Files
langgraph/libs/checkpoint-postgres
Sydney Runkle 79f5511e24 refactor(delta-channel): drop snapshot_every and saver Overwrite terminator
snapshot_every was a knob for bounding reconstruction cost on deep threads.
Benchmarks (notes/add_messages_replay_problem.md + scratch work on
sr/add-messages-replay-bench) showed the add_messages fast-path
(optimize/add-messages-fast-path) closes the quadratic replay cost for
threads under ~1000 turns, where the crossover to snapshots makes sense.
For deeper threads we'll ship a first-class compaction primitive instead.

Removals:

* DeltaChannel: snapshot_every ctor param, _writes_since_snapshot counter,
  should_snapshot() / snapshot_write() methods, counter threading through
  _apply_write / update / from_checkpoint / copy.
* Pregel loop: post-checkpoint snapshot-injection block and
  SNAPSHOT_TASK_ID import + constant.
* Checkpoint base: _overwrite_types() helper and the ancestor-walk
  short-circuit on user-emitted Overwrite in sync + async
  get_channel_writes.
* InMemory + Postgres savers: same walk-terminator shortcut. The
  pre-delta blob terminator (seed-from-ancestor-blob) stays — it's
  required for migration correctness, not a snapshot optimization.
* Tests for all of the above.

Preserved:

* Channel-level Overwrite semantics in DeltaChannel / BinOpAggregate:
  Overwrite still resets the value at reducer level; same-super-step
  dedup and InvalidUpdateError on multiple Overwrites still enforced.
* Pre-delta migration seeding.
2026-04-30 14:49:05 -04:00
..
2026-04-30 14:49:05 -04:00

LangGraph Checkpoint Postgres

Implementation of LangGraph CheckpointSaver that uses Postgres.

Dependencies

By default langgraph-checkpoint-postgres installs psycopg (Psycopg 3) without any extras. However, you can choose a specific installation that best suits your needs here (for example, psycopg[binary]).

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 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.

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:

# ❌ 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
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

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)]