---

代理式检索增强生成（Agentic RAG）

https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_agentic_rag/

在本教程中，我们将构建一个检索代理。当您希望大型语言模型(LLM)能够决定是从向量存储中检索上下文还是直接响应用户时，检索代理非常有用。

通过本教程，我们将完成以下内容：

1、获取并预处理用于检索的文档
2、为这些文档建立语义搜索索引，并为代理创建检索工具
3、构建一个能够决定何时使用检索工具的代理式RAG系统

---

安装

让我们下载所需的软件包并设置API密钥：

pip install -U --quiet langgraph "langchain[openai]" langchain-community langchain-text-splitters

---

import getpass
import osdef _set_env(key: str):if key not in os.environ:os.environ[key] = getpass.getpass(f"{key}:")_set_env("OPENAI_API_KEY")

---

为LangGraph开发设置LangSmith

注册LangSmith可快速发现问题并提升LangGraph项目性能。LangSmith允许您利用追踪数据来调试、测试和监控基于LangGraph构建的LLM应用——了解更多入门指南请点击此处。

---

1、预处理文档

1、获取用于RAG系统的文档。我们将使用Lilian Weng的优秀博客中最新的三篇文章。首先使用WebBaseLoader工具获取页面内容：

API参考：WebBaseLoader

---

from langchain_community.document_loaders import WebBaseLoaderurls = ["https://lilianweng.github.io/posts/2024-11-28-reward-hacking/","https://lilianweng.github.io/posts/2024-07-07-hallucination/","https://lilianweng.github.io/posts/2024-04-12-diffusion-video/",
]docs = [WebBaseLoader(url).load() for url in urls]

---

docs[0][0].page_content.strip()[:1000]

---

"Reward Hacking in Reinforcement Learning | Lil'Log\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLil'Log\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|\n\n\n\n\n\n\nPosts\n\n\n\n\nArchive\n\n\n\n\nSearch\n\n\n\n\nTags\n\n\n\n\nFAQ\n\n\n\n\n\n\n\n\n\n      Reward Hacking in Reinforcement Learning\n    \nDate: November 28, 2024 | Estimated Reading Time: 37 min | Author: Lilian Weng\n\n\n \n\n\nTable of Contents\n\n\n\nBackground\n\nReward Function in RL\n\nSpurious Correlation\n\n\nLet’s Define Reward Hacking\n\nList of Examples\n\nReward hacking examples in RL tasks\n\nReward hacking examples in LLM tasks\n\nReward hacking examples in real life\n\n\nWhy does Reward Hacking Exist?\n\n\nHacking RL Environment\n\nHacking RLHF of LLMs\n\nHacking the Training Process\n\nHacking the Evaluator\n\nIn-Context Reward Hacking\n\n\nGeneralization of Hacking Skills\n\nPeek into Mitigations\n\nRL Algorithm Improvement\n\nDetecting Reward Hacking\n\nData Analysis of RLHF\n\n\nCitation\n\nReferences\n\n\n\n\n\nReward hacking occurs when a reinforcement learning (RL) agent exploits flaws or ambiguities in the reward function to ac"

---

2、将获取的文档分割成更小的块，以便索引到我们的向量存储中：

API参考：RecursiveCharacterTextSplitter

---

from langchain_text_splitters import RecursiveCharacterTextSplitterdocs_list = [item for sublist in docs for item in sublist]text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(chunk_size=100, chunk_overlap=50
)
doc_splits = text_splitter.split_documents(docs_list)

---

doc_splits[0].page_content.strip()

---

"Reward Hacking in Reinforcement Learning | Lil'Log\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLil'Log\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|\n\n\n\n\n\n\nPosts\n\n\n\n\nArchive\n\n\n\n\nSearch\n\n\n\n\nTags\n\n\n\n\nFAQ"

---

2、创建检索器工具

现在我们已经完成了文档分割，接下来可以将它们索引到向量存储中，用于语义搜索。

1、使用内存向量存储和OpenAI嵌入：

API参考：InMemoryVectorStore | OpenAIEmbeddings

---

from langchain_core.vectorstores import InMemoryVectorStore
from langchain_openai import OpenAIEmbeddingsvectorstore = InMemoryVectorStore.from_documents(documents=doc_splits, embedding=OpenAIEmbeddings()
)
retriever = vectorstore.as_retriever()

---

2、使用LangChain预置的create_retriever_tool创建检索器工具：

API参考文档：create_retriever_tool

---

from langchain.tools.retriever import create_retriever_toolretriever_tool = create_retriever_tool(retriever,"retrieve_blog_posts","Search and return information about Lilian Weng blog posts.",
)

---

3、测试工具：

retriever_tool.invoke({"query": "types of reward hacking"})

---

'(Note: Some work defines reward tampering as a distinct category of misalignment behavior from reward hacking. But I consider reward hacking as a broader concept here.)\nAt a high level, reward hacking can be categorized into two types: environment or goal misspecification, and reward tampering.\n\nWhy does Reward Hacking Exist?#\n\nPan et al. (2022) investigated reward hacking as a function of agent capabilities, including (1) model size, (2) action space resolution, (3) observation space noise, and (4) training time. They also proposed a taxonomy of three types of misspecified proxy rewards:\n\nLet’s Define Reward Hacking#\nReward shaping in RL is challenging. Reward hacking occurs when an RL agent exploits flaws or ambiguities in the reward function to obtain high rewards without genuinely learning the intended behaviors or completing the task as designed. In recent years, several related concepts have been proposed, all referring to some form of reward hacking:'

---

3、生成查询

现在我们将开始为智能RAG图构建组件（节点和边）。注意这些组件将在MessagesState上运行——该图状态包含一个messages键，其中存储着聊天消息列表。

1、构建generate_query_or_respond节点。该节点将调用LLM基于当前图状态（消息列表）生成响应。根据输入消息，它会决定使用检索工具进行检索，或直接回复用户。注意我们通过.bind_tools为聊天模型提供了之前创建的retriever_tool访问权限：

API参考：init_chat_model

---

from langgraph.graph import MessagesState
from langchain.chat_models import init_chat_modelresponse_model = init_chat_model("openai:gpt-4.1", temperature=0)def generate_query_or_respond(state: MessagesState):"""Call the model to generate a response based on the current state. Giventhe question, it will decide to retrieve using the retriever tool, or simply respond to the user."""response = (response_model.bind_tools([retriever_tool]).invoke(state["messages"]))return {"messages": [response]}

---

2、在随机输入上尝试：

input = {"messages": [{"role": "user", "content": "hello!"}]}
generate_query_or_respond(input)["messages"][-1].pretty_print()

---

================================== Ai Message ==================================Hello! How can I help you today?

---

3、提出一个需要语义搜索的问题：

input = {"messages": [{"role": "user","content": "What does Lilian Weng say about types of reward hacking?",}]
}
generate_query_or_respond(input)["messages"][-1].pretty_print()

---

================================== Ai Message ==================================
Tool Calls:retrieve_blog_posts (call_tYQxgfIlnQUDMdtAhdbXNwIM)Call ID: call_tYQxgfIlnQUDMdtAhdbXNwIMArgs:query: types of reward hacking

---

4、文档分级

1、添加一个条件边——grade_documents——用于判断检索到的文档是否与问题相关。我们将使用一个具有结构化输出模式GradeDocuments的模型进行文档分级。grade_documents函数将根据分级决策返回下一步应转向的节点名称（generate_answer或rewrite_question）：

from pydantic import BaseModel, Field
from typing import LiteralGRADE_PROMPT = ("You are a grader assessing relevance of a retrieved document to a user question. \n ""Here is the retrieved document: \n\n {context} \n\n""Here is the user question: {question} \n""If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \n""Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question."
)class GradeDocuments(BaseModel):"""Grade documents using a binary score for relevance check."""binary_score: str = Field(description="Relevance score: 'yes' if relevant, or 'no' if not relevant")grader_model = init_chat_model("openai:gpt-4.1", temperature=0)def grade_documents(state: MessagesState,
) -> Literal["generate_answer", "rewrite_question"]:"""Determine whether the retrieved documents are relevant to the question."""question = state["messages"][0].contentcontext = state["messages"][-1].contentprompt = GRADE_PROMPT.format(question=question, context=context)response = (grader_model.with_structured_output(GradeDocuments).invoke([{"role": "user", "content": prompt}]))score = response.binary_scoreif score == "yes":return "generate_answer"else:return "rewrite_question"

---

2、在工具响应中使用无关文档运行此操作：

API参考：convert_to_messages

---

from langchain_core.messages import convert_to_messagesinput = {"messages": convert_to_messages([{"role": "user","content": "What does Lilian Weng say about types of reward hacking?",},{"role": "assistant","content": "","tool_calls": [{"id": "1","name": "retrieve_blog_posts","args": {"query": "types of reward hacking"},}],},{"role": "tool", "content": "meow", "tool_call_id": "1"},])
}
grade_documents(input)

---

'rewrite_question'

---

3、确认相关文档已按以下类别进行分类：

input = {"messages": convert_to_messages([{"role": "user","content": "What does Lilian Weng say about types of reward hacking?",},{"role": "assistant","content": "","tool_calls": [{"id": "1","name": "retrieve_blog_posts","args": {"query": "types of reward hacking"},}],},{"role": "tool","content": "reward hacking can be categorized into two types: environment or goal misspecification, and reward tampering","tool_call_id": "1",},])
}
grade_documents(input)

---

'generate_answer'

---

5、问题重写

1、构建 rewrite_question 节点。检索工具可能会返回不相关的文档，这表明需要改进原始用户问题。为此，我们将调用 rewrite_question 节点：

REWRITE_PROMPT = ("Look at the input and try to reason about the underlying semantic intent / meaning.\n""Here is the initial question:""\n ------- \n""{question}""\n ------- \n""Formulate an improved question:"
)def rewrite_question(state: MessagesState):"""Rewrite the original user question."""messages = state["messages"]question = messages[0].contentprompt = REWRITE_PROMPT.format(question=question)response = response_model.invoke([{"role": "user", "content": prompt}])return {"messages": [{"role": "user", "content": response.content}]}

---

2、动手尝试：

input = {"messages": convert_to_messages([{"role": "user","content": "What does Lilian Weng say about types of reward hacking?",},{"role": "assistant","content": "","tool_calls": [{"id": "1","name": "retrieve_blog_posts","args": {"query": "types of reward hacking"},}],},{"role": "tool", "content": "meow", "tool_call_id": "1"},])
}response = rewrite_question(input)
print(response["messages"][-1]["content"])

---

What are the different types of reward hacking described by Lilian Weng, and how does she explain them?

---

6、生成答案

1、构建 generate_answer 节点：如果通过评分器检查，我们可以基于原始问题和检索到的上下文生成最终答案：

GENERATE_PROMPT = ("You are an assistant for question-answering tasks. ""Use the following pieces of retrieved context to answer the question. ""If you don't know the answer, just say that you don't know. ""Use three sentences maximum and keep the answer concise.\n""Question: {question} \n""Context: {context}"
)def generate_answer(state: MessagesState):"""Generate an answer."""question = state["messages"][0].contentcontext = state["messages"][-1].contentprompt = GENERATE_PROMPT.format(question=question, context=context)response = response_model.invoke([{"role": "user", "content": prompt}])return {"messages": [response]}

---

2、试一试：

input = {"messages": convert_to_messages([{"role": "user","content": "What does Lilian Weng say about types of reward hacking?",},{"role": "assistant","content": "","tool_calls": [{"id": "1","name": "retrieve_blog_posts","args": {"query": "types of reward hacking"},}],},{"role": "tool","content": "reward hacking can be categorized into two types: environment or goal misspecification, and reward tampering","tool_call_id": "1",},])
}response = generate_answer(input)
response["messages"][-1].pretty_print()

---

================================== Ai Message ==================================Lilian Weng says that reward hacking can be categorized into two types: environment or goal misspecification, and reward tampering. These categories describe different ways in which an agent might exploit flaws in the reward system. Environment or goal misspecification involves unintended behaviors due to poorly specified objectives, while reward tampering involves directly manipulating the reward signal.

---

7、构建流程图

• 从 generate_query_or_respond 开始，判断是否需要调用 retriever_tool
• 使用 tools_condition 路由到下一步：
  • 如果 generate_query_or_respond 返回 tool_calls，则调用 retriever_tool 检索上下文
  • 否则直接响应用户
• 对检索到的文档内容进行问题相关性评分 (grade_documents) 并路由到下一步：
  • 如果不相关，使用 rewrite_question 重写问题后再次调用 generate_query_or_respond
  • 如果相关，则进入 generate_answer 并使用 ToolMessage 结合检索到的文档上下文生成最终响应

API参考：StateGraph | START | END | ToolNode | tools_condition

---

from langgraph.graph import StateGraph, START, END
from langgraph.prebuilt import ToolNode
from langgraph.prebuilt import tools_conditionworkflow = StateGraph(MessagesState)# Define the nodes we will cycle between
workflow.add_node(generate_query_or_respond)
workflow.add_node("retrieve", ToolNode([retriever_tool]))
workflow.add_node(rewrite_question)
workflow.add_node(generate_answer)workflow.add_edge(START, "generate_query_or_respond")# Decide whether to retrieve
workflow.add_conditional_edges("generate_query_or_respond",# Assess LLM decision (call `retriever_tool` tool or respond to the user)tools_condition,{# Translate the condition outputs to nodes in our graph"tools": "retrieve",END: END,},
)# Edges taken after the `action` node is called.
workflow.add_conditional_edges("retrieve",# Assess agent decisiongrade_documents,
)
workflow.add_edge("generate_answer", END)
workflow.add_edge("rewrite_question", "generate_query_or_respond")# Compile
graph = workflow.compile()

---

可视化图表：

from IPython.display import Image, displaydisplay(Image(graph.get_graph().draw_mermaid_png()))

---

8、运行智能RAG代理

---

for chunk in graph.stream({"messages": [{"role": "user","content": "What does Lilian Weng say about types of reward hacking?",}]}
):for node, update in chunk.items():print("Update from node", node)update["messages"][-1].pretty_print()print("\n\n")

---

Update from node generate_query_or_respond
================================== Ai Message ==================================
Tool Calls:retrieve_blog_posts (call_NYu2vq4km9nNNEFqJwefWKu1)Call ID: call_NYu2vq4km9nNNEFqJwefWKu1Args:query: types of reward hackingUpdate from node retrieve
================================= Tool Message =================================
Name: retrieve_blog_posts(Note: Some work defines reward tampering as a distinct category of misalignment behavior from reward hacking. But I consider reward hacking as a broader concept here.)
At a high level, reward hacking can be categorized into two types: environment or goal misspecification, and reward tampering.Why does Reward Hacking Exist?#Pan et al. (2022) investigated reward hacking as a function of agent capabilities, including (1) model size, (2) action space resolution, (3) observation space noise, and (4) training time. They also proposed a taxonomy of three types of misspecified proxy rewards:Let’s Define Reward Hacking#
Reward shaping in RL is challenging. Reward hacking occurs when an RL agent exploits flaws or ambiguities in the reward function to obtain high rewards without genuinely learning the intended behaviors or completing the task as designed. In recent years, several related concepts have been proposed, all referring to some form of reward hacking:Update from node generate_answer
================================== Ai Message ==================================Lilian Weng categorizes reward hacking into two types: environment or goal misspecification, and reward tampering. She considers reward hacking as a broad concept that includes both of these categories. Reward hacking occurs when an agent exploits flaws or ambiguities in the reward function to achieve high rewards without performing the intended behaviors.

---

多智能体监督系统

https://langchain-ai.github.io/langgraph/tutorials/multi_agent/agent_supervisor/

监督者是一种多智能体架构，其中专业化智能体由中央监督智能体协调。监督智能体控制所有通信流和任务委派，根据当前上下文和任务需求决定调用哪个智能体。

在本教程中，您将构建一个包含研究专家和数学专家的监督系统。通过本教程您将：

1、构建专业化的研究和数学智能体
2、使用预建的langgraph-supervisor构建协调它们的监督者
3、从零开始构建监督者
4、实现高级任务委派

---

安装配置

首先，我们需要安装必要的软件包并设置API密钥

---

pip install -U langgraph langgraph-supervisor langchain-tavily "langchain[openai]"

---

import getpass
import osdef _set_if_undefined(var: str):if not os.environ.get(var):os.environ[var] = getpass.getpass(f"Please provide your {var}")_set_if_undefined("OPENAI_API_KEY")
_set_if_undefined("TAVILY_API_KEY")

---

为 LangGraph 开发设置 LangSmith

注册 LangSmith 可以快速发现问题并提升 LangGraph 项目的性能。LangSmith 允许您利用追踪数据来调试、测试和监控基于 LangGraph 构建的 LLM 应用——了解更多入门指南请访问这里。

---

1、创建工作代理

首先，我们创建两个专业的工作代理——研究代理和数学代理：

• 研究代理将能够使用Tavily API进行网络搜索
• 数学代理将可以使用基础数学工具（add、multiply、divide）

---

研究代理

对于网页搜索，我们将使用 langchain-tavily 中的 TavilySearch 工具：

API参考文档：TavilySearch

---

from langchain_tavily import TavilySearchweb_search = TavilySearch(max_results=3)
web_search_results = web_search.invoke("who is the mayor of NYC?")print(web_search_results["results"][0]["content"])

---

Mayor Eric Adams has served the people of New York City as an NYPD officer, State Senator, Brooklyn Borough President, and now as the 110th Mayor of the City of New York. He gave voice to a diverse coalition of working families in all five boroughs and is leading the fight to bring back New York City’s economy, reduce inequality, improve public safety, and build a stronger, healthier city that delivers for all New Yorkers. Mayor Eric Adams has served the people of New York City as an NYPD officer, State Senator, Brooklyn Borough President, and now as the 110th Mayor of the City of New York. NYC is a trademark and service mark of the City of New York.

