How to Use Memory Tools

LangMem provides tools that let your agent store and search memories in a LangGraph store.

Basic Usage

Create an agent with memory tools:

^{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)

1. For production deployments, use a persistent store like AsyncPostgresStore. InMemoryStore works fine for development but doesn't persist data between restarts.

# 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,
)

1. The {user_id} placeholder lets memory tools access LangGraph's BaseStore namespace, with both tools sharing the same namespace for consistency.

   # 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")
   

   The shared namespace structure ("memories", "{user_id}") supports different memory organization patterns:

   # 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"}]})
   

Shared Storage

The store is shared within a given deployment. This lets you do things like create namespaced memories to share data between agents in a team.

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"}]})

For storage patterns, see Storage System.

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