本示例展示了如何利用LangGraph构建一个能够查询产品信息的AI代理。通过定义一个`product_info`工具函数，该代理可以根据用户输入的产品名称，从预设的产品目录中检索并返回产品信息。代码中演示了如何初始化模型、定义工具、创建ReAct代理图，并通过调用`graph.invoke`来执行查询。代理能够准确识别用户意图，调用相应的工具，并返回包含产品详情的文本。

🎯 **智能产品信息检索**：该AI代理能够理解用户关于产品信息的查询，并调用预定义的`product_info`工具来获取具体的产品详情。例如，当用户询问“介绍下新的iPhone 20”时，代理能够识别出产品名称“iPhone 20”，并从内置的产品目录中查找信息。

🛠️ **工具调用机制**：代理的核心在于其工具调用能力。`product_info`函数模拟了一个实际的数据库查询，返回产品名称对应的描述。代理通过`create_react_agent`进行配置，能够根据用户输入动态选择和调用最适合的工具。

🔄 **多轮交互支持**：示例展示了代理能够处理连续的用户查询。在第一次查询“介绍下新的iPhone 20”后，代理能够根据上下文理解“进一步介绍下iPhone 20”的意图，并再次调用工具提供信息，体现了其在多轮对话中的连贯性。

⚠️ **信息准确性与边界提示**：代理在返回信息时，会结合其知识边界进行提示。例如，对于不存在的“iPhone 20”，代理会说明其知识截止日期，并提示用户可能指的是当前最新型号，增加了回答的准确性和用户体验。

示例 2 产品信息查询的例子

from dotenv import load_dotenvimport osload_dotenv()llm = ChatOpenAI(model="qwen-max",                 base_url=os.getenv("BASE_URL"),                 api_key=os.getenv("OPENAI_API_KEY"),                 streaming=True)# toolsdef product_info(product_name: str) -> str:    """Fetch product infomation"""    product_catalog = {        'iPhone 20': 'The latest iPhone features an A15 chip and improved camera.',        'MacBook': 'The new MacBook has an M2 chip and a 14-inch Retina display.',    }    print(f"调用工具 {product_name}")    return product_catalog.get(product_name, 'Sorry, product not found.')checkpointer = MemorySaver()tools = [product_info]# ReAct agentgraph = create_react_agent(model=llm, checkpointer=checkpointer, tools=tools, debug=False)#config = {"configurable": {"thread_id": "thread_1"}}inputs = {"messages": [("user", "介绍下新的iPhone 20")]}messages = graph.invoke(inputs, config)for message in messages["messages"]:    message.pretty_print()inputs = {"messages": [("user", "进一步介绍下iPhone 20")]}messages = graph.invoke(inputs, config)for message in messages["messages"]:    message.pretty_print()

输出结果为

调用工具 iPhone 20================================ Human Message =================================介绍下新的iPhone 20================================== Ai Message ==================================Tool Calls:  product_info (call_08b215e1a2c945108de654) Call ID: call_08b215e1a2c945108de654  Args:    product_name: iPhone 20================================= Tool Message =================================Name: product_infoThe latest iPhone features an A15 chip and improved camera.================================== Ai Message ==================================最新的iPhone 20配备了A15芯片和改进的摄像头。请注意，这可能是指当前最新的iPhone型号，因为直到我的知识更新为止（2021年），并没有一款叫做iPhone 20的手机。如果您想了解的是另一个特定的产品，请提供更多信息。调用工具 iPhone 20================================ Human Message =================================介绍下新的iPhone 20================================== Ai Message ==================================Tool Calls:  product_info (call_08b215e1a2c945108de654) Call ID: call_08b215e1a2c945108de654  Args:    product_name: iPhone 20================================= Tool Message =================================Name: product_infoThe latest iPhone features an A15 chip and improved camera.================================== Ai Message ==================================最新的iPhone 20配备了A15芯片和改进的摄像头。请注意，这可能是指当前最新的iPhone型号，因为直到我的知识更新为止（2021年），并没有一款叫做iPhone 20的手机。如果您想了解的是另一个特定的产品，请提供更多信息。================================ Human Message =================================进一步介绍下iPhone 20================================== Ai Message ==================================Tool Calls:  product_info (call_d456afc25aac46e98e869a) Call ID: call_d456afc25aac46e98e869a  Args:    product_name: iPhone 20================================= Tool Message =================================Name: product_infoThe latest iPhone features an A15 chip and improved camera.================================== Ai Message ==================================再次确认，最新的iPhone配备了A15芯片以及升级的摄像头系统。不过，实际上并没有一款被正式命名为“iPhone 20”的产品。可能您指的是最新发布的iPhone型号。如果您需要更详细的信息，比如具体型号（如iPhone 13, iPhone 14等）、特色功能、设计变化或其他细节，请告知我，这样我可以为您提供更加准确的信息。  如果有其他特定的方面或者功能您感兴趣的话，也请告诉我！