---

要创建独立的 worker 代理，我们将使用 LangGraph 预构建的 agent。

API 参考文档: create_react_agent

---

from langgraph.prebuilt import create_react_agentresearch_agent = create_react_agent(model="openai:gpt-4.1",tools=[web_search],prompt=("You are a research agent.\n\n""INSTRUCTIONS:\n""- Assist ONLY with research-related tasks, DO NOT do any math\n""- After you're done with your tasks, respond to the supervisor directly\n""- Respond ONLY with the results of your work, do NOT include ANY other text."),name="research_agent",
)

---

让我们运行代理来验证其行为是否符合预期。

我们将使用pretty_print_messages辅助工具来优雅地呈现流式代理输出。

---

from langchain_core.messages import convert_to_messagesdef pretty_print_message(message, indent=False):pretty_message = message.pretty_repr(html=True)if not indent:print(pretty_message)returnindented = "\n".join("\t" + c for c in pretty_message.split("\n"))print(indented)def pretty_print_messages(update, last_message=False):is_subgraph = Falseif isinstance(update, tuple):ns, update = update# skip parent graph updates in the printoutsif len(ns) == 0:returngraph_id = ns[-1].split(":")[0]print(f"Update from subgraph {graph_id}:")print("\n")is_subgraph = Truefor node_name, node_update in update.items():update_label = f"Update from node {node_name}:"if is_subgraph:update_label = "\t" + update_labelprint(update_label)print("\n")messages = convert_to_messages(node_update["messages"])if last_message:messages = messages[-1:]for m in messages:pretty_print_message(m, indent=is_subgraph)print("\n")

---

for chunk in research_agent.stream({"messages": [{"role": "user", "content": "who is the mayor of NYC?"}]}
):pretty_print_messages(chunk)

---

Update from node agent:================================== Ai Message ==================================
Name: research_agent
Tool Calls:tavily_search (call_T4wrj7DKAG5hVtNVhjRYGdei)Call ID: call_T4wrj7DKAG5hVtNVhjRYGdeiArgs:query: current mayor of New York CityUpdate from node tools:================================= Tool Message =================================
Name: tavily_search{"query": "current mayor of New York City", "follow_up_questions": null, "answer": null, "images": [], "results": [{"title": "List of mayors of New York City - Wikipedia", "url": "https://en.wikipedia.org/wiki/List_of_mayors_of_New_York_City", "content": "The mayor of New York City is the chief executive of the Government of New York City, as stipulated by New York City's charter.The current officeholder, the 110th in the sequence of regular mayors, is Eric Adams, a member of the Democratic Party.. During the Dutch colonial period from 1624 to 1664, New Amsterdam was governed by the Director of New Netherland.", "score": 0.9039154, "raw_content": null}, {"title": "Office of the Mayor | Mayor's Bio | City of New York - NYC.gov", "url": "https://www.nyc.gov/office-of-the-mayor/bio.page", "content": "Mayor Eric Adams has served the people of New York City as an NYPD officer, State Senator, Brooklyn Borough President, and now as the 110th Mayor of the City of New York. He gave voice to a diverse coalition of working families in all five boroughs and is leading the fight to bring back New York City's economy, reduce inequality, improve", "score": 0.8405867, "raw_content": null}, {"title": "Eric Adams - Wikipedia", "url": "https://en.wikipedia.org/wiki/Eric_Adams", "content": "Eric Leroy Adams (born September 1, 1960) is an American politician and former police officer who has served as the 110th mayor of New York City since 2022、Adams was an officer in the New York City Transit Police and then the New York City Police Department (NYPD) for more than 20 years, retiring at the rank of captain.He served in the New York State Senate from 2006 to 2013, representing the", "score": 0.77731717, "raw_content": null}], "response_time": 1.31}Update from node agent:================================== Ai Message ==================================
Name: research_agentThe current mayor of New York City is Eric Adams.

---

数学代理工具

对于数学代理工具，我们将使用原生Python函数来实现：

def add(a: float, b: float):"""Add two numbers."""return a + bdef multiply(a: float, b: float):"""Multiply two numbers."""return a * bdef divide(a: float, b: float):"""Divide two numbers."""return a / bmath_agent = create_react_agent(model="openai:gpt-4.1",tools=[add, multiply, divide],prompt=("You are a math agent.\n\n""INSTRUCTIONS:\n""- Assist ONLY with math-related tasks\n""- After you're done with your tasks, respond to the supervisor directly\n""- Respond ONLY with the results of your work, do NOT include ANY other text."),name="math_agent",
)

---

让我们运行数学代理程序：

for chunk in math_agent.stream({"messages": [{"role": "user", "content": "what's (3 + 5) x 7"}]}
):pretty_print_messages(chunk)

---

Update from node agent:================================== Ai Message ==================================
Name: math_agent
Tool Calls:add (call_LqyOrR1Ktr2LVLDlXpbWNAsp)Call ID: call_LqyOrR1Ktr2LVLDlXpbWNAspArgs:a: 3b: 5Update from node tools:================================= Tool Message =================================
Name: add8.0Update from node agent:================================== Ai Message ==================================
Name: math_agent
Tool Calls:multiply (call_IBXYtlEMdZrfDZ8g8bWC31pM)Call ID: call_IBXYtlEMdZrfDZ8g8bWC31pMArgs:a: 8b: 7Update from node tools:================================= Tool Message =================================
Name: multiply56.0Update from node agent:================================== Ai Message ==================================
Name: math_agent56

---

2、使用 langgraph-supervisor 创建监督器

为了实现我们的多智能体系统，我们将使用预构建的 langgraph-supervisor 库中的 create_supervisor 方法：

API 参考文档：create_supervisor | init_chat_model

---

from langgraph_supervisor import create_supervisor
from langchain.chat_models import init_chat_modelsupervisor = create_supervisor(model=init_chat_model("openai:gpt-4.1"),agents=[research_agent, math_agent],prompt=("You are a supervisor managing two agents:\n""- a research agent. Assign research-related tasks to this agent\n""- a math agent. Assign math-related tasks to this agent\n""Assign work to one agent at a time, do not call agents in parallel.\n""Do not do any work yourself."),add_handoff_back_messages=True,output_mode="full_history",
).compile()

---

from IPython.display import display, Imagedisplay(Image(supervisor.get_graph().draw_mermaid_png()))

---

for chunk in supervisor.stream({"messages": [{"role": "user","content": "find US and New York state GDP in 2024、what % of US GDP was New York state?",}]},
):pretty_print_messages(chunk, last_message=True)final_message_history = chunk["supervisor"]["messages"]

---

Update from node supervisor:================================= Tool Message =================================
Name: transfer_to_research_agentSuccessfully transferred to research_agentUpdate from node research_agent:================================= Tool Message =================================
Name: transfer_back_to_supervisorSuccessfully transferred back to supervisorUpdate from node supervisor:================================= Tool Message =================================
Name: transfer_to_math_agentSuccessfully transferred to math_agentUpdate from node math_agent:================================= Tool Message =================================
Name: transfer_back_to_supervisorSuccessfully transferred back to supervisorUpdate from node supervisor:================================== Ai Message ==================================
Name: supervisorThe US GDP in 2024 was $29.017 trillion, and New York State's GDP was $2.284 trillion. New York State accounted for approximately 7.87% of the total US GDP in 2024、```

---

3、从零开始创建监督者

现在让我们从头开始实现这个相同的多智能体系统。我们需要完成以下步骤：

1、设置监督者与各个智能体之间的通信机制
2、创建监督者智能体
3、将监督者智能体和工作智能体组合成一个统一的多智能体图

---

设置代理通信机制

我们需要定义一种方式让监督代理与工作代理进行通信。在多代理架构中，常见的实现方式是使用交接机制，即一个代理将控制权交接给另一个代理。通过交接机制可以指定：

• 目标对象：要转移到的目标代理
• 传递内容：需要传递给该代理的Info : 我们将通过交接工具来实现这一机制，并将这些工具赋予监督代理：当监督代理调用这些工具时，会将控制权移交给工作代理，同时将完整的消息历史记录传递给该代理。

API参考文档：tool | InjectedToolCallId | InjectedState | StateGraph | START | Command

---

from typing import Annotated
from langchain_core.tools import tool, InjectedToolCallId
from langgraph.prebuilt import InjectedState
from langgraph.graph import StateGraph, START, MessagesState
from langgraph.types import Commanddef create_handoff_tool(*, agent_name: str, description: str | None = None):name = f"transfer_to_{agent_name}"description = description or f"Ask {agent_name} for help."@tool(name, description=description)def handoff_tool(state: Annotated[MessagesState, InjectedState],tool_call_id: Annotated[str, InjectedToolCallId],) -> Command:tool_message = {"role": "tool","content": f"Successfully transferred to {agent_name}","name": name,"tool_call_id": tool_call_id,}return Command(goto=agent_name,  # (1)!update={**state, "messages": state["messages"] + [tool_message]},  # (2)!graph=Command.PARENT,  # (3)!)return handoff_tool# Handoffs
assign_to_research_agent = create_handoff_tool(agent_name="research_agent",description="Assign task to a researcher agent.",
)assign_to_math_agent = create_handoff_tool(agent_name="math_agent",description="Assign task to a math agent.",
)

---

1、要交接给的目标代理或节点名称。

2、在交接过程中，将当前代理的消息添加到父级状态中。下一个代理将能够看到父级状态。

3、向LangGraph表明我们需要导航到父级多代理图中的某个代理节点。

---

创建监督员代理

接下来，我们将使用刚刚定义好的交接工具来创建监督员代理。这里会用到预构建的 create_react_agent 方法：

（说明：严格遵循了代码保护原则，保留了所有代码块和API名称的原始格式；将被动语态转换为主动语态；保持了技术文档的简洁性和准确性）

---

supervisor_agent = create_react_agent(model="openai:gpt-4.1",tools=[assign_to_research_agent, assign_to_math_agent],prompt=("You are a supervisor managing two agents:\n""- a research agent. Assign research-related tasks to this agent\n""- a math agent. Assign math-related tasks to this agent\n""Assign work to one agent at a time, do not call agents in parallel.\n""Do not do any work yourself."),name="supervisor",
)

---

创建多智能体图

将所有组件整合起来，让我们为整个多智能体系统创建一个图。我们将把监督者和各个智能体作为子图节点添加进来。

API参考：END

---

from langgraph.graph import END# Define the multi-agent supervisor graph
supervisor = (StateGraph(MessagesState)# NOTE: `destinations` is only needed for visualization and doesn't affect runtime behavior.add_node(supervisor_agent, destinations=("research_agent", "math_agent", END)).add_node(research_agent).add_node(math_agent).add_edge(START, "supervisor")# always return back to the supervisor.add_edge("research_agent", "supervisor").add_edge("math_agent", "supervisor").compile()
)

---

请注意，我们已明确添加了从工作代理返回到监督者的边缘——这意味着系统能确保将控制权交还给监督者。若希望代理直接响应用户（例如将系统转变为路由器模式），则可以移除这些边缘连接。

---

from IPython.display import display, Imagedisplay(Image(supervisor.get_graph().draw_mermaid_png()))

---

for chunk in supervisor.stream({"messages": [{"role": "user","content": "find US and New York state GDP in 2024、what % of US GDP was New York state?",}]},
):pretty_print_messages(chunk, last_message=True)final_message_history = chunk["supervisor"]["messages"]

---

Update from node supervisor:================================= Tool Message =================================
Name: transfer_to_research_agentSuccessfully transferred to research_agentUpdate from node research_agent:================================== Ai Message ==================================
Name: research_agent
- US GDP in 2024: $29.017 trillion (nominal, current prices)
- New York state GDP in 2024: $2.284 trillion
- New York state's share of US GDP in 2024: 7.87%Update from node supervisor:================================= Tool Message =================================
Name: transfer_to_math_agentSuccessfully transferred to math_agentUpdate from node math_agent:================================== Ai Message ==================================
Name: math_agentUS GDP in 2024: $29.017 trillion
New York state GDP in 2024: $2.284 trillion
New York state's share of US GDP: 7.87%Update from node supervisor:================================== Ai Message ==================================
Name: supervisorUS GDP in 2024 was $29.017 trillion. New York state GDP in 2024 was $2.284 trillion. New York state's GDP was about 7.87% of the US GDP.

---

让我们来看看完整的消息历史记录：

for message in final_message_history:message.pretty_print()

---

================================ Human Message =================================find US and New York state GDP in 2024、what % of US GDP was New York state?
================================== Ai Message ==================================
Name: supervisor
Tool Calls:transfer_to_research_agent (call_qxk9abrxWYQT6a9hPNpXiuM0)Call ID: call_qxk9abrxWYQT6a9hPNpXiuM0Args:
================================= Tool Message =================================
Name: transfer_to_research_agentSuccessfully transferred to research_agent
================================== Ai Message ==================================
Name: research_agent
Tool Calls:tavily_search (call_A3cVm1BXDD8dYv6uLwO132gg)Call ID: call_A3cVm1BXDD8dYv6uLwO132ggArgs:query: US GDP 2024search_depth: advancedtavily_search (call_77JyoUYwGDXlRNKOwvQFUUYJ)Call ID: call_77JyoUYwGDXlRNKOwvQFUUYJArgs:query: New York state GDP 2024search_depth: advanced
================================= Tool Message =================================
Name: tavily_search{"query": "US GDP 2024", "follow_up_questions": null, "answer": null, "images": [], "results": [{"url": "https://tradingeconomics.com/united-states/gdp-growth", "title": "United States GDP Growth Rate - Trading Economics", "content": "The US economy expanded an annualized 2.4% in Q4 2024, slightly higher than 2.3% in the previous estimates, primarily reflecting a downward revision to imports. Exports fell slightly less (-0.2% vs -0.5%) and imports declined more than initially anticipated (-1.9% vs -1.2%), leaving the contribution from net trade positive at 0.26 pp (vs 0.12 pp). Government expenditure also rose more (3.1% vs 2.9%) and fixed investment contracted less (-1.1% vs -1.4%), due to equipment (-8.7% vs -9%) while [...] The Gross Domestic Product (GDP) in the United States expanded 2.40 percent in the fourth quarter of 2024 over the previous quarter. GDP Growth Rate in the United States averaged 3.21 percent from 1947 until 2024, reaching an all time high of 35.20 percent in the third quarter of 2020 and a record low of -28.10 percent in the second quarter of 2020、This page provides the latest reported value for - United States GDP Growth Rate - plus previous releases, historical high and low, short-term [...] The Gross Domestic Product (GDP) in the United States expanded 2.40 percent in the fourth quarter of 2024 over the previous quarter. GDP Growth Rate in the United States is expected to be 0.90 percent by the end of this quarter, according to Trading Economics global macro models and analysts expectations. In the long-term, the United States GDP Growth Rate is projected to trend around 2.00 percent in 2026, according to our econometric models.\n%\n3Y5Y10YMAX\nExport API\nOK\nLoading...", "score": 0.9071234, "raw_content": null}, {"url": "https://www.bea.gov/data/gdp/gross-domestic-product", "title": "Gross Domestic Product | U.S. Bureau of Economic Analysis (BEA)", "content": "Real gross domestic product (GDP) increased at an annual rate of 2.4 percent in the fourth quarter of 2024 (October, November, and December), according to the third estimate released by the U.S. Bureau of Economic Analysis. In the third quarter, real GDP increased 3.1 percent. The increase in real GDP in the fourth quarter primarily reflected increases in consumer spending and government spending that were partly offset by a decrease in investment. Imports, which are a subtraction in the", "score": 0.9008183, "raw_content": null}, {"url": "https://www.nerdwallet.com/article/finance/gdp-report", "title": "GDP Report: Final Estimate Shows Growth Increased 2.4% in Q4 2024", "content": "NerdWallet's content is fact-checked for accuracy, timeliness and relevance. It undergoes a thorough review process involving writers and editors to ensure the information is as clear and complete as possible.\n\nAnna Helhoski\n\nRick VanderKnyff\n\nUpdated on April 7\n\nReal gross domestic product increased by an annual rate of 2.4% in the fourth quarter of 2024, according to the third estimate report released on March 27 by the Bureau of Economic Analysis. [...] The third estimate also showed that in 2024, the U.S. GDP grew 2.8% compared to a 2.9% increase in 2023.\n\nQ1 2025 GDP forecasts are negative\n\nA forecasting tool published by the Atlanta Federal Reserve shows negative growth for the first quarter of 2025; it expects gross domestic product (GDP) to contract by rate of -2.8%, according to data released on March 28、If the forecast is correct, it would be the first quarter where GDP declined since the first and second quarters of 2022、[...] GDP could be the next concern: On March 3, a forecasting tool published by the Atlanta Federal Reserve showed that GDP could contract by a rate of -2.8% in the first quarter of 2025、If GDP does indeed decline, it would be the first time since the first and second quarters of 2022.\n\nIn 2024, the U.S. GDP grew 2.8% compared to a 2.9% increase in 2023, according to a second estimate of real gross domestic product from the Bureau of Economic Analysis, released Feb. 27.", "score": 0.89053273, "raw_content": null}], "response_time": 1.78}
================================= Tool Message =================================
Name: tavily_search{"query": "New York state GDP 2024", "follow_up_questions": null, "answer": null, "images": [], "results": [{"url": "https://en.wikipedia.org/wiki/Economy_of_New_York_(state)", "title": "Economy of New York (state) - Wikipedia", "content": "Jump to content\nMain menu\nSearch\nDonate\nCreate account\nLog in\nPersonal tools\nToggle the table of contents\nEconomy of New York (state)\n1 language\nArticle\nTalk\nRead\nEdit\nView history\nTools\nFrom Wikipedia, the free encyclopedia\nThis article is about the overall economy of New York State. For the economy of New York City, see Economy of New York City.\nEconomy of New York\nNew York City, the economic capital of New York (state)\nStatistics\nGDP $2.3 trillion (2024)[1]\nGDP per capita  $117,332 (2024)[2] [...] The economy of the State of New York is reflected in its gross state product in 2024 of $2.284 trillion, ranking third in size behind the larger states of California and Texas. If New York State were an independent nation, it would rank as the 10th largest economy in the world by nominal GDP. However, in 2019, the multi-state, New York City-centered metropolitan statistical area produced a gross metropolitan product (GMP) of $US2.0 trillion, ranking first nationally by a wide margin and would [...] Population below poverty line   13.6%[3]\nGini coefficient    0.5157 ± 0.0029 (2023)[4]\nLabour force    9,645,984 (2023)[5]\nUnemployment    4.4% (August 2024)[6]\nPublic finances\nRevenues    $63.5 billion[7]\nExpenses    $54.6 billion[8]", "score": 0.9511106, "raw_content": null}, {"url": "https://en.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_GDP", "title": "List of U.S. states and territories by GDP - Wikipedia", "content": "GDP per capita also varied widely throughout the United States in 2024, with New York ($117,332), Massachusetts ($110,561), and Washington (state) ($108,468) recording the three highest GDP per capita figures in the U.S., while Mississippi ($53,061), Arkansas ($60,276), and West Virginia ($60,783) recorded the three lowest GDP per capita figures in the U.S. The District of Columbia, though, recorded a GDP per capita figure far higher than any U.S. state in 2024 at $263,220、[...] Overall, in the calendar year 2024, the United States' Nominal GDP at Current Prices totaled at $29.017 trillion, as compared to $25.744 trillion in 2022.\nThe three U.S. states with the highest GDPs were California ($4.080 trillion), Texas ($2.695 trillion), and New York ($2.284 trillion). The three U.S. states with the lowest GDPs were Vermont ($45.4 billion), Wyoming ($53.0 billion), and Alaska ($69.8 billion).", "score": 0.8947989, "raw_content": null}, {"url": "https://edc.nyc/sites/default/files/2025-01/NYCEDC-State-of-the-NYC-Economy-2024-v3.pdf", "title": "[PDF] State of the New York City Economy - NYCEDC", "content": "for talent faced a limited supply. STATE OF THE NEW YORK CITY ECONOMY 2024 / 21 STATE OF THE NEW YORK CITY ECONOMY 2024 / 22 After losing nearly a million jobs during the COVID-19 pandemic, New York City is now at record-high levels of private and total employment. The city’s gross city product (GCP) stands at $1.18 trillion as of 2023.24 While legacy sectors such as Finance and Insurance have continued to play a vital part in the city’s economic successes, emerging sectors like Tech, the Green [...] STATE OF THE NEW YORK CITY ECONOMY 2024 / 11 New York City’s economy is the largest in the nation, with $2 trillion in gross metropolitan product (GMP) for the metro area, representing 9% of the total US economy. As such, the city’s economy is closely intertwined with the broader national economic landscape, and US macroeconomic conditions play a significant role in shaping the economic backdrop for the city. National interest rates, inflation, gross domestic product (GDP), and employment [...] 1 Macro Trends STATE OF THE NEW YORK CITY ECONOMY 2024 / 12 Output Has Grown Faster in NYC than Nationally Since 2022 Actual and Projected US Real GDP and NYC Real GCP Growth Rates 2019 2020 2021 2022 2023 2024* 2025* NYC 2.6% -4.2% 5.5% 2.6% 2.8% 3.6% 1.9% US 2.5% -2.2% 5.8% 1.9% 2.5% 2.5% 1.4% Source: NYC OMB and US Bureau of Economic Analysis. Projections for 2024 and and 2025 from NYC OMB.", "score": 0.85797083, "raw_content": null}], "response_time": 0.63}
================================== Ai Message ==================================
Name: research_agent
- US GDP in 2024: $29.017 trillion (nominal, current prices)
- New York state GDP in 2024: $2.284 trillion
- New York state's share of US GDP in 2024: 7.87%
================================== Ai Message ==================================
Name: supervisor
Tool Calls:transfer_to_math_agent (call_m5ICqaoAtRXHWb8BI3638dJL)Call ID: call_m5ICqaoAtRXHWb8BI3638dJLArgs:
================================= Tool Message =================================
Name: transfer_to_math_agentSuccessfully transferred to math_agent
================================== Ai Message ==================================
Name: math_agent
Tool Calls:divide (call_Sf7nvyvEgIaoDlaioPmkCdqz)Call ID: call_Sf7nvyvEgIaoDlaioPmkCdqzArgs:a: 2.284b: 29.017
================================= Tool Message =================================
Name: divide0.07871247889168417
================================== Ai Message ==================================
Name: math_agentUS GDP in 2024: $29.017 trillion
New York state GDP in 2024: $2.284 trillion
New York state's share of US GDP: 7.87%
================================== Ai Message ==================================
Name: supervisorUS GDP in 2024 was $29.017 trillion. New York state GDP in 2024 was $2.284 trillion. New York state's GDP was about 7.87% of the US GDP.

