> ## Documentation Index > Fetch the complete documentation index at: https://cndoc-langchain.site/llms.txt > Use this file to discover all available pages before exploring further. # 子图 本指南解释了使用子图的机制。子图是一个被用作另一个图中[节点](/oss/python/langgraph/graph-api#nodes)的[图](/oss/python/langgraph/graph-api#graphs)。 子图适用于： * 构建[多智能体系统](/oss/python/langchain/multi-agent) * 在多个图中复用一组节点 * 分布式开发：当您希望不同团队独立处理图的不同部分时，可以将每个部分定义为一个子图，只要遵循子图接口（输入和输出模式），父图就可以在不了解子图任何细节的情况下构建。 ## 设置 ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U langgraph ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add langgraph ``` **为 LangGraph 开发设置 LangSmith** 注册 [LangSmith](https://smith.langchain.com) 以快速发现问题并提升 LangGraph 项目的性能。LangSmith 允许您使用跟踪数据来调试、测试和监控使用 LangGraph 构建的 LLM 应用——阅读更多关于[如何开始使用 LangSmith](https://docs.smith.langchain.com)的信息。 ## 定义子图通信 添加子图时，您需要定义父图和子图如何通信： | 模式 | 何时使用 | 状态模式 | | ------------------------------------------ | ---------------------------------------- | ---------------------------------- | | [在节点内调用子图](#call-a-subgraph-inside-a-node) | 父图和子图具有**不同的状态模式**（没有共享键），或者您需要在它们之间转换状态 | 您编写一个包装函数，将父状态映射到子图输入，并将子图输出映射回父状态 | | [将子图添加为节点](#add-a-subgraph-as-a-node) | 父图和子图**共享状态键**——子图从与父图相同的通道读取和写入 | 您将编译后的子图直接传递给 `add_node`——不需要包装函数 | ### 在节点内调用子图 当父图和子图具有**不同的状态模式**（没有共享键）时，在节点函数内调用子图。当您想在[多智能体](/oss/python/langchain/multi-agent)系统中为每个智能体保留私有消息历史时，这很常见。 节点函数在调用子图之前将父状态转换为子图状态，并在返回之前将结果转换回父状态。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from typing_extensions import TypedDict from langgraph.graph.state import StateGraph, START class SubgraphState(TypedDict): bar: str # Subgraph def subgraph_node_1(state: SubgraphState): return {"bar": "hi! " + state["bar"]} subgraph_builder = StateGraph(SubgraphState) subgraph_builder.add_node(subgraph_node_1) subgraph_builder.add_edge(START, "subgraph_node_1") subgraph = subgraph_builder.compile() # Parent graph class State(TypedDict): foo: str def call_subgraph(state: State): # Transform the state to the subgraph state subgraph_output = subgraph.invoke({"bar": state["foo"]}) # [!code highlight] # Transform response back to the parent state return {"foo": subgraph_output["bar"]} builder = StateGraph(State) builder.add_node("node_1", call_subgraph) builder.add_edge(START, "node_1") graph = builder.compile() ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from typing_extensions import TypedDict from langgraph.graph.state import StateGraph, START # Define subgraph class SubgraphState(TypedDict): # note that none of these keys are shared with the parent graph state bar: str baz: str def subgraph_node_1(state: SubgraphState): return {"baz": "baz"} def subgraph_node_2(state: SubgraphState): return {"bar": state["bar"] + state["baz"]} subgraph_builder = StateGraph(SubgraphState) subgraph_builder.add_node(subgraph_node_1) subgraph_builder.add_node(subgraph_node_2) subgraph_builder.add_edge(START, "subgraph_node_1") subgraph_builder.add_edge("subgraph_node_1", "subgraph_node_2") subgraph = subgraph_builder.compile() # Define parent graph class ParentState(TypedDict): foo: str def node_1(state: ParentState): return {"foo": "hi! " + state["foo"]} def node_2(state: ParentState): # Transform the state to the subgraph state response = subgraph.invoke({"bar": state["foo"]}) # Transform response back to the parent state return {"foo": response["bar"]} builder = StateGraph(ParentState) builder.add_node("node_1", node_1) builder.add_node("node_2", node_2) builder.add_edge(START, "node_1") builder.add_edge("node_1", "node_2") graph = builder.compile() for chunk in graph.stream({"foo": "foo"}, subgraphs=True, version="v2"): if chunk["type"] == "updates": print(chunk["ns"], chunk["data"]) ``` ``` () {'node_1': {'foo': 'hi! foo'}} ('node_2:577b710b-64ae-31fb-9455-6a4d4cc2b0b9',) {'subgraph_node_1': {'baz': 'baz'}} ('node_2:577b710b-64ae-31fb-9455-6a4d4cc2b0b9',) {'subgraph_node_2': {'bar': 'hi! foobaz'}} () {'node_2': {'foo': 'hi! foobaz'}} ``` 这是一个包含两级子图的示例：父图 -> 子图 -> 孙图。