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内存工具 API 参考

函数

create_manage_memory_tool 

create_manage_memory_tool(
    namespace: tuple[str, ...] | str,
    *,
    instructions: str = "Proactively call this tool when you:\n\n1. Identify a new USER preference.\n2. Receive an explicit USER request to remember something or otherwise alter your behavior.\n3. Are working and want to record important context.\n4. Identify that an existing MEMORY is incorrect or outdated.\n",
    schema: Type = str,
    actions_permitted: Optional[
        tuple[Literal["create", "update", "delete"], ...]
    ] = ("create", "update", "delete"),
    store: Optional[BaseStore] = None,
    name: str = "manage_memory",
)

创建一个用于在对话中管理持久性记忆的工具。

此函数创建一个工具，允许 AI 助手创建、更新和删除跨对话持久存在的记忆。该工具帮助在会话之间保持上下文和用户偏好。

参数

  • instructions (str, 默认值: '当您满足以下条件时，主动调用此工具：\n\n1. 识别出新的用户偏好。\n2. 收到用户明确要求记住某事或改变您的行为。\n3. 正在工作并希望记录重要上下文。\n4. 识别出现有记忆不正确或已过时。\n' ) –

    关于何时使用内存工具的自定义说明。默认为一组预定义的、用于主动进行内存管理的指南。

  • namespace (tuple[str, ...] | str) –

    用于在 LangGraph 的 BaseStore 中组织记忆的命名空间结构。使用带有诸如 {langgraph_user_id} 之类占位符的运行时配置。

  • store (Optional[BaseStore], 默认值: None ) –

    用于搜索的 BaseStore。如果未提供，该工具将使用您的图或入口点中配置的 BaseStore。仅当您打算在 LangGraph 上下文之外使用这些工具时才设置此项。

返回值

  • memory_tool ( Tool ) –

    一个装饰过的异步函数，可用作内存管理的工具。该工具支持创建、更新和删除记忆，并进行适当的验证。

生成的工具具有如下签名 

from typing import Literal


def manage_memory(
    content: str | None = None,  # Content for new/updated memory
    id: str | None = None,  # ID of existing memory to update/delete
    action: Literal["create", "update", "delete"] = "create",
) -> str: ...
注意：该工具支持同步和异步使用。

命名空间配置

命名空间通过 config 参数在运行时配置

# Example: Per-user memory storage
config = {"configurable": {"langgraph_user_id": "user-123"}}
# Results in namespace: ("memories", "user-123")

# Example: Team-wide memory storage
config = {"configurable": {"langgraph_user_id": "team-x"}}
# Results in namespace: ("memories", "team-x")

提示

此工具连接到您的图或入口点中配置的 LangGraph BaseStore。如果您不提供存储，它将无法工作。

示例

from langmem import create_manage_memory_tool
from langgraph.func import entrypoint
from langgraph.store.memory import InMemoryStore

memory_tool = create_manage_memory_tool(
    # All memories saved to this tool will live within this namespace
    # The brackets will be populated at runtime by the configurable values
    namespace=("project_memories", "{langgraph_user_id}"),
)

store = InMemoryStore(
    index={
        "dims": 1536,
        "embed": "openai:text-embedding-3-small",
    }
)


@entrypoint(store=store)
async def workflow(state: dict, *, previous=None):
    # Other work....
    result = await memory_tool.ainvoke(state)
    print(result)
    return entrypoint.final(value=result, save={})


config = {
    "configurable": {
        # This value will be formatted into the namespace you configured above ("project_memories", "{langgraph_user_id}")
        "langgraph_user_id": "123e4567-e89b-12d3-a456-426614174000"
    }
}
# Create a new memory
await workflow.ainvoke(
    {"content": "Team prefers to use Python for backend development"},
    config=config,
)
# Output: 'created memory 123e4567-e89b-12d3-a456-426614174000'

# Update an existing memory
result = await workflow.ainvoke(
    {
        "id": "123e4567-e89b-12d3-a456-426614174000",
        "content": "Team uses Python for backend and TypeScript for frontend",
        "action": "update",
    },
    config=config,
)
print(result)
# Output: 'updated memory 123e4567-e89b-12d3-a456-426614174000'

您可以在 LangGraph 预构建的 create_react_agent 中使用

from langgraph.prebuilt import create_react_agent
from langgraph.config import get_config, get_store

def prompt(state):
    config = get_config()
    memories = get_store().search(
        # Search within the same namespace as the one
        # we've configured for the agent
        ("memories", config["configurable"]["langgraph_user_id"]),
    )
    system_prompt = f"""You are a helpful assistant.
<memories>
{memories}
</memories>
"""
    system_message = {"role": "system", "content": system_prompt}
    return [system_message, *state["messages"]]

agent = create_react_agent(
    "anthropic:claude-3-5-sonnet-latest",
    tools=[
        create_manage_memory_tool(namespace=("memories", "{langgraph_user_id}")),
    ],
    store=store,
)

agent.invoke(
    {"messages": [{"role": "user", "content": "We've decided we like golang more than python for backend work"}]},
    config=config,
)

