refactor: 优化 multi-agent 设计

backend/routes/__init__.py

@@ -17,7 +17,7 @@ def register_routes(app: Flask):
     client = LLMClient(MODEL_CONFIG)
     init_chat_service(client)
 
-    # Register LLM client in service locator so tools (e.g. agent_task) can access it
+    # Register LLM client in service locator so tools (e.g. multi_agent) can access it
     from backend.tools import register_service
     register_service("llm_client", client)
 

backend/services/chat.py

@@ -61,6 +61,9 @@ class ChatService:
         """
         conv_id = conv.id
         conv_model = conv.model
+        conv_max_tokens = conv.max_tokens
+        conv_temperature = conv.temperature
+        conv_thinking_enabled = conv.thinking_enabled
         app = current_app._get_current_object()
         tools = registry.list_all() if tools_enabled else None
         initial_messages = build_messages(conv, project_id)
@@ -85,7 +88,9 @@ class ChatService:
             for iteration in range(MAX_ITERATIONS):
                 try:
                     stream_result = self._stream_llm_response(
-                        app, conv_id, messages, tools, tool_choice, step_index
+                        app, messages, tools, tool_choice, step_index,
+                        conv_model, conv_max_tokens, conv_temperature,
+                        conv_thinking_enabled,
                     )
                 except requests.exceptions.HTTPError as e:
                     resp = e.response
@@ -185,7 +190,7 @@ class ChatService:
                     # Append assistant message + tool results for the next iteration
                     messages.append({
                         "role": "assistant",
-                        "content": full_content or None,
+                        "content": full_content or "",
                         "tool_calls": tool_calls_list,
                     })
                     messages.extend(tool_results)
@@ -232,7 +237,8 @@ class ChatService:
     # ------------------------------------------------------------------
 
     def _stream_llm_response(
-        self, app, conv_id, messages, tools, tool_choice, step_index
+        self, app, messages, tools, tool_choice, step_index,
+        model, max_tokens, temperature, thinking_enabled,
     ):
         """Call LLM streaming API and parse the response.
 
@@ -253,13 +259,12 @@ class ChatService:
         sse_chunks = []  # Collect SSE events to yield later
 
         with app.app_context():
-            active_conv = db.session.get(Conversation, conv_id)
             resp = self.llm.call(
-                model=active_conv.model,
+                model=model,
                 messages=messages,
-                max_tokens=active_conv.max_tokens,
+                max_tokens=max_tokens,
-                temperature=active_conv.temperature,
+                temperature=temperature,
-                thinking_enabled=active_conv.thinking_enabled,
+                thinking_enabled=thinking_enabled,
                 tools=tools,
                 tool_choice=tool_choice,
                 stream=True,
@@ -327,39 +332,8 @@ class ChatService:
             sse_chunks,
         )
 
-    def _execute_tools_safe(self, app, executor, tool_calls_list, context):
+    def _truncate_tool_results(self, tool_results):
-        """Execute tool calls with top-level error wrapping.
+        """Truncate oversized tool result content in-place and return the list."""
-
-        If an unexpected exception occurs during tool execution, it is
-        converted into error tool results instead of crashing the stream.
-        """
-        try:
-            if len(tool_calls_list) > 1:
-                with app.app_context():
-                    tool_results = executor.process_tool_calls_parallel(
-                        tool_calls_list, context, max_workers=TOOL_MAX_WORKERS
-                    )
-            else:
-                with app.app_context():
-                    tool_results = executor.process_tool_calls(
-                        tool_calls_list, context
-                    )
-        except Exception as e:
-            logger.exception("Error during tool execution")
-            tool_results = [
-                {
-                    "role": "tool",
-                    "tool_call_id": tc["id"],
-                    "name": tc["function"]["name"],
-                    "content": json.dumps({
-                        "success": False,
-                        "error": f"Tool execution failed: {e}",
-                    }, ensure_ascii=False),
-                }
-                for tc in tool_calls_list
-            ]
-
-        # Truncate oversized tool result content
         for tr in tool_results:
             if len(tr["content"]) > TOOL_RESULT_MAX_LENGTH:
                 try:
@@ -380,9 +354,45 @@ class ChatService:
                     ensure_ascii=False,
                     default=str,
                 )[:TOOL_RESULT_MAX_LENGTH]
-
         return tool_results
 
