添加长期记忆

app/agent.py

@@ -11,8 +11,11 @@ from langchain_openai import ChatOpenAI
 from pydantic import SecretStr
 
 # 本地模块
-from app.graph_builder import GraphBuilder
+from app.graph_builder import GraphBuilder, GraphContext
 from app.tools import AVAILABLE_TOOLS, TOOLS_BY_NAME
+from app.logger import debug, info, warning, error
+from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
+from langgraph.store.postgres.aio import AsyncPostgresStore
 
 load_dotenv()
 
@@ -20,13 +23,15 @@ load_dotenv()
 class AIAgentService:
     """异步 AI Agent 服务，支持多模型动态切换，使用外部传入的 checkpointer"""
 
-    def __init__(self, checkpointer):
+    def __init__(self, checkpointer: AsyncPostgresSaver, store: AsyncPostgresStore):
         """
         初始化服务
         Args:
             checkpointer: 已经初始化的 AsyncPostgresSaver 实例
+            store: 已经初始化的 AsyncPostgresStore 实例
         """
         self.checkpointer = checkpointer
+        self.store = store
         self.graphs = {}  # 存储不同模型对应的 graph 实例
 
     def _create_zhipu_llm(self):
@@ -39,49 +44,108 @@ class AIAgentService:
             api_key=api_key,
             temperature=0.1,
             max_tokens=4096,
+            timeout=60.0,          # 请求超时时间（秒）
+            max_retries=2,          # 失败后自动重试次数
+        )
+
+    def _create_deepseek_llm(self):
+        """创建 DeepSeek LLM（使用 OpenAI 兼容 API）"""
+        api_key = os.getenv("DEEPSEEK_API_KEY")
+        if not api_key:
+            raise ValueError("DEEPSEEK_API_KEY not set in environment")
+        return ChatOpenAI(
+            base_url="https://api.deepseek.com",
+            api_key=SecretStr(api_key),
+            model="deepseek-reasoner",  # deepseek-chat: 非思考模式, deepseek-reasoner: 思考模式
+            temperature=0.1,
+            max_tokens=4096,
+            timeout=60.0,          # 请求超时时间（秒）
+            max_retries=2,          # 失败后自动重试次数
         )
 
     def _create_local_llm(self):
         """创建本地 vLLM 服务 LLM"""
+        # vLLM 服务地址：优先从环境变量读取，适配 Docker、FRP 穿透和本地开发
+        vllm_base_url = os.getenv(
+            "VLLM_BASE_URL",
+            "http://115.190.121.151:18000/v1"
+        )
+        
         return ChatOpenAI(
-            # 原来是 http://localhost:8000/v1
-            # 改为 FRP 穿透后的公网地址
-            base_url = "http://115.190.121.151:18000/v1",
+            base_url=vllm_base_url,
             api_key=SecretStr(os.getenv("VLLM_LOCAL_KEY", "")),
             model="gemma-4-E2B-it",
+            timeout=60.0,          # 请求超时时间（秒）
+            max_retries=2,          # 失败后自动重试次数
         )
 
     async def initialize(self):
-        """预编译所有模型的 graph（使用传入的 checkpointer）"""
+        """预编译所有模型的 graph（使用传入的 checkpointer 和 store）"""
         model_configs = {
             "zhipu": self._create_zhipu_llm,
+            "deepseek": self._create_deepseek_llm,
             "local": self._create_local_llm,
         }
 
