添加持久化¶

许多 AI 应用需要内存来在多次交互中共享上下文。LangGraph 支持两种对于构建对话代理至关重要的内存类型：

短期记忆: 通过在会话中维护消息历史记录来跟踪正在进行的对话。
长期记忆: 跨会话存储用户特定或应用级别的数据。

术语

在 LangGraph 中：

短期记忆 也称为 线程级内存。

长期记忆 也称为 跨线程内存。

线程表示由相同的 thread_id 分组的相关运行序列。

添加短期记忆¶

**短期**记忆（线程级持久性）使代理能够跟踪多轮对话。要添加短期记忆：

^{API Reference: init_chat_model | StateGraph | START | InMemorySaver}

from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START

from langgraph.checkpoint.memory import InMemorySaver

model = init_chat_model(model="anthropic:claude-3-5-haiku-latest")


def call_model(state: MessagesState):
    response = model.invoke(state["messages"])
    return {"messages": response}


builder = StateGraph(MessagesState)
builder.add_node(call_model)
builder.add_edge(START, "call_model")

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

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

for chunk in graph.stream(
    {"messages": [{"role": "user", "content": "hi! I'm bob"}]},
    config,
    stream_mode="values",
):
    chunk["messages"][-1].pretty_print()

for chunk in graph.stream(
    {"messages": [{"role": "user", "content": "what's my name?"}]},
    config,
    stream_mode="values",
):
    chunk["messages"][-1].pretty_print()

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

hi! I'm bob
================================== Ai Message ==================================

Hi Bob! How are you doing today? Is there anything I can help you with?
================================ Human Message =================================

what's my name?
================================== Ai Message ==================================

Your name is Bob.

LangGraph API用户无需此操作

如果您使用的是LangGraph API，**无需**在编译图时提供checkpointer。API会自动为您处理检查点。

在生产环境中使用¶

在生产环境中，你可能希望使用一个由数据库支持的 checkpointer：

^{API Reference: PostgresSaver}

from langgraph.checkpoint.postgres import PostgresSaver

DB_URI = "postgresql://postgres:postgres@localhost:5442/postgres?sslmode=disable"
with PostgresSaver.from_conn_string(DB_URI) as checkpointer:
    builder = StateGraph(...)
    graph = builder.compile(checkpointer=checkpointer)

示例：使用 Postgres checkpointer

pip install -U "psycopg[binary,pool]" langgraph langgraph-checkpoint-postgres

Setup

第一次使用 Postgres checkpointer 时，你需要调用 checkpointer.setup()

同步异步

from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.postgres import PostgresSaver

model = init_chat_model(model="anthropic:claude-3-5-haiku-latest")

DB_URI = "postgresql://postgres:postgres@localhost:5442/postgres?sslmode=disable"
with PostgresSaver.from_conn_string(DB_URI) as checkpointer:
    # checkpointer.setup()

    def call_model(state: MessagesState):
        response = model.invoke(state["messages"])
        return {"messages": response}

    builder = StateGraph(MessagesState)
    builder.add_node(call_model)
    builder.add_edge(START, "call_model")

    graph = builder.compile(checkpointer=checkpointer)

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

    for chunk in graph.stream(
        {"messages": [{"role": "user", "content": "hi! I'm bob"}]},
        config,
        stream_mode="values"
    ):
        chunk["messages"][-1].pretty_print()

    for chunk in graph.stream(
        {"messages": [{"role": "user", "content": "what's my name?"}]},
        config,
        stream_mode="values"
    ):
        chunk["messages"][-1].pretty_print()

from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver

model = init_chat_model(model="anthropic:claude-3-5-haiku-latest")

DB_URI = "postgresql://postgres:postgres@localhost:5442/postgres?sslmode=disable"
async with AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer:
    # await checkpointer.setup()

    async def call_model(state: MessagesState):
        response = await model.ainvoke(state["messages"])
        return {"messages": response}

    builder = StateGraph(MessagesState)
    builder.add_node(call_model)
    builder.add_edge(START, "call_model")

    graph = builder.compile(checkpointer=checkpointer)

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

    async for chunk in graph.astream(
        {"messages": [{"role": "user", "content": "hi! I'm bob"}]},
        config,
        stream_mode="values"
    ):
        chunk["messages"][-1].pretty_print()

    async for chunk in graph.astream(
        {"messages": [{"role": "user", "content": "what's my name?"}]},
        config,
        stream_mode="values"
    ):
        chunk["messages"][-1].pretty_print()

示例：使用 MongoDB checkpointer

pip install -U pymongo langgraph langgraph-checkpoint-mongodb

设置

要使用 MongoDB checkpointer，你需要一个 MongoDB 集群。如果你还没有一个，请按照这个指南创建一个集群。

同步异步

from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.mongodb import MongoDBSaver

model = init_chat_model(model="anthropic:claude-3-5-haiku-latest")

