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Long-term memory lets your agent store and recall information across different conversations and sessions. Unlike short-term memory, which is scoped to a single thread, long-term memory persists across threads and can be recalled at any time. Long-term memory is built on LangGraph stores, which save data as JSON documents organized by namespace and key.

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Usage

To add long-term memory to an agent, create a store and pass it to 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 in production use.
store = InMemoryStore()

agent: Runnable = create_agent(
    "claude-sonnet-4-6",
    tools=[],
    store=store,
)
Tools can then read from and write to the store using the runtime.store parameter. See Read long-term memory in tools and Write long-term memory from tools for examples.
For a deeper dive into memory types (semantic, episodic, procedural) and strategies for writing memories, see the Memory conceptual guide.

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Memory storage

LangGraph stores long-term memories as JSON documents in a store. Each memory is organized under a custom namespace (similar to a folder) and a distinct key (like a file name). Namespaces often include user or org IDs or other labels that makes it easier to organize information. This structure enables hierarchical organization of memories. Cross-namespace searching is then supported through content filters. 
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"
)
For more information about the memory store, see the Persistence guide.

​
Read long-term memory in tools

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 in production.
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"),
)

​
Write long-term memory from tools

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 in production.
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")
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