         for model_name, llm_creator in model_configs.items():
             try:
+                info(f"🔄 正在初始化模型 '{model_name}'...")
                 llm = llm_creator()
+                
+                # 测试 LLM 连接（可选，用于调试）
+                if model_name == "local":
+                    debug(f"   测试 vLLM 连接: {os.getenv('VLLM_BASE_URL', '未设置')}")
+                elif model_name == "deepseek":
+                    debug(f"   测试 DeepSeek API 连接: https://api.deepseek.com")
+                
                 builder = GraphBuilder(llm, AVAILABLE_TOOLS, TOOLS_BY_NAME).build()
-                graph = builder.compile(checkpointer=self.checkpointer)
+                graph = builder.compile(checkpointer=self.checkpointer, store=self.store)
                 self.graphs[model_name] = graph
-                print(f"✅ 模型 '{model_name}' 初始化成功")
+                info(f"✅ 模型 '{model_name}' 初始化成功")
             except Exception as e:
-                print(f"⚠️ 模型 '{model_name}' 初始化失败: {e}")
+                import traceback
+                error_detail = traceback.format_exc()
+                warning(f"⚠️ 模型 '{model_name}' 初始化失败: {e}")
+                debug(f"   详细错误:\n{error_detail}")
 
         if not self.graphs:
-            raise RuntimeError("没有可用的模型，请检查配置")
+            raise RuntimeError("没有可用的模型，请检查配置。可能的原因：\n"
+                             "1. ZHIPUAI_API_KEY 未配置或无效\n"
+                             "2. DEEPSEEK_API_KEY 未配置或无效\n"
+                             "3. vLLM 服务未启动或地址错误 (VLLM_BASE_URL)\n"
+                             "4. 网络连接问题")
 
         return self
 
-    async def process_message(self, message: str, thread_id: str, model: str = "zhipu") -> str:
-        """处理用户消息，返回最终答案"""
+    async def process_message(self, message: str, thread_id: str, model: str = "zhipu", user_id: str = "default_user") -> dict:
+        """
+        处理用户消息，返回包含回复、token统计和耗时的字典
+        
+        Returns:
+            dict: {
+                "reply": str,           # AI 回复内容
+                "token_usage": dict,    # Token 使用详情
+                "elapsed_time": float   # 调用耗时（秒）
+            }
+        """
         if model not in self.graphs:
             fallback_model = next(iter(self.graphs.keys()))
-            print(f"警告: 模型 '{model}' 不可用，已切换到 '{fallback_model}'")
+            warning(f"警告: 模型 '{model}' 不可用，已切换到 '{fallback_model}'")
             model = fallback_model
 
         graph = self.graphs[model]
         config = {"configurable": {"thread_id": thread_id}}
         input_state = {"messages": [HumanMessage(content=message)]}
-        result = await graph.ainvoke(input_state, config=config)
-        return result["messages"][-1].content
+        context = GraphContext(user_id=user_id)
+        
+        result = await graph.ainvoke(input_state, config=config, context=context)
+        
+        reply = result["messages"][-1].content
+        token_usage = result.get("last_token_usage", {})
+        elapsed_time = result.get("last_elapsed_time", 0.0)
+        
+        return {
+            "reply": reply,
+            "token_usage": token_usage,
+            "elapsed_time": elapsed_time
+        }

app/backend.py

@@ -7,17 +7,23 @@ import os
 import uuid
 from contextlib import asynccontextmanager
 
+from dotenv import load_dotenv
 from fastapi import FastAPI, HTTPException, WebSocket, WebSocketDisconnect, Depends, Request
 from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
 from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
-
+from langgraph.store.postgres.aio import AsyncPostgresStore 
 from app.agent import AIAgentService
+from app.logger import debug, info, warning, error
 
-# PostgreSQL 连接字符串（优先从环境变量读取，适配 Docker 和本地开发）
+# 加载 .env 文件
+load_dotenv()
+
+# PostgreSQL 连接字符串配置
+# 优先级：环境变量 DB_URI > Docker 内部服务名 > 本地开发地址
 DB_URI = os.getenv(
     "DB_URI",
-    "postgresql://postgres:mysecretpassword@postgres:5432/langgraph_db?sslmode=disable"
+    "postgresql://postgres:mysecretpassword@localhost:5432/langgraph_db?sslmode=disable"
 )
 