DB_URI = "localhost:27017"
with MongoDBSaver.from_conn_string(DB_URI) as checkpointer:

    def call_model(state: MessagesState):
        response = model.invoke(state["messages"])
        return {"messages": response}

    builder = StateGraph(MessagesState)
    builder.add_node(call_model)
    builder.add_edge(START, "call_model")

    graph = builder.compile(checkpointer=checkpointer)

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

    for chunk in graph.stream(
        {"messages": [{"role": "user", "content": "hi! I'm bob"}]},
        config,
        stream_mode="values"
    ):
        chunk["messages"][-1].pretty_print()

    for chunk in graph.stream(
        {"messages": [{"role": "user", "content": "what's my name?"}]},
        config,
        stream_mode="values"
    ):
        chunk["messages"][-1].pretty_print()

from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.mongodb.aio import AsyncMongoDBSaver

model = init_chat_model(model="anthropic:claude-3-5-haiku-latest")

DB_URI = "localhost:27017"
async with AsyncMongoDBSaver.from_conn_string(DB_URI) as checkpointer:

    async def call_model(state: MessagesState):
        response = await model.ainvoke(state["messages"])
        return {"messages": response}

    builder = StateGraph(MessagesState)
    builder.add_node(call_model)
    builder.add_edge(START, "call_model")

    graph = builder.compile(checkpointer=checkpointer)

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

    async for chunk in graph.astream(
        {"messages": [{"role": "user", "content": "hi! I'm bob"}]},
        config,
        stream_mode="values"
    ):
        chunk["messages"][-1].pretty_print()

    async for chunk in graph.astream(
        {"messages": [{"role": "user", "content": "what's my name?"}]},
        config,
        stream_mode="values"
    ):
        chunk["messages"][-1].pretty_print()

示例：使用 Redis checkpointer

pip install -U langgraph langgraph-checkpoint-redis

Setup

第一次使用 Redis checkpointer 时，你需要调用 checkpointer.setup()

同步异步

from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.redis import RedisSaver

model = init_chat_model(model="anthropic:claude-3-5-haiku-latest")

DB_URI = "redis://localhost:6379"
with RedisSaver.from_conn_string(DB_URI) as checkpointer:
    # checkpointer.setup()

    def call_model(state: MessagesState):
        response = model.invoke(state["messages"])
        return {"messages": response}

    builder = StateGraph(MessagesState)
    builder.add_node(call_model)
    builder.add_edge(START, "call_model")

    graph = builder.compile(checkpointer=checkpointer)

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

    for chunk in graph.stream(
        {"messages": [{"role": "user", "content": "hi! I'm bob"}]},
        config,
        stream_mode="values"
    ):
        chunk["messages"][-1].pretty_print()

    for chunk in graph.stream(
        {"messages": [{"role": "user", "content": "what's my name?"}]},
        config,
        stream_mode="values"
    ):
        chunk["messages"][-1].pretty_print()

from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.redis.aio import AsyncRedisSaver

model = init_chat_model(model="anthropic:claude-3-5-haiku-latest")

DB_URI = "redis://localhost:6379"
async with AsyncRedisSaver.from_conn_string(DB_URI) as checkpointer:
    # await checkpointer.asetup()

    async def call_model(state: MessagesState):
        response = await model.ainvoke(state["messages"])
        return {"messages": response}

    builder = StateGraph(MessagesState)
    builder.add_node(call_model)
    builder.add_edge(START, "call_model")

    graph = builder.compile(checkpointer=checkpointer)

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

    async for chunk in graph.astream(
        {"messages": [{"role": "user", "content": "hi! I'm bob"}]},
        config,
        stream_mode="values"
    ):
        chunk["messages"][-1].pretty_print()

    async for chunk in graph.astream(
        {"messages": [{"role": "user", "content": "what's my name?"}]},
        config,
        stream_mode="values"
    ):
        chunk["messages"][-1].pretty_print()

与子图一起使用¶

如果你的图中包含subgraphs，你只需在**编译父图时提供checkpointer**。LangGraph会自动将checkpointer传播到子图中。

^{API Reference: START | StateGraph | InMemorySaver}

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

class State(TypedDict):
    foo: str

# 子图

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()

# 父图

def node_1(state: State):
    return {"foo": "hi! " + state["foo"]}

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

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

如果你想让子图拥有自己的内存，可以在编译时使用checkpointer=True。这在multi-agent系统中很有用，如果你希望代理能够记录它们的内部消息历史：

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

与 Functional API 一起使用¶

要向 Functional API 的 LangGraph 工作流添加短期记忆：

将 checkpointer 实例传递给 entrypoint() 装饰器：

from langgraph.func import entrypoint

@entrypoint(checkpointer=checkpointer)
def workflow(inputs):
    ...

