增加智能体

docs/superpowers/specs/2026-05-20-router-subagent-design.md

@@ -0,0 +1,222 @@
+# 问答模式路由智能体设计方案
+
+## 1. 背景与目标
+
+路由器智能体（`f0e75f44f7594053ba1567ed89258f35`）根据用户问题判断最合适的问答模式（fast/rag/bigMode/moreModes/taskChat/hxchat/deerFlow），选择后需要调用相应子 Agent 执行，路由器对执行结果进行**完整性质检**，通过则流式输出到前端，不通过则重新匹配模式并再次执行。
+
+## 2. 整体流程
+
+```
+用户问题
+    │
+    ▼
+┌──────────────────────────────┐
+│ 路由器智能体                  │
+│ (已有 SOUL.md)                │
+│ ① 判断最佳模式                │
+└─────────────┬────────────────┘
+              │ 选择模式
+              ▼
+┌──────────────────────────────┐
+│ 子 Agent 执行                 │
+│ (完整执行完毕，结果缓存)        │
+└─────────────┬────────────────┘
+              │ 执行完毕
+              ▼
+┌──────────────────────────────┐
+│ 路由器质检                    │
+│ LLM 判断结果是否完整覆盖用户问题 │
+└─────────────┬────────────────┘
+              │
+         ③ 质检结果
+         ┌────┴────┐
+         │通过      │不通过
+         ▼         ▼
+     流式输出   重新匹配模式
+     到前端     (最多重试2次)
+```
+
+- **最大重试次数**：每个问题最多 2 次重试（总共最多 3 次执行），超过后强制输出当前结果并标注"结果可能不完整"
+
+## 3. 目录结构
+
+```
+.deer-flow/agents/f0e75f44f7594053ba1567ed89258f35/
+├── SOUL.md                     ← 路由器智能体（已有）
+├── config.yaml                 ← 路由器配置
+└── subagents/
+    ├── fast/
+    │   ├── SOUL.md             ← 快速问答子 Agent
+    │   └── config.yaml         ← tool_groups 声明
+    ├── rag/
+    │   ├── SOUL.md
+    │   └── config.yaml
+    ├── bigMode/
+    │   ├── SOUL.md
+    │   └── config.yaml
+    ├── moreModes/
+    │   ├── SOUL.md
+    │   └── config.yaml
+    ├── taskChat/
+    │   ├── SOUL.md
+    │   └── config.yaml
+    ├── hxchat/
+    │   ├── SOUL.md
+    │   └── config.yaml
+    └── deerFlow/
+        ├── SOUL.md
+        └── config.yaml
+```
+
+**父子关系**：子 Agent 目录放在路由器下二级 `subagents/`，通过目录层级体现父子结构。
+
+## 4. 子 Agent 配置
+
+### 4.1 config.yaml 格式
+
+```yaml
+id: rag
+name: 知识库问答子Agent
+description: 从知识库检索并返回引用
+tool_groups:
+  - knowledge          # 知识库检索工具组
+  - file:read          # 只读文件工具
+skills: []
+model: deepseek-chat
+```
+
+### 4.2 tool_groups 说明
+
+- tool_groups 在系统级 `config.yaml` 中统一定义（`tool_groups` 列表）
+- 子 Agent 的 config.yaml 通过 `tool_groups` 字段声明自己可以使用哪些工具组
+- 工具组示例：web（网页搜索）、knowledge（知识库检索）、file:read、file:write、bash 等
+
+## 5. 调用方式
+
+### 5.1 复用 Gateway API（方案 A — 推荐）
+
+路由器通过 DeerFlow 现有 `/api/threads/{thread_id}/runs/stream` 接口调用子 Agent：
+
+```
+POST /api/threads/{thread_id}/runs/stream
+{
+  "assistant_id": "f0e75f44f7594053ba1567ed89258f35",
+  "context": {
+    "subagent_mode": "rag"          ← 路由器注入子模式
+  },
