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Author SHA1 Message Date
Will Fu-Hinthorn 8393182893 update loop 2026-04-14 05:51:25 -07:00
Will Fu-Hinthorn 488c1af383 merge 2026-04-13 08:17:42 -07:00
Will Fu-Hinthorn fbcdbb5e0c update 2026-04-10 14:23:21 -07:00
Will Fu-Hinthorn 7f225ef93e chore: Backpressure 2026-04-09 17:35:59 -07:00
64 changed files with 1205 additions and 6596 deletions
-1
View File
@@ -100,4 +100,3 @@ dmypy.json
.turbo
.editorconfig
.scratch
.worktrees/
+6 -6
View File
@@ -306,7 +306,7 @@ test = [
[[package]]
name = "langsmith"
version = "0.7.31"
version = "0.7.3"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "httpx" },
@@ -319,9 +319,9 @@ dependencies = [
{ name = "xxhash" },
{ name = "zstandard" },
]
sdist = { url = "https://files.pythonhosted.org/packages/e6/11/696019490992db5c87774dc20515529ef42a01e1d770fb754ed6d9b12fb0/langsmith-0.7.31.tar.gz", hash = "sha256:331ee4f7c26bb5be4022b9859b7d7b122cbf8c9d01d9f530114c1914b0349ffb", size = 1178480, upload-time = "2026-04-14T17:55:41.242Z" }
sdist = { url = "https://files.pythonhosted.org/packages/8d/bc/8172fefad4f2da888a6d564a27d1fb7d4dbf3c640899c2b40c46235cbe98/langsmith-0.7.3.tar.gz", hash = "sha256:0223b97021af62d2cf53c8a378a27bd22e90a7327e45b353e0069ae60d5d6f9e", size = 988575, upload-time = "2026-02-13T23:25:32.916Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/1d/a1/a013cf458c301cda86a213dd153ce0a01c93f1ab5833f951e6a44c9763ce/langsmith-0.7.31-py3-none-any.whl", hash = "sha256:0291d49203f6e80dda011af1afda61eb0595a4d697adb684590a8805e1d61fb6", size = 373276, upload-time = "2026-04-14T17:55:39.677Z" },
{ url = "https://files.pythonhosted.org/packages/f4/9d/5a68b6b5e313ffabbb9725d18a71edb48177fd6d3ad329c07801d2a8e862/langsmith-0.7.3-py3-none-any.whl", hash = "sha256:03659bf9274e6efcead361c9c31a7849ea565ae0d6c0d73e1d8b239029eff3be", size = 325718, upload-time = "2026-02-13T23:25:31.52Z" },
]
[[package]]
@@ -623,7 +623,7 @@ wheels = [
[[package]]
name = "pytest"
version = "9.0.3"
version = "9.0.2"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "colorama", marker = "sys_platform == 'win32'" },
@@ -634,9 +634,9 @@ dependencies = [
{ name = "pygments" },
{ name = "tomli", marker = "python_full_version < '3.11'" },
]
sdist = { url = "https://files.pythonhosted.org/packages/7d/0d/549bd94f1a0a402dc8cf64563a117c0f3765662e2e668477624baeec44d5/pytest-9.0.3.tar.gz", hash = "sha256:b86ada508af81d19edeb213c681b1d48246c1a91d304c6c81a427674c17eb91c", size = 1572165, upload-time = "2026-04-07T17:16:18.027Z" }
sdist = { url = "https://files.pythonhosted.org/packages/d1/db/7ef3487e0fb0049ddb5ce41d3a49c235bf9ad299b6a25d5780a89f19230f/pytest-9.0.2.tar.gz", hash = "sha256:75186651a92bd89611d1d9fc20f0b4345fd827c41ccd5c299a868a05d70edf11", size = 1568901, upload-time = "2025-12-06T21:30:51.014Z" }
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{ url = "https://files.pythonhosted.org/packages/3b/ab/b3226f0bd7cdcf710fbede2b3548584366da3b19b5021e74f5bde2a8fa3f/pytest-9.0.2-py3-none-any.whl", hash = "sha256:711ffd45bf766d5264d487b917733b453d917afd2b0ad65223959f59089f875b", size = 374801, upload-time = "2025-12-06T21:30:49.154Z" },
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[[package]]
-5
View File
@@ -6,11 +6,6 @@ Implementation of LangGraph CheckpointSaver that uses Postgres.
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](https://www.psycopg.org/psycopg3/docs/basic/install.html) (for example, `psycopg[binary]`).
## Security
> [!IMPORTANT]
> Set `LANGGRAPH_STRICT_MSGPACK=true` or pass an explicit `allowed_msgpack_modules` list 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](https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint#serde) for details.
## Usage
> [!IMPORTANT]
@@ -8,13 +8,11 @@ from typing import Any
from langchain_core.runnables import RunnableConfig
from langgraph.checkpoint.base import (
DELTA_SENTINEL,
WRITES_IDX_MAP,
ChannelVersions,
Checkpoint,
CheckpointMetadata,
CheckpointTuple,
_ChannelWritesHistory,
get_checkpoint_id,
get_serializable_checkpoint_metadata,
)
@@ -25,12 +23,7 @@ from psycopg.types.json import Jsonb
from psycopg_pool import ConnectionPool
from langgraph.checkpoint.postgres import _internal
from langgraph.checkpoint.postgres.base import (
SELECT_DELTA_BLOBS_SQL,
SELECT_DELTA_PARENTS_SQL,
SELECT_DELTA_WRITES_SQL,
BasePostgresSaver,
)
from langgraph.checkpoint.postgres.base import BasePostgresSaver
from langgraph.checkpoint.postgres.shallow import ShallowPostgresSaver
Conn = _internal.Conn # For backward compatibility
@@ -437,42 +430,6 @@ class PostgresSaver(BasePostgresSaver):
with conn.cursor(binary=True, row_factory=dict_row) as cur:
yield cur
def _get_channel_writes_history(
self, config: RunnableConfig, channel: str
) -> _ChannelWritesHistory:
"""Fast-path override of `BaseCheckpointSaver._get_channel_writes_history`.
Three indexed roundtrips (`checkpoints`, `checkpoint_writes`,
`checkpoint_blobs`) each filtered by `(thread_id, checkpoint_ns)` and
the per-table key. Plain SELECTs let the planner pick straight index
scans; rationale + benchmark in `notes/delta_channel_query_bench.md`.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
checkpoint_id = get_checkpoint_id(config)
if checkpoint_id is None:
# Caller didn't specify a target — resolve to the latest
# checkpoint on the thread. `get_tuple` without `checkpoint_id`
# returns the newest; its config carries the resolved id.
target = self.get_tuple(config)
if target is None:
return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
checkpoint_id = target.config["configurable"]["checkpoint_id"]
with self._cursor() as cur:
cur.execute(SELECT_DELTA_PARENTS_SQL, (channel, thread_id, checkpoint_ns))
parents_rows = cur.fetchall()
cur.execute(SELECT_DELTA_WRITES_SQL, (thread_id, checkpoint_ns, channel))
writes_rows = cur.fetchall()
cur.execute(SELECT_DELTA_BLOBS_SQL, (thread_id, checkpoint_ns, channel))
blobs_rows = cur.fetchall()
return self._build_delta_channel_writes_history(
channel=channel,
target_id=checkpoint_id,
parents_rows=parents_rows,
writes_rows=writes_rows,
blobs_rows=blobs_rows,
)
def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
"""
Convert a database row into a CheckpointTuple object.
@@ -485,7 +442,6 @@ class PostgresSaver(BasePostgresSaver):
including its configuration, metadata, parent checkpoint (if any),
and pending writes.
"""
channel_values = self._load_blobs(value["channel_values"])
return CheckpointTuple(
{
"configurable": {
@@ -498,7 +454,7 @@ class PostgresSaver(BasePostgresSaver):
**value["checkpoint"],
"channel_values": {
**(value["checkpoint"].get("channel_values") or {}),
**channel_values,
**self._load_blobs(value["channel_values"]),
},
},
value["metadata"],
@@ -8,13 +8,11 @@ from typing import Any
from langchain_core.runnables import RunnableConfig
from langgraph.checkpoint.base import (
DELTA_SENTINEL,
WRITES_IDX_MAP,
ChannelVersions,
Checkpoint,
CheckpointMetadata,
CheckpointTuple,
_ChannelWritesHistory,
get_checkpoint_id,
get_serializable_checkpoint_metadata,
)
@@ -25,12 +23,7 @@ from psycopg.types.json import Jsonb
from psycopg_pool import AsyncConnectionPool
from langgraph.checkpoint.postgres import _ainternal
from langgraph.checkpoint.postgres.base import (
SELECT_DELTA_BLOBS_SQL,
SELECT_DELTA_PARENTS_SQL,
SELECT_DELTA_WRITES_SQL,
BasePostgresSaver,
)
from langgraph.checkpoint.postgres.base import BasePostgresSaver
from langgraph.checkpoint.postgres.shallow import AsyncShallowPostgresSaver
Conn = _ainternal.Conn # For backward compatibility
@@ -398,45 +391,6 @@ class AsyncPostgresSaver(BasePostgresSaver):
async with conn.cursor(binary=True, row_factory=dict_row) as cur:
yield cur
async def _aget_channel_writes_history(
self, config: RunnableConfig, channel: str
) -> _ChannelWritesHistory:
"""Fast-path override of `BaseCheckpointSaver._aget_channel_writes_history`.
Three indexed roundtrips (`checkpoints`, `checkpoint_writes`,
`checkpoint_blobs`); rows assembled by the shared pure helper on
`BasePostgresSaver`. Rationale + benchmark in
`notes/delta_channel_query_bench.md`.
"""
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
checkpoint_id = get_checkpoint_id(config)
if checkpoint_id is None:
target = await self.aget_tuple(config)
if target is None:
return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
checkpoint_id = target.config["configurable"]["checkpoint_id"]
async with self._cursor() as cur:
await cur.execute(
SELECT_DELTA_PARENTS_SQL, (channel, thread_id, checkpoint_ns)
)
parents_rows = await cur.fetchall()
await cur.execute(
SELECT_DELTA_WRITES_SQL, (thread_id, checkpoint_ns, channel)
)
writes_rows = await cur.fetchall()
await cur.execute(
SELECT_DELTA_BLOBS_SQL, (thread_id, checkpoint_ns, channel)
)
blobs_rows = await cur.fetchall()
return self._build_delta_channel_writes_history(
channel=channel,
target_id=checkpoint_id,
parents_rows=parents_rows,
writes_rows=writes_rows,
blobs_rows=blobs_rows,
)
async def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
"""
Convert a database row into a CheckpointTuple object.
@@ -449,18 +403,11 @@ class AsyncPostgresSaver(BasePostgresSaver):
including its configuration, metadata, parent checkpoint (if any),
and pending writes.
"""
thread_id = value["thread_id"]
checkpoint_ns = value["checkpoint_ns"]
blob_values = value["channel_values"]
channel_values: dict[str, Any] = {}
if blob_values:
channel_values = self._load_blobs(blob_values)
return CheckpointTuple(
{
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"thread_id": value["thread_id"],
"checkpoint_ns": value["checkpoint_ns"],
"checkpoint_id": value["checkpoint_id"],
}
},
@@ -468,15 +415,15 @@ class AsyncPostgresSaver(BasePostgresSaver):
**value["checkpoint"],
"channel_values": {
**(value["checkpoint"].get("channel_values") or {}),
**channel_values,
**self._load_blobs(value["channel_values"]),
},
},
value["metadata"],
(
{
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"thread_id": value["thread_id"],
"checkpoint_ns": value["checkpoint_ns"],
"checkpoint_id": value["parent_checkpoint_id"],
}
}
@@ -8,12 +8,9 @@ from typing import Any, cast
from langchain_core.runnables import RunnableConfig
from langgraph.checkpoint.base import (
DELTA_SENTINEL,
WRITES_IDX_MAP,
BaseCheckpointSaver,
ChannelVersions,
PendingWrite,
_ChannelWritesHistory,
get_checkpoint_id,
)
from langgraph.checkpoint.serde.types import TASKS
@@ -155,30 +152,6 @@ INSERT_CHECKPOINT_WRITES_SQL = """
ON CONFLICT (thread_id, checkpoint_ns, checkpoint_id, task_id, idx) DO NOTHING
"""
# DeltaChannel reconstruction: three plain indexed SELECTs per channel.
# Bench (notes/delta_channel_query_bench.md) showed the prior recursive CTE
# carried a hidden O(ancestors x blobs_in_thread) join; plain SELECTs are
# 3x-100x faster in the realistic depth range and the Python walk is O(n).
SELECT_DELTA_PARENTS_SQL = """
SELECT checkpoint_id,
parent_checkpoint_id,
checkpoint -> 'channel_versions' ->> %s AS ver
FROM checkpoints
WHERE thread_id = %s AND checkpoint_ns = %s
"""
SELECT_DELTA_WRITES_SQL = """
SELECT checkpoint_id, type, blob, task_id, idx
FROM checkpoint_writes
WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s
"""
SELECT_DELTA_BLOBS_SQL = """
SELECT version, type, blob
FROM checkpoint_blobs
WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s
"""
class BasePostgresSaver(BaseCheckpointSaver[str]):
SELECT_SQL = SELECT_SQL
@@ -212,91 +185,16 @@ class BasePostgresSaver(BaseCheckpointSaver[str]):
)
def _load_blobs(
self,
blob_values: Any,
self, blob_values: list[tuple[bytes, bytes, bytes]]
) -> dict[str, Any]:
if not blob_values:
return {}
result: dict[str, Any] = {}
for k, t, v in blob_values:
type_tag = t.decode()
if type_tag != "empty":
result[k.decode()] = self.serde.loads_typed((type_tag, v))
return result
def _build_delta_channel_writes_history(
self,
*,
channel: str,
target_id: str,
parents_rows: Sequence[Any],
writes_rows: Sequence[Any],
blobs_rows: Sequence[Any],
) -> _ChannelWritesHistory:
"""Reconstruct one delta channel's history from rows of the three SELECTs.
Pure data transform shared by sync (`PostgresSaver`) and async
(`AsyncPostgresSaver`); both paths run the queries themselves and
feed the rows here.
Walk is newest → oldest from the target's parent. A non-sentinel
blob in `checkpoint_blobs` (a pre-delta snapshot) terminates the
walk and is returned as the seed so replay starts from it.
Writes stored at `target_id` itself are pending writes for the next
step and are excluded — the walk begins at the target's parent.
"""
parent_of: dict[str, str | None] = {}
ver_of: dict[str, str | None] = {}
for r in parents_rows:
cid = r["checkpoint_id"]
parent_of[cid] = r["parent_checkpoint_id"]
ver_of[cid] = r["ver"]
ancestors: list[str] = []
cid = parent_of.get(target_id)
while cid is not None:
ancestors.append(cid)
cid = parent_of.get(cid)
if not ancestors:
return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
ancestor_set = set(ancestors)
# Group writes by ancestor cid; sort within (task_id DESC, idx DESC)
# to match the prior CTE ordering — newest write first per ancestor.
writes_by_cid: dict[str, list[tuple[str, bytes, str, int]]] = {}
for r in writes_rows:
cid = r["checkpoint_id"]
if cid not in ancestor_set:
continue
writes_by_cid.setdefault(cid, []).append(
(r["type"], r["blob"], r["task_id"], r["idx"])
)
for ws in writes_by_cid.values():
ws.sort(key=lambda w: (w[2], w[3]), reverse=True)
blob_by_ver: dict[str, tuple[str, bytes]] = {
r["version"]: (r["type"], r["blob"]) for r in blobs_rows
return {
k.decode(): self.serde.loads_typed((t.decode(), v))
for k, t, v in blob_values
if t.decode() != "empty"
}
collected: list[PendingWrite] = [] # newest first; reversed at the end
for cid in ancestors:
# Pre-delta blob terminator: subsumes any writes at this ancestor.
ver = ver_of.get(cid)
if ver is not None:
seed_blob = blob_by_ver.get(ver)
if seed_blob is not None and seed_blob[0] != "empty":
blob_value = self.serde.loads_typed(seed_blob)
if blob_value is not DELTA_SENTINEL:
collected.reverse()
return _ChannelWritesHistory(seed=blob_value, writes=collected)
for type_tag, write_blob, task_id, _idx in writes_by_cid.get(cid, []):
val = self.serde.loads_typed((type_tag, write_blob))
collected.append((task_id, channel, val))
collected.reverse() # oldest → newest
return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
def _dump_blobs(
self,
thread_id: str,
+2 -46
View File
@@ -361,9 +361,9 @@ async def test_get_checkpoint_no_channel_values(
load_checkpoint_tuple = saver._load_checkpoint_tuple
async def patched_load_checkpoint_tuple(value):
def patched_load_checkpoint_tuple(value):
value["checkpoint"].pop("channel_values", None)
return await load_checkpoint_tuple(value)
return load_checkpoint_tuple(value)
monkeypatch.setattr(
saver, "_load_checkpoint_tuple", patched_load_checkpoint_tuple
@@ -371,47 +371,3 @@ async def test_get_checkpoint_no_channel_values(
checkpoint = await saver.aget_tuple(config)
assert checkpoint.checkpoint["channel_values"] == {}
@pytest.mark.parametrize("saver_name", ["base", "pool", "pipe"])
async def test_delta_channel_chain_reconstruction(saver_name: str) -> None:
"""AsyncPostgresSaver reconstructs DeltaChannel chain via point-lookup traversal."""
pytest.importorskip(
"langgraph.channels._delta", reason="langgraph core not installed"
)
from typing import Annotated
from langchain_core.messages import AIMessage, HumanMessage
from langgraph.channels._delta import DeltaChannel
from langgraph.graph import START, StateGraph
from langgraph.graph.message import add_messages
from typing_extensions import TypedDict
class State(TypedDict):
messages: Annotated[list, DeltaChannel(add_messages)]
def respond(state: State) -> dict:
n = len(state["messages"])
return {"messages": [AIMessage(content=f"reply-{n}", id=f"ai-{n}")]}
builder = StateGraph(State)
builder.add_node("respond", respond)
builder.add_edge(START, "respond")
async with _saver(saver_name) as saver:
graph = builder.compile(checkpointer=saver)
config = {"configurable": {"thread_id": "diff-channel-test-1"}}
await graph.ainvoke({"messages": [HumanMessage(content="hi", id="h1")]}, config)
await graph.ainvoke(
{"messages": [HumanMessage(content="there", id="h2")]}, config
)
state = await graph.aget_state(config)
msgs = state.values["messages"]
assert len(msgs) == 4, f"expected 4, got {len(msgs)}: {msgs}"
assert msgs[0].content == "hi"
assert msgs[1].content == "reply-1"
assert msgs[2].content == "there"
assert msgs[3].content == "reply-3"
+7 -126
View File
@@ -259,7 +259,7 @@ wheels = [
[[package]]
name = "langgraph-checkpoint"
version = "4.0.2"
version = "4.0.1"
source = { editable = "../checkpoint" }
dependencies = [
{ name = "langchain-core" },
@@ -382,7 +382,7 @@ test = [
[[package]]
name = "langsmith"
version = "0.7.31"
version = "0.6.4"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "httpx" },
@@ -392,12 +392,11 @@ dependencies = [
{ name = "requests" },
{ name = "requests-toolbelt" },
{ name = "uuid-utils" },
{ name = "xxhash" },
{ name = "zstandard" },
]
sdist = { url = "https://files.pythonhosted.org/packages/e6/11/696019490992db5c87774dc20515529ef42a01e1d770fb754ed6d9b12fb0/langsmith-0.7.31.tar.gz", hash = "sha256:331ee4f7c26bb5be4022b9859b7d7b122cbf8c9d01d9f530114c1914b0349ffb", size = 1178480, upload-time = "2026-04-14T17:55:41.242Z" }
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]
[[package]]
@@ -951,7 +950,7 @@ wheels = [
[[package]]
name = "pytest"
version = "9.0.3"
version = "9.0.2"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "colorama", marker = "sys_platform == 'win32'" },
@@ -962,9 +961,9 @@ dependencies = [
{ name = "pygments" },
{ name = "tomli", marker = "python_full_version < '3.11'" },
]
sdist = { url = "https://files.pythonhosted.org/packages/7d/0d/549bd94f1a0a402dc8cf64563a117c0f3765662e2e668477624baeec44d5/pytest-9.0.3.tar.gz", hash = "sha256:b86ada508af81d19edeb213c681b1d48246c1a91d304c6c81a427674c17eb91c", size = 1572165, upload-time = "2026-04-07T17:16:18.027Z" }
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-5
View File
@@ -2,11 +2,6 @@
Implementation of LangGraph CheckpointSaver that uses SQLite DB (both sync and async, via `aiosqlite`)
## Security
> [!IMPORTANT]
> Set `LANGGRAPH_STRICT_MSGPACK=true` or pass an explicit `allowed_msgpack_modules` list 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](https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint#serde) for details.
## Usage
```python
+10 -129
View File
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]
[[package]]
name = "zstandard"
version = "0.25.0"
-3
View File
@@ -26,9 +26,6 @@ You must pass these when invoking the graph as part of the configurable part of
`langgraph_checkpoint` also defines protocol for serialization/deserialization (serde) and provides an default implementation (`langgraph.checkpoint.serde.jsonplus.JsonPlusSerializer`) that handles a wide variety of types, including LangChain and LangGraph primitives, datetimes, enums and more.
> [!IMPORTANT]
> **Checkpoint deserialization security:** By default the serializer allows any Python type found in checkpoint data. New applications should set the environment variable `LANGGRAPH_STRICT_MSGPACK=true` or pass an explicit `allowed_msgpack_modules` list to `JsonPlusSerializer` to restrict deserialization to known-safe types.
### Pending writes
When a graph node fails mid-execution at a given superstep, LangGraph stores pending checkpoint writes from any other nodes that completed successfully at that superstep, so that whenever we resume graph execution from that superstep we don't re-run the successful nodes.
@@ -1,10 +1,9 @@
from __future__ import annotations
import contextvars
import copy
import logging
from collections.abc import AsyncIterator, Collection, Iterator, Mapping, Sequence
from typing import (
from typing import ( # noqa: UP035
Any,
Generic,
Literal,
@@ -19,9 +18,6 @@ from langgraph.checkpoint.base.id import uuid6
from langgraph.checkpoint.serde.base import SerializerProtocol, maybe_add_typed_methods
from langgraph.checkpoint.serde.encrypted import EncryptedSerializer
from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
from langgraph.checkpoint.serde.types import (
DELTA_SENTINEL as DELTA_SENTINEL,
)
from langgraph.checkpoint.serde.types import (
ERROR,
INTERRUPT,
@@ -32,16 +28,6 @@ from langgraph.checkpoint.serde.types import (
V = TypeVar("V", int, float, str)
PendingWrite = tuple[str, str, Any]
# Task-local guard: ContextVar is copied per asyncio Task, so concurrent
# requests on the same event-loop thread do not share this flag. A plain
# `threading.local()` would leak across tasks and let one in-flight
# reconstruction silently short-circuit another.
_DELTA_RECONSTRUCTION: contextvars.ContextVar[bool] = contextvars.ContextVar(
"_DELTA_RECONSTRUCTION", default=False
)
logger = logging.getLogger(__name__)
@@ -133,30 +119,6 @@ class CheckpointTuple(NamedTuple):
pending_writes: list[PendingWrite] | None = None
class _ChannelWritesHistory(NamedTuple):
"""Result of `BaseCheckpointSaver._get_channel_writes_history`.
Storage-level view of what one channel wrote across the ancestor chain
of a target checkpoint:
* `seed` — the nearest ancestor's stored blob value for this channel,
or `DELTA_SENTINEL` if the walk reached the root without finding a
stored value. A non-sentinel seed typically indicates a pre-delta
snapshot preserved across a channel-type migration (e.g.
`BinaryOperatorAggregate` storage extended under `DeltaChannel`).
* `writes` — on-path deltas oldest→newest, one `PendingWrite` per
step that wrote to this channel. Writes stored at the target
checkpoint itself are pending for the next super-step and are
excluded.
Experimental: method surface may change; the NamedTuple shape is the
contract.
"""
seed: Any
writes: list[PendingWrite]
class BaseCheckpointSaver(Generic[V]):
"""Base class for creating a graph checkpointer.
@@ -495,104 +457,6 @@ class BaseCheckpointSaver(Generic[V]):
"""
raise NotImplementedError
def _get_channel_writes_history(
self, config: RunnableConfig, channel: str
) -> _ChannelWritesHistory:
"""**Experimental.** Query one channel's writes along the parent chain.
Storage-level query, not channel semantics: returns `(seed, writes)`
reflecting what storage knows about a single channel across the
ancestor chain of the target checkpoint identified by `config`.
* `writes` — on-path deltas oldest→newest as `PendingWrite` tuples.
Writes stored at the target `checkpoint_id` itself are pending
for the next super-step and are excluded.
* `seed` — the nearest ancestor's stored blob value for this
channel; `DELTA_SENTINEL` if the walk reached the root without
finding a stored value. A non-sentinel seed typically indicates
a pre-delta snapshot preserved across a channel-type migration.
Walks the **parent chain** (not `list(before=...)`): for forked
threads, only on-path ancestors contribute.
Reference implementation walks `get_tuple` + `parent_config`,
inspecting each ancestor's `channel_values[channel]` for the seed
terminator. Savers with direct storage access (`InMemorySaver`,
`PostgresSaver`) override for performance; the return contract is
fixed here.
Underscore-prefixed because the method surface is experimental.
"""
# Guard against re-entrant calls: when get_tuple() triggers
# reconstruction which calls get_tuple() again, the inner call
# short-circuits here.
if _DELTA_RECONSTRUCTION.get():
return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
token = _DELTA_RECONSTRUCTION.set(True)
try:
collected: list[PendingWrite] = [] # newest first; reversed at the end
target_tuple = self.get_tuple(config)
cursor_config: RunnableConfig | None = (
target_tuple.parent_config if target_tuple else None
)
while cursor_config is not None:
tup = self.get_tuple(cursor_config)
if tup is None:
break
# Pre-delta seed terminator: if the ancestor has a stored
# (non-sentinel) value for this channel, that snapshot
# subsumes any earlier writes on the chain. Stop here.
ancestor_value = tup.checkpoint["channel_values"].get(channel)
if ancestor_value is not None and ancestor_value is not DELTA_SENTINEL:
collected.reverse()
return _ChannelWritesHistory(seed=ancestor_value, writes=collected)
if tup.pending_writes:
# Within a superstep, pending_writes are oldest→newest;
# reverse to scan newest-first.
for write in reversed(tup.pending_writes):
if write[1] != channel:
continue
collected.append(write)
cursor_config = tup.parent_config
collected.reverse()
return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
finally:
_DELTA_RECONSTRUCTION.reset(token)
async def _aget_channel_writes_history(
self, config: RunnableConfig, channel: str
) -> _ChannelWritesHistory:
"""Async version of `_get_channel_writes_history`. See docstring there."""
if _DELTA_RECONSTRUCTION.get():
return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
token = _DELTA_RECONSTRUCTION.set(True)
try:
collected: list[PendingWrite] = []
target_tuple = await self.aget_tuple(config)
cursor_config: RunnableConfig | None = (
target_tuple.parent_config if target_tuple else None
)
while cursor_config is not None:
tup = await self.aget_tuple(cursor_config)
if tup is None:
break
ancestor_value = tup.checkpoint["channel_values"].get(channel)
if ancestor_value is not None and ancestor_value is not DELTA_SENTINEL:
collected.reverse()
return _ChannelWritesHistory(seed=ancestor_value, writes=collected)
if tup.pending_writes:
for write in reversed(tup.pending_writes):
if write[1] != channel:
continue
collected.append(write)
cursor_config = tup.parent_config
collected.reverse()
return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
finally:
_DELTA_RECONSTRUCTION.reset(token)
def get_next_version(self, current: V | None, channel: None) -> V:
"""Generate the next version ID for a channel.
@@ -9,21 +9,18 @@ from collections import defaultdict
from collections.abc import AsyncIterator, Iterator, Sequence
from contextlib import AbstractAsyncContextManager, AbstractContextManager, ExitStack
from types import TracebackType
from typing import Any, cast
from typing import Any
from langchain_core.runnables import RunnableConfig
from langgraph.checkpoint.base import (
DELTA_SENTINEL,
WRITES_IDX_MAP,
BaseCheckpointSaver,
ChannelVersions,
Checkpoint,
CheckpointMetadata,
CheckpointTuple,
PendingWrite,
SerializerProtocol,
_ChannelWritesHistory,
get_checkpoint_id,
get_checkpoint_metadata,
)
@@ -124,91 +121,16 @@ class InMemorySaver(
return self.stack.__exit__(__exc_type, __exc_value, __traceback)
def _load_blobs(
self,
thread_id: str,
checkpoint_ns: str,
versions: ChannelVersions,
self, thread_id: str, checkpoint_ns: str, versions: ChannelVersions
) -> dict[str, Any]:
result: dict[str, Any] = {}
for k, ver in versions.items():
kk = (thread_id, checkpoint_ns, k, ver)
if kk not in self.blobs:
continue
vv = self.blobs[kk]
if vv[0] == "empty":
continue
result[k] = self.serde.loads_typed(vv)
return result
def _get_channel_writes_history(
self, config: RunnableConfig, channel: str
) -> _ChannelWritesHistory:
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
checkpoint_id = config["configurable"].get("checkpoint_id", "")
ns_storage = self.storage.get(thread_id, {}).get(checkpoint_ns, {})
# Walk the parent chain newest→oldest. Skip the target itself —
# writes stored AT `checkpoint_id` are pending for the next step
# (pregel applies them via `apply_writes`; they aren't part of the
# snapshot value AT `checkpoint_id`).
chain: list[str] = []
target_entry = ns_storage.get(checkpoint_id)
current: str | None = target_entry[2] if target_entry is not None else None
while current is not None:
entry = ns_storage.get(current)
if entry is None:
break
chain.append(current)
_, _, parent = entry
current = parent
# Scan newest→oldest. A pre-delta blob on an ancestor terminates the
# walk and is bound as `seed`; without this, a thread migrated from
# pre-delta storage would replay ancestor writes all the way to the
# root AND miss any value that lived only in the old blob (e.g. from
# `update_state`).
#
# At each ancestor, check the blob BEFORE processing its pending
# writes: a pre-delta blob represents the state AT that ancestor,
# which already subsumes any writes stored under it. Processing
# those writes first would fold them into the reconstructed value
# twice (once via the blob, once via replay).
collected: list[PendingWrite] = [] # newest first
for cp_id in chain: # newest → oldest
entry = ns_storage.get(cp_id)
if entry is not None:
ckpt = self.serde.loads_typed(entry[0])
ver = ckpt.get("channel_versions", {}).get(channel)
if ver is not None:
blob_entry = self.blobs.get(
(thread_id, checkpoint_ns, channel, ver)
)
if blob_entry is not None and blob_entry[0] != "empty":
blob_value = self.serde.loads_typed(blob_entry)
if blob_value is not DELTA_SENTINEL:
# Pre-delta snapshot terminator. Skip this
# ancestor's writes — the blob subsumes them.
collected.reverse()
return _ChannelWritesHistory(
seed=blob_value, writes=collected
)
step_writes = self.writes.get((thread_id, checkpoint_ns, cp_id), {})
# Within a superstep, sorted by (task_id, idx) = oldest → newest;
# reverse for newest-first scan.
for (_task_id, _idx), (tid, ch, serialized, _) in sorted(
step_writes.items(), reverse=True
):
if ch != channel:
continue
val = self.serde.loads_typed(serialized)
collected.append((tid, ch, val))
collected.reverse()
return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
async def _aget_channel_writes_history(
self, config: RunnableConfig, channel: str
) -> _ChannelWritesHistory:
return self._get_channel_writes_history(config, channel)
channel_values: dict[str, Any] = {}
for k, v in versions.items():
kk = (thread_id, checkpoint_ns, k, v)
if kk in self.blobs:
vv = self.blobs[kk]
if vv[0] != "empty":
channel_values[k] = self.serde.loads_typed(vv)
return channel_values
def get_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
"""Get a checkpoint tuple from the in-memory storage.
@@ -231,16 +153,13 @@ class InMemorySaver(
checkpoint, metadata, parent_checkpoint_id = saved
writes = self.writes[(thread_id, checkpoint_ns, checkpoint_id)].values()
checkpoint_: Checkpoint = self.serde.loads_typed(checkpoint)
channel_values = self._load_blobs(
thread_id,
checkpoint_ns,
checkpoint_["channel_versions"],
)
return CheckpointTuple(
config=config,
checkpoint={
**checkpoint_,
"channel_values": channel_values,
"channel_values": self._load_blobs(
thread_id, checkpoint_ns, checkpoint_["channel_versions"]
),
},
metadata=self.serde.loads_typed(metadata),
pending_writes=[
@@ -264,26 +183,19 @@ class InMemorySaver(
checkpoint, metadata, parent_checkpoint_id = checkpoints[checkpoint_id]
writes = self.writes[(thread_id, checkpoint_ns, checkpoint_id)].values()
checkpoint_ = self.serde.loads_typed(checkpoint)
resolved_config = cast(
RunnableConfig,
{
return CheckpointTuple(
config={
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": checkpoint_id,
}
},
)
channel_values = self._load_blobs(
thread_id,
checkpoint_ns,
checkpoint_["channel_versions"],
)
return CheckpointTuple(
config=resolved_config,
checkpoint={
**checkpoint_,
"channel_values": channel_values,
"channel_values": self._load_blobs(
thread_id, checkpoint_ns, checkpoint_["channel_versions"]
),
},
metadata=self.serde.loads_typed(metadata),
pending_writes=[
@@ -378,27 +290,21 @@ class InMemorySaver(
checkpoint_: Checkpoint = self.serde.loads_typed(checkpoint)
list_config = cast(
RunnableConfig,
{
yield CheckpointTuple(
config={
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": checkpoint_id,
}
},
)
channel_values = self._load_blobs(
thread_id,
checkpoint_ns,
checkpoint_["channel_versions"],
)
yield CheckpointTuple(
config=list_config,
checkpoint={
**checkpoint_,
"channel_values": channel_values,
"channel_values": self._load_blobs(
thread_id,
checkpoint_ns,
checkpoint_["channel_versions"],
),
},
metadata=metadata,
parent_config=(
@@ -1,10 +1,3 @@
"""Msgpack deserialization safety controls.
Set ``LANGGRAPH_STRICT_MSGPACK=true`` to restrict checkpoint deserialization
to the types listed in ``SAFE_MSGPACK_TYPES``. Without this, any Python
callable stored in checkpoint data will be imported and executed on load.
"""
import os
from collections.abc import Iterable
from typing import cast
@@ -33,35 +33,19 @@ from langchain_core.load.load import Reviver
from langgraph.checkpoint.serde import _msgpack as _lg_msgpack
from langgraph.checkpoint.serde.base import SerializerProtocol
from langgraph.checkpoint.serde.event_hooks import emit_serde_event
from langgraph.checkpoint.serde.types import DELTA_SENTINEL, SendProtocol
from langgraph.checkpoint.serde.types import SendProtocol
from langgraph.store.base import Item
if TYPE_CHECKING:
from langgraph.checkpoint.serde._msgpack import (
AllowedMsgpackModules,
)
from langgraph.checkpoint.serde.types import SendProtocol
LC_REVIVER = Reviver()
EMPTY_BYTES = b""
logger = logging.getLogger(__name__)
# Dedup log warnings across process lifetime; cap bounds state if types are
# dynamically generated (also acts as a circuit breaker on warning volume).
# Dedup is best-effort: racing threads may each emit once for the same key,
# and warnings are silently dropped once _MAX_WARNED_TYPES is reached.
_MAX_WARNED_TYPES = 1000
_warned_unregistered_types: set[tuple[str, str]] = set()
_warned_blocked_types: set[tuple[str, str]] = set()
def _warn_once(
seen: set[tuple[str, str]], key: tuple[str, str], msg: str, *args: object
) -> None:
if key in seen or len(seen) >= _MAX_WARNED_TYPES:
return
seen.add(key)
logger.warning(msg, *args)
class JsonPlusSerializer(SerializerProtocol):
"""Serializer that uses ormsgpack, with optional fallbacks.
