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使用子图

本指南解释了使用子图的机制。子图的一个常见应用是构建多智能体系统。

添加子图时，您需要定义父图和子图如何进行通信

设置

pip install -U langgraph

为LangGraph开发设置LangSmith

注册LangSmith以快速发现问题并提高您的LangGraph项目的性能。LangSmith允许您使用跟踪数据来调试、测试和监控您使用LangGraph构建的LLM应用程序——了解更多如何开始使用这里。

共享状态模式

一个常见情况是父图和子图通过模式中的共享状态键（通道）进行通信。例如，在多智能体系统中，智能体通常通过共享的消息键进行通信。

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您的图中

  1. 定义子图工作流（在下面的示例中为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方法

API 参考：StateGraph

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)
builder.add_edge(START, "node_1")
graph = builder.compile()
完整示例：共享状态模式 
from typing_extensions import TypedDict
from langgraph.graph.state import StateGraph, START

# Define subgraph
class SubgraphState(TypedDict):
    foo: str  # (1)! 
    bar: str  # (2)!

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"}):
    print(chunk)
  1. 此键与父图状态共享
  2. 此键对于SubgraphState是私有的，对父图不可见
{'node_1': {'foo': 'hi! foo'}}
{'node_2': {'foo': 'hi! foobar'}}

不同状态模式

对于更复杂的系统，您可能希望定义与父图完全不同的模式（无共享键）的子图。例如，您可能希望为多智能体系统中的每个智能体保留私有消息历史。

如果您的应用程序属于这种情况，您需要定义一个节点用于调用子图的函数。此函数需要在调用子图之前将输入（父）状态转换为子图状态，并在从节点返回状态更新之前将结果转换回父状态。

API 参考：StateGraph

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):
    subgraph_output = subgraph.invoke({"bar": state["foo"]})  # (1)!
    return {"foo": subgraph_output["bar"]}  # (2)!

builder = StateGraph(State)
builder.add_node("node_1", call_subgraph)
builder.add_edge(START, "node_1")
graph = builder.compile()
  1. 将状态转换为子图状态
  2. 将响应转换回父状态
完整示例：不同状态模式 
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):
    response = subgraph.invoke({"bar": state["foo"]})  # (1)!
    return {"foo": response["bar"]}  # (2)!


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):
    print(chunk)
  1. 将状态转换为子图状态
  2. 将响应转换回父状态
((), {'node_1': {'foo': 'hi! foo'}})
(('node_2:9c36dd0f-151a-cb42-cbad-fa2f851f9ab7',), {'grandchild_1': {'my_grandchild_key': 'hi Bob, how are you'}})
(('node_2:9c36dd0f-151a-cb42-cbad-fa2f851f9ab7',), {'grandchild_2': {'bar': 'hi! foobaz'}})
((), {'node_2': {'foo': 'hi! foobaz'}})
完整示例：不同状态模式（两层子图）

这是一个包含两层子图的示例：父图 -> 子图 -> 孙图。

# 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"]}  # (1)!
    grandchild_graph_output = grandchild_graph.invoke(grandchild_graph_input)
    return {"my_child_key": grandchild_graph_output["my_grandchild_key"] + " today?"}  # (2)!

child = StateGraph(ChildState)
child.add_node("child_1", call_grandchild_graph)  # (3)!
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"]}  # (4)!
    child_graph_output = child_graph.invoke(child_graph_input)
    return {"my_key": child_graph_output["my_child_key"]}  # (5)!

parent = StateGraph(ParentState)
parent.add_node("parent_1", parent_1)
parent.add_node("child", call_child_graph)  # (6)!
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):
    print(chunk)
  1. 我们正在将状态从子状态通道（my_child_key）转换为孙状态通道（my_grandchild_key）
  2. 我们正在将状态从孙状态通道（my_grandchild_key）转换回子状态通道（my_child_key）
  3. 我们在这里传递一个函数，而不仅仅是编译后的图（grandchild_graph）
  4. 我们正在将状态从父状态通道（my_key）转换为子状态通道（my_child_key）
  5. 我们正在将状态从子状态通道（my_child_key）转换回父状态通道（my_key）
  6. 我们在这里传递一个函数，而不仅仅是编译后的图（child_graph）
((), {'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!'}})

添加持久化

您只需在编译父图时提供检查点器。LangGraph会自动将检查点器传播到子图。

API参考：START | StateGraph | InMemorySaver

from langgraph.graph import START, StateGraph
from langgraph.checkpoint.memory import InMemorySaver
from typing_extensions import TypedDict

class State(TypedDict):
    foo: str

# Subgraph

def subgraph_node_1(state: State):
    return {"foo": state["foo"] + "bar"}

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)
builder.add_edge(START, "node_1")

checkpointer = InMemorySaver()
graph = builder.compile(checkpointer=checkpointer)

如果您希望子图拥有自己的内存，您可以使用with checkpointer=True编译它。这在多智能体系统中很有用，如果您希望智能体跟踪其内部消息历史。

subgraph_builder = StateGraph(...)
subgraph = subgraph_builder.compile(checkpointer=True)

查看子图状态

启用持久化后，您可以通过graph.get_state(config)检查图状态（检查点）。要查看子图状态，您可以使用graph.get_state(config, subgraphs=True)。

仅在中断时可用

子图状态仅在子图中断时可见。一旦您恢复图，将无法访问子图状态。

查看中断的子图状态 
from langgraph.graph import START, StateGraph
from langgraph.checkpoint.memory import InMemorySaver
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()

# Parent graph

builder = StateGraph(State)
builder.add_node("node_1", subgraph)
builder.add_edge(START, "node_1")

checkpointer = InMemorySaver()
graph = builder.compile(checkpointer=checkpointer)

config = {"configurable": {"thread_id": "1"}}

graph.invoke({"foo": ""}, config)
parent_state = graph.get_state(config)
subgraph_state = graph.get_state(config, subgraphs=True).tasks[0].state  # (1)!

# resume the subgraph
graph.invoke(Command(resume="bar"), config)
  1. 此功能仅在子图中断时可用。一旦您恢复图，将无法访问子图状态。

流式传输子图输出

要在流式输出中包含来自子图的输出，您可以在父图的.stream()方法中设置subgraphs=True。这将流式传输来自父图和任何子图的输出。

for chunk in graph.stream(
    {"foo": "foo"},
    subgraphs=True, # (1)!
    stream_mode="updates",
):
    print(chunk)
  1. 设置subgraphs=True以从子图流式传输输出。
从子图流式传输 
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, # (1)!
):
    print(chunk)
  1. 设置subgraphs=True以从子图流式传输输出。

``` ((), {'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'}})