---

重要提示：

您可以看到监督系统会将所有独立代理的消息（即它们的内部工具调用循环）附加到完整消息历史中。这意味着监督代理在每次轮转时都能看到完整的历史记录。如果您希望更精细地控制以下方面：

• 输入如何传递给代理：可以使用LangGraph的Send()](https://langchain-ai.github.io/langgraph/reference/types/#langgraph.types.Send)原语，在交接时直接将数据发送给工作代理。具体示例请参考下方的[任务委派部分
• 代理输出如何添加：通过将代理封装在独立的节点函数中，您可以控制将代理内部消息历史的多少内容添加到监督器的总体消息历史中

---

def call_research_agent(state):# return agent's final response,# excluding inner monologueresponse = research_agent.invoke(state)return {"messages": response["messages"][-1]}

---

4、创建委派任务

目前各个智能体都依赖解析完整消息历史来确定任务。另一种方法是让监督者明确制定任务。我们可以通过在handoff_tool函数中添加task_description参数来实现。

API参考：Send

---

from langgraph.types import Senddef create_task_description_handoff_tool(*, agent_name: str, description: str | None = None
):name = f"transfer_to_{agent_name}"description = description or f"Ask {agent_name} for help."@tool(name, description=description)def handoff_tool(# this is populated by the supervisor LLMtask_description: Annotated[str,"Description of what the next agent should do, including all of the relevant context.",],# these parameters are ignored by the LLMstate: Annotated[MessagesState, InjectedState],) -> Command:task_description_message = {"role": "user", "content": task_description}agent_input = {**state, "messages": [task_description_message]}return Command(goto=[Send(agent_name, agent_input)],graph=Command.PARENT,)return handoff_toolassign_to_research_agent_with_description = create_task_description_handoff_tool(agent_name="research_agent",description="Assign task to a researcher agent.",
)assign_to_math_agent_with_description = create_task_description_handoff_tool(agent_name="math_agent",description="Assign task to a math agent.",
)supervisor_agent_with_description = create_react_agent(model="openai:gpt-4.1",tools=[assign_to_research_agent_with_description,assign_to_math_agent_with_description,],prompt=("You are a supervisor managing two agents:\n""- a research agent. Assign research-related tasks to this assistant\n""- a math agent. Assign math-related tasks to this assistant\n""Assign work to one agent at a time, do not call agents in parallel.\n""Do not do any work yourself."),name="supervisor",
)supervisor_with_description = (StateGraph(MessagesState).add_node(supervisor_agent_with_description, destinations=("research_agent", "math_agent")).add_node(research_agent).add_node(math_agent).add_edge(START, "supervisor").add_edge("research_agent", "supervisor").add_edge("math_agent", "supervisor").compile()
)

---

注意：我们在 handoff_tool 中使用了 Send() 原语。这意味着每个工作代理不会接收到完整的 supervisor 图状态作为输入，而只会看到 Send 负载的内容。在本示例中，我们将任务描述作为一条单独的"human"消息发送。

现在让我们用相同的输入查询来运行它：

for chunk in supervisor.stream({"messages": [{"role": "user","content": "find US and New York state GDP in 2024、what % of US GDP was New York state?",}]},subgraphs=True,
):pretty_print_messages(chunk, last_message=True)

---

Update from subgraph supervisor:Update from node agent:================================== Ai Message ==================================Name: supervisorTool Calls:transfer_to_research_agent (call_TtKzjGQBe4X9Xh0VzmjStVgZ)Call ID: call_TtKzjGQBe4X9Xh0VzmjStVgZArgs:Update from subgraph research_agent:Update from node agent:================================== Ai Message ==================================Name: research_agentTool Calls:tavily_search (call_AfeRYBJxJtmD4EKqifYcx8EI)Call ID: call_AfeRYBJxJtmD4EKqifYcx8EIArgs:query: US GDP in 2024search_depth: advancedtavily_search (call_n7Dn8QnDLu2ZpEDzswS2MOJ8)Call ID: call_n7Dn8QnDLu2ZpEDzswS2MOJ8Args:query: New York state GDP in 2024search_depth: advancedUpdate from subgraph research_agent:Update from node tools:================================= Tool Message =================================Name: tavily_search{"query": "New York state GDP in 2024", "follow_up_questions": null, "answer": null, "images": [], "results": [{"url": "https://en.wikipedia.org/wiki/Economy_of_New_York_(state)", "title": "Economy of New York (state) - Wikipedia", "content": "Jump to content\nMain menu\nSearch\nDonate\nCreate account\nLog in\nPersonal tools\nToggle the table of contents\nEconomy of New York (state)\n1 language\nArticle\nTalk\nRead\nEdit\nView history\nTools\nFrom Wikipedia, the free encyclopedia\nThis article is about the overall economy of New York State. For the economy of New York City, see Economy of New York City.\nEconomy of New York\nNew York City, the economic capital of New York (state)\nStatistics\nGDP $2.3 trillion (2024)[1]\nGDP per capita  $117,332 (2024)[2] [...] The economy of the State of New York is reflected in its gross state product in 2024 of $2.284 trillion, ranking third in size behind the larger states of California and Texas. If New York State were an independent nation, it would rank as the 10th largest economy in the world by nominal GDP. However, in 2019, the multi-state, New York City-centered metropolitan statistical area produced a gross metropolitan product (GMP) of $US2.0 trillion, ranking first nationally by a wide margin and would [...] Population below poverty line   13.6%[3]\nGini coefficient    0.5157 ± 0.0029 (2023)[4]\nLabour force    9,645,984 (2023)[5]\nUnemployment    4.4% (August 2024)[6]\nPublic finances\nRevenues    $63.5 billion[7]\nExpenses    $54.6 billion[8]", "score": 0.9530353, "raw_content": null}, {"url": "https://en.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_GDP", "title": "List of U.S. states and territories by GDP - Wikipedia", "content": "GDP per capita also varied widely throughout the United States in 2024, with New York ($117,332), Massachusetts ($110,561), and Washington (state) ($108,468) recording the three highest GDP per capita figures in the U.S., while Mississippi ($53,061), Arkansas ($60,276), and West Virginia ($60,783) recorded the three lowest GDP per capita figures in the U.S. The District of Columbia, though, recorded a GDP per capita figure far higher than any U.S. state in 2024 at $263,220、[...] Overall, in the calendar year 2024, the United States' Nominal GDP at Current Prices totaled at $29.017 trillion, as compared to $25.744 trillion in 2022.\nThe three U.S. states with the highest GDPs were California ($4.080 trillion), Texas ($2.695 trillion), and New York ($2.284 trillion). The three U.S. states with the lowest GDPs were Vermont ($45.4 billion), Wyoming ($53.0 billion), and Alaska ($69.8 billion).", "score": 0.89997756, "raw_content": null}, {"url": "https://edc.nyc/sites/default/files/2025-01/NYCEDC-State-of-the-NYC-Economy-2024-v3.pdf", "title": "[PDF] State of the New York City Economy - NYCEDC", "content": "for talent faced a limited supply. STATE OF THE NEW YORK CITY ECONOMY 2024 / 21 STATE OF THE NEW YORK CITY ECONOMY 2024 / 22 After losing nearly a million jobs during the COVID-19 pandemic, New York City is now at record-high levels of private and total employment. The city’s gross city product (GCP) stands at $1.18 trillion as of 2023.24 While legacy sectors such as Finance and Insurance have continued to play a vital part in the city’s economic successes, emerging sectors like Tech, the Green [...] STATE OF THE NEW YORK CITY ECONOMY 2024 / 11 New York City’s economy is the largest in the nation, with $2 trillion in gross metropolitan product (GMP) for the metro area, representing 9% of the total US economy. As such, the city’s economy is closely intertwined with the broader national economic landscape, and US macroeconomic conditions play a significant role in shaping the economic backdrop for the city. National interest rates, inflation, gross domestic product (GDP), and employment [...] 1 Macro Trends STATE OF THE NEW YORK CITY ECONOMY 2024 / 12 Output Has Grown Faster in NYC than Nationally Since 2022 Actual and Projected US Real GDP and NYC Real GCP Growth Rates 2019 2020 2021 2022 2023 2024* 2025* NYC 2.6% -4.2% 5.5% 2.6% 2.8% 3.6% 1.9% US 2.5% -2.2% 5.8% 1.9% 2.5% 2.5% 1.4% Source: NYC OMB and US Bureau of Economic Analysis. Projections for 2024 and and 2025 from NYC OMB.", "score": 0.8616433, "raw_content": null}], "response_time": 2.35}Update from subgraph research_agent:Update from node agent:================================== Ai Message ==================================Name: research_agent- US GDP in 2024 was $29.017 trillion (nominal, current prices) ([source](https://en.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_GDP)).- New York State GDP in 2024 was $2.284 trillion ([source](https://en.wikipedia.org/wiki/Economy_of_New_York_(state))).Percentage of US GDP attributed to New York State in 2024: approximately 7.9%.Update from subgraph supervisor:Update from node agent:================================== Ai Message ==================================Name: supervisorTool Calls:transfer_to_math_agent (call_oYbIXhQQeTWlj2zvZSoStUgO)Call ID: call_oYbIXhQQeTWlj2zvZSoStUgOArgs:Update from subgraph math_agent:Update from node agent:================================== Ai Message ==================================Name: math_agentTool Calls:divide (call_K5QxmkQYFfCZw5Vzkbz43VIG)Call ID: call_K5QxmkQYFfCZw5Vzkbz43VIGArgs:a: 2.284b: 29.017Update from subgraph math_agent:Update from node tools:================================= Tool Message =================================Name: divide0.07871247889168417Update from subgraph math_agent:Update from node agent:================================== Ai Message ==================================Name: math_agentNew York state's GDP was approximately 7.87% of US GDP in 2024.Update from subgraph supervisor:Update from node agent:================================== Ai Message ==================================Name: supervisorHere are the findings:- US GDP in 2024: $29.017 trillion- New York State GDP in 2024: $2.284 trillion- New York State accounted for approximately 7.87% of US GDP in 2024、```

---

构建SQL代理

https://langchain-ai.github.io/langgraph/tutorials/sql-agent/

在本教程中，我们将逐步介绍如何构建一个能够回答关于SQL数据库问题的代理程序。

从高层次来看，该代理将完成以下工作：

1、从数据库中获取可用表
2、确定哪些表与问题相关
3、获取相关表的模式结构
4、根据问题和模式信息生成查询语句
5、使用LLM检查查询语句中的常见错误
6、执行查询并返回结果
7、修正数据库引擎发现的错误，直到查询成功
8、根据结果生成响应

安全注意事项

构建SQL数据库问答系统需要执行模型生成的SQL查询语句。这样做存在固有风险。请确保您的数据库连接权限始终按照代理需求进行最小化范围限定。这将减轻（但不能完全消除）构建模型驱动系统所带来的风险。

---

1、环境配置

首先我们需要安装一些依赖项。本教程将使用来自langchain-community的SQL数据库和工具抽象层。同时还需要安装LangChain的聊天模型组件。

---

pip install -U langgraph langchain_community "langchain[openai]"

---

为LangGraph开发配置LangSmith

注册LangSmith可快速发现并优化LangGraph项目的性能问题。通过LangSmith，您能利用追踪数据来调试、测试和监控基于LangGraph构建的LLM应用——了解更多入门指南请访问这里。

---

选择大语言模型

首先我们需要初始化大语言模型。任何支持工具调用功能的模型都可以使用。以下示例我们采用OpenAI。

API参考文档：init_chat_model

---

from langchain.chat_models import init_chat_modelllm = init_chat_model("openai:gpt-4.1")

---

配置数据库

在本教程中，我们将创建一个SQLite数据库。SQLite是一种轻量级数据库，易于设置和使用。我们将加载chinook示例数据库，该数据库模拟了一个数字媒体商店。

有关该数据库的更多信息，请参阅此处。

为方便起见，我们已将数据库文件(Chinook.db)托管在公共GCS存储桶中。

---

import requestsurl = "https://storage.googleapis.com/benchmarks-artifacts/chinook/Chinook.db"response = requests.get(url)if response.status_code == 200:# Open a local file in binary write modewith open("Chinook.db", "wb") as file:# Write the content of the response (the file) to the local filefile.write(response.content)print("File downloaded and saved as Chinook.db")
else:print(f"Failed to download the file. Status code: {response.status_code}")

---

我们将使用langchain_community包中提供的一个便捷SQL数据库封装器来与数据库交互。该封装器提供了执行SQL查询和获取结果的简单接口：

API参考文档：SQLDatabase

---

from langchain_community.utilities import SQLDatabasedb = SQLDatabase.from_uri("sqlite:///Chinook.db")print(f"Dialect: {db.dialect}")
print(f"Available tables: {db.get_usable_table_names()}")
print(f'Sample output: {db.run("SELECT * FROM Artist LIMIT 5;")}')

---

Dialect: sqlite
Available tables: ['Album', 'Artist', 'Customer', 'Employee', 'Genre', 'Invoice', 'InvoiceLine', 'MediaType', 'Playlist', 'PlaylistTrack', 'Track']
Sample output: [(1, 'AC/DC'), (2, 'Accept'), (3, 'Aerosmith'), (4, 'Alanis Morissette'), (5, 'Alice In Chains')]

---

数据库交互工具

langchain-community 实现了一些内置工具，用于与 SQLDatabase 进行交互，包括列出表、读取表结构、检查和运行查询等工具：

API 参考：SQLDatabaseToolkit

---

from langchain_community.agent_toolkits import SQLDatabaseToolkittoolkit = SQLDatabaseToolkit(db=db, llm=llm)tools = toolkit.get_tools()for tool in tools:print(f"{tool.name}: {tool.description}\n")

---

sql_db_query: Input to this tool is a detailed and correct SQL query, output is a result from the database. If the query is not correct, an error message will be returned. If an error is returned, rewrite the query, check the query, and try again. If you encounter an issue with Unknown column 'xxxx' in 'field list', use sql_db_schema to query the correct table fields.sql_db_schema: Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables. Be sure that the tables actually exist by calling sql_db_list_tables first! Example Input: table1, table2, table3sql_db_list_tables: Input is an empty string, output is a comma-separated list of tables in the database.sql_db_query_checker: Use this tool to double check if your query is correct before executing it. Always use this tool before executing a query with sql_db_query!