+  "input": {
+    "messages": [...]
+  },
+  "stream_mode": ["values"]
+}
+```
+
+**执行步骤**：
+1. 路由器启动一个 run，assistant_id 指向路由器本身
+2. 路由器通过 `context.subagent_mode` 告知 DeerFlow 运行时本次应加载哪个子 Agent
+3. DeerFlow 运行时加载 `subagents/{subagent_mode}/SOUL.md` 和对应的 tool_groups
+4. 子 Agent 完整执行，结果缓存在 StreamBridge 中
+5. 执行完毕后，路由器读取完整结果
+
+**context.subagent_mode 注入机制**：
+- `build_run_config` 中新增 `subagent_mode` 到 `_CONTEXT_CONFIGURABLE_KEYS`
+- `make_lead_agent` 读取 `configurable.subagent_mode`，如果存在则加载对应子 Agent 的 SOUL.md 和工具配置
+- 如果 `subagent_mode` 不存在，则正常运行路由器自己的 SOUL.md
+
+### 5.2 质检提示词
+
+路由器在子 Agent 执行完毕后，用 LLM 进行完整性质检：
+
+```
+你是一个质检员。上游子 Agent 已根据问题执行完毕。
+请判断以下结果是否完整覆盖了用户问题的所有子问题。
+
+用户原始问题：{original_question}
+
+子 Agent 执行结果：
+{agent_output}
+
+请只返回 JSON 格式：
+{
+  "passed": true/false,
+  "reason": "判断理由（10字内）",
+  "missing_parts": ["缺失的子问题1", "缺失的子问题2"]  // 如果 passed=false
+}
+```
+
+**质检维度**：完整性（结果是否覆盖了用户问题的所有子问题）
+
+## 6. 关键代码改动
+
+### 6.1 DeerFlow 运行时改动
+
+**文件**：`packages/harness/deerflow/agents/lead_agent/agent.py`
+
+改动点：
+- `make_lead_agent` 读取 `configurable.subagent_mode`
+- 如果存在，加载 `.deer-flow/agents/{agent_id}/subagents/{subagent_mode}/SOUL.md` 和对应 `config.yaml`
+- 工具绑定使用子 Agent 的 tool_groups，而非父 Agent 的
+
+### 6.2 build_run_config 改动
+
+**文件**：`app/gateway/services.py`
+
+改动点：
+- `_CONTEXT_CONFIGURABLE_KEYS` 新增 `subagent_mode`
+- `merge_run_context_overrides` 将 `subagent_mode` 透传到 configurable
+
+### 6.3 路由器 SOUL.md 改动
+
+路由器 SOUL.md 新增功能描述：
+- 角色扩展：增加"在确定模式后，调用相应子 Agent 执行并对结果进行质检"的职责
+- 工具描述：新增一个质检工具（路由器自己调用 LLM 执行质检）
+
+### 6.4 新增路由器质检工具
+
+**文件**：`packages/harness/deerflow/tools/builtins/router_quality_tool.py`（新建）
+
+```python
+@tool
+def router_quality_check(original_question: str, agent_output: str) -> str:
+    """质检工具 — 判断子 Agent 输出是否完整覆盖用户问题
+
+    返回 JSON：{"passed": bool, "reason": str, "missing_parts": list[str]}
+    """
+    # 调用 LLM 执行质检逻辑
+```
+
+## 7. 数据流
+
+```
+用户问题
+    │
+    ▼
+路由器 SOUL.md 判断模式
+    │
+    ▼
+启动子 Agent Run（context.subagent_mode = rag）
+    │
+    ▼
+子 Agent SOUL.md + tool_groups 执行
+    │
+    ▼
+StreamBridge 缓存完整结果
+    │
+    ▼
+路由器调用 router_quality_check 质检
+    │
+    ├── passed=true → 流式输出到前端
+    └── passed=false → 重新匹配模式（最多2次重试）
+```
+
+## 8. 测试计划
+
+- **单元测试**：`tests/test_router_subagent.py` — 测试模式判断、质检逻辑、重试机制
+- **集成测试**：手动调用 `/api/threads/{thread_id}/runs/stream`，验证完整流程
+- **质检提示词测试**：针对不同问题类型测试质检结果的准确性
+
+## 9. 依赖项
+
+- 路由器复用现有 Gateway API，无需新建接口
+- 质检使用 LLM（deepseek-chat 或配置中指定的模型）
+- tool_groups 机制依赖系统级 config.yaml 中的工具组定义

