Renames `operator` → `reducer` and flips arg order to `(reducer, typ=None)`,
matching the new batch contract: `reducer(state, list[writes]) -> state`. The
reducer receives all writes for a step in one call instead of being folded
pairwise, enabling single-pass implementations that avoid O(N²) reprocessing.
`typ` is now optional — `_is_field_channel` in `graph/state.py` always
overwrites it from the `Annotated[T, ...]` outer type, so users can write
`DeltaChannel(my_reducer)` rather than `DeltaChannel(list, my_reducer)`.
Adds `_messages_delta_reducer` to `langgraph.graph.message` (experimental):
a single-pass bulk reducer for message lists that deduplicates by ID and
handles `RemoveMessage` tombstoning without calling `add_messages`, avoiding
repeated dedup passes that `add_messages` would incur in a fold.
Also fixes the `_delta_write_futs` mypy error in `AsyncPregelLoop` by moving
the type annotation to the class body, and unignores `new_pr_desc.md` from
the repo via `.gitignore`.
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
In durability="async" mode (the default), put_writes calls are
fire-and-forget coroutines — a process crash between write submission and
checkpoint commit leaves a DELTA_SENTINEL blob with no backing writes,
causing silent data loss on replay.
AsyncPregelLoop now maintains _delta_write_futs: any write to a
DeltaChannel channel appends its asyncio.Future to this list in
accept_writes. _checkpointer_put_after_previous drains the list with
await asyncio.gather() before calling aput(), guaranteeing
checkpoint_writes are durable before the sentinel blob is committed.
The sync loop is unchanged: BackgroundExecutor.__exit__ already ensures
all background tasks complete before invoke() returns.
Also fixes DeltaChannel(list, add_messages) constructor call in
checkpoint-postgres async test (missing typ arg).
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
Two fixes exposed by running the postgres test suite against a local
postgres instance:
1. `PostgresSaver._get_channel_writes_history` /
`AsyncPostgresSaver._aget_channel_writes_history` required
`checkpoint_id` in the passed config, raising `KeyError` when called
with just `thread_id` (e.g. `graph.aget_state({"thread_id": "..."})`).
Now resolves to the latest checkpoint via `get_tuple`/`aget_tuple`
when the id is missing.
2. `test_get_checkpoint_no_channel_values` (sync + async) monkeypatched
`_load_checkpoint_tuple` with the old `(value, cur)` signature. Method
now takes `(value)` only since delta reconstruction moved out of the
tuple-load path — updated both tests.
Local postgres (`brew install pgvector postgresql@16`, running on port
5441) now exercises all 40 non-vector postgres tests green.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Add `_load_diff_chains_async` to `AsyncPostgresSaver` and override
`_load_checkpoint_tuple` to inline blob-parsing and diff-chain
resolution via async point-lookup traversal, mirroring the sync
`PostgresSaver._load_diff_chains` implementation. Add integration test
`test_diff_channel_chain_reconstruction` that skips gracefully when
`langgraph` core is not installed.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Issue
Support for `Checkpoint.metadata.writes` was dropped in `langgraph`
v0.5.x.
In `langgraph-checkpoint-postgres` v2.0.23, metadata was serialized with
`BasePostgresSaver._dump_metadata` -> `JsonPlusSerializer.dumps` which
handles `pydantic.BaseModel`.
In v2.0.23, metadata is serialized with `psycopg.types.json.Jsonb`,
which raises `TypeError: Object of type AIMessage is not JSON
serializable` when trying to serialize `writes`.
Solution
- Add `BaseCheckpointSaver.get_serializable_checkpoint_metadata` which
pops the `writes` key.
- Log deprecation warning when strange version combinations are used
Solves https://github.com/langchain-ai/langgraph/issues/5769
---------
Co-authored-by: Alex Kondratev <56111142+soapun@users.noreply.github.com>
### Description
https://github.com/langchain-ai/langgraph/issues/6137 and
https://github.com/langchain-ai/langgraph/issues/5677 reported issues
where older checkpoints read by AsyncPostgresSaver/PostgresSaver from
`langgraph-checkpoint-postgres==2.0.19` fail to read channel values,
throwing `NoneType object is not a mapping`. This was due to a bug in
how `channel_values` is assembled:
```python
"channel_values": {
**value["checkpoint"].get("channel_values"), # <--- if channel_values doesn't exist (old checkpoint), **None errors
**self._load_blobs(value["channel_values"]),
},
```
This bug was observed for checkpoints generated by
`langgraph-checkpoint-postgres<=2.0.19`.
Fixed by providing a fallback to
`value["checkpoint"].get("channel_values")`:
```python
**value["checkpoint"],
"channel_values": {
**(
value["checkpoint"].get("channel_values") or {}
), # 'or {}' needed for backwards compat with v3 checkpoints and below, as v4 introduced channel_values key
**self._load_blobs(value["channel_values"]),
},
```
### Tests
Added test for AsyncPostgresSaver and test for PostgresSaver, using
monkeypatch to remove `channel_values` before CheckpointTuple is
assembled in `_load_checkpoint_tuple`.