---

2、使用预构建的Agent

基于这些工具，我们可以用一行代码初始化一个预构建的Agent。为了定制Agent的行为，我们需要编写一个描述性的系统提示。

API参考文档: create_react_agent

---

from langgraph.prebuilt import create_react_agentsystem_prompt = """
You are an agent designed to interact with a SQL database.
Given an input question, create a syntactically correct {dialect} query to run,
then look at the results of the query and return the answer. Unless the user
specifies a specific number of examples they wish to obtain, always limit your
query to at most {top_k} results.You can order the results by a relevant column to return the most interesting
examples in the database. Never query for all the columns from a specific table,
only ask for the relevant columns given the question.You MUST double check your query before executing it. If you get an error while
executing a query, rewrite the query and try again.DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the
database.To start you should ALWAYS look at the tables in the database to see what you
can query. Do NOT skip this step.Then you should query the schema of the most relevant tables.
""".format(dialect=db.dialect,top_k=5,
)agent = create_react_agent(llm,tools,prompt=system_prompt,
)

---

注意：本系统提示包含多项指令，例如始终在运行其他工具之前或之后执行特定工具。在下一节中，我们将通过图结构强制执行这些行为，从而提供更高程度的控制，并允许我们简化提示。

让我们在示例查询上运行此代理并观察其行为：

question = "Which sales agent made the most in sales in 2009?"for step in agent.stream({"messages": [{"role": "user", "content": question}]},stream_mode="values",
):step["messages"][-1].pretty_print()

---

================================ Human Message =================================Which sales agent made the most in sales in 2009?
================================== Ai Message ==================================
Tool Calls:sql_db_list_tables (call_0MfnhUYmRNVe03m6fYClaoa0)Call ID: call_0MfnhUYmRNVe03m6fYClaoa0Args:tool_input:
================================= Tool Message =================================
Name: sql_db_list_tablesAlbum, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track
================================== Ai Message ==================================
Tool Calls:sql_db_schema (call_XwI6uES0HeZ2xCRxkkdWzB3I)Call ID: call_XwI6uES0HeZ2xCRxkkdWzB3IArgs:table_names: Employee, Invoice, InvoiceLine
================================= Tool Message =================================
Name: sql_db_schemaCREATE TABLE "Employee" ("EmployeeId" INTEGER NOT NULL, "LastName" NVARCHAR(20) NOT NULL, "FirstName" NVARCHAR(20) NOT NULL, "Title" NVARCHAR(30), "ReportsTo" INTEGER, "BirthDate" DATETIME, "HireDate" DATETIME, "Address" NVARCHAR(70), "City" NVARCHAR(40), "State" NVARCHAR(40), "Country" NVARCHAR(40), "PostalCode" NVARCHAR(10), "Phone" NVARCHAR(24), "Fax" NVARCHAR(24), "Email" NVARCHAR(60), PRIMARY KEY ("EmployeeId"), FOREIGN KEY("ReportsTo") REFERENCES "Employee" ("EmployeeId")
)/*
3 rows from Employee table:
EmployeeId  LastName    FirstName   Title   ReportsTo   BirthDate   HireDate    Address City    State   Country PostalCode  Phone   Fax Email
1   Adams   Andrew  General Manager None    1962-02-18 00:00:00 2002-08-14 00:00:00 11120 Jasper Ave NW Edmonton    AB  Canada  T5K 2N1 +1 (780) 428-9482   +1 (780) 428-3457   andrew@chinookcorp.com
2   Edwards Nancy   Sales Manager   1   1958-12-08 00:00:00 2002-05-01 00:00:00 825 8 Ave SW    Calgary AB  Canada  T2P 2T3 +1 (403) 262-3443   +1 (403) 262-3322   nancy@chinookcorp.com
3   Peacock Jane    Sales Support Agent 2   1973-08-29 00:00:00 2002-04-01 00:00:00 1111 6 Ave SW   Calgary AB  Canada  T2P 5M5 +1 (403) 262-3443   +1 (403) 262-6712   jane@chinookcorp.com
*/CREATE TABLE "Invoice" ("InvoiceId" INTEGER NOT NULL, "CustomerId" INTEGER NOT NULL, "InvoiceDate" DATETIME NOT NULL, "BillingAddress" NVARCHAR(70), "BillingCity" NVARCHAR(40), "BillingState" NVARCHAR(40), "BillingCountry" NVARCHAR(40), "BillingPostalCode" NVARCHAR(10), "Total" NUMERIC(10, 2) NOT NULL, PRIMARY KEY ("InvoiceId"), FOREIGN KEY("CustomerId") REFERENCES "Customer" ("CustomerId")
)/*
3 rows from Invoice table:
InvoiceId   CustomerId  InvoiceDate BillingAddress  BillingCity BillingState    BillingCountry  BillingPostalCode   Total
1   2   2009-01-01 00:00:00 Theodor-Heuss-Straße 34 Stuttgart   None    Germany 70174   1.98
2   4   2009-01-02 00:00:00 Ullevålsveien 14    Oslo    None    Norway  0171    3.96
3   8   2009-01-03 00:00:00 Grétrystraat 63 Brussels    None    Belgium 1000    5.94
*/CREATE TABLE "InvoiceLine" ("InvoiceLineId" INTEGER NOT NULL, "InvoiceId" INTEGER NOT NULL, "TrackId" INTEGER NOT NULL, "UnitPrice" NUMERIC(10, 2) NOT NULL, "Quantity" INTEGER NOT NULL, PRIMARY KEY ("InvoiceLineId"), FOREIGN KEY("TrackId") REFERENCES "Track" ("TrackId"), FOREIGN KEY("InvoiceId") REFERENCES "Invoice" ("InvoiceId")
)/*
3 rows from InvoiceLine table:
InvoiceLineId   InvoiceId   TrackId UnitPrice   Quantity
1   1   2   0.99    1
2   1   4   0.99    1
3   2   6   0.99    1
*/
================================== Ai Message ==================================
Tool Calls:sql_db_schema (call_dHE5FKSycFhYIkdPKtpEk9rM)Call ID: call_dHE5FKSycFhYIkdPKtpEk9rMArgs:table_names: Customer
================================= Tool Message =================================
Name: sql_db_schemaCREATE TABLE "Customer" ("CustomerId" INTEGER NOT NULL, "FirstName" NVARCHAR(40) NOT NULL, "LastName" NVARCHAR(20) NOT NULL, "Company" NVARCHAR(80), "Address" NVARCHAR(70), "City" NVARCHAR(40), "State" NVARCHAR(40), "Country" NVARCHAR(40), "PostalCode" NVARCHAR(10), "Phone" NVARCHAR(24), "Fax" NVARCHAR(24), "Email" NVARCHAR(60) NOT NULL, "SupportRepId" INTEGER, PRIMARY KEY ("CustomerId"), FOREIGN KEY("SupportRepId") REFERENCES "Employee" ("EmployeeId")
)/*
3 rows from Customer table:
CustomerId  FirstName   LastName    Company Address City    State   Country PostalCode  Phone   Fax Email   SupportRepId
1   Luís    Gonçalves   Embraer - Empresa Brasileira de Aeronáutica S.A.    Av. Brigadeiro Faria Lima, 2170 São José dos Campos SP  Brazil  12227-000   +55 (12) 3923-5555  +55 (12) 3923-5566  luisg@embraer.com.br    3
2   Leonie  Köhler  None    Theodor-Heuss-Straße 34 Stuttgart   None    Germany 70174   +49 0711 2842222    None    leonekohler@surfeu.de   5
3   François    Tremblay    None    1498 rue Bélanger   Montréal    QC  Canada  H2G 1A7 +1 (514) 721-4711   None    ftremblay@gmail.com 3
*/
================================== Ai Message ==================================
Tool Calls:sql_db_query_checker (call_QPtWY5cUZWgWhr13Kz6eBJkU)Call ID: call_QPtWY5cUZWgWhr13Kz6eBJkUArgs:query: SELECT e.EmployeeId, e.FirstName, e.LastName, SUM(i.Total) as TotalSales
FROM Employee e
JOIN Customer c ON e.EmployeeId = c.SupportRepId
JOIN Invoice i ON c.CustomerId = i.CustomerId
WHERE i.InvoiceDate BETWEEN '2009-01-01' AND '2009-12-31'
GROUP BY e.EmployeeId, e.FirstName, e.LastName
ORDER BY TotalSales DESC
LIMIT 1;
================================= Tool Message =================================
Name: sql_db_query_checker\`\`\`sql
SELECT e.EmployeeId, e.FirstName, e.LastName, SUM(i.Total) as TotalSales
FROM Employee e
JOIN Customer c ON e.EmployeeId = c.SupportRepId
JOIN Invoice i ON c.CustomerId = i.CustomerId
WHERE i.InvoiceDate BETWEEN '2009-01-01' AND '2009-12-31'
GROUP BY e.EmployeeId, e.FirstName, e.LastName
ORDER BY TotalSales DESC
LIMIT 1;
\`\`\`
================================== Ai Message ==================================
Tool Calls:sql_db_query (call_X747YMElSeY5D3CfhE6KzLUj)Call ID: call_X747YMElSeY5D3CfhE6KzLUjArgs:query: SELECT e.EmployeeId, e.FirstName, e.LastName, SUM(i.Total) as TotalSales
FROM Employee e
JOIN Customer c ON e.EmployeeId = c.SupportRepId
JOIN Invoice i ON c.CustomerId = i.CustomerId
WHERE i.InvoiceDate BETWEEN '2009-01-01' AND '2009-12-31'
GROUP BY e.EmployeeId, e.FirstName, e.LastName
ORDER BY TotalSales DESC
LIMIT 1;
================================= Tool Message =================================
Name: sql_db_query[(5, 'Steve', 'Johnson', 164.34)]
================================== Ai Message ==================================The sales agent who made the most in sales in 2009 was Steve Johnson, with total sales of 164.34.

---

效果相当不错：智能体正确地列出了表格、获取了模式、编写了查询语句、检查了查询，并最终执行查询以生成最终响应。

提示：

您可以在LangSmith跟踪记录中查看上述运行的所有细节，包括采取的步骤、调用的工具、LLM接收到的提示等内容。

---

3、自定义智能体

预构建的智能体让我们能够快速上手，但在每个步骤中，该智能体都可以访问完整的工具集。上文我们依赖系统提示来约束其行为——例如，我们指示智能体始终以"列出表格"工具开始，并在执行查询前始终运行查询检查工具。

在LangGraph中，我们可以通过自定义智能体来实现更高程度的控制。下面我们将实现一个简单的ReAct智能体设置，为特定的工具调用创建专用节点。我们将使用与预构建智能体相同的状态。

我们为以下步骤构建专用节点：

• 列出数据库表格
• 调用"获取模式"工具
• 生成查询
• 检查查询

将这些步骤放入专用节点使我们能够：(1) 在需要时强制进行工具调用，(2) 自定义与每个步骤关联的提示。

API参考：AIMessage | RunnableConfig | END | START | StateGraph | ToolNode

---

from typing import Literal
from langchain_core.messages import AIMessage
from langchain_core.runnables import RunnableConfig
from langgraph.graph import END, START, MessagesState, StateGraph
from langgraph.prebuilt import ToolNodeget_schema_tool = next(tool for tool in tools if tool.name == "sql_db_schema")
get_schema_node = ToolNode([get_schema_tool], name="get_schema")run_query_tool = next(tool for tool in tools if tool.name == "sql_db_query")
run_query_node = ToolNode([run_query_tool], name="run_query")# Example: create a predetermined tool call
def list_tables(state: MessagesState):tool_call = {"name": "sql_db_list_tables","args": {},"id": "abc123","type": "tool_call",}tool_call_message = AIMessage(content="", tool_calls=[tool_call])list_tables_tool = next(tool for tool in tools if tool.name == "sql_db_list_tables")tool_message = list_tables_tool.invoke(tool_call)response = AIMessage(f"Available tables: {tool_message.content}")return {"messages": [tool_call_message, tool_message, response]}# Example: force a model to create a tool call
def call_get_schema(state: MessagesState):# Note that LangChain enforces that all models accept `tool_choice="any"`# as well as `tool_choice=<string name of tool>`.llm_with_tools = llm.bind_tools([get_schema_tool], tool_choice="any")response = llm_with_tools.invoke(state["messages"])return {"messages": [response]}generate_query_system_prompt = """
You are an agent designed to interact with a SQL database.
Given an input question, create a syntactically correct {dialect} query to run,
then look at the results of the query and return the answer. Unless the user
specifies a specific number of examples they wish to obtain, always limit your
query to at most {top_k} results.You can order the results by a relevant column to return the most interesting
examples in the database. Never query for all the columns from a specific table,
only ask for the relevant columns given the question.DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.
""".format(dialect=db.dialect,top_k=5,
)def generate_query(state: MessagesState):system_message = {"role": "system","content": generate_query_system_prompt,}# We do not force a tool call here, to allow the model to# respond naturally when it obtains the solution.llm_with_tools = llm.bind_tools([run_query_tool])response = llm_with_tools.invoke([system_message] + state["messages"])return {"messages": [response]}check_query_system_prompt = """
You are a SQL expert with a strong attention to detail.
Double check the {dialect} query for common mistakes, including:
- Using NOT IN with NULL values
- Using UNION when UNION ALL should have been used
- Using BETWEEN for exclusive ranges
- Data type mismatch in predicates
- Properly quoting identifiers
- Using the correct number of arguments for functions
- Casting to the correct data type
- Using the proper columns for joinsIf there are any of the above mistakes, rewrite the query. If there are no mistakes,
just reproduce the original query.You will call the appropriate tool to execute the query after running this check.
""".format(dialect=db.dialect)def check_query(state: MessagesState):system_message = {"role": "system","content": generate_query_system_prompt,}# Generate an artificial user message to checktool_call = state["messages"][-1].tool_calls[0]user_message = {"role": "user", "content": tool_call["args"]["query"]}llm_with_tools = llm.bind_tools([run_query_tool], tool_choice="any")response = llm_with_tools.invoke([system_message, user_message])response.id = state["messages"][-1].idreturn {"messages": [response]}

---

最后，我们使用 Graph API 将这些步骤组装成一个工作流。我们在查询生成步骤定义了一个条件边，当生成查询时会路由至查询检查器；若不存在工具调用（即LLM已直接响应查询），则流程终止。

---

def should_continue(state: MessagesState) -> Literal[END, "check_query"]:messages = state["messages"]last_message = messages[-1]if not last_message.tool_calls:return ENDelse:return "check_query"builder = StateGraph(MessagesState)
builder.add_node(list_tables)
builder.add_node(call_get_schema)
builder.add_node(get_schema_node, "get_schema")
builder.add_node(generate_query)
builder.add_node(check_query)
builder.add_node(run_query_node, "run_query")builder.add_edge(START, "list_tables")
builder.add_edge("list_tables", "call_get_schema")
builder.add_edge("call_get_schema", "get_schema")
builder.add_edge("get_schema", "generate_query")
builder.add_conditional_edges("generate_query",should_continue,
)
builder.add_edge("check_query", "run_query")
builder.add_edge("run_query", "generate_query")agent = builder.compile()

---

我们通过以下方式可视化该应用：

API参考文档：CurveStyle | MermaidDrawMethod | NodeStyles

---

from IPython.display import Image, display
from langchain_core.runnables.graph import CurveStyle, MermaidDrawMethod, NodeStylesdisplay(Image(agent.get_graph().draw_mermaid_png()))

---

question = "Which sales agent made the most in sales in 2009?"for step in agent.stream({"messages": [{"role": "user", "content": question}]},stream_mode="values",
):step["messages"][-1].pretty_print()

