sqlite pool and change agent cache to tools cache

agent/agent_memory_cache.py

@@ -2,10 +2,12 @@
 基于内存的 Agent 缓存管理模块
 使用 cachetools 库实现 TTLCache 和 LRUCache
 """
+import hashlib
+import json
 import logging
 import time
 import threading
-from typing import Any, Optional, Dict, Tuple
+from typing import Any, Optional, Dict, Tuple, List
 from collections import OrderedDict
 from datetime import datetime, timedelta
 
@@ -310,6 +312,48 @@ class AgentMemoryCacheManager:
         """返回缓存中的项数"""
         return len(self.cache)
 
+    def get_mcp_tools(self, mcp_settings: dict) -> Optional[List]:
+        """
+        获取缓存的 MCP tools
+
+        Args:
+            mcp_settings: MCP 配置字典
+
+        Returns:
+            缓存的 tools 列表或 None
+        """
+        cache_key = self._get_mcp_cache_key(mcp_settings)
+        return self.get(cache_key)
+
+    def set_mcp_tools(self, mcp_settings: dict, tools: List, ttl: Optional[int] = None) -> bool:
+        """
+        缓存 MCP tools
+
+        Args:
+            mcp_settings: MCP 配置字典
+            tools: 要缓存的 tools 列表
+            ttl: 过期时间（秒），如果为 None 则使用默认值
+
+        Returns:
+            是否成功设置缓存
+        """
+        cache_key = self._get_mcp_cache_key(mcp_settings)
+        return self.set(cache_key, tools, ttl=ttl)
+
+    def _get_mcp_cache_key(self, mcp_settings: dict) -> str:
+        """
+        根据 mcp_settings 生成缓存键
+
+        Args:
+            mcp_settings: MCP 配置字典
+
+        Returns:
+            缓存键字符串
+        """
+        # 将 mcp_settings 转换为 JSON 字符串并生成哈希
+        settings_str = json.dumps(mcp_settings, sort_keys=True)
+        return f"mcp_tools:{hashlib.md5(settings_str.encode()).hexdigest()}"
+
 