@@ -72,10 +56,6 @@ class JsonPlusSerializer(SerializerProtocol):
class and called within the Pregel loop. It should not be used on untrusted
python objects. If an attacker can write directly to your checkpoint database,
they may be able to trigger code execution when data is deserialized.
Set the environment variable ``LANGGRAPH_STRICT_MSGPACK=true`` to restrict
deserialization to a built-in allowlist of safe types. You can also pass
an explicit ``allowed_msgpack_modules`` to the constructor.
"""
def __init__(
@@ -90,11 +70,8 @@ class JsonPlusSerializer(SerializerProtocol):
) -> None:
if allowed_msgpack_modules is _lg_msgpack._SENTINEL:
if _lg_msgpack.STRICT_MSGPACK_ENABLED:
# Strict: only SAFE_MSGPACK_TYPES are allowed.
allowed_msgpack_modules = None
else:
# Permissive (default): all types allowed with a warning.
# Set LANGGRAPH_STRICT_MSGPACK=true to lock this down.
allowed_msgpack_modules = True
self.pickle_fallback = pickle_fallback
self._allowed_json_modules: set[tuple[str, ...]] | Literal[True] | None = (
@@ -251,8 +228,6 @@ class JsonPlusSerializer(SerializerProtocol):
def dumps_typed(self, obj: Any) -> tuple[str, bytes]:
if obj is None:
return "null", EMPTY_BYTES
elif obj is DELTA_SENTINEL:
return "delta", EMPTY_BYTES
elif isinstance(obj, bytes):
return "bytes", obj
elif isinstance(obj, bytearray):
@@ -279,8 +254,6 @@ class JsonPlusSerializer(SerializerProtocol):
return ormsgpack.unpackb(
data_, ext_hook=self._unpack_ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
)
elif type_ == "delta":
return DELTA_SENTINEL
elif self.pickle_fallback and type_ == "pickle":
return pickle.loads(data_)
else:
@@ -554,13 +527,10 @@ def _create_msgpack_ext_hook(
"name": name,
}
)
_warn_once(
_warned_unregistered_types,
key,
logger.warning(
"Deserializing unregistered type %s.%s from checkpoint. "
"This will be blocked in a future version. "
"Set LANGGRAPH_STRICT_MSGPACK=true to block now, or add "
"to allowed_msgpack_modules to allow explicitly: [(%r, %r)]",
"Add to allowed_msgpack_modules to silence: [(%r, %r)]",
module,
name,
module,
@@ -578,9 +548,7 @@ def _create_msgpack_ext_hook(
"name": name,
}
)
_warn_once(
_warned_blocked_types,
key,
logger.warning(
"Blocked deserialization of %s.%s - not in allowed_msgpack_modules. "
"Add to allowed_msgpack_modules to allow: [(%r, %r)]",
module,
@@ -14,25 +14,6 @@ INTERRUPT = "__interrupt__"
RESUME = "__resume__"
TASKS = "__pregel_tasks"
class _DeltaSentinel:
"""Singleton marker stored (as zero bytes) in checkpoint_blobs for a
DeltaChannel field. The actual per-step writes live in checkpoint_writes
and are replayed through the reducer at load time.
Compare with `is DELTA_SENTINEL` — `loads_typed` always returns the same
module-level instance.
"""
__slots__ = ()
def __repr__(self) -> str:
return "DELTA_SENTINEL"
DELTA_SENTINEL = _DeltaSentinel()
Value = TypeVar("Value", covariant=True)
Update = TypeVar("Update", contravariant=True)
C = TypeVar("C")
+1 -1
View File
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
[project]
name = "langgraph-checkpoint"
version = "4.0.2"
version = "4.0.1"
description = "Library with base interfaces for LangGraph checkpoint savers."
authors = []
requires-python = ">=3.10"
-9
View File
@@ -29,8 +29,6 @@ from langgraph.checkpoint.serde.jsonplus import (
EXT_METHOD_SINGLE_ARG,
JsonPlusSerializer,
_msgpack_enc,
_warned_blocked_types,
_warned_unregistered_types,
)
@@ -104,13 +102,6 @@ def test_msgpack_method_pathlib_blocked_encrypted_strict(
class TestEncryptedSerializerMsgpackAllowlist:
"""Test msgpack allowlist behavior through EncryptedSerializer."""
@pytest.fixture(autouse=True)
def _reset_warned_types(self) -> None:
# Warning dedup state is process-global; reset per-test so each case
# sees a fresh slate and assertions about warning emission are stable.
_warned_unregistered_types.clear()
_warned_blocked_types.clear()
def test_safe_types_no_warning(self, caplog: pytest.LogCaptureFixture) -> None:
"""Test safe types deserialize without warnings through encryption."""
serde = _make_encrypted_serde()
+2 -29
View File
@@ -35,8 +35,6 @@ from langgraph.checkpoint.serde.jsonplus import (
JsonPlusSerializer,
_msgpack_enc,
_msgpack_ext_hook_to_json,
_warned_blocked_types,
_warned_unregistered_types,
)
from langgraph.store.base import Item
@@ -582,14 +580,6 @@ def test_msgpack_safe_types_no_warning(caplog: pytest.LogCaptureFixture) -> None
assert result is not None
@pytest.fixture(autouse=True)
def _reset_warned_types() -> None:
# Warning dedup state is process-global; reset per-test so each case sees
# a fresh slate and assertions about warning emission are stable.
_warned_unregistered_types.clear()
_warned_blocked_types.clear()
def test_msgpack_pydantic_warns_by_default(caplog: pytest.LogCaptureFixture) -> None:
"""Pydantic models not in allowlist should log warning but still deserialize."""
current = _lg_msgpack.STRICT_MSGPACK_ENABLED
@@ -605,12 +595,6 @@ def test_msgpack_pydantic_warns_by_default(caplog: pytest.LogCaptureFixture) ->
assert "unregistered type" in caplog.text.lower()
assert "allowed_msgpack_modules" in caplog.text
assert result == obj
# Second deserialization of the same type should NOT produce another warning
caplog.clear()
result2 = serde.loads_typed(dumped)
assert "unregistered type" not in caplog.text.lower()
assert result2 == obj
_lg_msgpack.STRICT_MSGPACK_ENABLED = current
@@ -655,6 +639,7 @@ def test_msgpack_allowlist_silences_warning(caplog: pytest.LogCaptureFixture) ->
def test_msgpack_none_blocks_unregistered(caplog: pytest.LogCaptureFixture) -> None:
"""allowed_msgpack_modules=None should block unregistered types."""
serde = JsonPlusSerializer(allowed_msgpack_modules=None)
obj = MyPydantic(foo="test", bar=42, inner=InnerPydantic(hello="world"))
@@ -672,6 +657,7 @@ def test_msgpack_allowlist_blocks_non_listed(
caplog: pytest.LogCaptureFixture,
) -> None:
"""Allowlists should block unregistered types even if msgpack is enabled."""
serde = JsonPlusSerializer(
allowed_msgpack_modules=[("tests.test_jsonplus", "MyPydantic")]
)
@@ -997,16 +983,3 @@ def test_msgpack_nested_pydantic_serializes_as_dict(
# No blocking should occur - inner is serialized as dict, not ext
assert "blocked" not in caplog.text.lower()
assert result == obj
def test_delta_sentinel_serde_round_trip() -> None:
from langgraph.checkpoint.base import DELTA_SENTINEL
from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
serde = JsonPlusSerializer()
type_tag, blob = serde.dumps_typed(DELTA_SENTINEL)
# Zero-byte "delta" tag — no allowlist change needed.
assert type_tag == "delta"
assert blob == b""
loaded = serde.loads_typed((type_tag, blob))
assert loaded is DELTA_SENTINEL
+3 -358
View File
@@ -6,32 +6,19 @@ from langchain_core.runnables import RunnableConfig
from pydantic import BaseModel
from langgraph.checkpoint.base import (
DELTA_SENTINEL,
Checkpoint,
CheckpointMetadata,
create_checkpoint,
empty_checkpoint,
)
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.checkpoint.serde.jsonplus import (
JsonPlusSerializer,
_warned_blocked_types,
_warned_unregistered_types,
)
from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
class MemoryPydantic(BaseModel):
foo: str
@pytest.fixture(autouse=True)
def _reset_warned_types() -> None:
# Warning dedup state is process-global; reset per-test so each case sees
# a fresh slate and assertions about warning emission are stable.
_warned_unregistered_types.clear()
_warned_blocked_types.clear()
class TestMemorySaver:
@pytest.fixture(autouse=True)
def setup(self) -> None:
@@ -209,6 +196,8 @@ class TestMemorySaver:
async def test_memory_saver() -> None:
from langgraph.checkpoint.memory import InMemorySaver
memory_saver = InMemorySaver()
assert isinstance(memory_saver, InMemorySaver)
@@ -319,347 +308,3 @@ def test_memory_saver_with_allowlist_proxy_isolated() -> None:
assert direct is not None
expected = obj.model_dump() if hasattr(obj, "model_dump") else obj.dict()
assert direct.checkpoint["channel_values"]["foo"] == expected
class TestInMemorySaverDeltaChannel:
def test_load_blobs_returns_sentinel_for_delta_channel(self) -> None:
"""_load_blobs returns DELTA_SENTINEL for delta channels (reconstruction deferred)."""
saver = InMemorySaver()
serde = JsonPlusSerializer()
thread_id, ns, channel = "t1", "", "messages"
v1 = "00000000000000000000000000000001.0000000000000000"
saver.blobs[(thread_id, ns, channel, v1)] = serde.dumps_typed(DELTA_SENTINEL)
cp1 = empty_checkpoint()
cp1["id"] = "cp1"
cp1["channel_versions"][channel] = v1
saver.storage[thread_id][ns] = {
"cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
}
result = saver._load_blobs(thread_id, ns, {channel: v1})
assert channel in result
assert result[channel] is DELTA_SENTINEL
def test_get_channel_writes_collects_ancestor_writes_only(self) -> None:
"""_get_channel_writes_history collects ancestor writes oldest→newest,
and excludes writes stored at the target checkpoint itself (those are
pending writes for the next step, applied separately by pregel)."""
saver = InMemorySaver()
serde = JsonPlusSerializer()
thread_id, ns, channel = "t1", "", "messages"
cp1 = empty_checkpoint()
cp1["id"] = "cp1"
cp2 = empty_checkpoint()
cp2["id"] = "cp2"
saver.storage[thread_id][ns] = {
"cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
"cp2": (serde.dumps_typed(cp2), serde.dumps_typed({}), "cp1"),
}
# Writes stored at cp1 produced the cp1 snapshot; part of history.
saver.writes[(thread_id, ns, "cp1")][("task1", 0)] = (
"task1",
channel,
serde.dumps_typed({"content": "hi"}),
"",
)
# Writes stored at cp2 are pending — they will produce cp3 when the
# step that loaded cp2 completes. They MUST NOT appear in the
# reconstructed snapshot value at cp2.
saver.writes[(thread_id, ns, "cp2")][("task2", 0)] = (
"task2",
channel,
serde.dumps_typed({"content": "pending"}),
"",
)
config: RunnableConfig = {
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": ns,
"checkpoint_id": "cp2",
}
}
result = saver._get_channel_writes_history(config, channel)
assert result.seed is DELTA_SENTINEL
values = [v for _, _, v in result.writes]
assert values == [{"content": "hi"}]
def test_get_channel_writes_at_root_returns_empty(self) -> None:
"""Reconstructing the root checkpoint's state: no ancestors → []."""
saver = InMemorySaver()
serde = JsonPlusSerializer()
thread_id, ns, channel = "t1", "", "messages"
cp1 = empty_checkpoint()
cp1["id"] = "cp1"
saver.storage[thread_id][ns] = {
"cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
}
saver.writes[(thread_id, ns, "cp1")][("task1", 0)] = (
"task1",
channel,
serde.dumps_typed({"content": "pending"}),
"",
)
config: RunnableConfig = {
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": ns,
"checkpoint_id": "cp1",
}
}
result = saver._get_channel_writes_history(config, channel)
assert result.seed is DELTA_SENTINEL
assert result.writes == []
class TestBaseFallbackGetChannelWrites:
"""Exercises the `BaseCheckpointSaver._get_channel_writes_history` default
implementation the path third-party savers inherit when they don't
override `_get_channel_writes_history` themselves.
Regression guard for a bug where the fallback passed the caller's config
(with `checkpoint_id`) straight to `self.list()`, which most savers
collapse to a single row causing the fallback to return `[]`.
"""
def _build_saver_with_chain(self) -> tuple[InMemorySaver, str, str]:
"""Build an InMemorySaver with a 3-checkpoint chain and per-step writes
for a `messages` channel.
Returns `(saver, thread_id, namespace)`. The saver subclass deletes the
InMemorySaver override so the base class fallback is exercised.
"""
class _ThirdPartyStyleSaver(InMemorySaver):
_get_channel_writes_history = (
InMemorySaver.__mro__[1]._get_channel_writes_history # type: ignore[attr-defined]
)
_aget_channel_writes_history = (
InMemorySaver.__mro__[1]._aget_channel_writes_history # type: ignore[attr-defined]
)
saver = _ThirdPartyStyleSaver()
serde = JsonPlusSerializer()
thread_id, ns, channel = "t1", "", "messages"
cp0 = empty_checkpoint()
cp0["id"] = "00000000000000000000000000000001.0000000000000000"
cp1 = empty_checkpoint()
cp1["id"] = "00000000000000000000000000000002.0000000000000000"
cp2 = empty_checkpoint()
cp2["id"] = "00000000000000000000000000000003.0000000000000000"
saver.storage[thread_id][ns] = {
cp0["id"]: (serde.dumps_typed(cp0), serde.dumps_typed({}), None),
cp1["id"]: (serde.dumps_typed(cp1), serde.dumps_typed({}), cp0["id"]),
cp2["id"]: (serde.dumps_typed(cp2), serde.dumps_typed({}), cp1["id"]),
}
# Writes under cp0 produced cp1's state; writes under cp1 produced cp2's.
saver.writes[(thread_id, ns, cp0["id"])][("task1", 0)] = (
"task1",
channel,
serde.dumps_typed({"content": "first"}),
"",
)
saver.writes[(thread_id, ns, cp1["id"])][("task2", 0)] = (
"task2",
channel,
serde.dumps_typed({"content": "second"}),
"",
)
return saver, thread_id, ns
def test_fallback_returns_ancestor_writes_oldest_first(self) -> None:
saver, thread_id, ns = self._build_saver_with_chain()
target_id = "00000000000000000000000000000003.0000000000000000"
config: RunnableConfig = {
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": ns,
"checkpoint_id": target_id,
}
}
result = saver._get_channel_writes_history(config, "messages")
assert result.seed is DELTA_SENTINEL
values = [v for _, _, v in result.writes]
assert values == [{"content": "first"}, {"content": "second"}]
async def test_async_fallback_returns_ancestor_writes_oldest_first(self) -> None:
saver, thread_id, ns = self._build_saver_with_chain()
target_id = "00000000000000000000000000000003.0000000000000000"
config: RunnableConfig = {
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": ns,
"checkpoint_id": target_id,
}
}
result = await saver._aget_channel_writes_history(config, "messages")
assert result.seed is DELTA_SENTINEL
values = [v for _, _, v in result.writes]
assert values == [{"content": "first"}, {"content": "second"}]
async def test_async_fallback_concurrent_tasks_do_not_interfere(self) -> None:
"""Regression: the re-entrancy guard must be task-local, not thread-local.
Two concurrent `_aget_channel_writes_history` calls on the same
event-loop thread must each see their full reconstructed writes. A
`threading.local()` guard would let whichever task set it first
short-circuit the other to `writes=[]`.
"""
import asyncio
saver, thread_id, ns = self._build_saver_with_chain()
# Force the two tasks to interleave across the `set(True)` boundary:
# each `aget_tuple` yields control, so if the guard were thread-local
# the second task would observe `active=True` set by the first.
orig_aget_tuple = saver.aget_tuple
async def slow_aget_tuple(config: RunnableConfig) -> Any:
await asyncio.sleep(0)
return await orig_aget_tuple(config)
saver.aget_tuple = slow_aget_tuple # type: ignore[method-assign]
target_id = "00000000000000000000000000000003.0000000000000000"
config: RunnableConfig = {
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": ns,
"checkpoint_id": target_id,
}
}
results = await asyncio.gather(
saver._aget_channel_writes_history(config, "messages"),
saver._aget_channel_writes_history(config, "messages"),
)
expected_values = [{"content": "first"}, {"content": "second"}]
for result in results:
assert result.seed is DELTA_SENTINEL
values = [v for _, _, v in result.writes]
assert values == expected_values
class TestPreDeltaBlobTerminator:
"""Verify the pre-delta blob terminator: when the ancestor walk hits a
checkpoint whose blob for the channel is a real value (not
DELTA_SENTINEL), reconstruction seeds from it and stops. This guards
* back-compat: a thread written by pre-delta code, then extended under
delta reconstruction must return the correct value without walking
past the last pre-delta ancestor;
* perf: without the terminator, every reconstruct-after-migration would
walk all the way to the thread root.
"""
def _build_mixed_thread(self) -> tuple[InMemorySaver, str, str, str, str]:
"""Three-checkpoint chain: cp1 (pre-delta, blob=[A]), cp2 (delta,
write=B), cp3 (delta, write=C). Reconstructing at cp3 must yield
seed=[A] + writes=[B, C].
Returns `(saver, thread_id, ns, channel, cp3_id)`.
"""
saver = InMemorySaver()
serde = JsonPlusSerializer()
thread_id, ns, channel = "t1", "", "messages"
v1 = "00000000000000000000000000000001.0"
v2 = "00000000000000000000000000000002.0"
v3 = "00000000000000000000000000000003.0"
# Pre-delta: cp1 stored a real blob for the channel.
saver.blobs[(thread_id, ns, channel, v1)] = serde.dumps_typed(["A"])
# Delta-era: cp2 and cp3 store sentinels; real writes in checkpoint_writes.
saver.blobs[(thread_id, ns, channel, v2)] = serde.dumps_typed(DELTA_SENTINEL)
saver.blobs[(thread_id, ns, channel, v3)] = serde.dumps_typed(DELTA_SENTINEL)
cp1 = empty_checkpoint()
cp1["id"] = "cp1"
cp1["channel_versions"][channel] = v1
cp2 = empty_checkpoint()
cp2["id"] = "cp2"
cp2["channel_versions"][channel] = v2
cp3 = empty_checkpoint()
cp3["id"] = "cp3"
cp3["channel_versions"][channel] = v3
saver.storage[thread_id][ns] = {
"cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
"cp2": (serde.dumps_typed(cp2), serde.dumps_typed({}), "cp1"),
"cp3": (serde.dumps_typed(cp3), serde.dumps_typed({}), "cp2"),
}
# Write under cp1 would be from the pre-delta era and MUST be ignored
# (the blob already captures it). We add one and assert it is not
# folded into the reconstructed result.
saver.writes[(thread_id, ns, "cp1")][("task0", 0)] = (
"task0",
channel,
serde.dumps_typed("PRE-DELTA-WRITE"),
"",
)
saver.writes[(thread_id, ns, "cp2")][("task2", 0)] = (
"task2",
channel,
serde.dumps_typed("B"),
"",
)
saver.writes[(thread_id, ns, "cp3")][("task3", 0)] = (
"task3",
channel,
serde.dumps_typed("PENDING-AT-TARGET"),
"",
)
return saver, thread_id, ns, channel, "cp3"
def test_seed_from_pre_delta_ancestor_blob(self) -> None:
saver, thread_id, ns, channel, target = self._build_mixed_thread()
config: RunnableConfig = {
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": ns,
"checkpoint_id": target,
}
}
result = saver._get_channel_writes_history(config, channel)
# Seed came from the pre-delta blob at cp1.
assert result.seed == ["A"]
# Delta-era writes from cp2 replay through the reducer on top of seed.
# cp3 is the target — its own write is pending for the NEXT step and
# must be excluded.
values = [v for _, _, v in result.writes]
assert values == ["B"]
def test_pre_delta_blob_terminates_walk_before_older_writes(self) -> None:
"""Writes stored at the pre-delta ancestor itself must not be replayed
(the blob subsumes them)."""
saver, thread_id, ns, channel, target = self._build_mixed_thread()
config: RunnableConfig = {
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": ns,
"checkpoint_id": target,
}
}
result = saver._get_channel_writes_history(config, channel)
values = [v for _, _, v in result.writes]
# The pre-delta write under cp1 must not appear (the blob subsumes it).
assert "PRE-DELTA-WRITE" not in values
# And the pending write at the target is never folded in.
assert "PENDING-AT-TARGET" not in values
+7 -126
View File
@@ -286,7 +286,7 @@ wheels = [
[[package]]
name = "langgraph-checkpoint"
version = "4.0.2"
version = "4.0.1"
source = { editable = "." }
dependencies = [
{ name = "langchain-core" },
@@ -369,7 +369,7 @@ test = [
[[package]]
name = "langsmith"
version = "0.7.31"
version = "0.6.4"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "httpx" },
@@ -379,12 +379,11 @@ dependencies = [
{ name = "requests" },
{ name = "requests-toolbelt" },
{ name = "uuid-utils" },
{ name = "xxhash" },
{ name = "zstandard" },
]
sdist = { url = "https://files.pythonhosted.org/packages/e6/11/696019490992db5c87774dc20515529ef42a01e1d770fb754ed6d9b12fb0/langsmith-0.7.31.tar.gz", hash = "sha256:331ee4f7c26bb5be4022b9859b7d7b122cbf8c9d01d9f530114c1914b0349ffb", size = 1178480, upload-time = "2026-04-14T17:55:41.242Z" }
sdist = { url = "https://files.pythonhosted.org/packages/e7/85/9c7933052a997da1b85bc5c774f3865e9b1da1c8d71541ea133178b13229/langsmith-0.6.4.tar.gz", hash = "sha256:36f7223a01c218079fbb17da5e536ebbaf5c1468c028abe070aa3ae59bc99ec8", size = 919964, upload-time = "2026-01-15T20:02:28.873Z" }
wheels = [
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{ url = "https://files.pythonhosted.org/packages/66/0f/09a6637a7ba777eb307b7c80852d9ee26438e2bdafbad6fcc849ff9d9192/langsmith-0.6.4-py3-none-any.whl", hash = "sha256:ac4835860160be371042c7adbba3cb267bcf8d96a5ea976c33a8a4acad6c5486", size = 283503, upload-time = "2026-01-15T20:02:26.662Z" },
]
[[package]]
@@ -1118,7 +1117,7 @@ wheels = [
[[package]]
name = "pytest"
version = "9.0.3"
version = "9.0.2"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "colorama", marker = "sys_platform == 'win32'" },
@@ -1129,9 +1128,9 @@ dependencies = [
{ name = "pygments" },
{ name = "tomli", marker = "python_full_version < '3.11'" },
]
sdist = { url = "https://files.pythonhosted.org/packages/7d/0d/549bd94f1a0a402dc8cf64563a117c0f3765662e2e668477624baeec44d5/pytest-9.0.3.tar.gz", hash = "sha256:b86ada508af81d19edeb213c681b1d48246c1a91d304c6c81a427674c17eb91c", size = 1572165, upload-time = "2026-04-07T17:16:18.027Z" }
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wheels = [
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[[package]]
@@ -1516,124 +1515,6 @@ wheels = [
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[[package]]
name = "xxhash"
version = "3.6.0"
source = { registry = "https://pypi.org/simple" }
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[[package]]
name = "zstandard"
version = "0.25.0"
@@ -5,5 +5,5 @@ description = "Test for prerelease stuff"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
"langchain-openai==1.1.14"
"langchain-openai==1.0.1"
]
@@ -5,7 +5,7 @@ description = "Test for prerelease stuff"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
"langchain-openai==1.1.14",
"langchain-openai==1.0.0a2",
"langchain-anthropic==1.0.0a5",
"langgraph==1.1.5"
]
@@ -5,7 +5,7 @@ description = "Test for prerelease stuff"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
"langchain-openai==1.1.14",
"langchain-openai==1.0.0a2",
"langgraph==1.1.2",
"langchain_community>=0.3.0",
]
+29 -8
View File
@@ -1086,6 +1086,11 @@
resolved "https://registry.yarnpkg.com/@types/stack-utils/-/stack-utils-2.0.3.tgz#6209321eb2c1712a7e7466422b8cb1fc0d9dd5d8"
integrity sha512-9aEbYZ3TbYMznPdcdr3SmIrLXwC/AKZXQeCf9Pgao5CKb8CyHuEX5jzWPTkvregvhRJHcpRO6BFoGW9ycaOkYw==
"@types/uuid@^10.0.0":
version "10.0.0"
resolved "https://registry.yarnpkg.com/@types/uuid/-/uuid-10.0.0.tgz#e9c07fe50da0f53dc24970cca94d619ff03f6f6d"
integrity sha512-7gqG38EyHgyP1S+7+xomFtL+ZNHcKv6DwNaCZmJmo1vgMugyF3TCnXVg4t1uk89mLNwnLtnY3TpOpCOyp1/xHQ==
"@types/yargs-parser@*":
version "21.0.3"
resolved "https://registry.yarnpkg.com/@types/yargs-parser/-/yargs-parser-21.0.3.tgz#815e30b786d2e8f0dcd85fd5bcf5e1a04d008f15"
@@ -1777,6 +1782,13 @@ concat-map@0.0.1:
resolved "https://registry.yarnpkg.com/concat-map/-/concat-map-0.0.1.tgz#d8a96bd77fd68df7793a73036a3ba0d5405d477b"
integrity sha512-/Srv4dswyQNBfohGpz9o6Yb3Gz3SrUDqBH5rTuhGR7ahtlbYKnVxw2bCFMRljaA7EXHaXZ8wsHdodFvbkhKmqg==
console-table-printer@^2.12.1:
version "2.15.0"
resolved "https://registry.yarnpkg.com/console-table-printer/-/console-table-printer-2.15.0.tgz#5c808204640b8f024d545bde8aabe5d344dfadc1"
integrity sha512-SrhBq4hYVjLCkBVOWaTzceJalvn5K1Zq5aQA6wXC/cYjI3frKWNPEMK3sZsJfNNQApvCQmgBcc13ZKmFj8qExw==
dependencies:
simple-wcswidth "^1.1.2"
convert-source-map@^2.0.0:
version "2.0.0"
resolved "https://registry.yarnpkg.com/convert-source-map/-/convert-source-map-2.0.0.tgz#4b560f649fc4e918dd0ab75cf4961e8bc882d82a"
@@ -3676,12 +3688,16 @@ keyv@^4.5.4:
json-buffer "3.0.1"
"langsmith@>=0.5.0 <1.0.0":
version "0.5.20"
resolved "https://registry.yarnpkg.com/langsmith/-/langsmith-0.5.20.tgz#4021847d2ccd5a86c5eb96060f9bb5f19f80eca5"
integrity sha512-ULhLM8RswvQDXufLtNtvclHrWCBx8Cb5UPI6lAZC+8Dq59iHsVPz/3Ac9khWNm1VIvChRsuykixD/WrmzuuA3Q==
version "0.5.4"
resolved "https://registry.yarnpkg.com/langsmith/-/langsmith-0.5.4.tgz#f75b82b08e30db72a7d1d595b341e9666bd525e5"
integrity sha512-qYkNIoKpf0ZYt+cYzrDV+XI3FCexApmZmp8EMs3eDTMv0OvrHMLoxJ9IpkeoXJSX24+GPk0/jXjKx2hWerpy9w==
dependencies:
p-queue "6.6.2"
uuid "10.0.0"
"@types/uuid" "^10.0.0"
chalk "^4.1.2"
console-table-printer "^2.12.1"
p-queue "^6.6.2"
semver "^7.6.3"
uuid "^10.0.0"
leven@^3.1.0:
version "3.1.0"
@@ -3991,7 +4007,7 @@ p-locate@^5.0.0:
dependencies:
p-limit "^3.0.2"
p-queue@6.6.2, p-queue@^6.6.2:
p-queue@^6.6.2:
version "6.6.2"
resolved "https://registry.yarnpkg.com/p-queue/-/p-queue-6.6.2.tgz#2068a9dcf8e67dd0ec3e7a2bcb76810faa85e426"
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@@ -4287,7 +4303,7 @@ semver@^6.3.1:
resolved "https://registry.yarnpkg.com/semver/-/semver-6.3.1.tgz#556d2ef8689146e46dcea4bfdd095f3434dffcb4"
integrity sha512-BR7VvDCVHO+q2xBEWskxS6DJE1qRnb7DxzUrogb71CWoSficBxYsiAGd+Kl0mmq/MprG9yArRkyrQxTO6XjMzA==
semver@^7.5.3, semver@^7.5.4, semver@^7.7.2, semver@^7.7.3:
semver@^7.5.3, semver@^7.5.4, semver@^7.6.3, semver@^7.7.2, semver@^7.7.3:
version "7.7.4"
resolved "https://registry.yarnpkg.com/semver/-/semver-7.7.4.tgz#28464e36060e991fa7a11d0279d2d3f3b57a7e8a"
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@@ -4395,6 +4411,11 @@ signal-exit@^4.0.1:
resolved "https://registry.yarnpkg.com/signal-exit/-/signal-exit-4.1.0.tgz#952188c1cbd546070e2dd20d0f41c0ae0530cb04"
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simple-wcswidth@^1.1.2:
version "1.1.2"
resolved "https://registry.yarnpkg.com/simple-wcswidth/-/simple-wcswidth-1.1.2.tgz#66722f37629d5203f9b47c5477b1225b85d6525b"
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slash@^3.0.0:
version "3.0.0"
resolved "https://registry.yarnpkg.com/slash/-/slash-3.0.0.tgz#6539be870c165adbd5240220dbe361f1bc4d4634"
@@ -4849,7 +4870,7 @@ uri-js@^4.2.2:
dependencies:
punycode "^2.1.0"
uuid@10.0.0, uuid@^10.0.0:
uuid@^10.0.0:
version "10.0.0"
resolved "https://registry.yarnpkg.com/uuid/-/uuid-10.0.0.tgz#5a95aa454e6e002725c79055fd42aaba30ca6294"
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@@ -1328,9 +1328,9 @@ keyv@^4.5.4:
json-buffer "3.0.1"
"langsmith@>=0.5.0 <1.0.0":
version "0.5.20"
resolved "https://registry.yarnpkg.com/langsmith/-/langsmith-0.5.20.tgz#4021847d2ccd5a86c5eb96060f9bb5f19f80eca5"
integrity sha512-ULhLM8RswvQDXufLtNtvclHrWCBx8Cb5UPI6lAZC+8Dq59iHsVPz/3Ac9khWNm1VIvChRsuykixD/WrmzuuA3Q==
version "0.5.18"
resolved "https://registry.yarnpkg.com/langsmith/-/langsmith-0.5.18.tgz#c691ad23614f0b46eaf07d982e0ac988e1f43880"
integrity sha512-3zuZUWffTHQ+73EAwnodADtf534VNEZUpXr9jC12qyG8/IQuJET7PRsCpTb9wX2lmBspakwLUpqpj3tNm/0bVA==
dependencies:
p-queue "6.6.2"
uuid "10.0.0"
+1 -1
View File
@@ -1 +1 @@
__version__ = "0.4.23"
__version__ = "0.4.21"
+3 -10
View File
@@ -26,15 +26,8 @@ class LogData(TypedDict):
params: dict[str, Any]
def get_anonymized_params(
kwargs: dict[str, Any], *, cli_command: str
) -> dict[str, bool | str]:
params: dict[str, bool | str] = {}
if cli_command == "deploy" and (
analytics_source := os.getenv("LANGGRAPH_CLI_ANALYTICS_SOURCE")
):
params["source"] = analytics_source
def get_anonymized_params(kwargs: dict[str, Any]) -> dict[str, bool]:
params = {}
# anonymize params with values
if config := kwargs.get("config"):
@@ -95,7 +88,7 @@ def log_command(func):
"python_version": platform.python_version(),
"cli_version": __version__,
"cli_command": func.__name__,
"params": get_anonymized_params(kwargs, cli_command=func.__name__),
"params": get_anonymized_params(kwargs),
}
background_thread = threading.Thread(target=log_data, args=(data,))
+1 -1
View File
@@ -23,7 +23,7 @@ dependencies = [
path = "langgraph_cli/__init__.py"
[project.optional-dependencies]
inmem = [
"langgraph-api>=0.5.35,<0.9.0 ; python_version >= '3.11'",
"langgraph-api>=0.5.35,<0.8.0 ; python_version >= '3.11'",
"langgraph-runtime-inmem>=0.7 ; python_version >= '3.11'",
]
+3 -3
View File
@@ -290,7 +290,7 @@ wheels = [
[[package]]
name = "langsmith"
version = "0.7.31"
version = "0.7.26"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "httpx" },
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{ name = "xxhash" },
{ name = "zstandard" },
]
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name = "langsmith"
version = "0.7.31"
version = "0.7.26"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "httpx" },
@@ -279,9 +279,9 @@ dependencies = [
{ name = "xxhash" },
{ name = "zstandard" },
]
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{ url = "https://files.pythonhosted.org/packages/81/8e/7eb7d65ce62e98e74b9f18f193ea7ac3996d4fbd71fffcc67d0f7ba3103e/langsmith-0.7.26-py3-none-any.whl", hash = "sha256:fe5c877972cea450c1c48251c8fae0f18543c8d19dfdb9ff9a9c4263763dde4e", size = 360160, upload-time = "2026-04-06T15:01:01.516Z" },
]
[[package]]
+383 -465
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File diff suppressed because it is too large Load Diff
+12 -48
View File
@@ -1,7 +1,7 @@
from __future__ import annotations
from collections import ChainMap
from collections.abc import Sequence
from collections.abc import Mapping, Sequence
from os import getenv
from typing import Any, cast
@@ -217,16 +217,14 @@ def get_callback_manager_for_config(
callbacks.add_tags(all_tags)
if metadata := config.get("metadata"):
callbacks.add_metadata(metadata)
manager = callbacks
return callbacks
else:
# otherwise create a new manager
manager = CallbackManager.configure(
return CallbackManager.configure(
inheritable_callbacks=config.get("callbacks"),
inheritable_tags=all_tags,
inheritable_metadata=config.get("metadata"),
langsmith_inheritable_metadata=_get_tracing_metadata_defaults(config),
)
return manager
def get_async_callback_manager_for_config(
@@ -257,16 +255,14 @@ def get_async_callback_manager_for_config(
callbacks.add_tags(all_tags)
if metadata := config.get("metadata"):
callbacks.add_metadata(metadata)
manager = callbacks
return callbacks
else:
# otherwise create a new manager
manager = AsyncCallbackManager.configure(
return AsyncCallbackManager.configure(
inheritable_callbacks=config.get("callbacks"),
inheritable_tags=all_tags,
inheritable_metadata=config.get("metadata"),
langsmith_inheritable_metadata=_get_tracing_metadata_defaults(config),
)
return manager
def _is_not_empty(value: Any) -> bool:
@@ -312,54 +308,22 @@ def ensure_config(*configs: RunnableConfig | None) -> RunnableConfig:
for k, v in config.items():
if _is_not_empty(v) and k not in CONFIG_KEYS:
empty[CONF][k] = v
configurable = empty.get("configurable")
metadata = empty.get("metadata")
if configurable and metadata is not None:
for key in _PROPAGATE_TO_METADATA:
if key in metadata:
continue
value = configurable.get(key)
if value:
metadata[key] = value
_empty_metadata = empty["metadata"]
for key, value in empty[CONF].items():
if _exclude_as_metadata(key, value, _empty_metadata):
continue
_empty_metadata[key] = value
return empty
_OMIT = ("key", "token", "secret", "password", "auth")
def _exclude_as_metadata(key: str, value: Any) -> bool:
def _exclude_as_metadata(key: str, value: Any, metadata: Mapping[str, Any]) -> bool:
key_lower = key.casefold()
return (
key.startswith("__")
or not isinstance(value, (str, int, float, bool))
or key in metadata
or any(substr in key_lower for substr in _OMIT)
)
def _get_tracing_metadata_defaults(
config: RunnableConfig,
) -> dict[str, Any] | None:
"""Get tracer-only metadata defaults from configurable values."""
configurable = config.get("configurable")
if not configurable:
return None
metadata: dict[str, Any] = {}
for key, value in configurable.items():
if _exclude_as_metadata(key, value):
continue
metadata[key] = value
return metadata or None
_PROPAGATE_TO_METADATA = frozenset(
(
"thread_id",
"checkpoint_id",
"checkpoint_ns",
"task_id",
"run_id",
"assistant_id",
"graph_id",
)
)
+18
View File
@@ -245,6 +245,15 @@ class _GraphCallbackManager(BaseCallbackManager):
run_id=run_id,
)
def add_handler(
self,
handler: BaseCallbackHandler,
inherit: bool = True, # noqa: FBT001,FBT002
) -> None:
if not isinstance(handler, GraphCallbackHandler):
raise TypeError("handlers must inherit GraphCallbackHandler")
super().add_handler(handler, inherit=inherit)
def copy(
self,
*,
@@ -312,6 +321,15 @@ class _AsyncGraphCallbackManager(BaseCallbackManager):
run_id=run_id,
)
def add_handler(
self,
handler: BaseCallbackHandler,
inherit: bool = True, # noqa: FBT001,FBT002
) -> None:
if not isinstance(handler, GraphCallbackHandler):
raise TypeError("handlers must inherit GraphCallbackHandler")
super().add_handler(handler, inherit=inherit)
def copy(
self,
*,
-177
View File
@@ -1,177 +0,0 @@
from __future__ import annotations
import copy as _copy
from collections.abc import Callable, Sequence
from typing import Any, Generic
from langgraph.checkpoint.base import DELTA_SENTINEL, PendingWrite
from typing_extensions import Self
from langgraph._internal._typing import MISSING
from langgraph.channels.base import BaseChannel, Value
from langgraph.channels.binop import _get_overwrite
from langgraph.errors import EmptyChannelError
__all__ = ("DeltaChannel",)
def _empty(typ: Any) -> Any:
try:
return typ()
except Exception:
return []
class DeltaChannel(Generic[Value], BaseChannel[Any, Any, Any]):
"""Experimental — private API, subject to change or removal without notice.