可选地在工作流函数的参数签名中暴露 previous 参数：

@entrypoint(checkpointer=checkpointer)
def workflow(
    inputs,
    *,
    # 您可以在工作流函数的参数签名中选择性地指定 `previous`
    # 以访问上次执行时工作流的返回值
    previous
):
    previous = previous or []
    combined_inputs = previous + inputs
    result = do_something(combined_inputs)
    ...

可选地选择哪些值将从工作流返回，哪些值将被 checkpointer 保存为 previous：

@entrypoint(checkpointer=checkpointer)
def workflow(inputs, *, previous):
    ...
    result = do_something(...)
    return entrypoint.final(value=result, save=combine(inputs, result))

示例：为 Functional API 工作流添加短期记忆

from langchain_core.messages import AnyMessage
from langgraph.graph import add_messages
from langgraph.func import entrypoint, task
from langgraph.checkpoint.memory import InMemorySaver

@task
def call_model(messages: list[AnyMessage]):
    response = model.invoke(messages)
    return response

checkpointer = InMemorySaver()

@entrypoint(checkpointer=checkpointer)
def workflow(inputs: list[AnyMessage], *, previous: list[AnyMessage]):
    if previous:
        inputs = add_messages(previous, inputs)

    response = call_model(inputs).result()
    return entrypoint.final(value=response, save=add_messages(inputs, response))

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

for chunk in workflow.invoke(
    [{"role": "user", "content": "hi! I'm bob"}],
    config,
    stream_mode="values",
):
    chunk.pretty_print()

for chunk in workflow.stream(
    [{"role": "user", "content": "what's my name?"}],
    config,
    stream_mode="values",
):
    chunk.pretty_print()

管理检查点¶

你可以查看和删除检查点存储的信息：

查看线程状态（检查点）

图/函数式 API检查点 API

config = {
    "configurable": {
        "thread_id": "1",
        # 可选地提供一个特定检查点的 ID，
        # 否则将显示最新的检查点
        # "checkpoint_id": "1f029ca3-1f5b-6704-8004-820c16b69a5a"

    }
}
graph.get_state(config)

StateSnapshot(
    values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today?), HumanMessage(content="what's my name?"), AIMessage(content='Your name is Bob.')]}, next=(), 
    config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1f5b-6704-8004-820c16b69a5a'}},
    metadata={
        'source': 'loop',
        'writes': {'call_model': {'messages': AIMessage(content='Your name is Bob.')}},
        'step': 4,
        'parents': {},
        'thread_id': '1'
    },
    created_at='2025-05-05T16:01:24.680462+00:00',
    parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1790-6b0a-8003-baf965b6a38f'}}, 
    tasks=(),
    interrupts=()
)

config = {
    "configurable": {
        "thread_id": "1",
        # 可选地提供一个特定检查点的 ID，
        # 否则将显示最新的检查点
        # "checkpoint_id": "1f029ca3-1f5b-6704-8004-820c16b69a5a"

    }
}
checkpointer.get_tuple(config)

CheckpointTuple(
    config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1f5b-6704-8004-820c16b69a5a'}},
    checkpoint={
        'v': 3,
        'ts': '2025-05-05T16:01:24.680462+00:00',
        'id': '1f029ca3-1f5b-6704-8004-820c16b69a5a',
        'channel_versions': {'__start__': '00000000000000000000000000000005.0.5290678567601859', 'messages': '00000000000000000000000000000006.0.3205149138784782', 'branch:to:call_model': '00000000000000000000000000000006.0.14611156755133758'}, 'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000004.0.5736472536395331'}, 'call_model': {'branch:to:call_model': '00000000000000000000000000000005.0.1410174088651449'}},
        'channel_values': {'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today?), HumanMessage(content="what's my name?"), AIMessage(content='Your name is Bob.')]},
    },
    metadata={
        'source': 'loop',
        'writes': {'call_model': {'messages': AIMessage(content='Your name is Bob.')}},
        'step': 4,
        'parents': {},
        'thread_id': '1'
    },
    parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1790-6b0a-8003-baf965b6a38f'}},
    pending_writes=[]
)

查看线程历史（检查点）

图/函数式 API检查点 API

config = {
    "configurable": {
        "thread_id": "1"
    }
}
list(graph.get_state_history(config))