 # 全局缓存管理器实例
 _global_cache_manager: Optional[AgentMemoryCacheManager] = None

agent/checkpoint_manager.py

@@ -0,0 +1,186 @@
+"""
+全局 SQLite Checkpointer 管理器
+解决高并发场景下的数据库锁定问题
+"""
+import asyncio
+import logging
+import os
+from typing import Optional
+
+import aiosqlite
+from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
+
+from utils.settings import (
+    CHECKPOINT_DB_PATH,
+    CHECKPOINT_WAL_MODE,
+    CHECKPOINT_BUSY_TIMEOUT,
+    CHECKPOINT_POOL_SIZE,
+)
+
+logger = logging.getLogger('app')
+
+
+class CheckpointerManager:
+    """
+    全局 Checkpointer 管理器，使用连接池复用 SQLite 连接
+
+    主要功能：
+    1. 全局单例连接管理，避免每次请求创建新连接
+    2. 预配置 WAL 模式和 busy_timeout
+    3. 连接池支持高并发访问
+    4. 优雅关闭机制
+    """
+
+    def __init__(self):
+        self._pool: asyncio.Queue[AsyncSqliteSaver] = asyncio.Queue()
+        self._lock = asyncio.Lock()
+        self._initialized = False
+        self._closed = False
+        self._pool_size = CHECKPOINT_POOL_SIZE
+        self._db_path = CHECKPOINT_DB_PATH
+
+    async def initialize(self) -> None:
+        """初始化连接池"""
+        if self._initialized:
+            return
+
+        async with self._lock:
+            if self._initialized:
+                return
+
+            logger.info(f"Initializing CheckpointerManager with pool_size={self._pool_size}")
+
+            # 确保目录存在
+            os.makedirs(os.path.dirname(self._db_path), exist_ok=True)
+
+            # 创建连接池
+            for i in range(self._pool_size):
+                try:
+                    conn = await self._create_configured_connection()
+                    checkpointer = AsyncSqliteSaver(conn=conn)
+                    # 预先调用 setup 确保表结构已创建
+                    await checkpointer.setup()
+                    await self._pool.put(checkpointer)
+                    logger.debug(f"Created checkpointer connection {i+1}/{self._pool_size}")
+                except Exception as e:
+                    logger.error(f"Failed to create checkpointer connection {i+1}: {e}")
+                    raise
+
+            self._initialized = True
+            logger.info("CheckpointerManager initialized successfully")
+
+    async def _create_configured_connection(self) -> aiosqlite.Connection:
+        """
+        创建已配置的 SQLite 连接
+
+        配置包括：
+        1. WAL 模式 (Write-Ahead Logging) - 允许读写并发
+        2. busy_timeout - 等待锁定的最长时间
+        3. 其他优化参数
+        """
+        conn = aiosqlite.connect(self._db_path)
+
+        # 等待连接建立
+        await conn.__aenter__()
+
+        # 设置 busy timeout（必须在连接建立后设置）
+        await conn.execute(f"PRAGMA busy_timeout = {CHECKPOINT_BUSY_TIMEOUT}")
+
+        # 如果启用 WAL 模式
+        if CHECKPOINT_WAL_MODE:
+            await conn.execute("PRAGMA journal_mode = WAL")
+            await conn.execute("PRAGMA synchronous = NORMAL")
+            # WAL 模式下的优化配置
+            await conn.execute("PRAGMA wal_autocheckpoint = 1000")
+            await conn.execute("PRAGMA cache_size = -64000")  # 64MB 缓存
+            await conn.execute("PRAGMA temp_store = MEMORY")
+
+        await conn.commit()
+
+        return conn
+
+    async def acquire_for_agent(self) -> AsyncSqliteSaver:
+        """
+        为 agent 获取 checkpointer
+
+        注意：此方法获取的 checkpointer 需要手动归还
+        使用 return_to_pool() 方法归还
+
+        Returns:
+            AsyncSqliteSaver 实例
+        """
+        if not self._initialized:
+            raise RuntimeError("CheckpointerManager not initialized. Call initialize() first.")
+
+        checkpointer = await self._pool.get()
+        logger.debug(f"Acquired checkpointer from pool, remaining: {self._pool.qsize()}")
+        return checkpointer
+
+    async def return_to_pool(self, checkpointer: AsyncSqliteSaver) -> None:
+        """
+        归还 checkpointer 到池
+
+        Args:
+            checkpointer: 要归还的 checkpointer 实例
+        """
+        await self._pool.put(checkpointer)
+        logger.debug(f"Returned checkpointer to pool, remaining: {self._pool.qsize()}")
+
+    async def close(self) -> None:
+        """关闭所有连接"""
+        if self._closed:
+            return
+
+        async with self._lock:
+            if self._closed:
+                return
+
+            logger.info("Closing CheckpointerManager...")
+
+            # 清空池并关闭所有连接
+            while not self._pool.empty():
+                try:
+                    checkpointer = self._pool.get_nowait()
+                    if checkpointer.conn:
+                        await checkpointer.conn.close()
+                except asyncio.QueueEmpty:
+                    break
+
+            self._closed = True
+            self._initialized = False
+            logger.info("CheckpointerManager closed")
+
+    def get_pool_stats(self) -> dict:
+        """获取连接池状态统计"""
+        return {
+            "db_path": self._db_path,
+            "pool_size": self._pool_size,
+            "available_connections": self._pool.qsize(),
+            "initialized": self._initialized,
+            "closed": self._closed
+        }
+
+
+# 全局单例
+_global_manager: Optional[CheckpointerManager] = None
+
+
+def get_checkpointer_manager() -> CheckpointerManager:
+    """获取全局 CheckpointerManager 单例"""
+    global _global_manager
+    if _global_manager is None:
+        _global_manager = CheckpointerManager()
+    return _global_manager
+
+
+async def init_global_checkpointer() -> None:
+    """初始化全局 checkpointer 管理器"""
+    manager = get_checkpointer_manager()
+    await manager.initialize()
+
+
+async def close_global_checkpointer() -> None:
+    """关闭全局 checkpointer 管理器"""
+    global _global_manager
+    if _global_manager is not None:
+        await _global_manager.close()