示例3 产品查询并检查库存的例子

from langchain_openai import ChatOpenAIfrom langgraph.prebuilt import create_react_agentfrom langgraph.checkpoint.memory import MemorySaverfrom dotenv import load_dotenvimport osload_dotenv()llm = ChatOpenAI(model="qwen-max",                 base_url=os.getenv("BASE_URL"),                 api_key=os.getenv("OPENAI_API_KEY"),                 streaming=True)# toolsdef product_info(product_name: str) -> str:    """Fetch product infomation"""    product_catalog = {        'iPhone': 'The latest iPhone features an A15 chip and improved camera.',        'MacBook': 'The new MacBook has an M2 chip and a 14-inch Retina display.',    }    print(f"调用工具product_info {product_name}")    return product_catalog.get(product_name, 'Sorry, product not found.')def check_stock(product_name: str) -> str:    """Check product stock availability."""    stock_data = {        'iPhone': 'In stock.',        'MacBook': 'Out of stock.',    }    print(f"调用工具check_stock {product_name}")    return stock_data.get(product_name, 'Stock information unavailable.')checkpointer = MemorySaver()tools = [product_info, check_stock]# ReAct agentgraph = create_react_agent(model=llm, checkpointer=checkpointer, tools=tools, debug=False)#config = {"configurable": {"thread_id": "thread_1"}}inputs = {"messages": [("user", "我是小明，介绍下新的iPhone")]}messages = graph.invoke(inputs, config)for message in messages["messages"]:    message.pretty_print()inputs = {"messages": [("user", "iPhone 有库存吗")]}messages = graph.invoke(inputs, config)for message in messages["messages"]:    message.pretty_print()inputs = {"messages": [("user", "介绍下MacBook，另外还有库存吗")]}messages = graph.invoke(inputs, config)for message in messages["messages"]:    message.pretty_print()