[
    StateSnapshot(
        values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?'), HumanMessage(content="what's my name?"), AIMessage(content='Your name is Bob.')]}, 
        next=(), 
        config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1f5b-6704-8004-820c16b69a5a'}}, 
        metadata={'source': 'loop', 'writes': {'call_model': {'messages': AIMessage(content='Your name is Bob.')}}, 'step': 4, 'parents': {}, 'thread_id': '1'},
        created_at='2025-05-05T16:01:24.680462+00:00',
        parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1790-6b0a-8003-baf965b6a38f'}},
        tasks=(),
        interrupts=()
    ),
    StateSnapshot(
        values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?'), HumanMessage(content="what's my name?")]}, 
        next=('call_model',), 
        config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1790-6b0a-8003-baf965b6a38f'}},
        metadata={'source': 'loop', 'writes': None, 'step': 3, 'parents': {}, 'thread_id': '1'},
        created_at='2025-05-05T16:01:23.863421+00:00',
        parent_config={...}
        tasks=(PregelTask(id='8ab4155e-6b15-b885-9ce5-bed69a2c305c', name='call_model', path=('__pregel_pull', 'call_model'), error=None, interrupts=(), state=None, result={'messages': AIMessage(content='Your name is Bob.')}),),
        interrupts=()
    ),
    StateSnapshot(
        values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')]}, 
        next=('__start__',), 
        config={...}, 
        metadata={'source': 'input', 'writes': {'__start__': {'messages': [{'role': 'user', 'content': "what's my name?"}]}}, 'step': 2, 'parents': {}, 'thread_id': '1'},
        created_at='2025-05-05T16:01:23.863173+00:00',
        parent_config={...}
        tasks=(PregelTask(id='24ba39d6-6db1-4c9b-f4c5-682aeaf38dcd', name='__start__', path=('__pregel_pull', '__start__'), error=None, interrupts=(), state=None, result={'messages': [{'role': 'user', 'content': "what's my name?"}]}),),
        interrupts=()
    ),
    StateSnapshot(
        values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')]}, 
        next=(), 
        config={...}, 
        metadata={'source': 'loop', 'writes': {'call_model': {'messages': AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')}}, 'step': 1, 'parents': {}, 'thread_id': '1'},
        created_at='2025-05-05T16:01:23.862295+00:00',
        parent_config={...}
        tasks=(),
        interrupts=()
    ),
    StateSnapshot(
        values={'messages': [HumanMessage(content="hi! I'm bob")]}, 
        next=('call_model',), 
        config={...}, 
        metadata={'source': 'loop', 'writes': None, 'step': 0, 'parents': {}, 'thread_id': '1'}, 
        created_at='2025-05-05T16:01:22.278960+00:00', 
        parent_config={...}
        tasks=(PregelTask(id='8cbd75e0-3720-b056-04f7-71ac805140a0', name='call_model', path=('__pregel_pull', 'call_model'), error=None, interrupts=(), state=None, result={'messages': AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')}),), 
        interrupts=()
    ),
    StateSnapshot(
        values={'messages': []}, 
        next=('__start__',), 
        config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-0870-6ce2-bfff-1f3f14c3e565'}},
        metadata={'source': 'input', 'writes': {'__start__': {'messages': [{'role': 'user', 'content': "hi! I'm bob"}]}}, 'step': -1, 'parents': {}, 'thread_id': '1'}, 
        created_at='2025-05-05T16:01:22.277497+00:00', 
        parent_config=None,
        tasks=(PregelTask(id='d458367b-8265-812c-18e2-33001d199ce6', name='__start__', path=('__pregel_pull', '__start__'), error=None, interrupts=(), state=None, result={'messages': [{'role': 'user', 'content': "hi! I'm bob"}]}),), 
        interrupts=()
    )
]

config = {
    "configurable": {
        "thread_id": "1"
    }
}
list(checkpointer.list(config))

