQ0Q8v74PQioW0nU1kEb2MyMSygONpJVtpHOOh4r7f8A7PtP+eKflR9gtf8Ankv5ULiuv1pr8RPhmj/O/wAD4ltNA1u7lkittMuJpIyQ6KAWXBwcjORzVn/hEPFH/QBvv+/f/wBevtH+z7T/AJ4r+VH9n2n/ADwT8qT4rxHSEfx/zGuGqHWb/D/I+Lv+EQ8Uf9AG+/79/wD16P8AhEPFH/QBvv8Av3/9evtH+z7T/ngn5Uf2faf88E/Kl/rVif5I/j/mP/VrD/zv8P8AI+Lv+EQ8Uf8AQBvv+/f/ANej/hEPFH/QBvv+/f8A9evtH+z7T/ngn5Uf2faf88E/Kj/WrE/yR/H/ADD/AFaw/wDO/wAP8j4u/wCEQ8Uf9AG+/wC/f/16P+EQ8Uf9AG+/79//AF6+0f7PtP8Angn5Uf2faf8APBPyo/1qxP8AJH8f8w/1aw/87/D/ACPi7/hEPFH/AEAb7/v3/wDXo/4RDxR/0Ab7/v3/APXr7R/s+0/54J+VH9n2n/PBPyo/1qxP8kfx/wAw/wBWsP8Azv8AD/I+Lv8AhEPFH/QBvv8Av3/9ej/hEPFH/QBvv+/f/wBevtH+z7T/AJ4J+VH9n2n/ADwT8qP9asT/ACR/H/MP9WsP/O/w/wAj4u/4RDxR/wBAG+/79/8A16P+EQ8Uf9AG+/79/wD16+0f7PtP+eCflR/Z9p/zwT8qP9asT/JH8f8AMP8AVrD/AM7/AA/yPi7/AIRDxR/0Ab7/AL9//XoPhLxOOuhX3/fv/wCvX2j/AGfaf88E/KoptPtNw/cp09KP9asT/JH8f8w/1aw/87/D/I+Mv+ES8Tf9AK+/79//AF6P+ES8Tf8AQCvv+/f/ANevsr+z7T/nin5Uf2faf88U/Kj/AFqxP8kfx/zD/VrD/wA7/D/I8a/Zr8O6vpM2q3mpWclqtwIkjWThjt3EnHpyK9vqOGGKIYjQL9Kkrwcbi54utKtNWb7elj2sJho4WiqUXov+HPySooornPRCiiigBV6j61+m/wCzt/yQrwT/ANgO1/8ARYoopMipsd7RRRSMgqtJ/wAfR/3P60UUALRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUxP8AWy/8B/lRRQA+iiigCSHvSXX/AB7v9KKKcd0TL4WZ9VdQ0nTNWCRanp9teInKCaMNtPtnpRRXUpOOqZxOKkrSV0S2lrbWVutrZ28VvBHwkcSBVX6AVLRRSbbd2CSSsgwPSjA9KKKBhRRRQAYHpRgelFFABgelFFFABgelFFFABgelFFFABgUYHpRRQAYHpRgelFFABRgUUUAGBRgelFFABgelI6qwG5VOCCMjOD60UUALgUYFFFABgUUUUAGB6UUUUAGBRgUUUAFGBRRQAUYHpRRQAjAFSCAQRggjrQoAUAAAAcACiigQuB6UUUUDDA9KMD0oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACtKD/Up/uiiis6mxtR3EX/j4f/cX+ZqSiisToCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKim+8PpRRQAyiiigAooooA//2Q==",
+          "bounding_box": {
+            "x": 1.495016611295681,
+            "y": 637.1244416496716,
+            "width": 33.88704318936877,
+            "height": 32.805596465390295
+          },
+          "confidence_threshold": 0.8,
+          "description": "Élément sélectionné pour l'étape vwb_action_1768392179273",
+          "metadata": {
+            "capture_method": "screen_capture",
+            "capture_timestamp": "2026-01-14T12:03:16.921Z",
+            "screen_resolution": {
+              "width": 1920,
+              "height": 1080
+            }
+          }
+        }
+      },
+      "isVWBCatalogAction": true,
+      "vwbActionId": "click_anchor"
+    }
+  ],
+  "edges": [],
+  "variables": [],
+  "settings": {},
+  "tags": [],
+  "category": "default",
+  "is_template": false,
+  "version": "1.0.0",
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2026-01-20T14:41:18.028043",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_20260114_132430.json b/visual_workflow_builder/backend/data/workflows/wf_20260114_132430.json
new file mode 100644
index 000000000..61c8989cd
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_20260114_132430.json
@@ -0,0 +1,87 @@
+{
+  "id": "wf_20260114_132430",
+  "name": "test_3",
+  "description": "amélioration coordonnées",
+  "created_by": "unknown",
+  "created_at": "2026-01-14T13:24:30.861817",
+  "updated_at": "2026-01-14T13:24:30.861817",
+  "nodes": [
+    {
+      "id": "step_1768393229534",
+      "type": "click_anchor",
+      "name": "Cliquer sur Ancre",
+      "label": "Cliquer sur Ancre",
+      "position": {
+        "x": 489,
+        "y": 144.38333129882812
+      },
+      "data": {
+        "label": "Cliquer sur Ancre",
+        "stepType": "click_anchor",
+        "parameters": {
+          "visual_anchor": {
+            "anchor_id": "anchor_1768393261663",
+            "anchor_type": "generic",
+            "reference_image_base64": 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+            "bounding_box": {
+              "x": 2.5,
+              "y": 631.8999938964844,
+              "width": 36,
+              "height": 41
+            },
+            "confidence_threshold": 0.8,
+            "description": "Élément sélectionné pour l'étape vwb_action_1768393234127",
+            "metadata": {
+              "capture_method": "screen_capture",
+              "capture_timestamp": "2026-01-14T12:21:01.663Z",
+              "screen_resolution": {
+                "width": 1920,
+                "height": 1080
+              }
+            }
+          }
+        },
+        "isVWBCatalogAction": true,
+        "vwbActionId": "click_anchor"
+      },
+      "stepType": "click_anchor",
+      "parameters": {
+        "visual_anchor": {
+          "anchor_id": "anchor_1768393261663",
+          "anchor_type": "generic",
+          "reference_image_base64": 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swktZkSaLKkZUSfIwPQhvWq2l2Gq28uk3M9rB5lrp08UqQbUUSM0ZVQOgyFPTgH2xQBuHUdPAc/brX93MLd/3q/LKcYQ88McjjryKivda0iycJeapZW7FiuJJ1U5ABI5PYMPzFcPbeENZK/ZtQitrmC/eC4vTEdhSZZGZ2OWO5sOMMuP9WOOlXNM07xDYapJe32lHUGJuYiYpYgJQVgVHYMwwGEZyOcHt0oA7Vru1W4itmuYRNMpeKMuNzqMZIHUgZGSPUVSi8Q6DKk0kes6e6QLvlYXCkIucZPPAzxmuStvDXiC3vtNvh9lZtLW1t4k6vJGARNtbcAoO8jBGT5a9OKjk8L65Fo1kn7+9lisHi8l3hAhfzIm2oRtzkIRyT0HIpgd7YXtnfwefZXUNzFkrvicMMjqMjvU9Y2gW10NT1PUbi0azW7ePZC7KX+RcF22kjJzjqeFFbNIArnPEnjXQdC1KLS7mae51KZd0dlZW73E5HrtQHA+tdHXhPi3wPr+meK/Et3HFqt/pviKLb9u04CS6s23h9hQspZDt2nBGVx6V35fh6Neo1Vlbt5/8Nvbd7I4sdXrUYJ0o3/T+tvLdnrHhXxfoXiSW4t9NunF3anFxaXETQzxf7yMAfxraeaGOaOF5UWSXPloWAL4GTgd8CvHPh94N166+INl4pvYdQ0yx0yxSziN4yi7vyqFd8qgnAOehOcKo7Zr03xfaX0+mx3WkxLLqNlKJ7ZGcKHOCrKSegKs1PF4ajTrqFOV019z7X/qwsLXrVKLnUjZr8V6F5NU0xwSmo2jASiHiZf9YeidfvH061Dca/odvN5M+sWEcnPyNcKDwSD39QR9Qa5LUfCN+lxDDYxxyW8FgkquXALXsIIiOPfcST7Vl6/4S1+80C+0yKC8WW/0uKNzDNAITcbpHfzC3zbQz9V6iqhhcPJr95p8hTxFdJ+5/X9fqYmsftB6bb6s8GmaBPfWSNj7Q1wIy4/vKu08emSK9V8F+JdM8WaBDrOlOxhkJVkcYeNx1Vh6ivjx0vdBGs6FqltPbXU8axeS8XO9ZVbr1xgHBHByK+jP2bvD2p6F4Hml1OGS3kv7k3EcMgwyJtCgkdicZx6Yr2s5yvCYbDKpS0d1bW/N/XkeRlOY4rEYhwqarW+lrf15nqFFFFfJn0wUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUARr/x8P8A7i/zNcx8YZ57b4VeKbi2mlgmj0m5aOSJyjqwjOCpHIPuK6df+Ph/9xf5mq+uaXZa1o15pGoxGazvIWgnQOVLIwwRkYI4PUUpK6aHF2dzx2F9a8NLdeKdB8P+IdK0zTtCuZr2DW9TM0d7OFVoQiedIVIIfLgrwcc9uj1Lxb42ie/t7Sx0B7jR9MXUtREryhZBIZCkMRHQhImzI3GSMLjOO91XSbDVNEuNGvYTJZXMBgljDlSyEYIyDkcd6yfEngjw34huUuNTs5nkEH2aQxXUsInhznypQjASJnPytkcn1NOTben9b/8AAJirLX+tv+CcW3xF8TyRav4ittO0g+G9Ku7eOSN3kF5LFLDBIzDnYrKJxwQd2McdTLN4v1id7TUdUsrJNPi8Sz2EAt5pllKwLdBpGwwVsiMAIQRnJPbGxYfDTRT4i1bV9Ui+0G71OO8hhjnlSHbHFEsYkiDBJCjRkrkHGR6V0CeFdDSK3iFo2y31GTUowZWOLiQuWbryD5j8HjnpwKO1v62/4Ia7f11/4B5EnjfxPeaBr+sW1lNaTatoE+oWt0Le9CaeYogyRkyqsbEqxIaPA3g5DDFXtc8b6+NWhjGmyak3h1bV7qK2tr0/bZpYVdthjDRrtSQECUkFicgYBr0DT/h74UsormGLT5nhuLSSy8qa7mkSG3k+/FErMREp44THQegpJfh74WmmglmtLuQxxQxSK1/PtuVi/wBX5678TFexcE01Zf16g7/18jI0zxprcnxEbQtStdP06zkupre2iuEnSedEUlJYpSvky7sHMYIZRnOSCKrfEvxJ4mkXxTpfhqDTki0fRjcXk1zLIksjSxyFVhKcIVVCd7Z5IGBgmuntfBHhy21861Fa3H2j7S92sbXcrQJO4IeVYS3lq5DN8wXPzH1NJ4p8DeGvE1211q9lNJJJbm1n8m7lhFxDyfLlCMBIoJJAbOMn1qWrxsUnZ3OB034ka9C0FvZaTJqFlpxs7O5As7uW4uWeKJpJFlVTEu3zQdrnLbTyuRXT/EhNQvPFPh7SdP1S602S+tdShE0LkeW/2cBJMDglScjPQ1qN4B8LtqMV8bKcPGYWaIXcohlaEARPJEG2SMoVcMwJ+UegravNJsbvVbHU54S11YeZ9nfeRs8xQrcA4OQO9W2mTFNHlr+MtWuPHcT2+m3V3Z6XqX9jbFgvGZixSOS6LKvkEKx+6xztDEEE4pmh+MvFtp4N0lZ9R0e71W8ub875ba5mkaOKZlVUgh3O3PG8kKoCjkmvQZfBPhyTxB/bbWc32k3C3TRi6lEDTqAFmMIbyzIAB8xXPAPUCqv/AArzwuPL8u2vIfKkndDFqE6ELMweWPKuP3bMAdn3c9AKh3t/X9blaHH23xD8ZavaS6lo2laHHaWfh+01m5iu5ZfMcypIzQoV4XAjOHYHqMjrh0nxT1a61aSbR9De50q2uLaCSEWN1JcTeakTs6yIhhTYJh8rHJ2nlcitO1+EXhxdXujPFM2lGwtrG2tUvp0IhjMhaKQhx5kR3jCNkADHSuju/A/hq61karLYyCYyRSvFHcyJBK8QAjd4VYRuyhVwSp+6PQVSeuv9a/5Evb+u3+Z0gooopDCiiigAooooAKKKKACopvvD6VLUU33h9KAGUUUUAFFFFAH5GVueGvC+p66fMt1WG2BwZ5fu/QDqTVXwtpZ1rxDZaWGKrPIA7DqqDlj+QNfQPhTw5cajq9r4e0uKKMn5U3HbHFGoyXY9lCgkmuHHYqdG0KavJn0nD+CweOxM4YmpZQjzNbNrvfsuvqjziL4Y2xi+fWJ/M9VhXb/PNc94n8C6rosDXcbLfWi8u8SkMg9WX09xmvpJIvhlaTiznv8AxNqBU7XvrWOKOEn1SNssV+pBNdTL8O9A8i3urPUrq8sruIS286su2RDx0K8EdCD0rgzitmeQUo4nH02oS62X3O2zPVp1+Fs05qGDlaa6q/367o+Gl6iv08/Zz/5IR4H/AOwHa/8AosV+f/x08Gw+CvHkljZb/wCz7uJbq13dVBJDL+DA/gRX6Afs5/8AJCPA/wD2A7X/ANFivWwmLp4yhCvSd4yV0fHYmk6U3B9Dvqr219ZXNzcW1vdwSz2rBZ40kBaIkZAYDpkc81n+NBd/8IvfyWV+9hNFEZvOQZOE+Zl6HGQCMgZGcivPbG60G6udDt/DOsXNnq93IEuZxc+YzBg07l8rtuDuDpuXhSxGR92tznPWqhX/AI/G/wBwfzNTVCv/AB+N/uD+ZoAdcy+TCX2ljkADOMknA/U1Huvf+eFv/wB/j/8AE0X/APx7r/11j/8AQ1qxQBX3Xv8Azwt/+/x/+Jo3Xv8Azwt/+/x/+Jrl31zxLex6hqOj22mfYLKaWFIbgv51yYiVchgdsfzKwGQ3TJxmun0m9i1LSrTUbfPk3UCTR567WUMM/gaAF3Xv/PC3/wC/x/8AiaN17/zwt/8Av8f/AImor/UY7SeODyLieSRWcLDHuwAQCTz7ipLW/tLmGCSOdB54zGrHax9Rg85FAC7r3/nhb/8Af4//ABNG69/54W//AH+P/wATTbjULOD786f6wRNhgdrHP3vTp3qX7Tb5iHnxZlGYxvHz/T1oAZuvf+eFv/3+P/xNG69/54W//f4//E077Xa8f6TDy2wfOOW9Pr7Uy1v7W5VzFMmULblJAK4JBJHYZBoAXde/88Lf/v8AH/4mjde/88Lf/v8AH/4mnrdWzKjLcRMsn3CHBDfT1pzSxKSDIgIO05YcHGcUARbr3/nhb/8Af4//ABNG69/54W//AH+P/wATSi8tDE8ouYTGhw7CQYX6ntT5p4orZrh5FESruLZ4xQBHuvf+eFv/AN/j/wDE0br3/nhb/wDf4/8AxNMTUbNpPLM6JJ5InKswGEPc9qk+2Wn7v/SoP3v+r/eD5vp60AJuvf8Anhb/APf4/wDxNG69/wCeFv8A9/j/APE1Ks0LTNCsqGRBlkDDI+op9AFfde/88Lf/AL/H/wCJoaS8VSxt4DgZwJjn/wBBqxSP9xvpQAkMiyxJIudrqGGfQ1Af9fL/AMB/lTtP/wCPC3/65L/IU0/6+X/gP8qAFooooAkh70XX+of6UQ96Lr/UP9KT2E9ihRk+tFFZmQEknJOTRRWN42OpN4XvYNHhaW/uUFtDgkBDIQhdiAcKoJJOOMUm7IDYBBAIII7EUvFeX6cfFXh3R30K30vUI4rOOa1sBZxG4jwZY3hIcqOFjZ1yQB8hHXFaVvqHia2ljubqPxBPtncX0IslaNMrMEEO0bmTPlcjI6En7