 
@@ -25,11 +31,15 @@ DB_URI = os.getenv(
 async def lifespan(app: FastAPI):
     """应用生命周期管理：创建并注入全局服务"""
     # 1. 创建数据库连接池并初始化表
-    async with AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer:
+    async with (
+        AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer,
+        AsyncPostgresStore.from_conn_string(DB_URI) as store
+        ):
         await checkpointer.setup()
+        await store.setup()
         
         # 2. 构建 AI Agent 服务
-        agent_service = AIAgentService(checkpointer)
+        agent_service = AIAgentService(checkpointer,store)
         await agent_service.initialize()
         
         # 3. 将服务实例存入 app.state
@@ -39,7 +49,7 @@ async def lifespan(app: FastAPI):
         yield
         
         # 4. 关闭时自动清理数据库连接（async with 负责）
-        print("🛑 应用关闭，数据库连接池已释放")
+        info("🛑 应用关闭，数据库连接池已释放")
 
 
 app = FastAPI(lifespan=lifespan)
@@ -66,12 +76,17 @@ class ChatRequest(BaseModel):
     message: str
     thread_id: str | None = None
     model: str = "zhipu"
+    user_id: str = "default_user"
 
 
 class ChatResponse(BaseModel):
     reply: str
     thread_id: str
     model_used: str
+    input_tokens: int = 0
+    output_tokens: int = 0
+    total_tokens: int = 0
+    elapsed_time: float = 0.0
 
 
 # ========== 依赖注入函数 ==========
@@ -91,11 +106,27 @@ async def chat_endpoint(
         raise HTTPException(status_code=400, detail="message required")
 
     thread_id = request.thread_id or str(uuid.uuid4())
-    reply = await agent_service.process_message(
-        request.message, thread_id, request.model
+    result = await agent_service.process_message(
+        request.message, thread_id, request.model, request.user_id
     )
+    
+    # 提取 token 统计信息
+    token_usage = result.get("token_usage", {})
+    input_tokens = token_usage.get('prompt_tokens', token_usage.get('input_tokens', 0))
+    output_tokens = token_usage.get('completion_tokens', token_usage.get('output_tokens', 0))
+    elapsed_time = result.get("elapsed_time", 0.0)
+    
     actual_model = request.model if request.model in agent_service.graphs else next(iter(agent_service.graphs.keys()))
-    return ChatResponse(reply=reply, thread_id=thread_id, model_used=actual_model)
+    
+    return ChatResponse(
+        reply=result["reply"],
+        thread_id=thread_id,
+        model_used=actual_model,
+        input_tokens=input_tokens,
+        output_tokens=output_tokens,
+        total_tokens=input_tokens + output_tokens,
+        elapsed_time=elapsed_time
+    )
 
 
 # ========== WebSocket 端点（可选） ==========
@@ -111,10 +142,11 @@ async def websocket_endpoint(
             message = data.get("message")
             thread_id = data.get("thread_id", str(uuid.uuid4()))
             model = data.get("model", "zhipu")
+            user_id = data.get("user_id", "default_user")
             if not message:
                 await websocket.send_json({"error": "missing message"})
                 continue
-            reply = await agent_service.process_message(message, thread_id, model)
+            reply = await agent_service.process_message(message, thread_id, model, user_id)
             actual_model = model if model in agent_service.graphs else next(iter(agent_service.graphs.keys()))
             await websocket.send_json({"reply": reply, "thread_id": thread_id, "model_used": actual_model})
     except WebSocketDisconnect:

app/graph_builder.py

@@ -4,19 +4,34 @@ LangGraph 状态图构建模块 - 完全面向对象风格，无嵌套函数
 
 import operator
 import asyncio
+import time
 from typing import Literal, Annotated, Any
 from langchain_core.language_models import BaseLLM
 from langchain_core.messages import AnyMessage, AIMessage, ToolMessage, SystemMessage
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langgraph.graph import StateGraph, START, END
 from typing_extensions import TypedDict
+from langgraph.store.postgres.aio import AsyncPostgresStore
+from langgraph.runtime import Runtime
+from dataclasses import dataclass
+import uuid
+
+# 本地模块
+from app.logger import debug, info, warning, error
 
