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代理系统。
添加子图时，需要定义父图和子图如何通信
设置¶
为 LangGraph 开发设置 LangSmith
注册 LangSmith，快速发现问题并提高 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——在此处了解更多入门信息。
共享状态模式¶
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，在多代理系统中，代理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图中
- 定义子图工作流（在下面的示例中为
subgraph_builder）并编译它 - 在定义父图工作流时，将已编译的子图传递给
.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)
- 此键与父图状态共享
- 此键是
SubgraphState的私有键，对父图不可见
```
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模式（没有共享键）的子图。例如，您可能希望为多代理系统中的每个代理保留私有消息历史。
如果您的应用程序属于这种情况，您需要定义一个调用子图的节点函数。该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()
- 将状态转换为子图状态
- 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)
- 将状态转换为子图状态
- 将响应转换回父状态
完整示例：不同状态模式（两级子图）
这是一个两级子图的示例：父 -> 子 -> 孙。
# 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)
- 我们将状态从子状态通道（
my_child_key）转换为孙子状态通道（my_grandchild_key） - 我们将状态从孙子状态通道（
my_grandchild_key）转换回子状态通道（my_child_key） - 我们在这里传递一个函数，而不是仅仅编译图（
grandchild_graph） - 我们将状态从父状态通道（
my_key）转换为子状态通道（my_child_key） - 我们将状态从子状态通道（
my_child_key）转换回父状态通道（my_key） - 我们在这里传递一个函数，而不是仅仅编译图（
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器。LangGraph 会自动将检查器传播到子子图。
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，您可以将其编译为with checkpointer=True。这在多代理系统中很有用，如果您希望代理跟踪其内部消息历史。
查看子图状态¶
当您启用持久化时，可以通过graph.get_state(config)检查图状态（检查点）。要查看子图状态，可以使用graph.get_state(config, subgraphs=True)。
仅在中断时可用
子图状态只能在子图中断时查看。一旦您恢复图，您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)
- 这仅在子图中断时可用。一旦您恢复图，您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。这将同时流式传输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)
- 设置
subgraphs=True以流式传输子图的输出。
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)
- 设置
subgraphs=True以流式传输子图的输出。