wpOS3IUjvuKOPauI8Mnxa9/p91qc+p7Hljint5YkEaobJWZzhc7vPyM565FZUV94tvb25XT73W2jOozwTubZPKgiW6RE8ltvJ2b89cAEnBAp31t/X9ahz6XselkgdSBQWUHBZQenWvMdZHiqedYphr8sUN+slz5dqrCFUul8p7c7fnPlbmY/N64B4qpeW3jCRJp2g1ALNJEXney33DJGJjAxRRw+fL3YAwSM4BOJ59L2By1ses8e1LxXnNxH46P2m4OoawsoS5ZYoYIxGWjSExBRtPDMZOMnPIzxWr4Vl8WSa/qP9qvN5IWbZDJbFYVbzP3OxzwQU67c+5B4qrgpXOw3KAcsowMnnpS15Dc2/i25eWWSPW3W4tbaLUXubDcyuvnsyRKi5aMSFOgIwRyQSau3a+OLae4aCfVUed0knkS0Mg837LEECIMhULiTcM7QQAxAOaSkLn8j1GkBBJAIJHXnpXnlwvia5mi+2Pr/wBoh1KOSeOKAJaqizYTy2UbmUoQWALDGS2CKv8Aw5bXoPtR12O6Ml08ZcNYmPbcFWMp3d0+VQD90cAMc4DTuPm1sdqCDnBBx1orhru51eyTxZa6XY6gup3F609lL9hdomXyoQWV8bCcK+ATyQBUv2jxVb+BZb2I3t7qVvdGSGJrYxzXEAcDyyrc5IJOSFPAyB3XMPm/U7SkyM4yM15ikvxAi00rfz6yZofOt0ktLVGeeaJFEUjDbxFKxdieBwMlRU0kHjNdcstVk+2uFkuEvFSMb47f7THtSDAxyo3ZOSyqcc4p31sLn02PSAynOGU44ODnFLXk2mad4j0aECCHXLeKWAfaJrW3ElwZAbhkGCpypZl3HHdckAk1o2k/xEbxBa2t0LpLRpooZ5UiTYocLO8gOOihWg+rDvzSUruwc2l7HpFHbNcPrk3jAeLbpbKW7islTNukVn5sbJ5DEkkkLvEuMAnPAGMEkN8EjXp/EFjc6uNZ2La3EO+4DCOQ74mDMpRSvG/G8A/KQCRjIpXY3Kx3VAIOcEHHXHavMfDN74u1QaayX+tta3EyG9nlt0UJ+9kyIjt5jKBcnnHy8gk1H4esPE39pW1zqKahEW1SCe9WC0aIGX96rndj95HgpkgFcYO7qA072E5nqORkjIyOozSggsVBBI5IHWuKaTW1+JySJaXy6exNtK/k7omi8ncjBgmAPMyOWJByCACM4er6f4luNc1ud0v4xJJhjaWrKzwJPEYdko+8dgclRuP3gQOMrmBytc9RorziV/Hay6cVu9RxNudy1pv/AHn2jARwoAVBFgjcQOSc5wK1vEUviceM4Us5ruLSgsJUW9qZVdcv525ugYALtyc9MBjkUc2iHzbnYEgDJIA9zS15bdt47utOs0vFv1lWeR2P2ES4dfLNsdq8YK7mfJAEm4EqAtattf8Aia2u47i9TXpdl7/p8KWQaBY90gUQbRudSPLJxu9SQcijmFzHeMQo3MQB6ngUteSTQ+MZtOdpk1gNeG3luxLamYqwtgVCoBgfvVwwHAIGcA5r1HS5p5bNBdLILmNVSctEUDPtBYqOcjJPQkdRnimne41K5aoooplBRRRQAUUUUAX7f/UJ9KF/4+W/3B/M0W/+oT6UL/x8t/uD+ZrRbGq2JKKKKYwooooAKKKKACiiigAooooAKRmVVLMQAO5NLVbU7Cy1OxksdRtILu1lAEkMyB0bBBGQeDyAaatfUTvbQl8+H/ntH/30KPPg/wCesf8A30K5/wD4QHwR/wBCjof/AIAx/wCFH/CA+CP+hR0P/wAAY/8ACt+XD/zP7l/mY81f+Vfe/wDI6Dz4P+esf/fQo8+H/ntH/wB9Cuf/AOEB8Ef9Cjof/gDH/hR/wgPgj/oUdD/8AY/8KOXD/wAz+5f5hzV/5V97/wAjoPPh/wCe0f8A30KPPh/57R/99Cuf/wCEB8Ef9Cjof/gDH/hR/wAID4I/6FHQ/wDwBj/wo5cP/M/uX+Yc1f8AlX3v/I25RYyyrLILd5E+6zbSV+h7VKJoSQBKhJ6fMK5//hAfBH/Qo6H/AOAMf+FSW3gjwdbXMVzb+FtGimicPHIllGGRgcgg44INDVC3xP7l/mCdb+Vfe/8AI6Ciiiuc3CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCNf+Ph/wDcX+ZpbiaK3geeeRIoo1LO7thVA6kntSL/AMfD/wC4v8zVHxNYzajo01rb+X5pZHRZPuMUdW2t7Hbg/WgAtde0e5jDwahBICxXAPOQASMdehB+hrSrjtU03XtVnF2bK2sWG4KqzAydFGWYcHoQAOg+uBnWGla9Z62kq2MryQJG8xF4St2waUM4J4Uncpwewx2FAdD0KivPIfDPiPaTNPL55siiul2AqEwbPL+7uPz/ADZyB36jFbXifRr2Wy0yHS4S32U8I1wQgOBgt3bnPIO4ds5NAHU1Xur6ztbi2t7m5iilunMcCO2DIwUsQvqcAmuQ/sTXLm7iguottrDL87i7P75TceYeByBt4wfcdKs+LNC1jVNUW9s5reFbGFGs0kTcXlEgducjZkIq5weGahAzrs0Vw0/hvVFPnxxeYJZZZLyH7Ww89Tcb0TPQYQkdhxt6Go7vwzq0lqzhHBaeMtbpelyYhAFCBpAR8r88jnGewpdLh1O6lmiiaNZHVTI2xAT944JwPwBqOW8tYrlLZ5lWZxlU7kZxn6ZrA1bTNTax02GGeRriO1ltzMXLFJWiwshYAHqCN2P4s1lnw9rDSxy2NuulgKVVftjSmM565+vOBx+JNPqHQ7U3EIultjIomZDIEzyVBAJx6ZI/Opa4FPDurrcWl3DbLZxWozJam9MguDujLBmP3QdrMMdSBu6kVseJ2OuWCWeltHcHzA0q7ipCYIBGcA/MV659cEgUPRAdNUYnhNy1sJFMyoHZO4UkgH6ZB/KuFXw1rrWHneYY9SfCPIbtjmMWyoVyOmXUnIGed3Wp08NXrz/a4bQWMiCEWyC8aQw4nLP7fdJ45HOBR1F0O2Zgqlj0HJpIZI5oklidXjdQyspyCD0IritG8PapFeWcl5GY7aCRHuIxdtJ50iRyBpv+BMyfL/s5I4rpfDFvNbaHbRXClJMM2w9UDMWC/gCB+FAzSooooAKKKKACiiigAqKb7w+lS1FN94fSgBlFFFABRRRQB+ZXwv1qxTVdK0xtMgjuA8o+2Z+dyytgf0/AV9B/DYxTa7f6cZUhuNV0q5sLaRyABK6goCT0ztK5/wBqvkK2mltrmK4gcpLE4dGHYg5Br27wn4qs9fs0ZHWK9UDzYM4IPqvqPp0rgq03hqsa9NbO/fW9zxs8qYmlXji4bcvI7ae7qrO3dPfv52Nu7s7u0vnsLu2mgu432PA6EOremOte9+FNNvNH8AaLpWpK0d4pmuHhb70KyMCqEdjgZI7Zrzfw7428d395a6NZa5KZH/dpNJDG8sSdyJGUuAB712fjXxX4f8BeH/tmuagQQp8qJn3XF0/sDyxJ6seB3Nc3iLxpPOcBDKqFH35tN9Xp29X+p0cEYCnHETxt3yxVtdNX83ey9Oh8/wD7YlzA/jLQrRCpmgsHaTHUB5Plz/3ya+yv2dP+SEeB/wDsB2v/AKLFfm9448S3/i7xXe+INRws11JlY1OViQcIg9gOPfk96/SH9nT/AJIR4I/7Adr/AOixWuS4GWAwFLDS3itfV6v8We7jaqrVpVF1Z3xAIIIyDUa28C+VthjHlDEeFHyDGMD049Kkor0zjCoV/wCPxv8AcH8zU1Qr/wAfjf7g/maAG3//AB7r/wBdY/8A0NasVFdxtLAVQjcGVhnpkEH+lR+bef8APqn/AH+/+tQBhal4OtrqW8EGrarY2t+xe7tbaZVjlZhhjypZd3faRmrvhODV7KxfTdVEEq2jCK2uYsL58QHylkH3GHQgcHGR6Voebef8+qf9/v8A61Hm3n/Pqn/f7/61AFbUNMF5qcFxKzCKOGRCElZGyxUjlSOOD3rKh8PXavAHkjZFSJGIkI2eWxII+XJJGD1HOetb3m3n/Pqn/f7/AOtR5t5/z6p/3+/+tQBj2+iXAFvFLDY7IJEJkUHfKFYnLce4OOecnNRL4euVuEO+IxllyA5XywsrOMDbzwR0IwR3rd828/59U/7/AH/1qPNvP+fVP+/3/wBagDGTQ7iFItkNjMfLkjdZQdoLPu3jjk+o47c0lx4fmktfLR4Y3In3sON2+QOAeOmBg/1ra828/wCfVP8Av9/9ajzbz/n1T/v9/wDWoAzdL0eSC9iup1iJUysRu3FWbYAQdoHRTnAHWkvtMvpr+QxfZvIknWYszEMMR7MYxjrg5zWn5t5/z6p/3+/+tR5t5/z6p/3+/wDrUAYknh+dYoBB5CGGOAbFO0OyBwedp/vAg4PSrM2jzHQ7WyidRJBKsu3edrYJO3JB45447DitLzbz/n1T/v8Af/Wo828/59U/7/f/AFqAMQaHeRwbIfs2TEFYM2cHzS5AJUjoeDjgjp6VF0O/+0TWzW9o4kgdRM5J2b5WbIO3lhnpxzg103m3n/Pqn/f7/wCtR5t5/wA+qf8Af7/61AGTY6Pd2moNcho5ShlaNmlILlznDALx+Z6DgVsq1z5uGjiEe48hznGODjHXOaZ5t5/z6p/3+/8ArUebef8APqn/AH+/+tQBZpH+430qv5t5/wA+qf8Af7/61Iz3rKVFtGCRjJl4H6UAP0//AI8Lf/rkv8hTT/r5f+A/yqa3j8qCOLOdihc+uBioT/r5f+A/yoAWiiigCSHvRdf6h/pRD3p7KGUqRkGhgzNoq79mi/2vzo+zRf7X51HKzPlZSoq79mi/2vzo+zRf7X50crDlZSoq79mi/wBr86Ps0X+1+dHKw5WUqaiIgIRFUEkkAY5PJNX/ALNF/tfnR9mi/wBr86OVhyMpUVd+zRf7X50fZov9r86OVhyspUVd+zRf7X50fZov9r86OVhyspYFFXfs0X+1+dH2aL/a/OjlYcrKVFXfs0X+1+dH2aL/AGvzo5WHKylRV37NF/tfnR9mi/2vzo5WHKylRV37NF/tfnR9mi/2vzo5WHKylRV37NF/tfnR9mi/2vzo5WHKylRV37NF/tfnR9mi/wBr86OVhysoIiRoEjRUUdFUYA/CnVd+zRf7X50fZov9r86OVhyMpUVd+zRf7X50fZov9r86OVhyspUVd+zRf7X50fZov9r86OVhyspUVd+zRf7X50fZov8Aa/OjlYcrKVFXfs0X+1+dH2aL/a/OjlYcrKVFXfs0X+1+dH2aL/a/OjlYcrKVFXfs0X+1+dH2aL/a/OjlYcrKVFXfs0X+1+dH2aL/AGvzo5WHKx1v/qE+lC/8fLf7g/maeoCgAdBTF/4+W/3B/M1aNELMxRMrjJIAz7mkxN/z0j/74P8AjRcfcX/fX+YqQUAR7Zv+ekf/AHwf8aNs3/PSP/vg/wCNSUUAR7Zv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+          "bounding_box": {
+            "x": 2.5,
+            "y": 631.8999938964844,
+            "width": 36,
+            "height": 41
+          },
+          "confidence_threshold": 0.8,
+          "description": "Élément sélectionné pour l'étape vwb_action_1768393234127",
+          "metadata": {
+            "capture_method": "screen_capture",
+            "capture_timestamp": "2026-01-14T12:21:01.663Z",
+            "screen_resolution": {
+              "width": 1920,
+              "height": 1080
+            }
+          }
+        }
+      },
+      "isVWBCatalogAction": true,
+      "vwbActionId": "click_anchor"
+    }
+  ],
+  "edges": [],
+  "variables": [],
+  "settings": {},
+  "tags": [],
+  "category": "default",
+  "is_template": false,
+  "version": "1.0.0",
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2026-01-20T14:41:18.039291",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_20260114_211419.json b/visual_workflow_builder/backend/data/workflows/wf_20260114_211419.json
new file mode 100644
index 000000000..9ec063f30
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_20260114_211419.json
@@ -0,0 +1,21 @@
+{
+  "id": "wf_20260114_211419",
+  "name": "Test Apprentissage",
+  "description": "",
+  "created_by": "test",
+  "created_at": "2026-01-14T21:14:19.963587",
+  "updated_at": "2026-01-14T21:14:19.963587",
+  "nodes": [],
+  "edges": [],
+  "variables": [],
+  "settings": {},
+  "tags": [],
+  "category": "default",
+  "is_template": false,
+  "version": "1.0.0",
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2026-01-20T14:41:18.034248",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_20260114_211536.json b/visual_workflow_builder/backend/data/workflows/wf_20260114_211536.json
new file mode 100644
index 000000000..244b95df5
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_20260114_211536.json
@@ -0,0 +1,21 @@
+{
+  "id": "wf_20260114_211536",
+  "name": "Test Apprentissage v2",
+  "description": "",
+  "created_by": "dom",
+  "created_at": "2026-01-14T21:15:36.780837",
+  "updated_at": "2026-01-14T21:15:36.780837",
+  "nodes": [],
+  "edges": [],
+  "variables": [],
+  "settings": {},
+  "tags": [],
+  "category": "default",
+  "is_template": false,
+  "version": "1.0.0",
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2026-01-20T14:41:18.027473",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_20260114_211756.json b/visual_workflow_builder/backend/data/workflows/wf_20260114_211756.json
new file mode 100644
index 000000000..89256c686
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_20260114_211756.json
@@ -0,0 +1,22 @@
+{
+  "id": "wf_20260114_211756",
+  "name": "Test Apprentissage v3",
+  "description": "Updated",
+  "version": "1.0.0",
+  "created_at": "2026-01-14T21:17:56.229514",
+  "updated_at": "2026-01-22T09:28:35.393507",
+  "created_by": "dom",
+  "nodes": [],
+  "edges": [],
+  "variables": [],
+  "settings": {
+    "timeout": 300000,
+    "retry_on_failure": true,
+    "max_retries": 3,
+    "enable_self_healing": true,
+    "enable_analytics": true
+  },
+  "tags": [],
+  "category": "default",
+  "is_template": false
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_20260114_211833.json b/visual_workflow_builder/backend/data/workflows/wf_20260114_211833.json
new file mode 100644
index 000000000..054b1a1ff
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_20260114_211833.json
@@ -0,0 +1,21 @@
+{
+  "id": "wf_20260114_211833",
+  "name": "Test v4",
+  "description": "",
+  "created_by": "dom",
+  "created_at": "2026-01-14T21:18:33.091665",