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # Grandchild graph from typing_extensions import TypedDict from langgraph.graph.state import StateGraph, START, END class GrandChildState(TypedDict): my_grandchild_key: str def grandchild_1(state: GrandChildState) -> GrandChildState: # NOTE: child or parent keys will not be accessible here return {"my_grandchild_key": state["my_grandchild_key"] + ", how are you"} grandchild = StateGraph(GrandChildState) grandchild.add_node("grandchild_1", grandchild_1) grandchild.add_edge(START, "grandchild_1") grandchild.add_edge("grandchild_1", END) grandchild_graph = grandchild.compile() # Child graph class ChildState(TypedDict): my_child_key: str def call_grandchild_graph(state: ChildState) -> ChildState: # NOTE: parent or grandchild keys won't be accessible here grandchild_graph_input = {"my_grandchild_key": state["my_child_key"]} grandchild_graph_output = grandchild_graph.invoke(grandchild_graph_input) return {"my_child_key": grandchild_graph_output["my_grandchild_key"] + " today?"} child = StateGraph(ChildState) # We're passing a function here instead of just compiled graph (`grandchild_graph`) child.add_node("child_1", call_grandchild_graph) child.add_edge(START, "child_1") child.add_edge("child_1", END) child_graph = child.compile() # Parent graph class ParentState(TypedDict): my_key: str def parent_1(state: ParentState) -> ParentState: # NOTE: child or grandchild keys won't be accessible here return {"my_key": "hi " + state["my_key"]} def parent_2(state: ParentState) -> ParentState: return {"my_key": state["my_key"] + " bye!"} def call_child_graph(state: ParentState) -> ParentState: child_graph_input = {"my_child_key": state["my_key"]} child_graph_output = child_graph.invoke(child_graph_input) return {"my_key": child_graph_output["my_child_key"]} parent = StateGraph(ParentState) parent.add_node("parent_1", parent_1) # We're passing a function here instead of just a compiled graph (`child_graph`) parent.add_node("child", call_child_graph) parent.add_node("parent_2", parent_2) parent.add_edge(START, "parent_1") parent.add_edge("parent_1", "child") parent.add_edge("child", "parent_2") parent.add_edge("parent_2", END) parent_graph = parent.compile() for chunk in parent_graph.stream({"my_key": "Bob"}, subgraphs=True, version="v2"): if chunk["type"] == "updates": print(chunk["ns"], chunk["data"]) ``` ``` () {'parent_1': {'my_key': 'hi Bob'}} ('child:2e26e9ce-602f-862c-aa66-1ea5a4655e3b', 'child_1:781bb3b1-3971-84ce-810b-acf819a03f9c') {'grandchild_1': {'my_grandchild_key': 'hi Bob, how are you'}} ('child:2e26e9ce-602f-862c-aa66-1ea5a4655e3b',) {'child_1': {'my_child_key': 'hi Bob, how are you today?'}} () {'child': {'my_key': 'hi Bob, how are you today?'}} () {'parent_2': {'my_key': 'hi Bob, how are you today? bye!'}} ``` ### 将子图添加为节点 当父图和子图**共享状态键**时，您可以将编译后的子图直接传递给 `add_node`。不需要包装函数——子图会自动从父状态通道读取和写入。例如，在[多智能体](/oss/python/langchain/multi-agent)系统中，智能体通常通过共享的 [messages](/oss/python/langgraph/graph-api#why-use-messages) 键进行通信。 SQL agent graph 如果您的子图与父图共享状态键，您可以按照以下步骤将其添加到图中： 1. 定义子图工作流（下面示例中的 `subgraph_builder`）并编译它 2. 