输出结果为

调用工具product_info iPhone================================ Human Message =================================我是小明，介绍下新的iPhone================================== Ai Message ==================================Tool Calls:  product_info (call_7fe4fbbe6dc6426db484df) Call ID: call_7fe4fbbe6dc6426db484df  Args:    product_name: iPhone================================= Tool Message =================================Name: product_infoThe latest iPhone features an A15 chip and improved camera.================================== Ai Message ==================================最新的iPhone配备了A15芯片和改进的相机。调用工具check_stock iPhone================================ Human Message =================================我是小明，介绍下新的iPhone================================== Ai Message ==================================Tool Calls:  product_info (call_7fe4fbbe6dc6426db484df) Call ID: call_7fe4fbbe6dc6426db484df  Args:    product_name: iPhone================================= Tool Message =================================Name: product_infoThe latest iPhone features an A15 chip and improved camera.================================== Ai Message ==================================最新的iPhone配备了A15芯片和改进的相机。================================ Human Message =================================iPhone 有库存吗================================== Ai Message ==================================Tool Calls:  check_stock (call_1b24d717931a4b42807943) Call ID: call_1b24d717931a4b42807943  Args:    product_name: iPhone================================= Tool Message =================================Name: check_stockIn stock.================================== Ai Message ==================================iPhone目前有库存。调用工具product_info MacBook调用工具check_stock MacBook================================ Human Message =================================我是小明，介绍下新的iPhone================================== Ai Message ==================================Tool Calls:  product_info (call_7fe4fbbe6dc6426db484df) Call ID: call_7fe4fbbe6dc6426db484df  Args:    product_name: iPhone================================= Tool Message =================================Name: product_infoThe latest iPhone features an A15 chip and improved camera.================================== Ai Message ==================================最新的iPhone配备了A15芯片和改进的相机。================================ Human Message =================================iPhone 有库存吗================================== Ai Message ==================================Tool Calls:  check_stock (call_1b24d717931a4b42807943) Call ID: call_1b24d717931a4b42807943  Args:    product_name: iPhone================================= Tool Message =================================Name: check_stockIn stock.================================== Ai Message ==================================iPhone目前有库存。================================ Human Message =================================介绍下MacBook，另外还有库存吗================================== Ai Message ==================================Tool Calls:  product_info (call_a1a34117064840f0bda5c9) Call ID: call_a1a34117064840f0bda5c9  Args:    product_name: MacBook================================= Tool Message =================================Name: product_infoThe new MacBook has an M2 chip and a 14-inch Retina display.================================== Ai Message ==================================新款MacBook配备了M2芯片和14英寸Retina显示屏。让我查一下库存情况。Tool Calls:  check_stock (call_5cd17f2a9a5041caba454d) Call ID: call_5cd17f2a9a5041caba454d  Args:    product_name: MacBook================================= Tool Message =================================Name: check_stockOut of stock.================================== Ai Message ==================================目前MacBook没有库存。

示例4 多步推理和动态Action的例子

用一个父图+多个子图完成复杂的任务

from langchain_openai import ChatOpenAIfrom langgraph.prebuilt import create_react_agentfrom langgraph.checkpoint.memory import MemorySaverfrom dotenv import load_dotenvfrom langgraph.graph import StateGraph, MessagesState, START, ENDimport osfrom typing import TypedDictload_dotenv()llm = ChatOpenAI(model="qwen-max",                 base_url=os.getenv("BASE_URL"),                 api_key=os.getenv("OPENAI_API_KEY"),                 streaming=True)#class ReActAgentState(TypedDict):    message: str    action: str    sub_action: str#def reasoning_node(state: ReActAgentState):    query = state['message']    if 'weather' in query:        return {'action': 'fetch_weather'}    elif 'news' in query:        return {'action': 'fetch_news'}    elif 'recommend' in query:        return {'action': 'recommendation', 'sub_action': 'book'}    else:        return {'action': 'unknown'}# toolsdef weather_subgraph_node(state: ReActAgentState):    # Simulating a weather tool call    return {'message': 'The weather today is sunny.'}def news_subgraph_node(state: ReActAgentState):    # Simulating a news tool call    return {'message': 'Here are the latest news headlines.'}def recommendation_subgraph_node(state: ReActAgentState):    if state.get('sub_action') == 'book':        return {'message': "I recommend reading 'The Pragmatic Programmer'."}    else:        return {'message': 'I have no other recommendations at the moment.'}# 天气子图weather_subgraph_builder = StateGraph(ReActAgentState)weather_subgraph_builder.add_node("weather_action", weather_subgraph_node)weather_subgraph_builder.set_entry_point("weather_action")weather_subgraph = weather_subgraph_builder.compile()# 新闻子图news_subgraph_builder = StateGraph(ReActAgentState)news_subgraph_builder.add_node("news_action", news_subgraph_node)news_subgraph_builder.set_entry_point("news_action")news_subgraph = news_subgraph_builder.compile()# 推荐子图recommendation_subgraph_builder = StateGraph(ReActAgentState)recommendation_subgraph_builder.add_node('recommendation_action', recommendation_subgraph_node)recommendation_subgraph_builder.set_entry_point('recommendation_action')recommendation_subgraph = recommendation_subgraph_builder.compile()#def reasoning_state_manager(state: ReActAgentState):    if state['action'] == "fetch_weather":        return weather_subgraph    elif state['action'] == "fetch_news":        return news_subgraph    elif state['action'] == "recommendation":        return recommendation_subgraph    else:        return None#  parent graphparent_builder = StateGraph(ReActAgentState)parent_builder.add_node("reasoning", reasoning_node)parent_builder.add_node("action_dispatch", reasoning_state_manager)parent_builder.add_edge(START, "reasoning")parent_builder.add_edge("reasoning", "action_dispatch")#react_agent_graph = parent_builder.compile()#inputs_weather = {'message': 'What is the weather today?'}result_weather = react_agent_graph.invoke(inputs_weather)print(result_weather['message'])#inputs_news = {'message': 'Give me the latest news.'}result_news = react_agent_graph.invoke(inputs_news)print(result_news['message'])#inputs_recommendation = {'message': 'Can you recommend a good book?'}result_recommendation = react_agent_graph.invoke(inputs_recommendation)print(result_recommendation['message'])

