Restructure DeltaChannel reconstruction so the hydration path matches pregel's storage axes (blobs + writes) without leaking internal DTOs into the public checkpoint contract. Key changes: * Deleted `DeltaChannelWrites` dataclass and `SEED_UNSET` sentinel. Reconstruction data no longer flows through `Checkpoint.channel_values` as a wrapped DTO — that field now carries a value or `DELTA_SENTINEL`, never a reconstruction shape. * Added private `_ChannelWritesHistory(seed: Any, writes: list[PendingWrite])` NamedTuple as the return type for the new storage-level query. * Added private, experimental `_get_channel_writes_history` / `_aget_channel_writes_history` on `BaseCheckpointSaver` — reference impl via `get_tuple` + `parent_config` walk, overridden on `InMemorySaver` / `PostgresSaver` / `AsyncPostgresSaver` for perf. Fixes a latent migration bug in the base fallback (now inspects ancestor `channel_values` for pre-delta seed). * `DeltaChannel.from_checkpoint(seed)` simplified to two cases (sentinel/MISSING → empty, else → seed). New `replay_writes` method folds `list[PendingWrite]` through the reducer. * Delta hydration consolidated inside `channels_from_checkpoint` via optional `saver` + `config` kwargs (+ async mirror `achannels_from_checkpoint`). All six pregel call sites updated. `get_tuple` no longer patches `channel_values` — removed `_resolve_delta_channels` (memory) and per-tuple reconstruction from `_load_checkpoint_tuple` (postgres sync + async). * Hydration short-circuits on the target's own blob: if `channel_values[k]` is a real value (pre-migration tip, `update_state` result), use it directly. Only walks ancestors when the target holds sentinel or is missing. Fixes a correctness bug where migration-tip and `update_state` values would be lost. * New test_delta_channel_migration.py: 10 scenarios covering BinaryOperatorAggregate → DeltaChannel migration (basic + async, time-travel, fork, `update_state`, tip-of-pre-migration, base-saver fallback parity, cross-thread isolation). 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)]