+    def _execute_tools_safe(self, app, executor, tool_calls_list, context):
+        """Execute tool calls with top-level error wrapping.
+
+        If an unexpected exception occurs during tool execution, it is
+        converted into error tool results instead of crashing the stream.
+        """
+        try:
+            if len(tool_calls_list) > 1:
+                with app.app_context():
+                    return self._truncate_tool_results(
+                        executor.process_tool_calls_parallel(
+                            tool_calls_list, context, max_workers=TOOL_MAX_WORKERS
+                        )
+                    )
+            else:
+                with app.app_context():
+                    return self._truncate_tool_results(
+                        executor.process_tool_calls(
+                            tool_calls_list, context
+                        )
+                    )
+        except Exception as e:
+            logger.exception("Error during tool execution")
+            tool_results = [
+                {
+                    "role": "tool",
+                    "tool_call_id": tc["id"],
+                    "name": tc["function"]["name"],
+                    "content": json.dumps({
+                        "success": False,
+                        "error": f"Tool execution failed: {e}",
+                    }, ensure_ascii=False),
+                }
+                for tc in tool_calls_list
+            ]
+            return self._truncate_tool_results(tool_results)
+
     def _save_message(
         self, app, conv_id, conv_model, msg_id,
         full_content, all_tool_calls, all_tool_results,

backend/tools/__init__.py

@@ -21,7 +21,7 @@ from backend.tools.executor import ToolExecutor
 
 
 # ---------------------------------------------------------------------------
-# Service locator – allows tools (e.g. agent_task) to access LLM client
+# Service locator – allows tools (e.g. multi_agent) to access LLM client
 # ---------------------------------------------------------------------------
 _services: dict = {}
 

backend/tools/builtin/agent.py

@@ -1,8 +1,7 @@
-"""Multi-agent tools for concurrent and batch task execution.
+"""Multi-agent tool for spawning concurrent sub-agents.
 
 Provides:
-- parallel_execute: Run multiple tool calls concurrently
+- multi_agent: Spawn sub-agents with independent LLM conversation loops
-- agent_task: Spawn sub-agents with their own LLM conversation loops
 """
 import json
 from concurrent.futures import ThreadPoolExecutor, as_completed
@@ -13,118 +12,36 @@ from backend.tools.core import registry
 from backend.tools.executor import ToolExecutor
 
 
-# ---------------------------------------------------------------------------
+def _to_executor_calls(tool_calls: list, id_prefix: str = "tc") -> list:
-# parallel_execute – run multiple tool calls concurrently
+    """Normalize tool calls into executor-compatible format.
-# ---------------------------------------------------------------------------
 
-@tool(
+    Accepts two input shapes:
-    name="parallel_execute",
+      - LLM format: {"function": {"name": ..., "arguments": ...}}
-    description=(
+      - Simple format: {"name": ..., "arguments": ...}
-        "Execute multiple tool calls concurrently for better performance. "
-        "Use when you have several independent operations that don't depend on each other "
-        "(e.g. reading multiple files, running multiple searches, fetching several pages). "
-        "Results are returned in the same order as the input."
-    ),
-    parameters={
-        "type": "object",
-        "properties": {
-            "tool_calls": {
-                "type": "array",
-                "items": {
-                    "type": "object",
-                    "properties": {
-                        "name": {
-                            "type": "string",
-                            "description": "Tool name to execute",
-                        },
-                        "arguments": {
-                            "type": "object",
-                            "description": "Arguments for the tool",
-                        },
-                    },
-                    "required": ["name", "arguments"],
-                },
-                "description": "List of tool calls to execute in parallel (max 10)",
-            },
-            "concurrency": {
-                "type": "integer",
-                "description": "Max concurrent executions (1-5, default 3)",
-                "default": 3,
-            },
-        },
-        "required": ["tool_calls"],
-    },
-    category="agent",
-)
-def parallel_execute(arguments: dict) -> dict:
-    """Execute multiple tool calls concurrently.
-
-    Args:
-        arguments: {
-            "tool_calls": [
-                {"name": "file_read", "arguments": {"path": "a.py"}},
-                {"name": "web_search", "arguments": {"query": "python"}}
-            ],
-            "concurrency": 3,
-            "_project_id": "..."  // injected by executor
-        }
-
-    Returns:
-        {"results": [{index, tool_name, success, data/error}]}
     """
-    tool_calls = arguments["tool_calls"]
-    concurrency = min(max(arguments.get("concurrency", 3), 1), 5)
-
-    if len(tool_calls) > 10:
-        return {"success": False, "error": "Maximum 10 tool calls allowed per parallel execution"}
-
-    # Build executor context from injected fields
-    context = {}
-    project_id = arguments.get("_project_id")
-    if project_id:
-        context["project_id"] = project_id
-
-    # Format tool_calls into executor-compatible format
     executor_calls = []
     for i, tc in enumerate(tool_calls):
-        executor_calls.append({
+        if "function" in tc:
-            "id": f"pe-{i}",
+            func = tc["function"]
-            "type": "function",
+            executor_calls.append({
-            "function": {
+                "id": tc.get("id", f"{id_prefix}-{i}"),
-                "name": tc["name"],
+                "type": tc.get("type", "function"),
-                "arguments": json.dumps(tc["arguments"], ensure_ascii=False),
+                "function": {
-            },
+                    "name": func["name"],
-        })
+                    "arguments": func["arguments"],
+                },
+            })
+        else:
+            executor_calls.append({
+                "id": f"{id_prefix}-{i}",
+                "type": "function",
+                "function": {
+                    "name": tc["name"],
+                    "arguments": json.dumps(tc["arguments"], ensure_ascii=False),
+                },
+            })
+    return executor_calls
 