---

================================ Human Message =================================Which sales agent made the most in sales in 2009?
================================== Ai Message ==================================Available tables: Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track
================================== Ai Message ==================================
Tool Calls:sql_db_schema (call_rMs3sF1HVcAGggGf89ZNvtLE)Call ID: call_rMs3sF1HVcAGggGf89ZNvtLEArgs:table_names: Employee,Invoice,Customer
================================= Tool Message =================================
Name: sql_db_schemaCREATE TABLE "Customer" ("CustomerId" INTEGER NOT NULL, "FirstName" NVARCHAR(40) NOT NULL, "LastName" NVARCHAR(20) NOT NULL, "Company" NVARCHAR(80), "Address" NVARCHAR(70), "City" NVARCHAR(40), "State" NVARCHAR(40), "Country" NVARCHAR(40), "PostalCode" NVARCHAR(10), "Phone" NVARCHAR(24), "Fax" NVARCHAR(24), "Email" NVARCHAR(60) NOT NULL, "SupportRepId" INTEGER, PRIMARY KEY ("CustomerId"), FOREIGN KEY("SupportRepId") REFERENCES "Employee" ("EmployeeId")
)/*
3 rows from Customer table:
CustomerId  FirstName   LastName    Company Address City    State   Country PostalCode  Phone   Fax Email   SupportRepId
1   Luís    Gonçalves   Embraer - Empresa Brasileira de Aeronáutica S.A.    Av. Brigadeiro Faria Lima, 2170 São José dos Campos SP  Brazil  12227-000   +55 (12) 3923-5555  +55 (12) 3923-5566  luisg@embraer.com.br    3
2   Leonie  Köhler  None    Theodor-Heuss-Straße 34 Stuttgart   None    Germany 70174   +49 0711 2842222    None    leonekohler@surfeu.de   5
3   François    Tremblay    None    1498 rue Bélanger   Montréal    QC  Canada  H2G 1A7 +1 (514) 721-4711   None    ftremblay@gmail.com 3
*/CREATE TABLE "Employee" ("EmployeeId" INTEGER NOT NULL, "LastName" NVARCHAR(20) NOT NULL, "FirstName" NVARCHAR(20) NOT NULL, "Title" NVARCHAR(30), "ReportsTo" INTEGER, "BirthDate" DATETIME, "HireDate" DATETIME, "Address" NVARCHAR(70), "City" NVARCHAR(40), "State" NVARCHAR(40), "Country" NVARCHAR(40), "PostalCode" NVARCHAR(10), "Phone" NVARCHAR(24), "Fax" NVARCHAR(24), "Email" NVARCHAR(60), PRIMARY KEY ("EmployeeId"), FOREIGN KEY("ReportsTo") REFERENCES "Employee" ("EmployeeId")
)/*
3 rows from Employee table:
EmployeeId  LastName    FirstName   Title   ReportsTo   BirthDate   HireDate    Address City    State   Country PostalCode  Phone   Fax Email
1   Adams   Andrew  General Manager None    1962-02-18 00:00:00 2002-08-14 00:00:00 11120 Jasper Ave NW Edmonton    AB  Canada  T5K 2N1 +1 (780) 428-9482   +1 (780) 428-3457   andrew@chinookcorp.com
2   Edwards Nancy   Sales Manager   1   1958-12-08 00:00:00 2002-05-01 00:00:00 825 8 Ave SW    Calgary AB  Canada  T2P 2T3 +1 (403) 262-3443   +1 (403) 262-3322   nancy@chinookcorp.com
3   Peacock Jane    Sales Support Agent 2   1973-08-29 00:00:00 2002-04-01 00:00:00 1111 6 Ave SW   Calgary AB  Canada  T2P 5M5 +1 (403) 262-3443   +1 (403) 262-6712   jane@chinookcorp.com
*/CREATE TABLE "Invoice" ("InvoiceId" INTEGER NOT NULL, "CustomerId" INTEGER NOT NULL, "InvoiceDate" DATETIME NOT NULL, "BillingAddress" NVARCHAR(70), "BillingCity" NVARCHAR(40), "BillingState" NVARCHAR(40), "BillingCountry" NVARCHAR(40), "BillingPostalCode" NVARCHAR(10), "Total" NUMERIC(10, 2) NOT NULL, PRIMARY KEY ("InvoiceId"), FOREIGN KEY("CustomerId") REFERENCES "Customer" ("CustomerId")
)/*
3 rows from Invoice table:
InvoiceId   CustomerId  InvoiceDate BillingAddress  BillingCity BillingState    BillingCountry  BillingPostalCode   Total
1   2   2009-01-01 00:00:00 Theodor-Heuss-Straße 34 Stuttgart   None    Germany 70174   1.98
2   4   2009-01-02 00:00:00 Ullevålsveien 14    Oslo    None    Norway  0171    3.96
3   8   2009-01-03 00:00:00 Grétrystraat 63 Brussels    None    Belgium 1000    5.94
*/
================================== Ai Message ==================================
Tool Calls:sql_db_query (call_TQyoJSV78xEWHqc69PiYHjdS)Call ID: call_TQyoJSV78xEWHqc69PiYHjdSArgs:query: SELECT e.FirstName, e.LastName, SUM(i.Total) as TotalSales
FROM Employee e
JOIN Customer c ON e.EmployeeId = c.SupportRepId
JOIN Invoice i ON c.CustomerId = i.CustomerId
WHERE strftime('%Y', i.InvoiceDate) = '2009'
GROUP BY e.EmployeeId
ORDER BY TotalSales DESC
LIMIT 1;
================================== Ai Message ==================================
Tool Calls:sql_db_query (call_tSQOuxc7iQ1jvLzXSofCqK4V)Call ID: call_tSQOuxc7iQ1jvLzXSofCqK4VArgs:query: SELECT e.FirstName, e.LastName, SUM(i.Total) as TotalSales
FROM Employee e
JOIN Customer c ON e.EmployeeId = c.SupportRepId
JOIN Invoice i ON c.CustomerId = i.CustomerId
WHERE strftime('%Y', i.InvoiceDate) = '2009'
GROUP BY e.EmployeeId
ORDER BY TotalSales DESC
LIMIT 1;
================================= Tool Message =================================
Name: sql_db_query[('Steve', 'Johnson', 164.34)]
================================== Ai Message ==================================The sales agent who made the most in sales in 2009 was Steve Johnson, with total sales of 164.34.

---

提示：

查看上述运行的 LangSmith 追踪记录。

---

后续步骤

查看本指南，了解如何使用LangSmith评估LangGraph应用程序（包括本文中的SQL代理）。

---

如何在LangSmith中为图运行传递自定义运行ID或设置标签和元数据

https://langchain-ai.github.io/langgraph/how-tos/run-id-langsmith/

---

前提条件

本指南假设您已熟悉以下内容：

• LangSmith文档
• LangSmith平台
• RunnableConfig
• 向追踪记录添加元数据和标签
• 自定义运行名称

---

概述

在IDE或终端中调试图运行有时可能比较困难。LangSmith允许您使用追踪数据来调试、测试和监控使用LangGraph构建的LLM应用——请参阅LangSmith文档获取更多入门信息。

为了更容易识别和分析图调用期间生成的追踪记录，您可以在运行时设置额外的配置（参见RunnableConfig）：

字段	类型	描述
run_name	str	此调用的追踪运行名称。默认为类名。
run_id	UUID	此调用的追踪运行唯一标识符。如果未提供，将生成新的UUID。
tags	List[str]	此调用及任何子调用（例如，Chain调用LLM）的标签。可用于筛选调用。
metadata	Dict[str, Any]	此调用及任何子调用（例如，Chain调用LLM）的元数据。键应为字符串，值应可JSON序列化。

---

实现方式

LangGraph图实现了LangChain Runnable接口，并在invoke、ainvoke、stream等方法中接受第二个参数(RunnableConfig)。

LangSmith平台允许您基于run_name、run_id、tags和metadata来搜索和筛选追踪记录。

---

TLDR

---

import uuid
# Generate a random UUID -- it must be a UUID
config = {"run_id": uuid.uuid4()}, "tags": ["my_tag1"], "metadata": {"a": 5}}
# Works with all standard Runnable methods 
# like invoke, batch, ainvoke, astream_events etc
graph.stream(inputs, config, stream_mode="values")

---

本操作指南的剩余部分将展示一个完整的代理。

---

安装配置

首先，我们需要安装必要的软件包并设置API密钥

---

pip install --quiet -U langgraph langchain_openai

---

import getpass
import osdef _set_env(var: str):if not os.environ.get(var):os.environ[var] = getpass.getpass(f"{var}: ")_set_env("OPENAI_API_KEY")
_set_env("LANGSMITH_API_KEY")

---

为LangGraph开发配置LangSmith

注册LangSmith可快速发现并优化LangGraph项目的性能问题。通过LangSmith，您能利用追踪数据来调试、测试和监控基于LangGraph构建的LLM应用——了解更多入门指南请点击此处。

---

定义图结构

本示例将使用预构建的ReAct智能体。

API参考文档: ChatOpenAI | create_react_agent | tool

---

from langchain_openai import ChatOpenAI
from typing import Literal
from langgraph.prebuilt import create_react_agent
from langchain_core.tools import tool# First we initialize the model we want to use.
model = ChatOpenAI(model="gpt-4o", temperature=0)# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)
@tool
def get_weather(city: Literal["nyc", "sf"]):"""Use this to get weather information."""if city == "nyc":return "It might be cloudy in nyc"elif city == "sf":return "It's always sunny in sf"else:raise AssertionError("Unknown city")tools = [get_weather]# Define the graph
graph = create_react_agent(model, tools=tools)

---

运行你的图

现在我们已经定义好了图结构，接下来运行一次并在LangSmith中查看追踪记录。为了让追踪记录在LangSmith中易于访问，我们将在配置中传入一个自定义的run_id。

这要求您已设置好LANGSMITH_API_KEY环境变量。

请注意，您还可以通过设置LANGCHAIN_PROJECT环境变量来配置追踪到哪个项目，默认情况下运行记录会被追踪到default项目。

---

import uuiddef print_stream(stream):for s in stream:message = s["messages"][-1]if isinstance(message, tuple):print(message)else:message.pretty_print()inputs = {"messages": [("user", "what is the weather in sf")]}config = {"run_name": "agent_007", "tags": ["cats are awesome"]}print_stream(graph.stream(inputs, config, stream_mode="values"))

---

================================ Human Message =================================what is the weather in sf
================================== Ai Message ==================================
Tool Calls:get_weather (call_9ZudXyMAdlUjptq9oMGtQo8o)Call ID: call_9ZudXyMAdlUjptq9oMGtQo8oArgs:city: sf
================================= Tool Message =================================
Name: get_weatherIt's always sunny in sf
================================== Ai Message ==================================The weather in San Francisco is currently sunny.

---

在LangSmith中查看追踪记录

既然我们已经运行了图，现在让我们前往LangSmith查看追踪记录。首先点击您追踪到的项目（在我们的案例中是默认项目）。您应该会看到一个名为"agent_007"的自定义运行名称。

---

设置自定义认证

https://langchain-ai.github.io/langgraph/tutorials/auth/getting_started/

在本教程中，我们将构建一个仅允许特定用户访问的聊天机器人。我们将从LangGraph模板开始，逐步添加基于令牌的安全机制。最终，您将获得一个在允许访问前会检查有效令牌的工作聊天机器人。

这是认证系列的第一部分：

1、设置自定义认证（当前所在部分）- 控制谁可以访问您的机器人
2、实现私密对话 - 让用户进行私密对话
3、连接认证提供商 - 添加真实用户账户并使用OAuth2进行生产环境验证

本指南假设您已基本了解以下概念：

• 认证与访问控制
• LangGraph平台

---

注意：自定义认证仅适用于LangGraph平台SaaS部署或企业自托管部署。

---

1、创建你的应用

使用LangGraph启动模板创建一个新的聊天机器人：

pip install -U "langgraph-cli[inmem]"
langgraph new --template=new-langgraph-project-python custom-auth
cd custom-auth

---

模板为我们提供了一个占位符 LangGraph 应用。您可以通过安装本地依赖项并运行开发服务器来试用它：

pip install -e .
langgraph dev

---

服务器将启动并在浏览器中打开工作室：

- 🚀 API: http://127.0.0.1:2024
- 🎨 Studio UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
- 📚 API Docs: http://127.0.0.1:2024/docsThis in-memory server is designed for development and testing.
For production use, please use LangGraph Platform.

---

如果你将这项服务自托管在公共互联网上，任何人都能访问它！

---

---

2、添加认证功能

现在你已经有了一个基础的LangGraph应用，接下来需要为其添加认证功能。

---

注意：在本教程中，为了示例目的，我们将从一个硬编码的令牌开始。在第三个教程中，你将学习如何实现"生产就绪"的认证方案。

Auth对象允许你注册一个认证函数，LangGraph平台会在每个请求上运行该函数。这个函数接收每个请求，并决定是否接受或拒绝。

创建一个新文件src/security/auth.py，这里将存放用于检查用户是否有权访问你的机器人的代码：

src/security/auth.py

---

from langgraph_sdk import Auth# This is our toy user database. Do not do this in production
VALID_TOKENS = {"user1-token": {"id": "user1", "name": "Alice"},"user2-token": {"id": "user2", "name": "Bob"},
}# The "Auth" object is a container that LangGraph will use to mark our authentication function
auth = Auth()# The `authenticate` decorator tells LangGraph to call this function as middleware
# for every request. This will determine whether the request is allowed or not
@auth.authenticate
async def get_current_user(authorization: str | None) -> Auth.types.MinimalUserDict:"""Check if the user's token is valid."""assert authorizationscheme, token = authorization.split()assert scheme.lower() == "bearer"# Check if token is validif token not in VALID_TOKENS:raise Auth.exceptions.HTTPException(status_code=401, detail="Invalid token")# Return user info if validuser_data = VALID_TOKENS[token]return {"identity": user_data["id"],}

---

请注意，您的身份验证处理程序需要完成两项重要工作：

1、检查请求的Authorization头是否包含有效令牌

2、返回用户的身份信息

现在，通过将以下内容添加到langgraph.json配置文件来启用LangGraph的身份验证功能：

langgraph.json

{"dependencies": ["."],"graphs": {"agent": "./src/agent/graph.py:graph"},"env": ".env","auth": {"path": "src/security/auth.py:auth"}
}

---

3、测试你的机器人

重新启动服务器以测试所有功能：

langgraph dev --no-browser

---

如果你没有添加 --no-browser 参数，LangGraph Studio 的界面会在浏览器中自动打开。你可能会好奇：Studio 是如何连接到我们的服务器的？默认情况下，即使启用了自定义认证，我们也允许来自 LangGraph Studio 的访问。这样能更方便地在 Studio 中开发和测试你的机器人。如需禁用这种备用认证方式，可以在认证配置中添加 disable_studio_auth: "true" 设置：

{"auth": {"path": "src/security/auth.py:auth","disable_studio_auth": "true"}
}

---

4、与你的机器人对话

现在，只有当你提供有效的请求头令牌时才能访问机器人。不过，在教程下一节添加资源授权处理器之前，用户仍然可以访问彼此的资源。

---

---

在文件或笔记本中运行以下代码：

from langgraph_sdk import get_client# Try without a token (should fail)
client = get_client(url="http://localhost:2024")
try:thread = await client.threads.create()print("❌ Should have failed without token!")
except Exception as e:print("✅ Correctly blocked access:", e)# Try with a valid token
client = get_client(url="http://localhost:2024", headers={"Authorization": "Bearer user1-token"}
)# Create a thread and chat
thread = await client.threads.create()
print(f"✅ Created thread as Alice: {thread['thread_id']}")response = await client.runs.create(thread_id=thread["thread_id"],assistant_id="agent",input={"messages": [{"role": "user", "content": "Hello!"}]},
)
print("✅ Bot responded:")
print(response)

---

你会看到：

1、没有有效令牌时，我们无法访问机器人

2、拥有有效令牌时，我们可以创建会话并进行聊天

恭喜！你已经构建了一个仅允许"已认证"用户访问的聊天机器人。虽然这个系统（目前）还未实现生产环境级别的安全方案，但我们已经掌握了控制机器人访问权限的基本机制。在下一个教程中，我们将学习如何为每个用户提供专属的私密对话空间。

---

后续步骤

现在您已掌握如何控制对机器人的访问权限，接下来可以：

1、继续教程，前往设置私密会话了解资源授权机制
2、深入阅读身份验证概念
3、查阅API参考文档获取更多认证细节

---

实现私密对话功能

https://langchain-ai.github.io/langgraph/tutorials/auth/resource_auth/

在本教程中，您将扩展上一个教程中创建的聊天机器人，为每位用户提供独立的私密对话空间。您需要添加资源级访问控制机制，确保用户只能查看自己的对话线程。

---

---

前提条件

在开始本教程前，请确保您已成功运行第一个教程中的机器人且无报错。

---

1、添加资源授权

回顾上一教程，Auth 对象允许您注册一个认证函数，LangGraph 平台使用该函数来验证传入请求中的承载令牌。现在您将使用它来注册一个授权处理器。

授权处理器是在认证成功后运行的函数。这些处理器可以为资源添加元数据（例如资源所有者信息），并过滤每个用户可见的内容。

更新您的 src/security/auth.py 文件，添加一个在每次请求时运行的授权处理器：

src/security/auth.py

---

from langgraph_sdk import Auth# Keep our test users from the previous tutorial
VALID_TOKENS = {"user1-token": {"id": "user1", "name": "Alice"},"user2-token": {"id": "user2", "name": "Bob"},
}auth = Auth()@auth.authenticate
async def get_current_user(authorization: str | None) -> Auth.types.MinimalUserDict:"""Our authentication handler from the previous tutorial."""assert authorizationscheme, token = authorization.split()assert scheme.lower() == "bearer"if token not in VALID_TOKENS:raise Auth.exceptions.HTTPException(status_code=401, detail="Invalid token")user_data = VALID_TOKENS[token]return {"identity": user_data["id"],}@auth.on
async def add_owner(ctx: Auth.types.AuthContext,  # Contains info about the current uservalue: dict,  # The resource being created/accessed
):"""Make resources private to their creator."""# Examples:# ctx: AuthContext(#     permissions=[],#     user=ProxyUser(#         identity='user1',#         is_authenticated=True,#         display_name='user1'#     ),#     resource='threads',#     action='create_run'# )# value: # {#     'thread_id': UUID('1e1b2733-303f-4dcd-9620-02d370287d72'),#     'assistant_id': UUID('fe096781-5601-53d2-b2f6-0d3403f7e9ca'),#     'run_id': UUID('1efbe268-1627-66d4-aa8d-b956b0f02a41'),#     'status': 'pending',#     'metadata': {},#     'prevent_insert_if_inflight': True,#     'multitask_strategy': 'reject',#     'if_not_exists': 'reject',#     'after_seconds': 0,#     'kwargs': {#         'input': {'messages': [{'role': 'user', 'content': 'Hello!'}]},#         'command': None,#         'config': {#             'configurable': {#                 'langgraph_auth_user': ... Your user object...#                 'langgraph_auth_user_id': 'user1'#             }#         },#         'stream_mode': ['values'],#         'interrupt_before': None,#         'interrupt_after': None,#         'webhook': None,#         'feedback_keys': None,#         'temporary': False,#         'subgraphs': False#     }# }# Does 2 things:# 1、Add the user's ID to the resource's metadata. Each LangGraph resource has a `metadata` dict that persists with the resource.# this metadata is useful for filtering in read and update operations# 2、Return a filter that lets users only see their own resourcesfilters = {"owner": ctx.user.identity}metadata = value.setdefault("metadata", {})metadata.update(filters)# Only let users see their own resourcesreturn filters