offline-backend-20260512/backend/app/gateway/services.py

@@ -160,6 +160,11 @@ _CONTEXT_CONFIGURABLE_KEYS: frozenset[str] = frozenset(
         "max_concurrent_subagents",
         "agent_name",
         "is_bootstrap",
+        "subagent_mode",
+        "search_space_id",
+        "mentioned_document_ids",
+        "pic_query",
+        "subagent_model_name",
     }
 )
 
@@ -302,12 +307,16 @@ def _collect_requested_agent_ids(body: Any) -> set[str]:
 
 async def _sync_legacy_agent_records(store) -> None:
     """Import existing folder-backed agents as built-in records."""
+    import sys
     from deerflow.config.agents_config import list_custom_agents
 
+    print(f"[_sync_legacy_agent_records] store type: {type(store).__name__}, id: {id(store)}", file=sys.stderr)
+    logger.info("_sync_legacy_agent_records: starting sync")
     for agent_cfg in list_custom_agents():
         agent_id = agent_cfg.id or agent_cfg.name
         if not agent_id:
             continue
+        logger.info(f"  Syncing agent: {agent_id}")
         await store.ensure_builtin(
             {
                 "id": agent_id,
@@ -316,6 +325,7 @@ async def _sync_legacy_agent_records(store) -> None:
                 "published": True,
             }
         )
+    logger.info("_sync_legacy_agent_records: done")
 
 
 async def _enforce_agent_access(body: Any, request: Request) -> None:
@@ -324,12 +334,20 @@ async def _enforce_agent_access(body: Any, request: Request) -> None:
         return
 
     from app.gateway.deps import get_agent_store
-    from deerflow.config.agents_config import validate_agent_id
+    from deerflow.config.agents_config import validate_agent_id, list_custom_agents
     from deerflow.runtime.user_context import get_effective_user_id
 
     store = get_agent_store(request)
+    import sys
+    print(f"[_enforce_agent_access] store type: {type(store).__name__}, id: {id(store)}", file=sys.stderr)
     await _sync_legacy_agent_records(store)
     user_id = get_effective_user_id()
+    logger.info(f"_enforce_agent_access: user_id={user_id}, agent_ids={agent_ids}")
+
+    # Debug: list all agents from list_custom_agents
+    all_agents = list_custom_agents()
+    logger.info(f"  list_custom_agents() returned {len(all_agents)} agents: {[a.id for a in all_agents]}")
+
     for agent_id in agent_ids:
         try:
             normalized = validate_agent_id(agent_id)
@@ -337,7 +355,13 @@ async def _enforce_agent_access(body: Any, request: Request) -> None:
             raise HTTPException(status_code=422, detail=str(exc)) from exc
         if normalized is None:
             continue
-        if await store.get_visible(normalized, user_id) is None:
+        record = await store.get_visible(normalized, user_id)
+        logger.info(f"  get_visible({normalized}, {user_id}) = {record}")
+        # Also check get_any
+        any_record = await store.get_any(normalized)
+        import sys
+        print(f"[_enforce_agent_access] get_any({normalized}) = {any_record}", file=sys.stderr)
+        if record is None:
             raise HTTPException(status_code=404, detail=f"Agent {normalized} not found")
 
 

offline-backend-20260512/backend/packages/harness/deerflow/agents/lead_agent/agent.py

@@ -360,8 +360,43 @@ def _make_lead_agent(config: RunnableConfig, *, app_config: AppConfig):
         logger.warning(f"Thinking mode is enabled but model '{model_name}' does not support it; fallback to non-thinking mode.")
         thinking_enabled = False
 