-    # Use ToolExecutor for proper context injection, caching and dedup
-    executor = ToolExecutor(registry=registry, enable_cache=False)
-    executor_results = executor.process_tool_calls_parallel(
-        executor_calls, context, max_workers=concurrency
-    )
-
-    # Format output
-    results = []
-    for er in executor_results:
-        try:
-            content = json.loads(er["content"]) if isinstance(er["content"], str) else er["content"]
-        except (json.JSONDecodeError, TypeError):
-            content = {"success": False, "error": "Failed to parse result"}
-        results.append({
-            "index": len(results),
-            "tool_name": er["name"],
-            **content,
-        })
-
-    return {
-        "success": True,
-        "results": results,
-        "total": len(results),
-    }
-
-
-# ---------------------------------------------------------------------------
-# agent_task – spawn sub-agents with independent LLM conversation loops
-# ---------------------------------------------------------------------------
 
 def _run_sub_agent(
     task_name: str,
@@ -160,7 +77,9 @@ def _run_sub_agent(
         tools = all_tools
 
     executor = ToolExecutor(registry=registry)
-    context = {"project_id": project_id} if project_id else None
+    context = {"model": model}
+    if project_id:
+        context["project_id"] = project_id
 
     # System prompt: instruction + reminder to give a final text answer
     system_msg = (
@@ -170,13 +89,17 @@ def _run_sub_agent(
     )
     messages = [{"role": "system", "content": system_msg}]
 
-    for _ in range(max_iterations):
+    for i in range(max_iterations):
+        is_final = (i == max_iterations - 1)
         try:
             with app.app_context():
                 resp = llm_client.call(
                     model=model,
                     messages=messages,
-                    tools=tools if tools else None,
+                    # On the last iteration, don't pass tools so the LLM is
+                    # forced to produce a final text response instead of calling
+                    # more tools.
+                    tools=None if is_final else (tools if tools else None),
                     stream=False,
                     max_tokens=min(max_tokens, 4096),
                     temperature=0.7,
@@ -196,19 +119,15 @@ def _run_sub_agent(
             message = choice["message"]
 
             if message.get("tool_calls"):
-                messages.append(message)
+                # Only extract needed fields — LLM response may contain extra
+                # fields (e.g. reasoning_content) that the API rejects on re-send
+                messages.append({
+                    "role": "assistant",
+                    "content": message.get("content") or "",
+                    "tool_calls": message["tool_calls"],
+                })
                 tc_list = message["tool_calls"]
-                # Convert OpenAI tool_calls to executor format
+                executor_calls = _to_executor_calls(tc_list)
-                executor_calls = []
-                for tc in tc_list:
-                    executor_calls.append({
-                        "id": tc.get("id", ""),
-                        "type": tc.get("type", "function"),
-                        "function": {
-                            "name": tc["function"]["name"],
-                            "arguments": tc["function"]["arguments"],
-                        },
-                    })
                 tool_results = executor.process_tool_calls(executor_calls, context)
                 messages.extend(tool_results)
             else:
@@ -226,7 +145,7 @@ def _run_sub_agent(
                 "error": str(e),
             }
 
-    # Exhausted iterations without final response — return last LLM output if any
+    # Exhausted iterations without final response
     return {
         "task_name": task_name,
         "success": True,
@@ -234,49 +153,49 @@ def _run_sub_agent(
     }
 
