自定义状态¶

在本教程中，您将向状态添加其他字段，以定义复杂的行为，而不依赖消息列表。聊天机器人将使用其搜索工具查找特定信息，并将它们转发给人工审核。

Note

1. 向状态添加键¶

通过向状态添加 name 和 birthday 键，更新聊天机器人，使其能够研究实体的生日：

^{API Reference: add_messages}

from typing import Annotated

from typing_extensions import TypedDict

from langgraph.graph.message import add_messages


class State(TypedDict):
    messages: Annotated[list, add_messages]
    name: str
    birthday: str

将此信息添加到状态可以使其易于被其他图节点（如存储或处理信息的下游节点）以及图的持久化层访问。

2. 在工具内更新状态¶

现在，在 human_assistance 工具内填充状态键。这允许人工在信息存储到状态之前对其进行审核。使用 Command 从工具内部发出状态更新。

^{API Reference: ToolMessage | InjectedToolCallId | tool | Command | interrupt}

from langchain_core.messages import ToolMessage
from langchain_core.tools import InjectedToolCallId, tool

from langgraph.types import Command, interrupt

@tool
# 请注意，由于我们正在为状态更新生成 ToolMessage，
# 因此我们通常需要相应地工具调用的 ID。我们可以使用
# LangChain 的 InjectedToolCallId 来指示此参数不应在工具的
# schema 中向模型显示。
def human_assistance(
    name: str, birthday: str, tool_call_id: Annotated[str, InjectedToolCallId]
) -> str:
    """请求人工协助。"""
    human_response = interrupt(
        {
            "question": "Is this correct?",
            "name": name,
            "birthday": birthday,
        },
    )
    # 如果信息正确，则按原样更新状态。
    if human_response.get("correct", "").lower().startswith("y"):
        verified_name = name
        verified_birthday = birthday
        response = "Correct"
    # 否则，接收来自人工审阅者的信息。
    else:
        verified_name = human_response.get("name", name)
        verified_birthday = human_response.get("birthday", birthday)
        response = f"Made a correction: {human_response}"

    # 这次我们显式地使用工具内部的 ToolMessage 来更新状态。
    state_update = {
        "name": verified_name,
        "birthday": verified_birthday,
        "messages": [ToolMessage(response, tool_call_id=tool_call_id)],
    }
    # 我们在工具中返回一个 Command 对象来更新我们的状态。
    return Command(update=state_update)

图的其余部分保持不变。

3. 提示聊天机器人¶

提示聊天机器人查找 LangGraph 库的“生日”，并指示聊天机器人在获取所需信息后联系 human_assistance 工具。通过为工具的参数设置 name 和 birthday，您可以强制聊天机器人为这些字段生成提案。

user_input = (
    "Can you look up when LangGraph was released? "
    "When you have the answer, use the human_assistance tool for review."
)
config = {"configurable": {"thread_id": "1"}}

events = graph.stream(
    {"messages": [{"role": "user", "content": user_input}]},
    config,
    stream_mode="values",
)
for event in events:
    if "messages" in event:
        event["messages"][-1].pretty_print()

================================ Human Message =================================

Can you look up when LangGraph was released? When you have the answer, use the human_assistance tool for review.
================================== Ai Message ==================================

[{'text': "Certainly! I'll start by searching for information about LangGraph's release date using the Tavily search function. Then, I'll use the human_assistance tool for review.", 'type': 'text'}, {'id': 'toolu_01JoXQPgTVJXiuma8xMVwqAi', 'input': {'query': 'LangGraph release date'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
Tool Calls:
  tavily_search_results_json (toolu_01JoXQPgTVJXiuma8xMVwqAi)
 Call ID: toolu_01JoXQPgTVJXiuma8xMVwqAi
  Args:
    query: LangGraph release date
================================= Tool Message =================================
Name: tavily_search_results_json

