langchain-ai--langgraph/libs/checkpoint-postgres/README.md

# LangGraph Checkpoint Postgres

[![PyPI - Version](https://img.shields.io/pypi/v/langgraph-checkpoint-postgres?label=%20)](https://pypi.org/project/langgraph-checkpoint-postgres/#history)
[![PyPI - License](https://img.shields.io/pypi/l/langgraph-checkpoint-postgres)](https://opensource.org/licenses/MIT)
[![PyPI - Downloads](https://img.shields.io/pepy/dt/langgraph-checkpoint-postgres)](https://pypistats.org/packages/langgraph-checkpoint-postgres)
[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchain_oss.svg?style=social&label=Follow%20%40LangChain)](https://x.com/langchain_oss)

To help you ship LangGraph apps to production faster, check out [LangSmith](https://www.langchain.com/langsmith).
[LangSmith](https://www.langchain.com/langsmith) is a unified developer platform for building, testing, and monitoring LLM applications.

## Quick Install

```bash
uv add langgraph-checkpoint-postgres
```

## 🤔 What is this?

This library provides a Postgres implementation of LangGraph's checkpoint saver. Use it when you want LangGraph state persistence backed by Postgres for durable, long-running workflows and agents.

By default, `langgraph-checkpoint-postgres` installs `psycopg` (Psycopg 3) without any extras. You can choose a specific installation that best suits your needs in the [Psycopg installation docs](https://www.psycopg.org/psycopg3/docs/basic/install.html), for example `psycopg[binary]`.

## 📖 Documentation

For full documentation, see the [API reference](https://reference.langchain.com/python/langgraph.checkpoint.postgres). For conceptual guides on persistence and memory, see the [LangGraph Docs](https://docs.langchain.com/oss/python/langgraph/overview).

## Security

> [!IMPORTANT]
> Set `LANGGRAPH_STRICT_MSGPACK=true` or pass an explicit `allowed_msgpack_modules` list when creating your checkpointer. This restricts checkpoint deserialization to known-safe types, preventing code execution if the database is compromised. See the [langgraph-checkpoint README](https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint#serde) for details.

## Usage

> [!IMPORTANT]
> When using Postgres checkpointers for the first time, make sure to call `.setup()` method on them to create required tables. See example below.

> [!IMPORTANT]
> When manually creating Postgres connections and passing them to `PostgresSaver` or `AsyncPostgresSaver`, make sure to include `autocommit=True` and `row_factory=dict_row` (`from psycopg.rows import dict_row`). See a full example in this [how-to guide](https://langchain-ai.github.io/langgraph/how-tos/persistence_postgres/).
>
> **Why these parameters are required:**
>
> - `autocommit=True`: Required for the `.setup()` method to properly commit the checkpoint tables to the database. Without this, table creation may not be persisted.
> - `row_factory=dict_row`: Required because the PostgresSaver implementation accesses database rows using dictionary-style syntax (e.g., `row["column_name"]`). The default `tuple_row` factory returns tuples that only support index-based access (e.g., `row[0]`), which will cause `TypeError` exceptions when the checkpointer tries to access columns by name.
>
> **Example of incorrect usage:**
>
> ```python
> # ❌ This will fail with TypeError during checkpointer operations
> with psycopg.connect(DB_URI) as conn:  # Missing autocommit=True and row_factory=dict_row
>     checkpointer = PostgresSaver(conn)
>     checkpointer.setup()  # May not persist tables properly
>     # Any operation that reads from database will fail with:
>     # TypeError: tuple indices must be integers or slices, not str
> ```

```python
from langgraph.checkpoint.postgres import PostgresSaver

write_config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}}
read_config = {"configurable": {"thread_id": "1"}}

DB_URI = "postgres://postgres:postgres@localhost:5432/postgres?sslmode=disable"
with PostgresSaver.from_conn_string(DB_URI) as checkpointer:
    # call .setup() the first time you're using the checkpointer
    checkpointer.setup()
    checkpoint = {
        "v": 4,
        "ts": "2024-07-31T20:14:19.804150+00:00",
        "id": "1ef4f797-8335-6428-8001-8a1503f9b875",
        "channel_values": {
            "my_key": "meow",
            "node": "node"
        },
        "channel_versions": {
            "__start__": 2,
            "my_key": 3,
            "start:node": 3,
            "node": 3
        },
        "versions_seen": {
            "__input__": {},
            "__start__": {
            "__start__": 1
            },
            "node": {
            "start:node": 2
            }
        },
    }

    # store checkpoint
    checkpointer.put(write_config, checkpoint, {}, {})

    # load checkpoint
    checkpointer.get(read_config)

    # list checkpoints
    list(checkpointer.list(read_config))
```

### Async

```python
from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver

async with AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer:
    checkpoint = {
        "v": 4,
        "ts": "2024-07-31T20:14:19.804150+00:00",
        "id": "1ef4f797-8335-6428-8001-8a1503f9b875",
        "channel_values": {
            "my_key": "meow",
            "node": "node"
        },
        "channel_versions": {
            "__start__": 2,
            "my_key": 3,
            "start:node": 3,
            "node": 3
        },
        "versions_seen": {
            "__input__": {},
            "__start__": {
            "__start__": 1
            },
            "node": {
            "start:node": 2
            }
        },
    }

    # store checkpoint
    await checkpointer.aput(write_config, checkpoint, {}, {})

    # load checkpoint
    await checkpointer.aget(read_config)

    # list checkpoints
    [c async for c in checkpointer.alist(read_config)]
```

## 📕 Releases & Versioning

See our [Releases](https://docs.langchain.com/oss/python/release-policy) and [Versioning](https://docs.langchain.com/oss/python/versioning) policies.

## 💁 Contributing

As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.

For detailed information on how to contribute, see the [Contributing Guide](https://docs.langchain.com/oss/python/contributing/overview).