 
-# @tool(
+@tool(
-#     name="agent_task",
+    name="multi_agent",
-#     description=(
+    description=(
-#         "Spawn one or more sub-agents to work on tasks concurrently. "
+        "Spawn multiple sub-agents to work on tasks concurrently. "
-#         "Each agent runs its own independent conversation with the LLM and can use tools. "
+        "Each agent runs its own independent conversation with the LLM and can use tools. "
-#         "Useful for parallel research, multi-file analysis, or dividing complex tasks into sub-tasks. "
+        "Useful for parallel research, multi-file analysis, or dividing complex tasks into sub-tasks. "
-#         "Each agent is limited to 3 iterations and 4096 tokens to control cost."
+        "Each agent is limited to 3 iterations and 4096 tokens to control cost."
-#     ),
+    ),
-#     parameters={
+    parameters={
-#         "type": "object",
+        "type": "object",
-#         "properties": {
+        "properties": {
-#             "tasks": {
+            "tasks": {
-#                 "type": "array",
+                "type": "array",
-#                 "items": {
+                "items": {
-#                     "type": "object",
+                    "type": "object",
-#                     "properties": {
+                    "properties": {
-#                         "name": {
+                        "name": {
-#                             "type": "string",
+                            "type": "string",
-#                             "description": "Short name/identifier for this task",
+                            "description": "Short name/identifier for this task",
-#                         },
+                        },
-#                         "instruction": {
+                        "instruction": {
-#                             "type": "string",
+                            "type": "string",
-#                             "description": "Detailed instruction for the sub-agent",
+                            "description": "Detailed instruction for the sub-agent",
-#                         },
+                        },
-#                         "tools": {
+                        "tools": {
-#                             "type": "array",
+                            "type": "array",
-#                             "items": {"type": "string"},
+                            "items": {"type": "string"},
-#                             "description": (
+                            "description": (
-#                                 "Tool names this agent can use (empty = all tools). "
+                                "Tool names this agent can use (empty = all tools). "
-#                                 "e.g. ['file_read', 'file_list', 'web_search']"
+                                "e.g. ['file_read', 'file_list', 'web_search']"
-#                             ),
+                            ),
-#                         },
+                        },
-#                     },
+                    },
-#                     "required": ["name", "instruction"],
+                    "required": ["name", "instruction"],
-#                 },
+                },
-#                 "description": "Tasks for parallel sub-agents (max 5)",
+                "description": "Tasks for parallel sub-agents (max 5)",
-#             },
+            },
-#         },
+        },
-#         "required": ["tasks"],
+        "required": ["tasks"],
-#     },
+    },
-#     category="agent",
+    category="agent",
-# )
+)
-def agent_task(arguments: dict) -> dict:
+def multi_agent(arguments: dict) -> dict:
     """Spawn sub-agents to work on tasks concurrently.
 
     Args:
@@ -296,7 +215,7 @@ def agent_task(arguments: dict) -> dict:
         }
 
     Returns:
-        {"success": true, "results": [{task_name, success, response/error}]}
+        {"success": true, "results": [{task_name, success, response/error}], "total": int}
     """
     from flask import current_app
 
@@ -309,7 +228,8 @@ def agent_task(arguments: dict) -> dict:
     app = current_app._get_current_object()
 
     # Use injected model/project_id from executor context, fall back to defaults
-    model = arguments.get("_model", "glm-5")
+    from backend.config import DEFAULT_MODEL
+    model = arguments.get("_model") or DEFAULT_MODEL
     project_id = arguments.get("_project_id")
 
     # Execute agents concurrently (max 3 at a time)

backend/tools/executor.py

@@ -56,21 +56,80 @@ class ToolExecutor:
         """Inject context fields into tool arguments in-place.
 