在定义父图工作流时，将编译后的子图传递给 [`add_node`](https://reference.langchain.com/python/langgraph/graph/state/StateGraph/add_node) 方法 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from typing_extensions import TypedDict from langgraph.graph.state import StateGraph, START class State(TypedDict): foo: str # Subgraph def subgraph_node_1(state: State): return {"foo": "hi! " + state["foo"]} subgraph_builder = StateGraph(State) subgraph_builder.add_node(subgraph_node_1) subgraph_builder.add_edge(START, "subgraph_node_1") subgraph = subgraph_builder.compile() # Parent graph builder = StateGraph(State) builder.add_node("node_1", subgraph) # [!code highlight] builder.add_edge(START, "node_1") graph = builder.compile() ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from typing_extensions import TypedDict from langgraph.graph.state import StateGraph, START # Define subgraph class SubgraphState(TypedDict): foo: str # shared with parent graph state bar: str # private to SubgraphState def subgraph_node_1(state: SubgraphState): return {"bar": "bar"} def subgraph_node_2(state: SubgraphState): # note that this node is using a state key ('bar') that is only available in the subgraph # and is sending update on the shared state key ('foo') return {"foo": state["foo"] + state["bar"]} subgraph_builder = StateGraph(SubgraphState) subgraph_builder.add_node(subgraph_node_1) subgraph_builder.add_node(subgraph_node_2) subgraph_builder.add_edge(START, "subgraph_node_1") subgraph_builder.add_edge("subgraph_node_1", "subgraph_node_2") subgraph = subgraph_builder.compile() # Define parent graph class ParentState(TypedDict): foo: str def node_1(state: ParentState): return {"foo": "hi! " + state["foo"]} builder = StateGraph(ParentState) builder.add_node("node_1", node_1) builder.add_node("node_2", subgraph) builder.add_edge(START, "node_1") builder.add_edge("node_1", "node_2") graph = builder.compile() for chunk in graph.stream({"foo": "foo"}, version="v2"): if chunk["type"] == "updates": print(chunk["data"]) ``` ``` {'node_1': {'foo': 'hi! foo'}} {'node_2': {'foo': 'hi! foobar'}} ``` ## 子图持久化 当您使用子图时，需要决定其内部数据在调用之间如何处理。考虑一个委托给专业子智能体的客户支持机器人：“计费专家”子智能体是应该记住客户之前的问题，还是每次调用时都重新开始？ `.compile()` 上的 `checkpointer` 参数控制子图持久化： | 模式 | `checkpointer=` | 行为 | | ------------------------------- | --------------- | ------------------------------------------------------------------------------------------------------------------- | | [每次调用](#per-invocation-default) | `None`（默认） | 每次调用都重新开始，并继承父图的检查点以支持单次调用内的[中断](/oss/python/langgraph/interrupts)和[持久执行](/oss/python/langgraph/durable-execution)。 | | [每线程](#per-thread) | `True` | 状态在同一线程上的多次调用中累积。每次调用都从上次停止的地方继续。 | | [无状态](#stateless) | `False` | 完全没有检查点——像普通函数调用一样运行。没有中断或持久执行。 | 对于大多数应用程序，包括子智能体处理独立请求的[多智能体](/oss/python/langchain/multi-agent)系统，每次调用是正确的选择。当子智能体需要多轮对话记忆时（例如，一个在多次交互中构建上下文的研究助手），请使用每线程模式。 父图必须使用检查点编译，子图持久化功能（中断、状态检查、每线程记忆）才能工作。参见[持久化](/oss/python/langgraph/persistence)。 下面的示例使用 LangChain 的 [`create_agent`](https://reference.langchain.com/python/langchain/agents/factory/create_agent)，这是构建智能体的常用方式。`create_agent` 底层生成一个 [LangGraph 图](/oss/python/langgraph/graph-api)，因此所有子图持久化概念都直接适用。如果您使用原始的 LangGraph `StateGraph` 构建，相同的模式和配置选项也适用——详情请参阅 [Graph API](/oss/python/langgraph/graph-api)。 ### 有状态 有状态子图继承父图的检查点，这支持[中断](/oss/python/langgraph/interrupts)、[持久执行](/oss/python/langgraph/durable-execution)和状态检查。两种有状态模式在状态保留时长上有所不同。 #### 每次调用（默认） 这是大多数应用程序的推荐模式，包括子智能体作为工具调用的[多智能体](/oss/python/langchain/multi-agent)系统。它支持中断、[持久执行](/oss/python/langgraph/durable-execution)和并行调用，同时保持每次调用的隔离性。 