 
-class MessageState(TypedDict):
+class MessagesState(TypedDict):
     """对话状态类型定义"""
     messages: Annotated[list[AnyMessage], operator.add]
     llm_calls: int
+    memory_context:str
+    last_token_usage: dict  # 本次调用的 token 使用详情
+    last_elapsed_time: float  # 本次调用耗时（秒）
 
+@dataclass
+class GraphContext:
+    user_id: str
+    # 可扩展更多上下文信息
 
 class GraphBuilder:
     """LangGraph 状态图构建器 - 所有节点均为类方法"""
@@ -42,33 +57,132 @@ class GraphBuilder:
         """创建系统提示模板（静态方法，无需访问实例）"""
         return ChatPromptTemplate.from_messages([
             SystemMessage(content=(
-                "你是一个个人生活助手和数据分析助手。请说中文。"
-                "当用户询问天气或温度时,使用get_current_temperature工具获取信息。"
-                "当用户要求读文本文件时,请使用 read_local_file 工具,只能读取 './user_docs' 目录下的文件。"
-                "当用户要求读PDF文件时,请使用 read_pdf_summary 工具,只能读取 './user_docs' 目录下的文件。"
-                "当用户要求读Excel文件时,请使用 read_excel_as_markdown 工具,只能读取 './user_docs' 目录下的文件。"
-                "当用户要求抓取网页时,请使用 fetch_webpage_content 工具。"
-                "重要:你的回答必须简洁、直接,不要包含任何关于思考过程的描述。"
+                "你是一个个人生活助手和数据分析助手，请使用中文交流。\n\n"
+                "【用户背景信息】\n"
+                "以下是对当前用户的已知信息和长期记忆，你必须优先采纳并在回答中体现：\n"
+                "{memory_context}\n"
+                "若包含姓名、偏好等个人信息，请自然融入回应（例如称呼名字、提及偏好）。\n\n"
+                "【可用工具与使用规则】\n"
+                "- 获取温度/天气：`get_current_temperature`\n"
+                "- 读取文本文件：`read_local_file`（限定目录 `./user_docs`）\n"
+                "- 读取PDF摘要：`read_pdf_summary`（限定目录 `./user_docs`）\n"
+                "- 读取Excel表格：`read_excel_as_markdown`（限定目录 `./user_docs`）\n"
+                "- 抓取网页内容：`fetch_webpage_content`\n"
+                "工具调用时请直接返回所需参数，无需额外说明。\n\n"
+                "【回答要求（必须遵守）】\n"
+                "1. 回答必须简洁、直接，禁止描述任何思考过程或内心活动。\n"
+                "2. 优先利用已知用户信息进行个性化回复。\n"
+                "3. 若无信息可依，礼貌询问或提供通用帮助。"
             )),
-            MessagesPlaceholder(variable_name="message")
+            MessagesPlaceholder(variable_name="messages")
         ])
 
