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Short-term memory with MongoDB Atlas

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Short-term memory lets your agent remember previous interactions within a single thread or conversation. This guide shows how to use MongoDB Atlas as the persistent checkpointer backend.

MongoDB stores conversation state as documents in a collection, so threads can resume across process restarts and deployments.

Tip

Need to remember information across conversations? Use long-term memory with MongoDB Atlas to store and recall data across different threads and sessions.

Setup

Installation

pip install -U langgraph-checkpoint-mongodb pymongo
uv add langgraph-checkpoint-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 MongoDBSaver with from_conn_string and pass it to create_agent. The context manager creates the required collections and indexes when it opens. You do not call a separate setup() method (unlike the Postgres checkpointer).

import os

from langchain.agents import create_agent
from langgraph.checkpoint.mongodb import MongoDBSaver

def get_user_info() -> str:
    """Look up information about the current user."""
    return "No user profile on file."

MONGODB_ATLAS_URI = os.environ["MONGODB_ATLAS_URI"]

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

    thread_config = {"configurable": {"thread_id": "1"}}
    response = agent.invoke(
        {"messages": [{"role": "user", "content": "Hi! My name is Bob."}]},
        thread_config,
    )["messages"][-1].content

    print(response)

    response = agent.invoke(
        {"messages": [{"role": "user", "content": "What's my name?"}]},
        thread_config,
    )["messages"][-1].content

    print(response)

MongoDBSaver also exposes async methods such as aget and aput for use in async graphs. There is no separate AsyncMongoDBSaver class.

Next steps

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