+  "updated_at": "2026-01-14T21:18:33.091665",
+  "nodes": [],
+  "edges": [],
+  "variables": [],
+  "settings": {},
+  "tags": [],
+  "category": "default",
+  "is_template": false,
+  "version": "1.0.0",
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2026-01-20T14:41:18.045627",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_2c0aca849e14.json b/visual_workflow_builder/backend/data/workflows/wf_2c0aca849e14.json
new file mode 100644
index 000000000..eb042599b
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_2c0aca849e14.json
@@ -0,0 +1,27 @@
+{
+  "id": "wf_2c0aca849e14",
+  "name": "Test Workflow",
+  "description": "Test",
+  "version": "1.0.0",
+  "created_at": "2026-01-07T15:59:28.553385",
+  "updated_at": "2026-01-07T15:59:28.553421",
+  "created_by": "test_user",
+  "nodes": [],
+  "edges": [],
+  "variables": [],
+  "settings": {
+    "timeout": 300000,
+    "retry_on_failure": true,
+    "max_retries": 3,
+    "enable_self_healing": true,
+    "enable_analytics": true
+  },
+  "tags": [],
+  "category": null,
+  "is_template": false,
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2026-01-07T15:59:28.553495",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_363cd6de262b.json b/visual_workflow_builder/backend/data/workflows/wf_363cd6de262b.json
new file mode 100644
index 000000000..fbe67a77f
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_363cd6de262b.json
@@ -0,0 +1,27 @@
+{
+  "id": "wf_363cd6de262b",
+  "name": "test",
+  "description": "test workflow",
+  "version": "1.0.0",
+  "created_at": "2026-01-08T01:16:42.202713",
+  "updated_at": "2026-01-08T01:16:42.202748",
+  "created_by": "test_user",
+  "nodes": [],
+  "edges": [],
+  "variables": [],
+  "settings": {
+    "timeout": 300000,
+    "retry_on_failure": true,
+    "max_retries": 3,
+    "enable_self_healing": true,
+    "enable_analytics": true
+  },
+  "tags": [],
+  "category": null,
+  "is_template": false,
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2026-01-08T01:16:42.202819",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_3a1a6f402335.json b/visual_workflow_builder/backend/data/workflows/wf_3a1a6f402335.json
new file mode 100644
index 000000000..98fa9f879
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_3a1a6f402335.json
@@ -0,0 +1,27 @@
+{
+  "id": "wf_3a1a6f402335",
+  "name": "test",
+  "description": "test",
+  "version": "1.0.0",
+  "created_at": "2026-01-08T12:50:18.403937",
+  "updated_at": "2026-01-08T12:50:18.403977",
+  "created_by": "test",
+  "nodes": [],
+  "edges": [],
+  "variables": [],
+  "settings": {
+    "timeout": 300000,
+    "retry_on_failure": true,
+    "max_retries": 3,
+    "enable_self_healing": true,
+    "enable_analytics": true
+  },
+  "tags": [],
+  "category": null,
+  "is_template": false,
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2026-01-08T12:50:18.404053",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_46a49a8014d9.json b/visual_workflow_builder/backend/data/workflows/wf_46a49a8014d9.json
new file mode 100644
index 000000000..b237fd47c
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_46a49a8014d9.json
@@ -0,0 +1,100 @@
+{
+  "id": "wf_46a49a8014d9",
+  "name": "Test Execution Workflow",
+  "description": "",
+  "version": "1.0.0",
+  "created_at": "2025-12-03T20:26:42.904388",
+  "updated_at": "2025-12-03T20:26:42.904424",
+  "created_by": "system",
+  "nodes": [
+    {
+      "id": "start_node",
+      "type": "wait",
+      "position": {
+        "x": 100,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 100
+      },
+      "input_ports": [],
+      "output_ports": [
+        {
+          "id": "out",
+          "name": "Output",
+          "type": "output",
+          "data_type": null
+        }
+      ],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "end_node",
+      "type": "click",
+      "position": {
+        "x": 400,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "target": "Test Button"
+      },
+      "input_ports": [
+        {
+          "id": "in",
+          "name": "Input",
+          "type": "input",
+          "data_type": null
+        }
+      ],
+      "output_ports": [],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    }
+  ],
+  "edges": [
+    {
+      "id": "edge_1",
+      "source": "start_node",
+      "target": "end_node",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    }
+  ],
+  "variables": [],
+  "settings": {
+    "timeout": 300000,
+    "retry_on_failure": true,
+    "max_retries": 3,
+    "enable_self_healing": true,
+    "enable_analytics": true
+  },
+  "tags": [],
+  "category": null,
+  "is_template": false,
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2025-12-03T20:26:42.904450",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_54c765497489.json b/visual_workflow_builder/backend/data/workflows/wf_54c765497489.json
new file mode 100644
index 000000000..da4e71eb8
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_54c765497489.json
@@ -0,0 +1,65 @@
+{
+  "id": "wf_54c765497489",
+  "name": "Variables Workflow",
+  "description": "",
+  "version": "1.0.0",
+  "created_at": "2025-12-03T20:47:09.402214",
+  "updated_at": "2025-12-03T20:47:09.402296",
+  "created_by": "system",
+  "nodes": [
+    {
+      "id": "type_username",
+      "type": "type",
+      "position": {
+        "x": 100,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "target": "input",
+        "text": "${username}"
+      },
+      "input_ports": [],
+      "output_ports": [],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    }
+  ],
+  "edges": [],
+  "variables": [
+    {
+      "name": "username",
+      "type": "string",
+      "value": "default_user",
+      "description": null
+    },
+    {
+      "name": "count",
+      "type": "number",
+      "value": 5,
+      "description": null
+    }
+  ],
+  "settings": {
+    "timeout": 300000,
+    "retry_on_failure": true,
+    "max_retries": 3,
+    "enable_self_healing": true,
+    "enable_analytics": true
+  },
+  "tags": [],
+  "category": null,
+  "is_template": false,
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2025-12-03T20:47:09.402332",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_6065a7e99355.json b/visual_workflow_builder/backend/data/workflows/wf_6065a7e99355.json
new file mode 100644
index 000000000..95425dd5c
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_6065a7e99355.json
@@ -0,0 +1,27 @@
+{
+  "id": "wf_6065a7e99355",
+  "name": "Test",
+  "description": "",
+  "version": "1.0.0",
+  "created_at": "2025-12-12T09:30:13.870152",
+  "updated_at": "2025-12-12T09:30:13.870190",
+  "created_by": "test",
+  "nodes": [],
+  "edges": [],
+  "variables": [],
+  "settings": {
+    "timeout": 300000,
+    "retry_on_failure": true,
+    "max_retries": 3,
+    "enable_self_healing": true,
+    "enable_analytics": true
+  },
+  "tags": [],
+  "category": null,
+  "is_template": false,
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2025-12-12T09:30:13.870263",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_6431e27d58dd.json b/visual_workflow_builder/backend/data/workflows/wf_6431e27d58dd.json
new file mode 100644
index 000000000..9e39a14ae
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_6431e27d58dd.json
@@ -0,0 +1,484 @@
+{
+  "id": "wf_6431e27d58dd",
+  "name": "Long Workflow",
+  "description": "",
+  "version": "1.0.0",
+  "created_at": "2025-12-03T20:47:50.902425",
+  "updated_at": "2025-12-03T20:47:50.902543",
+  "created_by": "system",
+  "nodes": [
+    {
+      "id": "wait_0",
+      "type": "wait",
+      "position": {
+        "x": 100,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [],
+      "output_ports": [
+        {
+          "id": "out",
+          "name": "Output",
+          "type": "output",
+          "data_type": null
+        }
+      ],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "wait_1",
+      "type": "wait",
+      "position": {
+        "x": 150,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [
+        {
+          "id": "in",
+          "name": "Input",
+          "type": "input",
+          "data_type": null
+        }
+      ],
+      "output_ports": [
+        {
+          "id": "out",
+          "name": "Output",
+          "type": "output",
+          "data_type": null
+        }
+      ],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "wait_2",
+      "type": "wait",
+      "position": {
+        "x": 200,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [
+        {
+          "id": "in",
+          "name": "Input",
+          "type": "input",
+          "data_type": null
+        }
+      ],
+      "output_ports": [
+        {
+          "id": "out",
+          "name": "Output",
+          "type": "output",
+          "data_type": null
+        }
+      ],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "wait_3",
+      "type": "wait",
+      "position": {
+        "x": 250,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [
+        {
+          "id": "in",
+          "name": "Input",
