feat(moa): expose MoA presets as selectable virtual models

acp_adapter/tools.py

@@ -74,7 +74,7 @@
     "kanban_create", "kanban_show", "kanban_comment", "kanban_complete",
     "kanban_block", "kanban_link", "kanban_heartbeat",
     "yb_query_group_info", "yb_query_group_members", "yb_search_sticker",
-    "yb_send_dm", "yb_send_sticker", "mixture_of_agents",
+    "yb_send_dm", "yb_send_sticker",
 }
 
 

agent/agent_init.py

@@ -719,6 +719,15 @@ def init_agent(
                     print("🔑 Using credentials: Microsoft Entra ID")
                 elif isinstance(effective_key, str) and len(effective_key) > 12:
                     print(f"🔑 Using token: {effective_key[:8]}...{effective_key[-4:]}")
+    elif agent.provider == "moa":
+        from agent.moa_loop import MoAClient
+        agent.api_mode = "chat_completions"
+        agent.client = MoAClient(agent.model or "default")
+        agent._client_kwargs = {}
+        agent.api_key = api_key or "moa-virtual-provider"
+        agent.base_url = base_url or "moa://local"
+        if not agent.quiet_mode:
+            print(f"🤖 AI Agent initialized with MoA preset: {agent.model}")
     elif agent.api_mode == "bedrock_converse":
         # AWS Bedrock — uses boto3 directly, no OpenAI client needed.
         # Region is extracted from the base_url or defaults to us-east-1.

agent/conversation_loop.py

@@ -502,6 +502,7 @@ def run_conversation(
     stream_callback: Optional[callable] = None,
     persist_user_message: Optional[str] = None,
     persist_user_timestamp: Optional[float] = None,
+    moa_config: Optional[dict[str, Any]] = None,
 ) -> Dict[str, Any]:
     """
     Run a complete conversation with tool calling until completion.
@@ -524,6 +525,19 @@ def run_conversation(
     Returns:
         Dict: Complete conversation result with final response and message history
     """
+    if moa_config is None:
+        try:
+            from hermes_cli.moa_config import decode_moa_turn
+
+            _decoded_message, _decoded_moa_config = decode_moa_turn(user_message)
+            if _decoded_moa_config is not None:
+                user_message = _decoded_message
+                moa_config = _decoded_moa_config
+                if persist_user_message is None:
+                    persist_user_message = _decoded_message
+        except Exception:
+            pass
+
     # ── Per-turn setup (the prologue) ──
     # All once-per-turn setup — stdio guarding, retry-counter resets, user
     # message sanitization, todo/nudge hydration, system-prompt restore-or-
@@ -802,6 +816,29 @@ def run_conversation(
         if effective_system:
             api_messages = [{"role": "system", "content": effective_system}] + api_messages
 
+        if moa_config:
+            try:
+                from agent.moa_loop import aggregate_moa_context
+
+                _moa_context = aggregate_moa_context(
+                    user_prompt=original_user_message if isinstance(original_user_message, str) else str(original_user_message),
+                    api_messages=api_messages,
+                    reference_models=moa_config.get("reference_models") or [],
+                    aggregator=moa_config.get("aggregator") or {},
+                    temperature=float(moa_config.get("reference_temperature", 0.6) or 0.6),
+                    aggregator_temperature=float(moa_config.get("aggregator_temperature", 0.4) or 0.4),
+                    max_tokens=int(moa_config.get("max_tokens", 4096) or 4096),
+                )
+                if _moa_context:
+                    for _msg in reversed(api_messages):
+                        if _msg.get("role") == "user":
+                            _base = _msg.get("content", "")
+                            if isinstance(_base, str):
+                                _msg["content"] = _base + "\n\n" + _moa_context
+                            break
+            except Exception as _moa_exc:
+                logger.warning("MoA context aggregation failed: %s", _moa_exc)
+
         # Inject ephemeral prefill messages right after the system prompt
         # but before conversation history. Same API-call-time-only pattern.
         if agent.prefill_messages:
@@ -1123,7 +1160,7 @@ def _stop_spinner():
                 # stream.  Mirror the ACP exclusion used for Responses
                 # API upgrade (lines ~1083-1085).
                 elif (
-                    agent.provider == "copilot-acp"
+                    agent.provider in {"copilot-acp", "moa"}
                     or str(agent.base_url or "").lower().startswith("acp://copilot")
                     or str(agent.base_url or "").lower().startswith("acp+tcp://")
                 ):

agent/display.py

@@ -368,7 +368,7 @@ def build_tool_preview(tool_name: str, args: dict, max_len: int | None = None) -
         "search_files": "pattern", "browser_navigate": "url",
         "browser_click": "ref", "browser_type": "text",
         "image_generate": "prompt", "text_to_speech": "text",
-        "vision_analyze": "question", "mixture_of_agents": "user_prompt",
+        "vision_analyze": "question",
         "skill_view": "name", "skills_list": "category",
         "cronjob": "action",
         "execute_code": "code", "delegate_task": "goal",
@@ -1216,8 +1216,6 @@ def _wrap(line: str) -> str:
         return _wrap(f"┊ 🔊 speak     {_trunc(args.get('text', ''), 30)}  {dur}")
     if tool_name == "vision_analyze":
         return _wrap(f"┊ 👁️  vision    {_trunc(args.get('question', ''), 30)}  {dur}")
-    if tool_name == "mixture_of_agents":
-        return _wrap(f"┊ 🧠 reason    {_trunc(args.get('user_prompt', ''), 30)}  {dur}")
     if tool_name == "send_message":
         return _wrap(f"┊ 📨 send      {args.get('target', '?')}: \"{_trunc(args.get('message', ''), 25)}\"  {dur}")
     if tool_name == "cronjob":