Files
langgraph/libs/checkpoint-postgres
d8b7800183 chore(langgraph): use two phase read to avoid unnecessary data transport (#7660)
## Summary

Replaces the single-roundtrip `UNION ALL` DeltaChannel read with a
two-stage query that avoids fetching unused snapshot blobs, then removes
the old combined path entirely.

### Problem

`_get_channel_writes_history` used a single `UNION ALL` query that
fetched **all** checkpoint metadata, writes, and blobs for a
`(thread_id, channel)` in one shot. With `snapshot_frequency=N`, this
pulled back O(N/freq) full-size snapshot blobs even though only the
nearest one is needed to seed reconstruction. At 500 turns with
`snapshot_frequency=10`, this meant fetching ~100 complete
message-history snapshots per read.

### Solution

Two-stage read:
- **Stage 1** — lightweight scan of `checkpoints` only (no blob bytes):
walks the parent chain from the target checkpoint and stops at the first
ancestor with a snapshot, returning `chain_cids` and `seed_version`
- **Stage 2** — targeted fetch: only the writes for `chain_cids` and the
single seed blob at `seed_version`

The two-stage path is now unconditional — the old combined query and
`LG_DELTA_TWO_STAGE_QUERY` env-var gate have been removed.

### Sentinel cleanup

`DELTA_SENTINEL` is now a pure in-memory signal and is never written to
storage:
- Postgres `put()` already stripped it from `channel_values` before
writing blobs
- Memory saver `put()` now stores `"empty"` instead of serializing the
sentinel
- `EXT_DELTA_SENTINEL` (msgpack ext code 8) removed from
`JsonPlusSerializer`
- `DELTA_SENTINEL` is kept as an in-memory marker:
`DeltaChannel.checkpoint()` returns it so savers know to skip it, and
`_ChannelWritesHistory.seed` uses it to mean "no snapshot found, start
from empty"

## Performance

Benchmarked at `snapshot_frequency=10` on Postgres (`~100 tok/msg`):

| turns | old combined query | two-stage |
|------:|-------------------:|----------:|
| 50    | 6.0ms              | 2.8ms  (2.1x faster) |
| 100   | 10.1ms             | 5.6ms  (1.8x faster) |
| 500   | **216.1ms**        | 15.3ms (**14x faster**) |

The old query's read time grew super-linearly with turn count because
each read fetched O(N/freq) full snapshot blobs. Two-stage keeps read
depth bounded by `snapshot_frequency` regardless of thread length.

## Test plan

- `make test` in `libs/checkpoint`, `libs/checkpoint-postgres`,
`libs/langgraph`
- Removed `test_delta_sentinel_serde_round_trip` (sentinel no longer
serializable)
- Updated `test_memory.py` — delta channel blobs stored as `"empty"`,
not serialized sentinel
- Updated `test_channels.py` — `channel_values` no longer contains
sentinel key for DeltaChannels
- Deleted `test_delta_channel_two_stage_benchmark.py` (one-stage vs
two-stage comparison; path no longer exists)

---------

Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
Co-authored-by: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
2026-05-01 11:06:54 -04:00
..
2026-04-29 17:49:01 -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)]