当对子图的每次调用都是独立的，并且子智能体不需要记住之前调用的任何内容时，请使用每次调用持久化。这是最常见的模式，特别是对于处理一次性请求（如“查找此客户的订单”或“总结此文档”）的[多智能体](/oss/python/langchain/multi-agent)系统。 省略 `checkpointer` 或将其设置为 `None`。每次调用都重新开始，但在单次调用内，子图继承父图的检查点，并可以使用 `interrupt()` 暂停和恢复。 以下示例使用两个子智能体（水果专家、蔬菜专家）包装为外部智能体的工具： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain.agents import create_agent from langchain.tools import tool from langgraph.checkpoint.memory import MemorySaver from langgraph.types import Command, interrupt @tool def fruit_info(fruit_name: str) -> str: """Look up fruit info.""" return f"Info about {fruit_name}" @tool def veggie_info(veggie_name: str) -> str: """Look up veggie info.""" return f"Info about {veggie_name}" # Subagents - no checkpointer setting (inherits parent) fruit_agent = create_agent( model="gpt-5.4-mini", tools=[fruit_info], prompt="You are a fruit expert. Use the fruit_info tool. Respond in one sentence.", ) veggie_agent = create_agent( model="gpt-5.4-mini", tools=[veggie_info], prompt="You are a veggie expert. Use the veggie_info tool. Respond in one sentence.", ) # Wrap subagents as tools for the outer agent @tool def ask_fruit_expert(question: str) -> str: """Ask the fruit expert. Use for ALL fruit questions.""" response = fruit_agent.invoke( {"messages": [{"role": "user", "content": question}]}, ) return response["messages"][-1].content @tool def ask_veggie_expert(question: str) -> str: """Ask the veggie expert. Use for ALL veggie questions.""" response = veggie_agent.invoke( {"messages": [{"role": "user", "content": question}]}, ) return response["messages"][-1].content # Outer agent with checkpointer agent = create_agent( model="gpt-5.4-mini", tools=[ask_fruit_expert, ask_veggie_expert], prompt=( "You have two experts: ask_fruit_expert and ask_veggie_expert. " "ALWAYS delegate questions to the appropriate expert." ), checkpointer=MemorySaver(), ) ``` 每次调用都可以使用 `interrupt()` 暂停和恢复。在工具函数中添加 `interrupt()` 以要求用户批准后才能继续： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} @tool def fruit_info(fruit_name: str) -> str: """Look up fruit info.""" interrupt("continue?") # [!code highlight] return f"Info about {fruit_name}" ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} config = {"configurable": {"thread_id": "1"}} # Invoke - the subagent's tool calls interrupt() response = agent.invoke( {"messages": [{"role": "user", "content": "Tell me about apples"}]}, config=config, ) # response contains __interrupt__ # Resume - approve the interrupt response = agent.invoke(Command(resume=True), config=config) # [!code highlight] # Subagent message count: 4 ``` 每次调用都从全新的子智能体状态开始。子智能体不记得之前的调用： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} config = {"configurable": {"thread_id": "1"}} # First call response = agent.invoke( {"messages": [{"role": "user", "content": "Tell me about apples"}]}, config=config, ) # Subagent message count: 4 # Second call - subagent starts fresh, no memory of apples response = agent.invoke( {"messages": [{"role": "user", "content": "Now tell me about bananas"}]}, config=config, ) # Subagent message count: 4 (still fresh!) ``` 对同一子图的多次调用可以无冲突地工作，因为每次调用都有自己的检查点命名空间： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} config = {"configurable": {"thread_id": "1"}} # LLM calls ask_fruit_expert for both apples and bananas response = agent.invoke( {"messages": [{"role": "user", "content": "Tell me about apples and bananas"}]}, config=config, ) # Subagent message count: 4 (apples - fresh) # Subagent message count: 4 (bananas - fresh) ``` #### 每线程 当子智能体需要记住之前的交互时，请使用每线程持久化。