[
    CheckpointTuple(
        config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1f5b-6704-8004-820c16b69a5a'}}, 
        checkpoint={
            'v': 3, 
            'ts': '2025-05-05T16:01:24.680462+00:00', 
            'id': '1f029ca3-1f5b-6704-8004-820c16b69a5a', 
            'channel_versions': {'__start__': '00000000000000000000000000000005.0.5290678567601859', 'messages': '00000000000000000000000000000006.0.3205149138784782', 'branch:to:call_model': '00000000000000000000000000000006.0.14611156755133758'}, 
            'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000004.0.5736472536395331'}, 'call_model': {'branch:to:call_model': '00000000000000000000000000000005.0.1410174088651449'}},
            'channel_values': {'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?'), HumanMessage(content="what's my name?"), AIMessage(content='Your name is Bob.')]},
        },
        metadata={'source': 'loop', 'writes': {'call_model': {'messages': AIMessage(content='Your name is Bob.')}}, 'step': 4, 'parents': {}, 'thread_id': '1'}, 
        parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1790-6b0a-8003-baf965b6a38f'}}, 
        pending_writes=[]
    ),
    CheckpointTuple(
        config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1790-6b0a-8003-baf965b6a38f'}},
        checkpoint={
            'v': 3, 
            'ts': '2025-05-05T16:01:23.863421+00:00', 
            'id': '1f029ca3-1790-6b0a-8003-baf965b6a38f', 
            'channel_versions': {'__start__': '00000000000000000000000000000005.0.5290678567601859', 'messages': '00000000000000000000000000000005.0.7935064215293443', 'branch:to:call_model': '00000000000000000000000000000005.0.1410174088651449'}, 
            'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000004.0.5736472536395331'}, 'call_model': {'branch:to:call_model': '00000000000000000000000000000002.0.9300422176788571'}}, 
            'channel_values': {'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?'), HumanMessage(content="what's my name?")], 'branch:to:call_model': None}
        }, 
        metadata={'source': 'loop', 'writes': None, 'step': 3, 'parents': {}, 'thread_id': '1'}, 
        parent_config={...}, 
        pending_writes=[('8ab4155e-6b15-b885-9ce5-bed69a2c305c', 'messages', AIMessage(content='Your name is Bob.'))]
    ),
    CheckpointTuple(
        config={...}, 
        checkpoint={
            'v': 3, 
            'ts': '2025-05-05T16:01:23.863173+00:00', 
            'id': '1f029ca3-1790-616e-8002-9e021694a0cd', 
            'channel_versions': {'__start__': '00000000000000000000000000000004.0.5736472536395331', 'messages': '00000000000000000000000000000003.0.7056767754077798', 'branch:to:call_model': '00000000000000000000000000000003.0.22059023329132854'}, 
            'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.0.7040775356287469'}, 'call_model': {'branch:to:call_model': '00000000000000000000000000000002.0.9300422176788571'}}, 
            'channel_values': {'__start__': {'messages': [{'role': 'user', 'content': "what's my name?"}]}, 'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')]}
        }, 
        metadata={'source': 'input', 'writes': {'__start__': {'messages': [{'role': 'user', 'content': "what's my name?"}]}}, 'step': 2, 'parents': {}, 'thread_id': '1'}, 
        parent_config={...}, 
        pending_writes=[('24ba39d6-6db1-4c9b-f4c5-682aeaf38dcd', 'messages', [{'role': 'user', 'content': "what's my name?"}]), ('24ba39d6-6db1-4c9b-f4c5-682aeaf38dcd', 'branch:to:call_model', None)]
    ),
    CheckpointTuple(
        config={...}, 
        checkpoint={
            'v': 3, 
            'ts': '2025-05-05T16:01:23.862295+00:00', 
            'id': '1f029ca3-178d-6f54-8001-d7b180db0c89', 
            'channel_versions': {'__start__': '00000000000000000000000000000002.0.18673090920108737', 'messages': '00000000000000000000000000000003.0.7056767754077798', 'branch:to:call_model': '00000000000000000000000000000003.0.22059023329132854'}, 
            'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.0.7040775356287469'}, 'call_model': {'branch:to:call_model': '00000000000000000000000000000002.0.9300422176788571'}}, 
            'channel_values': {'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')]}
        }, 
        metadata={'source': 'loop', 'writes': {'call_model': {'messages': AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')}}, 'step': 1, 'parents': {}, 'thread_id': '1'}, 
        parent_config={...}, 
        pending_writes=[]
    ),
    CheckpointTuple(
        config={...}, 
        checkpoint={
            'v': 3, 
            'ts': '2025-05-05T16:01:22.278960+00:00', 
            'id': '1f029ca3-0874-6612-8000-339f2abc83b1', 
            'channel_versions': {'__start__': '00000000000000000000000000000002.0.18673090920108737', 'messages': '00000000000000000000000000000002.0.30296526818059655', 'branch:to:call_model': '00000000000000000000000000000002.0.9300422176788571'}, 
            'versions_seen': {'__input__': {}, '__start__': {'__start__': '00000000000000000000000000000001.0.7040775356287469'}}, 
            'channel_values': {'messages': [HumanMessage(content="hi! I'm bob")], 'branch:to:call_model': None}
        }, 
        metadata={'source': 'loop', 'writes': None, 'step': 0, 'parents': {}, 'thread_id': '1'}, 
        parent_config={...}, 
        pending_writes=[('8cbd75e0-3720-b056-04f7-71ac805140a0', 'messages', AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?'))]
    ),
    CheckpointTuple(
        config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-0870-6ce2-bfff-1f3f14c3e565'}}, 
        checkpoint={
            'v': 3, 
            'ts': '2025-05-05T16:01:22.277497+00:00', 
            'id': '1f029ca3-0870-6ce2-bfff-1f3f14c3e565', 
            'channel_versions': {'__start__': '00000000000000000000000000000001.0.7040775356287469'}, 
            'versions_seen': {'__input__': {}}, 
            'channel_values': {'__start__': {'messages': [{'role': 'user', 'content': "hi! I'm bob"}]}}
        }, 
        metadata={'source': 'input', 'writes': {'__start__': {'messages': [{'role': 'user', 'content': "hi! I'm bob"}]}}, 'step': -1, 'parents': {}, 'thread_id': '1'}, 
        parent_config=None, 
        pending_writes=[('d458367b-8265-812c-18e2-33001d199ce6', 'messages', [{'role': 'user', 'content': "hi! I'm bob"}]), ('d458367b-8265-812c-18e2-33001d199ce6', 'branch:to:call_model', None)]
    )
]

