refactor(agents): 实现统一工具加载逻辑并使用中间件动态配置

将各agent的工具加载逻辑统一改为通过RuntimeConfigMiddleware动态配置
移除直接的工具参数传递，改为从MCP服务获取工具并合并到中间件
在chat_stream_service中添加知识库权限过滤功能

TODO：目前历史记录的加载导致会话混乱

src/agents/chatbot/graph.py

@@ -6,7 +6,7 @@ from src.agents.common.middlewares import (
     RuntimeConfigMiddleware,
     inject_attachment_context,
 )
-from src.agents.common.tools import get_buildin_tools
+from src.services.mcp_service import get_tools_from_all_servers
 
 
 class ChatbotAgent(BaseAgent):
@@ -20,15 +20,16 @@ class ChatbotAgent(BaseAgent):
     async def get_graph(self, **kwargs):
         """构建图"""
         context = self.context_schema()
+        all_mcp_tools = await get_tools_from_all_servers()  # 因为异步加载，无法放在 RuntimeConfigMiddleware 的 __init__ 中
 
         # 使用 create_agent 创建智能体
+        # 注意：tools 参数由 RuntimeConfigMiddleware 在 wrap_model_call 中动态设置
         graph = create_agent(
             model=load_chat_model(context.model),
-            tools=get_buildin_tools(),
             system_prompt=context.system_prompt,
             middleware=[
                 inject_attachment_context,  # 附件上下文注入
-                RuntimeConfigMiddleware(),  # 运行时配置应用（模型/工具/知识库/MCP/提示词）
+                RuntimeConfigMiddleware(extra_tools=all_mcp_tools),  # 运行时配置应用（模型/工具/知识库/MCP/提示词）
                 ModelRetryMiddleware(),  # 模型重试中间件
             ],
             checkpointer=await self._get_checkpointer(),

src/agents/common/middlewares/runtime_config_middleware.py

@@ -1,14 +1,14 @@
 from __future__ import annotations
 
 from collections.abc import Callable
-from dataclasses import replace
 from typing import Any
 
 from langchain.agents.middleware import AgentMiddleware, ModelRequest, ModelResponse
 
-from src import knowledge_base
+from src.utils.logging_config import logger
 from src.agents.common import load_chat_model
-from src.agents.common.tools import get_tools_from_context
+from src.agents.common.tools import get_kb_based_tools, get_buildin_tools
+from src.services.mcp_service import get_enabled_mcp_tools
 
 
 def _is_system_message(msg: Any) -> bool:
@@ -28,38 +28,32 @@ def _get_message_content(msg: Any) -> str | None:
 
 
 class RuntimeConfigMiddleware(AgentMiddleware):
+    """运行时配置中间件 - 应用模型/工具/知识库/MCP/提示词配置
+
+    注意：所有可能用到的知识库工具必须在初始化时预加载并注册到 self.tools
+    运行时根据配置从 self.tools 中筛选工具，不能动态添加新工具
+    """
+
+    def __init__(self, *, extra_tools: list[Any] | None = None):
+        """初始化中间件
+
+        Args:
+            extra_tools: 额外工具列表（从 create_agent 的 tools 参数传入）
+        """
+        super().__init__()
+        # 这里的工具只是提供给 langchain 调用，并不是真正的绑定在模型上
+        self.kb_tools = get_kb_based_tools()
+        self.tools = self.kb_tools + (extra_tools or [])
+        logger.debug(f"Initialized tools: {len(self.tools)}")
+
     async def awrap_model_call(
         self, request: ModelRequest, handler: Callable[[ModelRequest], ModelResponse]
     ) -> ModelResponse:
-        # 虽然功能实现了， 但是总感觉怪怪的 TODO 优化下
         runtime_context = request.runtime.context
 