agent/deep_assistant.py

@@ -1,6 +1,7 @@
 import json
 import logging
 import time
+import copy
 from typing import Any, Dict
 from langchain.chat_models import init_chat_model
 # from deepagents import create_deep_agent
@@ -41,14 +42,26 @@ def read_mcp_settings():
 
 
 async def get_tools_from_mcp(mcp):
-    """从MCP配置中提取工具"""
+    """从MCP配置中提取工具（带缓存）"""
+    start_time = time.time()
     # 防御式处理：确保 mcp 是列表且长度大于 0，且包含 mcpServers
     if not isinstance(mcp, list) or len(mcp) == 0 or "mcpServers" not in mcp[0]:
+        logger.info(f"get_tools_from_mcp: invalid mcp config, elapsed: {time.time() - start_time:.3f}s")
         return []
 
-    # 修改 mcp[0]["mcpServers"] 列表，把 type 字段改成 transport
+    # 尝试从缓存获取
+    cache_manager = get_memory_cache_manager()
+    cached_tools = cache_manager.get_mcp_tools(mcp)
+    if cached_tools is not None:
+        logger.info(f"get_tools_from_mcp: cached {len(cached_tools)} tools, elapsed: {time.time() - start_time:.3f}s")
+        return cached_tools
+
+    # 深拷贝 mcp 配置，避免修改原始配置（影响缓存键）
+    mcp_copy = copy.deepcopy(mcp)
+
+    # 修改 mcp_copy[0]["mcpServers"] 列表，把 type 字段改成 transport
     # 如果没有 transport，则根据是否存在 url 默认 transport 为 http 或 stdio
-    for cfg in mcp[0]["mcpServers"].values():
+    for cfg in mcp_copy[0]["mcpServers"].values():
         if "type" in cfg:
             cfg.pop("type")
         if "transport" not in cfg:
@@ -62,53 +75,49 @@ async def get_tools_from_mcp(mcp):
             if "sse_read_timeout" not in cfg:
                 cfg["sse_read_timeout"] = MCP_SSE_READ_TIMEOUT
 
-    # 确保 mcp[0]["mcpServers"] 是字典类型
-    if not isinstance(mcp[0]["mcpServers"], dict):
+    # 确保 mcp_copy[0]["mcpServers"] 是字典类型
+    if not isinstance(mcp_copy[0]["mcpServers"], dict):
+        logger.info(f"get_tools_from_mcp: mcpServers is not dict, elapsed: {time.time() - start_time:.3f}s")
         return []
 
     try:
-        mcp_client = MultiServerMCPClient(mcp[0]["mcpServers"])
+        mcp_client = MultiServerMCPClient(mcp_copy[0]["mcpServers"])
         mcp_tools = await mcp_client.get_tools()
+
+        # 缓存结果
+        cache_manager.set_mcp_tools(mcp, mcp_tools)
+
+        logger.info(f"get_tools_from_mcp: loaded {len(mcp_tools)} tools, elapsed: {time.time() - start_time:.3f}s")
         return mcp_tools
-    except Exception:
+    except Exception as e:
         # 发生异常时返回空列表，避免上层调用报错
+        logger.info(f"get_tools_from_mcp: error {e}, elapsed: {time.time() - start_time:.3f}s")
         return []
 
 async def init_agent(config: AgentConfig):
     """
     初始化 Agent，支持持久化内存和对话摘要
 
+    注意：不再缓存 agent，只缓存 mcp_tools
+    返回 (agent, checkpointer) 元组，调用后需要归还 checkpointer
+
     Args:
         config: AgentConfig 对象，包含所有初始化参数
-        mcp: MCP配置（如果为None则使用配置中的mcp_settings）
+
+    Returns:
+        (agent, checkpointer) 元组
     """
 