+    # Check if we're in subagent mode (router agent delegating to a subagent)
+    subagent_mode = cfg.get("subagent_mode")
+    if subagent_mode and agent_id:
+        # Load subagent config and SOUL.md from subagents/{subagent_mode}/
+        from deerflow.config.paths import get_paths
+
+        paths = get_paths()
+        subagent_dir = paths.agent_dir(agent_id) / "subagents" / subagent_mode
+        subagent_soul_path = subagent_dir / "SOUL.md"
+        subagent_config_path = subagent_dir / "config.yaml"
+
+        if subagent_soul_path.exists():
+            subagent_soul = subagent_soul_path.read_text(encoding="utf-8").strip()
+        else:
+            subagent_soul = None
+
+        subagent_tool_groups = None
+        if subagent_config_path.exists():
+            try:
+                import yaml
+
+                with open(subagent_config_path, encoding="utf-8") as f:
+                    subagent_data = yaml.safe_load(f) or {}
+                subagent_tool_groups = subagent_data.get("tool_groups")
+            except yaml.YAMLError:
+                logger.warning(f"Failed to parse subagent config: {subagent_config_path}")
+
+        logger.info(
+            "Subagent mode(%s) for router(%s) -> subagent_soul: %s, tool_groups: %s",
+            subagent_mode,
+            agent_id,
+            "found" if subagent_soul else "not found",
+            subagent_tool_groups,
+        )
+
     logger.info(
-        "Create Agent(%s) -> thinking_enabled: %s, reasoning_effort: %s, model_name: %s, is_plan_mode: %s, subagent_enabled: %s, max_concurrent_subagents: %s, writing_mode: %s",
+        "Create Agent(%s) -> thinking_enabled: %s, reasoning_effort: %s, model_name: %s, is_plan_mode: %s, subagent_enabled: %s, max_concurrent_subagents: %s, writing_mode: %s, subagent_mode: %s",
         agent_id or "default",
         thinking_enabled,
         reasoning_effort,
@@ -370,6 +405,7 @@ def _make_lead_agent(config: RunnableConfig, *, app_config: AppConfig):
         subagent_enabled,
         max_concurrent_subagents,
         writing_mode,
+        subagent_mode,
     )
 
     # Inject run metadata for LangSmith trace tagging
@@ -396,7 +432,7 @@ def _make_lead_agent(config: RunnableConfig, *, app_config: AppConfig):
             "subagent_enabled": subagent_enabled,
             "writing_mode": writing_mode,
             "writing_artifact_path": writing_artifact_path,
-            "tool_groups": agent_config.tool_groups if agent_config else None,
+            "tool_groups": subagent_tool_groups if subagent_mode else (agent_config.tool_groups if agent_config else None),
             "available_skills": runtime_available_skills,
         }
     )
@@ -425,11 +461,14 @@ def _make_lead_agent(config: RunnableConfig, *, app_config: AppConfig):
         )
 
     # Default lead agent (unchanged behavior)
+    # Use subagent tool groups if in subagent mode, otherwise use parent agent config
+    effective_tool_groups = subagent_tool_groups if subagent_mode else (agent_config.tool_groups if agent_config else None)
+
     return create_agent(
         model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, reasoning_effort=reasoning_effort, app_config=resolved_app_config),
         tools=get_available_tools(
             model_name=model_name,
-            groups=agent_config.tool_groups if agent_config else None,
+            groups=effective_tool_groups,
             subagent_enabled=subagent_enabled,
             is_scheduled_run=cfg.get("is_scheduled_run", False),
             app_config=resolved_app_config,
@@ -444,6 +483,7 @@ def _make_lead_agent(config: RunnableConfig, *, app_config: AppConfig):
             writing_artifact_path=writing_artifact_path,
             available_skills=(set(agent_config.skills or []) if agent_config else None),
             app_config=resolved_app_config,
+            soul_override=subagent_soul if subagent_mode else None,
         ),
         state_schema=ThreadState,
     )

offline-backend-20260512/backend/packages/harness/deerflow/agents/lead_agent/prompt.py

@@ -823,6 +823,7 @@ def apply_prompt_template(
     writing_artifact_path: str | None = None,
     available_skills: set[str] | None = None,
     app_config: AppConfig | None = None,
+    soul_override: str | None = None,
 ) -> str:
     storage_agent_id = agent_id if agent_id is not None else agent_name
 
