Orchestration complète : planning, scheduling, CLI

- agent1.py : listener MQTT (agents/agent1/inbox), MAX_STEPS 10
- skills/plan.py : exécution séquentielle PLAN: avec contexte entre étapes
- skills/schedule_tasks.py : SCHEDULE: / PLAN_LIST: / PLAN_CANCEL: via APScheduler
- cli.py : interface CLI rich (MQTT, multi-agents, /plans, /agent)
- system_prompt.txt : mis à jour avec tous les nouveaux skills

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

agent1.py

@@ -3,14 +3,14 @@
 
 import asyncio
 import sys
+import threading
 import requests
 import json
 from pathlib import Path
 from slixmpp import ClientXMPP
+import paho.mqtt.client as mqtt
 
-# Ajouter /opt/agent au path pour importer les skills
 sys.path.insert(0, "/opt/agent")
-
 from skills.loader import load_skills, run_skills
 
 # ── CONFIG ───────────────────────────────────────────────────────────────
@@ -32,12 +32,15 @@ MODEL         = cfg["model"]
 XMPP_JID      = cfg["xmpp_jid"]
 XMPP_PASS     = cfg["xmpp_pass"]
 ADMIN_JID     = cfg["admin_jid"]
+MQTT_HOST     = cfg.get("mqtt_host", "localhost")
+MQTT_PORT     = int(cfg.get("mqtt_port", 1883))
+MQTT_INBOX    = "agents/agent1/inbox"
 SYSTEM_PROMPT = load_system_prompt()
 
-# Charger les skills au démarrage
 load_skills()
 
 conversation_history = []
+xmpp_bot = None  # référence globale pour répondre via XMPP depuis MQTT
 
 # ── LLM ──────────────────────────────────────────────────────────────────
 def call_ollama(messages: list) -> str:
@@ -48,38 +51,71 @@ def call_ollama(messages: list) -> str:
         "options" : {"temperature": 0.3}
     }
     response = requests.post(OLLAMA_URL, json=payload, timeout=180)
-    data     = response.json()
-    return data["message"]["content"]
-
-def ask_llm(user_message: str) -> str:
-    conversation_history.append({"role": "user", "content": user_message})
-    messages = [{"role": "system", "content": SYSTEM_PROMPT}] + conversation_history
+    return response.json()["message"]["content"]
 
+def ask_llm(user_message: str, history: list = None) -> str:
+    if history is None:
+        history = conversation_history
+    history.append({"role": "user", "content": user_message})
+    messages = [{"role": "system", "content": SYSTEM_PROMPT}] + history
     try:
-        # Boucle agentique : le LLM peut enchaîner plusieurs skills
-        MAX_STEPS = 5
+        MAX_STEPS = 10
         for _ in range(MAX_STEPS):
             reply = call_ollama(messages)
             skill_triggered, result = run_skills(reply)
-
             if not skill_triggered:
-                # Réponse finale sans commande
-                conversation_history.append({"role": "assistant", "content": reply})
+                history.append({"role": "assistant", "content": reply})
                 return reply
-
-            # Injecter le résultat du skill et relancer le LLM
             messages.append({"role": "assistant", "content": reply})
             messages.append({"role": "user",      "content": "[Résultat skill]\n" + result})
-
-        # Sécurité : trop d'étapes
         reply = call_ollama(messages)
-        conversation_history.append({"role": "assistant", "content": reply})
+        history.append({"role": "assistant", "content": reply})
         return reply
-
     except Exception as e:
-        error_reply = "Erreur : " + str(e)
-        conversation_history.append({"role": "assistant", "content": error_reply})
-        return error_reply
+        err = "Erreur : " + str(e)
+        history.append({"role": "assistant", "content": err})
+        return err
+
+# ── MQTT LISTENER (pour CLI) ──────────────────────────────────────────────
+mqtt_pub_client = None
+
+def mqtt_publish(topic: str, message: str):
+    if mqtt_pub_client:
+        mqtt_pub_client.publish(topic, message)
+
+def on_mqtt_message(client, userdata, msg):
+    raw = msg.payload.decode(errors="replace")
+
+    # Support JSON avec reply_to optionnel
+    reply_to = "agents/cli/outbox"
+    task = raw
+    try:
+        data = json.loads(raw)
+        task     = data.get("task", raw)
+        reply_to = data.get("reply_to", reply_to)
+    except json.JSONDecodeError:
+        pass
+
+    print("[MQTT] Message CLI reçu : {}".format(task[:80]))
+    mqtt_history = []
+    reply = ask_llm(task, history=mqtt_history)
+    mqtt_publish(reply_to, reply)
+    print("[MQTT] Réponse envoyée sur {}".format(reply_to))
+
+def start_mqtt_listener():
+    global mqtt_pub_client
+
+    mqtt_pub_client = mqtt.Client(mqtt.CallbackAPIVersion.VERSION2,
+                                  client_id="agent1_pub")
+    mqtt_pub_client.connect(MQTT_HOST, MQTT_PORT)
+    mqtt_pub_client.loop_start()
+
+    sub = mqtt.Client(mqtt.CallbackAPIVersion.VERSION2, client_id="agent1_sub")
+    sub.on_message = on_mqtt_message
+    sub.connect(MQTT_HOST, MQTT_PORT)
+    sub.subscribe(MQTT_INBOX)
+    print("[MQTT] Agent1 écoute sur {}".format(MQTT_INBOX))
+    sub.loop_forever()
 
 # ── BOT XMPP ─────────────────────────────────────────────────────────────
 class AgentBot(ClientXMPP):
@@ -93,7 +129,7 @@ class AgentBot(ClientXMPP):
     async def session_start(self, event):
         self.send_presence()
         await self.get_roster()
-        self.send_message(mto=ADMIN_JID, mbody="Agent en ligne !", mtype='chat')
+        self.send_message(mto=ADMIN_JID, mbody="Agent1 (orchestrateur) en ligne !", mtype='chat')
 
     async def message(self, msg):
         if msg['type'] not in ('chat', 'normal'):
@@ -114,6 +150,9 @@ class AgentBot(ClientXMPP):
 
 # ── MAIN ─────────────────────────────────────────────────────────────────
 if __name__ == "__main__":
-    bot = AgentBot()
-    bot.connect()
-    bot.loop.run_forever()
+    mqtt_thread = threading.Thread(target=start_mqtt_listener, daemon=True)
+    mqtt_thread.start()
+
+    xmpp_bot = AgentBot()
+    xmpp_bot.connect()
+    xmpp_bot.loop.run_forever()