+          "type": "input",
+          "data_type": null
+        }
+      ],
+      "output_ports": [
+        {
+          "id": "out",
+          "name": "Output",
+          "type": "output",
+          "data_type": null
+        }
+      ],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "wait_4",
+      "type": "wait",
+      "position": {
+        "x": 300,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [
+        {
+          "id": "in",
+          "name": "Input",
+          "type": "input",
+          "data_type": null
+        }
+      ],
+      "output_ports": [
+        {
+          "id": "out",
+          "name": "Output",
+          "type": "output",
+          "data_type": null
+        }
+      ],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "wait_5",
+      "type": "wait",
+      "position": {
+        "x": 350,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [
+        {
+          "id": "in",
+          "name": "Input",
+          "type": "input",
+          "data_type": null
+        }
+      ],
+      "output_ports": [
+        {
+          "id": "out",
+          "name": "Output",
+          "type": "output",
+          "data_type": null
+        }
+      ],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "wait_6",
+      "type": "wait",
+      "position": {
+        "x": 400,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [
+        {
+          "id": "in",
+          "name": "Input",
+          "type": "input",
+          "data_type": null
+        }
+      ],
+      "output_ports": [
+        {
+          "id": "out",
+          "name": "Output",
+          "type": "output",
+          "data_type": null
+        }
+      ],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "wait_7",
+      "type": "wait",
+      "position": {
+        "x": 450,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [
+        {
+          "id": "in",
+          "name": "Input",
+          "type": "input",
+          "data_type": null
+        }
+      ],
+      "output_ports": [
+        {
+          "id": "out",
+          "name": "Output",
+          "type": "output",
+          "data_type": null
+        }
+      ],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "wait_8",
+      "type": "wait",
+      "position": {
+        "x": 500,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [
+        {
+          "id": "in",
+          "name": "Input",
+          "type": "input",
+          "data_type": null
+        }
+      ],
+      "output_ports": [
+        {
+          "id": "out",
+          "name": "Output",
+          "type": "output",
+          "data_type": null
+        }
+      ],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "wait_9",
+      "type": "wait",
+      "position": {
+        "x": 550,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [
+        {
+          "id": "in",
+          "name": "Input",
+          "type": "input",
+          "data_type": null
+        }
+      ],
+      "output_ports": [],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    }
+  ],
+  "edges": [
+    {
+      "id": "edge_1",
+      "source": "wait_0",
+      "target": "wait_1",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    },
+    {
+      "id": "edge_2",
+      "source": "wait_1",
+      "target": "wait_2",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    },
+    {
+      "id": "edge_3",
+      "source": "wait_2",
+      "target": "wait_3",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    },
+    {
+      "id": "edge_4",
+      "source": "wait_3",
+      "target": "wait_4",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    },
+    {
+      "id": "edge_5",
+      "source": "wait_4",
+      "target": "wait_5",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    },
+    {
+      "id": "edge_6",
+      "source": "wait_5",
+      "target": "wait_6",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    },
+    {
+      "id": "edge_7",
+      "source": "wait_6",
+      "target": "wait_7",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    },
+    {
+      "id": "edge_8",
+      "source": "wait_7",
+      "target": "wait_8",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    },
+    {
+      "id": "edge_9",
+      "source": "wait_8",
+      "target": "wait_9",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    }
+  ],
+  "variables": [],
+  "settings": {
+    "timeout": 300000,
+    "retry_on_failure": true,
+    "max_retries": 3,
+    "enable_self_healing": true,
+    "enable_analytics": true
+  },
+  "tags": [],
+  "category": null,
+  "is_template": false,
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2025-12-03T20:47:50.902593",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_81ce5ea46c3f.json b/visual_workflow_builder/backend/data/workflows/wf_81ce5ea46c3f.json
new file mode 100644
index 000000000..88348f66e
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_81ce5ea46c3f.json
@@ -0,0 +1,70 @@
+{
+  "id": "wf_81ce5ea46c3f",
+  "name": "Variables Workflow",
+  "description": "",
+  "version": "1.0.0",
+  "created_at": "2025-12-03T20:25:37.575726",
+  "updated_at": "2026-01-22T09:29:38.515757",
+  "created_by": "system",
+  "nodes": [
+    {
+      "id": "type_username",
+      "type": "type",
+      "position": {
+        "x": 100,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "target": "input",
+        "text": "${username}"
+      },
+      "input_ports": [],
+      "output_ports": [],
+      "self_healing": null,
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null,
+      "data": {
+        "label": "Étape 1",
+        "stepType": "type",
+        "parameters": {
+          "target": "input",
+          "text": "${username}"
+        },
+        "isVWBCatalogAction": false
+      }
+    }
+  ],
+  "edges": [],
+  "variables": [
+    {
+      "name": "count",
+      "type": "number",
+      "value": 5,
+      "description": null
+    },
+    {
+      "name": "username",
+      "type": "string",
+      "value": "default_user",
+      "description": null
+    }
+  ],
+  "settings": {
+    "timeout": 300000,
+    "retry_on_failure": true,
+    "max_retries": 3,
+    "enable_self_healing": true,
+    "enable_analytics": true
+  },
+  "tags": [],
+  "category": null,
+  "is_template": false
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_820b2a9e6270.json b/visual_workflow_builder/backend/data/workflows/wf_820b2a9e6270.json
new file mode 100644
index 000000000..080914b4c
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_820b2a9e6270.json
@@ -0,0 +1,27 @@
+{
+  "id": "wf_820b2a9e6270",
+  "name": "Test Corrections VWB",
+  "description": "Workflow de test pour vérifier les corrections",
+  "version": "1.0.0",
+  "created_at": "2026-01-08T01:17:36.536995",
+  "updated_at": "2026-01-08T01:17:36.537023",
+  "created_by": "test_corrections",
+  "nodes": [],
+  "edges": [],
+  "variables": [],
+  "settings": {
+    "timeout": 300000,
+    "retry_on_failure": true,
+    "max_retries": 3,
+    "enable_self_healing": true,
+    "enable_analytics": true
+  },
+  "tags": [],
+  "category": null,
+  "is_template": false,
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2026-01-08T01:17:36.537077",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_90ecda5de0ed.json b/visual_workflow_builder/backend/data/workflows/wf_90ecda5de0ed.json
new file mode 100644
index 000000000..29fdcea7a
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_90ecda5de0ed.json
@@ -0,0 +1,27 @@
+{
+  "id": "wf_90ecda5de0ed",
+  "name": "Test Workflow",
+  "description": "Test des nouvelles fonctionnalités",
+  "version": "1.0.0",
+  "created_at": "2025-12-13T10:59:07.152509",
+  "updated_at": "2025-12-13T10:59:07.152536",
+  "created_by": "test_user",
+  "nodes": [],
+  "edges": [],
+  "variables": [],
+  "settings": {
+    "timeout": 300000,
+    "retry_on_failure": true,
+    "max_retries": 3,
+    "enable_self_healing": true,
+    "enable_analytics": true
+  },
+  "tags": [],
+  "category": null,
+  "is_template": false,
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2025-12-13T10:59:07.152596",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_9c8ce0d625a4.json b/visual_workflow_builder/backend/data/workflows/wf_9c8ce0d625a4.json
new file mode 100644
index 000000000..c0a363c37
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_9c8ce0d625a4.json
@@ -0,0 +1,65 @@
+{
+  "id": "wf_9c8ce0d625a4",
+  "name": "Variables Workflow",
+  "description": "",
+  "version": "1.0.0",
+  "created_at": "2025-12-03T20:26:43.205017",
+  "updated_at": "2025-12-03T20:26:43.205101",
+  "created_by": "system",
+  "nodes": [
+    {
+      "id": "type_username",
+      "type": "type",
+      "position": {
+        "x": 100,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "target": "input",
+        "text": "${username}"
+      },
+      "input_ports": [],
+      "output_ports": [],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    }
+  ],
+  "edges": [],
+  "variables": [
+    {
+      "name": "username",
+      "type": "string",