         - file_* tools: inject project_id
-        - agent_task: inject model and project_id (prefixed with _ to avoid collisions)
+        - agent tools (multi_agent): inject _model and _project_id
-        - parallel_execute: inject project_id (prefixed with _ to avoid collisions)
         """
         if not context:
             return
         if name.startswith("file_") and "project_id" in context:
             args["project_id"] = context["project_id"]
-        if name == "agent_task":
+        if name == "multi_agent":
             if "model" in context:
                 args["_model"] = context["model"]
             if "project_id" in context:
                 args["_project_id"] = context["project_id"]
-        if name == "parallel_execute":
+
-            if "project_id" in context:
+    def _prepare_call(
-                args["_project_id"] = context["project_id"]
+        self,
+        call: dict,
+        context: Optional[dict],
+        seen_calls: set,
+    ) -> tuple:
+        """Parse, inject context, check dedup/cache for a single tool call.
+
+        Returns a tagged tuple:
+          ("error",   call_id, name, error_msg)
+          ("cached",  call_id, name, result_dict)  -- dedup or cache hit
+          ("execute", call_id, name, args, cache_key)
+        """
+        name = call["function"]["name"]
+        args_str = call["function"]["arguments"]
+        call_id = call["id"]
+
+        # Parse JSON arguments
+        try:
+            args = json.loads(args_str) if isinstance(args_str, str) else args_str
+        except json.JSONDecodeError:
+            return ("error", call_id, name, "Invalid JSON arguments")
+
+        # Inject context
+        self._inject_context(name, args, context)
+
+        # Dedup within same batch
+        call_key = f"{name}:{json.dumps(args, sort_keys=True)}"
+        if call_key in seen_calls:
+            return ("cached", call_id, name,
+                    {"success": True, "data": None, "cached": True, "duplicate": True})
+        seen_calls.add(call_key)
+
+        # History dedup
+        history_result = self._check_duplicate_in_history(name, args)
+        if history_result is not None:
+            return ("cached", call_id, name, {**history_result, "cached": True})
+
+        # Cache check
+        cache_key = self._make_cache_key(name, args)
+        cached_result = self._get_cached(cache_key)
+        if cached_result is not None:
+            return ("cached", call_id, name, {**cached_result, "cached": True})
+
+        return ("execute", call_id, name, args, cache_key)
+
+    def _execute_and_record(
+        self,
+        name: str,
+        args: dict,
+        cache_key: str,
+    ) -> dict:
+        """Execute a tool, cache result, record history, and return raw result dict."""
+        result = self._execute_tool(name, args)
+        if result.get("success"):
+            self._set_cache(cache_key, result)
+        self._call_history.append({
+            "name": name,
+            "args_str": json.dumps(args, sort_keys=True, ensure_ascii=False),
+            "result": result,
+        })
+        return result
 
     def process_tool_calls_parallel(
         self,
@@ -81,10 +140,6 @@ class ToolExecutor:
         """
         Process tool calls concurrently and return message list (ordered by input).
 
-        Identical logic to process_tool_calls but uses ThreadPoolExecutor so that
-        independent tool calls (e.g. reading 3 files, running 2 searches) execute
-        in parallel instead of sequentially.
-
         Args:
             tool_calls: Tool call list returned by LLM
             context: Optional context info (user_id, project_id, etc.)
@@ -98,80 +153,31 @@ class ToolExecutor:
 
         max_workers = min(max(max_workers, 1), 6)
 
-        # Phase 1: prepare each call (parse args, inject context, check dedup/cache)
+        # Phase 1: prepare (sequential – avoids race conditions on shared state)
-        # This phase is fast and sequential – it must be done before parallelism
+        prepared = [self._prepare_call(call, context, set()) for call in tool_calls]
-        # to avoid race conditions on seen_calls / _call_history / _cache.
-        prepared: List[Optional[tuple]] = [None] * len(tool_calls)
-        seen_calls: set = set()
 
-        for i, call in enumerate(tool_calls):
+        # Phase 2: separate pre-resolved from tasks needing execution
-            name = call["function"]["name"]
-            args_str = call["function"]["arguments"]
-            call_id = call["id"]
-
-            # Parse JSON arguments
-            try:
-                args = json.loads(args_str) if isinstance(args_str, str) else args_str
-            except json.JSONDecodeError:
-                prepared[i] = self._create_error_result(call_id, name, "Invalid JSON arguments")
-                continue
-
-            # Inject context into tool arguments
-            self._inject_context(name, args, context)
-
-            # Dedup within same batch
-            call_key = f"{name}:{json.dumps(args, sort_keys=True)}"
-            if call_key in seen_calls:
-                prepared[i] = self._create_tool_result(
-                    call_id, name,
-                    {"success": True, "data": None, "cached": True, "duplicate": True}
-                )
-                continue
-            seen_calls.add(call_key)
-
-            # History dedup
-            history_result = self._check_duplicate_in_history(name, args)
-            if history_result is not None:
-                prepared[i] = self._create_tool_result(call_id, name, {**history_result, "cached": True})
-                continue
-
-            # Cache check
-            cache_key = self._make_cache_key(name, args)
-            cached_result = self._get_cached(cache_key)
-            if cached_result is not None:
-                prepared[i] = self._create_tool_result(call_id, name, {**cached_result, "cached": True})
-                continue
-
-            # Mark as needing actual execution
-            prepared[i] = ("execute", call_id, name, args, cache_key)
-
-        # Separate pre-resolved results from tasks needing execution
         results: List[dict] = [None] * len(tool_calls)
-        exec_tasks: Dict[int, tuple] = {}  # index -> (call_id, name, args, cache_key)
+        exec_tasks: Dict[int, tuple] = {}
 