删除线程的所有检查点

thread_id = "1"
checkpointer.delete_thread(thread_id)

添加长期记忆¶

使用**长期**记忆（跨线程持久化）来在对话之间存储用户特定或应用特定的数据。这对于像聊天机器人这样的应用程序非常有用，您希望记住用户的偏好或其他信息。

要使用长期记忆，创建图时需要提供一个存储：

^{API Reference: RunnableConfig | StateGraph | START | InMemorySaver}

import uuid
from typing_extensions import Annotated, TypedDict

from langchain_core.runnables import RunnableConfig
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.memory import InMemorySaver

from langgraph.store.memory import InMemoryStore
from langgraph.store.base import BaseStore

model = init_chat_model(model="anthropic:claude-3-5-haiku-latest")


def call_model(
    state: MessagesState,
    config: RunnableConfig,
    *,
    store: BaseStore,  # (1)!
):
    user_id = config["configurable"]["user_id"]
    namespace = ("memories", user_id)
    memories = store.search(namespace, query=str(state["messages"][-1].content))
    info = "\n".join([d.value["data"] for d in memories])
    system_msg = f"You are a helpful assistant talking to the user. User info: {info}"

    # Store new memories if the user asks the model to remember
    last_message = state["messages"][-1]
    if "remember" in last_message.content.lower():
        memory = "User name is Bob"
        store.put(namespace, str(uuid.uuid4()), {"data": memory})

    response = model.invoke(
        [{"role": "system", "content": system_msg}] + state["messages"]
    )
    return {"messages": response}


builder = StateGraph(MessagesState)
builder.add_node(call_model)
builder.add_edge(START, "call_model")

checkpointer = InMemorySaver()
store = InMemoryStore()

graph = builder.compile(
    checkpointer=checkpointer,
    store=store,
)

这是我们在其中编译图的 store

config = {
    "configurable": {
        "thread_id": "1",
        "user_id": "1",
    }
}
for chunk in graph.stream(
    {"messages": [{"role": "user", "content": "Hi! Remember: my name is Bob"}]},
    config,
    stream_mode="values",
):
    chunk["messages"][-1].pretty_print()

config = {
    "configurable": {
        "thread_id": "2",
        "user_id": "1",
    }
}

for chunk in graph.stream(
    {"messages": [{"role": "user", "content": "what is my name?"}]},
    config,
    stream_mode="values",
):
    chunk["messages"][-1].pretty_print()

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

Hi! Remember: my name is Bob
================================== Ai Message ==================================

Hi Bob! I'll remember that your name is Bob. How are you doing today?
================================ Human Message =================================

what is my name?
================================== Ai Message ==================================

Your name is Bob.

LangGraph API用户无需提供

如果您使用的是LangGraph API，则**无需**在编译图时提供存储。API会为您自动处理存储基础设施。

在生产环境中使用¶

在生产环境中，您希望使用由数据库支持的 checkpointer：

^{API Reference: PostgresSaver}

from langgraph.checkpoint.postgres import PostgresSaver

DB_URI = "postgresql://postgres:postgres@localhost:5442/postgres?sslmode=disable"
with PostgresStore.from_conn_string(DB_URI) as store:
    builder = StateGraph(...)
    graph = builder.compile(store=store)

示例：使用 Postgres 存储

pip install -U "psycopg[binary,pool]" langgraph langgraph-checkpoint-postgres

Setup

第一次使用 Postgres 存储时，您需要调用 store.setup()

同步异步

from langchain_core.runnables import RunnableConfig
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.postgres import PostgresSaver
from langgraph.store.postgres import PostgresStore
from langgraph.store.base import BaseStore

model = init_chat_model(model="anthropic:claude-3-5-haiku-latest")

DB_URI = "postgresql://postgres:postgres@localhost:5442/postgres?sslmode=disable"

with (
    PostgresStore.from_conn_string(DB_URI) as store,
    PostgresSaver.from_conn_string(DB_URI) as checkpointer,
):
    # store.setup()
    # checkpointer.setup()

    def call_model(
        state: MessagesState,
        config: RunnableConfig,
        *,
        store: BaseStore,
    ):
        user_id = config["configurable"]["user_id"]
        namespace = ("memories", user_id)
        memories = store.search(namespace, query=str(state["messages"][-1].content))
        info = "\n".join([d.value["data"] for d in memories])
        system_msg = f"You are a helpful assistant talking to the user. User info: {info}"