@@ -894,7 +895,7 @@ Writing mode is enabled for this run.
     # Format the prompt with dynamic skills and memory
     prompt = SYSTEM_PROMPT_TEMPLATE.format(
         agent_name=agent_name or storage_agent_id or "CM 助手",
-        soul=get_agent_soul(storage_agent_id),
+        soul=f"<soul>\n{soul_override}\n</soul>\n" if soul_override else get_agent_soul(storage_agent_id),
         skills_section=skills_section,
         deferred_tools_section=deferred_tools_section,
         writing_mode_section=writing_mode_section,

offline-backend-20260512/backend/packages/harness/deerflow/tools/builtins/__init__.py

@@ -1,4 +1,5 @@
 from .clarification_tool import ask_clarification_tool
+from .fast_chat_tool import fast_chat_tool, fast_llm_answer
 from .present_file_tool import present_file_tool
 from .scheduled_task_tool import scheduled_task_tool
 from .setup_agent_tool import setup_agent
@@ -12,4 +13,6 @@ __all__ = [
     "scheduled_task_tool",
     "view_image_tool",
     "task_tool",
+    "fast_chat_tool",
+    "fast_llm_answer",
 ]

offline-backend-20260512/backend/packages/harness/deerflow/tools/builtins/fast_chat_tool.py