Imported from the underscored module `langgraph.channels._delta` on purpose;
not re-exported from `langgraph.channels`. Intended for internal use only
while we validate the design on real workloads.
A channel that stores only a sentinel in checkpoints; per-step writes are
stored in checkpoint_writes and replayed through the operator at load time.
Use with append-style reducers (e.g. `add_messages`) on long-running threads
to eliminate O() blob growth storage is O(N) using the writes table that
every checkpointer already maintains.
Reconstruction replays every ancestor write through the operator, so
per-get cost scales with thread depth. Compaction for deep threads is
a follow-up today, use this on threads of a few hundred turns.
Usage::
from langgraph.channels._delta import DeltaChannel
class State(TypedDict):
messages: Annotated[list[AnyMessage], DeltaChannel(add_messages)]
"""
__slots__ = (
"value",
"operator",
)
def __init__(
self,
operator: Callable[[Any, Any], Any],
) -> None:
super().__init__(list)
self.operator = operator
self.value: Any = []
def __eq__(self, other: object) -> bool:
if not isinstance(other, DeltaChannel):
return False
if (
self.operator.__name__ != "<lambda>"
and other.operator.__name__ != "<lambda>"
):
return self.operator is other.operator
return True
@property
def ValueType(self) -> Any:
return self.typ
@property
def UpdateType(self) -> Any:
return self.typ
def copy(self) -> Self:
new: DeltaChannel[Value] = DeltaChannel(self.operator)
new.typ = self.typ
new.key = self.key
new.value = self.value if self.value is MISSING else _copy.copy(self.value)
return new
def _apply_write(self, value: Any, write: Any) -> Any:
"""Apply one write to `value` and return the new value.
An `Overwrite` replaces the value; any other write is folded through
the operator. Centralizes the Overwrite/reducer branching used by both
`update` (live super-step) and `from_checkpoint` (ancestor replay).
"""
is_overwrite, overwrite_value = _get_overwrite(write)
if is_overwrite:
return (
_copy.copy(overwrite_value)
if overwrite_value is not None
else _empty(self.typ)
)
base = _empty(self.typ) if value is MISSING else value
return self.operator(base, write)
def from_checkpoint(self, checkpoint: Any) -> Self:
"""Initialize from a seed value.
Pregel's hydration path calls this with the `seed` returned by
`saver.get_channel_history`:
* `MISSING` / `DELTA_SENTINEL` channel starts empty. The walk
either reached the root (fresh delta thread) or found nothing
to seed from.
* any other value use as the base value. Typically a pre-delta
blob preserved across a channel-type migration; `replay_writes`
folds subsequent deltas on top.
"""
new: DeltaChannel[Value] = DeltaChannel(self.operator)
new.typ = self.typ
new.key = self.key
if checkpoint is MISSING or checkpoint is DELTA_SENTINEL:
new.value = _empty(new.typ)
else:
new.value = checkpoint
return new
def replay_writes(self, writes: Sequence[PendingWrite]) -> None:
"""Fold a sequence of `PendingWrite` tuples into the current value.
Called after `from_checkpoint` during pregel hydration to replay
per-step deltas from on-path ancestors through the reducer. Writes
are oldestnewest. `Overwrite` values inside the stream reset the
reducer state at that point, same as during a live super-step.
The `task_id` and `channel` fields of each `PendingWrite` are
ignored `_get_channel_writes_history` has already filtered to
this channel.
"""
for _, _, value in writes:
self.value = self._apply_write(self.value, value)
def update(self, values: Sequence[Any]) -> bool:
if not values:
return False
seen_overwrite = False
for value in values:
is_overwrite, _ = _get_overwrite(value)
if is_overwrite:
if seen_overwrite:
from langgraph.errors import (
ErrorCode,
InvalidUpdateError,
create_error_message,
)
msg = create_error_message(
message="Can receive only one Overwrite value per super-step.",
error_code=ErrorCode.INVALID_CONCURRENT_GRAPH_UPDATE,
)
raise InvalidUpdateError(msg)
seen_overwrite = True
elif seen_overwrite:
# Post-Overwrite writes within the same super-step are dropped.
continue
self.value = self._apply_write(self.value, value)
return True
def get(self) -> Any:
if self.value is MISSING:
raise EmptyChannelError()
return self.value
def is_available(self) -> bool:
return self.value is not MISSING
def checkpoint(self) -> Any:
return DELTA_SENTINEL
+40 -74
View File
@@ -184,97 +184,63 @@ def add_messages(
```
"""
# 1. Coerce scalars to lists.
remove_all_idx = None
# coerce to list
if not isinstance(left, list):
left = [left] # type: ignore[assignment]
if not isinstance(right, list):
right = [right] # type: ignore[assignment]
left = cast(list, left)
# 2. Normalize `left`. After the first call, `left` is the previous return
# value of `add_messages` — a list of fully-resolved BaseMessages with
# IDs — and needs no work. Fresh user input (dicts, tuples, message
# chunks, BaseMessages without IDs) falls through to full conversion.
left_msgs: list[BaseMessage]
if (
left
and isinstance(left[0], BaseMessage)
and not isinstance(left[0], BaseMessageChunk)
and left[0].id is not None
):
left_msgs = left
else:
left_msgs = [
message_chunk_to_message(cast(BaseMessageChunk, m))
for m in convert_to_messages(left)
]
for m in left_msgs:
if m.id is None:
m.id = str(uuid.uuid4())
# 3. Normalize `right` — always fresh input. Assign missing IDs and detect
# any RemoveMessage sentinels in a single pass.
right_msgs: list[BaseMessage] = [
# coerce to message
left = [
message_chunk_to_message(cast(BaseMessageChunk, m))
for m in convert_to_messages(left)
]
right = [
message_chunk_to_message(cast(BaseMessageChunk, m))
for m in convert_to_messages(right)
]
remove_all_idx: int | None = None
has_remove = False
for idx, m in enumerate(right_msgs):
# assign missing ids
for m in left:
if m.id is None:
m.id = str(uuid.uuid4())
if isinstance(m, RemoveMessage):
has_remove = True
if m.id == REMOVE_ALL_MESSAGES:
remove_all_idx = idx
for idx, m in enumerate(right):
if m.id is None:
m.id = str(uuid.uuid4())
if isinstance(m, RemoveMessage) and m.id == REMOVE_ALL_MESSAGES:
remove_all_idx = idx
# 4. REMOVE_ALL_MESSAGES: discard everything up to and including the sentinel.
if remove_all_idx is not None:
return right_msgs[remove_all_idx + 1 :]
return right[remove_all_idx + 1 :]
# 5. Decide fast vs. slow path. The fast path (pure append) is only valid
# when `right` has no removals, no intra-right duplicate IDs, and no IDs
# that overlap with `left` — any of those would force the indexed merge
# below to update or dedup.
pure_append = False
if not has_remove:
left_ids = {m.id for m in left_msgs}
right_ids = {m.id for m in right_msgs}
pure_append = len(right_ids) == len(right_msgs) and right_ids.isdisjoint(
left_ids
)
if pure_append:
merged = left_msgs + right_msgs
else:
# 6. Slow path: build id→index map over `left`, then replay `right`.
# In-place replacement for matching IDs, append for new IDs, and a
# deferred removal pass so RemoveMessages can target either side.
merged = left_msgs.copy()
merged_by_id = {m.id: i for i, m in enumerate(merged)}
ids_to_remove = set()
for m in right_msgs:
if (existing_idx := merged_by_id.get(m.id)) is not None:
if isinstance(m, RemoveMessage):
ids_to_remove.add(m.id)
else:
ids_to_remove.discard(m.id)
merged[existing_idx] = m
# merge
merged = left.copy()
merged_by_id = {m.id: i for i, m in enumerate(merged)}
ids_to_remove = set()
for m in right:
if (existing_idx := merged_by_id.get(m.id)) is not None:
if isinstance(m, RemoveMessage):
ids_to_remove.add(m.id)
else:
if isinstance(m, RemoveMessage):
raise ValueError(
f"Attempting to delete a message with an ID that doesn't exist ('{m.id}')"
)
merged_by_id[m.id] = len(merged)
merged.append(m)
merged = [m for m in merged if m.id not in ids_to_remove]
ids_to_remove.discard(m.id)
merged[existing_idx] = m
else:
if isinstance(m, RemoveMessage):
raise ValueError(
f"Attempting to delete a message with an ID that doesn't exist ('{m.id}')"
)
merged_by_id[m.id] = len(merged)
merged.append(m)
merged = [m for m in merged if m.id not in ids_to_remove]
# 7. Apply optional output format.
if format == "langchain-openai":
return _format_messages(merged)
if format:
merged = _format_messages(merged)
elif format:
msg = f"Unrecognized {format=}. Expected one of 'langchain-openai', None."
raise ValueError(msg)
else:
pass
return merged
+1 -34
View File
@@ -1,6 +1,5 @@
from __future__ import annotations
import collections.abc
import inspect
import logging
import typing
@@ -48,8 +47,7 @@ from langgraph._internal._pydantic import create_model
from langgraph._internal._runnable import coerce_to_runnable
from langgraph._internal._typing import EMPTY_SEQ, MISSING, DeprecatedKwargs
from langgraph.channels.base import BaseChannel
from langgraph.channels.binop import BinaryOperatorAggregate, _strip_extras
from langgraph.channels._delta import DeltaChannel
from langgraph.channels.binop import BinaryOperatorAggregate
from langgraph.channels.ephemeral_value import EphemeralValue
from langgraph.channels.last_value import LastValue, LastValueAfterFinish
from langgraph.channels.named_barrier_value import (
@@ -1084,7 +1082,6 @@ class StateGraph(Generic[StateT, ContextT, InputT, OutputT]):
CompiledStateGraph: The compiled `StateGraph`.
"""
checkpointer = ensure_valid_checkpointer(checkpointer)
serde_allowlist: set[tuple[str, ...]] | None = None
if _serde.STRICT_MSGPACK_ENABLED:
schema_types: list[type[Any]] = [
@@ -1670,36 +1667,6 @@ def _is_field_channel(typ: type[Any]) -> BaseChannel | None:
# Search through all annotated medata to find channel annotations
for item in meta:
if isinstance(item, BaseChannel):
if isinstance(item, DeltaChannel) and hasattr(typ, "__origin__"):
origin = typ.__origin__
# Unwrap parameterized Required[X]/NotRequired[X] to X
# (e.g. Annotated[NotRequired[dict[...]], ...]).
if hasattr(origin, "__origin__") and origin.__origin__ in (
Required,
NotRequired,
):
origin = origin.__args__[0]
outer = _strip_extras(origin)
if outer in (
collections.abc.Sequence,
collections.abc.MutableSequence,
):
outer = list
elif outer in (
collections.abc.Mapping,
collections.abc.MutableMapping,
):
outer = dict
elif outer in (
collections.abc.Set,
collections.abc.MutableSet,
):
outer = set
item.typ = outer
try:
item.value = outer()
except Exception:
item.value = []
return item
elif isclass(item) and issubclass(item, BaseChannel):
# ex, Annotated[int, EphemeralValue, SomeOtherAnnotation]
+9 -79
View File
@@ -3,13 +3,11 @@ from __future__ import annotations
from collections.abc import Mapping
from datetime import datetime, timezone
from langchain_core.runnables import RunnableConfig
from langgraph.checkpoint.base import DELTA_SENTINEL, BaseCheckpointSaver, Checkpoint
from langgraph.checkpoint.base import Checkpoint
from langgraph.checkpoint.base.id import uuid6
from langgraph._internal._typing import MISSING
from langgraph.channels.base import BaseChannel
from langgraph.channels._delta import DeltaChannel
from langgraph.managed.base import ManagedValueMapping, ManagedValueSpec
LATEST_VERSION = 4
@@ -60,22 +58,8 @@ def create_checkpoint(
def channels_from_checkpoint(
specs: Mapping[str, BaseChannel | ManagedValueSpec],
checkpoint: Checkpoint,
*,
saver: BaseCheckpointSaver | None = None,
config: RunnableConfig | None = None,
) -> tuple[Mapping[str, BaseChannel], ManagedValueMapping]:
"""Hydrate channels from a checkpoint.
For most channels, `spec.from_checkpoint(checkpoint["channel_values"][k])`
is sufficient the stored value IS the reconstructed state.
`DeltaChannel` is the exception: its stored value is a sentinel; the
full state is spread across `checkpoint_writes` along the ancestor
chain. When `saver` and `config` are provided, this function fetches
that history via `saver._get_channel_writes_history` and folds it
through the channel's reducer. Without them (static contexts — graph
drawing, unit tests), delta channels fall back to empty.
"""
"""Get channels from a checkpoint."""
channel_specs: dict[str, BaseChannel] = {}
managed_specs: dict[str, ManagedValueSpec] = {}
for k, v in specs.items():
@@ -83,67 +67,13 @@ def channels_from_checkpoint(
channel_specs[k] = v
else:
managed_specs[k] = v
channels: dict[str, BaseChannel] = {}
for k, spec in channel_specs.items():
ch: BaseChannel
stored = checkpoint["channel_values"].get(k, MISSING)
if (
isinstance(spec, DeltaChannel)
and saver is not None
and config is not None
and (stored is MISSING or stored is DELTA_SENTINEL)
):
# Target's own blob is empty/sentinel — walk ancestors for
# seed + writes. Skipping this when `stored` is a real value
# preserves state written via `update_state` or sitting at the
# tip of a pre-migration thread: the saver's ancestor walk
# intentionally excludes the target's own blob, so without
# this short-circuit we'd lose it.
history = saver._get_channel_writes_history(config, k)
delta_ch = spec.from_checkpoint(history.seed)
delta_ch.replay_writes(history.writes)
ch = delta_ch
else:
ch = spec.from_checkpoint(stored)
channels[k] = ch
return channels, managed_specs
async def achannels_from_checkpoint(
specs: Mapping[str, BaseChannel | ManagedValueSpec],
checkpoint: Checkpoint,
*,
saver: BaseCheckpointSaver | None = None,
config: RunnableConfig | None = None,
) -> tuple[Mapping[str, BaseChannel], ManagedValueMapping]:
"""Async version of `channels_from_checkpoint`. See docstring there."""
channel_specs: dict[str, BaseChannel] = {}
managed_specs: dict[str, ManagedValueSpec] = {}
for k, v in specs.items():
if isinstance(v, BaseChannel):
channel_specs[k] = v
else:
managed_specs[k] = v
channels: dict[str, BaseChannel] = {}
for k, spec in channel_specs.items():
ch: BaseChannel
stored = checkpoint["channel_values"].get(k, MISSING)
if (
isinstance(spec, DeltaChannel)
and saver is not None
and config is not None
and (stored is MISSING or stored is DELTA_SENTINEL)
):
history = await saver._aget_channel_writes_history(config, k)
delta_ch = spec.from_checkpoint(history.seed)
delta_ch.replay_writes(history.writes)
ch = delta_ch
else:
ch = spec.from_checkpoint(stored)
channels[k] = ch
return channels, managed_specs
return (
{
k: v.from_checkpoint(checkpoint["channel_values"].get(k, MISSING))
for k, v in channel_specs.items()
},
managed_specs,
)
def copy_checkpoint(checkpoint: Checkpoint) -> Checkpoint:
@@ -0,0 +1,218 @@
from __future__ import annotations
import asyncio
import os
import queue
import threading
from collections.abc import Callable
from contextlib import AbstractAsyncContextManager, AbstractContextManager
from dataclasses import dataclass
from types import TracebackType
from typing import Any
from langchain_core.runnables import RunnableConfig
from langgraph.checkpoint.base import (
ChannelVersions,
Checkpoint,
CheckpointMetadata,
)
QUEUE_PUT_TIMEOUT = 0.05
CHECKPOINT_BACKLOG_ENV_VAR = "LANGGRAPH_CHECKPOINT_BACKLOG"
DEFAULT_CHECKPOINT_BACKLOG = 10
def _resolve_checkpoint_backlog() -> int:
if raw := os.getenv(CHECKPOINT_BACKLOG_ENV_VAR):
try:
backlog = int(raw)
except ValueError:
return DEFAULT_CHECKPOINT_BACKLOG
if backlog > 0:
return backlog
return DEFAULT_CHECKPOINT_BACKLOG
CHECKPOINT_BACKLOG = _resolve_checkpoint_backlog()
@dataclass(frozen=True, slots=True)
class CheckpointRequest:
config: RunnableConfig
checkpoint: Checkpoint
metadata: CheckpointMetadata
new_versions: ChannelVersions
def _raise(error: BaseException) -> None:
raise error
class SyncCheckpointWriter(AbstractContextManager):
__slots__ = ("put", "queue", "error", "closed", "thread")
def __init__(
self,
put: Callable[
[RunnableConfig, Checkpoint, CheckpointMetadata, ChannelVersions], Any
],
*,
max_pending: int | None = None,
) -> None:
self.put = put
max_pending = CHECKPOINT_BACKLOG if max_pending is None else max_pending
self.queue: queue.Queue[CheckpointRequest | None] = queue.Queue(max_pending)
self.error: BaseException | None = None
self.closed = False
self.thread = threading.Thread(
target=self._run,
name="langgraph-checkpoint-writer",
daemon=True,
)
def __enter__(self) -> SyncCheckpointWriter:
self.thread.start()
return self
def submit(self, request: CheckpointRequest) -> None:
self._ensure_open()
while True:
self._raise_if_broken()
try:
self.queue.put(request, timeout=QUEUE_PUT_TIMEOUT)
except queue.Full:
continue
else:
self._raise_if_broken()
return
def _run(self) -> None:
while True:
item = self.queue.get()
if item is None:
return
try:
self.put(
item.config,
item.checkpoint,
item.metadata,
item.new_versions,
)
except BaseException as exc:
self.error = exc
return
def _ensure_open(self) -> None:
if self.closed:
raise RuntimeError("Checkpoint writer is closed")
def _raise_if_broken(self) -> None:
if self.error is not None:
_raise(self.error)
def __exit__(
self,
exc_type: type[BaseException] | None,
exc_value: BaseException | None,
traceback: TracebackType | None,
) -> bool | None:
self.closed = True
while self.thread.is_alive():
if self.error is not None:
break
try:
self.queue.put(None, timeout=QUEUE_PUT_TIMEOUT)
except queue.Full:
continue
else:
break
self.thread.join()
if exc_type is None and self.error is not None:
_raise(self.error)
return None
class AsyncCheckpointWriter(AbstractAsyncContextManager):
__slots__ = ("put", "queue", "error", "closed", "task")
def __init__(
self,
put: Callable[
[RunnableConfig, Checkpoint, CheckpointMetadata, ChannelVersions], Any
],
*,
max_pending: int | None = None,
) -> None:
self.put = put
max_pending = CHECKPOINT_BACKLOG if max_pending is None else max_pending
self.queue: asyncio.Queue[CheckpointRequest | None] = asyncio.Queue(max_pending)
self.error: BaseException | None = None
self.closed = False
self.task: asyncio.Task[None] | None = None
async def __aenter__(self) -> AsyncCheckpointWriter:
self.task = asyncio.create_task(self._run(), name="langgraph-checkpoint-writer")
return self
async def submit(self, request: CheckpointRequest) -> None:
self._ensure_open()
while True:
self._raise_if_broken()
try:
await asyncio.wait_for(
self.queue.put(request),
timeout=QUEUE_PUT_TIMEOUT,
)
except asyncio.TimeoutError:
continue
else:
self._raise_if_broken()
return
async def _run(self) -> None:
while True:
item = await self.queue.get()
if item is None:
return
try:
await self.put(
item.config,
item.checkpoint,
item.metadata,
item.new_versions,
)
except BaseException as exc:
self.error = exc
return
def _ensure_open(self) -> None:
if self.closed:
raise RuntimeError("Checkpoint writer is closed")
def _raise_if_broken(self) -> None:
if self.error is not None:
_raise(self.error)
async def __aexit__(
self,
exc_type: type[BaseException] | None,
exc_value: BaseException | None,
traceback: TracebackType | None,
) -> None:
self.closed = True
while self.task is not None and not self.task.done():
if self.error is not None:
break
try:
await asyncio.wait_for(
self.queue.put(None),
timeout=QUEUE_PUT_TIMEOUT,
)
except asyncio.TimeoutError:
continue
else:
break
if self.task is not None:
await self.task
if exc_type is None and self.error is not None:
_raise(self.error)
+142 -140
View File
@@ -2,7 +2,6 @@ from __future__ import annotations
import asyncio
import binascii
import concurrent.futures
from collections import defaultdict, deque
from collections.abc import Callable, Iterator, Mapping, Sequence
from contextlib import (
@@ -27,7 +26,6 @@ from langgraph.cache.base import BaseCache
from langgraph.checkpoint.base import (
WRITES_IDX_MAP,
BaseCheckpointSaver,
ChannelVersions,
Checkpoint,
CheckpointMetadata,
CheckpointTuple,
@@ -92,12 +90,16 @@ from langgraph.pregel._algo import (
task_path_str,
)
from langgraph.pregel._checkpoint import (
achannels_from_checkpoint,
channels_from_checkpoint,
copy_checkpoint,
create_checkpoint,
empty_checkpoint,
)
from langgraph.pregel._checkpoint_writer import (
AsyncCheckpointWriter,
CheckpointRequest,
SyncCheckpointWriter,
)
from langgraph.pregel._executor import (
AsyncBackgroundExecutor,
BackgroundExecutor,
@@ -173,19 +175,6 @@ class PregelLoop:
checkpointer_get_next_version: GetNextVersion
checkpointer_put_writes: Callable[[RunnableConfig, WritesT, str], Any] | None
checkpointer_put_writes_accepts_task_path: bool
_checkpointer_put_after_previous: (
Callable[
[
concurrent.futures.Future | None,
RunnableConfig,
Checkpoint,
str,
ChannelVersions,
],
Any,
]
| None
)
_migrate_checkpoint: Callable[[Checkpoint], None] | None
submit: Submit
channels: Mapping[str, BaseChannel]
@@ -537,7 +526,7 @@ class PregelLoop:
)
# produce debug output
if self._checkpointer_put_after_previous is not None:
if self.checkpointer is not None:
self._emit(
"checkpoints",
map_debug_checkpoint,
@@ -583,7 +572,7 @@ class PregelLoop:
return True
def after_tick(self) -> None:
def _after_tick(self) -> CheckpointRequest | None:
# finish superstep
writes = [w for t in self.tasks.values() for w in t.writes]
# all tasks have finished
@@ -608,15 +597,7 @@ class PregelLoop:
# only replay (re-execute) done tasks on the first tick
self.is_replaying = False
# save checkpoint
self._put_checkpoint({"source": "loop"})
# after execution, check if we should interrupt
if self.interrupt_after and should_interrupt(
self.checkpoint, self.interrupt_after, self.tasks.values()
):
self.status = "interrupt_after"
raise GraphInterrupt()
# unset resuming flag
self.config[CONF].pop(CONFIG_KEY_RESUMING, None)
return self._prepare_checkpoint({"source": "loop"})
def match_cached_writes(self) -> Sequence[PregelExecutableTask]:
raise NotImplementedError
@@ -665,7 +646,7 @@ class PregelLoop:
def _first(
self, *, input_keys: str | Sequence[str], updated_channels: set[str] | None
) -> set[str] | None:
) -> tuple[set[str] | None, CheckpointRequest | None]:
# Resuming from a previous checkpoint requires two things:
# 1. A prior checkpoint exists (channel_versions is non-empty)
# 2. The input signals continuation (not a fresh run with new input)
@@ -693,7 +674,7 @@ class PregelLoop:
# writes so that interrupt() calls re-fire instead of returning
# stale values. But if we're actively resuming, keep them —
# multi-interrupt scenarios need previously resolved values preserved.
is_time_traveling = self.is_replaying and (
if self.is_replaying and (
# Time-travel to a subgraph checkpoint: the parent sets
# RESUMING=True (it can't distinguish time-travel from resume),
# so we check if this subgraph's own ns is in checkpoint_map.
@@ -711,8 +692,7 @@ class PregelLoop:
# (subgraph input is a Send arg, not a Command)
or configurable.get(CONFIG_KEY_RESUMING, False)
)
)
if is_time_traveling:
):
self.checkpoint_pending_writes = [
w for w in self.checkpoint_pending_writes if w[1] != RESUME
]
@@ -760,6 +740,7 @@ class PregelLoop:
)
if updated_channels is not None:
updated_channels.update(null_updated_channels)
checkpoint_request = None
# proceed past previous checkpoint
if is_resuming:
self.checkpoint["versions_seen"].setdefault(INTERRUPT, {})
@@ -767,26 +748,6 @@ class PregelLoop:
if k in self.checkpoint["channel_versions"]:
version = self.checkpoint["channel_versions"][k]
self.checkpoint["versions_seen"][INTERRUPT][k] = version
# When time-traveling (replaying from a specific checkpoint),
# save a fork checkpoint so the replayed execution creates a
# new branch. Without this, if the execution hits an interrupt
# before after_tick() runs, no new checkpoint is created —
# the parent's latest checkpoint remains the old one and
# subsequent resumes load the wrong state.
# Skip for update_state forks (source=update/fork) since they
# already have their own fork checkpoint.
if is_time_traveling and self.checkpoint_metadata.get("source") not in (
"update",
"fork",
):
# Clear old INTERRUPT writes from the loaded checkpoint.
# The fork will have a new checkpoint_id which changes
# task IDs — stale interrupt writes would accumulate and
# confuse the multiple-interrupt check in future resumes.
self.checkpoint_pending_writes = [
w for w in self.checkpoint_pending_writes if w[1] != INTERRUPT
]
self._put_checkpoint({"source": "fork"})
# produce values output
self._emit(
"values", map_output_values, self.output_keys, True, self.channels
@@ -822,35 +783,21 @@ class PregelLoop:
)
# save input checkpoint
self.updated_channels = updated_channels
self._put_checkpoint({"source": "input"})
checkpoint_request = self._prepare_checkpoint({"source": "input"})
elif CONFIG_KEY_RESUMING not in configurable:
raise EmptyInputError(f"Received no input for {input_keys}")
# Propagate resuming and replaying flags to subgraphs.
if not self.is_nested:
# Pass the resolved before-bound checkpoint ID so subgraphs can
# find their corresponding checkpoint without re-fetching the
# parent. For forks (source=update/fork), use the fork's parent
# parent. For forks (source=update), use the fork's parent
# checkpoint ID since the fork was created after the subgraph's
# checkpoints from the original execution.
#
# Only gate on is_time_traveling (not is_replaying). When the
# client resumes with an explicit checkpoint_id that happens to
# point at the current head (e.g. LangGraph Studio sending
# `checkpoint: {checkpoint_id}` alongside Command(resume=...)),
# is_replaying is True but is_time_traveling is False. In that
# case subgraphs should load their latest checkpoint normally,
# not go through ReplayState's before-bound lookup which would
# miss subgraph checkpoints created during processing of the
# current parent step.
replay_state: ReplayState | None = None
if is_time_traveling:
if self.is_replaying:
replay_checkpoint_id = self.checkpoint["id"]
if (
self.checkpoint_metadata.get("source")
in (
"update",
"fork",
)
self.checkpoint_metadata.get("source") == "update"
and self.prev_checkpoint_config
):
replay_checkpoint_id = self.prev_checkpoint_config[CONF].get(
@@ -868,9 +815,11 @@ class PregelLoop:
self.status = "pending"
if is_resuming:
self._push_graph_lifecycle_event("resume")
return updated_channels
return updated_channels, checkpoint_request
def _put_checkpoint(self, metadata: CheckpointMetadata) -> None:
def _prepare_checkpoint(
self, metadata: CheckpointMetadata
) -> CheckpointRequest | None:
# assign step and parents
exiting = metadata is self.checkpoint_metadata
if exiting and self.checkpoint["id"] == self.checkpoint_id_saved:
@@ -880,8 +829,7 @@ class PregelLoop:
metadata["step"] = self.step
metadata["parents"] = self.config[CONF].get(CONFIG_KEY_CHECKPOINT_MAP, {})
self.checkpoint_metadata = metadata
# do checkpoint?
do_checkpoint = self._checkpointer_put_after_previous is not None and (
do_checkpoint = self.checkpointer is not None and (
exiting or self.durability != "exit"
)
# create new checkpoint
@@ -903,9 +851,8 @@ class PregelLoop:
for value in self.checkpoint["channel_values"][TASKS]
]
self.checkpoint["channel_values"][TASKS] = sanitized_tasks
# bail if no checkpointer
if do_checkpoint and self._checkpointer_put_after_previous is not None:
request = None
if do_checkpoint:
self.prev_checkpoint_config = (
self.checkpoint_config
if CONFIG_KEY_CHECKPOINT_ID in self.checkpoint_config[CONF]
@@ -927,17 +874,11 @@ class PregelLoop:
self.checkpoint_previous_versions, channel_versions
)
self.checkpoint_previous_versions = channel_versions
# save it, without blocking
# if there's a previous checkpoint save in progress, wait for it
# ensuring checkpointers receive checkpoints in order
self._put_checkpoint_fut = self.submit(
self._checkpointer_put_after_previous,
getattr(self, "_put_checkpoint_fut", None),
self.checkpoint_config,
copy_checkpoint(self.checkpoint),
self.checkpoint_metadata,
new_versions,
request = CheckpointRequest(
config=self.checkpoint_config,
checkpoint=copy_checkpoint(self.checkpoint),
metadata=self.checkpoint_metadata,
new_versions=new_versions,
)
self.checkpoint_config = {
**self.checkpoint_config,
@@ -949,24 +890,13 @@ class PregelLoop:
if not exiting:
# increment step
self.step += 1
return request
def _suppress_interrupt(
def _finalize_suppress(
self,
exc_type: type[BaseException] | None,
exc_value: BaseException | None,
traceback: TracebackType | None,
) -> bool | None:
# persist current checkpoint and writes
if self.durability == "exit" and (
# if it's a top graph
not self.is_nested
# or a nested graph with error or interrupt
or exc_value is not None
# or a nested graph with checkpointer=True
or all(NS_END not in part for part in self.checkpoint_ns)
):
self._put_checkpoint(self.checkpoint_metadata)
self._put_pending_writes()
# suppress interrupt
if isinstance(exc_value, GraphInterrupt) and not self.is_nested:
interrupt = exc_value
@@ -1011,6 +941,7 @@ class PregelLoop:
elif exc_type is None:
# save final output
self.output = read_channels(self.channels, self.output_keys)
return None
def _emit(
self,
@@ -1158,26 +1089,57 @@ class SyncPregelLoop(PregelLoop, AbstractContextManager):
)
else:
self.checkpointer_get_next_version = increment
self._checkpointer_put_after_previous = None # type: ignore[assignment]
self.checkpointer_put_writes = None
self.checkpointer_put_writes_accepts_task_path = False
self._checkpoint_writer: SyncCheckpointWriter | None = None
def _checkpointer_put_after_previous(
self,
prev: concurrent.futures.Future | None,
config: RunnableConfig,
checkpoint: Checkpoint,
metadata: CheckpointMetadata,
new_versions: ChannelVersions,
) -> RunnableConfig:
try:
if prev is not None:
prev.result()
finally:
def _dispatch_checkpoint_request(self, request: CheckpointRequest) -> None:
if self.durability == "async" and self._checkpoint_writer is not None:
self._checkpoint_writer.submit(request)
else:
cast(BaseCheckpointSaver, self.checkpointer).put(
config, checkpoint, metadata, new_versions
request.config,
request.checkpoint,
request.metadata,
request.new_versions,
)
def _put_checkpoint(self, metadata: CheckpointMetadata) -> None:
if request := self._prepare_checkpoint(metadata):
self._dispatch_checkpoint_request(request)
def _suppress_interrupt(
self,
exc_type: type[BaseException] | None,
exc_value: BaseException | None,
traceback: TracebackType | None,
) -> bool | None:
# persist current checkpoint and writes
if self.durability == "exit" and (
# if it's a top graph
not self.is_nested
# or a nested graph with error or interrupt
or exc_value is not None
# or a nested graph with checkpointer=True
or all(NS_END not in part for part in self.checkpoint_ns)
):
self._put_checkpoint(self.checkpoint_metadata)
self._put_pending_writes()
return self._finalize_suppress(exc_type, exc_value)
def after_tick(self) -> None:
# compute tick state and save checkpoint
if request := self._after_tick():
self._dispatch_checkpoint_request(request)
# after execution, check if we should interrupt
if self.interrupt_after and should_interrupt(
self.checkpoint, self.interrupt_after, self.tasks.values()
):
self.status = "interrupt_after"
raise GraphInterrupt()
# unset resuming flag
self.config[CONF].pop(CONFIG_KEY_RESUMING, None)
def match_cached_writes(self) -> Sequence[PregelExecutableTask]:
if self.cache is None:
return ()
@@ -1273,23 +1235,26 @@ class SyncPregelLoop(PregelLoop, AbstractContextManager):
else []
)
self.submit = self.stack.enter_context(BackgroundExecutor(self.config))
if self.checkpointer is not None and self.durability == "async":
self._checkpoint_writer = self.stack.enter_context(
SyncCheckpointWriter(self.checkpointer.put)
)
self.channels, self.managed = channels_from_checkpoint(
self.specs,
self.checkpoint,
saver=self.checkpointer,
config=self.checkpoint_config,
self.specs, self.checkpoint
)
self.stack.push(self._suppress_interrupt)
self.status = "input"
self.step = self.checkpoint_metadata["step"] + 1
self.stop = self.step + self.config["recursion_limit"] + 1
self.checkpoint_previous_versions = self.checkpoint["channel_versions"].copy()
self.updated_channels = self._first(
self.updated_channels, checkpoint_request = self._first(
input_keys=self.input_keys,
updated_channels=set(self.checkpoint.get("updated_channels")) # type: ignore[arg-type]
if self.checkpoint.get("updated_channels")
else None,
)
if checkpoint_request is not None:
self._dispatch_checkpoint_request(checkpoint_request)
return self
@@ -1360,26 +1325,34 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
)
else:
self.checkpointer_get_next_version = increment
self._checkpointer_put_after_previous = None # type: ignore[assignment]
self.checkpointer_put_writes = None
self.checkpointer_put_writes_accepts_task_path = False
self._checkpoint_writer: AsyncCheckpointWriter | None = None
async def _checkpointer_put_after_previous(
self,
prev: asyncio.Task | None,
config: RunnableConfig,
checkpoint: Checkpoint,
metadata: CheckpointMetadata,
new_versions: ChannelVersions,
) -> RunnableConfig:
try:
if prev is not None:
await prev
finally:
async def _dispatch_checkpoint_request(self, request: CheckpointRequest) -> None:
if self.durability == "async" and self._checkpoint_writer is not None:
await self._checkpoint_writer.submit(request)
else:
await cast(BaseCheckpointSaver, self.checkpointer).aput(
config, checkpoint, metadata, new_versions
request.config,
request.checkpoint,
request.metadata,
request.new_versions,
)
async def aafter_tick(self) -> None:
# compute tick state and save checkpoint
if request := self._after_tick():
await self._dispatch_checkpoint_request(request)
# after execution, check if we should interrupt
if self.interrupt_after and should_interrupt(
self.checkpoint, self.interrupt_after, self.tasks.values()
):
self.status = "interrupt_after"
raise GraphInterrupt()
# unset resuming flag
self.config[CONF].pop(CONFIG_KEY_RESUMING, None)
async def amatch_cached_writes(self) -> Sequence[PregelExecutableTask]:
if self.cache is None:
return []
@@ -1424,6 +1397,32 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
},
)
async def _asuppress_interrupt(
self,
exc_type: type[BaseException] | None,
exc_value: BaseException | None,
traceback: TracebackType | None,
) -> bool | None:
"""Async counterpart of SyncPregelLoop._suppress_interrupt.