+      "value": "default_user",
+      "description": null
+    },
+    {
+      "name": "count",
+      "type": "number",
+      "value": 5,
+      "description": null
+    }
+  ],
+  "settings": {
+    "timeout": 300000,
+    "retry_on_failure": true,
+    "max_retries": 3,
+    "enable_self_healing": true,
+    "enable_analytics": true
+  },
+  "tags": [],
+  "category": null,
+  "is_template": false,
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2025-12-03T20:26:43.205134",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_ad7ccf92ca57.json b/visual_workflow_builder/backend/data/workflows/wf_ad7ccf92ca57.json
new file mode 100644
index 000000000..3bbf60b0a
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_ad7ccf92ca57.json
@@ -0,0 +1,484 @@
+{
+  "id": "wf_ad7ccf92ca57",
+  "name": "Long Workflow",
+  "description": "",
+  "version": "1.0.0",
+  "created_at": "2025-12-03T20:25:37.575921",
+  "updated_at": "2025-12-03T20:25:37.575959",
+  "created_by": "system",
+  "nodes": [
+    {
+      "id": "wait_0",
+      "type": "wait",
+      "position": {
+        "x": 100,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [],
+      "output_ports": [
+        {
+          "id": "out",
+          "name": "Output",
+          "type": "output",
+          "data_type": null
+        }
+      ],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "wait_1",
+      "type": "wait",
+      "position": {
+        "x": 150,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [
+        {
+          "id": "in",
+          "name": "Input",
+          "type": "input",
+          "data_type": null
+        }
+      ],
+      "output_ports": [
+        {
+          "id": "out",
+          "name": "Output",
+          "type": "output",
+          "data_type": null
+        }
+      ],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "wait_2",
+      "type": "wait",
+      "position": {
+        "x": 200,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [
+        {
+          "id": "in",
+          "name": "Input",
+          "type": "input",
+          "data_type": null
+        }
+      ],
+      "output_ports": [
+        {
+          "id": "out",
+          "name": "Output",
+          "type": "output",
+          "data_type": null
+        }
+      ],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "wait_3",
+      "type": "wait",
+      "position": {
+        "x": 250,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [
+        {
+          "id": "in",
+          "name": "Input",
+          "type": "input",
+          "data_type": null
+        }
+      ],
+      "output_ports": [
+        {
+          "id": "out",
+          "name": "Output",
+          "type": "output",
+          "data_type": null
+        }
+      ],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "wait_4",
+      "type": "wait",
+      "position": {
+        "x": 300,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [
+        {
+          "id": "in",
+          "name": "Input",
+          "type": "input",
+          "data_type": null
+        }
+      ],
+      "output_ports": [
+        {
+          "id": "out",
+          "name": "Output",
+          "type": "output",
+          "data_type": null
+        }
+      ],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "wait_5",
+      "type": "wait",
+      "position": {
+        "x": 350,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [
+        {
+          "id": "in",
+          "name": "Input",
+          "type": "input",
+          "data_type": null
+        }
+      ],
+      "output_ports": [
+        {
+          "id": "out",
+          "name": "Output",
+          "type": "output",
+          "data_type": null
+        }
+      ],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "wait_6",
+      "type": "wait",
+      "position": {
+        "x": 400,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [
+        {
+          "id": "in",
+          "name": "Input",
+          "type": "input",
+          "data_type": null
+        }
+      ],
+      "output_ports": [
+        {
+          "id": "out",
+          "name": "Output",
+          "type": "output",
+          "data_type": null
+        }
+      ],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "wait_7",
+      "type": "wait",
+      "position": {
+        "x": 450,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [
+        {
+          "id": "in",
+          "name": "Input",
+          "type": "input",
+          "data_type": null
+        }
+      ],
+      "output_ports": [
+        {
+          "id": "out",
+          "name": "Output",
+          "type": "output",
+          "data_type": null
+        }
+      ],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "wait_8",
+      "type": "wait",
+      "position": {
+        "x": 500,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [
+        {
+          "id": "in",
+          "name": "Input",
+          "type": "input",
+          "data_type": null
+        }
+      ],
+      "output_ports": [
+        {
+          "id": "out",
+          "name": "Output",
+          "type": "output",
+          "data_type": null
+        }
+      ],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "wait_9",
+      "type": "wait",
+      "position": {
+        "x": 550,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [
+        {
+          "id": "in",
+          "name": "Input",
+          "type": "input",
+          "data_type": null
+        }
+      ],
+      "output_ports": [],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    }
+  ],
+  "edges": [
+    {
+      "id": "edge_1",
+      "source": "wait_0",
+      "target": "wait_1",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    },
+    {
+      "id": "edge_2",
+      "source": "wait_1",
+      "target": "wait_2",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    },
+    {
+      "id": "edge_3",
+      "source": "wait_2",
+      "target": "wait_3",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    },
+    {
+      "id": "edge_4",
+      "source": "wait_3",
+      "target": "wait_4",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    },
+    {
+      "id": "edge_5",
+      "source": "wait_4",
+      "target": "wait_5",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    },
+    {
+      "id": "edge_6",
+      "source": "wait_5",
+      "target": "wait_6",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    },
+    {
+      "id": "edge_7",
+      "source": "wait_6",
+      "target": "wait_7",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    },
+    {
+      "id": "edge_8",
+      "source": "wait_7",
+      "target": "wait_8",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    },
+    {
+      "id": "edge_9",
+      "source": "wait_8",
+      "target": "wait_9",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    }
+  ],
+  "variables": [],
+  "settings": {
+    "timeout": 300000,
+    "retry_on_failure": true,
+    "max_retries": 3,
+    "enable_self_healing": true,
+    "enable_analytics": true
+  },
+  "tags": [],
+  "category": null,
+  "is_template": false,
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2025-12-03T20:25:37.575986",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_b40df255f3c7.json b/visual_workflow_builder/backend/data/workflows/wf_b40df255f3c7.json
new file mode 100644
index 000000000..150619e56
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_b40df255f3c7.json
@@ -0,0 +1,65 @@
+{
+  "id": "wf_b40df255f3c7",
+  "name": "Variables Workflow",
+  "description": "",
+  "version": "1.0.0",
+  "created_at": "2025-12-03T20:26:25.640410",
+  "updated_at": "2025-12-03T20:26:25.640508",
+  "created_by": "system",
+  "nodes": [
+    {
+      "id": "type_username",
+      "type": "type",
+      "position": {
+        "x": 100,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "target": "input",
+        "text": "${username}"
+      },
+      "input_ports": [],
+      "output_ports": [],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    }
+  ],
+  "edges": [],
+  "variables": [
+    {
+      "name": "username",
+      "type": "string",
+      "value": "default_user",
+      "description": null
+    },
+    {
+      "name": "count",
+      "type": "number",
+      "value": 5,
+      "description": null
+    }
+  ],
+  "settings": {
+    "timeout": 300000,
+    "retry_on_failure": true,
+    "max_retries": 3,
+    "enable_self_healing": true,
+    "enable_analytics": true
+  },
+  "tags": [],
+  "category": null,
+  "is_template": false,
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2025-12-03T20:26:25.640548",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_b86cac3286b3.json b/visual_workflow_builder/backend/data/workflows/wf_b86cac3286b3.json
new file mode 100644
index 000000000..c43151be3
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_b86cac3286b3.json
@@ -0,0 +1,27 @@
+{
+  "id": "wf_b86cac3286b3",
+  "name": "test",
+  "description": "test",
+  "version": "1.0.0",
+  "created_at": "2026-01-08T12:50:25.266435",