例如，一个在多次交互中构建上下文的研究助手，或一个跟踪已编辑文件的编码助手。子智能体的对话历史和数据在同一线程上的多次调用中累积。每次调用都从上次停止的地方继续。 使用 `checkpointer=True` 编译以启用此行为。 每线程子图不支持并行工具调用。当 LLM 可以访问每线程子智能体作为工具时，它可能会尝试并行多次调用该工具（例如，同时向水果专家询问苹果和香蕉）。这会导致检查点冲突，因为两次调用都写入相同的命名空间。 下面的示例使用 LangChain 的 `ToolCallLimitMiddleware` 来防止这种情况。如果您使用纯 LangGraph `StateGraph` 构建，您需要自己防止并行工具调用——例如，通过配置模型禁用并行工具调用，或添加逻辑确保同一子图不会被并行多次调用。 以下示例使用一个使用 `checkpointer=True` 编译的水果专家子智能体： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain.agents import create_agent from langchain.agents.middleware import ToolCallLimitMiddleware from langchain.tools import tool from langgraph.checkpoint.memory import MemorySaver from langgraph.types import Command, interrupt @tool def fruit_info(fruit_name: str) -> str: """Look up fruit info.""" return f"Info about {fruit_name}" # Subagent with checkpointer=True for persistent state fruit_agent = create_agent( model="gpt-5.4-mini", tools=[fruit_info], prompt="You are a fruit expert. Use the fruit_info tool. Respond in one sentence.", checkpointer=True, # [!code highlight] ) # Wrap subagent as a tool for the outer agent @tool def ask_fruit_expert(question: str) -> str: """Ask the fruit expert. Use for ALL fruit questions.""" response = fruit_agent.invoke( {"messages": [{"role": "user", "content": question}]}, ) return response["messages"][-1].content # Outer agent with checkpointer # Use ToolCallLimitMiddleware to prevent parallel calls to per-thread subagents, # which would cause checkpoint conflicts. agent = create_agent( model="gpt-5.4-mini", tools=[ask_fruit_expert], prompt="You have a fruit expert. ALWAYS delegate fruit questions to ask_fruit_expert.", middleware=[ # [!code highlight] ToolCallLimitMiddleware(tool_name="ask_fruit_expert", run_limit=1), # [!code highlight] ], # [!code highlight] checkpointer=MemorySaver(), ) ``` 每线程子智能体与每次调用一样支持 `interrupt()`。在工具函数中添加 `interrupt()` 以要求用户批准： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} @tool def fruit_info(fruit_name: str) -> str: """Look up fruit info.""" interrupt("continue?") # [!code highlight] return f"Info about {fruit_name}" ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} config = {"configurable": {"thread_id": "1"}} # Invoke - the subagent's tool calls interrupt() response = agent.invoke( {"messages": [{"role": "user", "content": "Tell me about apples"}]}, config=config, ) # response contains __interrupt__ # Resume - approve the interrupt response = agent.invoke(Command(resume=True), config=config) # [!code highlight] # Subagent message count: 4 ``` 状态在多次调用中累积——子智能体记住过去的对话： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} config = {"configurable": {"thread_id": "1"}} # First call response = agent.invoke( {"messages": [{"role": "user", "content": "Tell me about apples"}]}, config=config, ) # Subagent message count: 4 # Second call - subagent REMEMBERS apples conversation response = agent.invoke( {"messages": [{"role": "user", "content": "Now tell me about bananas"}]}, config=config, ) # Subagent message count: 8 (accumulated!) ``` 当您有多个**不同的**每线程子图（例如，水果专家和蔬菜专家）时，每个子图都需要自己的存储空间，这样它们的检查点才不会相互覆盖。这称为**命名空间隔离**。 如果您[在节点内调用子图](#call-a-subgraph-inside-a-node)，LangGraph 会根据调用顺序（第一次调用、第二次调用等）分配命名空间。这意味着重新排序调用可能会混淆哪个子图加载哪个状态。