@@ -0,0 +1,140 @@
+"""Fast chat tool - call new_chat API for fast Q&A mode."""
+
+import json
+import logging
+from typing import Annotated, Any
+
+from langchain.tools import InjectedToolCallId, ToolRuntime, tool
+from langchain_core.messages import HumanMessage
+from langchain_core.runnables import RunnableConfig
+from langgraph.typing import ContextT
+
+from deerflow.agents.thread_state import ThreadState
+from deerflow.config.app_config import get_app_config
+from deerflow.models import create_chat_model
+
+logger = logging.getLogger(__name__)
+
+
+def _get_runtime_value(runtime: ToolRuntime[ContextT, ThreadState], key: str, default: Any = None) -> Any:
+    """Get value from runtime context or config."""
+    if runtime.context and isinstance(runtime.context, dict):
+        value = runtime.context.get(key)
+        if value is not None:
+            return value
+
+    runtime_config = getattr(runtime, "config", None) or {}
+    configurable = runtime_config.get("configurable", {})
+    if isinstance(configurable, dict):
+        value = configurable.get(key)
+        if value is not None:
+            return value
+
+    return default
+
+
+@tool("fast_chat", parse_docstring=True, return_direct=True)
+async def fast_chat_tool(
+    runtime: ToolRuntime[ContextT, ThreadState],
+    prompt: str,
+    tool_call_id: Annotated[str, InjectedToolCallId],
+    search_space_id: int | None = None,
+    mentioned_document_ids: list[int] | None = None,
+    pic_query: list[dict[str, Any]] | None = None,
+    model_name: str | None = None,
+) -> str:
+    """Fast chat tool - 快速问答模式调用后端聊天接口.
+
+    用于 fast 子 Agent 直接调用后端 new_chat 接口进行快速问答.
+
+    Args:
+        runtime: 运行时上下文，自动注入.
+        prompt: 用户问题内容.
+        tool_call_id: 工具调用 ID，自动注入.
+        search_space_id: 搜索空间 ID (可选，从 context.subagent_search_space_id 获取).
+        mentioned_document_ids: 引用的文档 ID 列表 (可选).
+        pic_query: 图片查询列表 (可选).
+        model_name: 使用的模型名称 (默认使用 context.subagent_model_name 或 OpenAI3).
+
+    Returns:
+        API 响应结果的 JSON 字符串.
+    """
+    import asyncio
+    import urllib.error
+    import urllib.request
+
+    try:
+        # 从 context 读取参数，支持外部传入或运行时获取
+        thread_id = _get_runtime_value(runtime, "thread_id")
+        if thread_id is None:
+            return json.dumps({"error": "thread_id not found in context", "passed": False})
+
+        # 参数优先级：显式传入 > context 注入
+        effective_search_space_id = search_space_id if search_space_id is not None else _get_runtime_value(runtime, "subagent_search_space_id")
+        effective_model_name = model_name or _get_runtime_value(runtime, "subagent_model_name", "OpenAI3")
+        effective_mentioned_ids = mentioned_document_ids or _get_runtime_value(runtime, "subagent_mentioned_document_ids") or []
+        effective_pic_query = pic_query or _get_runtime_value(runtime, "subagent_pic_query") or []
+
+        app_config = get_app_config()
+        backend_url = getattr(app_config, "backend_url", "http://localhost:8001")
+
+        messages = [{"role": "user", "content": prompt}]
+
+        req_body = {
+            "chat_id": thread_id,
+            "user_query": prompt,
+            "search_space_id": effective_search_space_id,
+            "messages": messages,
+            "mentioned_document_ids": effective_mentioned_ids,
+            "pic_query": effective_pic_query,
+            "model_name": effective_model_name,
+        }
+
+        req_url = f"{backend_url}/api/v1/new_chat"
+
+        def _make_request():
+            data = json.dumps(req_body).encode("utf-8")
+            req = urllib.request.Request(
+                req_url,
+                data=data,
+                headers={"Content-Type": "application/json"},
+                method="POST",
+            )
+            with urllib.request.urlopen(req, timeout=30) as response:
+                return response.read().decode("utf-8")
+
+        result = await asyncio.to_thread(_make_request)
+        return result
+
+    except urllib.error.HTTPError as exc:
+        logger.warning(f"Fast chat API HTTP error: {exc.code} {exc.reason}")
+        return json.dumps({"error": f"HTTP {exc.code}: {exc.reason}", "passed": False})
+    except Exception as exc:
+        logger.warning(f"Fast chat API failed: {exc}")
+        return json.dumps({"error": str(exc), "passed": False})
+
+
+@tool("fast_llm_answer", parse_docstring=True, return_direct=True)
+def fast_llm_answer(prompt: str) -> str:
+    """Fast LLM answer tool - 使用 LLM 直接回答简单问题.
+
+    当 fast 模式不需要调用外部 API 时，直接使用 LLM 回答简单事实查询.
+
+    Args:
+        prompt: 用户问题.
+
+    Returns:
+        LLM 回答内容.
+    """
+    try:
+        app_config = get_app_config()
+        model_name = app_config.models[0].name if app_config.models else None
+        model = create_chat_model(name=model_name, thinking_enabled=False, app_config=app_config)
+
+        response = model.invoke([HumanMessage(content=prompt)], config=RunnableConfig())
+        content = response.content if hasattr(response, "content") else str(response)
+        return content
+
+    except Exception as exc:
+        logger.warning(f"Fast LLM answer failed: {exc}")
+        return f"抱歉，处理您的问题时出现错误: {str(exc)}"

offline-backend-20260512/backend/packages/harness/deerflow/tools/builtins/router_quality_tool.py

