deep agent

app/api/routes/device_ws.py

@@ -200,6 +200,9 @@ async def _message_loop(websocket: WebSocket, user_id: str) -> None:
 
 # ── v3 Chat Handlers ──────────────────────────────────────────────────
 
+_WS_TOOL_CALL_TIMEOUT = 30  # seconds to wait for Electron tool_result
+
+
 async def _make_ws_executor(websocket: WebSocket, user_id: str):
     """Return a callback that sends tool_call frames and awaits tool_result."""
     async def _executor(payload: dict) -> dict:
@@ -208,7 +211,18 @@ async def _make_ws_executor(websocket: WebSocket, user_id: str):
         logger.info("ws_executor: sending tool_call id=%s action=%s", call_id, payload.get("action"))
         await websocket.send_text(json.dumps(payload))
         future = device_manager.create_pending_call(user_id, call_id)
-        result = await future
+        try:
+            result = await asyncio.wait_for(future, timeout=_WS_TOOL_CALL_TIMEOUT)
+        except asyncio.TimeoutError:
+            logger.error(
+                "ws_executor: timeout waiting for tool_result id=%s action=%s user=%s",
+                call_id, payload.get("action"), user_id,
+            )
+            # Clean up the pending future so it doesn't leak
+            conn = device_manager._connections.get(user_id)
+            if conn:
+                conn.pending_calls.pop(call_id, None)
+            return {"error": f"Tool call timed out after {_WS_TOOL_CALL_TIMEOUT}s", "rows": []}
         logger.info("ws_executor: tool_result id=%s result_type=%s result_keys=%s",
                      call_id, type(result).__name__,
                      list(result.keys()) if isinstance(result, dict) else "N/A")

app/core/deep_agent.py

@@ -114,7 +114,8 @@ def _make_subagent_specs() -> list[dict[str, Any]]:
             "name": "task_agent",
             "description": (
                 "Manages tasks and comments: list, create, update, delete, "
-                "due-today, comments. Delegate task-related queries here."
+                "due-today, and comments. Use when the user asks about tasks, "
+                "to-dos, assignments, deadlines, or anything task-related."
             ),
             "system_prompt": (
                 "You are a task management assistant. You create, update, list, "
@@ -128,14 +129,13 @@ def _make_subagent_specs() -> list[dict[str, Any]]:
                 "  - For update_task, use -1 for integer fields you do not want to change\n"
                 "  - Always confirm the action in plain, user-friendly language."
             ),
-            "tools": _TASK_TOOLS,
-            "model": llm,
+            "tools": _TASK_TOOLS
         },
         {
             "name": "note_agent",
             "description": (
                 "Manages notes: list, get, create, update, delete. "
-                "Delegate note-related queries here."
+                "Use when the user asks about notes, documents, or written content."
             ),
             "system_prompt": (
                 "You are a note-taking assistant. You help users create, retrieve, "
@@ -146,14 +146,13 @@ def _make_subagent_specs() -> list[dict[str, Any]]:
                 "content before appending or replacing sections\n"
                 "  - Do not fabricate note content."
             ),
-            "tools": _NOTE_TOOLS,
-            "model": llm,
+            "tools": _NOTE_TOOLS
         },
         {
             "name": "project_agent",
             "description": (
                 "Manages projects: list, get, create, update, archive, delete. "
-                "Delegate project-related queries here."
+                "Use when the user asks about projects, workspaces, or project status."
             ),
             "system_prompt": (
                 "You are a project management assistant. You help users create, "
@@ -163,14 +162,14 @@ def _make_subagent_specs() -> list[dict[str, Any]]:
                 "  - Prefer archiving over deletion\n"
                 "  - ai_summary is populated only when the user asks for a summary."
             ),
-            "tools": _PROJECT_TOOLS,
-            "model": llm,
+            "tools": _PROJECT_TOOLS
         },
         {
             "name": "timeline_agent",
             "description": (
-                "Manages project timelines (milestones): list, create, update, "
-                "delete. Delegate timeline/milestone queries here."
+                "Manages project timelines and milestones: list, create, update, "
+                "delete. Use when the user asks about timelines, milestones, "
+                "deadlines, or project scheduling."
             ),
             "system_prompt": (
                 "You are a project timeline assistant. Timelines are milestone "
@@ -181,8 +180,7 @@ def _make_subagent_specs() -> list[dict[str, Any]]:
                 "  - For update_timeline, use -1 for integer fields you do not "
                 "want to change."
             ),
-            "tools": _TIMELINE_TOOLS,
-            "model": llm,
+            "tools": _TIMELINE_TOOLS
         },
     ]
 
@@ -221,6 +219,11 @@ _HOME_SYSTEM = (
     "multiple sub-agents in parallel if needed.\n\n"
     "You also have an update_core_memory tool — use it when the user states "
     "a preference or important fact worth remembering long-term.\n\n"
+    "IMPORTANT: You do NOT have direct access to workspace data. Always "
+    "delegate to your subagents using the task() tool. Do not attempt to "
+    "answer workspace queries yourself — the subagents have the tools to "
+    "fetch and modify data. You can call multiple subagents in parallel "
+    "when the request spans multiple domains.\n\n"
     "## Entity References\n"
     "When your response mentions specific workspace entities, embed them "
     "inline using entity tags so the UI can render interactive components.\n"
@@ -263,6 +266,10 @@ _FLOATING_SYSTEM = (
     "if the request requires it.\n\n"
     "You also have an update_core_memory tool — use it when the user states "
     "a preference or important fact worth remembering long-term.\n\n"
+    "IMPORTANT: You do NOT have direct access to workspace data. Always "
+    "delegate to your subagents using the task() tool. Do not attempt to "
+    "answer workspace queries yourself — the subagents have the tools to "
+    "fetch and modify data.\n\n"
     "Provide direct, conversational responses.\n\n"
     "Memory context:\n{memory_context}"
 )
@@ -367,6 +374,42 @@ async def _run_graph_stream(
         ):
             if stream_mode == "messages":
                 msg, metadata = chunk
+                agent_name = (
+                    metadata.get("lc_agent_name", "?")
+                    if isinstance(metadata, dict) else "?"
+                )
+                node = (
+                    metadata.get("langgraph_node", "?")
+                    if isinstance(metadata, dict) else "?"
+                )
+
+                # Log every message event with agent attribution
+                if isinstance(msg, (AIMessage, AIMessageChunk)) and msg.content:
+                    logger.info(
+                        "[%s] %s node=%s content=%s",
+                        agent_name,
+                        type(msg).__name__,
+                        node,
+                        str(msg.content),
+                    )
+                elif isinstance(msg, (AIMessage, AIMessageChunk)) and msg.tool_calls:
+                    tool_names = [tc["name"] for tc in msg.tool_calls]
+                    logger.info(
+                        "[%s] %s node=%s tool_calls=%s",
+                        agent_name,
+                        type(msg).__name__,
+                        node,
+                        tool_names,
+                    )
+                elif hasattr(msg, "name") and hasattr(msg, "content") and msg.content:
+                    # ToolMessage — log tool result
+                    logger.info(
+                        "[%s] ToolMessage tool=%s node=%s result=%s",
+                        agent_name,
+                        getattr(msg, "name", "?"),
+                        node,
+                        str(msg.content),
+                    )
                 # Only yield tokens from the supervisor's final response
                 # (not from sub-agent internal LLM calls).
                 # Accept both AIMessageChunk (streamed tokens) and AIMessage