为避免这种情况，请将每个子智能体包装在自己的 `StateGraph` 中，并使用唯一的节点名称——这为每个子图提供稳定、唯一的命名空间： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langgraph.graph import MessagesState, StateGraph def create_sub_agent(model, *, name, **kwargs): """Wrap an agent with a unique node name for namespace isolation.""" agent = create_agent(model=model, name=name, **kwargs) return ( StateGraph(MessagesState) .add_node(name, agent) # unique name → stable namespace # [!code highlight] .add_edge("__start__", name) .compile() ) fruit_agent = create_sub_agent( "gpt-5.4-mini", name="fruit_agent", tools=[fruit_info], prompt="...", checkpointer=True, ) veggie_agent = create_sub_agent( "gpt-5.4-mini", name="veggie_agent", tools=[veggie_info], prompt="...", checkpointer=True, ) config = {"configurable": {"thread_id": "1"}} # First call - LLM calls both fruit and veggie experts response = agent.invoke( {"messages": [{"role": "user", "content": "Tell me about cherries and broccoli"}]}, config=config, ) # Fruit subagent message count: 4 # Veggie subagent message count: 4 # Second call - both agents accumulate independently response = agent.invoke( {"messages": [{"role": "user", "content": "Now tell me about oranges and carrots"}]}, config=config, ) # Fruit subagent message count: 8 (remembers cherries!) # Veggie subagent message count: 8 (remembers broccoli!) ``` [添加为节点](#add-a-subgraph-as-a-node)的子图已经自动获得基于名称的命名空间，因此不需要此包装。 ### 无状态 当您希望像普通函数调用一样运行子智能体，没有检查点开销时，请使用此模式。子图无法暂停/恢复，也无法从[持久执行](/oss/python/langgraph/persistence)中受益。使用 `checkpointer=False` 编译。 没有检查点，子图就没有持久执行。如果进程在运行过程中崩溃，子图无法恢复，必须从头开始重新运行。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} subgraph_builder = StateGraph(...) subgraph = subgraph_builder.compile(checkpointer=False) # [!code highlight] ``` ### 检查点参考 使用 `.compile()` 上的 `checkpointer` 参数控制子图持久化： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} subgraph = builder.compile(checkpointer=False) # or True / None ``` | 功能 | 每次调用（默认） | 每线程 | 无状态 | | --------------- | ---------------------------------------------------------------------------------------- | --------------------------------------------------------------- | ------- | | `checkpointer=` | `None` | `True` | `False` | | 中断 (HITL) | ✅ | ✅ | ❌ | | 多轮对话记忆 | ❌ | ✅ | ❌ | | 多次调用（不同子图） | ✅ | ⚠️ | ✅ | | 多次调用（同一子图） | ✅ | ❌ | ✅ | | 状态检查 | ⚠️ | ✅ | ❌ | * **中断 (HITL)**：子图可以使用 [interrupt()](/oss/python/langgraph/interrupts) 暂停执行并等待用户输入，然后从停止的地方恢复。 * **多轮对话记忆**：子图在同一线程内的多次调用中保留其状态。每次调用都从上次停止的地方继续，而不是重新开始。 * **多次调用（不同子图）**：可以在单个节点内调用多个不同的子图实例，而不会发生检查点命名空间冲突。 * **多次调用（同一子图）**：可以在单个节点内多次调用同一子图实例。使用有状态持久化时，这些调用会写入相同的检查点命名空间并发生冲突——请改用每次调用持久化。 * **状态检查**：子图的状态可通过 `get_state(config, subgraphs=True)` 用于调试和监控。 ## 查看子图状态 当您启用[持久化](/oss/python/langgraph/persistence)时，可以使用 subgraphs 选项检查子图状态。使用[无状态](#stateless)检查点（`checkpointer=False`）时，不会保存子图检查点，因此子图状态不可用。 查看子图状态要求 LangGraph 能够**静态发现**子图——即它被[添加为节点](#add-a-subgraph-as-a-node)或[在节点内调用](#call-a-subgraph-inside-a-node)。当子图在[工具](/oss/python/langchain/tools)函数或其他间接方式（例如 [subagents](/oss/python/langchain/multi-agent/subagents) 模式）内调用时，此方法无效。无论嵌套如何，中断仍然会传播到顶层图。 仅返回**当前调用**的子图状态。每次调用都重新开始。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langgraph.graph import START, StateGraph from langgraph.checkpoint.memory import MemorySaver from langgraph.types import interrupt, Command from typing_extensions import TypedDict class State(TypedDict): foo: str # Subgraph def subgraph_node_1(state: State): value = interrupt("Provide value:") return {"foo": state["foo"] + value} subgraph_builder = StateGraph(State) subgraph_builder.add_node(subgraph_node_1) subgraph_builder.add_edge(START, "subgraph_node_1") subgraph = subgraph_builder.compile() # inherits parent checkpointer # Parent graph builder = StateGraph(State) builder.add_node("node_1", subgraph) builder.add_edge(START, "node_1") checkpointer = MemorySaver() graph = builder.compile(checkpointer=checkpointer) config = {"configurable": {"thread_id": "1"}} graph.invoke({"foo": ""}, config) # View subgraph state for the current invocation subgraph_state = graph.get_state(config, subgraphs=True).tasks[0].state # [!code highlight] # Resume the subgraph graph.invoke(Command(resume="bar"), config) ``` 返回此线程上所有调用的**累积**子图状态。