---

处理函数接收两个参数：

1、ctx (AuthContext)：包含当前用户信息、用户权限、操作的资源（“threads”、“crons”、“assistants”）以及正在执行的操作（“create”、“read”、“update”、“delete”、“search”、“create_run”）

2、value (dict)：正在创建或访问的数据。该字典的内容取决于所访问的资源和操作。有关如何获取更精细范围访问控制的信息，请参阅下方的添加范围授权处理函数。

注意，这个简单处理函数做了两件事：

1、将用户ID添加到资源的元数据中。

2、返回一个元数据过滤器，确保用户只能看到自己拥有的资源。

---

2、测试私有对话

测试您的授权配置。如果设置正确，您将看到所有✅标记的消息。请确保开发服务器正在运行（执行langgraph dev命令）：

from langgraph_sdk import get_client# Create clients for both users
alice = get_client(url="http://localhost:2024",headers={"Authorization": "Bearer user1-token"}
)bob = get_client(url="http://localhost:2024",headers={"Authorization": "Bearer user2-token"}
)# Alice creates an assistant
alice_assistant = await alice.assistants.create()
print(f"✅ Alice created assistant: {alice_assistant['assistant_id']}")# Alice creates a thread and chats
alice_thread = await alice.threads.create()
print(f"✅ Alice created thread: {alice_thread['thread_id']}")await alice.runs.create(thread_id=alice_thread["thread_id"],assistant_id="agent",input={"messages": [{"role": "user", "content": "Hi, this is Alice's private chat"}]}
)# Bob tries to access Alice's thread
try:await bob.threads.get(alice_thread["thread_id"])print("❌ Bob shouldn't see Alice's thread!")
except Exception as e:print("✅ Bob correctly denied access:", e)# Bob creates his own thread
bob_thread = await bob.threads.create()
await bob.runs.create(thread_id=bob_thread["thread_id"],assistant_id="agent",input={"messages": [{"role": "user", "content": "Hi, this is Bob's private chat"}]}
)
print(f"✅ Bob created his own thread: {bob_thread['thread_id']}")# List threads - each user only sees their own
alice_threads = await alice.threads.search()
bob_threads = await bob.threads.search()
print(f"✅ Alice sees {len(alice_threads)} thread")
print(f"✅ Bob sees {len(bob_threads)} thread")

---

Output:

✅ Alice created assistant: fc50fb08-78da-45a9-93cc-1d3928a3fc37
✅ Alice created thread: 533179b7-05bc-4d48-b47a-a83cbdb5781d
✅ Bob correctly denied access: Client error '404 Not Found' for url 'http://localhost:2024/threads/533179b7-05bc-4d48-b47a-a83cbdb5781d'
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/404
✅ Bob created his own thread: 437c36ed-dd45-4a1e-b484-28ba6eca8819
✅ Alice sees 1 thread
✅ Bob sees 1 thread

---

这意味着：

1、每位用户都可以创建自己的会话线程并在其中聊天

2、用户无法查看他人的会话线程

3、线程列表仅显示用户自己的线程

---

3、添加作用域授权处理器

宽泛的@auth.on处理器会匹配所有授权事件。这种方式虽然简洁，但意味着value字典的内容没有明确的作用域范围，且相同的用户级访问控制会应用于每个资源。如果需要更精细的控制，您还可以针对资源的特定操作进行管理。

更新src/security/auth.py文件，为特定资源类型添加处理器：

# Keep our previous handlers...from langgraph_sdk import Auth@auth.on.threads.create
async def on_thread_create(ctx: Auth.types.AuthContext,value: Auth.types.on.threads.create.value,
):"""Add owner when creating threads.This handler runs when creating new threads and does two things:1、Sets metadata on the thread being created to track ownership2、Returns a filter that ensures only the creator can access it"""# Example value:#  {'thread_id': UUID('99b045bc-b90b-41a8-b882-dabc541cf740'), 'metadata': {}, 'if_exists': 'raise'}# Add owner metadata to the thread being created# This metadata is stored with the thread and persistsmetadata = value.setdefault("metadata", {})metadata["owner"] = ctx.user.identity# Return filter to restrict access to just the creatorreturn {"owner": ctx.user.identity}@auth.on.threads.read
async def on_thread_read(ctx: Auth.types.AuthContext,value: Auth.types.on.threads.read.value,
):"""Only let users read their own threads.This handler runs on read operations. We don't need to setmetadata since the thread already exists - we just need toreturn a filter to ensure users can only see their own threads."""return {"owner": ctx.user.identity}@auth.on.assistants
async def on_assistants(ctx: Auth.types.AuthContext,value: Auth.types.on.assistants.value,
):# For illustration purposes, we will deny all requests# that touch the assistants resource# Example value:# {#     'assistant_id': UUID('63ba56c3-b074-4212-96e2-cc333bbc4eb4'),#     'graph_id': 'agent',#     'config': {},#     'metadata': {},#     'name': 'Untitled'# }raise Auth.exceptions.HTTPException(status_code=403,detail="User lacks the required permissions.",)# Assumes you organize information in store like (user_id, resource_type, resource_id)
@auth.on.store()
async def authorize_store(ctx: Auth.types.AuthContext, value: dict):# The "namespace" field for each store item is a tuple you can think of as the directory of an item.namespace: tuple = value["namespace"]assert namespace[0] == ctx.user.identity, "Not authorized"

---

注意，现在不再使用单一的全局处理器，而是针对以下操作提供了特定的处理器：

1、创建线程
2、读取线程
3、访问助手

前三个处理器分别对应每个资源上的特定操作（参见资源操作），而最后一个处理器（@auth.on.assistants）则匹配assistants资源上的任意操作。对于每个请求，LangGraph 都会运行与所访问资源及操作最匹配的特定处理器。这意味着上述四个处理器会优先运行，而非作用域宽泛的"@auth.on"全局处理器。

尝试将以下测试代码添加到您的测试文件中：

# ... Same as before
# Try creating an assistant. This should fail
try:await alice.assistants.create("agent")print("❌ Alice shouldn't be able to create assistants!")
except Exception as e:print("✅ Alice correctly denied access:", e)# Try searching for assistants. This also should fail
try:await alice.assistants.search()print("❌ Alice shouldn't be able to search assistants!")
except Exception as e:print("✅ Alice correctly denied access to searching assistants:", e)# Alice can still create threads
alice_thread = await alice.threads.create()
print(f"✅ Alice created thread: {alice_thread['thread_id']}")

---

Output:

✅ Alice created thread: dcea5cd8-eb70-4a01-a4b6-643b14e8f754
✅ Bob correctly denied access: Client error '404 Not Found' for url 'http://localhost:2024/threads/dcea5cd8-eb70-4a01-a4b6-643b14e8f754'
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/404
✅ Bob created his own thread: 400f8d41-e946-429f-8f93-4fe395bc3eed
✅ Alice sees 1 thread
✅ Bob sees 1 thread
✅ Alice correctly denied access:
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/500
✅ Alice correctly denied access to searching assistants:

---

恭喜！您已经构建了一个让每位用户都能拥有私人对话的聊天机器人。虽然当前系统采用基于令牌的简单认证方式，但这些授权模式同样适用于实现任何实际的认证系统。在下一个教程中，您将通过OAuth2将测试用户替换为真实的用户账户。

---

下一步行动

现在您已经掌握了资源访问控制，接下来可以：

1、继续学习连接认证提供商来添加真实用户账户
2、深入了解授权模式
3、查阅API参考文档获取本教程中使用的接口和方法详情

---

连接认证提供商

https://langchain-ai.github.io/langgraph/tutorials/auth/add_auth_server/

在上一个教程中，您添加了资源授权功能来实现用户私密对话。但目前仍在使用硬编码令牌进行认证，这种方式并不安全。现在您将通过OAuth2使用真实用户账户来替换这些令牌。

您将继续使用相同的Auth对象和资源级访问控制，但会将认证升级为使用Supabase作为身份提供商。虽然本教程使用Supabase，但这些概念适用于任何OAuth2提供商。您将学习如何：

1、用真实的JWT令牌替换测试令牌
2、集成OAuth2提供商实现安全的用户认证
3、在保持现有授权逻辑的同时处理用户会话和元数据

---

背景

OAuth2 主要涉及三个角色：

1、授权服务器：身份提供商（如 Supabase、Auth0、Google），负责用户认证并颁发令牌
2、应用后端：您的 LangGraph 应用。这部分负责验证令牌并提供受保护资源（对话数据）
3、客户端应用：用户与之交互的网页或移动应用

标准的 OAuth2 流程大致如下：

sequenceDiagramparticipant Userparticipant Clientparticipant AuthServerparticipant LangGraph BackendUser->>Client: Initiate loginUser->>AuthServer: Enter credentialsAuthServer->>Client: Send tokensClient->>LangGraph Backend: Request with tokenLangGraph Backend->>AuthServer: Validate tokenAuthServer->>LangGraph Backend: Token validLangGraph Backend->>Client: Serve request (e.g., run agent or graph)

---

前提条件

在开始本教程前，请确保您已满足以下要求：

• 已正常运行第二个教程中的机器人且无报错
• 拥有一个Supabase项目以使用其认证服务

---

1、安装依赖

安装所需的依赖项。首先进入你的 custom-auth 目录，并确保已安装 langgraph-cli：

cd custom-auth
pip install -U "langgraph-cli[inmem]"

---

2、设置认证提供程序

接下来，获取您的认证服务器URL和用于身份验证的私钥。
由于您使用的是Supabase，可以在Supabase仪表板中完成此操作：

1、在左侧边栏中，点击"⚙ 项目设置"，然后点击"API"
2、复制您的项目URL并将其添加到.env文件中

---

echo "SUPABASE_URL=your-project-url" >> .env

---

1、复制您的服务角色密钥并将其添加到您的 .env 文件中：

echo "SUPABASE_SERVICE_KEY=your-service-role-key" >> .env

---

1、复制你的"anon public"密钥并记录下来。稍后在设置客户端代码时会用到它。

SUPABASE_URL=your-project-url
SUPABASE_SERVICE_KEY=your-service-role-key

---

3、实现令牌验证

在前面的教程中，您使用了 Auth 对象来验证硬编码令牌和添加资源所有权。

现在您需要升级认证逻辑，改为验证来自 Supabase 的真实 JWT 令牌。主要修改集中在 @auth.authenticate 装饰函数中：

• 不再检查硬编码的令牌列表，而是向 Supabase 发起 HTTP 请求来验证令牌
• 从已验证的令牌中提取真实的用户信息（ID、邮箱）
• 现有的资源授权逻辑保持不变

更新 src/security/auth.py 文件实现这些功能：

src/security/auth.py

---

import os
import httpx
from langgraph_sdk import Authauth = Auth()# This is loaded from the `.env` file you created above
SUPABASE_URL = os.environ["SUPABASE_URL"]
SUPABASE_SERVICE_KEY = os.environ["SUPABASE_SERVICE_KEY"]@auth.authenticate
async def get_current_user(authorization: str | None):"""Validate JWT tokens and extract user information."""assert authorizationscheme, token = authorization.split()assert scheme.lower() == "bearer"try:# Verify token with auth providerasync with httpx.AsyncClient() as client:response = await client.get(f"{SUPABASE_URL}/auth/v1/user",headers={"Authorization": authorization,"apiKey": SUPABASE_SERVICE_KEY,},)assert response.status_code == 200user = response.json()return {"identity": user["id"],  # Unique user identifier"email": user["email"],"is_authenticated": True,}except Exception as e:raise Auth.exceptions.HTTPException(status_code=401, detail=str(e))# ... the rest is the same as before# Keep our resource authorization from the previous tutorial
@auth.on
async def add_owner(ctx, value):"""Make resources private to their creator using resource metadata."""filters = {"owner": ctx.user.identity}metadata = value.setdefault("metadata", {})metadata.update(filters)return filters

---

最重要的变化是我们现在使用真实的认证服务器来验证令牌。我们的认证处理器拥有 Supabase 项目的私钥，可用于验证用户令牌并提取其信息。

---

4、测试认证流程

让我们来测试新的认证流程。你可以在文件或笔记本中运行以下代码。需要提供：

• 一个有效的电子邮箱地址
• Supabase 项目 URL（来自上文）
• Supabase 匿名公钥（同样来自上文）

---

import os
import httpx
from getpass import getpass
from langgraph_sdk import get_client# Get email from command line
email = getpass("Enter your email: ")
base_email = email.split("@")
password = "secure-password"  # CHANGEME
email1 = f"{base_email[0]}+1@{base_email[1]}"
email2 = f"{base_email[0]}+2@{base_email[1]}"SUPABASE_URL = os.environ.get("SUPABASE_URL")
if not SUPABASE_URL:SUPABASE_URL = getpass("Enter your Supabase project URL: ")# This is your PUBLIC anon key (which is safe to use client-side)
# Do NOT mistake this for the secret service role key
SUPABASE_ANON_KEY = os.environ.get("SUPABASE_ANON_KEY")
if not SUPABASE_ANON_KEY:SUPABASE_ANON_KEY = getpass("Enter your public Supabase anon  key: ")async def sign_up(email: str, password: str):"""Create a new user account."""async with httpx.AsyncClient() as client:response = await client.post(f"{SUPABASE_URL}/auth/v1/signup",json={"email": email, "password": password},headers={"apiKey": SUPABASE_ANON_KEY},)assert response.status_code == 200return response.json()# Create two test users
print(f"Creating test users: {email1} and {email2}")
await sign_up(email1, password)
await sign_up(email2, password)

---

⚠️ 继续之前：请检查您的邮箱并点击两个确认链接。在用户确认邮箱之前，Supabase 会拒绝所有 /login 请求。

现在测试用户只能查看自己的数据。请确保服务器正在运行（执行 langgraph dev 命令）后再继续操作。以下代码片段需要用到您之前在设置认证提供方时从 Supabase 仪表板复制的 “anon public” 密钥。

---

async def login(email: str, password: str):"""Get an access token for an existing user."""async with httpx.AsyncClient() as client:response = await client.post(f"{SUPABASE_URL}/auth/v1/token?grant_type=password",json={"email": email,"password": password},headers={"apikey": SUPABASE_ANON_KEY,"Content-Type": "application/json"},)assert response.status_code == 200return response.json()["access_token"]# Log in as user 1
user1_token = await login(email1, password)
user1_client = get_client(url="http://localhost:2024", headers={"Authorization": f"Bearer {user1_token}"}
)# Create a thread as user 1
thread = await user1_client.threads.create()
print(f"✅ User 1 created thread: {thread['thread_id']}")# Try to access without a token
unauthenticated_client = get_client(url="http://localhost:2024")
try:await unauthenticated_client.threads.create()print("❌ Unauthenticated access should fail!")
except Exception as e:print("✅ Unauthenticated access blocked:", e)# Try to access user 1's thread as user 2
user2_token = await login(email2, password)
user2_client = get_client(url="http://localhost:2024", headers={"Authorization": f"Bearer {user2_token}"}
)try:await user2_client.threads.get(thread["thread_id"])print("❌ User 2 shouldn't see User 1's thread!")
except Exception as e:print("✅ User 2 blocked from User 1's thread:", e)

---

输出结果应如下所示：

✅ User 1 created thread: d6af3754-95df-4176-aa10-dbd8dca40f1a
✅ Unauthenticated access blocked: Client error '403 Forbidden' for url 'http://localhost:2024/threads'
✅ User 2 blocked from User 1's thread: Client error '404 Not Found' for url 'http://localhost:2024/threads/d6af3754-95df-4176-aa10-dbd8dca40f1a'

---

您的身份验证与授权机制协同工作：

1、用户必须登录才能访问机器人

2、每个用户只能查看自己的会话线程

所有用户均由Supabase认证提供商管理，因此您无需实现额外的用户管理逻辑。

---

后续步骤

您已成功为LangGraph应用构建了一个生产就绪的认证系统！让我们回顾一下您已完成的工作：

1、设置认证服务提供商（本示例使用Supabase）
2、添加了支持邮箱/密码验证的真实用户账户
3、将JWT令牌验证集成到LangGraph服务端
4、实现完善的授权机制，确保用户只能访问自己的数据
5、建立了可应对未来认证需求的基础架构 🚀

现在您已拥有生产级认证系统，建议考虑：

1、使用您喜欢的框架构建Web界面（可参考自定义认证模板示例）
2、通过认证概念指南深入了解认证与授权的其他方面
3、阅读参考文档后进一步自定义处理程序和配置

---

运行时重建图

https://langchain-ai.github.io/langgraph/cloud/deployment/graph_rebuild/
您可能需要为新运行使用不同配置重新构建图。例如，根据配置的不同，您可能需要使用不同的图状态或图结构。本指南将展示如何实现这一点。

---

注意：在大多数情况下，基于配置自定义行为应通过单一图处理，其中每个节点可以读取配置并据此改变其行为

---

先决条件

在开始之前，请确保先查阅此操作指南了解如何为部署设置您的应用程序。

---

定义图结构

假设你有一个应用程序，其中包含一个简单的图结构，该结构会调用大语言模型(LLM)并将响应返回给用户。应用程序的文件目录结构如下：

my-app/
|-- requirements.txt
|-- .env
|-- openai_agent.py     # code for your graph

---

其中图定义在 openai_agent.py 文件中。

---

无需重新构建

在标准LangGraph API配置中，服务器使用的是定义在openai_agent.py顶层的已编译图实例，其结构如下所示：

API参考文档：ChatOpenAI | END | START

---

from langchain_openai import ChatOpenAI
from langgraph.graph import END, START, MessageGraphmodel = ChatOpenAI(temperature=0)graph_workflow = MessageGraph()graph_workflow.add_node("agent", model)
graph_workflow.add_edge("agent", END)
graph_workflow.add_edge(START, "agent")agent = graph_workflow.compile()

