> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-zh.cn/llms.txt
> Use this file to discover all available pages before exploring further.

# 长期记忆

> 为 LangChain 智能体添加长期记忆，实现跨对话和会话的数据存储与回忆

长期记忆使您的智能体能够跨不同对话和会话存储并回忆信息。
与仅限于单个线程的[短期记忆](/oss/python/langchain/short-term-memory)不同，长期记忆可跨线程持久保存，并可在任意时刻被回忆。

长期记忆构建于 [LangGraph 存储](/oss/python/langgraph/persistence#memory-store)之上，该存储将数据保存为按命名空间和键组织的 JSON 文档。

## 使用方法

要为智能体添加长期记忆，请创建一个存储并将其传递给 [`create_agent`](https://reference.langchain.com/python/langchain/agents/factory/create_agent)：

<Tabs>
  <Tab title="InMemoryStore">
    ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
    from langchain.agents import create_agent
    from langchain_core.runnables import Runnable
    from langgraph.store.memory import InMemoryStore

    # InMemoryStore 将数据存储到内存字典中。在生产环境中请使用基于数据库的存储。
    store = InMemoryStore()

    agent: Runnable = create_agent(
        "claude-sonnet-4-6",
        tools=[],
        store=store,
    )
    ```
  </Tab>

  <Tab title="PostgreSQL">
    ```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
    pip install langgraph-checkpoint-postgres
    ```

    ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
    from langchain.agents import create_agent
    from langchain_core.runnables import Runnable
    from langgraph.store.postgres import PostgresStore  # type: ignore[import-not-found]

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

    with PostgresStore.from_conn_string(DB_URI) as store:
        store.setup()
        agent: Runnable = create_agent(
            "claude-sonnet-4-6",
            tools=[],
            store=store,
        )
    ```
  </Tab>
</Tabs>

随后，工具可通过 `runtime.store` 参数从存储中读取数据或向存储写入数据。具体示例请参阅[在工具中读取长期记忆](#read-long-term-memory-in-tools)和[从工具写入长期记忆](#write-long-term-memory-from-tools)。

<Tip>
  若需深入了解记忆类型（语义记忆、情景记忆、程序性记忆）及记忆写入策略，请参阅[记忆概念指南](/oss/python/concepts/memory#long-term-memory)。
</Tip>

## 记忆存储

LangGraph 将长期记忆作为 JSON 文档存储在[存储](/oss/python/langgraph/persistence#memory-store)中。

每条记忆都组织在自定义的 `namespace`（类似于文件夹）和唯一的 `key`（类似于文件名）之下。命名空间通常包含用户或组织 ID 或其他便于信息组织的标签。

这种结构支持记忆的层次化组织。跨命名空间的搜索则通过内容过滤器实现。

<Tabs>
  <Tab title="InMemoryStore">
    ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
    from collections.abc import Sequence

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


    def embed(texts: Sequence[str]) -> list[list[float]]:
        # Replace with an actual embedding function or LangChain embeddings object
        return [[1.0, 2.0] for _ in texts]


    # InMemoryStore saves data to an in-memory dictionary. Use a DB-backed store in production use.
    store = InMemoryStore(index=IndexConfig(embed=embed, dims=2))
    user_id = "my-user"
    application_context = "chitchat"
    namespace = (user_id, application_context)
    store.put(
        namespace,
        "a-memory",
        {
            "rules": [
                "User likes short, direct language",
                "User only speaks English & python",
            ],
            "my-key": "my-value",
        },
    )
    # get the "memory" by ID
    item = store.get(namespace, "a-memory")
    # search for "memories" within this namespace, filtering on content equivalence, sorted by vector similarity
    items = store.search(
        namespace, filter={"my-key": "my-value"}, query="language preferences"
    )
    ```
  </Tab>

  <Tab title="PostgreSQL">
    ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
    from collections.abc import Sequence

    from langgraph.store.base import IndexConfig
    from langgraph.store.postgres import PostgresStore  # type: ignore[import-not-found]


    def embed(texts: Sequence[str]) -> list[list[float]]:
        # 替换为实际的嵌入函数或 LangChain 嵌入对象
        return [[1.0, 2.0] for _ in texts]


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

    with PostgresStore.from_conn_string(
        DB_URI,
        index=IndexConfig(embed=embed, dims=2),  # type: ignore[arg-type]
    ) as store:
        store.setup()
        user_id = "my-user"
        application_context = "chitchat"
        namespace = (user_id, application_context)
        store.put(
            namespace,
            "a-memory",
            {
                "rules": [
                    "User likes short, direct language",
                    "User only speaks English & python",
                ],
                "my-key": "my-value",
            },
        )
        item = store.get(namespace, "a-memory")
        items = store.search(
            namespace, filter={"my-key": "my-value"}, query="language preferences"
        )
    ```
  </Tab>
</Tabs>

有关记忆存储的更多信息，请参阅[持久化](/oss/python/langgraph/persistence#memory-store)指南。

## 在工具中读取长期记忆

<Tabs>
  <Tab title="InMemoryStore">
    ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
    from dataclasses import dataclass

    from langchain.agents import create_agent
    from langchain.tools import ToolRuntime, tool
    from langchain_core.runnables import Runnable
    from langgraph.store.memory import InMemoryStore