-    # 初始化 checkpointer 和中间件
+
+    # 从连接池获取 checkpointer
     checkpointer = None
     if config.session_id:
-        os.makedirs("projects/memory", exist_ok=True)
-        conn = aiosqlite.connect("projects/memory/checkpoints.db")
-        checkpointer = AsyncSqliteSaver(conn=conn)
+        from .checkpoint_manager import get_checkpointer_manager
+        manager = get_checkpointer_manager()
+        checkpointer = await manager.acquire_for_agent()
         await prepare_checkpoint_message(config, checkpointer)
-    # 获取缓存管理器
-    cache_manager = get_memory_cache_manager()
-
-    # 获取唯一的缓存键
-    cache_key = config.get_unique_cache_id()
-
-    # 如果有缓存键，检查缓存
-    if cache_key:
-        # 尝试从缓存中获取 agent
-        cached_agent = cache_manager.get(cache_key)
-        if cached_agent is not None:
-            logger.info(f"Using cached agent for session: {config.session_id}, cache_key: {cache_key}")
-            return cached_agent
-        else:
-            logger.info(f"Cache miss for session: {config.session_id}, cache_key: {cache_key}")
-
-    # 没有缓存或缓存已过期，创建新的 agent
-    logger.info(f"Creating new agent for session: {getattr(config, 'session_id', 'no-session')}")
 
+    # 加载配置
     final_system_prompt = await load_system_prompt_async(
         config.project_dir, config.language, config.system_prompt, config.robot_type, config.bot_id, config.user_identifier
     )
@@ -123,10 +132,11 @@ async def init_agent(config: AgentConfig):
     config.system_prompt = mcp_settings
     config.mcp_settings = system_prompt
 
+    # 获取 mcp_tools（缓存逻辑已内置到 get_tools_from_mcp 中）
     mcp_tools = await get_tools_from_mcp(mcp_settings)
 
     # 检测或使用指定的提供商
-    model_provider,base_url = detect_provider(config.model_name, config.model_server)
+    model_provider, base_url = detect_provider(config.model_name, config.model_server)
     # 构建模型参数
     model_kwargs = {
         "model": config.model_name,
@@ -139,6 +149,10 @@ async def init_agent(config: AgentConfig):
         model_kwargs.update(config.generate_cfg)
     llm_instance = init_chat_model(**model_kwargs)
 
+    # 创建新的 agent（不再缓存）
+    logger.info(f"Creating new agent for session: {getattr(config, 'session_id', 'no-session')}")
+
+    create_start = time.time()
     if config.robot_type == "deep_agent":
         # 使用 DeepAgentX 创建 agent
         agent, composite_backend = create_cli_agent(
@@ -160,8 +174,8 @@ async def init_agent(config: AgentConfig):
         tool_output_middleware = ToolOutputLengthMiddleware(
             max_length=getattr(config.generate_cfg, 'tool_output_max_length', None) if config.generate_cfg else None or TOOL_OUTPUT_MAX_LENGTH,
             truncation_strategy=getattr(config.generate_cfg, 'tool_output_truncation_strategy', 'smart') if config.generate_cfg else 'smart',
-            tool_filters=getattr(config.generate_cfg, 'tool_output_filters', None) if config.generate_cfg else None,  # 可配置特定工具
-            exclude_tools=getattr(config.generate_cfg, 'tool_output_exclude', []) if config.generate_cfg else [],  # 排除的工具
+            tool_filters=getattr(config.generate_cfg, 'tool_output_filters', None) if config.generate_cfg else None,
+            exclude_tools=getattr(config.generate_cfg, 'tool_output_exclude', []) if config.generate_cfg else [],
             preserve_code_blocks=getattr(config.generate_cfg, 'preserve_code_blocks', True) if config.generate_cfg else True,
             preserve_json=getattr(config.generate_cfg, 'preserve_json', True) if config.generate_cfg else True
         )
@@ -171,7 +185,7 @@ async def init_agent(config: AgentConfig):
             summarization_middleware = SummarizationMiddleware(
                 model=llm_instance,
                 max_tokens_before_summary=SUMMARIZATION_MAX_TOKENS,
-                messages_to_keep=20,  # 摘要后保留最近 20 条消息
+                messages_to_keep=20,
                 summary_prompt="请简洁地总结以上对话的要点，包括重要的用户信息、讨论过的话题和关键结论。"
             )
             middleware.append(summarization_middleware)
@@ -181,13 +195,7 @@ async def init_agent(config: AgentConfig):
             system_prompt=system_prompt,
             tools=mcp_tools,
             middleware=middleware,
-            checkpointer=checkpointer  # 传入 checkpointer 以启用持久化
+            checkpointer=checkpointer
         )
-    
-    # 如果有缓存键，将 agent 加入缓存
-    if cache_key:
-        # 使用 DiskCache 缓存管理器存储 agent
-        cache_manager.set(cache_key, agent)
-        logger.info(f"Cached agent for session: {config.session_id}, cache_key: {cache_key}")
-
-    return agent
+    logger.info(f"create {config.robot_type} elapsed: {time.time() - create_start:.3f}s")
+    return agent, checkpointer

