> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-ccdata-1770345743-a5afa16.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Long-term memory

## Overview

LangChain agents use [LangGraph persistence](/oss/python/langgraph/persistence#memory-store) to enable long-term memory. This is a more advanced topic and requires knowledge of LangGraph to use.

## Memory storage

LangGraph stores long-term memories as JSON documents in a [store](/oss/python/langgraph/persistence#memory-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.

```python theme={null}
from langgraph.store.memory import InMemoryStore


def embed(texts: list[str]) -> list[list[float]]:
    # Replace with an actual embedding function or LangChain embeddings object
    return [[1.0, 2.0] * len(texts)]


# InMemoryStore saves data to an in-memory dictionary. Use a DB-backed store in production use.
store = InMemoryStore(index={"embed": embed, "dims": 2}) # [!code highlight]
user_id = "my-user"
application_context = "chitchat"
namespace = (user_id, application_context) # [!code highlight]
store.put( # [!code highlight]
    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") # [!code highlight]
# search for "memories" within this namespace, filtering on content equivalence, sorted by vector similarity
items = store.search( # [!code highlight]
    namespace, filter={"my-key": "my-value"}, query="language preferences"
)
```

For more information about the memory store, see the [Persistence](/oss/python/langgraph/persistence#memory-store) guide.

## Read long-term memory in tools

```python A tool the agent can use to look up user information theme={null}
from dataclasses import dataclass

from langchain_core.runnables import RunnableConfig
from langchain.agents import create_agent
from langchain.tools import tool, ToolRuntime
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() # [!code highlight]

# Write sample data to the store using the put method
store.put( # [!code highlight]
    ("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`
    store = runtime.store # [!code highlight]
    user_id = runtime.context.user_id
    # Retrieve data from store - returns StoreValue object with value and metadata
    user_info = store.get(("users",), user_id) # [!code highlight]
    return str(user_info.value) if user_info else "Unknown user"

agent = create_agent(
    model="claude-sonnet-4-5-20250929",
    tools=[get_user_info],
    # Pass store to agent - enables agent to access store when running tools
    store=store, # [!code highlight]
    context_schema=Context
)

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

<a id="write-long-term" />

## Write long-term memory from tools

```python Example of a tool that updates user information theme={null}
from dataclasses import dataclass
from typing_extensions import TypedDict

from langchain.agents import create_agent
from langchain.tools import tool, ToolRuntime
from langgraph.store.memory import InMemoryStore


# InMemoryStore saves data to an in-memory dictionary. Use a DB-backed store in production.
store = InMemoryStore() # [!code highlight]

@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`
    store = runtime.store # [!code highlight]
    user_id = runtime.context.user_id # [!code highlight]
    # Store data in the store (namespace, key, data)
    store.put(("users",), user_id, user_info) # [!code highlight]
    return "Successfully saved user info."

agent = create_agent(
    model="claude-sonnet-4-5-20250929",
    tools=[save_user_info],
    store=store, # [!code highlight]
    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") # [!code highlight]
)

# You can access the store directly to get the value
store.get(("users",), "user_123").value
```

***

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