---

为了让服务器识别您的图，您需要在LangGraph API配置（langgraph.json）中指定包含CompiledStateGraph实例的变量路径，例如：

{"dependencies": ["."],"graphs": {"openai_agent": "./openai_agent.py:agent",},"env": "./.env"
}

---

重建

为了让你的图表在每次使用自定义配置重新运行时都能重建，你需要重写 openai_agent.py，改为提供一个函数，该函数接收配置并返回一个图表（或已编译的图表）实例。假设我们希望为用户 ID 为 ‘1’ 的情况返回现有的图表，而为其他用户返回一个工具调用代理。我们可以按如下方式修改 openai_agent.py：

API 参考：ChatOpenAI | END | START | StateGraph | add_messages | ToolNode | tool | BaseMessage | RunnableConfig

---

from typing import Annotated
from typing_extensions import TypedDict
from langchain_openai import ChatOpenAI
from langgraph.graph import END, START, MessageGraph
from langgraph.graph.state import StateGraph
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode
from langchain_core.tools import tool
from langchain_core.messages import BaseMessage
from langchain_core.runnables import RunnableConfigclass State(TypedDict):messages: Annotated[list[BaseMessage], add_messages]model = ChatOpenAI(temperature=0)def make_default_graph():"""Make a simple LLM agent"""graph_workflow = StateGraph(State)def call_model(state):return {"messages": [model.invoke(state["messages"])]}graph_workflow.add_node("agent", call_model)graph_workflow.add_edge("agent", END)graph_workflow.add_edge(START, "agent")agent = graph_workflow.compile()return agentdef make_alternative_graph():"""Make a tool-calling agent"""@tooldef add(a: float, b: float):"""Adds two numbers."""return a + btool_node = ToolNode([add])model_with_tools = model.bind_tools([add])def call_model(state):return {"messages": [model_with_tools.invoke(state["messages"])]}def should_continue(state: State):if state["messages"][-1].tool_calls:return "tools"else:return ENDgraph_workflow = StateGraph(State)graph_workflow.add_node("agent", call_model)graph_workflow.add_node("tools", tool_node)graph_workflow.add_edge("tools", "agent")graph_workflow.add_edge(START, "agent")graph_workflow.add_conditional_edges("agent", should_continue)agent = graph_workflow.compile()return agent# this is the graph making function that will decide which graph to
# build based on the provided config
def make_graph(config: RunnableConfig):user_id = config.get("configurable", {}).get("user_id")# route to different graph state / structure based on the user IDif user_id == "1":return make_default_graph()else:return make_alternative_graph()

---

最后，你需要在langgraph.json中指定图表生成函数(make_graph)的路径：

{"dependencies": ["."],"graphs": {"openai_agent": "./openai_agent.py:make_graph",},"env": "./.env"
}

---

更多关于LangGraph API配置文件的详细信息，请参阅此处。

---

如何使用RemoteGraph与部署进行交互

https://langchain-ai.github.io/langgraph/how-tos/use-remote-graph/

---

前提条件

• LangGraph平台
• LangGraph服务端

RemoteGraph是一个接口，它允许您像操作常规本地定义的LangGraph图(例如CompiledGraph)一样与LangGraph平台部署进行交互。本指南将向您展示如何初始化RemoteGraph并与之交互。

---

初始化图

初始化 RemoteGraph 时，必须始终指定以下参数：

• name：要交互的图名称。该名称与部署时 langgraph.json 配置文件中的图名称一致。
• api_key：有效的 LangSmith API 密钥。可通过环境变量 (LANGSMITH_API_KEY) 设置，或直接通过 api_key 参数传入。如果 LangGraphClient / SyncLangGraphClient 初始化时已传入 api_key 参数，也可通过 client / sync_client 参数提供该密钥。

此外，还需提供以下任一参数：

• url：要交互的部署 URL。传入 url 参数后，同步和异步客户端将基于该 URL、请求头（如提供）及默认配置值（如超时等）创建。
• client：用于异步交互的 LangGraphClient 实例（例如使用 .astream()、.ainvoke()、.aget_state()、.aupdate_state() 等方法）。
• sync_client：用于同步交互的 SyncLangGraphClient 实例（例如使用 .stream()、.invoke()、.get_state()、.update_state() 等方法）。

---

注意：若同时传入 client/sync_client 和 url 参数，前者将覆盖后者。若未提供 client/sync_client/url 任一参数，运行时 RemoteGraph 将抛出 ValueError 异常。

---

使用 URL

Python

from langgraph.pregel.remote import RemoteGraphurl = <DEPLOYMENT_URL>
graph_name = "agent"
remote_graph = RemoteGraph(graph_name, url=url)

---

Javascript

import { RemoteGraph } from "@langchain/langgraph/remote";const url = `<DEPLOYMENT_URL>`;
const graphName = "agent";
const remoteGraph = new RemoteGraph({ graphId: graphName, url });

---

使用客户端

Python

from langgraph_sdk import get_client, get_sync_client
from langgraph.pregel.remote import RemoteGraphurl = <DEPLOYMENT_URL>
graph_name = "agent"
client = get_client(url=url)
sync_client = get_sync_client(url=url)
remote_graph = RemoteGraph(graph_name, client=client, sync_client=sync_client)

---

Javascript

import { Client } from "@langchain/langgraph-sdk";
import { RemoteGraph } from "@langchain/langgraph/remote";const client = new Client({ apiUrl: `<DEPLOYMENT_URL>` });
const graphName = "agent";
const remoteGraph = new RemoteGraph({ graphId: graphName, client });

---

调用图结构

由于 RemoteGraph 是一个实现了与 CompiledGraph 相同方法的 Runnable，因此您可以像平常操作已编译图结构那样与之交互，即通过调用 .invoke()、.stream()、.get_state()、.update_state() 等方法（以及它们的异步版本）。

---

异步使用

---

注意：要异步使用图表，在初始化 RemoteGraph 时必须提供 url 或 client 参数。

Python

# invoke the graph
result = await remote_graph.ainvoke({"messages": [{"role": "user", "content": "what's the weather in sf"}]
})# stream outputs from the graph
async for chunk in remote_graph.astream({"messages": [{"role": "user", "content": "what's the weather in la"}]
}):print(chunk)

---

Javascript

// invoke the graph
const result = await remoteGraph.invoke({messages: [{role: "user", content: "what's the weather in sf"}]
})// stream outputs from the graph
for await (const chunk of await remoteGraph.stream({messages: [{role: "user", content: "what's the weather in la"}]
})):console.log(chunk)

---

同步模式

---

注意：要同步使用图数据库，在初始化 RemoteGraph 时必须提供 url 或 sync_client 参数。

Python

# invoke the graph
result = remote_graph.invoke({"messages": [{"role": "user", "content": "what's the weather in sf"}]
})# stream outputs from the graph
for chunk in remote_graph.stream({"messages": [{"role": "user", "content": "what's the weather in la"}]
}):print(chunk)

---

线程级持久化

默认情况下，图的运行（即调用.invoke()或.stream()）是无状态的——图的检查点和最终状态不会被持久化。如果您希望持久化图运行的输出（例如，为了实现人在回路功能），可以创建一个线程并通过config参数提供线程ID，这与常规编译图的操作方式相同：

Python

from langgraph_sdk import get_sync_client
url = <DEPLOYMENT_URL>
graph_name = "agent"
sync_client = get_sync_client(url=url)
remote_graph = RemoteGraph(graph_name, url=url)# create a thread (or use an existing thread instead)
thread = sync_client.threads.create()# invoke the graph with the thread config
config = {"configurable": {"thread_id": thread["thread_id"]}}
result = remote_graph.invoke({"messages": [{"role": "user", "content": "what's the weather in sf"}]
}, config=config)# verify that the state was persisted to the thread
thread_state = remote_graph.get_state(config)
print(thread_state)

---

Javascript

import { Client } from "@langchain/langgraph-sdk";
import { RemoteGraph } from "@langchain/langgraph/remote";const url = `<DEPLOYMENT_URL>`;
const graphName = "agent";
const client = new Client({ apiUrl: url });
const remoteGraph = new RemoteGraph({ graphId: graphName, url });// create a thread (or use an existing thread instead)
const thread = await client.threads.create();// invoke the graph with the thread config
const config = { configurable: { thread_id: thread.thread_id }};
const result = await remoteGraph.invoke({messages: [{ role: "user", content: "what's the weather in sf" }],
}, config);// verify that the state was persisted to the thread
const threadState = await remoteGraph.getState(config);
console.log(threadState);

---

作为子图使用

---

注意：如果需要在包含 RemoteGraph 子图节点的图中使用 checkpointer，请确保使用 UUID 作为线程 ID。

由于 RemoteGraph 的行为与常规 CompiledGraph 相同，因此也可以将其作为子图嵌入到另一个图中。例如：

Python

from langgraph_sdk import get_sync_client
from langgraph.graph import StateGraph, MessagesState, START
from typing import TypedDicturl = <DEPLOYMENT_URL>
graph_name = "agent"
remote_graph = RemoteGraph(graph_name, url=url)# define parent graph
builder = StateGraph(MessagesState)
# add remote graph directly as a node
builder.add_node("child", remote_graph)
builder.add_edge(START, "child")
graph = builder.compile()# invoke the parent graph
result = graph.invoke({"messages": [{"role": "user", "content": "what's the weather in sf"}]
})
print(result)# stream outputs from both the parent graph and subgraph
for chunk in graph.stream({"messages": [{"role": "user", "content": "what's the weather in sf"}]
}, subgraphs=True):print(chunk)

---

Javascript

import { MessagesAnnotation, StateGraph, START } from "@langchain/langgraph";
import { RemoteGraph } from "@langchain/langgraph/remote";const url = `<DEPLOYMENT_URL>`;
const graphName = "agent";
const remoteGraph = new RemoteGraph({ graphId: graphName, url });// define parent graph and add remote graph directly as a node
const graph = new StateGraph(MessagesAnnotation).addNode("child", remoteGraph).addEdge(START, "child").compile()// invoke the parent graph
const result = await graph.invoke({messages: [{ role: "user", content: "what's the weather in sf" }]
});
console.log(result);// stream outputs from both the parent graph and subgraph
for await (const chunk of await graph.stream({messages: [{ role: "user", content: "what's the weather in la" }]
}, { subgraphs: true })) {console.log(chunk);
}

---

如何使用LangGraph平台部署CrewAI、AutoGen等框架

https://langchain-ai.github.io/langgraph/how-tos/autogen-langgraph-platform/

LangGraph平台为智能体部署提供了基础设施。该平台与LangGraph无缝集成，同时也兼容其他框架。实现方式是将智能体封装为单个LangGraph节点，使其成为整个图结构。

这种部署方式能带来诸多优势，包括：水平可扩展的基础设施、处理突发操作的任务队列、支持短期记忆的持久层，以及长期记忆功能。

本指南将演示如何部署AutoGen智能体，但该方法同样适用于CrewAI、LlamaIndex等其他框架定义的智能体。

---

安装

---

%pip install autogen langgraph

---

import getpass
import osdef _set_env(var: str):if not os.environ.get(var):os.environ[var] = getpass.getpass(f"{var}: ")_set_env("OPENAI_API_KEY")

---

定义自动生成代理

在这里，我们定义自己的AutoGen代理。参考自https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_web_info.ipynb

---

import autogen
import osconfig_list = [{"model": "gpt-4o", "api_key": os.environ["OPENAI_API_KEY"]}]llm_config = {"timeout": 600,"cache_seed": 42,"config_list": config_list,"temperature": 0,
}autogen_agent = autogen.AssistantAgent(name="assistant",llm_config=llm_config,
)user_proxy = autogen.UserProxyAgent(name="user_proxy",human_input_mode="NEVER",max_consecutive_auto_reply=10,is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),code_execution_config={"work_dir": "web","use_docker": False,},  # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.llm_config=llm_config,system_message="Reply TERMINATE if the task has been solved at full satisfaction. Otherwise, reply CONTINUE, or the reason why the task is not solved yet.",
)

---

使用LangGraph封装

现在我们将AutoGen智能体封装为一个单独的LangGraph节点，并使其成为整个图结构。这主要涉及为节点定义输入和输出模式——即使手动部署也需要完成这项工作，因此不会增加额外负担。

API参考文档：StateGraph

---

from langgraph.graph import StateGraph, MessagesStatedef call_autogen_agent(state: MessagesState):last_message = state["messages"][-1]response = user_proxy.initiate_chat(autogen_agent, message=last_message.content)# get the final response from the agentcontent = response.chat_history[-1]["content"]return {"messages": {"role": "assistant", "content": content}}graph = StateGraph(MessagesState)
graph.add_node(call_autogen_agent)
graph.set_entry_point("call_autogen_agent")
graph = graph.compile()

---

通过LangGraph平台部署

您现在可以像往常一样使用LangGraph平台进行部署。更多详情请参阅这些说明。

---

如何将LangGraph集成到React应用中

https://langchain-ai.github.io/langgraph/cloud/how-tos/use_stream_react/

---

前提条件

• LangGraph平台
• LangGraph服务

useStream() React钩子提供了一种无缝集成LangGraph到React应用的方式。它处理了所有流式传输、状态管理和分支逻辑的复杂性，让你能专注于构建出色的聊天体验。

主要特性：

• 消息流式处理：处理消息分块流以形成完整消息
• 自动状态管理：管理消息、中断、加载状态和错误
• 对话分支：从聊天历史中的任意点创建替代对话路径
• UI无关设计：可使用自定义组件和样式

下面我们来探索如何在React应用中使用useStream()。

useStream()为创建定制聊天体验提供了坚实基础。如需预构建的聊天组件和界面，我们也推荐查看CopilotKit和assistant-ui。

---

安装

---

npm install @langchain/langgraph-sdk @langchain/core

---

示例

---

"use client";import { useStream } from "@langchain/langgraph-sdk/react";
import type { Message } from "@langchain/langgraph-sdk";export default function App() {const thread = useStream<{ messages: Message[] }>({apiUrl: "http://localhost:2024",assistantId: "agent",messagesKey: "messages",});*  return (<div><div>{thread.messages.map((message) => (<div key={message.id}>{message.content as string}</div>))}</div><formonSubmit={(e) => {e.preventDefault();const form = e.target as HTMLFormElement;const message = new FormData(form).get("message") as string;form.reset();thread.submit({ messages: [{ type: "human", content: message }] });}}><input type="text" name="message" />{thread.isLoading ? (<button key="stop" type="button" onClick={() => thread.stop()}>Stop</button>) : (<button keytype="submit">Send</button>)}</form></div>);
}

---

自定义用户界面

useStream() 钩子函数在幕后处理了所有复杂的状态管理，为您提供了简洁的接口来构建用户界面。以下是开箱即用的功能：

• 线程状态管理
• 加载与错误状态处理
• 中断机制
• 消息处理与更新
• 分支支持

以下是如何有效使用这些功能的示例：

加载状态

isLoading 属性可以告诉你数据流何时处于活动状态，使你可以：

• 显示加载指示器
• 在处理期间禁用输入字段
• 显示取消按钮

---

export default function App() {const { isLoading, stop } = useStream<{ messages: Message[] }>({apiUrl: "http://localhost:2024",assistantId: "agent",messagesKey: "messages",});*  return (<form>{isLoading && (<button key="stop" type="button" onClick={() => stop()}>Stop</button>)}</form>);
}

---

线程管理

通过内置的线程管理功能跟踪对话。您可以访问当前线程ID，并在新线程创建时收到通知：

const [threadId, setThreadId] = useState<string | null>(null);const thread = useStream<{ messages: Message[] }>({apiUrl: "http://localhost:2024",assistantId: "agent",*  threadId: threadId,onThreadId: setThreadId,
});

---

我们建议将 threadId 存储在 URL 的查询参数中，以便用户在页面刷新后能恢复对话。

---

消息处理

useStream() 钩子会持续追踪从服务端接收到的消息分片，并将它们拼接成完整的消息。拼接完成的消息分片可通过 messages 属性获取。

默认情况下，messagesKey 设置为 messages，此时新的消息分片会被追加到 values["messages"] 中。如果您将消息存储在其他键名下，可以修改 messagesKey 的值。

---

import type { Message } from "@langchain/langgraph-sdk";
import { useStream } from "@langchain/langgraph-sdk/react";export default function HomePage() {const thread = useStream<{ messages: Message[] }>({apiUrl: "http://localhost:2024",assistantId: "agent",messagesKey: "messages",});*  return (<div>{thread.messages.map((message) => (<div key={message.id}>{message.content as string}</div>))}</div>);
}

---

在底层实现中，useStream() 钩子会使用 streamMode: "messages-tuple" 模式，从图节点内部的任何 LangChain 聊天模型调用中接收消息流（即独立的 LLM 令牌）。有关消息流的更多信息，请参阅流式传输指南。

---

中断机制

useStream() 钩子函数提供了 interrupt 属性，该属性会存储来自线程的最新中断信息。您可以通过中断机制实现以下功能：

• 在执行某个节点前渲染确认界面
• 等待人工输入，使智能体能够向用户提出澄清性问题

更多关于中断机制的使用方法，请参阅如何处理中断指南。

---

const thread = useStream<{ messages: Message[] }, { InterruptType: string }>({apiUrl: "http://localhost:2024",assistantId: "agent",messagesKey: "messages",
});if (thread.interrupt) {return (<div>Interrupted! {thread.interrupt.value}<buttontype="button"onClick={() => {// `resume` can be any value that the agent acceptsthread.submit(undefined, { command: { resume: true } });}}>Resume</button></div>);
}

---

分支管理

对于每条消息，您可以使用 getMessagesMetadata() 获取该消息首次出现时的检查点。然后，您可以从首次出现检查点的前一个检查点创建新运行，从而在线程中创建新分支。