[{"url": "https://blog.langchain.dev/langgraph-cloud/", "content": "We also have a new stable release of LangGraph. By LangChain 6 min read Jun 27, 2024 (Oct '24) Edit: Since the launch of LangGraph Platform, we now have multiple deployment options alongside LangGraph Studio - which now fall under Langgraph Platform. LangGraph Platform is synonymous with our Cloud SaaS deployment option."}, {"url": "https://changelog.langchain.com/announcements/langgraph-cloud-deploy-at-scale-monitor-carefully-iterate-boldly", "content": "LangChain - Changelog | ☁ 🚀 LangGraph Platform: Deploy at scale, monitor LangChain LangSmith LangGraph LangChain LangSmith LangGraph LangChain LangSmith LangGraph LangChain Changelog Sign up for our newsletter to stay up to date DATE: The LangChain Team LangGraph LangGraph Platform ☁ 🚀 LangGraph Platform: Deploy at scale, monitor carefully, iterate boldly DATE: June 27, 2024 AUTHOR: The LangChain Team LangGraph Platform is now in closed beta, offering scalable, fault-tolerant deployment for LangGraph agents. LangGraph Platform also includes a new playground-like studio for debugging agent failure modes and quick iteration: Join the waitlist today for LangGraph Platform. And to learn more, read our blog post announcement or check out our docs. Subscribe By clicking subscribe, you accept our privacy policy and terms and conditions."}]
================================== Ai Message ==================================

[{'text': "Based on the search results, it appears that LangGraph was already in existence before June 27, 2024, when LangGraph Platform was announced. However, the search results don't provide a specific release date for the original LangGraph. \n\nGiven this information, I'll use the human_assistance tool to review and potentially provide more accurate information about LangGraph's initial release date.", 'type': 'text'}, {'id': 'toolu_01JDQAV7nPqMkHHhNs3j3XoN', 'input': {'name': 'Assistant', 'birthday': '2023-01-01'}, 'name': 'human_assistance', 'type': 'tool_use'}]
Tool Calls:
  human_assistance (toolu_01JDQAV7nPqMkHHhNs3j3XoN)
 Call ID: toolu_01JDQAV7nPqMkHHhNs3j3XoN
  Args:
    name: Assistant
    birthday: 2023-01-01

我们再次触发了 human_assistance 工具中的 interrupt。

4. 添加人工协助¶

聊天机器人未能识别正确的日期，因此请为其提供信息：

human_command = Command(
    resume={
        "name": "LangGraph",
        "birthday": "Jan 17, 2024",
    },
)

events = graph.stream(human_command, config, stream_mode="values")
for event in events:
    if "messages" in event:
        event["messages"][-1].pretty_print()

================================== Ai Message ==================================

[{'text': "Based on the search results, it appears that LangGraph was already in existence before June 27, 2024, when LangGraph Platform was announced. However, the search results don't provide a specific release date for the original LangGraph. \n\nGiven this information, I'll use the human_assistance tool to review and potentially provide more accurate information about LangGraph's initial release date.", 'type': 'text'}, {'id': 'toolu_01JDQAV7nPqMkHHhNs3j3XoN', 'input': {'name': 'Assistant', 'birthday': '2023-01-01'}, 'name': 'human_assistance', 'type': 'tool_use'}]
Tool Calls:
  human_assistance (toolu_01JDQAV7nPqMkHHhNs3j3XoN)
 Call ID: toolu_01JDQAV7nPqMkHHhNs3j3XoN
  Args:
    name: Assistant
    birthday: 2023-01-01
================================= Tool Message =================================
Name: human_assistance

Made a correction: {'name': 'LangGraph', 'birthday': 'Jan 17, 2024'}
================================== Ai Message ==================================

Thank you for the human assistance. I can now provide you with the correct information about LangGraph's release date.

LangGraph was initially released on January 17, 2024. This information comes from the human assistance correction, which is more accurate than the search results I initially found.

To summarize:
1. LangGraph's original release date: January 17, 2024
2. LangGraph Platform announcement: June 27, 2024

It's worth noting that LangGraph had been in development and use for some time before the LangGraph Platform announcement, but the official initial release of LangGraph itself was on January 17, 2024.

请注意，这些字段现在已反映在状态中：

snapshot = graph.get_state(config)

{k: v for k, v in snapshot.values.items() if k in ("name", "birthday")}

{'name': 'LangGraph', 'birthday': 'Jan 17, 2024'}

这使得它们可以轻松地被下游节点访问（例如，进一步处理或存储信息的节点）。

5. 手动更新状态¶

LangGraph 对应用程序状态提供了高度的控制。例如，在任何时候（包括中断时），您都可以使用 graph.update_state 手动覆盖一个键：

graph.update_state(config, {"name": "LangGraph (library)"})

{'configurable': {'thread_id': '1',
  'checkpoint_ns': '',
  'checkpoint_id': '1efd4ec5-cf69-6352-8006-9278f1730162'}}

6. 查看新值¶

如果调用 graph.get_state，您可以看到新值已反映出来：

snapshot = graph.get_state(config)