+  "updated_at": "2026-01-08T12:50:25.266469",
+  "created_by": "test",
+  "nodes": [],
+  "edges": [],
+  "variables": [],
+  "settings": {
+    "timeout": 300000,
+    "retry_on_failure": true,
+    "max_retries": 3,
+    "enable_self_healing": true,
+    "enable_analytics": true
+  },
+  "tags": [],
+  "category": null,
+  "is_template": false,
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2026-01-08T12:50:25.266526",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_bb224c0d2ada.json b/visual_workflow_builder/backend/data/workflows/wf_bb224c0d2ada.json
new file mode 100644
index 000000000..0969434f4
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_bb224c0d2ada.json
@@ -0,0 +1,65 @@
+{
+  "id": "wf_bb224c0d2ada",
+  "name": "Variables Workflow",
+  "description": "",
+  "version": "1.0.0",
+  "created_at": "2025-12-03T20:47:50.401853",
+  "updated_at": "2025-12-03T20:47:50.401939",
+  "created_by": "system",
+  "nodes": [
+    {
+      "id": "type_username",
+      "type": "type",
+      "position": {
+        "x": 100,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "target": "input",
+        "text": "${username}"
+      },
+      "input_ports": [],
+      "output_ports": [],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    }
+  ],
+  "edges": [],
+  "variables": [
+    {
+      "name": "username",
+      "type": "string",
+      "value": "default_user",
+      "description": null
+    },
+    {
+      "name": "count",
+      "type": "number",
+      "value": 5,
+      "description": null
+    }
+  ],
+  "settings": {
+    "timeout": 300000,
+    "retry_on_failure": true,
+    "max_retries": 3,
+    "enable_self_healing": true,
+    "enable_analytics": true
+  },
+  "tags": [],
+  "category": null,
+  "is_template": false,
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2025-12-03T20:47:50.401975",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_d6c3d79a9f3b.json b/visual_workflow_builder/backend/data/workflows/wf_d6c3d79a9f3b.json
new file mode 100644
index 000000000..4351b42a2
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_d6c3d79a9f3b.json
@@ -0,0 +1,443 @@
+{
+  "id": "wf_d6c3d79a9f3b",
+  "name": "Long Workflow",
+  "description": "",
+  "version": "1.0.0",
+  "created_at": "2025-12-03T20:26:26.141033",
+  "updated_at": "2026-01-22T09:29:19.076505",
+  "created_by": "system",
+  "nodes": [
+    {
+      "id": "wait_0",
+      "type": "wait",
+      "position": {
+        "x": 100,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 80
+      },
+      "parameters": {
+        "duration": 200
+      },
+      "input_ports": [],
+      "output_ports": [],
+      "self_healing": null,
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null,
+      "data": {
+        "label": "Étape 1",
+        "stepType": "wait",
+        "parameters": {
+          "duration": 200
+        },
+        "isVWBCatalogAction": false
+      }
+    },
+    {
+      "id": "wait_1",
+      "type": "wait",
+      "position": {
+        "x": 150,
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+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_e0406995738f.json b/visual_workflow_builder/backend/data/workflows/wf_e0406995738f.json
new file mode 100644
index 000000000..4089a51e7
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_e0406995738f.json
@@ -0,0 +1,93 @@
+{
+  "id": "wf_e0406995738f",
+  "name": "Test Export Workflow",
+  "description": "Workflow pour tester l export",
+  "version": "1.0.0",
+  "created_at": "2025-12-12T09:34:55.703125",
+  "updated_at": "2025-12-12T09:34:55.703165",
+  "created_by": "test_user",
+  "nodes": [
+    {
+      "id": "node-1",
+      "type": "click",
+      "position": {
+        "x": 100,
+        "y": 100
+      },
+      "size": {
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+        "height": 100
+      },
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+        "y": 100
+      },
+      "size": {
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+        "height": 100
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+      "name": "username",
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+      "value": "testuser",
+      "description": "Nom d utilisateur"
+    }
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+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_f9605ef7334b.json b/visual_workflow_builder/backend/data/workflows/wf_f9605ef7334b.json
new file mode 100644
index 000000000..162dc70b0
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_f9605ef7334b.json
@@ -0,0 +1,484 @@
+{
+  "id": "wf_f9605ef7334b",
+  "name": "Long Workflow",
+  "description": "",
+  "version": "1.0.0",
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+        "height": 80
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+      "color": null
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+      "selected": false,
+      "animated": false
+    },
+    {
+      "id": "edge_6",
+      "source": "wait_5",
+      "target": "wait_6",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    },
+    {
+      "id": "edge_7",
+      "source": "wait_6",
+      "target": "wait_7",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    },
+    {
+      "id": "edge_8",
+      "source": "wait_7",
+      "target": "wait_8",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    },
+    {
+      "id": "edge_9",
+      "source": "wait_8",
+      "target": "wait_9",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    }
+  ],
+  "variables": [],
+  "settings": {
+    "timeout": 300000,
+    "retry_on_failure": true,
+    "max_retries": 3,
+    "enable_self_healing": true,
+    "enable_analytics": true
+  },
+  "tags": [],
+  "category": null,
+  "is_template": false,
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2025-12-03T20:26:43.705734",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/data/workflows/wf_fcb0cfdd419b.json b/visual_workflow_builder/backend/data/workflows/wf_fcb0cfdd419b.json
new file mode 100644
index 000000000..fbdc28f70
--- /dev/null
+++ b/visual_workflow_builder/backend/data/workflows/wf_fcb0cfdd419b.json
@@ -0,0 +1,93 @@
+{
+  "id": "wf_fcb0cfdd419b",
+  "name": "Test Export Workflow",
+  "description": "Workflow pour tester l export",
+  "version": "1.0.0",
+  "created_at": "2025-12-12T09:30:28.846387",
+  "updated_at": "2025-12-12T09:30:28.846439",
+  "created_by": "test_user",
+  "nodes": [
+    {
+      "id": "node-1",
+      "type": "click",
+      "position": {
+        "x": 100,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 100
+      },
+      "parameters": {
+        "target": "button"
+      },
+      "input_ports": [],
+      "output_ports": [],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    },
+    {
+      "id": "node-2",
+      "type": "type",
+      "position": {
+        "x": 300,
+        "y": 100
+      },
+      "size": {
+        "width": 200,
+        "height": 100
+      },
+      "parameters": {
+        "text": "Hello World"
+      },
+      "input_ports": [],
+      "output_ports": [],
+      "selected": false,
+      "highlighted": false,
+      "status": null,
+      "label": null,
+      "description": null,
+      "color": null
+    }
+  ],
+  "edges": [
+    {
+      "id": "edge-1",
+      "source": "node-1",
+      "target": "node-2",
+      "source_port": "out",
+      "target_port": "in",
+      "condition": null,
+      "style": null,
+      "selected": false,
+      "animated": false
+    }
+  ],
+  "variables": [
+    {
+      "name": "username",
+      "type": "string",
+      "value": "testuser",
+      "description": "Nom d utilisateur"
+    }
+  ],
+  "settings": {
+    "timeout": 300000,
+    "retry_on_failure": true,
+    "max_retries": 3,
+    "enable_self_healing": true,
+    "enable_analytics": true
+  },
+  "tags": [],
+  "category": null,
+  "is_template": false,
+  "_serialization": {
+    "version": "1.0.0",
+    "serialized_at": "2025-12-12T09:30:28.846516",
+    "format": "json"
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/instance/vwb_v3.db b/visual_workflow_builder/backend/instance/vwb_v3.db
new file mode 100644
index 000000000..443c0cded
Binary files /dev/null and b/visual_workflow_builder/backend/instance/vwb_v3.db differ
diff --git a/visual_workflow_builder/backend/instance/workflows.db b/visual_workflow_builder/backend/instance/workflows.db
new file mode 100644
index 000000000..1b69e9124
Binary files /dev/null and b/visual_workflow_builder/backend/instance/workflows.db differ
diff --git a/visual_workflow_builder/backend/training_data/workflow_wf_372eabf626f8_1773752892_1773833925.json b/visual_workflow_builder/backend/training_data/workflow_wf_372eabf626f8_1773752892_1773833925.json
new file mode 100644
index 000000000..6d539b6fa
--- /dev/null
+++ b/visual_workflow_builder/backend/training_data/workflow_wf_372eabf626f8_1773752892_1773833925.json
@@ -0,0 +1,95 @@
+{
+  "workflow_id": "wf_372eabf626f8_1773752892",
+  "workflow_name": "test_windows",
+  "description": "",
+  "tags": [],
+  "steps": [
+    {
+      "order": 0,
