## Summary
- Promotes the private K-channel batched ancestor-walk to a stable
public `get_delta_channel_history` / `aget_delta_channel_history` API on
`BaseCheckpointSaver` (returns `Mapping[str, DeltaChannelHistory]`, a
TypedDict with `writes` always present and `seed` `NotRequired`)
- Removes `DELTA_SENTINEL` / `_DeltaSentinel` entirely — the saver layer
is now delta-agnostic on both write and read paths
- Reworks `DeltaChannel` snapshot cadence from "every Nth superstep" to
"every N updates to this channel," persisted in
`CheckpointMetadata.delta_updates_since_snapshot`
- Adds Postgres optimizations: paged stage-1 with cursor (1024-row
pages) and per-channel UNION ALL stage-2 (no over-fetch when channels
have different chain depths)
- Default `snapshot_frequency` becomes a positive int (default `1000`);
the previous `None` opt-out is removed
## Public API
```python
class DeltaChannelHistory(TypedDict):
writes: list[PendingWrite] # always present, possibly empty
seed: NotRequired[Any] # absent if walk reached root
def get_delta_channel_history(
self, *, config: RunnableConfig, channels: Sequence[str]
) -> Mapping[str, DeltaChannelHistory]: ...
async def aget_delta_channel_history(
self, *, config: RunnableConfig, channels: Sequence[str]
) -> Mapping[str, DeltaChannelHistory]: ...
```
`config` and `channels` are keyword-only so later additions (e.g.
`page_size`) don't shift the positional API.
The TypedDict-with-`NotRequired[seed]` shape matches the existing
checkpoint-package convention (`CheckpointMetadata` is
`TypedDict(total=False)`) — absence-via-key-omission rather than
introducing a new sentinel. Pregel translates `"seed" not in hist` to
`MISSING` on its side at consume time.
The default impl walks `get_tuple` + `parent_config` correctly but is
slow on long chains; savers that care override (`InMemorySaver`,
`PostgresSaver`).
## Sentinel removal
`DELTA_SENTINEL` and `_DeltaSentinel` are deleted entirely. The saver
layer becomes delta-agnostic:
- `DeltaChannel.checkpoint()` returns `MISSING` for non-snapshot steps;
pregel's `create_checkpoint` skips MISSING so delta channels without a
snapshot simply don't appear in `channel_values`
- `InMemorySaver.put` and Postgres `put` no longer filter sentinels
(they have nothing to filter)
- `_needs_replay` becomes `stored is MISSING`
- `DeltaChannel.from_checkpoint` accepts: `MISSING` → empty,
`_DeltaSnapshot(value)` → snapshot value, plain value → pre-migration
legacy
## Snapshot cadence
`DeltaChannel.snapshot_frequency: int` (default `1000`, positive). The
previous `None` opt-out is gone.
```python
def should_snapshot(ch_name, ch):
if force_delta_snapshot: # durability="exit"
return True
return updates_since_snapshot.get(ch_name, 0) >= ch.snapshot_frequency
```
Per-channel update counters are persisted in
`CheckpointMetadata.delta_updates_since_snapshot` (`NotRequired`,
`total=False`). The counter is incremented by `_put_checkpoint` for any
delta channel in `updated_channels` and reset to `0` by
`create_checkpoint` for channels that fire a snapshot this step.
Version-format-independent — works for `int`, `float`, and `str`
versioning schemes alike.
## Postgres optimization
Two improvements internal to the override:
**Stage-1 paged with cursor** (`LIMIT 1024` internal const, `AND
checkpoint_id < ?` for subsequent pages). The previous unpaged form
scanned every checkpoint in `(thread_id, ns)` and was pathological at
high thread depths.
**Stage-2 per-channel UNION ALL**: one `WHERE channel='X' AND
checkpoint_id = ANY(chain_X)` branch per channel plus one seed-blob
branch per channel with a seed. The previous form filtered by `channel =
ANY(channels) AND checkpoint_id = ANY(union_chain_cids)`, over-fetching
writes when channels had different chain depths (`K ×
max(chain_lengths)` vs the correct `sum(chain_lengths)`).
Both improvements stay internal to `PostgresSaver`/`AsyncPostgresSaver`;
the public contract returns a single `Mapping`.
## Benchmarks
`libs/langgraph/tests/test_delta_channel_benchmark.py`. Run via `python
libs/langgraph/tests/test_delta_channel_benchmark.py`. Postgres against
local pg:5441.