输出结果

The weather today is sunny.Here are the latest news headlines.I recommend reading 'The Pragmatic Programmer'.

父图推理节点负责处理用户输入，并判断需要执行的动作父图管理节点负责根据状态路由到不同的子图上各个子图负责处理各自的不同的任务

示例5 多个子图和上下文记忆的高级 ReAct 代理

from langchain_openai import ChatOpenAIfrom langgraph.prebuilt import create_react_agentfrom langgraph.checkpoint.memory import MemorySaverfrom dotenv import load_dotenvfrom langgraph.graph import StateGraph, MessagesState, START, ENDimport osfrom typing import TypedDictload_dotenv()llm = ChatOpenAI(model="qwen-max",                 base_url=os.getenv("BASE_URL"),                 api_key=os.getenv("OPENAI_API_KEY"),                 streaming=True)#class ReActAgentState(TypedDict):    message: str    action: str    sub_action: str    memory: dict  # Memory of past interactions#def reasoning_node(state: ReActAgentState):    query = state['message']    past_interactions = state.get('memory', {})    if 'weather' in query:        return {'action': 'fetch_weather'}    elif 'news' in query:        return {'action': 'fetch_news'}    elif 'recommend' in query:        if past_interactions.get('favorite_genre') == 'science':            return {'action': 'recommendation', 'sub_action': 'science_book'}        else:            return {'action': 'recommendation', 'sub_action': 'general_book'}    else:        return {'action': 'unknown'}# toolsdef weather_subgraph_node(state: ReActAgentState):    # Simulating a weather tool call    return {'message': 'The weather today is sunny.'}def news_subgraph_node(state: ReActAgentState):    # Simulating a news tool call    return {'message': 'Here are the latest news headlines.'}def general_recommendation_node(state: ReActAgentState):    return {'message': "I recommend reading 'The Pragmatic Programmer'."}def science_recommendation_node(state: ReActAgentState):    return {'message': "Since you like science, I recommend 'A Brief History of Time' by Stephen Hawking."}def update_memory_node(state: ReActAgentState):    if "recommend" in state["message"]:        state['memory']['favorite_genre'] = 'science'    return state# 天气子图weather_subgraph_builder = StateGraph(ReActAgentState)weather_subgraph_builder.add_node("weather_action", weather_subgraph_node)weather_subgraph_builder.set_entry_point("weather_action")weather_subgraph = weather_subgraph_builder.compile()# 新闻子图news_subgraph_builder = StateGraph(ReActAgentState)news_subgraph_builder.add_node("news_action", news_subgraph_node)news_subgraph_builder.set_entry_point("news_action")news_subgraph = news_subgraph_builder.compile()# 推荐子图recommendation_subgraph_builder = StateGraph(ReActAgentState)recommendation_subgraph_builder.add_node('general_recommendation_node', general_recommendation_node)recommendation_subgraph_builder.set_entry_point('general_recommendation_node')general_recommendation_subgraph = recommendation_subgraph_builder.compile()#science_recommendation_builder = StateGraph(ReActAgentState)science_recommendation_builder.add_node('science_recommendation_action', science_recommendation_node)science_recommendation_builder.set_entry_point('science_recommendation_action')science_recommendation_subgraph = science_recommendation_builder.compile()#memory_update_builder = StateGraph(ReActAgentState)memory_update_builder.add_node('update_memory_action', update_memory_node)memory_update_builder.set_entry_point('update_memory_action')memory_update_subgraph = memory_update_builder.compile()#def reasoning_state_manager(state: ReActAgentState):    if state['action'] == "fetch_weather":        return weather_subgraph    elif state['action'] == "fetch_news":        return news_subgraph    elif state['action'] == "recommendation":        if state['sub_action'] == 'science_book':            return science_recommendation_subgraph        else:            return general_recommendation_subgraph    else:        return None#  parent graphparent_builder = StateGraph(ReActAgentState)parent_builder.add_node("reasoning", reasoning_node)parent_builder.add_node("action_dispatch", reasoning_state_manager)parent_builder.add_node('update_memory', memory_update_subgraph)parent_builder.add_edge(START, "reasoning")parent_builder.add_edge("reasoning", "action_dispatch")parent_builder.add_edge('action_dispatch', 'update_memory')react_agent_graph = parent_builder.compile()checkpointer = MemorySaver()#inputs_weather = {'message': 'What is the weather today?', 'memory': {}}result_weather = react_agent_graph.invoke(inputs_weather)print(result_weather['message'])#inputs_recommendation_first = {'message': 'Can you recommend a good book?', 'memory': {}}result_recommendation_first = react_agent_graph.invoke(inputs_recommendation_first)print(result_recommendation_first['message'])#inputs_recommendation_second = {'message': 'Can you recommend another book?',                                'memory': {'favorite_genre': 'science'}}result_recommendation_second = react_agent_graph.invoke(inputs_recommendation_second)print(result_recommendation_second['message'])