-        effective_context = runtime_context
-        blocked_knowledges: list[str] = []
-        requested_knowledges = getattr(runtime_context, "knowledges", None)
-        department_id = getattr(runtime_context, "department_id", None)
-
-        if department_id and isinstance(requested_knowledges, list) and requested_knowledges:
-            user_info = {"role": "user", "department_id": department_id}
-            accessible_databases = await knowledge_base.get_databases_by_user(user_info)
-            accessible_kb_names = {
-                db.get("name")
-                for db in accessible_databases.get("databases", [])
-                if isinstance(db, dict) and db.get("name")
-            }
-            filtered_knowledges = [kb for kb in requested_knowledges if kb in accessible_kb_names]
-            blocked_knowledges = [kb for kb in requested_knowledges if kb not in accessible_kb_names]
-            if blocked_knowledges:
-                effective_context = replace(runtime_context)
-                effective_context.knowledges = filtered_knowledges
-
         model = load_chat_model(getattr(runtime_context, "model", None))
-        tools = await get_tools_from_context(effective_context)
+        enabled_tools = await self.get_tools_from_context(runtime_context)
-
         system_prompt = getattr(runtime_context, "system_prompt", None)
-        notice = None
-        if blocked_knowledges:
-            notice = f"注意：已自动过滤无权访问的知识库：{', '.join(blocked_knowledges)}"
 
         existing_systems: list[Any] = []
         remaining: list[Any] = []
@@ -83,10 +77,33 @@ class RuntimeConfigMiddleware(AgentMiddleware):
                 new_systems.append(existing_systems.pop(idx))
                 existing_contents.pop(idx)
 
-        if notice and notice not in existing_contents:
-            new_systems.append({"role": "system", "content": notice})
-
         messages = [*new_systems, *existing_systems, *remaining]
 
-        request = request.override(model=model, tools=tools, messages=messages)
+        request = request.override(model=model, tools=enabled_tools, messages=messages)
         return await handler(request)
+
+
+    async def get_tools_from_context(self, context) -> list:
+        """从上下文配置中获取工具列表"""
+        # 1. 基础工具 (从 context.tools 中筛选)
+        selected_tools = []
+
+        if context.tools:
+            # 创建工具映射表
+            tools_map = {t.name: t for t in self.tools}
+            for tool_name in context.tools:
+                if tool_name in tools_map:
+                    selected_tools.append(tools_map[tool_name])
+
+        # 2. 知识库工具
+        if context.knowledges:
+            kb_tools = get_kb_based_tools(db_names=context.knowledges)
+            selected_tools.extend(kb_tools)
+
+        # 3. MCP 工具（使用统一入口，自动过滤 disabled_tools）
+        if context.mcps:
+            for server_name in context.mcps:
+                mcp_tools = await get_enabled_mcp_tools(server_name)
+                selected_tools.extend(mcp_tools)
+
+        return selected_tools

src/agents/deep_agent/graph.py

@@ -4,11 +4,12 @@ from deepagents.middleware.filesystem import FilesystemMiddleware
 from deepagents.middleware.patch_tool_calls import PatchToolCallsMiddleware
 from deepagents.middleware.subagents import SubAgentMiddleware
 from langchain.agents import create_agent
-from langchain.agents.middleware import ModelRequest, SummarizationMiddleware, TodoListMiddleware, dynamic_prompt
+from langchain.agents.middleware import ModelRequest, ModelRetryMiddleware, SummarizationMiddleware, TodoListMiddleware, dynamic_prompt
 
 from src.agents.common import BaseAgent, load_chat_model
-from src.agents.common.middlewares import inject_attachment_context
+from src.agents.common.middlewares import RuntimeConfigMiddleware, inject_attachment_context
 from src.agents.common.tools import get_tavily_search
+from src.services.mcp_service import get_tools_from_all_servers
 
 from .context import DeepContext
 from .prompts import DEEP_PROMPT
@@ -93,24 +94,26 @@ class DeepAgent(BaseAgent):
 
         model = load_chat_model(context.model)
         sub_model = load_chat_model(context.subagents_model)
-        tools = await self.get_tools()
+        search_tools = await self.get_tools()
+        all_mcp_tools = await get_tools_from_all_servers()
+        # 合并搜索工具和 MCP 工具
 
         # Build subagents with search tools
-        research_sub_agent = _get_research_sub_agent(tools)
+        research_sub_agent = _get_research_sub_agent(search_tools)
 