-    async def call_llm(self, state: MessageState) -> dict:
+    async def call_llm(self, state: MessagesState, runtime: Runtime[GraphContext]) -> dict:
         """
         LLM 调用节点（异步方法）
         注意：因为 self._chain.invoke 是同步方法，使用 run_in_executor 避免阻塞事件循环
         """
+        memory_context = state.get("memory_context", "暂无用户信息")
+        
+        # 构建完整的输入消息列表（用于调试打印）
+        system_prompt = self._prompt.messages[0]  # SystemMessage
+        if isinstance(system_prompt, SystemMessage):
+            system_content = system_prompt.content.format(memory_context=memory_context)
+        else:
+            system_content = str(system_prompt.content)
+        
+        input_messages = [SystemMessage(content=system_content)] + state["messages"]
+        
+        # 打印发送给大模型的最终输入
+        debug("\n" + "="*80)
+        debug("📤 [LLM输入] 发送给大模型的完整消息:")
+        debug(f"   总消息数: {len(input_messages)}")
+        for i, msg in enumerate(input_messages):
+            content_preview = str(msg.content)  # 不截断，完整输出
+            debug(f"   [{i}] {msg.type.upper():10s}: {content_preview}")
+        debug("="*80 + "\n")
+        
         loop = asyncio.get_event_loop()
-        response = await loop.run_in_executor(
-            None, 
-            lambda: self._chain.invoke({"message": state["messages"]})
-        )
-        return {
-            "messages": [response],
-            "llm_calls": state.get('llm_calls', 0) + 1
-        }
+        start_time = time.time()
+        
+        try:
+            response = await loop.run_in_executor(
+                None, 
+                lambda: self._chain.invoke({
+                    "messages": state["messages"],
+                    "memory_context": memory_context
+                })
+            )
+            
+            elapsed_time = time.time() - start_time
+            
+            # 提取 token 用量（兼容不同 LLM 提供商的元数据格式）
+            token_usage = {}
+            input_tokens = 0
+            output_tokens = 0
+            
+            # 尝试从 response_metadata 中提取
+            if hasattr(response, 'response_metadata') and response.response_metadata:
+                meta = response.response_metadata
+                if 'token_usage' in meta:
+                    token_usage = meta['token_usage']
+                elif 'usage' in meta:
+                    token_usage = meta['usage']
+            
+            # 尝试从 additional_kwargs 中提取
+            if not token_usage and hasattr(response, 'additional_kwargs'):
+                add_kwargs = response.additional_kwargs
+                if 'llm_output' in add_kwargs and 'token_usage' in add_kwargs['llm_output']:
+                    token_usage = add_kwargs['llm_output']['token_usage']
+            
+            # 提取具体的 token 数值
+            if token_usage:
+                input_tokens = token_usage.get('prompt_tokens', token_usage.get('input_tokens', 0))
+                output_tokens = token_usage.get('completion_tokens', token_usage.get('output_tokens', 0))
+            
+            # 打印响应统计信息
+            info(f"⏱️  [LLM统计] 调用耗时: {elapsed_time:.2f}秒")
+            info(f"📊 [LLM统计] Token用量: 输入={input_tokens}, 输出={output_tokens}, 总计={input_tokens + output_tokens}")
+            if token_usage:
+                debug(f"📋 [LLM统计] 详细用量: {token_usage}")
+            
+            # 打印 LLM 的完整输出
+            debug("\n" + "="*80)
+            debug("📥 [LLM输出] 大模型返回的完整响应:")
+            debug(f"   消息类型: {response.type.upper()}")
+            debug(f"   内容长度: {len(str(response.content))} 字符")
+            debug("-"*80)
+            debug(f"{response.content}")
+            debug("="*80 + "\n")
+            
+            return {
+                "messages": [response],
+                "llm_calls": state.get('llm_calls', 0) + 1,
+                "last_token_usage": token_usage,
+                "last_elapsed_time": elapsed_time
+            }
+            
+        except Exception as e:
+            elapsed_time = time.time() - start_time
+            error(f"\n❌ [LLM错误] 调用失败 (耗时: {elapsed_time:.2f}秒)")
+            error(f"   错误类型: {type(e).__name__}")
+            error(f"   错误信息: {str(e)}")
+            import traceback
+            traceback.print_exc()
+            debug("="*80 + "\n")
+            
+            # 返回一个友好的错误消息
+            error_response = AIMessage(
+                content="抱歉，模型暂时无法响应，可能是网络超时或服务繁忙，请稍后再试。"
+            )
+            return {
+                "messages": [error_response],
+                "llm_calls": state.get('llm_calls', 0),
+                "last_token_usage": {},
+                "last_elapsed_time": elapsed_time
+            }
 