如果您想自定义记忆的预期 schema，可以通过提供 schema 参数来实现。

from pydantic import BaseModel


class UserProfile(BaseModel):
    name: str
    age: int | None = None
    recent_memories: list[str] = []
    preferences: dict | None = None


memory_tool = create_manage_memory_tool(
    # All memories saved to this tool will live within this namespace
    # The brackets will be populated at runtime by the configurable values
    namespace=("memories", "{langgraph_user_id}", "user_profile"),
    schema=UserProfile,
    actions_permitted=["create", "update"],
    instructions="Update the existing user profile (or create a new one if it doesn't exist) based on the shared information.",
)
store = InMemoryStore(
    index={
        "dims": 1536,
        "embed": "openai:text-embedding-3-small",
    }
)
agent = create_react_agent(
    "anthropic:claude-3-5-sonnet-latest",
    prompt=prompt,
    tools=[
        memory_tool,
    ],
    store=store,
)

result = agent.invoke(
    {
        "messages": [
            {
                "role": "user",
                "content": "I'm 60 years old and have been programming for 5 days.",
            }
        ]
    },
    config=config,
)
result["messages"][-1].pretty_print()
# I've created a memory with your age of 60 and noted that you started programming 5 days ago...

result = agent.invoke(
    {
        "messages": [
            {"role": "user", "content": "Just had by 61'st birthday today!!"}
        ]
    },
    config=config,
)
result["messages"][-1].pretty_print()
# Happy 61st birthday! 🎂 I've updated your profile to reflect your new age. Is there anything else I can help you with?
print(
    store.search(
        ("memories", "123e4567-e89b-12d3-a456-426614174000", "user_profile")
    )
)
# [Item(
#     namespace=['memories', '123e4567-e89b-12d3-a456-426614174000', 'user_profile'],
#     key='1528553b-0900-4363-8dc2-c6b72844096e',
#     value={
# highlight-next-line
#         'content': UserProfile(
#             name='User',
#             age=61,
#             recent_memories=['Started programming 5 days ago'],
#             preferences={'programming_experience': '5 days'}
#         )
#     },
#     created_at='2025-02-07T01:12:14.383762+00:00',
#     updated_at='2025-02-07T01:12:14.383763+00:00',
#     score=None
# )]

如果您想限制该工具可以执行的操作，可以通过提供 actions_permitted 参数来实现。

create_search_memory_tool 

create_search_memory_tool(
    namespace: tuple[str, ...] | str,
    *,
    instructions: str = _MEMORY_SEARCH_INSTRUCTIONS,
    store: BaseStore | None = None,
    response_format: Literal[
        "content", "content_and_artifact"
    ] = "content",
    name: str = "search_memory",
)

创建一个用于搜索存储在 LangGraph BaseStore 中的记忆的工具。

此函数创建一个工具，允许 AI 助手使用语义或精确匹配来搜索之前存储的记忆。该工具返回记忆内容和原始记忆对象，以供高级使用。

参数

  • instructions (str, 默认值: _MEMORY_SEARCH_INSTRUCTIONS ) –

    关于何时使用搜索工具的自定义说明。默认为一组预定义的指南。

  • namespace (tuple[str, ...] | str) –

    用于在 LangGraph 的 BaseStore 中组织记忆的命名空间结构。使用带有诸如 {langgraph_user_id} 之类占位符的运行时配置。请参阅 内存命名空间。

  • store (BaseStore | None, 默认值: None ) –

    用于搜索的 BaseStore。如果未提供，该工具将使用您的图或入口点中配置的 BaseStore。仅当您打算在 LangGraph 上下文之外使用这些工具时才设置此项。

返回值

  • search_tool ( Tool ) –

    一个装饰过的函数，可用作内存搜索工具。该工具返回序列化记忆和原始记忆对象。

生成的工具具有如下签名 
def search_memory(
    query: str,  # Search query to match against memories
    limit: int = 10,  # Maximum number of results to return
    offset: int = 0,  # Number of results to skip
    filter: dict | None = None,  # Additional filter criteria
) -> tuple[list[dict], list]: ...  # Returns (serialized memories, raw memories)

注意：该工具支持同步和异步使用。

提示

此工具连接到您的图或入口点中配置的 LangGraph BaseStore。如果您不提供存储，它将无法工作。

示例

from langmem import create_search_memory_tool
from langgraph.func import entrypoint
from langgraph.store.memory import InMemoryStore

search_tool = create_search_memory_tool(
    namespace=("project_memories", "{langgraph_user_id}"),
)

store = InMemoryStore(
    index={
        "dims": 1536,
        "embed": "openai:text-embedding-3-small",
    }
)


@entrypoint(store=store)
async def workflow(state: dict, *, previous=None):
    # Search for memories about Python
    memories, _ = await search_tool.ainvoke(
        {"query": "Python preferences", "limit": 5}
    )
    print(memories)
    return entrypoint.final(value=memories, save={})

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