{k: v for k, v in snapshot.values.items() if k in ("name", "birthday")}

{'name': 'LangGraph (library)', 'birthday': 'Jan 17, 2024'}

手动状态更新将生成一个跟踪在 LangSmith 中。如果需要，它们也可以用于控制人工干预工作流。通常建议使用 interrupt 函数，因为它允许数据在人工干预交互中独立于状态更新进行传输。

恭喜！ 您已向状态添加了自定义键以促进更复杂的工作流，并学习了如何从工具内部生成状态更新。

查看下面的代码片段以回顾本教程中的图：

OpenAIAnthropicAzureGoogle GeminiAWS Bedrock

pip install -U "langchain[openai]"

import os
from langchain.chat_models import init_chat_model

os.environ["OPENAI_API_KEY"] = "sk-..."

llm = init_chat_model("openai:gpt-4.1")

👉 阅读 OpenAI 集成文档

pip install -U "langchain[anthropic]"

import os
from langchain.chat_models import init_chat_model

os.environ["ANTHROPIC_API_KEY"] = "sk-..."

llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")

👉 阅读 Anthropic 集成文档

pip install -U "langchain[openai]"

import os
from langchain.chat_models import init_chat_model

os.environ["AZURE_OPENAI_API_KEY"] = "..."
os.environ["AZURE_OPENAI_ENDPOINT"] = "..."
os.environ["OPENAI_API_VERSION"] = "2025-03-01-preview"

llm = init_chat_model(
    "azure_openai:gpt-4.1",
    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
)

👉 阅读 Azure 集成文档

pip install -U "langchain[google-genai]"

import os
from langchain.chat_models import init_chat_model

os.environ["GOOGLE_API_KEY"] = "..."

llm = init_chat_model("google_genai:gemini-2.0-flash")

👉 阅读 Google GenAI 集成文档

pip install -U "langchain[aws]"

from langchain.chat_models import init_chat_model

# Follow the steps here to configure your credentials:
# https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html

llm = init_chat_model(
    "anthropic.claude-3-5-sonnet-20240620-v1:0",
    model_provider="bedrock_converse",
)

👉 阅读 AWS Bedrock 集成文档

^{API Reference: TavilySearch | ToolMessage | InjectedToolCallId | tool | MemorySaver | StateGraph | START | END | add_messages | ToolNode | tools_condition | Command | interrupt}

from typing import Annotated

from langchain_tavily import TavilySearch
from langchain_core.messages import ToolMessage
from langchain_core.tools import InjectedToolCallId, tool
from typing_extensions import TypedDict

from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition
from langgraph.types import Command, interrupt

class State(TypedDict):
    messages: Annotated[list, add_messages]
    name: str
    birthday: str

@tool
def human_assistance(
    name: str, birthday: str, tool_call_id: Annotated[str, InjectedToolCallId]
) -> str:
    """Request assistance from a human."""
    human_response = interrupt(
        {
            "question": "Is this correct?",
            "name": name,
            "birthday": birthday,
        },
    )
    if human_response.get("correct", "").lower().startswith("y"):
        verified_name = name
        verified_birthday = birthday
        response = "Correct"
    else:
        verified_name = human_response.get("name", name)
        verified_birthday = human_response.get("birthday", birthday)
        response = f"Made a correction: {human_response}"

    state_update = {
        "name": verified_name,
        "birthday": verified_birthday,
        "messages": [ToolMessage(response, tool_call_id=tool_call_id)],
    }
    return Command(update=state_update)


tool = TavilySearch(max_results=2)
tools = [tool, human_assistance]
llm_with_tools = llm.bind_tools(tools)

def chatbot(state: State):
    message = llm_with_tools.invoke(state["messages"])
    assert(len(message.tool_calls) <= 1)
    return {"messages": [message]}

graph_builder = StateGraph(State)
graph_builder.add_node("chatbot", chatbot)

tool_node = ToolNode(tools=tools)
graph_builder.add_node("tools", tool_node)

graph_builder.add_conditional_edges(
    "chatbot",
    tools_condition,
)
graph_builder.add_edge("tools", "chatbot")
graph_builder.add_edge(START, "chatbot")

memory = MemorySaver()
graph = graph_builder.compile(checkpointer=memory)

后续步骤¶

在完成 LangGraph 基础教程之前，还有一个概念需要回顾：将 checkpointing 和 state updates 连接到时间旅行。