可以通过以下方式创建分支：

1、编辑之前的用户消息
2、请求重新生成之前的助手消息

---

"use client";import type { Message } from "@langchain/langgraph-sdk";
import { useStream } from "@langchain/langgraph-sdk/react";
import { useState } from "react";function BranchSwitcher({branch,branchOptions,onSelect,
}: {branch: string | undefined;branchOptions: string[] | undefined;onSelect: (branch: string) => void;
}) {if (!branchOptions || !branch) return null;const index = branchOptions.indexOf(branch);*  return (<div className="flex items-center gap-2"><buttontype="button"onClick={() => {const prevBranch = branchOptions[index - 1];if (!prevBranch) return;onSelect(prevBranch);}}>Prev</button><span>{index + 1} / {branchOptions.length}</span><buttontype="button"onClick={() => {const nextBranch = branchOptions[index + 1];if (!nextBranch) return;onSelect(nextBranch);}}>Next</button></div>);
}function EditMessage({message,onEdit,
}: {message: Message;onEdit: (message: Message) => void;
}) {const [editing, setEditing] = useState(false);*  if (!editing) {return (<button type="button" onClick={() => setEditing(true)}>Edit</button>);}*  return (<formonSubmit={(e) => {e.preventDefault();const form = e.target as HTMLFormElement;const content = new FormData(form).get("content") as string;form.reset();onEdit({ type: "human", content });setEditing(false);}}><input name="content" defaultValue={message.content as string} /><button type="submit">Save</button></form>);
}export default function App() {const thread = useStream({apiUrl: "http://localhost:2024",assistantId: "agent",messagesKey: "messages",});*  return (<div><div>{thread.messages.map((message) => {const meta = thread.getMessagesMetadata(message);const parentCheckpoint = meta?.firstSeenState?.parent_checkpoint;return (<div key={message.id}><div>{message.content as string}</div>{message.type === "human" && (<EditMessagemessage={message}onEdit={(message) =>thread.submit({ messages: [message] },{ checkpoint: parentCheckpoint })}/>)}{message.type === "ai" && (<buttontype="button"onClick={() =>thread.submit(undefined, { checkpoint: parentCheckpoint })}><span>Regenerate</span></button>)}<BranchSwitcherbranch={meta?.branch}branchOptions={meta?.branchOptions}onSelect={(branch) => thread.setBranch(branch)}/></div>);})}</div><formonSubmit={(e) => {e.preventDefault();const form = e.target as HTMLFormElement;const message = new FormData(form).get("message") as string;form.reset();thread.submit({ messages: [message] });}}><input type="text" name="message" />{thread.isLoading ? (<button key="stop" type="button" onClick={() => thread.stop()}>Stop</button>) : (<button key="submit" type="submit">Send</button>)}</form></div>);
}

---

对于高级使用场景，您可以使用 experimental_branchTree 属性获取线程的树状表示，这可用于为非基于消息的图表渲染分支控件。

---

乐观更新

您可以在向代理发起网络请求之前，乐观地更新客户端状态。这种方式能够为用户提供即时反馈，例如在代理尚未收到请求时，就立即显示用户消息。

---

const stream = useStream({apiUrl: "http://localhost:2024",assistantId: "agent",messagesKey: "messages",
});const handleSubmit = (text: string) => {const newMessage = { type: "human" as const, content: text };*  stream.submit({ messages: [newMessage] },{optimisticValues(prev) {const prevMessages = prev.messages ?? [];const newMessages = [...prevMessages, newMessage];return { ...prev, messages: newMessages };},});
};

---

TypeScript

useStream() 钩子对使用 TypeScript 编写的应用非常友好，你可以为状态指定类型以获得更好的类型安全性和 IDE 支持。

---

// Define your types
type State = {messages: Message[];context?: Record<string, unknown>;
};// Use them with the hook
const thread = useStream<State>({apiUrl: "http://localhost:2024",assistantId: "agent",messagesKey: "messages",
});

---

你也可以选择性地为不同场景指定类型，例如：

• ConfigurableType：用于 config.configurable 属性的类型（默认：Record<string, unknown>）
• InterruptType：用于中断值的类型——即 interrupt(...) 函数的内容（默认：unknown）
• CustomEventType：用于自定义事件的类型（默认：unknown）
• UpdateType：用于提交函数的类型（默认：Partial<State>）

---

const thread = useStream<State,{UpdateType: {messages: Message[] | Message;context?: Record<string, unknown>;};InterruptType: string;CustomEventType: {type: "progress" | "debug";payload: unknown;};ConfigurableType: {model: string;};}
>({apiUrl: "http://localhost:2024",assistantId: "agent",messagesKey: "messages",
});

---

如果你正在使用 LangGraph.js，也可以复用你的图注解类型。但请确保仅导入注解模式的类型，以避免引入整个 LangGraph.js 运行时（例如通过 import type { ... } 指令）。

---

import {Annotation,MessagesAnnotation,type StateType,type UpdateType,
} from "@langchain/langgraph/web";const AgentState = Annotation.Root({...MessagesAnnotation.spec,context: Annotation<string>(),
});const thread = useStream<StateType<typeof AgentState.spec>,{ UpdateType: UpdateType<typeof AgentState.spec> }
>({apiUrl: "http://localhost:2024",assistantId: "agent",messagesKey: "messages",
});

---

事件处理

useStream() 钩子提供了多个回调选项，帮助您响应不同事件：

• onError：发生错误时触发
• onFinish：流结束时触发
• onUpdateEvent：收到更新事件时触发
• onCustomEvent：收到自定义事件时触发。参阅流式传输指南了解如何传输自定义事件
• onMetadataEvent：收到元数据事件时触发，该事件包含运行ID和线程ID

---

了解更多

• JS/TS SDK 参考文档

---

如何使用LangGraph实现生成式用户界面

https://langchain-ai.github.io/langgraph/cloud/how-tos/generative_ui_react/

---

前提条件

• LangGraph平台
• LangGraph服务端
• useStream() React Hook

生成式用户界面（Generative UI）使智能体能够超越纯文本交互，生成丰富的用户界面。这可以创建更具交互性和上下文感知的应用程序，其中界面会根据对话流程和AI响应动态调整。

---

---

LangGraph平台支持将React组件与图代码协同定位。这样您可以专注于为图构建特定的UI组件，同时轻松集成到现有聊天界面（如Agent Chat）中，并且仅在真正需要时加载代码。

---

教程

---

1、定义与配置UI组件

首先创建你的第一个UI组件。每个组件都需要提供一个唯一标识符，该标识符将用于在图代码中引用该组件。

src/agent/ui.tsx

---

const WeatherComponent = (props: { city: string }) => {return <div>Weather for {props.city}</div>;
};export default {weather: WeatherComponent,
};

---

接下来，在 langgraph.json 配置文件中定义您的 UI 组件：

{"node_version": "20","graphs": {"agent": "./src/agent/index.ts:graph"},"ui": {"agent": "./src/agent/ui.tsx"}
}

---

ui 部分指向图表将使用的 UI 组件。默认情况下，我们建议使用与图表名称相同的键名，但您也可以按需拆分组件，更多细节请参阅自定义 UI 组件的命名空间。

LangGraph 平台会自动打包您的 UI 组件代码和样式，并将其作为可通过 LoadExternalComponent 组件加载的外部资源提供服务。部分依赖项（如 react 和 react-dom）会被自动排除在打包范围之外。

平台还开箱即用地支持 CSS 和 Tailwind 4.x，因此您可以在 UI 组件中自由使用 Tailwind 类以及 shadcn/ui。

src/agent/ui.tsx

import "./styles.css";const WeatherComponent = (props: { city: string }) => {return <div className="bg-red-500">Weather for {props.city}</div>;
};export default {weather: WeatherComponent,
};

---

src/agent/styles.css

@import "tailwindcss";

---

2、发送图中的UI组件

Python

src/agent.py

---

import uuid
from typing import Annotated, Sequence, TypedDictfrom langchain_core.messages import AIMessage, BaseMessage
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph
from langgraph.graph.message import add_messages
from langgraph.graph.ui import AnyUIMessage, ui_message_reducer, push_ui_messageclass AgentState(TypedDict):  # noqa: D101messages: Annotated[Sequence[BaseMessage], add_messages]ui: Annotated[Sequence[AnyUIMessage], ui_message_reducer]async def weather(state: AgentState):class WeatherOutput(TypedDict):city: strweather: WeatherOutput = (await ChatOpenAI(model="gpt-4o-mini").with_structured_output(WeatherOutput).with_config({"tags": ["nostream"]}).ainvoke(state["messages"]))message = AIMessage(id=str(uuid.uuid4()),content=f"Here's the weather for {weather['city']}",)# Emit UI elements associated with the messagepush_ui_message("weather", weather, message=message)return {"messages": [message]}workflow = StateGraph(AgentState)
workflow.add_node(weather)
workflow.add_edge("__start__", "weather")
graph = workflow.compile()

---

JS

使用 typedUi 工具从您的代理节点发出 UI 元素：

src/agent/index.ts

---

import {typedUi,uiMessageReducer,
} from "@langchain/langgraph-sdk/react-ui/server";import { ChatOpenAI } from "@langchain/openai";
import { v4 as uuidv4 } from "uuid";
import { z } from "zod";import type ComponentMap from "./ui.js";import {Annotation,MessagesAnnotation,StateGraph,type LangGraphRunnableConfig,
} from "@langchain/langgraph";const AgentState = Annotation.Root({...MessagesAnnotation.spec,ui: Annotation({ reducer: uiMessageReducer, default: () => [] }),
});export const graph = new StateGraph(AgentState).addNode("weather", async (state, config) => {// Provide the type of the component map to ensure// type safety of `ui.push()` calls as well as// pushing the messages to the `ui` and sending a custom event as well.const ui = typedUi<typeof ComponentMap>(config);const weather = await new ChatOpenAI({ model: "gpt-4o-mini" }).withStructuredOutput(z.object({ city: z.string() })).withConfig({ tags: ["nostream"] }).invoke(state.messages);const response = {id: uuidv4(),type: "ai",content: `Here's the weather for ${weather.city}`,};// Emit UI elements associated with the AI messageui.push({ name: "weather", props: weather }, { message: response });return { messages: [response] };}).addEdge("__start__", "weather").compile();

---

3、在React应用中处理UI元素

在客户端，你可以使用 useStream() 和 LoadExternalComponent 来展示UI元素。

src/app/page.tsx

"use client";import { useStream } from "@langchain/langgraph-sdk/react";
import { LoadExternalComponent } from "@langchain/langgraph-sdk/react-ui";export default function Page() {const { thread, values } = useStream({apiUrl: "http://localhost:2024",assistantId: "agent",});*  return (<div>{thread.messages.map((message) => (<div key={message.id}>{message.content}{values.ui?.filter((ui) => ui.metadata?.message_id === message.id).map((ui) => (<LoadExternalComponent key={ui.id} stream={thread} message={ui} />))}</div>))}</div>);
}

---

在幕后，LoadExternalComponent 会从 LangGraph 平台获取 UI 组件的 JS 和 CSS 文件，并在 shadow DOM 中渲染它们，从而确保这些组件的样式与应用程序的其他部分相互隔离。

---

操作指南

---

在客户端提供自定义组件

如果您的客户端应用中已加载了相关组件，可以直接提供一个组件映射表，无需从LangGraph平台获取UI代码即可直接渲染。

---

const clientComponents = {weather: WeatherComponent,
};<LoadExternalComponentstream={thread}message={ui}components={clientComponents}
/>;

---

组件加载时显示加载界面

当组件正在加载时，你可以提供一个备用界面进行渲染。

---

<LoadExternalComponentstream={thread}message={ui}fallback={<div>Loading...</div>}
/>

---

自定义 UI 组件的命名空间

默认情况下，LoadExternalComponent 会使用 useStream() 钩子中的 assistantId 来获取 UI 组件的代码。您可以通过向 LoadExternalComponent 组件提供 namespace 属性来自定义此行为。

src/app/page.tsx

---

<LoadExternalComponentstream={thread}message={ui}namespace="custom-namespace"
/>

---

langgraph.json

{"ui": {"custom-namespace": "./src/agent/ui.tsx"}
}

---

从UI组件访问和交互线程状态

您可以通过使用 useStreamContext 钩子在UI组件内部访问线程状态。

---

import { useStreamContext } from "@langchain/langgraph-sdk/react-ui";const WeatherComponent = (props: { city: string }) => {const { thread, submit } = useStreamContext();return (<><div>Weather for {props.city}</div><buttononClick={() => {const newMessage = {type: "human",content: `What's the weather in ${props.city}?`,};submit({ messages: [newMessage] });}}>Retry</button></>);
};

---

向客户端组件传递额外上下文

您可以通过向LoadExternalComponent组件提供meta属性，来向客户端组件传递额外的上下文信息。

---

<LoadExternalComponent stream={thread} message={ui} meta={{ userId: "123" }} />

---

然后，你可以通过使用 useStreamContext 钩子在 UI 组件中访问 meta 属性。

---

import { useStreamContext } from "@langchain/langgraph-sdk/react-ui";const WeatherComponent = (props: { city: string }) => {const { meta } = useStreamContext<{ city: string },{ MetaType: { userId?: string } }>();*  return (<div>Weather for {props.city} (user: {meta?.userId})</div>);
};

---

从服务器流式传输 UI 消息

您可以通过 useStream() 钩子的 onCustomEvent 回调，在节点执行完成前流式传输 UI 消息。这在 LLM 生成响应时更新 UI 组件特别有用。

---

import { uiMessageReducer } from "@langchain/langgraph-sdk/react-ui";const { thread, submit } = useStream({apiUrl: "http://localhost:2024",assistantId: "agent",onCustomEvent: (event, options) => {options.mutate((prev) => {const ui = uiMessageReducer(prev.ui ?? [], event);return { ...prev, ui };});},
});

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然后，您可以通过调用 ui.push() 或 push_ui_message() 并传入要更新的 UI 消息的相同 ID，来推送对该 UI 组件的更新。

Python

from typing import Annotated, Sequence, TypedDictfrom langchain_anthropic import ChatAnthropic
from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage
from langgraph.graph import StateGraph
from langgraph.graph.message import add_messages
from langgraph.graph.ui import AnyUIMessage, push_ui_message, ui_message_reducerclass AgentState(TypedDict):  # noqa: D101messages: Annotated[Sequence[BaseMessage], add_messages]ui: Annotated[Sequence[AnyUIMessage], ui_message_reducer]class CreateTextDocument(TypedDict):"""Prepare a document heading for the user."""title: strasync def writer_node(state: AgentState):model = ChatAnthropic(model="claude-3-5-sonnet-latest")message: AIMessage = await model.bind_tools(tools=[CreateTextDocument],tool_choice={"type": "tool", "name": "CreateTextDocument"},).ainvoke(state["messages"])tool_call = next((x["args"] for x in message.tool_calls if x["name"] == "CreateTextDocument"),None,)if tool_call:ui_message = push_ui_message("writer", tool_call, message=message)ui_message_id = ui_message["id"]# We're already streaming the LLM response to the client through UI messages# so we don't need to stream it again to the `messages` stream mode.content_stream = model.with_config({"tags": ["nostream"]}).astream(f"Create a document with the title: {tool_call['title']}")content: AIMessageChunk | None = Noneasync for chunk in content_stream:content = content + chunk if content else chunkpush_ui_message("writer",{"content": content.text()},id=ui_message_id,message=message,# Use `merge=rue` to merge props with the existing UI messagemerge=True,)return {"messages": [message]}

---

JS

import {Annotation,MessagesAnnotation,type LangGraphRunnableConfig,
} from "@langchain/langgraph";
import { z } from "zod";
import { ChatAnthropic } from "@langchain/anthropic";
import {typedUi,uiMessageReducer,
} from "@langchain/langgraph-sdk/react-ui/server";
import type { AIMessageChunk } from "@langchain/core/messages";import type ComponentMap from "./ui";const AgentState = Annotation.Root({...MessagesAnnotation.spec,ui: Annotation({ reducer: uiMessageReducer, default: () => [] }),
});async function writerNode(state: typeof AgentState.State,config: LangGraphRunnableConfig
): Promise<typeof AgentState.Update> {const ui = typedUi<typeof ComponentMap>(config);*  const model = new ChatAnthropic({ model: "claude-3-5-sonnet-latest" });const message = await model.bindTools([{name: "create_text_document",description: "Prepare a document heading for the user.",schema: z.object({ title: z.string() }),},],{ tool_choice: { type: "tool", name: "create_text_document" } }).invoke(state.messages);*  type ToolCall = { name: "create_text_document"; args: { title: string } };const toolCall = message.tool_calls?.find((tool): tool is ToolCall => tool.name === "create_text_document");*  if (toolCall) {const { id, name } = ui.push({ name: "writer", props: { title: toolCall.args.title } },{ message });const contentStream = await model// We're already streaming the LLM response to the client through UI messages// so we don't need to stream it again to the `messages` stream mode..withConfig({ tags: ["nostream"] }).stream(`Create a short poem with the topic: ${message.text}`);let content: AIMessageChunk | undefined;for await (const chunk of contentStream) {content = content?.concat(chunk) ?? chunk;ui.push({ id, name, props: { content: content?.text } },// Use `merge: true` to merge props with the existing UI message{ message, merge: true });}}*  return { messages: [message] };
}

---

ui.tsx

function WriterComponent(props: { title: string; content?: string }) {return (<article><h2>{props.title}</h2><p style={{ whiteSpace: "pre-wrap" }}>{props.content}</p></article>);
}export default {weather: WriterComponent,
};

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从状态中移除UI消息

类似于通过追加RemoveMessage从状态中移除消息的方式，您也可以通过调用remove_ui_message/ui.delete并传入UI消息的ID，从状态中移除UI消息。

---

from langgraph.graph.ui import push_ui_message, delete_ui_message# push message
message = push_ui_message("weather", {"city": "London"})# remove said message
delete_ui_message(message["id"])

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JS

// push message
const message = ui.push({ name: "weather", props: { city: "London" } });// remove said message
ui.delete(message.id);

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了解更多

• JS/TS SDK 参考文档

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