Uses async checkpoint dispatch so we don't block the event loop
(the sync version was previously used here via stack.push, which
would call the blocking checkpointer.put instead of aput).
"""
# persist current checkpoint and writes
if self.durability == "exit" and (
# if it's a top graph
not self.is_nested
# or a nested graph with error or interrupt
or exc_value is not None
# or a nested graph with checkpointer=True
or all(NS_END not in part for part in self.checkpoint_ns)
):
if request := self._prepare_checkpoint(self.checkpoint_metadata):
await self._dispatch_checkpoint_request(request)
self._put_pending_writes()
return self._finalize_suppress(exc_type, exc_value)
# context manager
async def __aenter__(self) -> Self:
@@ -1480,23 +1479,26 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
self.submit = await self.stack.enter_async_context(
AsyncBackgroundExecutor(self.config)
)
self.channels, self.managed = await achannels_from_checkpoint(
self.specs,
self.checkpoint,
saver=self.checkpointer,
config=self.checkpoint_config,
if self.checkpointer is not None and self.durability == "async":
self._checkpoint_writer = await self.stack.enter_async_context(
AsyncCheckpointWriter(self.checkpointer.aput)
)
self.channels, self.managed = channels_from_checkpoint(
self.specs, self.checkpoint
)
self.stack.push(self._suppress_interrupt)
self.stack.push_async_exit(self._asuppress_interrupt)
self.status = "input"
self.step = self.checkpoint_metadata["step"] + 1
self.stop = self.step + self.config["recursion_limit"] + 1
self.checkpoint_previous_versions = self.checkpoint["channel_versions"].copy()
self.updated_channels = self._first(
self.updated_channels, checkpoint_request = self._first(
input_keys=self.input_keys,
updated_channels=set(self.checkpoint.get("updated_channels")) # type: ignore[arg-type]
if self.checkpoint.get("updated_channels")
else None,
)
if checkpoint_request is not None:
await self._dispatch_checkpoint_request(checkpoint_request)
return self
+3 -28
View File
@@ -122,7 +122,6 @@ from langgraph.pregel._algo import (
)
from langgraph.pregel._call import identifier
from langgraph.pregel._checkpoint import (
achannels_from_checkpoint,
channels_from_checkpoint,
copy_checkpoint,
create_checkpoint,
@@ -1053,10 +1052,6 @@ class Pregel(
channels, managed = channels_from_checkpoint(
self.channels,
saved.checkpoint,
saver=self.checkpointer
if isinstance(self.checkpointer, BaseCheckpointSaver)
else None,
config=saved.config,
)
# tasks for this checkpoint
next_tasks = prepare_next_tasks(
@@ -1173,13 +1168,9 @@ class Pregel(
step = saved.metadata.get("step", -1) + 1
stop = step + 2
channels, managed = await achannels_from_checkpoint(
channels, managed = channels_from_checkpoint(
self.channels,
saved.checkpoint,
saver=self.checkpointer
if isinstance(self.checkpointer, BaseCheckpointSaver)
else None,
config=saved.config,
)
# tasks for this checkpoint
next_tasks = prepare_next_tasks(
@@ -1550,11 +1541,6 @@ class Pregel(
channels, managed = channels_from_checkpoint(
self.channels,
checkpoint,
saver=self.checkpointer
if saved is not None
and isinstance(self.checkpointer, BaseCheckpointSaver)
else None,
config=saved.config if saved is not None else None,
)
values, as_node = updates[0][:2]
@@ -1998,14 +1984,9 @@ class Pregel(
)
if saved:
checkpoint_config = patch_configurable(config, saved.config[CONF])
channels, managed = await achannels_from_checkpoint(
channels, managed = channels_from_checkpoint(
self.channels,
checkpoint,
saver=self.checkpointer
if saved is not None
and isinstance(self.checkpointer, BaseCheckpointSaver)
else None,
config=saved.config if saved is not None else None,
)
values, as_node = updates[0][:2]
# no values, just clear all tasks
@@ -2794,9 +2775,6 @@ class Pregel(
)
loop.after_tick()
emit_graph_lifecycle_events(loop)
# wait for checkpoint
if durability_ == "sync":
loop._put_checkpoint_fut.result()
emit_graph_lifecycle_events(loop)
# emit output
yield from _output(
@@ -3215,11 +3193,8 @@ class Pregel(
_state_mapper,
):
yield o
loop.after_tick()
await loop.aafter_tick()
await aemit_graph_lifecycle_events(loop)
# wait for checkpoint
if durability_ == "sync":
await cast(asyncio.Future, loop._put_checkpoint_fut)
finally:
# ensure waiter doesn't remain pending on cancel/shutdown
if _cleanup_waiter is not None:
+2 -2
View File
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
[project]
name = "langgraph"
version = "1.1.9"
version = "1.1.7a1"
description = "Building stateful, multi-actor applications with LLMs"
authors = []
requires-python = ">=3.10"
@@ -24,7 +24,7 @@ classifiers = [
'Programming Language :: Python :: 3.13',
]
dependencies = [
"langchain-core>=1.3.0,<2",
"langchain-core>=0.1",
"langgraph-checkpoint>=2.1.0,<5.0.0",
"langgraph-sdk>=0.3.0,<0.4.0",
"langgraph-prebuilt>=1.0.9,<1.1.0",
@@ -1,290 +0,0 @@
"""Benchmark: add_messages fast-path optimizations.
Both implementations are inlined so the benchmark is self-contained and
immune to import-cache or installed-vs-local confusion.
Run directly:
python tests/test_add_messages_benchmark.py
Or via pytest (correctness only, numbers printed to stdout):
pytest tests/test_add_messages_benchmark.py -s -v
"""
import statistics
import time
import tracemalloc
import uuid
from typing import cast
from langchain_core.messages import (
AIMessage,
BaseMessage,
BaseMessageChunk,
HumanMessage,
RemoveMessage,
convert_to_messages,
message_chunk_to_message,
)
from langgraph.graph.message import REMOVE_ALL_MESSAGES
# ── original implementation (pre-optimisation) ────────────────────────────────
def _add_messages_original(left, right):
remove_all_idx = None
if not isinstance(left, list):
left = [left]
if not isinstance(right, list):
right = [right]
left = [
message_chunk_to_message(cast(BaseMessageChunk, m))
for m in convert_to_messages(left)
]
right = [
message_chunk_to_message(cast(BaseMessageChunk, m))
for m in convert_to_messages(right)
]
for m in left:
if m.id is None:
m.id = str(uuid.uuid4())
for idx, m in enumerate(right):
if m.id is None:
m.id = str(uuid.uuid4())
if isinstance(m, RemoveMessage) and m.id == REMOVE_ALL_MESSAGES:
remove_all_idx = idx
if remove_all_idx is not None:
return right[remove_all_idx + 1 :]
merged = left.copy()
merged_by_id = {m.id: i for i, m in enumerate(merged)}
ids_to_remove = set()
for m in right:
if (existing_idx := merged_by_id.get(m.id)) is not None:
if isinstance(m, RemoveMessage):
ids_to_remove.add(m.id)
else:
ids_to_remove.discard(m.id)
merged[existing_idx] = m
else:
if isinstance(m, RemoveMessage):
raise ValueError(
f"Attempting to delete a message with an ID that doesn't exist ('{m.id}')"
)
merged_by_id[m.id] = len(merged)
merged.append(m)
return [m for m in merged if m.id not in ids_to_remove]
# ── optimised implementation ──────────────────────────────────────────────────
def _add_messages_optimized(left, right):
if not isinstance(left, list):
left = [left]
if not isinstance(right, list):
right = [right]
# Optimisation 1: skip conversion + ID assignment on left when it already
# contains fully-resolved BaseMessage objects (the common case after the
# first call, since add_messages always returns list[BaseMessage] with IDs).
if (
left
and isinstance(left[0], BaseMessage)
and not isinstance(left[0], BaseMessageChunk)
):
left = cast(list[BaseMessage], left)
else:
left = [
message_chunk_to_message(cast(BaseMessageChunk, m))
for m in convert_to_messages(left)
]
for m in left:
if m.id is None:
m.id = str(uuid.uuid4())
# always normalise right — it's fresh external input
right = [
message_chunk_to_message(cast(BaseMessageChunk, m))
for m in convert_to_messages(right)
]
remove_all_idx = None
has_remove = False
for idx, m in enumerate(right):
if m.id is None:
m.id = str(uuid.uuid4())
if isinstance(m, RemoveMessage):
has_remove = True
if m.id == REMOVE_ALL_MESSAGES:
remove_all_idx = idx
if remove_all_idx is not None:
return right[remove_all_idx + 1 :]
# Optimisation 2: pure-append fast path — no removals and no ID overlaps.
# Builds one set over left instead of copying left + building a full dict.
if not has_remove:
left_ids = {m.id for m in left}
if not any(m.id in left_ids for m in right):
return left + right
# slow path: updates or removals present — full indexed merge
merged = left.copy()
merged_by_id = {m.id: i for i, m in enumerate(merged)}
ids_to_remove = set()
for m in right:
if (existing_idx := merged_by_id.get(m.id)) is not None:
if isinstance(m, RemoveMessage):
ids_to_remove.add(m.id)
else:
ids_to_remove.discard(m.id)
merged[existing_idx] = m
else:
if isinstance(m, RemoveMessage):
raise ValueError(
f"Attempting to delete a message with an ID that doesn't exist ('{m.id}')"
)
merged_by_id[m.id] = len(merged)
merged.append(m)
return [m for m in merged if m.id not in ids_to_remove]
# ── helpers ───────────────────────────────────────────────────────────────────
def _make_messages(n: int) -> list[BaseMessage]:
return [
(HumanMessage if i % 2 == 0 else AIMessage)(
content=f"message {i}", id=str(uuid.uuid4())
)
for i in range(n)
]
def _bench_time(fn, left, right, *, iters: int = 2_000) -> float:
"""Return median latency in microseconds."""
for _ in range(100):
fn(list(left), list(right))
times = []
for _ in range(iters):
left_copy, right_copy = list(left), list(right)
t0 = time.perf_counter()
fn(left_copy, right_copy)
times.append(time.perf_counter() - t0)
return statistics.median(times) * 1e6
def _bench_memory(fn, left, right) -> int:
"""Return peak memory allocated during a single call (bytes)."""
# one warm-up so any lazy init is excluded
fn(list(left), list(right))
left_copy, right_copy = list(left), list(right)
tracemalloc.start()
tracemalloc.clear_traces()
fn(left_copy, right_copy)
_, peak = tracemalloc.get_traced_memory()
tracemalloc.stop()
return peak
# ── scenarios ─────────────────────────────────────────────────────────────────
SCENARIOS = [
("pure append 1 → 1 msg", 1, 1, "append"),
("pure append 10 → 1 msg", 10, 1, "append"),
("pure append 100 → 1 msg", 100, 1, "append"),
("pure append 1000 → 1 msg", 1000, 1, "append"),
("pure append 1000 → 5 msgs", 1000, 5, "append"),
("update existing 100 → 1 msg", 100, 1, "update"),
("remove message 100 → 1 msg", 100, 1, "remove"),
]
def _make_inputs(n_left, n_right, mode):
left = _make_messages(n_left)
right = _make_messages(n_right)
if mode == "update":
right[0] = AIMessage(content="updated", id=left[0].id)
elif mode == "remove":
right = [RemoveMessage(id=left[0].id)]
return left, right
# ── main output ───────────────────────────────────────────────────────────────
COL = 36
def run_benchmarks() -> None:
print()
print("=" * 88)
print("add_messages benchmark — time (µs, median of 2 000 iterations)")
print("=" * 88)
print(f"{'Scenario':<{COL}} {'Original':>10} {'Optimized':>11} {'Speedup':>8}")
print("-" * 88)
for label, n_left, n_right, mode in SCENARIOS:
left, right = _make_inputs(n_left, n_right, mode)
t_orig = _bench_time(_add_messages_original, left, right)
t_opt = _bench_time(_add_messages_optimized, left, right)
print(f"{label:<{COL}} {t_orig:>10.2f} {t_opt:>11.2f} {t_orig / t_opt:>7.2f}x")
print()
print("=" * 88)
print("add_messages benchmark — peak memory allocated per call (bytes)")
print("=" * 88)
print(f"{'Scenario':<{COL}} {'Original':>10} {'Optimized':>11} {'Reduction':>10}")
print("-" * 88)
for label, n_left, n_right, mode in SCENARIOS:
left, right = _make_inputs(n_left, n_right, mode)
m_orig = _bench_memory(_add_messages_original, left, right)
m_opt = _bench_memory(_add_messages_optimized, left, right)
reduction = (1 - m_opt / m_orig) * 100 if m_orig else 0.0
print(f"{label:<{COL}} {m_orig:>10,} {m_opt:>11,} {reduction:>9.1f}%")
print()
print("=" * 88)
print("Simulated long thread — 200 steps × 2 msgs appended per step")
print("=" * 88)
for name, fn in [
("original", _add_messages_original),
("optimized", _add_messages_optimized),
]:
state: list = []
t0 = time.perf_counter()
for step in range(200):
new_msgs = [
HumanMessage(content=f"step {step} human", id=str(uuid.uuid4())),
AIMessage(content=f"step {step} ai", id=str(uuid.uuid4())),
]
state = fn(state, new_msgs)
elapsed = (time.perf_counter() - t0) * 1_000
print(f" {name:<12} {elapsed:.2f} ms ({len(state)} messages)")
print()
# ── pytest entry-points ───────────────────────────────────────────────────────
def test_add_messages_correctness():
"""Optimised implementation must match original output for every scenario."""
for label, n_left, n_right, mode in SCENARIOS:
left, right = _make_inputs(n_left, n_right, mode)
expected = _add_messages_original(list(left), list(right))
actual = _add_messages_optimized(list(left), list(right))
assert len(actual) == len(expected), f"[{label}] length mismatch"
for a, b in zip(actual, expected):
assert type(a) is type(b), f"[{label}] type mismatch"
assert a.id == b.id, f"[{label}] id mismatch"
assert a.content == b.content, f"[{label}] content mismatch"
def test_add_messages_benchmark(capsys):
run_benchmarks()
out = capsys.readouterr().out
assert "Speedup" in out
assert "Optimized" in out
if __name__ == "__main__":
run_benchmarks()
-504
View File
@@ -2,17 +2,13 @@ import operator
from collections.abc import Sequence
import pytest
from langchain_core.messages import AIMessage, HumanMessage
from langgraph.checkpoint.base import DELTA_SENTINEL
from langgraph._internal._typing import MISSING
from langgraph.channels.binop import BinaryOperatorAggregate
from langgraph.channels._delta import DeltaChannel
from langgraph.channels.last_value import LastValue
from langgraph.channels.topic import Topic
from langgraph.channels.untracked_value import UntrackedValue
from langgraph.errors import EmptyChannelError, InvalidUpdateError
from langgraph.graph.message import add_messages
pytestmark = pytest.mark.anyio
@@ -121,503 +117,3 @@ def test_untracked_value() -> None:
new_channel = UntrackedValue(dict).from_checkpoint(checkpoint)
with pytest.raises(EmptyChannelError):
new_channel.get()
def test_delta_channel_basic_two_steps() -> None:
from langchain_core.messages import AIMessage, HumanMessage
from langgraph.checkpoint.base import DELTA_SENTINEL
from langgraph.channels._delta import DeltaChannel
from langgraph.graph.message import add_messages
ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
# Step 1: one message added
ch.update([HumanMessage(content="hi", id="h1")])
d1 = ch.checkpoint()
assert d1 is DELTA_SENTINEL
# Step 2: another message
ch.update([AIMessage(content="hello", id="a1")])
d2 = ch.checkpoint()
assert d2 is DELTA_SENTINEL
# Full accumulated value is preserved in memory
assert len(ch.get()) == 2
assert ch.get()[0].content == "hi"
assert ch.get()[1].content == "hello"
def test_delta_channel_from_checkpoint_writes_list() -> None:
"""replay_writes on a fresh channel replays through the operator."""
from langchain_core.messages import AIMessage, HumanMessage
from langgraph.channels._delta import DeltaChannel
from langgraph.graph.message import add_messages
spec = DeltaChannel(add_messages)
ch = spec.from_checkpoint(DELTA_SENTINEL)
ch.replay_writes(
[
("t0", "messages", HumanMessage(content="hi", id="h1")),
("t1", "messages", AIMessage(content="hello", id="a1")),
("t2", "messages", HumanMessage(content="bye", id="h2")),
]
)
msgs = ch.get()
assert len(msgs) == 3
assert msgs[0].content == "hi"
assert msgs[1].content == "hello"
assert msgs[2].content == "bye"
def test_delta_channel_from_checkpoint_backwards_compat() -> None:
from langchain_core.messages import HumanMessage
from langgraph.channels._delta import DeltaChannel
from langgraph.graph.message import add_messages
# Old BinaryOperatorAggregate checkpoint: plain list treated as backward compat
spec = DeltaChannel(add_messages)
old_value = [HumanMessage(content="old", id="h1")]
ch = spec.from_checkpoint(old_value)
assert ch.get() == old_value
def test_delta_channel_overwrite() -> None:
from langchain_core.messages import HumanMessage
from langgraph.checkpoint.base import DELTA_SENTINEL
from langgraph.channels._delta import DeltaChannel
from langgraph.graph.message import add_messages
from langgraph.types import Overwrite
ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
ch.update([HumanMessage(content="old", id="h1")])
ch.update([Overwrite([HumanMessage(content="new", id="h2")])])
d = ch.checkpoint()
assert d is DELTA_SENTINEL
# After overwrite, value is reset to only the new message
assert len(ch.get()) == 1
assert ch.get()[0].content == "new"
def test_delta_channel_remove_message_and_replay() -> None:
"""RemoveMessage must round-trip correctly when writes are replayed."""
from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage
from langgraph.channels._delta import DeltaChannel
from langgraph.graph.message import add_messages
spec = DeltaChannel(add_messages)
ch = spec.from_checkpoint(MISSING)
# Step 1: add two messages
ch.update([HumanMessage(content="hi", id="h1")])
ch.update([AIMessage(content="hello", id="a1")])
assert ch.get() == [
HumanMessage(content="hi", id="h1"),
AIMessage(content="hello", id="a1"),
]
# Step 2: remove the AI message
ch.update([RemoveMessage(id="a1")])
assert ch.get() == [HumanMessage(content="hi", id="h1")]
# Replay the writes list from scratch — must reproduce the post-remove state
ch2 = spec.from_checkpoint(DELTA_SENTINEL)
ch2.replay_writes(
[
("t0", "messages", HumanMessage(content="hi", id="h1")),
("t1", "messages", AIMessage(content="hello", id="a1")),
("t2", "messages", RemoveMessage(id="a1")),
]
)
assert ch2.get() == [HumanMessage(content="hi", id="h1")]
def test_delta_channel_update_by_id_and_replay() -> None:
"""Updating a message by ID must round-trip correctly through writes replay."""
from langchain_core.messages import HumanMessage
from langgraph.channels._delta import DeltaChannel
from langgraph.graph.message import add_messages
spec = DeltaChannel(add_messages)
ch = spec.from_checkpoint(MISSING)
# Step 1: add a message
ch.update([HumanMessage(content="original", id="h1")])
# Step 2: update the same message by ID
ch.update([HumanMessage(content="updated", id="h1")])
assert ch.get() == [HumanMessage(content="updated", id="h1")]
# Replay writes — must produce the updated message, not the original
ch2 = spec.from_checkpoint(DELTA_SENTINEL)
ch2.replay_writes(
[
("t0", "messages", HumanMessage(content="original", id="h1")),
("t1", "messages", HumanMessage(content="updated", id="h1")),
]
)
assert len(ch2.get()) == 1
assert ch2.get()[0].content == "updated"
def test_delta_channel_checkpoint_returns_sentinel() -> None:
"""checkpoint() always returns DELTA_SENTINEL regardless of state."""
from langgraph.checkpoint.base import DELTA_SENTINEL
from langgraph.channels._delta import DeltaChannel
from langgraph.graph.message import add_messages
ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
assert ch.checkpoint() is DELTA_SENTINEL
from langchain_core.messages import HumanMessage
ch.update([HumanMessage(content="hi", id="h1")])
assert ch.checkpoint() is DELTA_SENTINEL
def test_delta_channel_inmemory_saver_assembles_writes() -> None:
"""InMemorySaver assembles writes from checkpoint_writes inside get_tuple."""
from typing import Annotated
from langchain_core.messages import AIMessage, HumanMessage
from langgraph.checkpoint.memory import InMemorySaver
from typing_extensions import TypedDict
from langgraph.channels._delta import DeltaChannel
from langgraph.graph import START, StateGraph
from langgraph.graph.message import add_messages
class State(TypedDict):
messages: Annotated[list, DeltaChannel(add_messages)]
n = {"v": 0}
def respond(state: State) -> dict:
n["v"] += 1
return {"messages": [AIMessage(content=f"ok{n['v']}", id=f"ai{n['v']}")]}
builder = StateGraph(State)
builder.add_node("respond", respond)
builder.add_edge(START, "respond")
saver = InMemorySaver()
graph = builder.compile(checkpointer=saver)
config = {"configurable": {"thread_id": "t1"}}
graph.invoke({"messages": [HumanMessage(content="hi", id="h1")]}, config)
graph.invoke({"messages": [HumanMessage(content="bye", id="h2")]}, config)
# get_tuple returns raw storage shape — channel_values stores DELTA_SENTINEL
# for delta channels; the reconstructed writes flow separately via
# saver._get_channel_writes_history.
saved = saver.get_tuple(config)
assert saved is not None
assert "messages" in saved.checkpoint["channel_values"]
assert saved.checkpoint["channel_values"]["messages"] is DELTA_SENTINEL
state = graph.get_state(config)
assert len(state.values["messages"]) == 4 # 2 human + 2 AI
def _delta_channel_with_type(operator, typ):
"""Build a DeltaChannel with an explicit type via the Annotated injection path."""
from typing import Annotated
from langgraph.channels._delta import DeltaChannel
from langgraph.graph.state import _get_channel
return _get_channel("_test", Annotated[typ, DeltaChannel(operator)])
def test_delta_channel_dict_reducer_fresh_channel() -> None:
"""DeltaChannel with a dict reducer starts as empty dict on MISSING checkpoint."""
def merge_dicts(left: dict, right: dict) -> dict:
return {**left, **right}
ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
# Should be available (not raise EmptyChannelError) and start empty
assert ch.is_available()
assert ch.get() == {}
def test_delta_channel_dict_reducer_basic_updates() -> None:
"""DeltaChannel with a dict reducer accumulates key/value pairs across steps."""
from langgraph.checkpoint.base import DELTA_SENTINEL
def merge_dicts(left: dict, right: dict) -> dict:
return {**left, **right}
ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
ch.update([{"a": 1}])
d1 = ch.checkpoint()
assert d1 is DELTA_SENTINEL
ch.update([{"b": 2}])
d2 = ch.checkpoint()
assert d2 is DELTA_SENTINEL
assert ch.get() == {"a": 1, "b": 2}
def test_delta_channel_dict_reducer_writes_reconstruction() -> None:
"""replay_writes on a fresh channel replays through a dict merge reducer."""
def merge_dicts(left: dict, right: dict) -> dict:
return {**left, **right}
spec = _delta_channel_with_type(merge_dicts, dict)
ch = spec.from_checkpoint(DELTA_SENTINEL)
ch.replay_writes(
[
("t0", "files", {"a": 1}),
("t1", "files", {"b": 2}),
("t2", "files", {"c": 3}),
]
)
assert ch.get() == {"a": 1, "b": 2, "c": 3}
def test_delta_channel_dict_reducer_with_deletions() -> None:
"""Dict reducer that treats None values as deletions works end-to-end (deepagents pattern)."""
def merge_files(left: dict | None, right: dict) -> dict:
if left is None:
return {k: v for k, v in right.items() if v is not None}
result = {**left}
for k, v in right.items():
if v is None:
result.pop(k, None)
else:
result[k] = v
return result
ch = _delta_channel_with_type(merge_files, dict).from_checkpoint(MISSING)
ch.update([{"file1.py": "content1", "file2.py": "content2"}])
# Delete file1, add file3
ch.update([{"file1.py": None, "file3.py": "content3"}])
assert ch.get() == {"file2.py": "content2", "file3.py": "content3"}
# Confirm writes reconstruction produces the same result
spec = _delta_channel_with_type(merge_files, dict)
ch2 = spec.from_checkpoint(DELTA_SENTINEL)
ch2.replay_writes(
[
("t0", "files", {"file1.py": "content1", "file2.py": "content2"}),
("t1", "files", {"file1.py": None, "file3.py": "content3"}),
]
)
assert ch2.get() == {"file2.py": "content2", "file3.py": "content3"}
def test_delta_channel_dict_reducer_overwrite_in_update() -> None:
"""Overwrite(dict) in update() must preserve dict shape, not coerce to list."""
from langgraph.types import Overwrite
def merge_dicts(left: dict, right: dict) -> dict:
return {**left, **right}
ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
ch.update([{"a": 1}])
ch.update([Overwrite({"b": 2, "c": 3})])
assert ch.get() == {"b": 2, "c": 3}
def test_delta_channel_dict_reducer_overwrite_in_writes_replay() -> None:
"""Overwrite(dict) embedded in replayed writes must reconstruct as dict."""
from langgraph.types import Overwrite
def merge_dicts(left: dict, right: dict) -> dict:
return {**left, **right}
spec = _delta_channel_with_type(merge_dicts, dict)
ch = spec.from_checkpoint(DELTA_SENTINEL)
ch.replay_writes(
[
("t0", "files", {"a": 1}),
("t1", "files", Overwrite({"x": 10, "y": 20})),
("t2", "files", {"z": 30}),
]
)
assert ch.get() == {"x": 10, "y": 20, "z": 30}
def test_delta_channel_dict_reducer_with_notrequired_annotation() -> None:
"""DeltaChannel infers dict type through `Annotated[NotRequired[dict[...]], ch]`.
This is the shape the deepagents filesystem middleware uses for its
`files` field; without unwrapping NotRequired we'd fall through to `list`
and blow up on the first dict operator call.
"""
from typing import Annotated
from typing_extensions import NotRequired
from langgraph.channels._delta import DeltaChannel
from langgraph.graph.state import _get_channel
def merge_dicts(left: dict | None, right: dict) -> dict:
if left is None:
return dict(right)
return {**left, **right}
annotation = Annotated[
NotRequired[dict[str, int]],
DeltaChannel(merge_dicts),
]
ch = _get_channel("files", annotation).from_checkpoint(MISSING)
assert ch.get() == {}
ch.update([{"a": 1}])
ch.update([{"b": 2}])
assert ch.get() == {"a": 1, "b": 2}
def test_delta_channel_dict_reducer_end_to_end_filesystem() -> None:
"""End-to-end: graph with dict-reducer (filesystem-style) channel wrapped in DeltaChannel.
Mirrors the deepagents filesystem pattern: `files: Annotated[dict, reducer]`
where the reducer merges dicts and treats None values as deletions.
"""
from typing import Annotated
from langgraph.checkpoint.memory import InMemorySaver
from typing_extensions import TypedDict
from langgraph.channels._delta import DeltaChannel
from langgraph.graph import START, StateGraph
def merge_files(left: dict | None, right: dict) -> dict:
if left is None:
return {k: v for k, v in right.items() if v is not None}
result = {**left}
for k, v in right.items():
if v is None:
result.pop(k, None)
else:
result[k] = v
return result
class State(TypedDict):
files: Annotated[dict[str, str], DeltaChannel(merge_files)]
turn = {"v": 0}
def write_file(state: State) -> dict:
turn["v"] += 1
n = turn["v"]
return {"files": {f"/doc_{n}.txt": f"content for turn {n}"}}
builder = StateGraph(State)
builder.add_node("write_file", write_file)
builder.add_edge(START, "write_file")
saver = InMemorySaver()
graph = builder.compile(checkpointer=saver)
config = {"configurable": {"thread_id": "fs"}}
for _ in range(3):
graph.invoke({"files": {}}, config)
# Checkpoint stores only the sentinel — per-step writes live in checkpoint_writes.
saved = saver.get_tuple(config)
assert saved is not None
cv = saved.checkpoint["channel_values"]["files"]
assert cv is DELTA_SENTINEL
state = graph.get_state(config)
assert state.values["files"] == {
"/doc_1.txt": "content for turn 1",
"/doc_2.txt": "content for turn 2",
"/doc_3.txt": "content for turn 3",
}
# Deletion path must round-trip through writes replay.
def delete_file(state: State) -> dict:
return {"files": {"/doc_1.txt": None}}
builder2 = StateGraph(State)
builder2.add_node("write_file", write_file)
builder2.add_node("delete_file", delete_file)
builder2.add_edge(START, "write_file")
builder2.add_edge("write_file", "delete_file")
turn["v"] = 0
saver2 = InMemorySaver()
graph2 = builder2.compile(checkpointer=saver2)
config2 = {"configurable": {"thread_id": "fs2"}}
graph2.invoke({"files": {}}, config2)
state2 = graph2.get_state(config2)
assert state2.values["files"] == {}
def test_delta_channel_dict_reducer_backwards_compat() -> None:
"""A pre-DeltaChannel dict checkpoint must load as a dict, not be listified."""
def merge_dicts(left: dict, right: dict) -> dict:
return {**left, **right}
spec = _delta_channel_with_type(merge_dicts, dict)
old_value = {"a": 1, "b": 2}
ch = spec.from_checkpoint(old_value)
assert ch.get() == {"a": 1, "b": 2}
# ---------------------------------------------------------------------------
# seed / pre-delta migration
# ---------------------------------------------------------------------------
def test_delta_channel_from_checkpoint_honors_seed() -> None:
"""A non-sentinel value to from_checkpoint is used as the pre-delta seed.
Guards the pre-delta migration path: when the saver's ancestor walk hits
a pre-DeltaChannel blob it passes it as `seed` so replay reconstructs
the post-migration state correctly rather than replaying from empty.
"""
spec = DeltaChannel(add_messages)
seed = [HumanMessage(content="pre-delta", id="p1")]
ch = spec.from_checkpoint(seed)
ch.replay_writes(
[
("t0", "messages", AIMessage(content="delta-1", id="d1")),
("t1", "messages", HumanMessage(content="delta-2", id="d2")),
]
)
msgs = ch.get()
assert [m.content for m in msgs] == ["pre-delta", "delta-1", "delta-2"]
def test_delta_channel_from_checkpoint_seed_without_writes() -> None:
"""Reconstruction at a pre-delta ancestor with no newer deltas returns
just the seed the saver's terminator fired immediately."""
spec = DeltaChannel(add_messages)
seed = [HumanMessage(content="only-snap", id="s1")]
ch = spec.from_checkpoint(seed)
ch.replay_writes([])
assert ch.get() == seed
def test_delta_channel_from_checkpoint_seed_none_is_distinct_from_sentinel() -> None:
"""`seed=None` must start replay from None, not from an empty channel.
The DELTA_SENTINEL / MISSING sentinels mean 'no seed'; passing `None`
explicitly should feed None to the reducer as the left operand.
"""
def replace(left, right):
return right
spec = DeltaChannel(replace)
ch = spec.from_checkpoint(None)
ch.replay_writes([("t0", "x", "after")])
# Reducer replaces; seed=None → first write produces "after".
assert ch.get() == "after"
@@ -1,398 +0,0 @@
"""Benchmark: DeltaChannel vs BinaryOperatorAggregate storage and time.
Run directly: python tests/test_delta_channel_benchmark.py
Run via pytest: pytest tests/test_delta_channel_benchmark.py -s
Simulates realistic multi-turn conversations with paragraph-length messages
(~100 tokens each) scaling up to 1M-token-equivalent histories.
Token estimates: 1 token 4 chars; each turn 200 tokens (human + AI).
A 1M-token conversation 5,000 turns of realistic messages.
DeltaChannel stores only a zero-byte sentinel in checkpoint_blobs; the actual
write data lives in checkpoint_writes (already stored there). Reconstruction
walks the parent chain and replays writes through the operator O(N) total
storage vs O() for plain add_messages.
"""
from __future__ import annotations
import sys
import time
from typing import Annotated, Any
import pytest
from langchain_core.messages import AIMessage, HumanMessage
from langgraph.checkpoint.memory import MemorySaver
from typing_extensions import TypedDict
from langgraph.channels._delta import DeltaChannel
from langgraph.graph import END, StateGraph
from langgraph.graph.message import add_messages
try:
from langgraph.checkpoint.sqlite import SqliteSaver
_SQLITE_AVAILABLE = True
except ImportError:
_SQLITE_AVAILABLE = False
try:
from langgraph.checkpoint.postgres import PostgresSaver
_POSTGRES_AVAILABLE = True
_POSTGRES_URI = (
"postgres://postgres:postgres@localhost:5441/postgres?sslmode=disable"
)
except ImportError:
_POSTGRES_AVAILABLE = False
# ---------------------------------------------------------------------------
# Realistic message payload (~100 tokens / ~400 chars each)
# ---------------------------------------------------------------------------
_HUMAN_TEMPLATE = (
"I need help understanding the implications of {topic} on our system architecture. "
"Specifically, I'm concerned about how this interacts with our existing {concern} "
"and whether we need to refactor the {component} layer before proceeding. "
"We've had prior incidents in this area and want to be deliberate. "
"What should we prioritize first, and are there known failure modes we should design around from the start?"
)
_AI_TEMPLATE = (
"Great question about {topic}. The key insight here is that {concern} introduces "
"a subtle ordering dependency that most teams overlook until they hit it in production. "
"For your {component} layer specifically, I'd recommend starting with a careful audit "
"of the interface boundaries before making any structural changes. This will give you "
"a clear picture of the blast radius and let you sequence the migration safely."
)
_TOPICS = [
"distributed tracing",
"eventual consistency",
"schema migration",
"backpressure handling",
"idempotency guarantees",
"cache invalidation",
"connection pooling",
"rate limiting",
"circuit breaking",
"observability pipelines",
]
_CONCERNS = [
"concurrency model",
"retry semantics",
"state management",
"error propagation",
"latency budget",
]
_COMPONENTS = [
"persistence",
"routing",
"ingestion",
"aggregation",
"serialization",
]
def _human_content(i: int) -> str:
return _HUMAN_TEMPLATE.format(
topic=_TOPICS[i % len(_TOPICS)],
concern=_CONCERNS[i % len(_CONCERNS)],
component=_COMPONENTS[i % len(_COMPONENTS)],
)
def _ai_content(i: int) -> str:
return _AI_TEMPLATE.format(
topic=_TOPICS[i % len(_TOPICS)],
concern=_CONCERNS[i % len(_CONCERNS)],
component=_COMPONENTS[i % len(_COMPONENTS)],
)
# ---------------------------------------------------------------------------
# State definitions
# ---------------------------------------------------------------------------
class BinaryState(TypedDict):
messages: Annotated[list, add_messages]
class DeltaState(TypedDict):
messages: Annotated[list, DeltaChannel(add_messages)]
# ---------------------------------------------------------------------------
# Graph factory
# ---------------------------------------------------------------------------
def _make_graph(state_cls: type, checkpointer: Any = None) -> Any:
def human_node(state: Any) -> dict:
return {}
def ai_node(state: Any) -> dict:
i = len(state["messages"]) // 2
return {"messages": [AIMessage(content=_ai_content(i), id=f"a{i}")]}
g = StateGraph(state_cls)
g.add_node("human", human_node)
g.add_node("ai", ai_node)
g.add_edge("human", "ai")
g.add_edge("ai", END)
g.set_entry_point("human")
return g.compile(checkpointer=checkpointer or MemorySaver())
# ---------------------------------------------------------------------------
# Measurement helpers
# ---------------------------------------------------------------------------
def _total_blob_bytes(saver: MemorySaver) -> int:
total = 0
for (_, _, _, _), (type_tag, blob) in saver.blobs.items():
if blob is not None:
total += len(blob)
return total
def _run_turns(
n_turns: int,
state_cls: type,
checkpointer: Any = None,
) -> tuple[float, float, int]:
"""Run n_turns conversation turns.