fastapi_app.py

@@ -4,6 +4,7 @@ import uuid
 import time
 import multiprocessing
 import sys
+from contextlib import asynccontextmanager
 
 import uvicorn
 from fastapi import FastAPI
@@ -19,7 +20,26 @@ from utils.log_util.logger import init_with_fastapi
 # Import route modules
 from routes import chat, files, projects, system
 
-app = FastAPI(title="Database Assistant API", version="1.0.0")
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """FastAPI 应用生命周期管理"""
+    # 启动时初始化
+    logger.info("Starting up...")
+    from agent.checkpoint_manager import init_global_checkpointer
+    await init_global_checkpointer()
+    logger.info("Global checkpointer initialized")
+
+    yield
+
+    # 关闭时清理
+    logger.info("Shutting down...")
+    from agent.checkpoint_manager import close_global_checkpointer
+    await close_global_checkpointer()
+    logger.info("Global checkpointer closed")
+
+
+app = FastAPI(title="Database Assistant API", version="1.0.0", lifespan=lifespan)
 
 init_with_fastapi(app)
 

routes/chat.py

@@ -64,12 +64,13 @@ async def enhanced_generate_stream_response(
 
         # Agent 任务（准备 + 流式处理）
         async def agent_task():
+            checkpointer = None
             try:
                 # 开始流式处理
                 logger.info(f"Starting agent stream response")
                 chunk_id = 0
                 message_tag = ""
-                agent = await init_agent(config)
+                agent, checkpointer = await init_agent(config)
                 async for msg, metadata in agent.astream({"messages": config.messages}, stream_mode="messages", config=config.invoke_config(), max_tokens=MAX_OUTPUT_TOKENS):
                     new_content = ""
 
@@ -115,6 +116,11 @@ async def enhanced_generate_stream_response(
             except Exception as e:
                 logger.error(f"Error in agent task: {e}")
                 await output_queue.put(("agent_done", None))
+            finally:
+                if checkpointer:
+                    from agent.checkpoint_manager import get_checkpointer_manager
+                    manager = get_checkpointer_manager()
+                    await manager.return_to_pool(checkpointer)
 
         # 并发执行任务
         # 只有在 enable_thinking 为 True 时才执行 preamble 任务
@@ -203,41 +209,49 @@ async def create_agent_and_generate_response(
             headers={"Cache-Control": "no-cache", "Connection": "keep-alive"}
         )
     