输出结果

The weather today is sunny.I recommend reading 'The Pragmatic Programmer'.Since you like science, I recommend 'A Brief History of Time' by Stephen Hawking.

示例6 动态定价的Agent

from langchain_openai import ChatOpenAIfrom langgraph.prebuilt import create_react_agentfrom langgraph.checkpoint.memory import MemorySaverfrom dotenv import load_dotenvfrom langgraph.graph import StateGraph, MessagesState, START, ENDimport osfrom typing import TypedDictload_dotenv()model = ChatOpenAI(model="qwen-max",                   base_url=os.getenv("BASE_URL"),                   api_key=os.getenv("OPENAI_API_KEY"),                   streaming=True)# 模拟apidef get_demand_data(product_id: str) -> dict:    """Mock demand API to get demand data for a product."""    return {"product_id": product_id, "demand_level": "high"}def get_competitor_pricing(product_id: str) -> dict:    """Mock competitor pricing API."""    return {'product_id': product_id, 'competitor_price': 95.0}tools = [get_demand_data, get_competitor_pricing]graph = create_react_agent(model, tools=tools)initial_messages = [    ('system', '你是一个人工智能代理，能够根据市场需求和竞争对手的价格动态调整产品价格。'),    ('user', "产品ID“12345”的价格应该定多少？")]inputs = {"messages": initial_messages}for state in graph.stream(inputs, stream_mode="values"):    message = state["messages"][-1]    if isinstance(message, tuple):        print(message)    else:        message.pretty_print()

输出结果

================================ Human Message =================================产品ID“12345”的价格应该定多少？================================== Ai Message ==================================Tool Calls:  get_competitor_pricing (call_8212a1aad1044c488a9a53) Call ID: call_8212a1aad1044c488a9a53  Args:    product_id: 12345================================= Tool Message =================================Name: get_competitor_pricing{"product_id": "12345", "competitor_price": 95.0}================================== Ai Message ==================================Tool Calls:  get_demand_data (call_2450404f79774e85b1904a) Call ID: call_2450404f79774e85b1904a  Args:    product_id: 12345================================= Tool Message =================================Name: get_demand_data{"product_id": "12345", "demand_level": "high"}================================== Ai Message ==================================根据市场情况，对于产品ID为“12345”的商品，竞争对手的定价是95.0元。同时，当前市场上该产品的需求水平较高。鉴于高需求以及竞争对手的价格，我们可以考虑将价格设定得略高于或等于95.0元以最大化利润，同时保持竞争力。确切的价格还需要考虑成本、品牌定位等因素。如果我们决定跟随竞争者的价格，那么可以将价格也设为95.0元；如果想要利用高需求来增加收益，并且确定顾客愿意为此支付更多，我们则可以适度提价。您希望如何继续？