Returns (write_elapsed_s, read_elapsed_s, total_blob_bytes).
blob_bytes is -1 for savers without in-memory blob stores (e.g. SQLite).
Read latency is measured as the time to invoke the graph with no new
messages after the full history is built this forces state rehydration.
"""
graph = _make_graph(state_cls, checkpointer)
config = {"configurable": {"thread_id": "bench"}}
t0 = time.perf_counter()
for i in range(n_turns):
graph.invoke(
{"messages": [HumanMessage(content=_human_content(i), id=f"h{i}")]},
config,
)
write_elapsed = time.perf_counter() - t0
# Measure read/rehydration: get_state forces the channel to rebuild
t1 = time.perf_counter()
for _ in range(5):
graph.get_state(config)
read_elapsed = (time.perf_counter() - t1) / 5
if isinstance(graph.checkpointer, MemorySaver):
blob_bytes = _total_blob_bytes(graph.checkpointer)
else:
blob_bytes = -1
return write_elapsed, read_elapsed, blob_bytes
def _fmt_bytes(n: int) -> str:
if n >= 1_000_000:
return f"{n / 1_000_000:.1f} MB"
if n >= 1_000:
return f"{n / 1_000:.1f} KB"
return f"{n} B"
def _approx_tokens(n_turns: int) -> str:
# ~100 tokens human + ~100 tokens AI per turn
tokens = n_turns * 200
if tokens >= 1_000_000:
return f"~{tokens / 1_000_000:.1f}M tok"
if tokens >= 1_000:
return f"~{tokens / 1_000:.0f}K tok"
return f"~{tokens} tok"
# ---------------------------------------------------------------------------
# Benchmark matrix
# ---------------------------------------------------------------------------
# Turn counts chosen to demonstrate O(N²) vs O(N) storage growth without running too long.
# Extrapolation: 5,000 turns × ~200 tokens/turn ≈ 1M tokens (Claude's full context window).
TURN_COUNTS = [10, 25, 50, 100, 500]
# Deep-thread counts where add_messages blob storage would exceed 1 GB;
# only DeltaChannel runs here.
DELTA_ONLY_TURN_COUNTS = [1000]
def _checkpointer_factories() -> list[tuple[str, Any]]:
"""Return (label, context_manager_or_none) pairs for available checkpointers."""
return [("InMemory", None)]
def run_benchmark() -> None:
print()
print(
"DeltaChannel vs add_messages (BinaryOperatorAggregate) — checkpoint storage & latency"
)
print("Simulating realistic multi-turn conversations up to ~1M-token histories")
print("(5,000 turns × ~200 tokens/turn ≈ 1M tokens — Claude's full context window)")
print()
checkpointers: list[tuple[str, Any]] = [("InMemory", None)]
if _POSTGRES_AVAILABLE:
try:
import psycopg
psycopg.connect(_POSTGRES_URI).close()
checkpointers.append(("Postgres (plain SELECT)", "postgres"))
except Exception:
pass
for cp_label, cp_hint in checkpointers:
print(f"--- Checkpointer: {cp_label} ---")
_run_benchmark_for_checkpointer(cp_hint)
def _run_benchmark_for_checkpointer(cp_hint: Any) -> None:
import contextlib
import tempfile
@contextlib.contextmanager
def _make_saver():
if cp_hint is None:
yield None
elif cp_hint == "postgres":
with PostgresSaver.from_conn_string(_POSTGRES_URI) as saver:
saver.setup()
with saver._cursor() as cur:
cur.execute("DELETE FROM checkpoints WHERE thread_id = 'bench'")
cur.execute(
"DELETE FROM checkpoint_blobs WHERE thread_id = 'bench'"
)
cur.execute(
"DELETE FROM checkpoint_writes WHERE thread_id = 'bench'"
)
yield saver
else:
with tempfile.NamedTemporaryFile(suffix=".db") as f:
with SqliteSaver.from_conn_string(f.name) as saver:
yield saver
rows: list[tuple[int, Any, Any, Any, Any, Any, Any]] = []
for turns in TURN_COUNTS:
with _make_saver() as saver:
b_wt, b_rt, b_bytes = _run_turns(turns, BinaryState, saver)
with _make_saver() as saver:
d_wt, d_rt, d_bytes = _run_turns(turns, DeltaState, saver)
rows.append((turns, b_bytes, d_bytes, b_rt, d_rt, b_wt, d_wt))
for turns in DELTA_ONLY_TURN_COUNTS:
with _make_saver() as saver:
d_wt, d_rt, d_bytes = _run_turns(turns, DeltaState, saver)
rows.append((turns, None, d_bytes, None, d_rt, None, d_wt))
# ── Table 1: Storage ─────────────────────────────────────────────────────
W = 64
print("Storage (checkpoint blob bytes)")
print("=" * W)
print(
f"{'turns':>6} {'ctx size':>10} {'add_msgs':>12} {'delta':>12} "
f"{'savings':>8}"
)
print("-" * W)
def _bytes_or_na(v: Any) -> str:
if v is None:
return "n/a"
if v < 0:
return "n/a"
return _fmt_bytes(v)
def _ms_or_na(v: Any) -> str:
return "n/a" if v is None else f"{v * 1000:.1f}ms"
for turns, b_bytes, d_bytes, b_rt, d_rt, b_wt, d_wt in rows:
if b_bytes is None or b_bytes < 0 or d_bytes is None or d_bytes < 0:
ratio_str = "n/a"
else:
ratio = b_bytes / d_bytes if d_bytes else float("inf")
ratio_str = f"{ratio:.0f}x"
print(
f"{turns:>6} {_approx_tokens(turns):>10} "
f"{_bytes_or_na(b_bytes):>12} {_bytes_or_na(d_bytes):>12} "
f"{ratio_str:>8}"
)
print("=" * W)
print()
# ── Table 2: Read latency ─────────────────────────────────────────────────
print("Read latency (avg of 5 get_state calls)")
print("=" * W)
print(f"{'turns':>6} {'ctx size':>10} {'add_msgs':>12} {'delta':>12}")
print("-" * W)
for turns, b_bytes, d_bytes, b_rt, d_rt, b_wt, d_wt in rows:
print(
f"{turns:>6} {_approx_tokens(turns):>10} "
f"{_ms_or_na(b_rt):>12} {_ms_or_na(d_rt):>12}"
)
print("=" * W)
print()
# ── Table 3: Per-invoke latency (total write_elapsed / turns) ─────────────
print("Per-invoke latency (total graph.invoke time / turns)")
print("=" * W)
print(f"{'turns':>6} {'ctx size':>10} {'add_msgs':>12} {'delta':>12}")
print("-" * W)
for turns, b_bytes, d_bytes, b_rt, d_rt, b_wt, d_wt in rows:
def _per(wt: Any) -> str:
if wt is None:
return "n/a"
return f"{(wt / turns) * 1000:.1f}ms"
print(
f"{turns:>6} {_approx_tokens(turns):>10} "
f"{_per(b_wt):>12} {_per(d_wt):>12}"
)
print("=" * W)
print()
print("Legend:")
print(" add_msgs = Annotated[list, add_messages] — O(N²) storage")
print(" delta = Annotated[list, DeltaChannel(add_messages)] — O(N) storage")
print()
# ---------------------------------------------------------------------------
# Pytest entry point
# ---------------------------------------------------------------------------
@pytest.mark.skip(
reason="slow benchmark — run manually with: python tests/test_delta_channel_benchmark.py"
)
def test_delta_channel_benchmark(capsys: Any) -> None:
"""Storage grows O(N²) for add_messages, O(N) for DeltaChannel."""
with capsys.disabled():
run_benchmark()
# Correctness assertion: DeltaChannel must use less storage at scale.
for turns in [25, 50]:
_, _, b_bytes = _run_turns(turns, BinaryState)
_, _, d_bytes = _run_turns(turns, DeltaState)
assert d_bytes < b_bytes, (
f"DeltaChannel should use less storage at {turns} turns, "
f"got delta={d_bytes} binary={b_bytes}"
)
# ---------------------------------------------------------------------------
# Script entry point
# ---------------------------------------------------------------------------
if __name__ == "__main__":
run_benchmark()
sys.exit(0)
@@ -1,504 +0,0 @@
"""Tests for the BinaryOperatorAggregate -> DeltaChannel migration path.
A thread written under `BinaryOperatorAggregate(...)` must keep working
after its annotation is swapped to `DeltaChannel(...)` on the same
checkpointer pre-migration state visible at each *settled* ancestor
checkpoint is preserved, and post-migration writes fold on top through
the reducer.
Mechanism under test: the saver's `_get_channel_writes_history(config,
channel)` walks the parent chain; when it encounters an ancestor whose
`channel_values[channel]` is a real value (not `DELTA_SENTINEL`), it
returns that as the `seed`. `DeltaChannel.from_checkpoint(seed)` uses
it as the base value, and `replay_writes(writes)` folds on-path deltas.
Scenarios covered:
1. **Basic migration (sync + async)**: build pre-migration state with
`BinaryOperatorAggregate`, swap the annotation to `DeltaChannel` on
the same checkpointer, and verify that every settled pre-migration
super-step boundary (`next=('__start__',)`) round-trips exactly
under the delta-channel view.
2. **Time travel into a pre-migration checkpoint** after migration
`graph.get_state(pre_migration_config)` at a settled ancestor
returns the same state as under the binop channel.
3. **Continuing a migrated thread**: driving one more super-step after
migration produces a state that includes the pre-migration settled
prefix plus the new delta write proving `from_checkpoint(seed)` +
`replay_writes` correctly fold post-migration deltas onto the
pre-migration seed.
4. **Base-saver fallback path**: a third-party-style subclass that
removes the optimized `InMemorySaver` override and falls back to
`BaseCheckpointSaver._get_channel_writes_history` must produce the
same result as the optimized path.
5. **Channel-type isolation across threads**: two threads on the same
checkpointer under the delta-channel graph one freshly-started,
one migrated from pre-migration state don't cross-contaminate.
The parent-chain walk is scoped to the thread.
TODO: add postgres variants in the existing `libs/checkpoint-postgres`
test files (different fixture setup; not this file).
"""
from __future__ import annotations
import operator
from typing import Annotated, Any
import pytest
from langgraph.checkpoint.memory import InMemorySaver
from typing_extensions import TypedDict
from langgraph.channels.binop import BinaryOperatorAggregate
from langgraph.channels._delta import DeltaChannel
from langgraph.graph import END, START, StateGraph
pytestmark = pytest.mark.anyio
# ---------------------------------------------------------------------------
# Graph factories
#
# A minimal reducer (`operator.add` on lists of str) with a noop node keeps
# state change localized to the HumanMessage-like payload passed through
# `invoke`. That isolates the pre/post-migration parity assertions to
# channel-hydration semantics.
# ---------------------------------------------------------------------------
def _noop(_state: Any) -> dict:
return {}
def _binop_graph(checkpointer: Any) -> Any:
class BinopState(TypedDict):
items: Annotated[list, BinaryOperatorAggregate(list, operator.add)]
return (
StateGraph(BinopState)
.add_node("noop", _noop)
.add_edge(START, "noop")
.add_edge("noop", END)
.compile(checkpointer=checkpointer)
)
def _delta_graph(checkpointer: Any) -> Any:
class DeltaState(TypedDict):
items: Annotated[list, DeltaChannel(operator.add)]
return (
StateGraph(DeltaState)
.add_node("noop", _noop)
.add_edge(START, "noop")
.add_edge("noop", END)
.compile(checkpointer=checkpointer)
)
def _drive(graph: Any, config: dict, tag: str, n: int) -> None:
for i in range(n):
graph.invoke({"items": [f"{tag}{i}"]}, config)
async def _adrive(graph: Any, config: dict, tag: str, n: int) -> None:
for i in range(n):
await graph.ainvoke({"items": [f"{tag}{i}"]}, config)
def _settled_boundaries(history: list) -> list[tuple[dict, list]]:
"""Return `[(config, items), ...]` for every checkpoint in `history`
whose `next == ('__start__',)` the stable boundaries between invokes.
"""
return [
(s.config, list(s.values.get("items", [])))
for s in history
if s.next == ("__start__",)
]
# ---------------------------------------------------------------------------
# 1. Basic migration (sync + async)
# ---------------------------------------------------------------------------
def test_basic_migration_preserves_pre_migration_state() -> None:
"""Build state under `BinaryOperatorAggregate`, migrate to
`DeltaChannel` on the same checkpointer, and verify that every
settled pre-migration super-step boundary round-trips exactly.
Settled boundaries (`next=('__start__',)`) are the stable hydration
targets for the migration path: writes that produced the NEXT
super-step are kept as `pending_writes` on the ancestor, so walking
from a descendant finds the ancestor's blob as the seed and
reconstructs the correct state.
"""
checkpointer = InMemorySaver()
config = {"configurable": {"thread_id": "basic-sync"}}
# Pre-migration: accumulate items across 3 invokes.
binop = _binop_graph(checkpointer)
_drive(binop, config, "u", 3)
pre_boundaries = _settled_boundaries(list(binop.get_state_history(config)))
assert len(pre_boundaries) >= 2, "expected multiple settled boundaries"
# Migrate: swap the annotation on the same checkpointer.
delta = _delta_graph(checkpointer)
for cfg, items in pre_boundaries:
snap = delta.get_state(cfg)
assert list(snap.values.get("items", [])) == items, (
f"snapshot mismatch at {cfg['configurable']['checkpoint_id']}: "
f"expected {items}, got {snap.values.get('items', [])}"
)
async def test_basic_migration_preserves_pre_migration_state_async() -> None:
"""Async variant of the basic migration scenario."""
checkpointer = InMemorySaver()
config = {"configurable": {"thread_id": "basic-async"}}
binop = _binop_graph(checkpointer)
await _adrive(binop, config, "u", 3)
pre_history = [s async for s in binop.aget_state_history(config)]
pre_boundaries = _settled_boundaries(pre_history)
assert len(pre_boundaries) >= 2
delta = _delta_graph(checkpointer)
for cfg, items in pre_boundaries:
snap = await delta.aget_state(cfg)
assert list(snap.values.get("items", [])) == items, (
f"async snapshot mismatch at {cfg['configurable']['checkpoint_id']}"
)
# ---------------------------------------------------------------------------
# 2. Time travel into a pre-migration checkpoint after migration
# ---------------------------------------------------------------------------
def test_time_travel_into_pre_migration_checkpoint() -> None:
"""After migration, `graph.get_state(pre_migration_config)` at a
settled ancestor returns the state as stored at that point."""
checkpointer = InMemorySaver()
config = {"configurable": {"thread_id": "time-travel"}}
binop = _binop_graph(checkpointer)
_drive(binop, config, "u", 3)
pre_boundaries = _settled_boundaries(list(binop.get_state_history(config)))
assert pre_boundaries, "no settled ancestors to time-travel to"
delta = _delta_graph(checkpointer)
# Pick the oldest non-empty boundary — a long distance to walk back.
non_empty = [(cfg, items) for cfg, items in pre_boundaries if items]
assert non_empty, "expected at least one non-empty boundary"
target_cfg, expected_items = non_empty[-1]
snap = delta.get_state(target_cfg)
assert list(snap.values.get("items", [])) == expected_items
# ---------------------------------------------------------------------------
# 3. Continuing a migrated thread: deltas fold onto pre-migration seed
# ---------------------------------------------------------------------------
def test_continuing_migrated_thread_folds_deltas_on_seed() -> None:
"""Resume a pre-migration settled ancestor via `invoke(None, cfg)`
under the delta-channel graph. Since the pre-migration checkpoint
has an existing `pending_writes` entry (the input for the NEXT
super-step), re-running from that ancestor reproduces the same
post-ancestor state as the original binop run.
This proves the seed-terminator + write-replay pipeline works
end-to-end across the migration boundary.
"""
checkpointer = InMemorySaver()
config = {"configurable": {"thread_id": "continue"}}
binop = _binop_graph(checkpointer)
_drive(binop, config, "u", 2)
# Pick the oldest settled boundary with non-empty state.
pre_boundaries = _settled_boundaries(list(binop.get_state_history(config)))
target_cfg, seed_items = next(
(cfg, items) for cfg, items in reversed(pre_boundaries) if items
)
assert seed_items, "need a non-empty seed boundary"
# Migrate and resume from the pre-migration ancestor. `invoke(None,
# cfg)` replays the pending writes staged at `cfg` under the new
# channel; the reducer folds those deltas onto the seed.
delta = _delta_graph(checkpointer)
result = delta.invoke(None, target_cfg)
# The resumed state must include the pre-migration seed items in order.
result_items = list(result.get("items", []))
for idx, prefix_item in enumerate(seed_items):
assert result_items[idx] == prefix_item, (
f"pre-migration seed item at {idx} not preserved: "
f"got {result_items[: idx + 1]}, expected {seed_items}"
)
# ---------------------------------------------------------------------------
# 4. Base-saver fallback path
# ---------------------------------------------------------------------------
class _ThirdPartyStyleSaver(InMemorySaver):
"""Simulates a third-party saver that inherits the reference
`_get_channel_writes_history` implementation from
`BaseCheckpointSaver` rather than overriding it.
We rebind the two methods to the base-class versions (via MRO) so
the fallback path is exercised even though the storage layer is
still the in-memory one.
"""
# MRO: [_ThirdPartyStyleSaver, InMemorySaver, BaseCheckpointSaver, ...]
_get_channel_writes_history = ( # type: ignore[assignment]
InMemorySaver.__mro__[1]._get_channel_writes_history # type: ignore[attr-defined]
)
_aget_channel_writes_history = ( # type: ignore[assignment]
InMemorySaver.__mro__[1]._aget_channel_writes_history # type: ignore[attr-defined]
)
def test_base_saver_fallback_matches_optimized_override() -> None:
"""The reference `BaseCheckpointSaver` implementation must produce
the same migration behavior as the optimized `InMemorySaver`
override. We drive the same migration scenario through both savers
and assert per-snapshot parity in the delta-channel view."""
# Fast path: optimized InMemorySaver override.
fast_saver = InMemorySaver()
fast_config = {"configurable": {"thread_id": "fast"}}
fast_binop = _binop_graph(fast_saver)
_drive(fast_binop, fast_config, "u", 3)
fast_delta = _delta_graph(fast_saver)
fast_history = [
(s.next, list(s.values.get("items", [])))
for s in fast_delta.get_state_history(fast_config)
]
# Slow path: base-class fallback.
slow_saver = _ThirdPartyStyleSaver()
slow_config = {"configurable": {"thread_id": "slow"}}
slow_binop = _binop_graph(slow_saver)
_drive(slow_binop, slow_config, "u", 3)
slow_delta = _delta_graph(slow_saver)
slow_history = [
(s.next, list(s.values.get("items", [])))
for s in slow_delta.get_state_history(slow_config)
]
assert slow_history == fast_history, (
"base-saver fallback should match optimized-override behavior; "
f"fast={fast_history}, slow={slow_history}"
)
# ---------------------------------------------------------------------------
# 5. Thread isolation under mixed-generation storage
# ---------------------------------------------------------------------------
def test_delta_and_migrated_threads_do_not_cross_contaminate() -> None:
"""Two threads sharing a checkpointer — one migrated from
pre-migration state, one freshly-started under DeltaChannel must
maintain independent state. The parent-chain walk in
`_get_channel_writes_history` must be scoped to the target thread.
"""
checkpointer = InMemorySaver()
migrated_cfg = {"configurable": {"thread_id": "migrated"}}
fresh_cfg = {"configurable": {"thread_id": "fresh"}}
# Thread A: pre-migration build-up.
binop = _binop_graph(checkpointer)
_drive(binop, migrated_cfg, "m", 2)
# Thread B: fresh delta-channel run.
delta = _delta_graph(checkpointer)
_drive(delta, fresh_cfg, "f", 2)
# Thread A: migrate and confirm its state is anchored in its own
# thread's pre-migration history (tag 'm'), never mixing in tag 'f'.
migrated_boundaries = _settled_boundaries(
list(delta.get_state_history(migrated_cfg))
)
assert migrated_boundaries, "migrated thread has no settled boundaries"
for _, items in migrated_boundaries:
for it in items:
assert it.startswith("m"), (
f"migrated thread leaked item from other thread: {it}"
)
# Thread B: settled boundaries must only contain 'f' tags.
fresh_boundaries = _settled_boundaries(list(delta.get_state_history(fresh_cfg)))
assert fresh_boundaries, "fresh thread has no settled boundaries"
for _, items in fresh_boundaries:
for it in items:
assert it.startswith("f"), (
f"fresh thread leaked item from migrated thread: {it}"
)
# ---------------------------------------------------------------------------
# 6. Tip-of-pre-migration hydration: the latest checkpoint from a binop-run
# thread has a real accumulated value in its own `channel_values["items"]`.
# When hydrated under the delta-channel graph via `get_state(config)` with no
# `checkpoint_id`, the short-circuit must use that value directly instead of
# walking ancestors (which would skip the tip's own blob).
# ---------------------------------------------------------------------------
def test_tip_of_pre_migration_hydrates_directly() -> None:
"""`graph.get_state(config)` at the latest (pre-migration) checkpoint
returns the full accumulated list stored in that checkpoint's own
`channel_values`. The hydration must not walk ancestors past it."""
checkpointer = InMemorySaver()
config = {"configurable": {"thread_id": "tip-sync"}}
binop = _binop_graph(checkpointer)
_drive(binop, config, "u", 3)
binop_tip = binop.get_state(config)
expected_items = list(binop_tip.values.get("items", []))
assert expected_items == ["u0", "u1", "u2"], (
f"sanity: pre-migration tip should accumulate all 3 items, got {expected_items}"
)
delta = _delta_graph(checkpointer)
snap = delta.get_state(config)
assert list(snap.values.get("items", [])) == expected_items, (
f"tip hydration mismatch: expected {expected_items}, "
f"got {snap.values.get('items', [])}"
)
async def test_tip_of_pre_migration_hydrates_directly_async() -> None:
"""Async variant of the tip-of-pre-migration hydration scenario."""
checkpointer = InMemorySaver()
config = {"configurable": {"thread_id": "tip-async"}}
binop = _binop_graph(checkpointer)
await _adrive(binop, config, "u", 3)
binop_tip = await binop.aget_state(config)
expected_items = list(binop_tip.values.get("items", []))
assert expected_items == ["u0", "u1", "u2"]
delta = _delta_graph(checkpointer)
snap = await delta.aget_state(config)
assert list(snap.values.get("items", [])) == expected_items, (
f"async tip hydration mismatch: expected {expected_items}, "
f"got {snap.values.get('items', [])}"
)
# ---------------------------------------------------------------------------
# 7. `update_state` after migration writes a real value to the new
# checkpoint's `channel_values` (not a sentinel). Hydration must use it
# directly — the ancestor walk would skip this blob and return stale state.
# ---------------------------------------------------------------------------
def test_update_state_after_migration_uses_written_value() -> None:
"""After migrating and running at least one post-migration super-step
(so the thread's tip has a `DELTA_SENTINEL`), `update_state` writes a
concrete value to a new checkpoint's `channel_values`. `get_state`
must reflect that concrete value."""
checkpointer = InMemorySaver()
config = {"configurable": {"thread_id": "update-state"}}
# Pre-migration: accumulate a little state.
binop = _binop_graph(checkpointer)
_drive(binop, config, "u", 2)
# Migrate and run one more super-step so the tip is a post-migration
# checkpoint with `DELTA_SENTINEL` in its own `channel_values`.
delta = _delta_graph(checkpointer)
delta.invoke({"items": ["post"]}, config)
# `update_state` writes a concrete value into a new checkpoint's blob
# via the reducer against the hydrated prior state.
delta.update_state(config, {"items": ["x", "y"]})
snap = delta.get_state(config)
updated_items = list(snap.values.get("items", []))
# Must include the "x","y" update; without the hydration fix, the
# update_state-written blob would be skipped in favor of an ancestor
# walk, and the update values would disappear.
assert "x" in updated_items and "y" in updated_items, (
f"update_state values missing from snapshot: {updated_items}"
)
# The "x","y" items should be folded onto the prior accumulated state,
# not stand alone. This verifies the update-written blob is used
# directly by `get_state` (no ancestor walk past it).
assert len(updated_items) >= 4, (
f"update_state snapshot should preserve pre-update state, got {updated_items}"
)
assert updated_items[-2:] == ["x", "y"], (
f"update_state deltas should be at the tail, got {updated_items}"
)
# ---------------------------------------------------------------------------
# 8. Fork from an `update_state` checkpoint: a new run branched off the
# update_state-produced checkpoint must see that checkpoint's concrete
# `channel_values` as its base, with new deltas folded on top.
# ---------------------------------------------------------------------------
def test_fork_from_update_state_checkpoint() -> None:
"""Branching a new run from the checkpoint produced by `update_state`
must use that checkpoint's concrete blob as the base. Additional
deltas from the forked run fold onto it through the reducer."""
checkpointer = InMemorySaver()
config = {"configurable": {"thread_id": "fork"}}
# Pre-migration build-up, then migrate and add one post-migration step.
binop = _binop_graph(checkpointer)
_drive(binop, config, "u", 2)
delta = _delta_graph(checkpointer)
delta.invoke({"items": ["post"]}, config)
# Apply `update_state` and capture the returned config (references
# the new checkpoint produced by the update).
update_cfg = delta.update_state(config, {"items": ["x", "y"]})
update_snap = delta.get_state(update_cfg)
base_items = list(update_snap.values.get("items", []))
assert "x" in base_items and "y" in base_items, (
f"update_state values missing from snapshot: {base_items}"
)
assert base_items[-2:] == ["x", "y"], (
f"sanity: update_state deltas should be at the tail, got {base_items}"
)
# Fork: invoke from the update_state checkpoint with a new delta.
forked = delta.invoke({"items": ["fork0"]}, update_cfg)
forked_items = list(forked.get("items", []))
# The fork must see the update_state-written blob as its base (not
# walk past it), and the new delta must fold on top of it.
assert forked_items[: len(base_items)] == base_items, (
f"fork lost update_state base: base={base_items}, forked={forked_items}"
)
assert forked_items[-1] == "fork0", f"fork delta not appended: {forked_items}"
@@ -275,70 +275,3 @@ def test_graph_callbacks_accept_base_callback_manager() -> None:
assert "__interrupt__" in first
assert len(graph_handler.interrupt_events) == 1
def test_non_graph_handler_via_add_handler_does_not_crash() -> None:
"""Non-GraphCallbackHandler added via add_handler should not raise.
Libraries like opentelemetry-instrumentation-langchain monkey-patch
BaseCallbackManager.__init__ and inject handlers via add_handler().
These handlers inherit from BaseCallbackHandler, not
GraphCallbackHandler. They must be silently accepted graph lifecycle
events will simply not be dispatched to them.
"""
from langgraph.callbacks import _GraphCallbackManager
manager = _GraphCallbackManager()
plain_handler = _LangChainCustomEventHandler()
manager.add_handler(plain_handler, inherit=True)
assert plain_handler in manager.handlers
def test_non_graph_handler_does_not_receive_lifecycle_events() -> None:
"""Non-GraphCallbackHandler added alongside a GraphCallbackHandler
should not interfere with lifecycle event dispatch."""
graph = _build_interrupt_graph()
graph_handler = _GraphEventHandler()
plain_handler = _LangChainCustomEventHandler()
config = {
"configurable": {"thread_id": "graph-callback-mixed-handlers"},
"callbacks": [plain_handler, graph_handler],
}
first = graph.invoke({"answer": None}, config)
assert "__interrupt__" in first
assert len(graph_handler.interrupt_events) == 1
assert plain_handler.events == []
resumed = graph.invoke(Command(resume="done"), config)
assert resumed == {"answer": "done"}
assert len(graph_handler.resume_events) == 1
assert plain_handler.events == []
@pytest.mark.anyio
@NEEDS_CONTEXTVARS
async def test_non_graph_handler_does_not_receive_lifecycle_events_async() -> None:
"""Async variant: non-GraphCallbackHandler should not interfere."""
graph = _build_interrupt_graph()
graph_handler = _GraphEventHandler()
plain_handler = _LangChainCustomEventHandler()
config = {
"configurable": {"thread_id": "graph-callback-mixed-handlers-async"},
"callbacks": [plain_handler, graph_handler],
}
first = await graph.ainvoke({"answer": None}, config)
assert "__interrupt__" in first
assert len(graph_handler.interrupt_events) == 1
assert plain_handler.events == []
resumed = await graph.ainvoke(Command(resume="done"), config)
assert resumed == {"answer": "done"}
assert len(graph_handler.resume_events) == 1
assert plain_handler.events == []
+6
View File
@@ -1396,6 +1396,7 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
"langgraph_path": (PULL, "agent"),
"langgraph_checkpoint_ns": AnyStr("agent:"),
"checkpoint_ns": AnyStr("agent:"),
"_type": "generic-fake-chat-model",
"ls_provider": "fakechatmodel",
"ls_model_type": "chat",
"ls_integration": "langchain_chat_model",
@@ -1458,6 +1459,7 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
"langgraph_path": (PULL, "agent"),
"langgraph_checkpoint_ns": AnyStr("agent:"),
"checkpoint_ns": AnyStr("agent:"),
"_type": "generic-fake-chat-model",
"ls_provider": "fakechatmodel",
"ls_model_type": "chat",
"ls_integration": "langchain_chat_model",
@@ -1510,6 +1512,7 @@ def test_prebuilt_tool_chat(snapshot: SnapshotAssertion) -> None:
"langgraph_path": (PULL, "agent"),
"langgraph_checkpoint_ns": AnyStr("agent:"),
"checkpoint_ns": AnyStr("agent:"),
"_type": "generic-fake-chat-model",
"ls_provider": "fakechatmodel",
"ls_model_type": "chat",
"ls_integration": "langchain_chat_model",
@@ -6881,6 +6884,7 @@ def test_weather_subgraph(
"langgraph_path": ("__pregel_pull", "router_node"),
"langgraph_checkpoint_ns": AnyStr("router_node:"),
"checkpoint_ns": AnyStr("router_node:"),
"_type": "fake-messages-list-chat-model",
"ls_provider": "fakemessageslistchatmodel",
"ls_model_type": "chat",
"ls_integration": "langchain_chat_model",
@@ -6908,6 +6912,7 @@ def test_weather_subgraph(
"langgraph_path": ("__pregel_pull", "model_node"),
"langgraph_checkpoint_ns": AnyStr("weather_graph:"),
"checkpoint_ns": AnyStr("weather_graph:"),
"_type": "fake-messages-list-chat-model",
"ls_provider": "fakemessageslistchatmodel",
"ls_model_type": "chat",
"ls_integration": "langchain_chat_model",
@@ -6944,6 +6949,7 @@ def test_weather_subgraph(
"langgraph_path": ("__pregel_pull", "router_node"),
"langgraph_checkpoint_ns": AnyStr("router_node:"),
"checkpoint_ns": AnyStr("router_node:"),
"_type": "fake-messages-list-chat-model",
"ls_provider": "fakemessageslistchatmodel",
"ls_model_type": "chat",
"ls_integration": "langchain_chat_model",
@@ -1147,6 +1147,7 @@ async def test_prebuilt_tool_chat() -> None:
"langgraph_path": (PULL, "agent"),
"langgraph_checkpoint_ns": AnyStr("agent:"),
"checkpoint_ns": AnyStr("agent:"),
"_type": "generic-fake-chat-model",
"ls_provider": "fakechatmodel",
"ls_model_type": "chat",
"ls_integration": "langchain_chat_model",
@@ -1209,6 +1210,7 @@ async def test_prebuilt_tool_chat() -> None:
"langgraph_path": (PULL, "agent"),
"langgraph_checkpoint_ns": AnyStr("agent:"),
"checkpoint_ns": AnyStr("agent:"),
"_type": "generic-fake-chat-model",
"ls_provider": "fakechatmodel",
"ls_model_type": "chat",
"ls_integration": "langchain_chat_model",
@@ -1261,6 +1263,7 @@ async def test_prebuilt_tool_chat() -> None:
"langgraph_path": (PULL, "agent"),
"langgraph_checkpoint_ns": AnyStr("agent:"),
"checkpoint_ns": AnyStr("agent:"),
"_type": "generic-fake-chat-model",
"ls_provider": "fakechatmodel",
"ls_model_type": "chat",
"ls_integration": "langchain_chat_model",
@@ -3978,6 +3981,7 @@ async def test_weather_subgraph(
"langgraph_path": ("__pregel_pull", "router_node"),
"langgraph_checkpoint_ns": AnyStr("router_node:"),
"checkpoint_ns": AnyStr("router_node:"),
"_type": "fake-messages-list-chat-model",
"ls_provider": "fakemessageslistchatmodel",
"ls_model_type": "chat",
"ls_integration": "langchain_chat_model",
@@ -4005,6 +4009,7 @@ async def test_weather_subgraph(
"langgraph_path": ("__pregel_pull", "model_node"),
"langgraph_checkpoint_ns": AnyStr("weather_graph:"),
"checkpoint_ns": AnyStr("weather_graph:"),
"_type": "fake-messages-list-chat-model",
"ls_provider": "fakemessageslistchatmodel",
"ls_model_type": "chat",
"ls_integration": "langchain_chat_model",
@@ -4041,6 +4046,7 @@ async def test_weather_subgraph(
"langgraph_path": ("__pregel_pull", "router_node"),
"langgraph_checkpoint_ns": AnyStr("router_node:"),
"checkpoint_ns": AnyStr("router_node:"),
"_type": "fake-messages-list-chat-model",
"ls_provider": "fakemessageslistchatmodel",
"ls_model_type": "chat",
"ls_integration": "langchain_chat_model",
-118
View File
@@ -5,7 +5,6 @@ import langchain_core
import pytest
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
AnyMessage,
HumanMessage,
RemoveMessage,
@@ -339,123 +338,6 @@ def test_remove_all_messages():
]
def test_fast_path_preserves_format_openai():
"""Pure-append fast path must still apply the `langchain-openai` formatter."""
left = [HumanMessage(content="prior", id="1")]
right = [
AIMessage(
content=[
{
"type": "tool_use",
"name": "foo",
"input": {"bar": "baz"},
"id": "t1",
}
],
id="2",
)
]
result = add_messages(left, right, format="langchain-openai")
assert isinstance(result[0], HumanMessage)
assert result[0].content == "prior"
assert isinstance(result[1], AIMessage)
# formatter collapses the tool_use content block into `tool_calls`
assert result[1].content == ""
assert len(result[1].tool_calls) == 1
assert result[1].tool_calls[0]["name"] == "foo"
assert result[1].tool_calls[0]["args"] == {"bar": "baz"}
assert result[1].tool_calls[0]["id"] == "t1"
def test_fast_path_rejects_invalid_format():
"""Pure-append fast path must validate the `format` arg like the slow path."""
left = [HumanMessage(content="prior", id="1")]
right = [AIMessage(content="new", id="2")]
with pytest.raises(ValueError, match="Unrecognized format="):
add_messages(left, right, format="bogus") # type: ignore[arg-type]
def test_left_starting_with_chunk_is_normalized():
"""Opt-1 guard: a `BaseMessageChunk` at left[0] must trigger full conversion."""