+      "action_type": "click_anchor",
+      "label": "click_anchor",
+      "parameters": {
+        "visual_anchor": {
+          "anchor_id": "anchor_e54628b75227_1773829452",
+          "bounding_box": {
+            "x": 560,
+            "y": 1536,
+            "width": 230,
+            "height": 59
+          }
+        }
+      },
+      "has_anchor": true
+    },
+    {
+      "order": 0,
+      "action_type": "type_text",
+      "label": "type_text",
+      "parameters": {
+        "clear_before": true,
+        "text": "bloc note"
+      },
+      "has_anchor": false
+    },
+    {
+      "order": 0,
+      "action_type": "click_anchor",
+      "label": "click_anchor",
+      "parameters": {
+        "visual_anchor": {
+          "anchor_id": "anchor_dd32b09238ca_1773833606",
+          "bounding_box": {
+            "x": 670,
+            "y": 310,
+            "width": 51,
+            "height": 51
+          }
+        }
+      },
+      "has_anchor": true
+    },
+    {
+      "order": 0,
+      "action_type": "wait_for_anchor",
+      "label": "wait_for_anchor",
+      "parameters": {
+        "visual_anchor": {
+          "anchor_id": "anchor_2d51b88900d7_1773833805",
+          "bounding_box": {
+            "x": 287,
+            "y": 290,
+            "width": 70,
+            "height": 92
+          }
+        }
+      },
+      "has_anchor": true
+    },
+    {
+      "order": 0,
+      "action_type": "type_text",
+      "label": "type_text",
+      "parameters": {
+        "text": "hello"
+      },
+      "has_anchor": false
+    }
+  ],
+  "exported_at": "2026-03-18T11:38:45.271209",
+  "metadata": {
+    "step_count": 5,
+    "action_types": [
+      "wait_for_anchor",
+      "type_text",
+      "click_anchor"
+    ],
+    "has_anchors": true,
+    "warnings": [
+      "Étape 1 (click_anchor): pas de label personnalisé",
+      "Étape 2 (type_text): pas de label personnalisé",
+      "Étape 3 (click_anchor): pas de label personnalisé",
+      "Étape 4 (wait_for_anchor): pas de label personnalisé",
+      "Étape 5 (type_text): pas de label personnalisé"
+    ]
+  }
+}
\ No newline at end of file
diff --git a/visual_workflow_builder/backend/vlm_provider.py b/visual_workflow_builder/backend/vlm_provider.py
new file mode 100644
index 000000000..67395236d
--- /dev/null
+++ b/visual_workflow_builder/backend/vlm_provider.py
@@ -0,0 +1,165 @@
+import os
+import io
+import json
+import base64
+from typing import Optional, Dict, Any, List
+from PIL import Image
+from dotenv import load_dotenv
+
+# Charger les variables d'environnement
+env_paths = [
+    os.path.join(os.getcwd(), ".env.local"),
+    os.path.join(os.getcwd(), "rpa_vision_v3/.env.local"),
+    os.path.join(os.path.dirname(__file__), "../../../.env.local")
+]
+for path in env_paths:
+    if os.path.exists(path):
+        load_dotenv(path, override=True)
+        break
+
+class VLMProvider:
+    """Hub de Vision Sémantique Multi-Fournisseurs (OpenAI, Gemini, Anthropic, Ollama)"""
+
+    def __init__(self):
+        # Clés API
+        self.openai_key = os.getenv("OPENAI_API_KEY")
+        self.gemini_key = os.getenv("GOOGLE_API_KEY")
+        self.anthropic_key = os.getenv("ANTHROPIC_API_KEY")
+        self.deepseek_key = os.getenv("DEEPSEEK_API_KEY")
+        
+        # Configuration Ollama Local
+        self.ollama_url = os.getenv("OLLAMA_URL", "http://localhost:11434")
+        self.local_model = os.getenv("VLM_MODEL", "qwen3-vl:8b")
+
+        # Priorité par défaut
+        self.preferred_cloud = "openai" # gpt-4o est la référence UI
+        print(f"🔧 [VLM Hub] Initialisé. OpenAI: {bool(self.openai_key)}, Gemini: {bool(self.gemini_key)}, Anthropic: {bool(self.anthropic_key)}")
+
+    def _to_base64(self, image_input) -> str:
+        """Convertit n'importe quel input image en base64 pur"""
+        if isinstance(image_input, Image.Image):
+            buffer = io.BytesIO()
+            image_input.save(buffer, format="PNG")
+            return base64.b64encode(buffer.getvalue()).decode("utf-8")
+        elif isinstance(image_input, str):
+            if image_input.startswith("data:image"):
+                return image_input.split(",", 1)[1]
+            elif os.path.exists(image_input):
+                with open(image_input, "rb") as f:
+                    return base64.b64encode(f.read()).decode("utf-8")
+            return image_input # Base64 brut supposé
+        return base64.b64encode(image_input).decode("utf-8")
+
+    def detect_ui_element(self, screenshot, anchor_image=None, description: str = "") -> Optional[Dict[str, Any]]:
+        """Tente de localiser l'élément en essayant les fournisseurs par ordre de qualité"""
+        
+        # 1. Tenter OpenAI (Référence Vision UI)
+        if self.openai_key:
+            res = self._call_openai(screenshot, anchor_image, description)
+            if res and res.get('found'): return res
+
+        # 2. Tenter Gemini (Excellent backup Vision)
+        if self.gemini_key:
+            res = self._call_gemini(screenshot, anchor_image, description)
+            if res and res.get('found'): return res
+
+        # 3. Tenter Anthropic (Précision logique)
+        if self.anthropic_key:
+            res = self._call_anthropic(screenshot, anchor_image, description)
+            if res and res.get('found'): return res
+
+        # 4. Fallback Local (Ollama) - Crucial pour le DGX Spark
+        return self._call_ollama_local(screenshot, anchor_image, description)
+
+    def _call_openai(self, screenshot, anchor_image, description):
+        try:
+            from openai import OpenAI
+            client = OpenAI(api_key=self.openai_key)
+            prompt = f"Expert UI: Localise précisément '{description}'. Retourne JSON: {{'found': bool, 'bbox': [ymin, xmin, ymax, xmax] (0-1000), 'confidence': float}}"
+            
+            content = [{"type": "text", "text": prompt}]
+            content.append({"type": "image_url", "image_url": {"url": f"data:image/png;base64,{self._to_base64(screenshot)}"}})
+            if anchor_image:
+                content.append({"type": "text", "text": "Ancre de référence:"})
+                content.append({"type": "image_url", "image_url": {"url": f"data:image/png;base64,{self._to_base64(anchor_image)}"}})
+
+            response = client.chat.completions.create(
+                model="gpt-4o",
+                messages=[{"role": "user", "content": content}],
+                response_format={"type": "json_object"},
+                temperature=0
+            )
+            return json.loads(response.choices[0].message.content)
+        except Exception as e:
+            print(f"⚠️ [Hub] OpenAI Error: {e}")
+            return None
+
+    def _call_gemini(self, screenshot, anchor_image, description):
+        try:
+            from google import genai
+            client = genai.Client(api_key=self.gemini_key)
+            prompt = f"Expert UI: Localise précisément '{description}'. Retourne JSON: {{'found': bool, 'bbox': [ymin, xmin, ymax, xmax] (0-1000), 'confidence': float}}"
+            
+            contents = [prompt, Image.open(io.BytesIO(base64.b64decode(self._to_base64(screenshot))))]
+            if anchor_image:
+                contents.append(Image.open(io.BytesIO(base64.b64decode(self._to_base64(anchor_image)))))
+
+            response = client.models.generate_content(
+                model="gemini-1.5-flash",
+                contents=contents,
+                config={"response_mime_type": "application/json"}
+            )
+            return json.loads(response.text)
+        except Exception as e:
+            print(f"⚠️ [Hub] Gemini Error: {e}")
+            return None
+
+    def _call_anthropic(self, screenshot, anchor_image, description):
+        try:
+            import anthropic
+            client = anthropic.Anthropic(api_key=self.anthropic_key)
+            # Claude 3.5 Sonnet supporte la vision mais pas le format JSON strict en sortie nativement via config
+            # On utilise un prompt renforcé
+            prompt = f"Localise '{description}'. Réponds UNIQUEMENT en JSON : {{'found': bool, 'bbox': [ymin, xmin, ymax, xmax], 'confidence': float}}"
+            
+            content = [{"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": self._to_base64(screenshot)}},
+                       {"type": "text", "text": prompt}]
+            
+            response = client.messages.create(
+                model="claude-3-5-sonnet-20241022",
+                max_tokens=1000,
+                messages=[{"role": "user", "content": content}]
+            )
+            text = response.content[0].text
+            return json.loads(text[text.find('{'):text.rfind('}')+1])
+        except Exception as e:
+            print(f"⚠️ [Hub] Anthropic Error: {e}")
+            return None
+
+    def _call_ollama_local(self, screenshot, anchor_image, description):
+        """Appel à Ollama local (Mode DGX Spark / Offline)"""
+        try:
+            import requests
+            print(f"🏠 [Hub] Fallback Local Ollama ({self.local_model})...")