-    async def call_tools(self, state: MessageState) -> dict:
+    async def call_tools(self, state: MessagesState, runtime: Runtime[GraphContext]) -> dict:
         """
         工具执行节点（异步方法）
         对于每个工具调用，在线程池中执行同步工具函数
@@ -91,11 +205,15 @@ class GraphBuilder:
                 continue
 
             try:
-                # 同步工具函数在线程池中执行
-                observation = await loop.run_in_executor(
-                    None, 
-                    lambda: tool_func.invoke(tool_args)
-                )
+                # 修复闭包问题：将变量作为默认参数传入 lambda
+                # 如果工具支持异步 (ainvoke)，优先使用异步调用
+                if hasattr(tool_func, 'ainvoke'):
+                    observation = await tool_func.ainvoke(tool_args)
+                else:
+                    observation = await loop.run_in_executor(
+                        None,
+                        lambda args=tool_args: tool_func.invoke(args)  # 默认参数捕获当前值
+                    )
                 results.append(ToolMessage(content=str(observation), tool_call_id=tool_id))
             except Exception as e:
                 results.append(ToolMessage(content=f"Error: {e}", tool_call_id=tool_id))
@@ -103,25 +221,101 @@ class GraphBuilder:
         return {"messages": results}
 
     @staticmethod
-    def should_continue(state: MessageState) -> Literal['tool_node', END]:
-        """
-        条件边判断（静态方法）
-        决定下一步是进入工具节点还是结束
-        """
+    def should_continue(state: MessagesState) -> Literal['tool_node', 'save_memory', 'END']:
+        """决定下一步：工具调用、保存记忆还是结束"""
         last_message = state["messages"][-1]
-        if isinstance(last_message, AIMessage) and bool(last_message.tool_calls):
+    
+        # 1. 如果需要调用工具，优先进入工具节点
+        if isinstance(last_message, AIMessage) and last_message.tool_calls:
             return 'tool_node'
-        return END
+    
+        # 2. 如果是 AI 的最终回复，可以考虑进入记忆保存节点（可增加判断逻辑）
+        #    这里简单处理：只要没有工具调用，且是 AI 消息，就尝试保存记忆。
+        if isinstance(last_message, AIMessage):
+            return 'save_memory'
+    
+        # 3. 其他情况（如只有用户消息）直接结束
+        return 'END'
+    
+    async def retrieve_memory(self, state: MessagesState, runtime: Runtime[GraphContext]) -> dict:
+        """搜索并返回长期记忆"""
+        user_id = runtime.context.user_id
+        namespace = ("memories", user_id)
+        query = str(state["messages"][-1].content)
+        
+        debug(f"\n{'='*60}")
+        debug(f"🔎 [记忆检索] 开始检索")
+        debug(f"   ├─ 用户ID: {user_id}")
+        debug(f"   ├─ 命名空间: {namespace}")
+        debug(f"   ├─ 查询内容: '{query}'")
+        debug(f"   └─ 消息总数: {len(state['messages'])}")
+        
+        try:
+            memories = await runtime.store.asearch(namespace, query=query)
+            debug(f"✅ [记忆检索] 检索完成，找到 {len(memories)} 条相关记忆")
+            
+            if memories:
+                memory_text = "\n".join([m.value["data"] for m in memories])
+                debug(f"📚 [记忆内容]")
+                for i, memory in enumerate(memories, 1):
+                    debug(f"   [{i}] {memory.value['data']}")
+                debug(f"{'='*60}\n")
+                return {"memory_context": memory_text}
+            else:
+                debug(f"⚠️  [记忆检索] 未找到相关记忆")
+                debug(f"{'='*60}\n")
+                return {"memory_context": ""}
+                
+        except Exception as e:
+            error(f"❌ [记忆检索] 检索失败: {e}")
+            import traceback
+            traceback.print_exc()
+            debug(f"{'='*60}\n")
+            return {"memory_context": ""}
+    
+    async def save_memory(self, state: MessagesState, runtime: Runtime[GraphContext]) -> dict:
+        """尝试从对话中提取并保存长期记忆"""
+        # 获取最后一条用户消息（通常是要记住的内容的来源）
+        user_messages = [msg for msg in state["messages"] if msg.type == "human"]
+        if not user_messages:
+            return {}
+        
+        last_user_msg = user_messages[-1].content.lower()
+        
+        # 简单触发逻辑：包含"记住"或"保存"等关键词
+        if any(keyword in last_user_msg for keyword in ["记住", "保存", "别忘了"]):
+            # 提取记忆内容（这里仅作示例，实际可用 LLM 提取）
+            memory_content = f"用户说过：{last_user_msg}"
+            user_id = runtime.context.user_id
+            namespace = ("memories", user_id)
+            await runtime.store.aput(namespace, str(uuid.uuid4()), {"data": memory_content})
+            info(f"✅ 长期记忆已保存：{memory_content}")
+        
+        return {}
 