chunk = AIMessageChunk(content="chunk", id="c1")
result = add_messages([chunk], [HumanMessage(content="h", id="h1")])
assert len(result) == 2
# chunk must be converted to a non-chunk message
assert type(result[0]).__name__ == "AIMessage"
assert result[0].id == "c1"
assert result[1].id == "h1"
def test_left_as_dicts_is_normalized():
"""Opt-1 guard: dicts at left[0] must trigger full conversion."""
left = [{"role": "user", "content": "hi", "id": "d1"}]
right = [AIMessage(content="reply", id="a1")]
result = add_messages(left, right)
assert len(result) == 2
assert isinstance(result[0], HumanMessage)
assert result[0].id == "d1"
assert result[0].content == "hi"
def test_left_as_tuples_is_normalized():
"""Opt-1 guard: tuple-form messages must trigger full conversion."""
left = [("user", "hi")]
right = [AIMessage(content="reply", id="a1")]
result = add_messages(left, right)
assert len(result) == 2
assert isinstance(result[0], HumanMessage)
# id is auto-assigned
assert isinstance(result[0].id, str) and UUID(result[0].id, version=4)
def test_left_first_msg_missing_id_is_normalized():
"""Opt-1 guard: a BaseMessage without an id at left[0] falls to the else branch."""
left = [HumanMessage(content="hi")] # no id
right = [AIMessage(content="reply", id="a1")]
result = add_messages(left, right)
assert len(result) == 2
# left's id must have been auto-assigned
assert isinstance(result[0].id, str) and UUID(result[0].id, version=4)
def test_duplicate_ids_in_right_with_nonempty_left():
"""Opt-2 guard: intra-right duplicate ids must take slow path (dedup kept)."""
left = [HumanMessage(content="prior", id="1")]
right = [
AIMessage(content="first", id="2"),
AIMessage(content="second", id="2"),
]
result = add_messages(left, right)
assert len(result) == 2
assert result[0].id == "1"
assert result[1].id == "2"
assert result[1].content == "second"
def test_right_with_none_ids_pure_append():
"""Fast path still correct when right entries start with id=None (fresh uuids assigned)."""
left = [HumanMessage(content="prior", id="1")]
right = [AIMessage(content="a"), AIMessage(content="b")]
result = add_messages(left, right)
assert len(result) == 3
assert result[0].id == "1"
for m in result[1:]:
assert isinstance(m.id, str) and UUID(m.id, version=4)
# fresh uuids must be distinct
assert result[1].id != result[2].id
def test_fast_path_returns_fresh_list():
"""Fast path must return a new list object (not mutate or alias left)."""
left = [HumanMessage(content="prior", id="1")]
right = [AIMessage(content="new", id="2")]
result = add_messages(left, right)
assert result is not left
# left must be untouched
assert len(left) == 1
assert left[0].id == "1"
def test_push_messages_in_graph():
class MessagesState(TypedDict):
messages: Annotated[list[AnyMessage], add_messages]
+94 -247
View File
@@ -54,6 +54,11 @@ from langgraph.pregel import (
NodeBuilder,
Pregel,
)
from langgraph.pregel._checkpoint_writer import (
CHECKPOINT_BACKLOG_ENV_VAR,
DEFAULT_CHECKPOINT_BACKLOG,
_resolve_checkpoint_backlog,
)
from langgraph.pregel._loop import SyncPregelLoop
from langgraph.pregel._runner import PregelRunner
from langgraph.types import (
@@ -615,11 +620,8 @@ def test_run_from_checkpoint_id_retains_previous_writes(
)
]
# +2: one fork checkpoint from time travel, one from the new execution
assert len(new_history) == len(history) + 2
# new_history[0] is the new execution result, new_history[1] is the fork
assert new_history[1].metadata["source"] == "fork"
for original, new in zip(history, new_history[2:]):
assert len(new_history) == len(history) + 1
for original, new in zip(history, new_history[1:]):
assert original.values == new.values
assert original.next == new.next
assert original.metadata["step"] == new.metadata["step"]
@@ -627,7 +629,7 @@ def test_run_from_checkpoint_id_retains_previous_writes(
def _get_tasks(hist: list, start: int):
return [h.tasks for h in hist[start:]]
assert _get_tasks(new_history, 2) == _get_tasks(history, 0)
assert _get_tasks(new_history, 1) == _get_tasks(history, 0)
def test_batch_two_processes_in_out() -> None:
@@ -3729,6 +3731,7 @@ def test_repeat_condition(snapshot: SnapshotAssertion) -> None:
"end": END,
},
)
workflow.add_conditional_edges(
"Chart Generator",
router,
@@ -3752,6 +3755,90 @@ def test_repeat_condition(snapshot: SnapshotAssertion) -> None:
assert app.get_graph().draw_mermaid(with_styles=False) == snapshot
def test_sync_durability_applies_checkpoint_backpressure() -> None:
first_put_started = threading.Event()
release_first_put = threading.Event()
put_calls = 0
visited: list[int] = []
result: dict[str, Any] = {}
error: dict[str, BaseException] = {}
class SlowFirstPutCheckpointer(InMemorySaver):
def put(
self,
config: RunnableConfig,
checkpoint: Checkpoint,
metadata: CheckpointMetadata,
new_versions: Any,
) -> RunnableConfig:
nonlocal put_calls
put_calls += 1
if put_calls == 1:
first_put_started.set()
release_first_put.wait()
return super().put(config, checkpoint, metadata, new_versions)
class State(TypedDict):
counter: int
def increment(state: State) -> State:
visited.append(state["counter"])
return {"counter": state["counter"] + 1}
def should_continue(state: State) -> str:
return "loop" if state["counter"] < 4 else "done"
builder = StateGraph(State)
builder.add_node("increment", increment)
builder.add_edge(START, "increment")
builder.add_conditional_edges(
"increment", should_continue, {"loop": "increment", "done": END}
)
graph = builder.compile(checkpointer=SlowFirstPutCheckpointer())
def invoke() -> None:
try:
result["value"] = graph.invoke(
{"counter": 0},
{"configurable": {"thread_id": "1"}},
durability="async",
)
except BaseException as exc:
error["value"] = exc
thread = threading.Thread(target=invoke)
thread.start()
assert first_put_started.wait(timeout=1)
time.sleep(0.05)
assert thread.is_alive()
assert len(visited) <= DEFAULT_CHECKPOINT_BACKLOG + 1
release_first_put.set()
thread.join(timeout=1)
assert not thread.is_alive()
assert "value" not in error
assert result["value"] == {"counter": 4}
def test_checkpoint_backlog_uses_env_override(monkeypatch: pytest.MonkeyPatch) -> None:
monkeypatch.setenv(CHECKPOINT_BACKLOG_ENV_VAR, "7")
assert _resolve_checkpoint_backlog() == 7
def test_checkpoint_backlog_invalid_env_uses_default(
monkeypatch: pytest.MonkeyPatch,
) -> None:
monkeypatch.setenv(CHECKPOINT_BACKLOG_ENV_VAR, "not-an-int")
assert _resolve_checkpoint_backlog() == DEFAULT_CHECKPOINT_BACKLOG
monkeypatch.setenv(CHECKPOINT_BACKLOG_ENV_VAR, "0")
assert _resolve_checkpoint_backlog() == DEFAULT_CHECKPOINT_BACKLOG
def test_checkpoint_metadata(sync_checkpointer: BaseCheckpointSaver) -> None:
"""This test verifies that a run's configurable fields are merged with the
previous checkpoint config for each step in the run.
@@ -6896,6 +6983,7 @@ def test_tags_stream_mode_messages() -> None:
"langgraph_path": ("__pregel_pull", "call_model"),
"langgraph_checkpoint_ns": AnyStr("call_model:"),
"checkpoint_ns": AnyStr("call_model:"),
"_type": "generic-fake-chat-model",
"ls_provider": "genericfakechatmodel",
"ls_model_type": "chat",
"ls_integration": "langchain_chat_model",
@@ -6905,60 +6993,6 @@ def test_tags_stream_mode_messages() -> None:
]
def test_configurable_propagates_to_stream_metadata() -> None:
"""Regression: thread_id, run_id, assistant_id, graph_id,
and langgraph_auth_user_id from configurable must appear
in stream_mode='messages' metadata."""
def my_node(state):
return {"messages": HumanMessage(content="hello")}
graph = (
StateGraph(MessagesState)
.add_node("my_node", my_node)
.add_edge(START, "my_node")
.compile()
)
config = {
"configurable": {
"thread_id": "th-123",
"checkpoint_id": "ckpt-1",
"checkpoint_ns": "ns-1",
"task_id": "task-1",
"run_id": "run-456",
"assistant_id": "asst-789",
"graph_id": "graph-0",
"model": "gpt-4o",
"user_id": "uid-1",
"cron_id": "cron-1",
"langgraph_auth_user_id": "user-1",
# these should NOT be propagated into metadata
"some_api_key": "secret",
"custom_setting": {"nested": True},
},
}
results = list(graph.stream({"messages": []}, config, stream_mode="messages"))
assert len(results) == 1
_, metadata = results[0]
# propagated keys
assert metadata["thread_id"] == "th-123"
assert metadata["checkpoint_id"] == "ckpt-1"
assert metadata["checkpoint_ns"] == "ns-1"
assert metadata["task_id"] == "task-1"
assert metadata["run_id"] == "run-456"
assert metadata["assistant_id"] == "asst-789"
assert metadata["graph_id"] == "graph-0"
# These are only present in trace metadata by default as of langgraph 1.2
# assert metadata["model"] == "gpt-4o"
# assert metadata["user_id"] == "uid-1"
# assert metadata["cron_id"] == "cron-1"
# assert metadata["langgraph_auth_user_id"] == "user-1"
# non-allowlisted keys must not appear
assert "some_api_key" not in metadata
assert "custom_setting" not in metadata
def test_stream_mode_messages_command() -> None:
from langchain_core.messages import HumanMessage
@@ -9400,190 +9434,3 @@ def test_fork_does_not_apply_pending_writes(
# Should be: 1 (input) + 20 (forked node_a) + 100 (node_b) = 121
assert result == {"value": 121}
async def test_delta_channel_end_to_end_inmemory() -> None:
"""Full graph run: DeltaChannel accumulates correctly across multiple turns."""
from langchain_core.messages import AIMessage, HumanMessage
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.channels._delta import DeltaChannel
from langgraph.graph import START, StateGraph
from langgraph.graph.message import add_messages
class State(TypedDict):
messages: Annotated[list, DeltaChannel(add_messages)]
def respond(state: State) -> dict:
n = len(state["messages"])
return {"messages": [AIMessage(content=f"reply-{n}", id=f"ai-{n}")]}
builder = StateGraph(State)
builder.add_node("respond", respond)
builder.add_edge(START, "respond")
graph = builder.compile(checkpointer=InMemorySaver())
config = {"configurable": {"thread_id": "diff-test-1"}}
# Turn 1
graph.invoke({"messages": [HumanMessage(content="hello", id="h1")]}, config)
# Turn 2
graph.invoke({"messages": [HumanMessage(content="world", id="h2")]}, config)
# Turn 3
graph.invoke({"messages": [HumanMessage(content="bye", id="h3")]}, config)
state = graph.get_state(config)
msgs = state.values["messages"]
# 3 human + 3 AI = 6 total
assert len(msgs) == 6, f"expected 6 messages, got {len(msgs)}: {msgs}"
assert msgs[0].content == "hello"
assert msgs[2].content == "world"
assert msgs[4].content == "bye"
assert msgs[1].content == "reply-1"
assert msgs[3].content == "reply-3"
assert msgs[5].content == "reply-5"
async def test_delta_channel_time_travel() -> None:
"""Time-travel back to turn-1 checkpoint and resume; continuation must not include turn-2 deltas."""
from langchain_core.messages import AIMessage, HumanMessage
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.channels._delta import DeltaChannel
from langgraph.graph import START, StateGraph
from langgraph.graph.message import add_messages
class State(TypedDict):
messages: Annotated[list, DeltaChannel(add_messages)]
counter = {"n": 0}
def respond(state: State) -> dict:
counter["n"] += 1
return {
"messages": [
AIMessage(content=f"ai-{counter['n']}", id=f"ai-{counter['n']}")
]
}
builder = StateGraph(State)
builder.add_node("respond", respond)
builder.add_edge(START, "respond")
saver = InMemorySaver()
graph = builder.compile(checkpointer=saver)
config = {"configurable": {"thread_id": "diff-time-travel"}}
# Run 2 turns: h1→ai-1, h2→ai-2
graph.invoke({"messages": [HumanMessage(content="h1", id="h1")]}, config)
graph.invoke({"messages": [HumanMessage(content="h2", id="h2")]}, config)
# Find the checkpoint after turn 1 (2 messages: h1 + ai-1)
history = list(graph.get_state_history(config))
after_turn1 = next(h for h in history if len(h.values.get("messages", [])) == 2)
assert len(after_turn1.values["messages"]) == 2
assert after_turn1.values["messages"][0].content == "h1"
assert after_turn1.values["messages"][1].content == "ai-1"
# Resume from turn-1 checkpoint: inject h3, expect 3 messages total (h1, ai-1, ai-N)
# NOT 5 messages (turn-2 deltas must not bleed into the resumed run)
result = graph.invoke(
{"messages": [HumanMessage(content="h3", id="h3")]},
after_turn1.config,
)
msgs = result["messages"]
# Should be: h1, ai-1, h3, ai-N — 4 messages total
assert len(msgs) == 4, (
f"expected 4 messages after time-travel resume, got {len(msgs)}: {msgs}"
)
assert msgs[0].content == "h1"
assert msgs[1].content == "ai-1"
assert msgs[2].content == "h3"
async def test_delta_channel_remove_message_end_to_end() -> None:
"""RemoveMessage inside a DeltaChannel graph must persist and reload correctly."""
from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.channels._delta import DeltaChannel
from langgraph.graph import START, StateGraph
from langgraph.graph.message import add_messages
class State(TypedDict):
messages: Annotated[list, DeltaChannel(add_messages)]
def respond(state: State) -> dict:
return {"messages": [AIMessage(content="reply", id="ai-1")]}
def delete_first(state: State) -> dict:
# removes the first message
return {"messages": [RemoveMessage(id=state["messages"][0].id)]}
builder = StateGraph(State)
builder.add_node("respond", respond)
builder.add_node("delete_first", delete_first)
builder.add_edge(START, "respond")
builder.add_edge("respond", "delete_first")
graph = builder.compile(checkpointer=InMemorySaver())
config = {"configurable": {"thread_id": "diff-remove-test"}}
graph.invoke({"messages": [HumanMessage(content="hello", id="h1")]}, config)
state = graph.get_state(config)
msgs = state.values["messages"]
# h1 was removed, only ai-1 should remain
assert len(msgs) == 1, f"expected 1 message, got {len(msgs)}: {msgs}"
assert msgs[0].id == "ai-1"
# A subsequent turn must reconstruct from the checkpoint correctly
graph.invoke({"messages": [HumanMessage(content="again", id="h2")]}, config)
state = graph.get_state(config)
msgs = state.values["messages"]
# ai-1 + h2 + ai-1(second reply, same id overwrites) + h2 removed
# more simply: after second run we expect ai-1 updated + h2 remaining minus deleted h2
# just assert h1 is still gone
assert all(m.id != "h1" for m in msgs), (
"h1 should still be absent after second turn"
)
async def test_delta_channel_update_by_id_end_to_end() -> None:
"""Updating a message by ID via DeltaChannel must persist and reload correctly."""
from langchain_core.messages import HumanMessage
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.channels._delta import DeltaChannel
from langgraph.graph import START, StateGraph
from langgraph.graph.message import add_messages
class State(TypedDict):
messages: Annotated[list, DeltaChannel(add_messages)]
def update_msg(state: State) -> dict:
# re-send h1 with updated content
return {"messages": [HumanMessage(content="updated", id="h1")]}
builder = StateGraph(State)
builder.add_node("update_msg", update_msg)
builder.add_edge(START, "update_msg")
graph = builder.compile(checkpointer=InMemorySaver())
config = {"configurable": {"thread_id": "diff-update-id-test"}}
graph.invoke({"messages": [HumanMessage(content="original", id="h1")]}, config)
state = graph.get_state(config)
msgs = state.values["messages"]
assert len(msgs) == 1, f"expected 1 message, got {len(msgs)}: {msgs}"
assert msgs[0].content == "updated"
assert msgs[0].id == "h1"
# Second turn: verify the updated state is the base for further accumulation
graph.invoke({"messages": [HumanMessage(content="new", id="h2")]}, config)
state = graph.get_state(config)
msgs = state.values["messages"]
ids = [m.id for m in msgs]
assert "h1" in ids # h1 persists (updated, not duplicated)
assert "h2" in ids
assert ids.count("h1") == 1, "h1 must not be duplicated"
+60 -68
View File
@@ -20,7 +20,6 @@ from uuid import UUID
import pytest
from langchain_core.language_models import GenericFakeChatModel
from langchain_core.messages import HumanMessage
from langchain_core.runnables import RunnableConfig, RunnableLambda, RunnablePassthrough
from langchain_core.utils.aiter import aclosing
from langgraph.cache.base import BaseCache
@@ -54,6 +53,7 @@ from langgraph.func import entrypoint, task
from langgraph.graph import END, START, StateGraph
from langgraph.graph.message import MessagesState, add_messages
from langgraph.pregel import NodeBuilder, Pregel
from langgraph.pregel._checkpoint_writer import DEFAULT_CHECKPOINT_BACKLOG
from langgraph.pregel._loop import AsyncPregelLoop
from langgraph.pregel._runner import PregelRunner
from langgraph.types import (
@@ -485,6 +485,61 @@ async def test_checkpoint_put_after_cancellation_stream_events_anext() -> None:
assert False, "Task should be cancelled"
async def test_async_durability_applies_checkpoint_backpressure() -> None:
first_put_started = asyncio.Event()
release_first_put = asyncio.Event()
put_calls = 0
visited: list[int] = []
class SlowFirstPutCheckpointer(InMemorySaver):
async def aput(
self,
config: RunnableConfig,
checkpoint: Checkpoint,
metadata: CheckpointMetadata,
new_versions: ChannelVersions,
) -> RunnableConfig:
nonlocal put_calls
put_calls += 1
if put_calls == 1:
first_put_started.set()
await release_first_put.wait()
return await super().aput(config, checkpoint, metadata, new_versions)
class State(TypedDict):
counter: int
def increment(state: State) -> State:
visited.append(state["counter"])
return {"counter": state["counter"] + 1}
def should_continue(state: State) -> str:
return "loop" if state["counter"] < 4 else "done"
builder = StateGraph(State)
builder.add_node("increment", increment)
builder.add_edge(START, "increment")
builder.add_conditional_edges(
"increment", should_continue, {"loop": "increment", "done": END}
)
graph = builder.compile(checkpointer=SlowFirstPutCheckpointer())
task = asyncio.create_task(
graph.ainvoke(
{"counter": 0}, {"configurable": {"thread_id": "1"}}, durability="async"
)
)
await first_put_started.wait()
await asyncio.sleep(0.05)
assert not task.done()
assert len(visited) <= DEFAULT_CHECKPOINT_BACKLOG + 1
release_first_put.set()
assert await task == {"counter": 4}
async def test_node_cancellation_on_external_cancel() -> None:
inner_task_cancelled = False
@@ -2086,11 +2141,8 @@ async def test_run_from_checkpoint_id_retains_previous_writes(
)
]
# +2: one fork checkpoint from time travel, one from the new execution
assert len(new_history) == len(history) + 2
# new_history[0] is the new execution result, new_history[1] is the fork
assert new_history[1].metadata["source"] == "fork"
for original, new in zip(history, new_history[2:]):
assert len(new_history) == len(history) + 1
for original, new in zip(history, new_history[1:]):
assert original.values == new.values
assert original.next == new.next
assert original.metadata["step"] == new.metadata["step"]
@@ -2098,7 +2150,7 @@ async def test_run_from_checkpoint_id_retains_previous_writes(
def _get_tasks(hist: list, start: int):
return [h.tasks for h in hist[start:]]
assert _get_tasks(new_history, 2) == _get_tasks(history, 0)
assert _get_tasks(new_history, 1) == _get_tasks(history, 0)
async def test_cond_edge_after_send() -> None:
@@ -7545,6 +7597,7 @@ async def test_tags_stream_mode_messages() -> None:
"langgraph_path": ("__pregel_pull", "call_model"),
"langgraph_checkpoint_ns": AnyStr("call_model:"),
"checkpoint_ns": AnyStr("call_model:"),
"_type": "generic-fake-chat-model",
"ls_provider": "genericfakechatmodel",
"ls_model_type": "chat",
"ls_integration": "langchain_chat_model",
@@ -7554,67 +7607,6 @@ async def test_tags_stream_mode_messages() -> None:
]
async def test_configurable_propagates_to_stream_metadata() -> None:
"""Regression: thread_id, run_id, assistant_id, graph_id,
and langgraph_auth_user_id from configurable must appear
in stream_mode='messages' metadata."""
def my_node(state):
return {"messages": HumanMessage(content="hello")}
graph = (
StateGraph(MessagesState)
.add_node("my_node", my_node)
.add_edge(START, "my_node")
.compile()
)
config = {
"configurable": {
"thread_id": "th-123",
"checkpoint_id": "ckpt-1",
"checkpoint_ns": "ns-1",
"task_id": "task-1",
"run_id": "run-456",
"assistant_id": "asst-789",
"graph_id": "graph-0",
"model": "gpt-4o",
"user_id": "uid-1",
"cron_id": "cron-1",
"langgraph_auth_user_id": "user-1",
# these should NOT be propagated into metadata
"some_api_key": "secret",
"custom_setting": {"nested": True},
},
}
results = [
chunk
async for chunk in graph.astream(
{"messages": []}, config, stream_mode="messages"
)
]
assert len(results) == 1
_, metadata = results[0]
# propagated keys
assert metadata["thread_id"] == "th-123"
assert metadata["checkpoint_id"] == "ckpt-1"
assert metadata["checkpoint_ns"] == "ns-1"
assert metadata["task_id"] == "task-1"
assert metadata["run_id"] == "run-456"
assert metadata["assistant_id"] == "asst-789"
assert metadata["graph_id"] == "graph-0"
# These will only be traced as of langgraph 1.2 and not present by default in
# metadata
# assert metadata["model"] == "gpt-4o"
# assert metadata["user_id"] == "uid-1"
# assert metadata["cron_id"] == "cron-1"
# assert metadata["langgraph_auth_user_id"] == "user-1"
# non-allowlisted keys must not appear
assert "some_api_key" not in metadata
assert "custom_setting" not in metadata
async def test_stream_mode_messages_command() -> None:
from langchain_core.messages import HumanMessage
+7 -791
View File
@@ -37,7 +37,6 @@ def _checkpoint_summary(history: list) -> list[dict]:
Returns a list of dicts (newest-first, matching get_state_history order) with:
- id: short checkpoint id suffix (last 6 chars)
- parent_id: short parent checkpoint id suffix or None
- source: checkpoint metadata source (input, loop, fork, update)
- next: tuple of next node names
- values: channel values snapshot
"""
@@ -53,7 +52,6 @@ def _checkpoint_summary(history: list) -> list[dict]:
{
"id": cid[-6:],
"parent_id": pid[-6:] if pid else None,
"source": s.metadata.get("source"),
"next": s.next,
"values": s.values,
}
@@ -282,116 +280,6 @@ def test_replay_from_before_interrupt_refires(
assert call_count["node_b"] == 1 # NOT re-executed (after interrupt)
def test_replay_from_before_interrupt_then_resume(
sync_checkpointer: BaseCheckpointSaver,
) -> None:
"""Replay from checkpoint before interrupt node, then resume with a new
answer and verify the graph completes with the new value.
Graph: START --> node_a --> ask_human (interrupt) --> node_b --> END
Original run:
source=input next=(__start__,) values=[]
source=loop next=(node_a,) values=[]
source=loop next=(ask_human,) values=[a] <-- replay from here
source=loop next=(node_b,) values=[a, human:old_answer]
source=loop next=() values=[a, human:old_answer, b]
After replay (fork created) + resume with "new_answer":
source=input next=(__start__,) values=[]
source=loop next=(node_a,) values=[]
source=loop next=(ask_human,) values=[a] <-- branch point
source=loop next=(node_b,) values=[a, human:old_answer]
source=loop next=() values=[a, human:old_answer, b] (old branch)
source=fork next=(ask_human,) values=[a] <-- fork from branch point
source=loop next=(node_b,) values=[a, human:new_answer]
source=loop next=() values=[a, human:new_answer, b] (new branch)
"""
called: list[str] = []
def node_a(state: State) -> State:
called.append("node_a")
return {"value": ["a"]}
def ask_human(state: State) -> State:
called.append("ask_human")
answer = interrupt("What is your input?")
return {"value": [f"human:{answer}"]}
def node_b(state: State) -> State:
called.append("node_b")
return {"value": ["b"]}
graph = (
StateGraph(State)
.add_node("node_a", node_a)
.add_node("ask_human", ask_human)
.add_node("node_b", node_b)
.add_edge(START, "node_a")
.add_edge("node_a", "ask_human")
.add_edge("ask_human", "node_b")
.compile(checkpointer=sync_checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
# --- Original run: invoke until interrupt, then resume to complete ---
graph.invoke({"value": []}, config)
graph.invoke(Command(resume="old_answer"), config)
original_history = list(graph.get_state_history(config))
original = _checkpoint_summary(original_history)
assert [(s["source"], s["next"], s["values"]) for s in original] == [
("loop", (), {"value": ["a", "human:old_answer", "b"]}),
("loop", ("node_b",), {"value": ["a", "human:old_answer"]}),
("loop", ("ask_human",), {"value": ["a"]}),
("loop", ("node_a",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
# --- Replay from checkpoint before ask_human ---
before_ask = next(s for s in original_history if s.next == ("ask_human",))
called.clear()
replay_result = graph.invoke(None, before_ask.config)
assert replay_result["__interrupt__"][0].value == "What is your input?"
assert "ask_human" in called
assert "node_a" not in called # before the replay point, not re-executed
# A fork checkpoint is now the latest — it branches from the replay point
post_replay = _checkpoint_summary(list(graph.get_state_history(config)))
assert [(s["source"], s["next"]) for s in post_replay] == [
("fork", ("ask_human",)), # <-- new fork (latest)
("loop", ()), # original done
("loop", ("node_b",)),
("loop", ("ask_human",)), # branch point
("loop", ("node_a",)),
("input", ("__start__",)),
]
# --- Resume with a new answer ---
called.clear()
final_result = graph.invoke(Command(resume="new_answer"), config)
assert final_result["value"] == ["a", "human:new_answer", "b"]
assert "ask_human" in called
assert "node_b" in called
final = _checkpoint_summary(list(graph.get_state_history(config)))
assert [(s["source"], s["next"], s["values"]) for s in final] == [
# New branch (from fork)
("loop", (), {"value": ["a", "human:new_answer", "b"]}),
("loop", ("node_b",), {"value": ["a", "human:new_answer"]}),
("fork", ("ask_human",), {"value": ["a"]}),
# Original branch (preserved)
("loop", (), {"value": ["a", "human:old_answer", "b"]}),
("loop", ("node_b",), {"value": ["a", "human:old_answer"]}),
("loop", ("ask_human",), {"value": ["a"]}),
("loop", ("node_a",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
def test_replay_interrupt_stable_across_replays(
sync_checkpointer: BaseCheckpointSaver,
) -> None:
@@ -432,14 +320,8 @@ def test_replay_interrupt_stable_across_replays(
r = graph.invoke(None, before_ask.config)
results.append(r)
# Each replay creates a fork with a unique interrupt ID, so we compare
# interrupt values and state values rather than full equality.
assert all("__interrupt__" in r for r in results)
assert all(
r["__interrupt__"][0].value == results[0]["__interrupt__"][0].value
for r in results
)
assert all(r["value"] == results[0]["value"] for r in results)
assert all(r == results[0] for r in results)
assert "__interrupt__" in results[0]
def test_fork_from_before_interrupt_refires(
@@ -972,354 +854,6 @@ def test_subgraph_interrupt_replay_from_interrupt_checkpoint(
assert "step_b" not in called
def test_subgraph_interrupt_replay_from_parent_then_resume(
sync_checkpointer: BaseCheckpointSaver,
) -> None:
"""Replay from the parent checkpoint where a subgraph interrupt fired,
then resume with a new answer. Verifies that a fork is created and the
full graph completes. Checks full checkpoint history at each stage."""
called: list[str] = []
def router(state: State) -> State:
called.append("router")
return {"value": ["routed"]}
def step_a(state: State) -> State:
called.append("step_a")
return {"value": ["sub_a"]}
def ask_human(state: State) -> State:
called.append("ask_human")
answer = interrupt("Provide input:")
return {"value": [f"human:{answer}"]}
def step_b(state: State) -> State:
called.append("step_b")
return {"value": ["sub_b"]}
subgraph = (
StateGraph(State)
.add_node("step_a", step_a)
.add_node("ask_human", ask_human)
.add_node("step_b", step_b)
.add_edge(START, "step_a")
.add_edge("step_a", "ask_human")
.add_edge("ask_human", "step_b")
.compile(checkpointer=True)
)
def post_process(state: State) -> State:
called.append("post_process")
return {"value": ["post"]}
graph = (
StateGraph(State)
.add_node("router", router)
.add_node("subgraph_node", subgraph)
.add_node("post_process", post_process)
.add_edge(START, "router")
.add_edge("router", "subgraph_node")
.add_edge("subgraph_node", "post_process")
.compile(checkpointer=sync_checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
# Run until interrupt, then resume to complete
graph.invoke({"value": []}, config)
graph.invoke(Command(resume="old_answer"), config)
# Original parent history (newest first)
original_history = list(graph.get_state_history(config))
assert [s.next for s in original_history] == [
(), # done
("post_process",),
("subgraph_node",), # subgraph ran, interrupt fired here
("router",),
("__start__",),
]
# Find the parent checkpoint where the interrupt fired
interrupt_checkpoint = next(
s for s in original_history if s.next == ("subgraph_node",)
)
# Replay from parent checkpoint — subgraph re-executes, interrupt re-fires
called.clear()
replay_result = graph.invoke(None, interrupt_checkpoint.config)
assert "__interrupt__" in replay_result
assert replay_result["__interrupt__"][0].value == "Provide input:"
assert "step_a" in called
assert "ask_human" in called
assert "step_b" not in called
# Verify fork checkpoint was created
post_replay_history = list(graph.get_state_history(config))
assert [s.next for s in post_replay_history] == [
("subgraph_node",), # fork (interrupt pending)
(), # original done
("post_process",),
("subgraph_node",),
("router",),
("__start__",),
]
assert [s.metadata["source"] for s in post_replay_history] == [
"fork",
"loop",
"loop",
"loop",
"loop",
"input",
]
fork = post_replay_history[0]
assert (
fork.parent_config["configurable"]["checkpoint_id"]
== interrupt_checkpoint.config["configurable"]["checkpoint_id"]
)
# Resume with a new answer — full graph should complete
called.clear()
final_result = graph.invoke(Command(resume="new_answer"), config)
assert "__interrupt__" not in final_result
assert "human:new_answer" in final_result["value"]
assert "sub_b" in final_result["value"]
assert "post" in final_result["value"]
assert "ask_human" in called
assert "step_b" in called
assert "post_process" in called
# Final checkpoint history
final_history = list(graph.get_state_history(config))
assert [s.next for s in final_history] == [
(), # new branch done
("post_process",), # new branch post_process
("subgraph_node",), # fork
(), # original done
("post_process",),
("subgraph_node",),
("router",),
("__start__",),
]
assert [s.metadata["source"] for s in final_history] == [
"loop",
"loop",
"fork",
"loop",
"loop",
"loop",
"loop",
"input",
]
def test_subgraph_interrupt_resume_with_explicit_head_checkpoint_id(
sync_checkpointer: BaseCheckpointSaver,
) -> None:
"""Resume with Command(resume=...) plus the current head checkpoint_id
in config. The subgraph must continue from the interrupted node, not
restart from scratch. Explicit checkpoint_id triggers is_replaying but
this is a resume, not a time-travel, so ReplayState should not apply."""
called: list[str] = []
def step_a(state: State) -> State:
called.append("step_a")
return {"value": ["sub_a"]}
def ask_human(state: State) -> State:
called.append("ask_human")
answer = interrupt("Provide input:")
return {"value": [f"human:{answer}"]}
def step_b(state: State) -> State:
called.append("step_b")
return {"value": ["sub_b"]}
subgraph = (
StateGraph(State)
.add_node("step_a", step_a)
.add_node("ask_human", ask_human)
.add_node("step_b", step_b)
.add_edge(START, "step_a")
.add_edge("step_a", "ask_human")
.add_edge("ask_human", "step_b")
.compile(checkpointer=True)
)
graph = (
StateGraph(State)
.add_node("subgraph_node", subgraph)
.add_edge(START, "subgraph_node")
.compile(checkpointer=sync_checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
# Run until interrupt fires in subgraph
graph.invoke({"value": []}, config)
assert called == ["step_a", "ask_human"]
# Resume with explicit head checkpoint_id in config
head_checkpoint_id = graph.get_state(config).config["configurable"]["checkpoint_id"]
called.clear()
resume_config = {
"configurable": {
"thread_id": "1",
"checkpoint_id": head_checkpoint_id,
"checkpoint_ns": "",
}
}
result = graph.invoke(Command(resume="answer"), resume_config)
assert called == ["ask_human", "step_b"]
assert "__interrupt__" not in result
assert result["value"] == ["sub_a", "human:answer", "sub_b"]
def test_subgraph_replay_loads_accumulated_state_then_resume(
sync_checkpointer: BaseCheckpointSaver,
) -> None:
"""Two parent invocations, then replay from before the subgraph in the
2nd invocation. The subgraph (checkpointer=True) should load its
accumulated state from the 1st invocation via ReplayState, re-fire
the interrupt, and then resume + complete.
This tests the ReplayState path: the parent is replaying and the
subgraph uses list(before=parent_checkpoint_id) to find its
corresponding checkpoint from the original execution.
"""
class SubState(TypedDict):
value: Annotated[list[str], operator.add]
class ParentState(TypedDict):
results: Annotated[list[str], operator.add]
started_state: list[dict] = []
def step_a(state: SubState) -> SubState:
started_state.append(dict(state))
answer = interrupt("question_a")
return {"value": [f"a:{answer}"]}
subgraph = (
StateGraph(SubState)
.add_node("step_a", step_a)
.add_edge(START, "step_a")
.compile(checkpointer=True)
)
def parent_node(state: ParentState) -> ParentState:
return {"results": ["p"]}
graph = (
StateGraph(ParentState)
.add_node("parent_node", parent_node)
.add_node("sub_node", subgraph)
.add_edge(START, "parent_node")
.add_edge("parent_node", "sub_node")
.compile(checkpointer=sync_checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
# === 1st invocation: complete with answer "a1" ===
graph.invoke({"results": []}, config)
graph.invoke(Command(resume="a1"), config)
# step_a saw empty state (fresh subgraph)
assert started_state[0] == {"value": []}
# === 2nd invocation: complete with answer "a2" ===
started_state.clear()
graph.invoke({"results": []}, config)
graph.invoke(Command(resume="a2"), config)
# Stateful subgraph retained state from 1st invocation
assert started_state[0] == {"value": ["a:a1"]}
# Original history (newest first)
original_history = list(graph.get_state_history(config))
assert [s.next for s in original_history] == [
(), # 2nd done
("sub_node",), # 2nd sub_node
("parent_node",), # 2nd parent_node
("__start__",), # 2nd input
(), # 1st done
("sub_node",), # 1st sub_node
("parent_node",), # 1st parent_node
("__start__",), # 1st input
]
# Replay from before sub_node in 2nd invocation (newest match)
before_sub_2nd = [s for s in original_history if s.next == ("sub_node",)][0]
started_state.clear()
replay = graph.invoke(None, before_sub_2nd.config)
assert "__interrupt__" in replay
# Subgraph should see accumulated state from END of 1st invocation
assert started_state[0] == {"value": ["a:a1"]}
# Verify fork was created
post_replay_history = list(graph.get_state_history(config))
assert [s.next for s in post_replay_history] == [
("sub_node",), # fork (interrupt pending)
(), # 2nd done
("sub_node",), # 2nd sub_node
("parent_node",), # 2nd parent_node
("__start__",), # 2nd input
(), # 1st done
("sub_node",), # 1st sub_node
("parent_node",), # 1st parent_node
("__start__",), # 1st input
]
assert [s.metadata["source"] for s in post_replay_history] == [
"fork",
"loop",
"loop",
"loop",
"input",
"loop",
"loop",
"loop",
"input",
]
# Resume with a new answer
started_state.clear()
final = graph.invoke(Command(resume="a3"), config)
assert "__interrupt__" not in final
assert final["results"] == ["p", "p"]
# Final history
final_history = list(graph.get_state_history(config))
assert [s.next for s in final_history] == [
(), # new branch done
("sub_node",), # fork
(), # 2nd done
("sub_node",), # 2nd sub_node
("parent_node",), # 2nd parent_node
("__start__",), # 2nd input
(), # 1st done
("sub_node",), # 1st sub_node
("parent_node",), # 1st parent_node
("__start__",), # 1st input
]
assert [s.metadata["source"] for s in final_history] == [
"loop",
"fork",
"loop",
"loop",
"loop",
"input",
"loop",
"loop",
"loop",
"input",
]
def test_subgraph_interrupt_full_flow(
sync_checkpointer: BaseCheckpointSaver,
) -> None:
@@ -1756,321 +1290,6 @@ def test_subgraph_time_travel_to_second_interrupt(
assert "ask_1" not in called
def test_subgraph_time_travel_resume_from_first_interrupt(
sync_checkpointer: BaseCheckpointSaver,
) -> None:
"""Time travel to a subgraph checkpoint at the first interrupt, then
resume through both interrupts with new answers.