+            prompt = f"Localise l'élément '{description}'. Retourne JSON: {{'found': bool, 'bbox': [ymin, xmin, ymax, xmax] (0-1000)}}"
+            
+            payload = {
+                "model": self.local_model,
+                "prompt": prompt,
+                "images": [self._to_base64(screenshot)],
+                "stream": False,
+                "format": "json"
+            }
+            if anchor_image:
+                payload["images"].append(self._to_base64(anchor_image))
+
+            response = requests.post(f"{self.ollama_url}/api/generate", json=payload, timeout=60)
+            if response.status_code == 200:
+                return json.loads(response.json().get('response', '{}'))
+            return None
+        except Exception as e:
+            print(f"❌ [Hub] Local Ollama Error: {e}")
+            return {"found": False, "error": str(e)}
+
+# Instance unique
+vlm_hub = VLMProvider()
diff --git a/visual_workflow_builder/frontend_v4/src/App.tsx b/visual_workflow_builder/frontend_v4/src/App.tsx
index 86731b10a..ee35ed69a 100644
--- a/visual_workflow_builder/frontend_v4/src/App.tsx
+++ b/visual_workflow_builder/frontend_v4/src/App.tsx
@@ -248,6 +248,16 @@ function App() {
     }
   };
 
+  // Écouter les custom events de suppression (depuis StepNode via memo)
+  useEffect(() => {
+    const handler = (e: Event) => {
+      const stepId = (e as CustomEvent).detail;
+      if (stepId) handleDeleteStep(stepId);
+    };
+    window.addEventListener('rpa-delete-step', handler);
+    return () => window.removeEventListener('rpa-delete-step', handler);
+  });
+
   const handleUpdateStepParams = async (id: string, params: Record<string, unknown>) => {
     if (!appState?.session.active_workflow_id) return;
     try {
diff --git a/visual_workflow_builder/frontend_v4/src/components/ExecutionControls.tsx b/visual_workflow_builder/frontend_v4/src/components/ExecutionControls.tsx
index fa4c51b50..106d695ee 100644
--- a/visual_workflow_builder/frontend_v4/src/components/ExecutionControls.tsx
+++ b/visual_workflow_builder/frontend_v4/src/components/ExecutionControls.tsx
@@ -2,6 +2,9 @@
 import { useState } from 'react';
 import type { Execution } from '../types';
 
+// Détecter l'OS du navigateur pour n'afficher que le bouton d'exécution pertinent
+const userOS: 'windows' | 'linux' = navigator.platform.includes('Win') ? 'windows' : 'linux';
+
 interface Props {
   execution: Execution | null;
   onStart: () => void;
@@ -71,25 +74,28 @@ export default function ExecutionControls({ execution, onStart, onStop }: Props)
     <div className="execution-controls">
       {!isRunning ? (
         <div style={{ display: 'flex', gap: '4px', alignItems: 'center' }}>
-          <button className="btn-start" onClick={onStart} title="Exécuter sur cet écran (Linux)">
-            🐧 Linux
-          </button>
-          <button
-            className="btn-start"
-            onClick={handleExecuteWindows}
-            disabled={windowsStatus === 'sending'}
-            style={{
-              background: windowsStatus === 'sent' ? '#22c55e' : windowsStatus === 'error' ? '#ef4444' : '#0078d4',
-              fontSize: '12px',
-              opacity: windowsStatus === 'sending' ? 0.6 : 1,
-            }}
-            title="Exécuter sur le PC Windows distant"
-          >
-            {windowsStatus === 'sending' ? '⏳ Envoi...' :
-             windowsStatus === 'sent' ? '✅ Lancé !' :
-             windowsStatus === 'error' ? '❌ Erreur' :
-             '🖥️ Windows'}
-          </button>
+          {userOS === 'linux' ? (
+            <button className="btn-start" onClick={onStart} title="Exécuter sur cet écran">
+              Exécuter
+            </button>
+          ) : (
+            <button
+              className="btn-start"
+              onClick={handleExecuteWindows}
+              disabled={windowsStatus === 'sending'}
+              style={{
+                background: windowsStatus === 'sent' ? '#22c55e' : windowsStatus === 'error' ? '#ef4444' : '#0078d4',
+                fontSize: '12px',
+                opacity: windowsStatus === 'sending' ? 0.6 : 1,
+              }}
+              title="Exécuter sur ce PC"
+            >
+              {windowsStatus === 'sending' ? 'Envoi...' :
+               windowsStatus === 'sent' ? 'Lancé !' :
+               windowsStatus === 'error' ? 'Erreur' :
+               'Exécuter'}
+            </button>
+          )}
         </div>
       ) : (
         <>
diff --git a/visual_workflow_builder/frontend_v4/src/components/WorkflowList.tsx b/visual_workflow_builder/frontend_v4/src/components/WorkflowList.tsx
index 2b8b2e2cd..8d35fe6e4 100644
--- a/visual_workflow_builder/frontend_v4/src/components/WorkflowList.tsx
+++ b/visual_workflow_builder/frontend_v4/src/components/WorkflowList.tsx
@@ -41,7 +41,14 @@ export default function WorkflowList({ workflows, activeId, onSelect, onCreate,
               onClick={() => onSelect(wf.id)}
             >
               <div className="wf-main-row">
-                <span className="wf-name">{wf.name}</span>
+                <span className="wf-name">
+                  {wf.name}
+                  {wf.source === 'learned_import' && (
+                    <span className="imported-badge" title="Importé depuis Léa">
+                      appris
+                    </span>
+                  )}
+                </span>
 
                 {/* Indicateurs compacts des métadonnées */}
                 <span className="wf-meta-indicators">
diff --git a/visual_workflow_builder/frontend_v4/src/components/WorkflowSelector.tsx b/visual_workflow_builder/frontend_v4/src/components/WorkflowSelector.tsx
index 3ecebaf90..d9dc4ae07 100644
--- a/visual_workflow_builder/frontend_v4/src/components/WorkflowSelector.tsx
+++ b/visual_workflow_builder/frontend_v4/src/components/WorkflowSelector.tsx
@@ -12,6 +12,9 @@ interface Props {
   onRename: (id: string, newName: string) => void;
 }
 
+// Détecter l'OS du navigateur (pour ne montrer que les workflows compatibles)
+const userOS: 'windows' | 'linux' = navigator.platform.includes('Win') ? 'windows' : 'linux';
+
 export default function WorkflowSelector({
   workflows,
   activeWorkflow,
@@ -50,11 +53,11 @@ export default function WorkflowSelector({
     }
   }, [editingId]);
 
-  // Charger les workflows appris quand le dropdown s'ouvre
+  // Charger les workflows appris quand le dropdown s'ouvre (filtrés par OS)
   const loadLearnedWorkflows = useCallback(async () => {
     setLearnedLoading(true);
     try {
-      const data = await api.getLearnedWorkflows();
+      const data = await api.getLearnedWorkflows(undefined, userOS);
       // Ne garder que ceux qui ne sont pas encore importés
       setLearnedWorkflows(data.workflows.filter(w => !w.already_imported));
     } catch {
@@ -77,10 +80,11 @@ export default function WorkflowSelector({
     (wf.tags || []).some(tag => tag.toLowerCase().includes(search.toLowerCase()))
   );
 
-  // Filtrer les workflows appris
+  // Filtrer les workflows appris (par recherche + OS)
   const filteredLearned = learnedWorkflows.filter(wf =>
-    wf.name.toLowerCase().includes(search.toLowerCase()) ||
-    wf.workflow_id.toLowerCase().includes(search.toLowerCase())
+    (wf.name.toLowerCase().includes(search.toLowerCase()) ||
+     wf.workflow_id.toLowerCase().includes(search.toLowerCase())) &&
+    (wf.machine_id || '').toLowerCase().includes(userOS)
   );
 
   // Workflows récents (les 8 premiers)
@@ -202,6 +206,11 @@ export default function WorkflowSelector({
                     <>
                       <span className="item-name">
                         {wf.name}
+                        {wf.source === 'learned_import' && (
+                          <span className="imported-badge" title="Importé depuis Léa">
+                            appris
+                          </span>
+                        )}
                         {wf.review_status === 'pending_review' && (
                           <span className="review-badge-inline pending_review" title="En attente de validation">
                             &#9679; A valider
diff --git a/visual_workflow_builder/frontend_v4/src/services/api.ts b/visual_workflow_builder/frontend_v4/src/services/api.ts
index 1cdc28fe7..0fed9898e 100644
--- a/visual_workflow_builder/frontend_v4/src/services/api.ts
+++ b/visual_workflow_builder/frontend_v4/src/services/api.ts
@@ -322,12 +322,15 @@ export interface LearnedWorkflow {
   created_at?: string;
 }
 
-export async function getLearnedWorkflows(machineId?: string): Promise<{
+export async function getLearnedWorkflows(machineId?: string, os?: string): Promise<{
   workflows: LearnedWorkflow[];
   streaming_server_available: boolean;
 }> {
-  const params = machineId ? `?machine_id=${encodeURIComponent(machineId)}` : '';
-  return request('GET', `/learned-workflows${params}`);
+  const searchParams = new URLSearchParams();
+  if (machineId) searchParams.set('machine_id', machineId);
+  if (os) searchParams.set('os', os);
+  const qs = searchParams.toString();
+  return request('GET', `/learned-workflows${qs ? '?' + qs : ''}`);
 }
 
 export async function importLearnedWorkflow(
diff --git a/visual_workflow_builder/frontend_v4/src/styles.css b/visual_workflow_builder/frontend_v4/src/styles.css
index a2674c96d..a53212f52 100644
--- a/visual_workflow_builder/frontend_v4/src/styles.css
+++ b/visual_workflow_builder/frontend_v4/src/styles.css
@@ -2544,6 +2544,17 @@ body {
   gap: 0.3rem;
 }
 
+.learned-list .learned-item .item-name {
+  color: #1e293b;
+  font-weight: 600;
+}
+
+.learned-list .learned-item .item-meta {
+  color: #64748b;
+  font-size: 0.8rem;
+  width: 100%;
+}
+
 .learned-badge {
   display: inline-block;
   font-size: 0.65rem;
@@ -2555,6 +2566,18 @@ body {
   font-weight: 500;
 }
 
+.imported-badge {
+  display: inline-block;
+  font-size: 0.6rem;
+  padding: 0.1rem 0.35rem;
+  border-radius: 3px;
+  background: #e0f2fe;
+  color: #0369a1;
+  margin-left: 0.3rem;
+  font-weight: 500;
+  vertical-align: middle;
+}
+
 .import-btn {
   margin-left: auto;
   padding: 0.2rem 0.6rem;
diff --git a/visual_workflow_builder/frontend_v4/vite.config.ts b/visual_workflow_builder/frontend_v4/vite.config.ts
index 3119b8d20..e1fec44de 100644
--- a/visual_workflow_builder/frontend_v4/vite.config.ts
+++ b/visual_workflow_builder/frontend_v4/vite.config.ts
@@ -4,10 +4,18 @@ import react from '@vitejs/plugin-react'
 export default defineConfig({
   plugins: [react()],
   server: {
+    port: 3002,
+    allowedHosts: [
+      'vwb.labs.laurinebazin.design',
+    ],
     proxy: {
       '/api': {
-        target: 'http://localhost:5001',
+        target: 'http://localhost:5002',
         changeOrigin: true
+      },
+      '/socket.io': {
+        target: 'http://localhost:5002',
+        ws: true
       }
     }
   }