How to use a custom store ↗

Documentation Index#

Fetch the complete documentation index at: https://docs.langchain.com/llms.txt Use this file to discover all available pages before exploring further.

Replace the built-in Postgres store with a custom BaseStore implementation in your agent deployment.

When deploying agents to LangSmith, the server provides a built-in Postgres-backed long-term memory store with optional vector search via pgvector. You can replace this with your own BaseStore implementation to use a different storage backend, custom indexing, or specialized search capabilities.

You provide a path to an async context manager that yields a BaseStore instance, and the server manages the store’s lifecycle automatically.

Custom stores are in alpha. This feature may experience breaking changes in minor version updates.

Define the store#

Starting from an existing LangSmith application, create a file that defines an async context manager yielding your custom store. If you are beginning a new project, you can create an app from a template using the CLI.

langgraph new --template=new-langgraph-project-python my_new_project

The async context manager pattern lets the server open and close the store connection at the right points in the application lifecycle. The following example uses @[AsyncSqliteStore] with semantic search:

SQLite is not recommended for use in production deployments.

# ./src/agent/store.py
import contextlib
from typing import cast

from langchain.embeddings import init_embeddings, Embeddings
from langgraph.store.base import IndexConfig
from langgraph.store.sqlite import AsyncSqliteStore

embeddings = cast(Embeddings, init_embeddings("openai:text-embedding-3-small"))


@contextlib.asynccontextmanager
async def generate_store():
    """Yield a BaseStore, open for the duration of the server."""
    async with AsyncSqliteStore.from_conn_string(
        "./custom_store.sql",
        index=IndexConfig(
            dims=1536,
            embed=embeddings,
            fields=["$"],
        ),
    ) as store:
        await store.setup()
        yield store

When a custom store is configured, it replaces the built-in Postgres store entirely. Capabilities like semantic search and TTL sweeping depend on your implementation.

Configure langgraph.json#

Add the store key to your langgraph.json configuration file. The path points to the async context manager you defined earlier.

{
  "dependencies": ["."],
  "graphs": {
    "agent": "./src/agent/graph.py:graph"
  },
  "env": ".env",
  "store": {
    "path": "./src/agent/store.py:generate_store"
  }
}

Start server#

Test the server out locally:

langgraph dev --no-browser

The server logs will confirm that your custom store is active:

Using custom store. Skipping store TTL sweeper.

Deploying#

You can deploy this app as-is to LangSmith or to your self-hosted platform.

Next steps#

• Use a custom checkpointer to replace the built-in checkpoint storage.
• Learn about persistence and memory in LangGraph.

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