         # 使用 create_deep_agent 创建深度智能体
         graph = create_agent(
             model=model,
-            tools=tools,
             system_prompt=context.system_prompt,
             middleware=[
                 context_aware_prompt,  # 动态系统提示词
                 inject_attachment_context,  # 附件上下文注入
+                RuntimeConfigMiddleware(extra_tools=all_mcp_tools),
                 TodoListMiddleware(),
                 FilesystemMiddleware(),
                 SubAgentMiddleware(
                     default_model=sub_model,
-                    default_tools=tools,
+                    default_tools=search_tools,
                     subagents=[critique_sub_agent, research_sub_agent],
                     default_middleware=[
                         TodoListMiddleware(),  # 子智能体也有 todo 列表

src/agents/mini_agent/graph.py

@@ -1,7 +1,10 @@
 from langchain.agents import create_agent
 
 from src.agents.common import BaseAgent, load_chat_model
-from src.agents.common.tools import get_tools_from_context
+from src.agents.common.middlewares import (
+    RuntimeConfigMiddleware,
+)
+from src.services.mcp_service import get_tools_from_all_servers
 
 
 class MiniAgent(BaseAgent):
@@ -12,13 +15,16 @@ class MiniAgent(BaseAgent):
         super().__init__(**kwargs)
 
     async def get_graph(self, **kwargs):
+        """构建图"""
         context = self.context_schema.from_file(module_name=self.module_name)
+        all_mcp_tools = await get_tools_from_all_servers()
 
-        # 创建 MiniAgent
         graph = create_agent(
             model=load_chat_model(context.model),
             system_prompt=context.system_prompt,
-            tools=await get_tools_from_context(context),
+            middleware=[
+                RuntimeConfigMiddleware(extra_tools=all_mcp_tools),
+            ],
             checkpointer=await self._get_checkpointer(),
         )
 

src/agents/reporter/graph.py

@@ -2,10 +2,15 @@ from dataclasses import dataclass, field
 from typing import Annotated
 
 from langchain.agents import create_agent
+from langchain.agents.middleware import ModelRetryMiddleware
 
 from src.agents.common import BaseAgent, BaseContext, load_chat_model
+from src.agents.common.middlewares import (
+    RuntimeConfigMiddleware,
+)
 from src.agents.common.toolkits.mysql import get_mysql_tools
-from src.agents.common.tools import gen_tool_info, get_buildin_tools, get_tools_from_context
+from src.agents.common.tools import gen_tool_info, get_buildin_tools
+from src.services.mcp_service import get_tools_from_all_servers
 from src.utils import logger
 
 
@@ -35,13 +40,18 @@ class SqlReporterAgent(BaseAgent):
         super().__init__(**kwargs)
 
     async def get_graph(self, **kwargs):
+        """构建图"""
         context = self.context_schema.from_file(module_name=self.module_name)
+        all_mcp_tools = await get_tools_from_all_servers()
+        # 合并 MySQL 工具和 MCP 工具
+        extra_tools = get_mysql_tools() + all_mcp_tools
 
-        # 创建 SqlReporterAgent
         graph = create_agent(
-            model=load_chat_model(context.model),  # 使用 context 中的模型配置
+            model=load_chat_model(context.model),
             system_prompt=context.system_prompt,
-            tools=await get_tools_from_context(context, extra_tools=get_mysql_tools()),
+            middleware=[
+                RuntimeConfigMiddleware(extra_tools=extra_tools),
+            ],
             checkpointer=await self._get_checkpointer(),
         )
 

src/services/chat_stream_service.py

@@ -331,6 +331,24 @@ async def stream_agent_chat(
             logger.error(f"Error loading attachments for thread_id={thread_id}: {e}")
             input_context["attachments"] = []
 
+        # 根据用户权限过滤知识库
+        requested_knowledge_names = input_context.get("knowledges")
+        if requested_knowledge_names and isinstance(requested_knowledge_names, list) and requested_knowledge_names:
+            user_info = {"role": "user", "department_id": department_id}
+            accessible_databases = await knowledge_base.get_databases_by_user(user_info)
+            accessible_kb_names = {
+                db.get("name")
+                for db in accessible_databases.get("databases", [])
+                if isinstance(db, dict) and db.get("name")
+            }
+            filtered_knowledge_names = [kb for kb in requested_knowledge_names if kb in accessible_kb_names]
+            blocked_knowledge_names = [kb for kb in requested_knowledge_names if kb not in accessible_kb_names]
+            if blocked_knowledge_names:
+                logger.warning(
+                    f"用户 {user_id} 无权访问知识库: {blocked_knowledge_names}, 已自动过滤"
+                )
+            input_context["knowledges"] = filtered_knowledge_names
+
         full_msg = None
         accumulated_content = []
         langgraph_config = {"configurable": {"thread_id": thread_id, "user_id": user_id}}