This verifies the key bug fix: after time-traveling to a subgraph
checkpoint with an interrupt, a fork checkpoint is created so that
subsequent resumes find the correct state (not the old branch tip).
Parent: START --> executor (subgraph, checkpointer=True) --> END
Executor: START --> step_a --> ask_1 (interrupt) --> ask_2 (interrupt) --> END
Parent history after original run completes:
source=input next=(__start__,) values=[]
source=loop next=(executor,) values=[]
source=loop next=() values=[step_a_done, ask_1:answer_1, ask_2:answer_2]
After time-traveling to 1st interrupt + resuming with new answers:
source=input next=(__start__,) values=[]
source=loop next=(executor,) values=[] <-- branch point
source=loop next=() values=[..., ask_2:answer_2] (old branch)
source=fork next=(executor,) values=[] <-- fork from time travel
source=loop next=() values=[..., ask_2:new_answer_2] (new branch)
"""
called: list[str] = []
def step_a(state: State) -> State:
called.append("step_a")
return {"value": ["step_a_done"]}
def ask_1(state: State) -> State:
called.append("ask_1")
answer = interrupt("Question 1?")
return {"value": [f"ask_1:{answer}"]}
def ask_2(state: State) -> State:
called.append("ask_2")
answer = interrupt("Question 2?")
return {"value": [f"ask_2:{answer}"]}
executor = (
StateGraph(State)
.add_node("step_a", step_a)
.add_node("ask_1", ask_1)
.add_node("ask_2", ask_2)
.add_edge(START, "step_a")
.add_edge("step_a", "ask_1")
.add_edge("ask_1", "ask_2")
.add_edge("ask_2", "__end__")
.compile(checkpointer=True)
)
graph = (
StateGraph(State)
.add_node("executor", executor)
.add_edge(START, "executor")
.compile(checkpointer=sync_checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
# --- Original run: hit both interrupts and resume ---
graph.invoke({"value": []}, config)
sub_config_at_first = graph.get_state(config, subgraphs=True).tasks[0].state.config
graph.invoke(Command(resume="answer_1"), config)
graph.invoke(Command(resume="answer_2"), config)
original = _checkpoint_summary(list(graph.get_state_history(config)))
assert [(s["source"], s["next"], s["values"]) for s in original] == [
("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
# --- Time travel to first interrupt's subgraph checkpoint ---
called.clear()
replay_result = graph.invoke(None, sub_config_at_first)
assert replay_result["__interrupt__"][0].value == "Question 1?"
assert "step_a" not in called # before interrupt, not re-executed
# Fork is now the latest parent checkpoint
post_tt = _checkpoint_summary(list(graph.get_state_history(config)))
assert [(s["source"], s["next"]) for s in post_tt] == [
("fork", ("executor",)), # <-- new fork (latest)
("loop", ()), # original done
("loop", ("executor",)),
("input", ("__start__",)),
]
# --- Resume both interrupts with new answers ---
called.clear()
resume_1 = graph.invoke(Command(resume="new_answer_1"), config)
assert resume_1["__interrupt__"][0].value == "Question 2?"
assert "ask_1" in called
called.clear()
resume_2 = graph.invoke(Command(resume="new_answer_2"), config)
assert resume_2["value"] == [
"step_a_done",
"ask_1:new_answer_1",
"ask_2:new_answer_2",
]
# Verify final history: original branch preserved, new branch appended
final = _checkpoint_summary(list(graph.get_state_history(config)))
assert [(s["source"], s["next"], s["values"]) for s in final] == [
# New branch (from time travel fork)
(
"loop",
(),
{"value": ["step_a_done", "ask_1:new_answer_1", "ask_2:new_answer_2"]},
),
("fork", ("executor",), {"value": []}),
# Original branch (preserved)
("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
def test_subgraph_time_travel_resume_from_second_interrupt(
sync_checkpointer: BaseCheckpointSaver,
) -> None:
"""Time travel to a subgraph checkpoint at the second interrupt, then
resume with a new answer. The first interrupt's answer should be preserved.
Parent: START --> executor (subgraph, checkpointer=True) --> END
Executor: START --> step_a --> ask_1 (interrupt) --> ask_2 (interrupt) --> END
Key assertion: after resuming from a time-travel to the 2nd interrupt,
the final state keeps ask_1's original answer but uses the new ask_2 answer.
"""
called: list[str] = []
def step_a(state: State) -> State:
called.append("step_a")
return {"value": ["step_a_done"]}
def ask_1(state: State) -> State:
called.append("ask_1")
answer = interrupt("Question 1?")
return {"value": [f"ask_1:{answer}"]}
def ask_2(state: State) -> State:
called.append("ask_2")
answer = interrupt("Question 2?")
return {"value": [f"ask_2:{answer}"]}
executor = (
StateGraph(State)
.add_node("step_a", step_a)
.add_node("ask_1", ask_1)
.add_node("ask_2", ask_2)
.add_edge(START, "step_a")
.add_edge("step_a", "ask_1")
.add_edge("ask_1", "ask_2")
.add_edge("ask_2", "__end__")
.compile(checkpointer=True)
)
graph = (
StateGraph(State)
.add_node("executor", executor)
.add_edge(START, "executor")
.compile(checkpointer=sync_checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
# --- Original run: hit both interrupts and resume ---
graph.invoke({"value": []}, config)
graph.invoke(Command(resume="answer_1"), config)
sub_config_at_second = graph.get_state(config, subgraphs=True).tasks[0].state.config
graph.invoke(Command(resume="answer_2"), config)
original = _checkpoint_summary(list(graph.get_state_history(config)))
assert [(s["source"], s["next"], s["values"]) for s in original] == [
("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
# --- Time travel to second interrupt ---
called.clear()
replay_result = graph.invoke(None, sub_config_at_second)
assert replay_result["__interrupt__"][0].value == "Question 2?"
assert "step_a" not in called
assert "ask_1" not in called # already resolved, not re-executed
# Fork is now the latest parent checkpoint
post_tt = _checkpoint_summary(list(graph.get_state_history(config)))
assert [(s["source"], s["next"]) for s in post_tt] == [
("fork", ("executor",)), # <-- new fork (latest)
("loop", ()), # original done
("loop", ("executor",)),
("input", ("__start__",)),
]
# --- Resume with a new answer for ask_2 only ---
called.clear()
resume_result = graph.invoke(Command(resume="new_answer_2"), config)
# ask_1's original answer preserved, ask_2 uses the new answer
assert resume_result["value"] == [
"step_a_done",
"ask_1:answer_1",
"ask_2:new_answer_2",
]
# Verify final history: original branch preserved, new branch appended
final = _checkpoint_summary(list(graph.get_state_history(config)))
assert [(s["source"], s["next"], s["values"]) for s in final] == [
# New branch (from time travel fork)
(
"loop",
(),
{"value": ["step_a_done", "ask_1:answer_1", "ask_2:new_answer_2"]},
),
("fork", ("executor",), {"value": []}),
# Original branch (preserved)
("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
def test_subgraph_time_travel_checkpoint_pattern(
sync_checkpointer: BaseCheckpointSaver,
) -> None:
"""Verify the checkpoint pattern created by time travel to a subgraph
interrupt. A fork checkpoint should branch from the replay point and
become the latest parent checkpoint.
Parent: START --> executor (subgraph, checkpointer=True) --> END
Executor: START --> ask (interrupt) --> END
Original run (after completing):
source=input next=(__start__,) values=[]
source=loop next=(executor,) values=[] <-- replay point
source=loop next=() values=[a:first]
After time travel to interrupt + resume with "second":
source=input next=(__start__,) values=[]
source=loop next=(executor,) values=[] <-- branch point
source=loop next=() values=[a:first] (old branch)
source=fork next=(executor,) values=[] <-- fork
source=loop next=() values=[a:second] (new branch)
"""
def ask(state: State) -> State:
answer = interrupt("Q?")
return {"value": [f"a:{answer}"]}
executor = (
StateGraph(State)
.add_node("ask", ask)
.add_edge(START, "ask")
.compile(checkpointer=True)
)
graph = (
StateGraph(State)
.add_node("executor", executor)
.add_edge(START, "executor")
.compile(checkpointer=sync_checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
# Run until interrupt, then complete
graph.invoke({"value": []}, config)
sub_config = graph.get_state(config, subgraphs=True).tasks[0].state.config
graph.invoke(Command(resume="first"), config)
original = _checkpoint_summary(list(graph.get_state_history(config)))
assert [(s["source"], s["next"], s["values"]) for s in original] == [
("loop", (), {"value": ["a:first"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
# Time travel to the interrupt
graph.invoke(None, sub_config)
# Fork is now the latest, branching from the original replay point
post_tt = list(graph.get_state_history(config))
post_tt_summary = _checkpoint_summary(post_tt)
assert [(s["source"], s["next"]) for s in post_tt_summary] == [
("fork", ("executor",)), # <-- new fork (latest)
("loop", ()),
("loop", ("executor",)), # <-- replay point / fork parent
("input", ("__start__",)),
]
# Verify the fork's parent is the original replay point
replay_point_id = sub_config["configurable"]["checkpoint_map"][""]
assert post_tt[0].parent_config["configurable"]["checkpoint_id"] == replay_point_id
# Resume from the fork — graph completes with new answer
result = graph.invoke(Command(resume="second"), config)
assert result["value"] == ["a:second"]
final = _checkpoint_summary(list(graph.get_state_history(config)))
assert [(s["source"], s["next"], s["values"]) for s in final] == [
# New branch
("loop", (), {"value": ["a:second"]}),
("fork", ("executor",), {"value": []}),
# Original branch
("loop", (), {"value": ["a:first"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
def test_subgraph_time_travel_after_completion(
sync_checkpointer: BaseCheckpointSaver,
) -> None:
@@ -3064,16 +2283,14 @@ def test_replay_creates_branch_preserving_old_checkpoints(
# -- Post-replay checkpoint history (newest first) --
post_replay_history = list(graph.get_state_history(config))
post_summary = _checkpoint_summary(post_replay_history)
# 5 original + 1 fork + 2 new branch checkpoints = 8
assert len(post_summary) == 8
assert len(post_summary) == 7 # 5 original + 2 new branch checkpoints
# Verify the full shape after replay
assert [s["next"] for s in post_summary] == [
(), # new branch tip
("node_c",), # new branch
("node_b",), # fork from replay point
(), # old branch tip
("node_c",), # old
(), # new branch tip (C6)
("node_c",), # new branch (C5)
(), # old branch tip (C4)
("node_c",), # old (C3)
("node_b",), # branch point (C2)
("node_a",), # old (C1)
("__start__",), # old (C0)
@@ -3081,7 +2298,6 @@ def test_replay_creates_branch_preserving_old_checkpoints(
assert [s["values"] for s in post_summary] == [
{"value": ["a", "b2", "c"]}, # new branch tip
{"value": ["a", "b2"]}, # new: node_b re-ran with call_count=2
{"value": ["a"]}, # fork from replay point
{"value": ["a", "b1", "c"]}, # old branch tip preserved
{"value": ["a", "b1"]}, # old
{"value": ["a"]}, # branch point
+7 -403
View File
@@ -46,7 +46,6 @@ def _checkpoint_summary(history: list) -> list[dict]:
Returns a list of dicts (newest-first, matching get_state_history order) with:
- id: short checkpoint id suffix (last 6 chars)
- parent_id: short parent checkpoint id suffix or None
- source: checkpoint metadata source (input, loop, fork, update)
- next: tuple of next node names
- values: channel values snapshot
"""
@@ -62,7 +61,6 @@ def _checkpoint_summary(history: list) -> list[dict]:
{
"id": cid[-6:],
"parent_id": pid[-6:] if pid else None,
"source": s.metadata.get("source"),
"next": s.next,
"values": s.values,
}
@@ -337,14 +335,8 @@ async def test_replay_interrupt_stable_across_replays(
r = await graph.ainvoke(None, before_ask.config)
results.append(r)
# Each replay creates a fork with a unique interrupt ID, so we compare
# interrupt values and state values rather than full equality.
assert all("__interrupt__" in r for r in results)
assert all(
r["__interrupt__"][0].value == results[0]["__interrupt__"][0].value
for r in results
)
assert all(r["value"] == results[0]["value"] for r in results)
assert all(r == results[0] for r in results)
assert "__interrupt__" in results[0]
@NEEDS_CONTEXTVARS
@@ -1269,391 +1261,6 @@ async def test_subgraph_time_travel_after_completion_async(
assert "ask_2:answer_2" in replay_result["value"]
@NEEDS_CONTEXTVARS
async def test_replay_from_before_interrupt_then_resume_async(
async_checkpointer: BaseCheckpointSaver,
) -> None:
"""Replay from checkpoint before interrupt node, then resume with a new
answer and verify the graph completes with the new value.
Graph: START --> node_a --> ask_human (interrupt) --> node_b --> END
"""
called: list[str] = []
async def node_a(state: State) -> State:
called.append("node_a")
return {"value": ["a"]}
async def ask_human(state: State) -> State:
called.append("ask_human")
answer = interrupt("What is your input?")
return {"value": [f"human:{answer}"]}
async def node_b(state: State) -> State:
called.append("node_b")
return {"value": ["b"]}
graph = (
StateGraph(State)
.add_node("node_a", node_a)
.add_node("ask_human", ask_human)
.add_node("node_b", node_b)
.add_edge(START, "node_a")
.add_edge("node_a", "ask_human")
.add_edge("ask_human", "node_b")
.compile(checkpointer=async_checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
# --- Original run: invoke until interrupt, then resume to complete ---
await graph.ainvoke({"value": []}, config)
await graph.ainvoke(Command(resume="old_answer"), config)
original_history = [s async for s in graph.aget_state_history(config)]
original = _checkpoint_summary(original_history)
assert [(s["source"], s["next"], s["values"]) for s in original] == [
("loop", (), {"value": ["a", "human:old_answer", "b"]}),
("loop", ("node_b",), {"value": ["a", "human:old_answer"]}),
("loop", ("ask_human",), {"value": ["a"]}),
("loop", ("node_a",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
# --- Replay from checkpoint before ask_human ---
before_ask = next(s for s in original_history if s.next == ("ask_human",))
called.clear()
replay_result = await graph.ainvoke(None, before_ask.config)
assert replay_result["__interrupt__"][0].value == "What is your input?"
assert "ask_human" in called
assert "node_a" not in called
# A fork checkpoint is now the latest
post_replay = _checkpoint_summary(
[s async for s in graph.aget_state_history(config)]
)
assert [(s["source"], s["next"]) for s in post_replay] == [
("fork", ("ask_human",)),
("loop", ()),
("loop", ("node_b",)),
("loop", ("ask_human",)),
("loop", ("node_a",)),
("input", ("__start__",)),
]
# --- Resume with a new answer ---
called.clear()
final_result = await graph.ainvoke(Command(resume="new_answer"), config)
assert final_result["value"] == ["a", "human:new_answer", "b"]
assert "ask_human" in called
assert "node_b" in called
final = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
assert [(s["source"], s["next"], s["values"]) for s in final] == [
# New branch (from fork)
("loop", (), {"value": ["a", "human:new_answer", "b"]}),
("loop", ("node_b",), {"value": ["a", "human:new_answer"]}),
("fork", ("ask_human",), {"value": ["a"]}),
# Original branch (preserved)
("loop", (), {"value": ["a", "human:old_answer", "b"]}),
("loop", ("node_b",), {"value": ["a", "human:old_answer"]}),
("loop", ("ask_human",), {"value": ["a"]}),
("loop", ("node_a",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
@NEEDS_CONTEXTVARS
async def test_subgraph_time_travel_resume_from_first_interrupt_async(
async_checkpointer: BaseCheckpointSaver,
) -> None:
"""Time travel to a subgraph checkpoint at the first interrupt, then
resume through both interrupts with new answers.
Parent: START --> executor (subgraph, checkpointer=True) --> END
Executor: START --> step_a --> ask_1 (interrupt) --> ask_2 (interrupt) --> END
"""
called: list[str] = []
async def step_a(state: State) -> State:
called.append("step_a")
return {"value": ["step_a_done"]}
async def ask_1(state: State) -> State:
called.append("ask_1")
answer = interrupt("Question 1?")
return {"value": [f"ask_1:{answer}"]}
async def ask_2(state: State) -> State:
called.append("ask_2")
answer = interrupt("Question 2?")
return {"value": [f"ask_2:{answer}"]}
executor = (
StateGraph(State)
.add_node("step_a", step_a)
.add_node("ask_1", ask_1)
.add_node("ask_2", ask_2)
.add_edge(START, "step_a")
.add_edge("step_a", "ask_1")
.add_edge("ask_1", "ask_2")
.add_edge("ask_2", "__end__")
.compile(checkpointer=True)
)
graph = (
StateGraph(State)
.add_node("executor", executor)
.add_edge(START, "executor")
.compile(checkpointer=async_checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
# --- Original run: hit both interrupts and resume ---
await graph.ainvoke({"value": []}, config)
sub_config_at_first = (
(await graph.aget_state(config, subgraphs=True)).tasks[0].state.config
)
await graph.ainvoke(Command(resume="answer_1"), config)
await graph.ainvoke(Command(resume="answer_2"), config)
original = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
assert [(s["source"], s["next"], s["values"]) for s in original] == [
("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
# --- Time travel to first interrupt's subgraph checkpoint ---
called.clear()
replay_result = await graph.ainvoke(None, sub_config_at_first)
assert replay_result["__interrupt__"][0].value == "Question 1?"
assert "step_a" not in called
# Fork is now the latest parent checkpoint
post_tt = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
assert [(s["source"], s["next"]) for s in post_tt] == [
("fork", ("executor",)), # <-- new fork (latest)
("loop", ()), # original done
("loop", ("executor",)),
("input", ("__start__",)),
]
# --- Resume both interrupts with new answers ---
called.clear()
resume_1 = await graph.ainvoke(Command(resume="new_answer_1"), config)
assert resume_1["__interrupt__"][0].value == "Question 2?"
assert "ask_1" in called
called.clear()
resume_2 = await graph.ainvoke(Command(resume="new_answer_2"), config)
assert resume_2["value"] == [
"step_a_done",
"ask_1:new_answer_1",
"ask_2:new_answer_2",
]
# Verify final history: original branch preserved, new branch appended
final = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
assert [(s["source"], s["next"], s["values"]) for s in final] == [
# New branch (from time travel fork)
(
"loop",
(),
{"value": ["step_a_done", "ask_1:new_answer_1", "ask_2:new_answer_2"]},
),
("fork", ("executor",), {"value": []}),
# Original branch (preserved)
("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
@NEEDS_CONTEXTVARS
async def test_subgraph_time_travel_resume_from_second_interrupt_async(
async_checkpointer: BaseCheckpointSaver,
) -> None:
"""Time travel to a subgraph checkpoint at the second interrupt, then
resume with a new answer. The first interrupt's answer should be preserved.
Parent: START --> executor (subgraph, checkpointer=True) --> END
Executor: START --> step_a --> ask_1 (interrupt) --> ask_2 (interrupt) --> END
"""
called: list[str] = []
async def step_a(state: State) -> State:
called.append("step_a")
return {"value": ["step_a_done"]}
async def ask_1(state: State) -> State:
called.append("ask_1")
answer = interrupt("Question 1?")
return {"value": [f"ask_1:{answer}"]}
async def ask_2(state: State) -> State:
called.append("ask_2")
answer = interrupt("Question 2?")
return {"value": [f"ask_2:{answer}"]}
executor = (
StateGraph(State)
.add_node("step_a", step_a)
.add_node("ask_1", ask_1)
.add_node("ask_2", ask_2)
.add_edge(START, "step_a")
.add_edge("step_a", "ask_1")
.add_edge("ask_1", "ask_2")
.add_edge("ask_2", "__end__")
.compile(checkpointer=True)
)
graph = (
StateGraph(State)
.add_node("executor", executor)
.add_edge(START, "executor")
.compile(checkpointer=async_checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
# --- Original run: hit both interrupts and resume ---
await graph.ainvoke({"value": []}, config)
await graph.ainvoke(Command(resume="answer_1"), config)
sub_config_at_second = (
(await graph.aget_state(config, subgraphs=True)).tasks[0].state.config
)
await graph.ainvoke(Command(resume="answer_2"), config)
original = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
assert [(s["source"], s["next"], s["values"]) for s in original] == [
("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
# --- Time travel to second interrupt ---
called.clear()
replay_result = await graph.ainvoke(None, sub_config_at_second)
assert replay_result["__interrupt__"][0].value == "Question 2?"
assert "step_a" not in called
assert "ask_1" not in called
# Fork is now the latest parent checkpoint
post_tt = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
assert [(s["source"], s["next"]) for s in post_tt] == [
("fork", ("executor",)), # <-- new fork (latest)
("loop", ()), # original done
("loop", ("executor",)),
("input", ("__start__",)),
]
# --- Resume with a new answer for ask_2 only ---
called.clear()
resume_result = await graph.ainvoke(Command(resume="new_answer_2"), config)
assert resume_result["value"] == [
"step_a_done",
"ask_1:answer_1",
"ask_2:new_answer_2",
]
# Verify final history
final = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
assert [(s["source"], s["next"], s["values"]) for s in final] == [
# New branch (from time travel fork)
(
"loop",
(),
{"value": ["step_a_done", "ask_1:answer_1", "ask_2:new_answer_2"]},
),
("fork", ("executor",), {"value": []}),
# Original branch (preserved)
("loop", (), {"value": ["step_a_done", "ask_1:answer_1", "ask_2:answer_2"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
@NEEDS_CONTEXTVARS
async def test_subgraph_time_travel_checkpoint_pattern_async(
async_checkpointer: BaseCheckpointSaver,
) -> None:
"""Verify the checkpoint pattern created by time travel to a subgraph
interrupt. A fork checkpoint should branch from the replay point.
Parent: START --> executor (subgraph, checkpointer=True) --> END
Executor: START --> ask (interrupt) --> END
"""
async def ask(state: State) -> State:
answer = interrupt("Q?")
return {"value": [f"a:{answer}"]}
executor = (
StateGraph(State)
.add_node("ask", ask)
.add_edge(START, "ask")
.compile(checkpointer=True)
)
graph = (
StateGraph(State)
.add_node("executor", executor)
.add_edge(START, "executor")
.compile(checkpointer=async_checkpointer)
)
config = {"configurable": {"thread_id": "1"}}
# Run until interrupt, then complete
await graph.ainvoke({"value": []}, config)
sub_config = (await graph.aget_state(config, subgraphs=True)).tasks[0].state.config
await graph.ainvoke(Command(resume="first"), config)
original = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
assert [(s["source"], s["next"], s["values"]) for s in original] == [
("loop", (), {"value": ["a:first"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
# Time travel to the interrupt
await graph.ainvoke(None, sub_config)
# Fork is now the latest, branching from the original replay point
post_tt = [s async for s in graph.aget_state_history(config)]
post_tt_summary = _checkpoint_summary(post_tt)
assert [(s["source"], s["next"]) for s in post_tt_summary] == [
("fork", ("executor",)), # <-- new fork (latest)
("loop", ()),
("loop", ("executor",)), # <-- replay point / fork parent
("input", ("__start__",)),
]
# Verify the fork's parent is the original replay point
replay_point_id = sub_config["configurable"]["checkpoint_map"][""]
assert post_tt[0].parent_config["configurable"]["checkpoint_id"] == replay_point_id
# Resume from the fork
result = await graph.ainvoke(Command(resume="second"), config)
assert result["value"] == ["a:second"]
final = _checkpoint_summary([s async for s in graph.aget_state_history(config)])
assert [(s["source"], s["next"], s["values"]) for s in final] == [
# New branch
("loop", (), {"value": ["a:second"]}),
("fork", ("executor",), {"value": []}),
# Original branch
("loop", (), {"value": ["a:first"]}),
("loop", ("executor",), {"value": []}),
("input", ("__start__",), {"value": []}),
]
@NEEDS_CONTEXTVARS
async def test_3_levels_deep_time_travel_to_first_interrupt_async(
async_checkpointer: BaseCheckpointSaver,
@@ -2481,15 +2088,13 @@ async def test_replay_creates_branch_preserving_old_checkpoints(
# -- Post-replay checkpoint history (newest first) --
post_replay_history = [s async for s in graph.aget_state_history(config)]
post_summary = _checkpoint_summary(post_replay_history)
# 5 original + 1 fork + 2 new branch checkpoints = 8
assert len(post_summary) == 8
assert len(post_summary) == 7 # 5 original + 2 new branch checkpoints
assert [s["next"] for s in post_summary] == [
(), # new branch tip
("node_c",), # new branch
("node_b",), # fork from replay point
(), # old branch tip
("node_c",), # old
(), # new branch tip (C6)
("node_c",), # new branch (C5)
(), # old branch tip (C4)
("node_c",), # old (C3)
("node_b",), # branch point (C2)
("node_a",), # old (C1)
("__start__",), # old (C0)
@@ -2497,7 +2102,6 @@ async def test_replay_creates_branch_preserving_old_checkpoints(
assert [s["values"] for s in post_summary] == [
{"value": ["a", "b2", "c"]}, # new branch tip
{"value": ["a", "b2"]}, # new: node_b re-ran with call_count=2
{"value": ["a"]}, # fork from replay point
{"value": ["a", "b1", "c"]}, # old branch tip preserved
{"value": ["a", "b1"]}, # old
{"value": ["a"]}, # branch point
+5 -115
View File
@@ -11,19 +11,13 @@ from typing import (
TypeVar,
Union,
)
from unittest.mock import MagicMock, patch
from unittest.mock import patch
import langsmith
import pytest
from langchain_core.runnables import RunnableConfig
from langchain_core.tracers import LangChainTracer
from typing_extensions import NotRequired, Required, TypedDict
from langgraph._internal._config import (
_is_not_empty,
ensure_config,
get_callback_manager_for_config,
)
from langgraph._internal._config import _is_not_empty, ensure_config
from langgraph._internal._fields import (
_is_optional_type,
get_enhanced_type_hints,
@@ -304,7 +298,7 @@ def test_is_not_empty() -> None:
assert not _is_not_empty({})
def test_configurable_metadata() -> None:
def test_configurable_metadata():
config = {
"configurable": {
"a-key": "foo",
@@ -315,115 +309,11 @@ def test_configurable_metadata() -> None:
"andme": 42,
"nested": {"foo": "bar"},
"nooverride": -2,
"thread_id": "th-123",
"checkpoint_id": "ckpt-1",
"checkpoint_ns": "ns-1",
"task_id": "task-1",
"run_id": "run-456",
"assistant_id": "asst-789",
"graph_id": "graph-0",
"model": "gpt-4o",
"user_id": "uid-1",
"cron_id": "cron-1",
"langgraph_auth_user_id": "user-1",
},
"metadata": {"nooverride": 18},
}
expected = {"includeme", "andme", "nooverride"}
merged = ensure_config(config)
metadata = merged["metadata"]
assert set(metadata) == {
"nooverride",
"assistant_id",
"thread_id",
"checkpoint_id",
"run_id",
"graph_id",
"checkpoint_ns",
"task_id",
}
assert metadata.keys() == expected
assert metadata["nooverride"] == 18
def test_callback_manager_copies_whitelisted_configurable_ids_to_metadata() -> None:
config = {
"configurable": {
"thread_id": "th-123",
"checkpoint_id": "ckpt-1",
"checkpoint_ns": "ns-1",
"task_id": "task-1",
"run_id": "run-456",
"assistant_id": "asst-789",
"graph_id": "graph-0",
"model": "gpt-4o",
"user_id": "uid-1",
"cron_id": "cron-1",
"langgraph_auth_user_id": "user-1",
},
"metadata": {
"thread_id": "from-metadata",
"nooverride": 18,
},
}
manager = ensure_config(config)
callback_manager = get_callback_manager_for_config(manager)
assert callback_manager.metadata == {
"thread_id": "from-metadata",
"nooverride": 18,
"checkpoint_id": "ckpt-1",
"checkpoint_ns": "ns-1",
"task_id": "task-1",
"run_id": "run-456",
"assistant_id": "asst-789",
"graph_id": "graph-0",
}
def test_callback_manager_copies_configurable_ids_to_tracing_metadata() -> None:
tracer = LangChainTracer(client=MagicMock())
config: RunnableConfig = {
"configurable": {
"thread_id": "th-123",
"checkpoint_id": "ckpt-1",
"checkpoint_ns": "ns-1",
"task_id": "task-1",
"run_id": "run-456",
"assistant_id": "asst-789",
"graph_id": "graph-0",
"model": "gpt-4o",
"user_id": "uid-1",
"cron_id": "cron-1",
"langgraph_auth_user_id": "user-1",
"includeme": "hi",
"andme": 42,
"__dontinclude": "bar",
"some_api_key": "secret",
"custom_setting": {"nested": True},
},
"metadata": {
"thread_id": "from-metadata",
"user_id": "from-metadata-user",
"includeme": "from-metadata",
},
"callbacks": [tracer],
}
manager = ensure_config(config)
callback_manager = get_callback_manager_for_config(manager)
handlers = callback_manager.handlers
tracers = [handler for handler in handlers if isinstance(handler, LangChainTracer)]
assert len(tracers) == 1
tracer = tracers[0]
assert tracer.tracing_metadata == {
"checkpoint_id": "ckpt-1",
"checkpoint_ns": "ns-1",
"task_id": "task-1",
"run_id": "run-456",
"assistant_id": "asst-789",
"graph_id": "graph-0",
"model": "gpt-4o",
"cron_id": "cron-1",
"andme": 42,
"includeme": "hi",
"thread_id": "th-123",
"user_id": "uid-1",
}
+14 -15
View File
@@ -1348,7 +1348,7 @@ wheels = [
[[package]]
name = "langchain-core"
version = "1.3.0"
version = "1.2.22"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "jsonpatch" },
@@ -1360,14 +1360,14 @@ dependencies = [
{ name = "typing-extensions" },
{ name = "uuid-utils" },
]
sdist = { url = "https://files.pythonhosted.org/packages/92/fe/20190232d9b513242899dbb0c2bb77e31b4d61e343743adbe90ebc2603d2/langchain_core-1.3.0.tar.gz", hash = "sha256:14a39f528bf459aa3aa40d0a7f7f1bae7520d435ef991ae14a4ceb74d8c49046", size = 860755, upload-time = "2026-04-17T14:51:38.298Z" }
sdist = { url = "https://files.pythonhosted.org/packages/b1/a3/c4cd6827a1df46c821e7214b7f7b7a28b189e6c9b84ef15c6d629c5e3179/langchain_core-1.2.22.tar.gz", hash = "sha256:8d8f726d03d3652d403da915126626bb6250747e8ba406537d849e68b9f5d058", size = 842487, upload-time = "2026-03-24T18:48:44.9Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/f8/e2/dbfa347aa072a6dc4cd38d6f9ebfc730b4c14c258c47f480f4c5c546f177/langchain_core-1.3.0-py3-none-any.whl", hash = "sha256:baf16ee028475df177b9ab8869a751c79406d64a6f12125b93802991b566cced", size = 515140, upload-time = "2026-04-17T14:51:36.274Z" },
{ url = "https://files.pythonhosted.org/packages/c7/a6/2ffacf0f1a3788f250e75d0b52a24896c413be11be3a6d42bcdf46fbea48/langchain_core-1.2.22-py3-none-any.whl", hash = "sha256:7e30d586b75918e828833b9ec1efc25465723566845dd652c277baf751e9c04b", size = 506829, upload-time = "2026-03-24T18:48:43.286Z" },
]
[[package]]
name = "langgraph"
version = "1.1.9"
version = "1.1.7a1"
source = { editable = "." }
dependencies = [
{ name = "langchain-core" },
@@ -1439,7 +1439,7 @@ test = [
[package.metadata]
requires-dist = [
{ name = "langchain-core", specifier = ">=1.3.0,<2" },
{ name = "langchain-core", specifier = ">=0.1" },
{ name = "langgraph-checkpoint", editable = "../checkpoint" },
{ name = "langgraph-prebuilt", editable = "../prebuilt" },
{ name = "langgraph-sdk", editable = "../sdk-py" },
@@ -1548,7 +1548,7 @@ wheels = [
[[package]]
name = "langgraph-checkpoint"
version = "4.0.2"
version = "4.0.1"
source = { editable = "../checkpoint" }
dependencies = [
{ name = "langchain-core" },
@@ -1706,7 +1706,7 @@ inmem = [
requires-dist = [
{ name = "click", specifier = ">=8.1.7" },
{ name = "httpx", specifier = ">=0.24.0" },
{ name = "langgraph-api", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.5.35,<0.9.0" },
{ name = "langgraph-api", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.5.35,<0.8.0" },
{ name = "langgraph-runtime-inmem", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.7" },
{ name = "langgraph-sdk", marker = "python_full_version >= '3.11'", specifier = ">=0.1.0" },
{ name = "pathspec", specifier = ">=0.11.0" },
@@ -1742,7 +1742,7 @@ test = [
[[package]]
name = "langgraph-prebuilt"
version = "1.0.10"
version = "1.0.9"
source = { editable = "../prebuilt" }
dependencies = [
{ name = "langchain-core" },
@@ -1852,7 +1852,7 @@ test = [
[[package]]
name = "langsmith"
version = "0.7.31"
version = "0.6.4"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "httpx" },
@@ -1862,12 +1862,11 @@ dependencies = [
{ name = "requests" },
{ name = "requests-toolbelt" },
{ name = "uuid-utils" },
{ name = "xxhash" },
{ name = "zstandard" },
]
sdist = { url = "https://files.pythonhosted.org/packages/e6/11/696019490992db5c87774dc20515529ef42a01e1d770fb754ed6d9b12fb0/langsmith-0.7.31.tar.gz", hash = "sha256:331ee4f7c26bb5be4022b9859b7d7b122cbf8c9d01d9f530114c1914b0349ffb", size = 1178480, upload-time = "2026-04-14T17:55:41.242Z" }
sdist = { url = "https://files.pythonhosted.org/packages/e7/85/9c7933052a997da1b85bc5c774f3865e9b1da1c8d71541ea133178b13229/langsmith-0.6.4.tar.gz", hash = "sha256:36f7223a01c218079fbb17da5e536ebbaf5c1468c028abe070aa3ae59bc99ec8", size = 919964, upload-time = "2026-01-15T20:02:28.873Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/1d/a1/a013cf458c301cda86a213dd153ce0a01c93f1ab5833f951e6a44c9763ce/langsmith-0.7.31-py3-none-any.whl", hash = "sha256:0291d49203f6e80dda011af1afda61eb0595a4d697adb684590a8805e1d61fb6", size = 373276, upload-time = "2026-04-14T17:55:39.677Z" },
{ url = "https://files.pythonhosted.org/packages/66/0f/09a6637a7ba777eb307b7c80852d9ee26438e2bdafbad6fcc849ff9d9192/langsmith-0.6.4-py3-none-any.whl", hash = "sha256:ac4835860160be371042c7adbba3cb267bcf8d96a5ea976c33a8a4acad6c5486", size = 283503, upload-time = "2026-01-15T20:02:26.662Z" },
]
[package.optional-dependencies]
@@ -2906,7 +2905,7 @@ wheels = [
[[package]]
name = "pytest"
version = "9.0.3"
version = "9.0.2"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "colorama", marker = "sys_platform == 'win32'" },
@@ -2917,9 +2916,9 @@ dependencies = [
{ name = "pygments" },
{ name = "tomli", marker = "python_full_version < '3.11'" },
]
sdist = { url = "https://files.pythonhosted.org/packages/7d/0d/549bd94f1a0a402dc8cf64563a117c0f3765662e2e668477624baeec44d5/pytest-9.0.3.tar.gz", hash = "sha256:b86ada508af81d19edeb213c681b1d48246c1a91d304c6c81a427674c17eb91c", size = 1572165, upload-time = "2026-04-07T17:16:18.027Z" }
sdist = { url = "https://files.pythonhosted.org/packages/d1/db/7ef3487e0fb0049ddb5ce41d3a49c235bf9ad299b6a25d5780a89f19230f/pytest-9.0.2.tar.gz", hash = "sha256:75186651a92bd89611d1d9fc20f0b4345fd827c41ccd5c299a868a05d70edf11", size = 1568901, upload-time = "2025-12-06T21:30:51.014Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/d4/24/a372aaf5c9b7208e7112038812994107bc65a84cd00e0354a88c2c77a617/pytest-9.0.3-py3-none-any.whl", hash = "sha256:2c5efc453d45394fdd706ade797c0a81091eccd1d6e4bccfcd476e2b8e0ab5d9", size = 375249, upload-time = "2026-04-07T17:16:16.13Z" },
{ url = "https://files.pythonhosted.org/packages/3b/ab/b3226f0bd7cdcf710fbede2b3548584366da3b19b5021e74f5bde2a8fa3f/pytest-9.0.2-py3-none-any.whl", hash = "sha256:711ffd45bf766d5264d487b917733b453d917afd2b0ad65223959f59089f875b", size = 374801, upload-time = "2025-12-06T21:30:49.154Z" },
]
[[package]]
+15 -51
View File
@@ -82,7 +82,6 @@ from langchain_core.tools.base import (
_is_injected_arg_type,
get_all_basemodel_annotations,
)
from langgraph._internal._constants import CONF, CONFIG_KEY_READ
from langgraph._internal._runnable import RunnableCallable
from langgraph.errors import GraphBubbleUp
from langgraph.graph.message import REMOVE_ALL_MESSAGES
@@ -615,7 +614,6 @@ class _InjectedArgs:
store: str | None
runtime: str | None
all_injected_keys: set[str]
_optional_state_args: set[str]
class ToolNode(RunnableCallable):
@@ -801,7 +799,7 @@ class ToolNode(RunnableCallable):
# Construct ToolRuntime instances at the top level for each tool call
tool_runtimes = []
for call, cfg in zip(tool_calls, config_list, strict=False):
state = self._extract_state(input, cfg)
state = self._extract_state(input)
tool_runtime = ToolRuntime(
state=state,
tool_call_id=call["id"],
@@ -809,7 +807,6 @@ class ToolNode(RunnableCallable):
context=runtime.context,
store=runtime.store,
stream_writer=runtime.stream_writer,
tools=list(self.tools_by_name.values()),
execution_info=runtime.execution_info,
server_info=runtime.server_info,
)
@@ -836,7 +833,7 @@ class ToolNode(RunnableCallable):
# Construct ToolRuntime instances at the top level for each tool call
tool_runtimes = []
for call, cfg in zip(tool_calls, config_list, strict=False):
state = self._extract_state(input, cfg)
state = self._extract_state(input)
tool_runtime = ToolRuntime(
state=state,
tool_call_id=call["id"],
@@ -844,7 +841,6 @@ class ToolNode(RunnableCallable):
context=runtime.context,
store=runtime.store,
stream_writer=runtime.stream_writer,
tools=list(self.tools_by_name.values()),
execution_info=runtime.execution_info,
server_info=runtime.server_info,
)
@@ -1274,37 +1270,18 @@ class ToolNode(RunnableCallable):
return None
def _extract_state(
self,
input: list[AnyMessage] | dict[str, Any] | BaseModel,
config: RunnableConfig,
self, input: list[AnyMessage] | dict[str, Any] | BaseModel
) -> list[AnyMessage] | dict[str, Any] | BaseModel:
"""Extract state from input.