### Solves
https://github.com/langchain-ai/langgraph/issues/6137 and
https://github.com/langchain-ai/langgraph/issues/5677
---------
Co-authored-by: Shahrukh Shaik <144558473+shahrukh-shaik@users.noreply.github.com>
This PR updates the dependencies in all Python packages using `uv lock
--upgrade`.
This is an automated PR created by the UV Lock Upgrade workflow.
To make tests pass:
* linting fixes
* whitespace fixes in snapshots
---------
Co-authored-by: sydney-runkle <54324534+sydney-runkle@users.noreply.github.com>
Co-authored-by: Sydney Runkle <sydneymarierunkle@gmail.com>
### Description
* Set `ensure_ascii=False` for all `json.dumps` calls in
`get_text_at_path`. Preserves non-ASCII text instead of embedding
`\uXXXX` escapes.
**Before**
```python
store.put(("user_123", "memories"), "1", {"text": "这是中文"})
# embeds {"text": "\\u8fd9\\u662f\\u4e2d\\u6587"}
```
**After**
```python
store.put(("user_123", "memories"), "1", {"text": "这是中文"})
# embeds {"text": "这是中文"}
```
### Tests & Docs
* Add unit test `test_non_ascii` that writes three records (Chinese,
Japanese, Korean) to an `InMemoryStore`, searches with the same strings,
and asserts the correct top hit with a score >= 0.15 for each.
### Issue
Fixes#5946
- Leave it up to each checkpointer implementation to decide whether to merge in configurable/metadata (previously PregelLoop would do some of this always)
- Never copy over internal langgraph keys into checkpoint.metadata (these are redundant/misleading to include)
Prepare langgraph-checkpoint for 0.5
- Given we have no upper bound on langgraph-checkpoint dep need to undo all changes in langgraph-checkpoint that might break previous versions of langgraph
- Instead store sends in a Topic channel, removing the need to fetch sends as writes against the parent checkpoint
- Remove deprecated/unused functions in langgraph-checkpoint (will require bumping min range for langgraph-checkpoint in langgraph lib)
- Implement migration of old pending sends in langgraph-checkpoint-postgres
- Ensure parent config of `checkpoint_during=False` checkpoints always points to checkpoints that were also saved
The configuration expects the key "fields", not "text_fields": I had
failed to update across all implementations in the original PR
Thank you to Vincent Min for the fix!
---------
Co-authored-by: Vincent Min <93780551+VMinB12@users.noreply.github.com>
This PR adds a "shallow" version of `PostgresSaver` checkpointer that
ONLY stores the most recent checkpoint and does NOT retain any history.
It is meant to be a light-weight drop-in replacement for the
PostgresSaver that supports most of the LangGraph persistence
functionality with the exception of time travel.
Adds a few of preliminaries:
1. Makes the returned "score" actually the result of the requested
operation (cosine, inner_product, l2)
2. Sorts asc, etc. so that if you were to add an HNSW index (and not
have any WHERE filters), it would be used
3. Drop the inner WHERE statement if no namespace or other filters are
provided. See (2) for why.
I don't yet add an index to the migrations since I think we need to
agree on the right balance to ensure it's actually used in common query
patterns.
- Initializing the store with an 'embedding config' -> this contains the
'dims' (used to create the table) and the encoder object (rn langchain
embeddings object, though that is ......)
- Call setup() -> creates the vector table.
Each document has 1 or more vectors associated with it for each json
path in the embedding config.
Would welcome critique and requests!
Leaving the params as the defaults for pgvector but open to feedback if
you think it's important to be able to more transparently configure that
in setup()
```python
from typing import TypedDict, List, Dict, Any, Optional
from langchain_openai import OpenAIEmbeddings
from langgraph.graph import StateGraph
from langgraph.store.postgres import PostgresStore
emb_config = {
"dims": 1536, # OpenAI embedding dimensions
"embed": OpenAIEmbeddings(model="text-embedding-3-small"),
"distance_type": "cosine",
}
with PostgresStore.from_conn_string(
"postgres://postgres:postgres@localhost:5441",
embedding=emb_config,
) as store:
store.setup()
# Define the state type for our graph
class State(TypedDict):
query: str
results: Optional[List[Dict[str, Any]]]
def put_stuff(state: State) -> State:
docs = [
("doc1", {"text": "red apple in kitchen"}),
("doc2", {"text": "blue car in garage"}),
("doc3", {"text": "green apple on table"}),
]
for key, value in docs:
store.put(("docs",), key, value)
def search_stuff(state: State) -> State:
"""Search for documents using vector similarity."""
results = store.search(("docs",), query=state["query"])
return {"results": results}
builder = StateGraph(State)
builder.add_node(put_stuff)
builder.add_node(search_stuff)
builder.add_edge("__start__", "put_stuff")
builder.add_edge("put_stuff", "search_stuff")
# Compile
with PostgresStore.from_conn_string(
"postgres://postgres:postgres@localhost:5441",
embedding=emb_config,
) as store:
chain = builder.compile(store=store)
result = chain.invoke({"query": "sour apple"})
# Print results
for doc in result["results"]:
print(doc.key)
print(doc.value)
print(doc.response_metadata)
```