-    agent = await init_agent(config)
-    # 使用更新后的 messages
-    agent_responses = await agent.ainvoke({"messages": config.messages}, config=config.invoke_config(), max_tokens=MAX_OUTPUT_TOKENS)
-    append_messages = agent_responses["messages"][len(config.messages):]
-    response_text = ""
-    for msg in append_messages:
-        if isinstance(msg,AIMessage):
-            if len(msg.text)>0:
-                meta_message_tag = msg.additional_kwargs.get("message_tag", "ANSWER")
-                output_text = msg.text.replace("<think>","").replace("</think>","") if meta_message_tag == "THINK" else msg.text
-                response_text += f"[{meta_message_tag}]\n"+output_text+ "\n"
-            if len(msg.tool_calls)>0:
-                response_text += "".join([f"[TOOL_CALL] {tool['name']}\n{json.dumps(tool["args"]) if isinstance(tool["args"],dict) else tool["args"]}\n" for tool in msg.tool_calls])
-        elif isinstance(msg,ToolMessage) and config.tool_response:
-            response_text += f"[TOOL_RESPONSE] {msg.name}\n{msg.text}\n"
+    agent, checkpointer = await init_agent(config)
+    try:
+        # 使用更新后的 messages
+        agent_responses = await agent.ainvoke({"messages": config.messages}, config=config.invoke_config(), max_tokens=MAX_OUTPUT_TOKENS)
+        append_messages = agent_responses["messages"][len(config.messages):]
+        response_text = ""
+        for msg in append_messages:
+            if isinstance(msg,AIMessage):
+                if len(msg.text)>0:
+                    meta_message_tag = msg.additional_kwargs.get("message_tag", "ANSWER")
+                    output_text = msg.text.replace("````","").replace("````","") if meta_message_tag == "THINK" else msg.text
+                    response_text += f"[{meta_message_tag}]\n"+output_text+ "\n"
+                if len(msg.tool_calls)>0:
+                    response_text += "".join([f"[TOOL_CALL] {tool['name']}\n{json.dumps(tool["args"]) if isinstance(tool["args"],dict) else tool["args"]}\n" for tool in msg.tool_calls])
+            elif isinstance(msg,ToolMessage) and config.tool_response:
+                response_text += f"[TOOL_RESPONSE] {msg.name}\n{msg.text}\n"
 
-    if len(response_text) > 0:
-        # 构造OpenAI格式的响应
-        return ChatResponse(
-            choices=[{
-                "index": 0,
-                "message": {
-                    "role": "assistant",
-                    "content": response_text
-                },
-                "finish_reason": "stop"
-            }],
-            usage={
-                "prompt_tokens": sum(len(msg.get("content", "")) for msg in config.messages),
-                "completion_tokens": len(response_text),
-                "total_tokens": sum(len(msg.get("content", "")) for msg in config.messages) + len(response_text)
-            }
-        )
-    else:
-        raise HTTPException(status_code=500, detail="No response from agent")
+        if len(response_text) > 0:
+            # 构造OpenAI格式的响应
+            result = ChatResponse(
+                choices=[{
+                    "index": 0,
+                    "message": {
+                        "role": "assistant",
+                        "content": response_text
+                    },
+                    "finish_reason": "stop"
+                }],
+                usage={
+                    "prompt_tokens": sum(len(msg.get("content", "")) for msg in config.messages),
+                    "completion_tokens": len(response_text),
+                    "total_tokens": sum(len(msg.get("content", "")) for msg in config.messages) + len(response_text)
+                }
+            )
+        else:
+            raise HTTPException(status_code=500, detail="No response from agent")
+    finally:
+        if checkpointer:
+            from agent.checkpoint_manager import get_checkpointer_manager
+            manager = get_checkpointer_manager()
+            await manager.return_to_pool(checkpointer)
+
+    return result
 
 
 @router.post("/api/v1/chat/completions")
@@ -348,18 +362,27 @@ async def chat_warmup_v1(request: ChatRequest, authorization: Optional[str] = He
         # 创建 AgentConfig 对象
         config = AgentConfig.from_v1_request(request, api_key, project_dir, generate_cfg, messages)
 
-        # 预热：初始化agent（这会触发缓存）
-        logger.info(f"Warming up agent for bot_id: {bot_id}")
-        agent = await init_agent(config)
+        # 预热 mcp_tools 缓存
+        logger.info(f"Warming up mcp_tools for bot_id: {bot_id}")
+        from agent.deep_assistant import get_tools_from_mcp
+        from agent.prompt_loader import load_mcp_settings_async
 