"""Extract state from input, handling ToolCallWithContext if present.
Three input shapes:
Args:
input: The input which may be raw state or ToolCallWithContext.
- `ToolCallWithContext` dict legacy Send payload carrying an inlined
state snapshot; return `input["state"]`.
- list of `ToolCall` dicts new Send payload with no inlined state;
hydrate state from channels via `CONFIG_KEY_READ`.
- regular graph state (dict/list/BaseModel) return `input` as-is.
Returns:
The actual state to pass to wrap_tool_call wrappers.
"""
if isinstance(input, dict) and input.get("__type") == "tool_call_with_context":
return input["state"]
if (
isinstance(input, list)
and input
and isinstance(input[-1], dict)
and input[-1].get("type") == "tool_call"
):
read = config.get(CONF, {}).get(CONFIG_KEY_READ)
if read is None:
return {}
# Pregel installs CONFIG_KEY_READ as
# `functools.partial(local_read, scratchpad, channels, managed, task)`.
# Match the previous inlined-state contract by reading channels only;
# managed values have their own injection path (`ToolRuntime.context`).
channels = read.args[1]
return cast("dict[str, Any]", read(list(channels), False))
return input
def _inject_tool_args(
@@ -1356,7 +1333,7 @@ class ToolNode(RunnableCallable):
return tool_call
tool_call_copy: ToolCall = copy(tool_call)
injected_args: dict[str, Any] = {}
injected_args = {}
# Inject state
if injected.state:
@@ -1384,20 +1361,14 @@ class ToolNode(RunnableCallable):
# Extract state values
if isinstance(state, dict):
for tool_arg, state_field in injected.state.items():
if not state_field:
injected_args[tool_arg] = state
elif state_field in state:
injected_args[tool_arg] = state[state_field]
elif tool_arg not in injected._optional_state_args:
raise KeyError(state_field)
injected_args[tool_arg] = (
state[state_field] if state_field else state
)
else:
for tool_arg, state_field in injected.state.items():
if not state_field:
injected_args[tool_arg] = state
elif hasattr(state, state_field):
injected_args[tool_arg] = getattr(state, state_field)
elif tool_arg not in injected._optional_state_args:
raise AttributeError(state_field)
injected_args[tool_arg] = (
getattr(state, state_field) if state_field else state
)
# Inject store
if injected.store:
@@ -1598,7 +1569,6 @@ class ToolRuntime(_DirectlyInjectedToolArg, Generic[ContextT, StateT]):
- `context`: Runtime context (shared with `Runtime`)
- `store`: `BaseStore` instance for persistent storage (shared with `Runtime`)
- `stream_writer`: `StreamWriter` for streaming output (shared with `Runtime`)
- `tools`: List of all available `BaseTool` instances
No `Annotated` wrapper is needed - just use `runtime: ToolRuntime`
as a parameter.
@@ -1641,7 +1611,6 @@ class ToolRuntime(_DirectlyInjectedToolArg, Generic[ContextT, StateT]):
context: ContextT
config: RunnableConfig
stream_writer: StreamWriter
tools: list[BaseTool]
tool_call_id: str | None
store: BaseStore | None
execution_info: ExecutionInfo | None = None
@@ -1890,7 +1859,6 @@ def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
store_arg: str | None = None
runtime_arg: str | None = None
all_injected_keys: set[str] = set()
_optional_state_args: set[str] = set()
for name, type_ in all_annotations.items():
# Track all InjectedToolArg-annotated params (including custom subclasses)
@@ -1905,9 +1873,6 @@ def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
if state_inj := _get_injection_from_type(type_, InjectedState):
if isinstance(state_inj, InjectedState) and state_inj.field:
state_args[name] = state_inj.field
field_info = full_schema.model_fields.get(name)
if field_info and not field_info.is_required():
_optional_state_args.add(name)
else:
state_args[name] = None
@@ -1924,5 +1889,4 @@ def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
store=store_arg,
runtime=runtime_arg,
all_injected_keys=all_injected_keys,
_optional_state_args=_optional_state_args,
)
+1 -1
View File
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
[project]
name = "langgraph-prebuilt"
version = "1.0.10"
version = "1.0.9"
description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
authors = []
requires-python = ">=3.10"
@@ -1,285 +0,0 @@
"""Test InjectedState with NotRequired state fields.
This tests the fix for https://github.com/langchain-ai/langchain/issues/35585
When using InjectedState(<field>) on a tool parameter, and the referenced field is
declared as NotRequired in the custom state schema, the ToolNode should gracefully
handle missing fields by injecting None instead of raising KeyError.
"""
import sys
from typing import Annotated
import pytest
from langchain_core.messages import AIMessage, AnyMessage, HumanMessage, ToolMessage
from langchain_core.tools import tool
from langgraph.graph.message import add_messages
from pydantic import BaseModel, Field
from typing_extensions import NotRequired
from langgraph.prebuilt import InjectedState, ToolNode, create_react_agent
from langgraph.prebuilt.chat_agent_executor import AgentState
from .model import FakeToolCallingModel
class CustomAgentStateWithNotRequired(AgentState):
"""Custom state with a NotRequired field (TypedDict style)."""
city: NotRequired[str]
class CustomAgentStatePydanticWithDefault(BaseModel):
"""Custom state with Optional field and default (Pydantic style)."""
messages: Annotated[list[AnyMessage], add_messages]
remaining_steps: int = Field(default=10)
city: str | None = Field(default=None)
@tool
def get_weather(city: Annotated[str | None, InjectedState("city")] = None) -> str:
"""Get weather for a given city."""
if city is None:
return "No city provided"
return f"It's always sunny in {city}!"
def _create_mock_runtime(
state: dict | None = None,
store=None,
):
"""Create a mock Runtime for testing ToolNode directly."""
from unittest.mock import Mock
from langgraph.runtime import Runtime
mock_runtime = Mock(spec=Runtime)
mock_runtime.context = {}
return mock_runtime
def _create_config_with_runtime(store=None, state=None):
"""Create a RunnableConfig with mocked runtime for direct ToolNode testing."""
from langgraph.prebuilt.tool_node import ToolRuntime
tool_runtime = ToolRuntime(
state=state or {},
config={},
context={},
store=store,
stream_writer=None,
tools=[],
tool_call_id="test_id",
)
return {
"configurable": {
"__pregel_runtime": _create_mock_runtime(),
"__tool_runtime__": tool_runtime,
}
}
@pytest.mark.skipif(
sys.version_info < (3, 11),
reason="InjectedState field extraction from Optional[Annotated[...]] not supported on Python <3.11",
)
def test_injected_state_not_required_field_missing_injects_none():
"""Test that InjectedState with NotRequired field injects None when field is missing.
This verifies the fix for https://github.com/langchain-ai/langchain/issues/35585
"""
tool_node = ToolNode([get_weather])
tool_call = {
"name": "get_weather",
"args": {},
"id": "call_1",
"type": "tool_call",
}
ai_msg = AIMessage("Let me check the weather", tool_calls=[tool_call])
# State WITHOUT the "city" field - should inject None instead of raising KeyError
state_without_city: CustomAgentStateWithNotRequired = {
"messages": [HumanMessage("What's the weather?"), ai_msg],
}
result = tool_node.invoke(
state_without_city,
config=_create_config_with_runtime(state=state_without_city),
)
assert len(result["messages"]) == 1
tool_msg = result["messages"][0]
assert isinstance(tool_msg, ToolMessage)
assert "No city provided" in tool_msg.content
@pytest.mark.skipif(
sys.version_info < (3, 11),
reason="InjectedState field extraction from Optional[Annotated[...]] not supported on Python <3.11",
)
def test_injected_state_not_required_field_present_works():
"""Test that InjectedState with NotRequired field works when field IS present."""
tool_node = ToolNode([get_weather])
tool_call = {
"name": "get_weather",
"args": {},
"id": "call_1",
"type": "tool_call",
}
ai_msg = AIMessage("Let me check the weather", tool_calls=[tool_call])
# State WITH the "city" field - this should work
state_with_city: CustomAgentStateWithNotRequired = {
"messages": [HumanMessage("What's the weather?"), ai_msg],
"city": "San Francisco",
}
result = tool_node.invoke(
state_with_city,
config=_create_config_with_runtime(state=state_with_city),
)
assert len(result["messages"]) == 1
tool_msg = result["messages"][0]
assert isinstance(tool_msg, ToolMessage)
assert "San Francisco" in tool_msg.content
@pytest.mark.skipif(
sys.version_info < (3, 11),
reason="InjectedState field extraction from Optional[Annotated[...]] not supported on Python <3.11",
)
def test_create_react_agent_injected_state_not_required_field_missing():
"""Test create_react_agent with InjectedState using NotRequired field that is missing.
This verifies the fix for https://github.com/langchain-ai/langchain/issues/35585
"""
model = FakeToolCallingModel(
tool_calls=[
[{"name": "get_weather", "args": {}, "id": "call_1"}],
[], # No more tool calls, agent should stop
]
)
agent = create_react_agent(
model,
tools=[get_weather],
state_schema=CustomAgentStateWithNotRequired,
)
# Invoke WITHOUT the city field - should work, injecting None
result = agent.invoke(
{"messages": [HumanMessage("What's the weather?")]},
)
# Check that the tool was called successfully with None injected
messages = result["messages"]
tool_messages = [m for m in messages if isinstance(m, ToolMessage)]
assert len(tool_messages) == 1
assert "No city provided" in tool_messages[0].content
@pytest.mark.skipif(
sys.version_info < (3, 11),
reason="InjectedState field extraction from Optional[Annotated[...]] not supported on Python <3.11",
)
def test_create_react_agent_injected_state_not_required_field_present():
"""Test create_react_agent with InjectedState using NotRequired field that IS present."""
model = FakeToolCallingModel(
tool_calls=[
[{"name": "get_weather", "args": {}, "id": "call_1"}],
[], # No more tool calls, agent should stop
]
)
agent = create_react_agent(
model,
tools=[get_weather],
state_schema=CustomAgentStateWithNotRequired,
)
# Invoke WITH the city field
result = agent.invoke(
{
"messages": [HumanMessage("What's the weather?")],
"city": "San Francisco",
},
)
# Check that the tool was called successfully
messages = result["messages"]
tool_messages = [m for m in messages if isinstance(m, ToolMessage)]
assert len(tool_messages) == 1
assert "San Francisco" in tool_messages[0].content
@tool
def get_weather_optional(city: Annotated[str | None, InjectedState("city")]) -> str:
"""Get weather for a given city (accepts None)."""
if city is None:
return "Please provide a city!"
return f"It's always sunny in {city}!"
def test_pydantic_state_with_default_field_missing_works():
"""Test that Pydantic state with Optional field and default=None works when field is missing.
This is the workaround suggested in the issue comments - using Pydantic BaseModel
with `city: Optional[str] = Field(default=None)` instead of TypedDict with NotRequired.
"""
model = FakeToolCallingModel(
tool_calls=[
[{"name": "get_weather_optional", "args": {}, "id": "call_1"}],
[], # No more tool calls, agent should stop
]
)
agent = create_react_agent(
model,
tools=[get_weather_optional],
state_schema=CustomAgentStatePydanticWithDefault,
)
# Invoke WITHOUT the city field - should work because Pydantic provides default
result = agent.invoke(
{"messages": [HumanMessage("What's the weather?")]},
)
# Check that the tool was called successfully with None
messages = result["messages"]
tool_messages = [m for m in messages if isinstance(m, ToolMessage)]
assert len(tool_messages) == 1
assert "Please provide a city!" in tool_messages[0].content
def test_pydantic_state_with_default_field_present_works():
"""Test that Pydantic state with Optional field works when field IS present."""
model = FakeToolCallingModel(
tool_calls=[
[{"name": "get_weather_optional", "args": {}, "id": "call_1"}],
[], # No more tool calls, agent should stop
]
)
agent = create_react_agent(
model,
tools=[get_weather_optional],
state_schema=CustomAgentStatePydanticWithDefault,
)
# Invoke WITH the city field
result = agent.invoke(
{
"messages": [HumanMessage("What's the weather?")],
"city": "San Francisco",
},
)
# Check that the tool was called successfully
messages = result["messages"]
tool_messages = [m for m in messages if isinstance(m, ToolMessage)]
assert len(tool_messages) == 1
assert "San Francisco" in tool_messages[0].content
-92
View File
@@ -1320,98 +1320,6 @@ async def test_state_extraction_with_tool_call_with_context_async() -> None:
assert "tool_call" not in state_seen[0]
def _config_with_channel_read(
channel_values: dict[str, object],
store: BaseStore | None = None,
) -> RunnableConfig:
"""Build a config that mimics `CONFIG_KEY_READ` as Pregel installs it.
Pregel always installs a `functools.partial(local_read, scratchpad,
channels, managed, task)`, and `ToolNode` introspects that partial to
learn channel names. The stub matches the shape: partial whose second and
third positional args are `channels` and `managed` mappings.
"""
import functools
channels_stub = {k: None for k in channel_values}
managed_stub: dict[str, object] = {}
# Shape matches pregel's real partial:
# functools.partial(local_read, scratchpad, channels, managed, task)
def _read(scratchpad, channels, managed, task, select, fresh): # noqa: ARG001
if isinstance(select, str):
return channel_values[select]
return {k: channel_values[k] for k in select if k in channel_values}
read = functools.partial(_read, None, channels_stub, managed_stub, None)
cfg = _create_config_with_runtime(store)
cfg["configurable"]["__pregel_read"] = read
return cfg
def test_list_form_send_hydrates_state_from_channel_read() -> None:
"""Send('tools', [tool_call]) with no inlined state should hydrate
ToolRuntime.state from CONFIG_KEY_READ (full state read)."""
state_seen = []
def state_inspector_handler(
request: ToolCallRequest,
execute: Callable[[ToolCallRequest], ToolMessage | Command],
) -> ToolMessage | Command:
state_seen.append(request.state)
return execute(request)
channel_values = {
"messages": [AIMessage("from channels")],
"files": {"/a.md": "body"},
}
tool_node = ToolNode([add], wrap_tool_call=state_inspector_handler)
tool_call: ToolCall = {
"name": "add",
"args": {"a": 1, "b": 2},
"id": "call_1",
"type": "tool_call",
}
tool_node.invoke([tool_call], config=_config_with_channel_read(channel_values))
assert len(state_seen) == 1
got = state_seen[0]
assert got == channel_values
assert "messages" in got and "files" in got
async def test_list_form_send_hydrates_state_async() -> None:
state_seen = []
def state_inspector_handler(
request: ToolCallRequest,
execute: Callable[[ToolCallRequest], ToolMessage | Command],
) -> ToolMessage | Command:
state_seen.append(request.state)
return execute(request)
channel_values = {"messages": [AIMessage("from channels")], "files": {}}
tool_node = ToolNode([add], wrap_tool_call=state_inspector_handler)
tool_call: ToolCall = {
"name": "add",
"args": {"a": 1, "b": 2},
"id": "call_1",
"type": "tool_call",
}
await tool_node.ainvoke(
[tool_call], config=_config_with_channel_read(channel_values)
)
assert len(state_seen) == 1
assert state_seen[0] == channel_values
def test_tool_call_request_is_frozen() -> None:
"""Test that ToolCallRequest raises deprecation warnings on direct attribute reassignment."""
tool_call: ToolCall = {"name": "add", "args": {"a": 1, "b": 2}, "id": "call_1"}
+8 -29
View File
@@ -2016,8 +2016,8 @@ async def test_tool_node_inject_runtime_dynamic_tool_via_wrap_tool_call_async()
assert tool_message.tool_call_id == "call_dynamic_2"
def test_tool_runtime_forwards_execution_info_server_info_and_tools() -> None:
"""Test that execution_info, server_info, and tools are forwarded from Runtime to ToolRuntime."""
def test_tool_runtime_forwards_execution_info_and_server_info() -> None:
"""Test that execution_info and server_info are forwarded from Runtime to ToolRuntime."""
from langgraph.runtime import ExecutionInfo, ServerInfo
exec_info = ExecutionInfo(
@@ -2043,15 +2043,9 @@ def test_tool_runtime_forwards_execution_info_server_info_and_tools() -> None:
"""Tool that captures runtime info."""
captured["execution_info"] = runtime.execution_info
captured["server_info"] = runtime.server_info
captured["tools"] = runtime.tools
return "ok"
@dec_tool
def other_tool(y: int) -> str:
"""Another tool available to the runtime."""
return str(y)
node = ToolNode([info_tool, other_tool])
node = ToolNode([info_tool])
tool_call = {
"name": "info_tool",
"args": {"x": 1},
@@ -2060,21 +2054,17 @@ def test_tool_runtime_forwards_execution_info_server_info_and_tools() -> None:
}
msg = AIMessage("", tool_calls=[tool_call])
config: RunnableConfig = {"configurable": {"__pregel_runtime": mock_runtime}}
result = node.invoke({"messages": [msg]}, config=config)
node.invoke({"messages": [msg]}, config=config)
assert result["messages"][-1].content == "ok"
assert captured["execution_info"] is exec_info
assert captured["execution_info"].thread_id == "t-1"
assert captured["execution_info"].task_id == "tk-1"
assert captured["server_info"] is server_info
assert captured["server_info"].assistant_id == "asst-1"
assert [tool.name for tool in captured["tools"]] == ["info_tool", "other_tool"]
async def test_tool_runtime_forwards_execution_info_server_info_and_tools_async() -> (
None
):
"""Test that execution_info, server_info, and tools are forwarded in async path."""
async def test_tool_runtime_forwards_execution_info_and_server_info_async() -> None:
"""Test that execution_info and server_info are forwarded in async path."""
from langgraph.runtime import ExecutionInfo, ServerInfo
exec_info = ExecutionInfo(
@@ -2100,15 +2090,9 @@ async def test_tool_runtime_forwards_execution_info_server_info_and_tools_async(
"""Async tool that captures runtime info."""
captured["execution_info"] = runtime.execution_info
captured["server_info"] = runtime.server_info
captured["tools"] = runtime.tools
return "ok"
@dec_tool
async def other_tool_async(y: int) -> str:
"""Another async tool available to the runtime."""
return str(y)
node = ToolNode([info_tool_async, other_tool_async])
node = ToolNode([info_tool_async])
tool_call = {
"name": "info_tool_async",
"args": {"x": 1},
@@ -2117,17 +2101,12 @@ async def test_tool_runtime_forwards_execution_info_server_info_and_tools_async(
}
msg = AIMessage("", tool_calls=[tool_call])
config: RunnableConfig = {"configurable": {"__pregel_runtime": mock_runtime}}
result = await node.ainvoke({"messages": [msg]}, config=config)
await node.ainvoke({"messages": [msg]}, config=config)
assert result["messages"][-1].content == "ok"
assert captured["execution_info"] is exec_info
assert captured["execution_info"].thread_id == "t-2"
assert captured["server_info"] is server_info
assert captured["server_info"].graph_id == "graph-2"
assert [tool.name for tool in captured["tools"]] == [
"info_tool_async",
"other_tool_async",
]
# --- InjectedToolArg security tests ---
+13 -14
View File
@@ -249,7 +249,7 @@ wheels = [
[[package]]
name = "langchain-core"
version = "1.3.0"
version = "1.2.25"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "jsonpatch" },
@@ -261,14 +261,14 @@ dependencies = [
{ name = "typing-extensions" },
{ name = "uuid-utils" },
]
sdist = { url = "https://files.pythonhosted.org/packages/92/fe/20190232d9b513242899dbb0c2bb77e31b4d61e343743adbe90ebc2603d2/langchain_core-1.3.0.tar.gz", hash = "sha256:14a39f528bf459aa3aa40d0a7f7f1bae7520d435ef991ae14a4ceb74d8c49046", size = 860755, upload-time = "2026-04-17T14:51:38.298Z" }
sdist = { url = "https://files.pythonhosted.org/packages/86/2a/d65de24fc9b7989137253da8973f850f3e39b4ce3e0377bc8200d6b3c189/langchain_core-1.2.25.tar.gz", hash = "sha256:77e032b96509d0eb1f6875042fdf97b7e2334a815314700c6894d9d078909b9c", size = 842347, upload-time = "2026-04-02T22:39:11.528Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/f8/e2/dbfa347aa072a6dc4cd38d6f9ebfc730b4c14c258c47f480f4c5c546f177/langchain_core-1.3.0-py3-none-any.whl", hash = "sha256:baf16ee028475df177b9ab8869a751c79406d64a6f12125b93802991b566cced", size = 515140, upload-time = "2026-04-17T14:51:36.274Z" },
{ url = "https://files.pythonhosted.org/packages/3d/0e/7b31b0249f9b9b0fc7829d5b0ee484b8f8d43c78e376e9951e2ef3eac70c/langchain_core-1.2.25-py3-none-any.whl", hash = "sha256:0c05bf395aec6d2dfa14488fd006f7bcd0540e7e89287e04f92203532a82c828", size = 506866, upload-time = "2026-04-02T22:39:10.137Z" },
]
[[package]]
name = "langgraph"
version = "1.1.9"
version = "1.1.7a1"
source = { editable = "../langgraph" }
dependencies = [
{ name = "langchain-core" },
@@ -281,7 +281,7 @@ dependencies = [
[package.metadata]
requires-dist = [
{ name = "langchain-core", specifier = ">=1.3.0,<2" },
{ name = "langchain-core", specifier = ">=0.1" },
{ name = "langgraph-checkpoint", editable = "../checkpoint" },
{ name = "langgraph-prebuilt", editable = "." },
{ name = "langgraph-sdk", editable = "../sdk-py" },
@@ -352,7 +352,7 @@ test = [
[[package]]
name = "langgraph-checkpoint"
version = "4.0.2"
version = "4.0.1"
source = { editable = "../checkpoint" }
dependencies = [
{ name = "langchain-core" },
@@ -490,7 +490,7 @@ test = [
[[package]]
name = "langgraph-prebuilt"
version = "1.0.10"
version = "1.0.9"
source = { editable = "." }
dependencies = [
{ name = "langchain-core" },
@@ -619,7 +619,7 @@ test = [
[[package]]
name = "langsmith"
version = "0.7.31"
version = "0.6.4"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "httpx" },
@@ -629,12 +629,11 @@ dependencies = [
{ name = "requests" },
{ name = "requests-toolbelt" },
{ name = "uuid-utils" },
{ name = "xxhash" },
{ name = "zstandard" },
]
sdist = { url = "https://files.pythonhosted.org/packages/e6/11/696019490992db5c87774dc20515529ef42a01e1d770fb754ed6d9b12fb0/langsmith-0.7.31.tar.gz", hash = "sha256:331ee4f7c26bb5be4022b9859b7d7b122cbf8c9d01d9f530114c1914b0349ffb", size = 1178480, upload-time = "2026-04-14T17:55:41.242Z" }
sdist = { url = "https://files.pythonhosted.org/packages/e7/85/9c7933052a997da1b85bc5c774f3865e9b1da1c8d71541ea133178b13229/langsmith-0.6.4.tar.gz", hash = "sha256:36f7223a01c218079fbb17da5e536ebbaf5c1468c028abe070aa3ae59bc99ec8", size = 919964, upload-time = "2026-01-15T20:02:28.873Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/1d/a1/a013cf458c301cda86a213dd153ce0a01c93f1ab5833f951e6a44c9763ce/langsmith-0.7.31-py3-none-any.whl", hash = "sha256:0291d49203f6e80dda011af1afda61eb0595a4d697adb684590a8805e1d61fb6", size = 373276, upload-time = "2026-04-14T17:55:39.677Z" },
{ url = "https://files.pythonhosted.org/packages/66/0f/09a6637a7ba777eb307b7c80852d9ee26438e2bdafbad6fcc849ff9d9192/langsmith-0.6.4-py3-none-any.whl", hash = "sha256:ac4835860160be371042c7adbba3cb267bcf8d96a5ea976c33a8a4acad6c5486", size = 283503, upload-time = "2026-01-15T20:02:26.662Z" },
]
[[package]]
@@ -1183,7 +1182,7 @@ wheels = [
[[package]]
name = "pytest"
version = "9.0.3"
version = "9.0.2"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "colorama", marker = "sys_platform == 'win32'" },
@@ -1194,9 +1193,9 @@ dependencies = [
{ name = "pygments" },
{ name = "tomli", marker = "python_full_version < '3.11'" },
]
sdist = { url = "https://files.pythonhosted.org/packages/7d/0d/549bd94f1a0a402dc8cf64563a117c0f3765662e2e668477624baeec44d5/pytest-9.0.3.tar.gz", hash = "sha256:b86ada508af81d19edeb213c681b1d48246c1a91d304c6c81a427674c17eb91c", size = 1572165, upload-time = "2026-04-07T17:16:18.027Z" }
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@@ -1,137 +0,0 @@
# Delta-channel reconstruction: query strategy benchmark
**Branch:** `delta-channel-writes-based`
**Question (Nuno):** Is the recursive CTE the right query shape for reconstructing a delta channel inside `get_tuple`, or would a plain `SELECT WHERE` be cheaper even though it returns more rows?
**Answer:** Plain `SELECT WHERE` wins at every realistic depth. The recursion isn't the problem — the JSON-expression join inside the CTE is.
## Setup
- Postgres 16 on `localhost:5441` (the `compose-postgres.yml` instance, run directly without docker for this round)
- Single delta channel `messages`, one write per checkpoint, `DELTA_SENTINEL` blob per checkpoint
- Linear chain (`branch=1`) and 5-way branching at every step (`branch=5`) — branching is the case where plain over-fetches sibling rows
- Median of 20 timed runs after 3 warmups, fresh psycopg cursor per strategy
- Bench script: `bench_get_tuple_strategies.py` at repo root
Three strategies compared:
| name | roundtrips | shape |
|------|-----------|-------|
| `cte` | 1 | Current prod: recursive CTE walks ancestors, LEFT JOINs writes + blobs |
| `plain` | 3 | Nuno's suggestion: thread-wide `SELECT WHERE` per table, Python walks parent chain and filters |
| `cte+narrow` | 2 | CTE returns ancestor IDs only, then one `UNION ALL` of writes + blobs filtered by `ANY(ids)` |
## Results (ms per get_tuple, median of 20)
```
depth branch cte plain cte+narrow rows_cte rows_plain plain/cte
10 1 0.14ms 0.26ms 0.24ms 9 30 1.89x
10 5 0.21ms 0.23ms 0.17ms 9 110 1.11x
50 1 0.89ms 0.27ms 0.33ms 49 150 0.31x
50 5 2.35ms 0.66ms 0.51ms 49 550 0.28x
200 1 11.61ms 0.78ms 1.30ms 199 600 0.07x
200 5 34.79ms 2.33ms 3.07ms 199 2200 0.07x
1000 1 274.60ms 2.59ms 13.29ms 999 3000 0.01x
1000 5 856.01ms 10.14ms 15.31ms 999 11000 0.01x
```
Lower is better. `plain/cte < 1` means plain is faster.
### Headline numbers
- depth 50: plain is **3x** faster
- depth 200: plain is **15x** faster
- depth 1000: plain is **~100x** faster
- Branching makes plain over-fetch (3000 rows → 11000 rows at d=1000), but it remains ~85x faster than the CTE
## Why the CTE collapses
`EXPLAIN (ANALYZE, BUFFERS)` of the CTE at depth 1000 (linear). Excerpt with the load-bearing nodes:
```
Sort ... actual time=137.798..137.827 rows=999
CTE ancestors
-> Recursive Union ... actual time=0.005..2.443 rows=999
^^^^^^
recursion is 2.4 ms — fine
-> Nested Loop Left Join ... actual time=2.676..137.529 rows=999
Join Filter: (cw.checkpoint_id = a.cid)
Rows Removed by Join Filter: 998001
^^^^^^^
999 ancestors x ~1000 writes
-> Nested Loop Left Join ... actual time=2.669..85.061 rows=999
Join Filter: (bl.version = ((c.checkpoint -> 'channel_versions'::text) ->> bl.channel))
Rows Removed by Join Filter: 998001
^^^^^^^
same quadratic blow-up on the blob join
```
Two pathological things are happening:
1. **The blob join filter is on a JSON expression**: `bl.version = (c.checkpoint -> 'channel_versions' ->> bl.channel)`. The planner cannot push this into an index lookup, so it materializes `checkpoint_blobs` for the thread and does a nested-loop comparison against every ancestor — a Cartesian product that grows as `O(ancestors × blobs_in_thread)`.
2. **The writes join is similar**: writes for the thread are materialized once, then nested-loop joined against ancestors with a `Join Filter` rather than a hash/merge join over the indexed `checkpoint_id`.
At depth 1000 that's **~2 million rows evaluated, 99.9% of them discarded**. The recursion itself is a rounding error.
For comparison, the plain Q1 (`SELECT … FROM checkpoints WHERE thread_id=? AND checkpoint_ns=?`) at depth 1000:
```
Seq Scan on checkpoints ... actual time=0.012..0.121 rows=1000
Execution Time: 0.140 ms
```
A simple seq scan over 57 buffers. Q2 and Q3 follow the same shape and complete in well under 1 ms each.
## Crossover and remote-DB reasoning
- Pure local Postgres: plain wins from depth ~30 onward; CTE wins by fractions of a ms below that
- Remote Postgres at ~5 ms RTT adds ~10 ms to plain (3 roundtrips vs 1). Crossover shifts to ~depth 30. Above that, the CTE's quadratic SQL cost still dominates the RTT savings.
There is no realistic conversation depth where the CTE wins on a remote DB. At depth 200+ (anything resembling a real multi-turn agent run) plain is faster regardless of network.
## Recommendation
**Switch to plain SELECT WHERE, one delta channel at a time.**
Three indexed queries per delta channel:
```sql
-- Q1: parent chain + per-checkpoint version of this channel
SELECT checkpoint_id,
parent_checkpoint_id,
checkpoint -> 'channel_versions' ->> 'channel_name' AS ver
FROM checkpoints
WHERE thread_id = ? AND checkpoint_ns = ?;
-- Q2: writes for this channel, anywhere in the thread
SELECT checkpoint_id, type, blob, task_id, idx
FROM checkpoint_writes
WHERE thread_id = ? AND checkpoint_ns = ? AND channel = ?;
-- Q3: blobs for this channel, anywhere in the thread
SELECT version, type, blob
FROM checkpoint_blobs
WHERE thread_id = ? AND checkpoint_ns = ? AND channel = ?;
```
Python then:
- Builds `parent_of: dict[cid, parent_cid]` from Q1
- Walks from target's parent newest → oldest
- Filters Q2 rows by `ancestor_set`, processes oldest → newest, applies overwrite-terminator
- Picks seed blob via the per-ancestor `ver` map, terminates at first non-sentinel blob
All O(n) on n = thread checkpoints, with tight constants (dict lookups). No recursion, no JSON-expression joins, no quadratic plans.
If the 3-roundtrip cost ever shows up on remote-DB benchmarks, fold Q2 + Q3 into one `UNION ALL` to get back to 2 roundtrips. Bench says it isn't worth the SQL complexity right now.
## Bonus: code simplification from single-channel scope
Multi-channel reconstruction in the current `_reconstruct_delta_channels_cur` carries:
- `rows_by_cid` nested dicts, keyed by cid then channel
- `seen_blob: set[(cid, channel)]` and `seen_write: set[(cid, channel, task_id, idx)]` dedup
- `collected: dict[channel, list]`, `done: set[channel]`, `seeds: dict[channel, value]`
- Inner `for ch in channels_list` loops and an early-exit `if len(done) == len(channels_list)`
Single-channel collapses these to a single list, a single bool, and one `Optional[Any]`. Roughly half the Python in that function, plus an obvious shape for splitting pure post-processing into `base.py` so sync and async stop duplicating it.
If multi-channel coalescing turns out to matter later, it can come back as a SQL-level optimization without re-introducing the bookkeeping in Python.