         for i, item in enumerate(prepared):
-            if isinstance(item, dict):
+            tag = item[0]
-                results[i] = item
+            if tag == "error":
-            elif isinstance(item, tuple) and item[0] == "execute":
+                _, call_id, name, error_msg = item
+                results[i] = self._create_error_result(call_id, name, error_msg)
+            elif tag == "cached":
+                _, call_id, name, result_dict = item
+                results[i] = self._create_tool_result(call_id, name, result_dict)
+            else:  # "execute"
                 _, call_id, name, args, cache_key = item
                 exec_tasks[i] = (call_id, name, args, cache_key)
 
-        # Phase 2: execute remaining calls in parallel
+        # Phase 3: execute remaining calls in parallel
         if exec_tasks:
             def _run(idx: int, call_id: str, name: str, args: dict, cache_key: str) -> tuple:
                 t0 = time.time()
-                result = self._execute_tool(name, args)
+                result = self._execute_and_record(name, args, cache_key)
                 elapsed = time.time() - t0
-
-                if result.get("success"):
-                    self._set_cache(cache_key, result)
-
-                self._call_history.append({
-                    "name": name,
-                    "args_str": json.dumps(args, sort_keys=True, ensure_ascii=False),
-                    "result": result,
-                })
-
                 return idx, self._create_tool_result(call_id, name, result, execution_time=elapsed)
 
             with ThreadPoolExecutor(max_workers=max_workers) as pool:
@@ -201,65 +207,22 @@ class ToolExecutor:
             Tool response message list, can be appended to messages
         """
         results = []
-        seen_calls = set()  # Track calls within this batch
+        seen_calls: set = set()
 
         for call in tool_calls:
-            name = call["function"]["name"]
+            prepared = self._prepare_call(call, context, seen_calls)
-            args_str = call["function"]["arguments"]
+            tag = prepared[0]
-            call_id = call["id"]
 
-            try:
+            if tag == "error":
-                args = json.loads(args_str) if isinstance(args_str, str) else args_str
+                _, call_id, name, error_msg = prepared
-            except json.JSONDecodeError:
+                results.append(self._create_error_result(call_id, name, error_msg))
-                results.append(self._create_error_result(
+            elif tag == "cached":
-                    call_id, name, "Invalid JSON arguments"
+                _, call_id, name, result_dict = prepared
-                ))
+                results.append(self._create_tool_result(call_id, name, result_dict))
-                continue
+            else:  # "execute"
-
+                _, call_id, name, args, cache_key = prepared
-            # Inject context into tool arguments
+                result = self._execute_and_record(name, args, cache_key)
-            self._inject_context(name, args, context)
-
-            # Check for duplicate within same batch
-            call_key = f"{name}:{json.dumps(args, sort_keys=True)}"
-            if call_key in seen_calls:
-                # Skip duplicate, but still return a result
-                results.append(self._create_tool_result(
-                    call_id, name,
-                    {"success": True, "data": None, "cached": True, "duplicate": True}
-                ))
-                continue
-            seen_calls.add(call_key)
-
-            # Check history for previous call in this session
-            history_result = self._check_duplicate_in_history(name, args)
-            if history_result is not None:
-                result = {**history_result, "cached": True}
                 results.append(self._create_tool_result(call_id, name, result))
-                continue
-
-            # Check cache
-            cache_key = self._make_cache_key(name, args)
-            cached_result = self._get_cached(cache_key)
-            if cached_result is not None:
-                result = {**cached_result, "cached": True}
-                results.append(self._create_tool_result(call_id, name, result))
-                continue
-
-            # Execute tool with retry
-            result = self._execute_tool(name, args)
-            
-            # Cache the result (only cache successful results)
-            if result.get("success"):
-                self._set_cache(cache_key, result)
-            
-            # Add to history
-            self._call_history.append({
-                "name": name,
-                "args_str": json.dumps(args, sort_keys=True, ensure_ascii=False),
-                "result": result
-            })
-            
-            results.append(self._create_tool_result(call_id, name, result))
 
