The Postgres store removes expired items only via the background TTL
sweeper, so between sweeps a read can still return a logically expired
row. Adds an opt-in flag so reads can filter expired rows at query time,
closing the window without depending on sweep cadence.
## Changes
- Add `omit_expired: bool` to `TTLConfig` (default `False`). When unset
or `False`, behavior is unchanged
- When enabled, inject `(expires_at IS NULL OR expires_at > NOW())` into
the read query builders on `BasePostgresStore`, so
`get`/`search`/`list_namespaces` do not surface an expired row:
- **GET** (`_get_batch_GET_ops_queries`): predicate on both the final
SELECT *and* the refresh `UPDATE` — the update is driven from an
unfiltered key list, so gating only the SELECT would hide an expired row
yet still refresh it back to life.
- **SEARCH** (`_prepare_batch_search_queries`): inside the inner scans
(before `LIMIT`/`OFFSET`), which also gates the refresh `UPDATE` fed by
`search_results` and keeps pagination correct
- **list_namespaces** (`_get_batch_list_namespaces_queries`): predicate
appended to the existing scan conditions.
## Testing
- Four behaviors covered sync + async, across the
`default`/`pipe`/`pool` fixtures: expired-unswept row omitted from all
read paths (with a raw SQL check proving the row is still physically
present), default/explicit-`False` still returns it, `refresh_ttl=True`
doesn't resurrect an expired row while still extending live ones, and
search pagination stays correct when an expired row falls inside the
page window.
- Full `checkpoint-postgres` suite green on PG16 (210 passed); `ruff
format`/`check` clean on both packages.
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)
```
