God Mode+ v3 fix: agents/chat_agent.py

agents/chat_agent.py

@@ -1,18 +1,15 @@
 """
-ChatAgent — Conversational interface with streaming, Burmese support
+ChatAgent — Conversational AI via LLMRouter
 """
-from typing import Dict
 import structlog
+from typing import Dict
 from .base_agent import BaseAgent
 
 log = structlog.get_logger()
 
-CHAT_SYSTEM = """You are God Agent — an elite autonomous AI operating system.
-You support both English and Burmese (မြန်မာဘာသာ) languages.
-Be helpful, precise, and autonomous. 
-When users write in Burmese, respond in Burmese.
-When discussing code, provide production-quality examples.
-"""
+SYSTEM_PROMPT = """You are God Mode+ AI — an elite autonomous AI software engineer (Devin + Manus style).
+You can plan, code, debug, refactor, test, and deploy software autonomously.
+Be concise, helpful, and professional. Use markdown formatting."""
 
 
 class ChatAgent(BaseAgent):
@@ -20,25 +17,19 @@ class ChatAgent(BaseAgent):
         super().__init__("ChatAgent", ws_manager, ai_router)
 
     async def run(self, task: str, context: Dict = {}, **kwargs) -> str:
-        session_id = kwargs.get("session_id", "")
-        task_id = kwargs.get("task_id", "")
-
-        history = context.get("history", [])
-        messages = [{"role": "system", "content": CHAT_SYSTEM}]
-
-        for h in history[-10:]:
-            messages.append({"role": h.get("role", "user"), "content": h.get("content", "")})
-
-        messages.append({"role": "user", "content": task})
-
-        await self.emit_chat(session_id, "stream_start", {"agent": "ChatAgent", "status": "generating"})
-
-        response = await self.llm(messages, task_id=task_id, session_id=session_id)
-
-        await self.emit_chat(session_id, "stream_end", {
-            "agent": "ChatAgent",
-            "full_response": response,
-            "status": "complete",
-        })
-
-        return response
+        session_id = kwargs.get("session_id", context.get("session_id", ""))
+        task_id    = kwargs.get("task_id",    context.get("task_id", ""))
+
+        messages = [
+            {"role": "system",  "content": SYSTEM_PROMPT},
+            {"role": "user",    "content": task},
+        ]
+
+        await self.emit(task_id, "agent_thinking", {"agent": "chat", "message": task[:100]}, session_id)
+
+        result = await self.ask_llm(
+            messages=messages,
+            task_id=task_id,
+            session_id=session_id,
+        )
+        return result