示例 7 使用 LLM 和自定义情感分析工具的情感分析代理

from langchain_openai import ChatOpenAIfrom langgraph.prebuilt import create_react_agentfrom langgraph.checkpoint.memory import MemorySaverfrom dotenv import load_dotenvfrom langgraph.graph import StateGraph, MessagesState, START, ENDimport osfrom typing import TypedDictfrom typing import Annotated, Sequence, TypedDictfrom langchain_core.messages import BaseMessage, ToolMessage, SystemMessagefrom langgraph.graph.message import add_messagesfrom langchain_core.tools import tool# pip install textblobfrom textblob import TextBlobimport jsonfrom langchain_core.runnables import RunnableConfigload_dotenv()model = ChatOpenAI(model="qwen-max",                   base_url=os.getenv("BASE_URL"),                   api_key=os.getenv("OPENAI_API_KEY"),                   streaming=True)class AgentState(TypedDict):    messages: Annotated[Sequence[BaseMessage], add_messages]@tooldef analyze_sentiment(feedback: str) -> str:    """Analyze customer feedback sentiment with custom logic."""    analysis = TextBlob(feedback)    if analysis.sentiment.polarity > 0.5:        return "positive"    elif analysis.sentiment.polarity == 0.5:        return "neutral"    else:        return "negative"@tooldef respond_based_on_sentiment(sentiment: str) -> str:    """Respond to the customer based on the analyzed sentiment."""    if sentiment == 'positive':        return '感谢您的积极反馈！'    elif sentiment == 'neutral':        return '我们感谢您的反馈。'    else:        return "我们很遗憾听到你不满意。我们如何提供帮助？"tools = [analyze_sentiment, respond_based_on_sentiment]llm = model.bind_tools(tools)tools_by_name = {tool.name: tool for tool in tools}graph = create_react_agent(model, tools=tools)def tool_node(state: AgentState):    outputs = []    for tool_call in state['messages'][-1].tool_calls:        tool_result = tools_by_name[tool_call['name']].invoke(tool_call['args'])        outputs.append(            ToolMessage(content=json.dumps(tool_result),                        name=tool_call['name'],                        tool_call_id=tool_call['id']))    return {'messages': outputs}def call_model(state: AgentState, config: RunnableConfig):    system_prompt = SystemMessage(content='你是一位乐于助人的助手，负责回复客户反馈。')    response = llm.invoke([system_prompt] + state['messages'], config)    return {'messages': [response]}def should_continue(state: AgentState):    last_message = state['messages'][-1]    # If there is no tool call, then we finish    if not last_message.tool_calls:        return 'end'    else:        return 'continue'#workflow = StateGraph(AgentState)workflow.add_node('agent', call_model)workflow.add_node('tools', tool_node)workflow.set_entry_point('agent')workflow.add_conditional_edges(    'agent',    should_continue,    {'continue': 'tools', 'end': END, },)workflow.add_edge('tools', 'agent')graph = workflow.compile()def print_stream(stream):    for s in stream:        message = s['messages'][-1]    if isinstance(message, tuple):        print(message)    else:        message.pretty_print()initial_state = {'messages': [('user', '产品很好，但交货很差。')]}print_stream(graph.stream(initial_state, stream_mode='values'))

输出结果

================================== Ai Message ==================================我们很遗憾听到你不满意。我们如何提供帮助？