     def build(self) -> StateGraph:
         """
         构建未编译的状态图（返回 StateGraph 实例）
         图中节点直接使用实例方法 call_llm, call_tools
         """
-        builder = StateGraph(MessageState)
+        builder = StateGraph(MessagesState,context_schema=GraphContext)
+        builder.add_node("retrieve_memory", self.retrieve_memory)
         builder.add_node("llm_call", self.call_llm)
         builder.add_node("tool_node", self.call_tools)
-        builder.add_edge(START, "llm_call")
-        builder.add_conditional_edges("llm_call", self.should_continue, ["tool_node", END])
+        builder.add_node("save_memory", self.save_memory)
+
+        builder.add_edge(START, "retrieve_memory")
+        builder.add_edge("retrieve_memory", "llm_call")
+        builder.add_conditional_edges(
+            "llm_call",
+            self.should_continue,
+            {
+                "tool_node": "tool_node",
+                "save_memory": "save_memory",
+                'END': END
+            }
+        )
         builder.add_edge("tool_node", "llm_call")
+        builder.add_edge("save_memory", END)
+        
         return builder

app/logger.py

@@ -0,0 +1,55 @@
+"""
+统一的日志模块 - 基于环境变量控制日志级别
+类似 C# 的条件编译效果，开发时打印详细调试信息，生产环境只输出关键信息
+"""
+
+import os
+import logging
+from typing import Any
+from dotenv import load_dotenv
+
+# 先加载环境变量
+load_dotenv()
+
+# 从环境变量读取日志级别，默认 INFO
+LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO").upper()
+
+# 根据环境变量控制是否显示详细调试信息
+DEBUG_MODE = os.getenv("DEBUG", "false").lower() == "true"
+
+# 创建统一的日志器
+logger = logging.getLogger("ai_agent")
+logger.setLevel(getattr(logging, LOG_LEVEL, logging.INFO))
+
+# 避免重复添加 handler
+if not logger.handlers:
+    handler = logging.StreamHandler()
+    # 重要：handler 也需要设置级别，否则可能继承根 logger 的级别
+    handler.setLevel(getattr(logging, LOG_LEVEL, logging.INFO))
+    formatter = logging.Formatter(
+        fmt="%(asctime)s | %(levelname)-8s | %(name)s | %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S"
+    )
+    handler.setFormatter(formatter)
+    logger.addHandler(handler)
+
+
+def debug(msg: Any, *args, **kwargs):
+    """调试日志，仅在 DEBUG 环境变量为 true 时打印"""
+    if DEBUG_MODE:
+        logger.debug(msg, *args, **kwargs)
+
+
+def info(msg: Any, *args, **kwargs):
+    """信息日志"""
+    logger.info(msg, *args, **kwargs)
+
+
+def warning(msg: Any, *args, **kwargs):
+    """警告日志"""
+    logger.warning(msg, *args, **kwargs)
+
+
+def error(msg: Any, *args, **kwargs):
+    """错误日志"""
+    logger.error(msg, *args, **kwargs)