Results below trimmed to the high-signal cells. Sub-millisecond /
sub-100-turn rows omitted as warmup-bound; freq=1 omitted (chain depth =
1, nothing to optimize); peak read-time memory and Postgres storage are
flat between branches and omitted. Deep-thread reads and the
cadence-rework storage win are the load-bearing numbers.
### Postgres reads, 500 turns
| Scenario | main | branch | Δ |
|---|---:|---:|---:|
| Single-channel deep read | 17.7 ms | **6.1 ms** | **-66%** |
| Single-channel, 1000 turns | 35.0 ms | **14.3 ms** | **-59%** |
| K=3 channels, freq=50 uniform | 70.5 ms | **41.4 ms** | **-41%** |
| K=8 channels, freq=50 uniform | 214.2 ms | **139.4 ms** | **-35%** |
| K=8 channels, mixed freq (25/50/100/.../1000) | 295.6 ms | **214.4
ms** | **-27%** |
K-channel batching + paged stage-1 + per-channel UNION ALL stage-2 doing
exactly what they should at depth.
### InMemory reads, 500 turns
| Scenario | main | branch | Δ |
|---|---:|---:|---:|
| Single-channel deep read | 7.9 ms | **3.8 ms** | **-52%** |
| Single-channel, 1000 turns | 15.6 ms | **7.2 ms** | **-54%** |
| K=8 channels, freq=50 uniform | 112.3 ms | 94.6 ms | -16% |
| K=8 channels, mixed freq | 184.9 ms | **134.5 ms** | **-27%** |
### InMemory storage, 500 turns (cadence-rework win)
| Scenario | main | branch | Δ |
|---|---:|---:|---:|
| K=3, freq=50 uniform | 8.7 MB | **3.3 MB** | **-62%** |
| K=3 mixed freq | 3.8 MB | **1.3 MB** | **-66%** |
| K=8, freq=50 uniform | 23.1 MB | **8.7 MB** | **-62%** |
| K=8 mixed freq | 11.5 MB | **4.2 MB** | **-64%** |
Snapshot frequency now counts **channel updates** instead of
**supersteps**. On graphs where supersteps outpace per-channel updates
(e.g., input/end steps that don't write to channels), branch stores ~3×
fewer snapshot blobs.
### Tradeoff worth flagging
InMemory K=3 with mixed frequencies (50/200/1000) at 500 turns: **+64%
read latency** (46.6 → 76.5 ms). The mixed scenario has a channel with
`freq=1000` that goes the entire 500-turn run with no snapshot. On main,
the old superstep-counted cadence happened to fire at step=500 anyway.
New cadence gives users explicit control over walk depth via
`snapshot_frequency`. The K=8 mixed case still wins overall (-27%); this
regression is specific to the K=3 mixed shape.
Default `snapshot_frequency=1000` is the upper bound on walk depth —
it's a tunable knob.
## Tests
- New sqlite smoke test (`test_get_delta_channel_history.py`) exercises
the inherited default `BaseCheckpointSaver` impl via `SqliteSaver` /
`AsyncSqliteSaver` end-to-end with a real `DeltaChannel`-backed graph.
Sqlite uses the default unchanged — this validates the default path
actually works on a real second saver, not just on the optimized
override.
- Module-level `pytest.importorskip("langgraph.channels.delta")` guards
the test for sqlite's standalone CI environment (matches the postgres
pattern).
## Test plan
- [x] `libs/checkpoint`: 150 passed, 16 skipped
- [x] `libs/langgraph` (channels + delta migration): 41/41 (post-merge)
- [x] `libs/langgraph` (full pregel suite): 1784 passing — 6 "failures"
verified via `env -i` clean shell are local LangSmith env vars + `git
describe revision_id` polluting LangChain metadata fixtures; CI is
unaffected
- [x] `libs/checkpoint-postgres`: 40/40 saver tests + 3/3 delta channel
reconstruction tests against local Postgres
- [x] `libs/checkpoint-sqlite`: 105/105 (incl. retry-passed flake
`test_ttl_refresh`, unrelated to this PR)
- [x] Lint clean across all four libs (`ruff format`, `ruff check`,
`mypy`)
- [x] Branch-vs-main benchmarks — see results above
---------
Co-authored-by: Quanzheng Long <long@langchain.dev>
Co-authored-by: Cursor <cursoragent@cursor.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)]