如何使用记忆工具¶

LangMem 提供的工具可让您的智能体在 LangGraph 存储中存储和搜索记忆。

基本用法¶

使用记忆工具创建智能体

^{API：create_react_agent | create_manage_memory_tool | create_search_memory_tool}

from langgraph.prebuilt import create_react_agent
from langgraph.store.memory import InMemoryStore
from langmem import create_manage_memory_tool, create_search_memory_tool

# Set up store and memory saver
store = InMemoryStore(
    index={
        "dims": 1536,
        "embed": "openai:text-embedding-3-small",
    }
) # (1)!

对于生产部署，请使用持久性存储，如 AsyncPostgresStore。InMemoryStore 适用于开发，但在重启后不保留数据。

# Create agent with memory tools
agent = create_react_agent(
    "anthropic:claude-3-5-sonnet-latest",
    tools=[
        # Configure memory tools with runtime namespace (1)
        create_manage_memory_tool(namespace=("memories", "{user_id}")),
        create_search_memory_tool(namespace=("memories", "{user_id}")),
    ],
    store=store,
)

{user_id} 占位符让记忆工具可以访问 LangGraph 的 BaseStore 命名空间，并且所有工具共享同一个命名空间以保持一致性。

# Example 1: Store and search User A's memories
response_a = agent.invoke(
    {"messages": [{"role": "user", "content": "Remember my favorite color is blue"}]},
    config={"configurable": {"user_id": "user-a"}}
)  # Both tools use namespace ("memories", "user-a")

# Example 2: Store and search User B's memories
response_b = agent.invoke(
    {"messages": [{"role": "user", "content": "Remember I prefer dark mode"}]},
    config={"configurable": {"user_id": "user-b"}}
)  # Both tools use namespace ("memories", "user-b")

共享命名空间结构 ("memories", "{user_id}") 支持不同的记忆组织模式。

# Personal memories
namespace=("memories", "user-123")

# Shared team knowledge
namespace=("memories", "team-product")

# Project-specific memories
namespace=("memories", "project-x")

# Use the agent
config = {"configurable": {"user_id": "user-1"}}

# Store a preference
response = agent.invoke(
    {"messages": [{"role": "user", "content": "Remember I prefer dark mode"}]},
    config=config,
)

# Search preferences
response = agent.invoke(
    {"messages": [{"role": "user", "content": "What are my preferences?"}]},
    config=config,
)


agent_a_tools = [
    # Write to agent-specific namespace
    create_manage_memory_tool(namespace=("memories", "team_a", "agent_a")),
    # Read from shared team namespace
    create_search_memory_tool(namespace=("memories", "team_a"))
]



# Agents with different prompts sharing read access
agent_a = create_react_agent(
    "anthropic:claude-3-5-sonnet-latest",
    tools=agent_a_tools,
    store=store,
    prompt="You are a research assistant"
)

# Create tools for agent B with different write space
agent_b_tools = [
    create_manage_memory_tool(namespace=("memories", "team_a", "agent_b")),
    create_search_memory_tool(namespace=("memories", "team_a"))
]
agent_b = create_react_agent(
    "anthropic:claude-3-5-sonnet-latest",
    tools=agent_b_tools,
    store=store,
    prompt="You are a report writer."
)

agent_b.invoke({"messages": [{"role": "user", "content": "Hi"}]})

共享存储¶

在给定的部署中，存储是共享的。这让您可以创建命名空间的记忆，以便在团队中的智能体之间共享数据。

agent_a_tools = [
    # Write to agent-specific namespace
    create_manage_memory_tool(namespace=("memories", "team_a", "agent_a")),
    # Read from shared team namespace
    create_search_memory_tool(namespace=("memories", "team_a"))
]



# Agents with different prompts sharing read access
agent_a = create_react_agent(
    "anthropic:claude-3-5-sonnet-latest",
    tools=agent_a_tools,
    store=store,
    prompt="You are a research assistant"
)

# Create tools for agent B with different write space
agent_b_tools = [
    create_manage_memory_tool(namespace=("memories", "team_a", "agent_b")),
    create_search_memory_tool(namespace=("memories", "team_a"))
]
agent_b = create_react_agent(
    "anthropic:claude-3-5-sonnet-latest",
    tools=agent_b_tools,
    store=store,
    prompt="You are a report writer."
)

agent_b.invoke({"messages": [{"role": "user", "content": "Hi"}]})

有关存储模式，请参阅存储系统。

如何使用记忆工具¶

基本用法¶

共享存储¶

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