Long-term memory with MongoDB Atlas

Long-term memory lets your agent store and recall information across different conversations and sessions. This guide shows how to use MongoDB Atlas as the persistent store backend. MongoDB stores memories as documents in a collection. With an Atlas Vector Search index, the same store can run semantic search over stored memories.

For a deeper dive into memory types and strategies for writing memories, see the Memory conceptual guide.

Setup

Installation

Install the store package (langgraph-store-mongodb):

pip install -U langgraph-store-mongodb pymongo

uv add langgraph-store-mongodb pymongo

Credentials

Set your Atlas connection string:

import getpass
import os

if not os.environ.get("MONGODB_ATLAS_URI"):
    os.environ["MONGODB_ATLAS_URI"] = getpass.getpass("Enter your MongoDB Atlas URI: ")

If you want automated tracing of your model calls, set your LangSmith API key:

if not os.environ.get("LANGSMITH_API_KEY"):
    os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"

Usage

Create a MongoDBStore and pass it to create_agent. The from_conn_string context manager creates the required indexes when it opens. You do not call a separate setup() method.

import os

from langchain.agents import create_agent
from langgraph.store.mongodb import MongoDBStore

MONGODB_ATLAS_URI = os.environ["MONGODB_ATLAS_URI"]

with MongoDBStore.from_conn_string(MONGODB_ATLAS_URI) as store:
    agent = create_agent(
        "anthropic:claude-sonnet-4-6",
        tools=[],
        store=store,
    )

Memory storage

LangGraph stores memories as JSON documents organized by namespace and key. Namespaces typically include a user or org ID to keep memories scoped. The following example configures vector indexing so store.search can run semantic queries. Use create_vector_index_config and pass it as index_config:

import os
from collections.abc import Sequence

from langgraph.store.mongodb import MongoDBStore, create_vector_index_config

def embed(texts: Sequence[str]) -> list[list[float]]:
    # Replace with an embeddings model or LangChain Embeddings instance
    return [[1.0, 2.0] for _ in texts]

MONGODB_ATLAS_URI = os.environ["MONGODB_ATLAS_URI"]

with MongoDBStore.from_conn_string(
    MONGODB_ATLAS_URI,
    index_config=create_vector_index_config(embed=embed, dims=2),
) as store:
    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 and Python",
            ],
            "my-key": "my-value",
        },
    )
    item = store.get(namespace, "a-memory")
    items = store.search(
        namespace, filter={"my-key": "my-value"}, query="language preferences"
    )

For more information about store operations, see the Stores guide.

Read long-term memory in tools

Tools can read from the store using the runtime parameter, which LangGraph injects automatically:

import os
from dataclasses import dataclass

from langchain.agents import create_agent
from langchain.tools import ToolRuntime, tool
from langgraph.store.mongodb import MongoDBStore

@dataclass
class Context:
    user_id: str

MONGODB_ATLAS_URI = os.environ["MONGODB_ATLAS_URI"]

with MongoDBStore.from_conn_string(MONGODB_ATLAS_URI) as store:
    store.put(("users",), "user_123", {"name": "John Smith", "language": "English"})

    @tool
    def get_user_info(runtime: ToolRuntime[Context]) -> str:
        """Look up user info."""
        assert runtime.store is not None
        user_info = runtime.store.get(("users",), runtime.context.user_id)
        return str(user_info.value) if user_info else "Unknown user"

    agent = create_agent(
        "anthropic:claude-sonnet-4-6",
        tools=[get_user_info],
        store=store,
        context_schema=Context,
    )

    agent.invoke(
        {"messages": [{"role": "user", "content": "look up user information"}]},
        context=Context(user_id="user_123"),
    )

Write long-term memory from tools

Tools can also write to the store using runtime.store.put, persisting data that survives across threads:

import os
from dataclasses import dataclass

from langchain.agents import create_agent
from langchain.tools import ToolRuntime, tool
from langgraph.store.mongodb import MongoDBStore
from typing_extensions import TypedDict

@dataclass
class Context:
    user_id: str

class UserInfo(TypedDict):
    name: str

@tool
def save_user_info(user_info: UserInfo, runtime: ToolRuntime[Context]) -> str:
    """Save user info."""
    assert runtime.store is not None
    runtime.store.put(("users",), runtime.context.user_id, dict(user_info))
    return "Successfully saved user info."

MONGODB_ATLAS_URI = os.environ["MONGODB_ATLAS_URI"]

with MongoDBStore.from_conn_string(MONGODB_ATLAS_URI) as store:
    agent = create_agent(
        "anthropic:claude-sonnet-4-6",
        tools=[save_user_info],
        store=store,
        context_schema=Context,
    )

    agent.invoke(
        {"messages": [{"role": "user", "content": "My name is John Smith"}]},
        context=Context(user_id="user_123"),
    )

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Long-term memory with MongoDB Atlas

Setup

Installation

Credentials

Usage

Memory storage

Read long-term memory in tools

Write long-term memory from tools

See also

​Setup

​Installation

​Credentials

​Usage

​Memory storage

​Read long-term memory in tools

​Write long-term memory from tools

​See also

Setup

Installation

Credentials

Usage

Memory storage

Read long-term memory in tools

Write long-term memory from tools

See also