Skip to main content

Documentation Index

Fetch the complete documentation index at: https://nvd-54.mintlify.app/llms.txt

Use this file to discover all available pages before exploring further.

长期记忆允许你的智能体跨不同对话和会话存储及检索信息。 与短期记忆不同——短期记忆的作用范围仅限于单个线程——长期记忆可以跨线程持久化，并且可以随时检索。 长期记忆构建在 LangGraph 存储之上，它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，需要创建一个存储并将其传递给 create_agent： 
from langchain.agents import create_agent
from langchain_core.runnables import Runnable
from langgraph.store.memory import InMemoryStore

# InMemoryStore saves data to an in-memory dictionary. Use a DB-backed store 在生产环境中 use.
store = InMemoryStore()

agent: Runnable = create_agent(
    "claude-sonnet-4-6",
    tools=[],
    store=store,
)
然后，工具可以通过 runtime.store 参数读取和写入存储。有关示例，请参阅在工具中读取长期记忆和从工具写入长期记忆。
如需深入了解记忆类型（语义记忆、情景记忆、程序性记忆）和记忆写入策略，请参阅记忆概念指南。

​
记忆存储

LangGraph 将长期记忆存储为存储中的 JSON 文档。 每条记忆组织在自定义的 namespace（类似于文件夹）和唯一的 key（类似于文件名）下。命名空间通常包含用户 ID 或组织 ID 或其他标签，以便更轻松地组织信息。 这种结构支持记忆的层次化组织。然后通过内容过滤器支持跨命名空间搜索。 
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 在生产环境中 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"
)
有关记忆存储的更多信息，请参阅持久化指南。

​
在工具中读取长期记忆

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 saves data to an in-memory dictionary. Use a DB-backed store 在生产环境中.
store = InMemoryStore()

# Write sample data to the store using the put method
store.put(
    (
        "users",
    ),  # Namespace to group related data together (users namespace for user data)
    "user_123",  # Key within the namespace (user ID as key)
    {
        "name": "John Smith",
        "language": "English",
    },  # Data to store for the given user
)


@tool
def get_user_info(runtime: ToolRuntime[Context]) -> str:
    """Look up user info."""
    # Access the store - same as that provided to `create_agent`
    assert runtime.store is not None
    user_id = runtime.context.user_id
    # Retrieve data from store - returns StoreValue object with value and metadata
    user_info = runtime.store.get(("users",), user_id)
    return str(user_info.value) if user_info else "Unknown user"


agent: Runnable = create_agent(
    model="google_genai:gemini-3.1-pro-preview",
    tools=[get_user_info],
    # Pass store to agent - enables agent to access store when running tools
    store=store,
    context_schema=Context,
)

# Run the agent
agent.invoke(
    {"messages": [{"role": "user", "content": "look up user information"}]},
    context=Context(user_id="user_123"),
)

​
从工具写入长期记忆

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 saves data to an in-memory dictionary. Use a DB-backed store 在生产环境中.
store = InMemoryStore()


@dataclass
class Context:
    user_id: str


# TypedDict defines the structure of user information for the LLM
class UserInfo(TypedDict):
    name: str


# Tool that allows agent to update user information (useful for chat applications)
@tool
def save_user_info(user_info: UserInfo, runtime: ToolRuntime[Context]) -> str:
    """Save user info."""
    # Access the store - same as that provided to `create_agent`
    assert runtime.store is not None
    store = runtime.store
    user_id = runtime.context.user_id
    # Store data in the store (namespace, key, data)
    store.put(("users",), user_id, dict(user_info))
    return "Successfully saved user info."


agent: Runnable = create_agent(
    model="google_genai:gemini-3.1-pro-preview",
    tools=[save_user_info],
    store=store,
    context_schema=Context,
)

# Run the agent
agent.invoke(
    {"messages": [{"role": "user", "content": "My name is John Smith"}]},
    # user_id passed in context to identify whose information is being updated
    context=Context(user_id="user_123"),
)

# You can access the store directly to get the value
item = store.get(("users",), "user_123")
将这些文档连接到 Claude、VSCode 等工具，通过 MCP 获取实时答案。
在 GitHub 上编辑此页面或提交问题。