    @dataclass
    class Context:
        user_id: str


    # InMemoryStore 将数据保存到内存字典中。在生产环境中请使用基于数据库的存储。
    store = InMemoryStore()

    # 使用 put 方法向存储写入示例数据
    store.put(
        (
            "users",
        ),  # 用于分组相关数据的命名空间（用户数据的 users 命名空间）
        "user_123",  # 命名空间内的键（以用户 ID 为键）
        {
            "name": "John Smith",
            "language": "English",
        },  # 要存储的给定用户的数据
    )


    @tool
    def get_user_info(runtime: ToolRuntime[Context]) -> str:
        """查找用户信息。"""
        # 访问存储 - 与提供给 `create_agent` 的相同
        assert runtime.store is not None
        user_id = runtime.context.user_id
        # 从存储检索数据 - 返回包含值和元数据的 StoreValue 对象
        user_info = runtime.store.get(("users",), user_id)
        return str(user_info.value) if user_info else "Unknown user"


    agent: Runnable = create_agent(
        model="claude-sonnet-4-6",
        tools=[get_user_info],
        # 将存储传递给代理 - 使代理在运行工具时能够访问存储
        store=store,
        context_schema=Context,
    )

    # 运行代理
    agent.invoke(
        {"messages": [{"role": "user", "content": "look up user information"}]},
        context=Context(user_id="user_123"),
    )
    ```
  </Tab>

  <Tab title="PostgreSQL">
    ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
    from dataclasses import dataclass

    from langchain.agents import create_agent
    from langchain.tools import ToolRuntime, tool
    from langchain_core.runnables import Runnable
    from langgraph.store.postgres import PostgresStore  # type: ignore[import-not-found]


    @dataclass
    class Context:
        user_id: str


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

    with PostgresStore.from_conn_string(DB_URI) as store:
        store.setup()
        store.put(("users",), "user_123", {"name": "John Smith", "language": "English"})

        @tool
        def get_user_info(runtime: ToolRuntime[Context]) -> str:
            """Look up user info."""
            assert runtime.store is not None
            user_info = runtime.store.get(("users",), runtime.context.user_id)
            return str(user_info.value) if user_info else "Unknown user"

        agent: Runnable = create_agent(
            "claude-sonnet-4-6",
            tools=[get_user_info],
            store=store,
            context_schema=Context,
        )

        result = agent.invoke(
            {"messages": [{"role": "user", "content": "look up user information"}]},
            context=Context(user_id="user_123"),
        )
    ```
  </Tab>
</Tabs>

<a id="write-long-term" />

## 从工具写入长期记忆

<Tabs>
  <Tab title="InMemoryStore">
    ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
    from dataclasses import dataclass

    from langchain.agents import create_agent
    from langchain.tools import ToolRuntime, tool
    from langchain_core.runnables import Runnable
    from langgraph.store.memory import InMemoryStore
    from typing_extensions import TypedDict

    # InMemoryStore 将数据保存到内存字典中。在生产环境中请使用基于数据库的存储。
    store = InMemoryStore()


    @dataclass
    class Context:
        user_id: str


    # TypedDict 定义了 LLM 的用户信息结构
    class UserInfo(TypedDict):
        name: str


    # 允许代理更新用户信息的工具（适用于聊天应用程序）
    @tool
    def save_user_info(user_info: UserInfo, runtime: ToolRuntime[Context]) -> str:
        """保存用户信息。"""
        # 访问存储 - 与提供给 `create_agent` 的相同
        assert runtime.store is not None
        store = runtime.store
        user_id = runtime.context.user_id
        # 在存储中存储数据（命名空间，键，数据）
        store.put(("users",), user_id, dict(user_info))
        return "Successfully saved user info."


    agent: Runnable = create_agent(
        model="claude-sonnet-4-6",
        tools=[save_user_info],
        store=store,
        context_schema=Context,
    )

    # 运行代理
    agent.invoke(
        {"messages": [{"role": "user", "content": "My name is John Smith"}]},
        # 通过上下文传递的 user_id 用于标识正在更新谁的信息
        context=Context(user_id="user_123"),
    )

    # 您可以直接访问存储以获取值
    item = store.get(("users",), "user_123")
    ```
  </Tab>

  <Tab title="PostgreSQL">
    ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
    from dataclasses import dataclass

    from langchain.agents import create_agent
    from langchain.tools import ToolRuntime, tool
    from langchain_core.runnables import Runnable
    from langgraph.store.postgres import PostgresStore  # type: ignore[import-not-found]
    from typing_extensions import TypedDict


    @dataclass
    class Context:
        user_id: str


    class UserInfo(TypedDict):
        name: str


    @tool
    def save_user_info(user_info: UserInfo, runtime: ToolRuntime[Context]) -> str:
        """Save user info."""
        assert runtime.store is not None
        runtime.store.put(("users",), runtime.context.user_id, dict(user_info))
        return "Successfully saved user info."


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

    with PostgresStore.from_conn_string(DB_URI) as store:
        store.setup()
        agent: Runnable = create_agent(
            "claude-sonnet-4-6",
            tools=[save_user_info],
            store=store,
            context_schema=Context,
        )

        agent.invoke(
            {"messages": [{"role": "user", "content": "My name is John Smith"}]},
            context=Context(user_id="user_123"),
        )
    ```
  </Tab>
</Tabs>

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