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langgraph.graph import START, StateGraph, MessagesState from langgraph.checkpoint.memory import MemorySaver # Subgraph with its own persistent state subgraph_builder = StateGraph(MessagesState) # ... add nodes and edges subgraph = subgraph_builder.compile(checkpointer=True) # [!code highlight] # Parent graph builder = StateGraph(MessagesState) builder.add_node("agent", subgraph) builder.add_edge(START, "agent") checkpointer = MemorySaver() graph = builder.compile(checkpointer=checkpointer) config = {"configurable": {"thread_id": "1"}} graph.invoke({"messages": [{"role": "user", "content": "hi"}]}, config) graph.invoke({"messages": [{"role": "user", "content": "what did I say?"}]}, config) # View accumulated subgraph state (includes messages from both invocations) subgraph_state = graph.get_state(config, subgraphs=True).tasks[0].state # [!code highlight] ``` ## 流式传输子图输出 要将子图的输出包含在流式输出中，可以在父图的 stream 方法中设置 subgraphs 选项。这将流式传输父图和任何子图的输出。 使用 `version="v2"` 时，子图事件使用相同的 `StreamPart` 格式。`ns` 字段标识源图： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} for chunk in graph.stream( {"foo": "foo"}, subgraphs=True, # [!code highlight] stream_mode="updates", version="v2", # [!code highlight] ): print(chunk["type"]) # "updates" print(chunk["ns"]) # () for root, ("node_2:",) for subgraph print(chunk["data"]) # {"node_name": {"key": "value"}} ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} for chunk in graph.stream( {"foo": "foo"}, subgraphs=True, # [!code highlight] stream_mode="updates", ): print(chunk) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from typing_extensions import TypedDict from langgraph.graph.state import StateGraph, START # Define subgraph class SubgraphState(TypedDict): foo: str bar: str def subgraph_node_1(state: SubgraphState): return {"bar": "bar"} def subgraph_node_2(state: SubgraphState): # note that this node is using a state key ('bar') that is only available in the subgraph # and is sending update on the shared state key ('foo') return {"foo": state["foo"] + state["bar"]} subgraph_builder = StateGraph(SubgraphState) subgraph_builder.add_node(subgraph_node_1) subgraph_builder.add_node(subgraph_node_2) subgraph_builder.add_edge(START, "subgraph_node_1") subgraph_builder.add_edge("subgraph_node_1", "subgraph_node_2") subgraph = subgraph_builder.compile() # Define parent graph class ParentState(TypedDict): foo: str def node_1(state: ParentState): return {"foo": "hi! " + state["foo"]} builder = StateGraph(ParentState) builder.add_node("node_1", node_1) builder.add_node("node_2", subgraph) builder.add_edge(START, "node_1") builder.add_edge("node_1", "node_2") graph = builder.compile() for chunk in graph.stream( {"foo": "foo"}, stream_mode="updates", subgraphs=True, # [!code highlight] version="v2", # [!code highlight] ): if chunk["type"] == "updates": print(chunk["ns"], chunk["data"]) ``` ``` () {'node_1': {'foo': 'hi! foo'}} ('node_2:e58e5673-a661-ebb0-70d4-e298a7fc28b7',) {'subgraph_node_1': {'bar': 'bar'}} ('node_2:e58e5673-a661-ebb0-70d4-e298a7fc28b7',) {'subgraph_node_2': {'foo': 'hi! foobar'}} () {'node_2': {'foo': 'hi! foobar'}} ``` ***
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