Four fixes from an independent review of the reconstruction pipeline, plus a structural cleanup: 1. Ancestor walk excludes the target checkpoint itself (matches pregel: writes stored under checkpoint_id=T are pending for the NEXT step and applied separately via apply_writes). Memory saver previously included them, diverging from Postgres and causing pending writes to be folded into the reconstructed snapshot — visible via get_state during interrupts and time-travel into a non-leaf checkpoint. 2. Pre-delta blob terminator. When the walk hits an ancestor whose blob for the channel is a real value (not DELTA_SENTINEL), bind that blob as DeltaChannelWrites.seed and stop. Without this, threads migrated from pre-delta storage would replay ancestor writes to the root forever AND lose any value that lived only in the old blob (e.g. from update_state). Per-ancestor, the blob is checked BEFORE its writes — a pre-delta blob subsumes writes at the same checkpoint, so including them would double-count. 3. Base-fallback get_channel_writes follows parent_checkpoint_id instead of list(before=...). The previous form returned every tuple with id<target, including sibling branches on forked threads. 4. seed replaces the Overwrite-wrapping hack for pre-delta values. DeltaChannelWrites(writes, seed=SEED_UNSET) makes the saver's reconstruction terminator semantically explicit; drops the lazy _make_overwrite import dance. User-emitted Overwrite still reset the chain via _apply_write as before. Postgres: recursive CTE enumerates on-path ancestors and joins once against checkpoint_writes and once against checkpoint_blobs for every delta channel in the get_tuple — one roundtrip instead of the previous 3 queries × N channels. Tests added: - Pre-delta blob seeding (seed binding, no double-counting of ancestor writes at the terminator, pending-at-target excluded). - Root checkpoint returns empty writes. - Seed-based from_checkpoint replay (three scenarios: with writes, seed-only, seed=None distinct from SEED_UNSET). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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=trueor pass an explicitallowed_msgpack_moduleslist 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
PostgresSaverorAsyncPostgresSaver, make sure to includeautocommit=Trueandrow_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 defaulttuple_rowfactory returns tuples that only support index-based access (e.g.,row[0]), which will causeTypeErrorexceptions 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)]