示例8 使用 LLM 和内存的个性化产品推荐代理

from langchain_openai import ChatOpenAIfrom langgraph.prebuilt import create_react_agentfrom langgraph.checkpoint.memory import MemorySaverfrom dotenv import load_dotenvfrom langgraph.graph import StateGraph, MessagesState, START, ENDimport osfrom typing import TypedDictfrom typing import Annotated, Sequence, TypedDictfrom langchain_core.messages import BaseMessage, ToolMessage, SystemMessagefrom langgraph.graph.message import add_messagesfrom langchain_core.tools import tool# pip install textblobfrom textblob import TextBlobimport jsonfrom langchain_core.runnables import RunnableConfigload_dotenv()model = ChatOpenAI(model="qwen-max",                   base_url=os.getenv("BASE_URL"),                   api_key=os.getenv("OPENAI_API_KEY"),                   streaming=True)class RecommendationState(TypedDict):    user_id: str  # 用户标识符    preference: str  # 用户当前的偏好（例如，流派、类别）    reasoning: str  # 来自 LLM 的推理过程    recommendation: str  # 最终产品推荐    memory: dict  # 用于存储首选项的用户内存    messages: Annotated[Sequence[BaseMessage], add_messages]@tooldef recommend_product(preference: str) -> str:    """Recommend a product based on the user's preferences."""    product_db = {        'science': "I recommend 'A Brief History of Time' by Stephen Hawking.",        'technology': "I recommend 'The Innovators' by Walter Isaacson.",        'fiction': "I recommend 'The Alchemist' by Paulo Coelho."    }    return product_db.get(preference, 'I recommend exploring our latest products!')tools = [recommend_product]tools_by_name = {tool.name: tool for tool in tools}llm = model.bind_tools(tools)def update_memory(state: RecommendationState):    state["memory"][state['user_id']] = state["preference"]    return statedef tool_node(state: RecommendationState):    outputs = []    for tool_call in state["messages"][-1].tool_calls:        tool_result = tools_by_name[tool_call["name"]].invoke(tool_call["args"])        outputs.append(            ToolMessage(                content=json.dumps(tool_result),                name=tool_call["name"],                tool_call_id=tool_call["id"],            )        )    return {"messages": outputs}def call_model(state: RecommendationState, config: RunnableConfig):    system_prompt = SystemMessage(        content=f"You are a helpful assistant for recommending a product based on the user's preference."    )    response = llm.invoke([system_prompt] + state["messages"] + [("user", state["preference"])], config)    return {"messages": [response]}def should_continue(state: RecommendationState):    last_message = state["messages"][-1]    # If there is no tool call, then we finish    if not last_message.tool_calls:        return "end"    else:        return "continue"workflow = StateGraph(RecommendationState)workflow.add_node('agent', call_model)workflow.add_node('tools', tool_node)workflow.add_node('update_memory', update_memory)workflow.set_entry_point('agent')workflow.add_conditional_edges(    'agent',    should_continue,    {        'continue': 'tools',        'end': END,    })workflow.add_edge('tools', 'update_memory')workflow.add_edge('update_memory', 'agent')graph = workflow.compile()#initial_state = {    "messages": [("user", "I'm looking for a book.")],    "user_id": "user1", "preference": "science",    "memory": {},    "reasoning": ""}result = graph.invoke(initial_state)#print(f"Reasoning: {result['reasoning']}")print(f"Product Recommendation: {result['messages'][-1].content}")print(f"Updated Memory: {result['memory']}")# Helper function to print the conversationdef print_stream(stream):    for s in stream:        message = s["messages"][-1]        if isinstance(message, tuple):            print(message)        else:            message.pretty_print()# Run the agentprint_stream(graph.stream(initial_state, stream_mode="values"))

输出结果

Reasoning: Product Recommendation: I recommend 'A Brief History of Time' by Stephen Hawking. It's a great read for anyone interested in science, particularly in the areas of cosmology and theoretical physics.Updated Memory: {'user1': 'science'}================================ Human Message =================================I'm looking for a book.================================== Ai Message ==================================Tool Calls:  recommend_product (call_6b6a242cc9e24557a89db8) Call ID: call_6b6a242cc9e24557a89db8  Args:    preference: science================================= Tool Message =================================Name: recommend_product"I recommend 'A Brief History of Time' by Stephen Hawking."================================= Tool Message =================================Name: recommend_product"I recommend 'A Brief History of Time' by Stephen Hawking."================================== Ai Message ==================================I recommend 'A Brief History of Time' by Stephen Hawking. It's a great book that explores complex scientific concepts in an accessible way. Would you like more recommendations or information on this book?