         return results
 

backend/utils/helpers.py

@@ -133,6 +133,13 @@ def build_messages(conv, project_id=None):
     # Query messages directly to avoid detached instance warning
     messages = Message.query.filter_by(conversation_id=conv.id).order_by(Message.created_at.asc()).all()
     for m in messages:
+        # Skip tool messages — they are ephemeral intermediate results, not
+        # meant to be replayed as conversation history (would violate the API
+        # protocol that requires tool messages to follow an assistant message
+        # with matching tool_calls).
+        if m.role == "tool":
+            continue
+
         # Build full content from JSON structure
         full_content = m.content
         try:

docs/Design.md

@@ -266,8 +266,8 @@ classDiagram
         -ToolRegistry registry
         -dict _cache
         -list _call_history
-        +process_tool_calls(calls, context) list
+        +process_tool_calls(list, dict) list
-        +clear_history() void
+        +process_tool_calls_parallel(list, dict, int) list
     }
 
     ChatService --> LLMClient : 使用
@@ -295,18 +295,17 @@ classDiagram
         +register(ToolDefinition) void
         +get(str name) ToolDefinition?
         +list_all() list~dict~
-        +list_by_category(str) list~dict~
         +execute(str name, dict args) dict
-        +remove(str name) bool
-        +has(str name) bool
     }
 
     class ToolExecutor {
         -ToolRegistry registry
+        -bool enable_cache
+        -int cache_ttl
         -dict _cache
         -list _call_history
         +process_tool_calls(list, dict) list
-        +clear_history() void
+        +process_tool_calls_parallel(list, dict, int) list
     }
 
     class ToolResult {
@@ -394,18 +393,19 @@ def validate_path_in_project(path: str, project_dir: Path) -> Path:
 工具执行器自动为文件工具注入 `project_id`：
 
 ```python
-# backend/tools/executor.py
+# backend/tools/executor.py — _inject_context()
 
-def process_tool_calls(self, tool_calls, context=None):
+@staticmethod
-    for call in tool_calls:
+def _inject_context(name: str, args: dict, context: Optional[dict]) -> None:
-        name = call["function"]["name"]
+    # file_* 工具: 注入 project_id
-        args = json.loads(call["function"]["arguments"])
+    if name.startswith("file_") and "project_id" in context:
-
+        args["project_id"] = context["project_id"]
-        # 自动注入 project_id
+    # agent 工具: 注入 _model 和 _project_id
-        if context and name.startswith("file_") and "project_id" in context:
+    if name == "multi_agent":
-            args["project_id"] = context["project_id"]
+        if "model" in context:
-
+            args["_model"] = context["model"]
-        result = self.registry.execute(name, args)
+        if "project_id" in context:
+            args["_project_id"] = context["project_id"]
 ```
 
 ---

docs/ToolSystemDesign.md

@@ -27,19 +27,20 @@ classDiagram
         +register(ToolDefinition tool) void
         +get(str name) ToolDefinition?
         +list_all() list~dict~
-        +list_by_category(str category) list~dict~
         +execute(str name, dict args) dict
-        +remove(str name) bool
-        +has(str name) bool
     }
 
     class ToolExecutor {
         -ToolRegistry registry
+        -bool enable_cache
+        -int cache_ttl
         -dict _cache
         -list _call_history
         +process_tool_calls(list tool_calls, dict context) list~dict~
-        +build_request(list messages, str model, list tools, dict kwargs) dict
+        +process_tool_calls_parallel(list tool_calls, dict context, int max_workers) list~dict~
-        +clear_history() void
+        -_prepare_call(dict call, dict context, set seen_calls) tuple
+        -_execute_and_record(str name, dict args, str cache_key) dict
+        -_inject_context(str name, dict args, dict context) void
     }
 