-        # 获取缓存键
-        cache_key = config.get_unique_cache_id() if hasattr(config, 'get_unique_cache_id') else None
+        # 加载 mcp_settings
+        final_mcp_settings = await load_mcp_settings_async(
+            config.project_dir, config.mcp_settings, config.bot_id, config.robot_type
+        )
+        mcp_settings = final_mcp_settings if final_mcp_settings else []
+        if not isinstance(mcp_settings, list) or len(mcp_settings) == 0:
+            mcp_settings = []
+
+        # 预热 mcp_tools（缓存逻辑已内置到 get_tools_from_mcp）
+        mcp_tools = await get_tools_from_mcp(mcp_settings)
 
         return {
             "status": "warmed_up",
             "bot_id": bot_id,
-            "cache_key": cache_key,
-            "message": "Agent has been initialized and cached successfully"
+            "mcp_tools_count": len(mcp_tools),
+            "message": "MCP tools have been cached successfully"
         }
 
     except Exception as e:
@@ -435,18 +458,27 @@ async def chat_warmup_v2(request: ChatRequestV2, authorization: Optional[str] =
         # 创建 AgentConfig 对象
         config = AgentConfig.from_v2_request(request, bot_config, project_dir, messages)
 
-        # 预热：初始化agent（这会触发缓存）
-        logger.info(f"Warming up agent for bot_id: {bot_id}")
-        agent = await init_agent(config)
+        # 预热 mcp_tools 缓存
+        logger.info(f"Warming up mcp_tools for bot_id: {bot_id}")
+        from agent.deep_assistant import get_tools_from_mcp
+        from agent.prompt_loader import load_mcp_settings_async
 
-        # 获取缓存键
-        cache_key = config.get_unique_cache_id() if hasattr(config, 'get_unique_cache_id') else None
+        # 加载 mcp_settings
+        final_mcp_settings = await load_mcp_settings_async(
+            config.project_dir, config.mcp_settings, config.bot_id, config.robot_type
+        )
+        mcp_settings = final_mcp_settings if final_mcp_settings else []
+        if not isinstance(mcp_settings, list) or len(mcp_settings) == 0:
+            mcp_settings = []
+
+        # 预热 mcp_tools（缓存逻辑已内置到 get_tools_from_mcp）
+        mcp_tools = await get_tools_from_mcp(mcp_settings)
 
         return {
             "status": "warmed_up",
             "bot_id": bot_id,
-            "cache_key": cache_key,
-            "message": "Agent has been initialized and cached successfully"
+            "mcp_tools_count": len(mcp_tools),
+            "message": "MCP tools have been cached successfully"
         }
 
     except HTTPException:

utils/log_util/logger.py

@@ -45,9 +45,12 @@ class Formatter(logging.Formatter):
         return s
 
     def format(self, record):
-        # 处理 trace_id
+        # 处理 trace_id - 在没有请求上下文时使用默认值
         if not hasattr(record, "trace_id"):
-            record.trace_id = getattr(g, "trace_id")
+            try:
+                record.trace_id = getattr(g, "trace_id")
+            except LookupError:
+                record.trace_id = "N/A"
         # 处理 user_id
         # if not hasattr(record, "user_id"):
         #     record.user_id = getattr(g, "user_id")

utils/settings.py

@@ -37,4 +37,24 @@ DEFAULT_THINKING_ENABLE = os.getenv("DEFAULT_THINKING_ENABLE", "true") == "true"
 MCP_HTTP_TIMEOUT = int(os.getenv("MCP_HTTP_TIMEOUT", 60))  # HTTP 请求超时（秒）
 MCP_SSE_READ_TIMEOUT = int(os.getenv("MCP_SSE_READ_TIMEOUT", 300))  # SSE 读取超时（秒）
 
+# ============================================================
+# SQLite Checkpoint Configuration
+# ============================================================
+
+# Checkpoint 数据库路径
+CHECKPOINT_DB_PATH = os.getenv("CHECKPOINT_DB_PATH", "./projects/memory/checkpoints.db")
+
+# 启用 WAL 模式 (Write-Ahead Logging)
+# WAL 模式允许读写并发，大幅提升并发性能
+CHECKPOINT_WAL_MODE = os.getenv("CHECKPOINT_WAL_MODE", "true") == "true"
+
+# Busy Timeout (毫秒)
+# 当数据库被锁定时，等待的最长时间（毫秒）
+CHECKPOINT_BUSY_TIMEOUT = int(os.getenv("CHECKPOINT_BUSY_TIMEOUT", "10000"))
+
+
+# 连接池大小
+# 同时可以持有的最大连接数
+CHECKPOINT_POOL_SIZE = int(os.getenv("CHECKPOINT_POOL_SIZE", "30"))
+