2025.11.12

08_langgraph_basics/04_agent_with_memory.py

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+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+api_key = os.getenv("OPENAI_API_KEY")
+langchain_api_key = os.getenv("LANGCHAIN_API_KEY")
+
+from langchain.schema import HumanMessage
+from langchain.tools import tool
+from langchain_openai import ChatOpenAI
+from langgraph.graph import StateGraph, MessagesState, END,START
+from langgraph.prebuilt import ToolNode
+from langchain_core.messages import SystemMessage
+from langgraph.checkpoint.memory import MemorySaver
+
+os.environ["LANGCHAIN_TRACING_V2"] = "true" # 总开关，决定启用追踪功能
+os.environ["LANGCHAIN_PROJECT"] = "demo02" # 自定义项目名
+os.environ["LANGCHAIN_API_KEY"] = langchain_api_key
+
+# LLM配置
+llm = ChatOpenAI(
+    model="deepseek-chat",
+    api_key=api_key,
+    base_url="https://api.deepseek.com"
+)
+
+# Prompt配置
+sys_prompt = "你是一个强大的助手，能查天气，也能回答一般问题。请使用中文回答。"
+
+# 工具定义
+@tool
+def get_weather(loaction):
+    """模拟获取天气"""
+    return f'{loaction}当前天气：23℃，晴，风力2级'
+
+
+tools = [get_weather]
+llm_with_tools = llm.bind_tools(tools) # 让llm学会调用工具节点
+
+
+# --- 核心组件:拆解AgentExecutor ---
+# ReAct Step1:Thought(LLM决策)
+def call_model(state:MessagesState):
+    # 构造带system prompt 的完整消息列表(仅用于本次LLM调用)
+    message_for_llm = [SystemMessage(content=sys_prompt)]+state["messages"]
+    response = llm_with_tools.invoke(message_for_llm)
+    # 此处只会返回新生成的消息，不包含prompt，防止污染历史
+    return {"messages":[response]} # 新消息追加到状态
+
+
+# ReAct Step2-3:Action + Observation
+tool_node = ToolNode(tools)
+
+# ReAct Step4:Loop Controller(是否循环)
+def should_continue(state:MessagesState):
+    last_msg = state["messages"][-1]
+    if hasattr(last_msg,"tool_calls") and len(last_msg.tool_calls) > 0:
+        return "tools" # 有工具调用 -> 执行工具
+    return END         # 无工具调用 -> 返回答案
+
+
+# --- 构建 ReAct 循环图---
+workflow = StateGraph(MessagesState)
+
+workflow.add_node("agent",call_model) # Thought
+workflow.add_node("tools",tool_node) # Action + Observation
+
+workflow.add_edge(START,"agent")
+
+# 条件边:Thought -> 决定下一步
+workflow.add_conditional_edges(
+    "agent", # 从哪个节点出发
+    should_continue, # 决定下一步去哪
+    {
+        "tools":"tools", # 如果返回tools，去tools节点
+        END:END          # 如果返回END，直接结束工作流
+    }
+)
+
+workflow.add_edge("tools","agent")
+
+
+
+
+
+# 编译时启用记忆
+app = workflow.compile(checkpointer=MemorySaver())
+
+
+
+if __name__ == '__main__':
+    session_id = "user123"
+    config = {
+        "configurable":{"thread_id":session_id}
+    }
+    while 1:
+        user_input = input('\n你：')
+        if user_input.strip().lower() == 'quit':
+            break
+
+        result = app.invoke(
+            {'messages':[HumanMessage(content=user_input)]},
+            config=config
+        )
+
+        ai_msg = result["messages"][-1]
+        print(f'AI：{ai_msg.content}')
+
+
+
+
+
+