     class ToolResult {
@@ -88,32 +89,26 @@ classDiagram
 
 ### context 参数
 
-`process_tool_calls()` 接受 `context` 参数，用于自动注入工具参数：
+`process_tool_calls()` / `process_tool_calls_parallel()` 接受 `context` 参数，用于自动注入工具参数：
 
 ```python
-# backend/tools/executor.py
+# backend/tools/executor.py — _inject_context()
 
-def process_tool_calls(
+@staticmethod
-    self,
+def _inject_context(name: str, args: dict, context: Optional[dict]) -> None:
-    tool_calls: List[dict],
-    context: Optional[dict] = None
-) -> List[dict]:
     """
-    Args:
+    - file_* 工具: 注入 project_id
-        tool_calls: LLM 返回的工具调用列表
+    - agent 工具 (multi_agent): 注入 _model 和 _project_id
-        context: 上下文信息，支持：
-            - project_id: 自动注入到文件工具
     """
-    for call in tool_calls:
+    if not context:
-        name = call["function"]["name"]
+        return
-        args = json.loads(call["function"]["arguments"])
+    if name.startswith("file_") and "project_id" in context:
-        
+        args["project_id"] = context["project_id"]
-        # 自动注入 project_id 到文件工具
+    if name == "multi_agent":
-        if context:
+        if "model" in context:
-            if name.startswith("file_") and "project_id" in context:
+            args["_model"] = context["model"]
-                args["project_id"] = context["project_id"]
+        if "project_id" in context:
-        
+            args["_project_id"] = context["project_id"]
-        result = self.registry.execute(name, args)
 ```
 
 ### 使用示例
@@ -122,12 +117,12 @@ def process_tool_calls(
 # backend/services/chat.py
 
 def stream_response(self, conv, tools_enabled=True, project_id=None):
-    # 构建上下文（优先使用请求传递的 project_id，否则回退到对话绑定的）
+    # 构建上下文（包含 model 和 project_id）
-    context = None
+    context = {"model": conv.model}
     if project_id:
-        context = {"project_id": project_id}
+        context["project_id"] = project_id
     elif conv.project_id:
-        context = {"project_id": conv.project_id}
+        context["project_id"] = conv.project_id
 
     # 处理工具调用时自动注入
     tool_results = self.executor.process_tool_calls(tool_calls, context)
@@ -250,6 +245,19 @@ file_read({"path": "src/main.py", "project_id": "xxx"})
 |---------|------|------|
 | `get_weather` | 查询天气信息（模拟） | `city`: 城市名称 |
 
+### 5.6 多智能体工具 (agent)
+
+| 工具名称 | 描述 | 参数 |
+|---------|------|------|
+| `multi_agent` | 派生子 Agent 并发执行任务（最多 5 个） | `tasks`: 任务数组（name, instruction, tools）<br>`_model`: 模型名称（自动注入）<br>`_project_id`: 项目 ID（自动注入） |
+
+**`multi_agent` 工作原理：**
+1. 接收任务数组，每个任务指定 name、instruction 和可选的 tools 列表
+2. 为每个子 Agent 创建独立线程，各自拥有 LLM 对话循环（最多 3 轮迭代，4096 tokens）
+3. 通过 Service Locator 获取 `llm_client` 实例
+4. 子 Agent 在 `app.app_context()` 中运行，可独立调用所有注册工具
+5. 返回 `{success, results: [{task_name, success, response/error}], total}`
+
 ---
 
 ## 六、核心特性
@@ -285,7 +293,6 @@ def my_tool(arguments: dict) -> dict:
 
 - **批次内去重**：同一批次中相同工具+参数的调用会被跳过
 - **历史去重**：同一会话内已调用过的工具会直接返回缓存结果
-- **自动清理**：新会话开始时调用 `clear_history()` 清理历史
 
 ### 6.4 无自动重试
 
@@ -308,13 +315,45 @@ def my_tool(arguments: dict) -> dict:
 def init_tools() -> None:
     """初始化所有内置工具"""
     from backend.tools.builtin import (
-        code, crawler, data, weather, file_ops
+        code, crawler, data, weather, file_ops, agent
     )
 ```
 
 ---
 
-## 八、扩展新工具
+## 八、Service Locator
+
+工具系统提供 Service Locator 模式，允许工具访问共享服务（如 LLM 客户端）：
+
+```python
+# backend/tools/__init__.py
+
+_services: dict = {}
+
+def register_service(name: str, service) -> None:
+    """注册共享服务"""
+    _services[name] = service
+
+def get_service(name: str):
+    """获取已注册的服务，不存在则返回 None"""
+    return _services.get(name)
+```
+
+### 使用方式
+
+```python
+# 在应用初始化时注册（routes/__init__.py）
+from backend.tools import register_service
+register_service("llm_client", llm_client)
+
+# 在工具中使用（agent.py）
+from backend.tools import get_service
+llm_client = get_service("llm_client")
+```
+
+---
+
+## 九、扩展新工具
 
 ### 添加新工具