        # 如果用户要求模型记住信息，则存储新记忆
        last_message = state["messages"][-1]
        if "remember" in last_message.content.lower():
            memory = "User name is Bob"
            store.put(namespace, str(uuid.uuid4()), {"data": memory})

        response = model.invoke(
            [{"role": "system", "content": system_msg}] + state["messages"]
        )
        return {"messages": response}

    builder = StateGraph(MessagesState)
    builder.add_node(call_model)
    builder.add_edge(START, "call_model")

    graph = builder.compile(
        checkpointer=checkpointer,
        store=store,
    )

    config = {
        "configurable": {
            "thread_id": "1",
            "user_id": "1",
        }
    }
    for chunk in graph.stream(
        {"messages": [{"role": "user", "content": "Hi! Remember: my name is Bob"}]},
        config,
        stream_mode="values",
    ):
        chunk["messages"][-1].pretty_print()

    config = {
        "configurable": {
            "thread_id": "2",
            "user_id": "1",
        }
    }

    for chunk in graph.stream(
        {"messages": [{"role": "user", "content": "what is my name?"}]},
        config,
        stream_mode="values",
    ):
        chunk["messages"][-1].pretty_print()

from langchain_core.runnables import RunnableConfig
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
from langgraph.store.postgres.aio import AsyncPostgresStore
from langgraph.store.base import BaseStore

model = init_chat_model(model="anthropic:claude-3-5-haiku-latest")

DB_URI = "postgresql://postgres:postgres@localhost:5442/postgres?sslmode=disable"

async with (
    AsyncPostgresStore.from_conn_string(DB_URI) as store,
    AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer,
):
    # await store.setup()
    # await checkpointer.setup()

    async def call_model(
        state: MessagesState,
        config: RunnableConfig,
        *,
        store: BaseStore,
    ):
        user_id = config["configurable"]["user_id"]
        namespace = ("memories", user_id)
        memories = await store.asearch(namespace, query=str(state["messages"][-1].content))
        info = "\n".join([d.value["data"] for d in memories])
        system_msg = f"You are a helpful assistant talking to the user. User info: {info}"

        # 如果用户要求模型记住信息，则存储新记忆
        last_message = state["messages"][-1]
        if "remember" in last_message.content.lower():
            memory = "User name is Bob"
            await store.aput(namespace, str(uuid.uuid4()), {"data": memory})

        response = await model.ainvoke(
            [{"role": "system", "content": system_msg}] + state["messages"]
        )
        return {"messages": response}

    builder = StateGraph(MessagesState)
    builder.add_node(call_model)
    builder.add_edge(START, "call_model")

    graph = builder.compile(
        checkpointer=checkpointer,
        store=store,
    )

    config = {
        "configurable": {
            "thread_id": "1",
            "user_id": "1",
        }
    }
    async for chunk in graph.astream(
        {"messages": [{"role": "user", "content": "Hi! Remember: my name is Bob"}]},
        config,
        stream_mode="values",
    ):
        chunk["messages"][-1].pretty_print()

    config = {
        "configurable": {
            "thread_id": "2",
            "user_id": "1",
        }
    }

    async for chunk in graph.astream(
        {"messages": [{"role": "user", "content": "what is my name?"}]},
        config,
        stream_mode="values",
    ):
        chunk["messages"][-1].pretty_print()

示例：使用 Redis 存储

pip install -U langgraph langgraph-checkpoint-redis

Setup

第一次使用 Redis 存储时，您需要调用 store.setup()

同步异步

from langchain_core.runnables import RunnableConfig
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.redis import RedisSaver
from langgraph.store.redis import RedisStore
from langgraph.store.base import BaseStore

model = init_chat_model(model="anthropic:claude-3-5-haiku-latest")

DB_URI = "redis://localhost:6379"

with (
    RedisStore.from_conn_string(DB_URI) as store,
    RedisSaver.from_conn_string(DB_URI) as checkpointer,
):
    store.setup()
    checkpointer.setup()

    def call_model(
        state: MessagesState,
        config: RunnableConfig,
        *,
        store: BaseStore,
    ):
        user_id = config["configurable"]["user_id"]
        namespace = ("memories", user_id)
        memories = store.search(namespace, query=str(state["messages"][-1].content))
        info = "\n".join([d.value["data"] for d in memories])
        system_msg = f"You are a helpful assistant talking to the user. User info: {info}"

        # 如果用户要求模型记住信息，则存储新记忆
        last_message = state["messages"][-1]
        if "remember" in last_message.content.lower():
            memory = "User name is Bob"
            store.put(namespace, str(uuid.uuid4()), {"data": memory})

        response = model.invoke(
            [{"role": "system", "content": system_msg}] + state["messages"]
        )
        return {"messages": response}

    builder = StateGraph(MessagesState)
    builder.add_node(call_model)
    builder.add_edge(START, "call_model")

    graph = builder.compile(
        checkpointer=checkpointer,
        store=store,
    )

    config = {
        "configurable": {
            "thread_id": "1",
            "user_id": "1",
        }
    }
    for chunk in graph.stream(
        {"messages": [{"role": "user", "content": "Hi! Remember: my name is Bob"}]},
        config,
        stream_mode="values",
    ):
        chunk["messages"][-1].pretty_print()

    config = {
        "configurable": {
            "thread_id": "2",
            "user_id": "1",
        }
    }

    for chunk in graph.stream(
        {"messages": [{"role": "user", "content": "what is my name?"}]},
        config,
        stream_mode="values",
    ):
        chunk["messages"][-1].pretty_print()

from langchain_core.runnables import RunnableConfig
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.checkpoint.redis.aio import AsyncRedisSaver
from langgraph.store.redis.aio import AsyncRedisStore
from langgraph.store.base import BaseStore

model = init_chat_model(model="anthropic:claude-3-5-haiku-latest")