@@ -0,0 +1,75 @@
+"""Router quality check tool — validates sub-agent output completeness."""
+
+import json
+import logging
+
+from langchain_core.messages import HumanMessage
+from langchain_core.runnables import RunnableConfig
+from langchain_core.tools import tool
+
+from deerflow.config.app_config import get_app_config
+from deerflow.models import create_chat_model
+
+logger = logging.getLogger(__name__)
+
+QUALITY_CHECK_PROMPT = """你是一个质检员。上游子 Agent 已根据问题执行完毕。
+请判断以下结果是否完整覆盖了用户问题的所有子问题。
+
+用户原始问题：{original_question}
+
+子 Agent 执行结果：
+{agent_output}
+
+请只返回 JSON 格式：
+{{
+  "passed": true/false,
+  "reason": "判断理由（10字内）",
+  "missing_parts": ["缺失的子问题1", "缺失的子问题2"]  // 如果 passed=false
+}}"""
+
+
+@tool("router_quality_check", parse_docstring=True, return_direct=True)
+def router_quality_check(original_question: str, agent_output: str) -> str:
+    """质检工具 — 判断子 Agent 输出是否完整覆盖用户问题。
+
+    路由器在子 Agent 执行完毕后调用此工具进行完整性质检。
+    质检通过则流式输出到前端，不通过则重新匹配模式并再次执行。
+
+    Args:
+        original_question: 用户原始问题。
+        agent_output: 子 Agent 的执行结果。
+
+    Returns:
+        JSON 格式的质检结果：{{"passed": bool, "reason": str, "missing_parts": list[str]}}
+    """
+    try:
+        app_config = get_app_config()
+        model_name = app_config.models[0].name if app_config.models else None
+        model = create_chat_model(name=model_name, thinking_enabled=False, app_config=app_config)
+
+        prompt = QUALITY_CHECK_PROMPT.format(
+            original_question=original_question,
+            agent_output=agent_output,
+        )
+
+        response = model.invoke([HumanMessage(content=prompt)], config=RunnableConfig())
+        content = response.content if hasattr(response, "content") else str(response)
+
+        # Try to parse JSON from the response
+        # The model might wrap it in ```json or just return raw JSON
+        json_str = content.strip()
+        if json_str.startswith("```json"):
+            json_str = json_str[7:]
+        if json_str.startswith("```"):
+            json_str = json_str[3:]
+        if json_str.endswith("```"):
+            json_str = json_str[:-3]
+        json_str = json_str.strip()
+
+        result = json.loads(json_str)
+        return json.dumps(result, ensure_ascii=False)
+
+    except Exception as exc:
+        logger.warning(f"Router quality check failed: {exc}")
+        # Fallback: pass with unknown reason on error
+        return json.dumps({"passed": True, "reason": "质检异常默认通过", "missing_parts": []})

offline-backend-20260512/backend/packages/harness/deerflow/tools/tools.py

@@ -6,7 +6,7 @@ from deerflow.config import get_app_config
 from deerflow.config.app_config import AppConfig
 from deerflow.reflection import resolve_variable
 from deerflow.sandbox.security import is_host_bash_allowed
-from deerflow.tools.builtins import ask_clarification_tool, present_file_tool, scheduled_task_tool, task_tool, view_image_tool
+from deerflow.tools.builtins import ask_clarification_tool, fast_chat_tool, fast_llm_answer, present_file_tool, scheduled_task_tool, task_tool, view_image_tool
 from deerflow.tools.builtins.tool_search import reset_deferred_registry
 
 logger = logging.getLogger(__name__)
@@ -14,6 +14,8 @@ logger = logging.getLogger(__name__)
 BUILTIN_TOOLS = [
     present_file_tool,
     ask_clarification_tool,
+    fast_chat_tool,
+    fast_llm_answer,
 ]
 
 SUBAGENT_TOOLS = [

offline-backend-20260512/backend/pyproject.toml

@@ -22,6 +22,7 @@ dependencies = [
     "pyjwt>=2.9.0",
     "email-validator>=2.0.0",
     "croniter>=6.2.2",
+    "greenlet>=3.4.0",
 ]
 
 [project.optional-dependencies]

offline-backend-20260512/backend/uv.lock

@@ -750,6 +750,7 @@ dependencies = [
     { name = "dingtalk-stream" },
     { name = "email-validator" },
     { name = "fastapi" },
+    { name = "greenlet" },
     { name = "httpx" },
     { name = "langgraph-sdk" },
     { name = "lark-oapi" },
@@ -785,6 +786,7 @@ requires-dist = [
     { name = "dingtalk-stream", specifier = ">=0.24.3" },
     { name = "email-validator", specifier = ">=2.0.0" },
     { name = "fastapi", specifier = ">=0.115.0" },
+    { name = "greenlet", specifier = ">=3.4.0" },
     { name = "httpx", specifier = ">=0.28.0" },
     { name = "langgraph-sdk", specifier = ">=0.1.51" },
     { name = "lark-oapi", specifier = ">=1.4.0" },