DB_URI = "redis://localhost:6379"

async with (
    AsyncRedisStore.from_conn_string(DB_URI) as store,
    AsyncRedisSaver.from_conn_string(DB_URI) as checkpointer,
):
    # await store.setup()
    # await checkpointer.asetup()

    async def call_model(
        state: MessagesState,
        config: RunnableConfig,
        *,
        store: BaseStore,
    ):
        user_id = config["configurable"]["user_id"]
        namespace = ("memories", user_id)
        memories = await store.asearch(namespace, query=str(state["messages"][-1].content))
        info = "\n".join([d.value["data"] for d in memories])
        system_msg = f"You are a helpful assistant talking to the user. User info: {info}"

        # 如果用户要求模型记住信息，则存储新记忆
        last_message = state["messages"][-1]
        if "remember" in last_message.content.lower():
            memory = "User name is Bob"
            await store.aput(namespace, str(uuid.uuid4()), {"data": memory})

        response = await model.ainvoke(
            [{"role": "system", "content": system_msg}] + state["messages"]
        )
        return {"messages": response}

    builder = StateGraph(MessagesState)
    builder.add_node(call_model)
    builder.add_edge(START, "call_model")

    graph = builder.compile(
        checkpointer=checkpointer,
        store=store,
    )

    config = {
        "configurable": {
            "thread_id": "1",
            "user_id": "1",
        }
    }
    async for chunk in graph.astream(
        {"messages": [{"role": "user", "content": "Hi! Remember: my name is Bob"}]},
        config,
        stream_mode="values",
    ):
        chunk["messages"][-1].pretty_print()

    config = {
        "configurable": {
            "thread_id": "2",
            "user_id": "1",
        }
    }

    async for chunk in graph.astream(
        {"messages": [{"role": "user", "content": "what is my name?"}]},
        config,
        stream_mode="values",
    ):
        chunk["messages"][-1].pretty_print()

使用语义搜索¶

你可以在图的内存存储中启用语义搜索：这使得图代理可以通过语义相似性来在存储中搜索项目。

^{API Reference: init_embeddings}

from langchain.embeddings import init_embeddings
from langgraph.store.memory import InMemoryStore

# 创建一个启用了语义搜索的存储
embeddings = init_embeddings("openai:text-embedding-3-small")
store = InMemoryStore(
    index={
        "embed": embeddings,
        "dims": 1536,
    }
)

store.put(("user_123", "memories"), "1", {"text": "I love pizza"})
store.put(("user_123", "memories"), "2", {"text": "I am a plumber"})

items = store.search(
    ("user_123", "memories"), query="I'm hungry", limit=1
)

使用语义搜索的长期记忆

from typing import Optional

from langchain.embeddings import init_embeddings
from langchain.chat_models import init_chat_model
from langgraph.store.base import BaseStore
from langgraph.store.memory import InMemoryStore
from langgraph.graph import START, MessagesState, StateGraph

llm = init_chat_model("openai:gpt-4o-mini")

# 创建一个启用了语义搜索的存储
embeddings = init_embeddings("openai:text-embedding-3-small")
store = InMemoryStore(
    index={
        "embed": embeddings,
        "dims": 1536,
    }
)

store.put(("user_123", "memories"), "1", {"text": "I love pizza"})
store.put(("user_123", "memories"), "2", {"text": "I am a plumber"})

def chat(state, *, store: BaseStore):
    # 基于用户的最后一条消息进行搜索
    items = store.search(
        ("user_123", "memories"), query=state["messages"][-1].content, limit=2
    )
    memories = "\n".join(item.value["text"] for item in items)
    memories = f"## Memories of user\n{memories}" if memories else ""
    response = llm.invoke(
        [
            {"role": "system", "content": f"You are a helpful assistant.\n{memories}"},
            *state["messages"],
        ]
    )
    return {"messages": [response]}


builder = StateGraph(MessagesState)
builder.add_node(chat)
builder.add_edge(START, "chat")
graph = builder.compile(store=store)

for message, metadata in graph.stream(
    input={"messages": [{"role": "user", "content": "I'm hungry"}]},
    stream_mode="messages",
):
    print(message.content, end="")

有关如何在 LangGraph 内存存储中使用语义搜索的更多信息，请参阅此指南。