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Author SHA1 Message Date
Chester Curme 5638502015 factor out helper 2026-04-05 20:30:52 -04:00
Chester Curme 442c6d90b0 merge callbacks in ensure_config 2026-04-05 20:15:56 -04:00
Chester Curme 0c52212098 add test 2026-04-05 20:15:15 -04:00
98 changed files with 1457 additions and 12745 deletions
+2 -32
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@@ -116,8 +116,8 @@ jobs:
timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-h
echo "Finished starting up langgraph-test-h"
LANGGRAPH_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langgraph'); print(v);")
if [ "$LANGGRAPH_VERSION" != "1.1.5" ]; then
echo "LANGGRAPH_VERSION != 1.1.5; $LANGGRAPH_VERSION"
if [ "$LANGGRAPH_VERSION" != "1.1.2" ]; then
echo "LANGGRAPH_VERSION != 1.1.2; $LANGGRAPH_VERSION"
exit 1
fi
LANGCHAIN_OPENAI_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langchain-openai'); print(v);")
@@ -136,33 +136,3 @@ jobs:
working-directory: libs/cli/examples/graph_prerelease_reqs_fail
run: |
langgraph build -t langgraph-test-i || [ $? -eq 1 ]
- name: Build uv simple service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
working-directory: libs/cli/uv-examples/simple
run: |
langgraph build -t langgraph-test-uv-simple
- name: Test uv simple service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' && env.HAS_LANGSMITH_API_KEY == 'true' }}
working-directory: libs/cli/uv-examples/simple
env:
LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
run: |
cp .env.example .env
echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env
timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-uv-simple
- name: Build uv monorepo service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
working-directory: libs/cli/uv-examples/monorepo/apps/agent
run: |
langgraph build -t langgraph-test-uv-monorepo
- name: Test uv monorepo service
if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' && env.HAS_LANGSMITH_API_KEY == 'true' }}
working-directory: libs/cli/uv-examples/monorepo/apps/agent
env:
LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
run: |
cp .env.example .env
echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env
timeout 60 python ../../../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-uv-monorepo
-1
View File
@@ -100,4 +100,3 @@ dmypy.json
.turbo
.editorconfig
.scratch
.worktrees/
@@ -339,6 +339,37 @@ async def test_list_metadata_custom_keys(
assert results[0].metadata["run_id"] == "run-abc"
async def test_list_global_search(
saver: BaseCheckpointSaver,
) -> None:
"""alist(None, filter=...) searches across all threads."""
tid1, tid2 = str(uuid4()), str(uuid4())
# Use a unique marker so we don't collide with other tests' data
marker = str(uuid4())
cfg1 = generate_config(tid1)
cp1 = generate_checkpoint()
await saver.aput(cfg1, cp1, generate_metadata(source="input", marker=marker), {})
cfg2 = generate_config(tid2)
cp2 = generate_checkpoint()
await saver.aput(cfg2, cp2, generate_metadata(source="loop", marker=marker), {})
# Search across all threads with filter
results = []
async for tup in saver.alist(None, filter={"source": "input", "marker": marker}):
results.append(tup)
assert len(results) == 1
assert results[0].config["configurable"]["thread_id"] == tid1
# Search with marker only — should find both
results = []
async for tup in saver.alist(None, filter={"marker": marker}):
results.append(tup)
assert len(results) == 2
ALL_LIST_TESTS = [
test_list_all,
test_list_by_thread,
@@ -349,6 +380,7 @@ ALL_LIST_TESTS = [
test_list_metadata_filter_multiple_keys,
test_list_metadata_filter_no_match,
test_list_metadata_custom_keys,
test_list_global_search,
test_list_before,
test_list_limit,
test_list_limit_plus_before,
+1 -1
View File
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
[project]
name = "langgraph-checkpoint-conformance"
version = "0.0.2"
version = "0.0.1"
description = "Conformance test suite for LangGraph checkpointer implementations."
authors = [{name = "William FH", email = "13333726+hinthornw@users.noreply.github.com"}]
requires-python = ">=3.10"
+10 -10
View File
@@ -231,7 +231,7 @@ wheels = [
[[package]]
name = "langchain-core"
version = "1.2.28"
version = "1.2.22"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "jsonpatch" },
@@ -243,9 +243,9 @@ dependencies = [
{ name = "typing-extensions" },
{ name = "uuid-utils" },
]
sdist = { url = "https://files.pythonhosted.org/packages/f8/a4/317a1a3ac1df33a64adb3670bf88bbe3b3d5baa274db6863a979db472897/langchain_core-1.2.28.tar.gz", hash = "sha256:271a3d8bd618f795fdeba112b0753980457fc90537c46a0c11998516a74dc2cb", size = 846119, upload-time = "2026-04-08T18:19:34.867Z" }
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/a8/92/32f785f077c7e898da97064f113c73fbd9ad55d1e2169cf3a391b183dedb/langchain_core-1.2.28-py3-none-any.whl", hash = "sha256:80764232581eaf8057bcefa71dbf8adc1f6a28d257ebd8b95ba9b8b452e8c6ac", size = 508727, upload-time = "2026-04-08T18:19:32.823Z" },
{ 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]]
@@ -263,7 +263,7 @@ wheels = [
[[package]]
name = "langgraph-checkpoint-conformance"
version = "0.0.2"
version = "0.0.1"
source = { editable = "." }
dependencies = [
{ name = "langgraph-checkpoint" },
@@ -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" }
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]]
-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]
@@ -4,17 +4,15 @@ import threading
from collections import defaultdict
from collections.abc import Iterator, Sequence
from contextlib import contextmanager
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,
ChannelVersions,
Checkpoint,
CheckpointMetadata,
CheckpointTuple,
_ChannelWritesHistory,
get_checkpoint_id,
get_serializable_checkpoint_metadata,
)
@@ -25,11 +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_COMBINED_SQL,
BasePostgresSaver,
_DeltaCombinedRow,
)
from langgraph.checkpoint.postgres.base import BasePostgresSaver
from langgraph.checkpoint.postgres.shallow import ShallowPostgresSaver
Conn = _internal.Conn # For backward compatibility
@@ -436,48 +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`.
One combined UNION ALL query (`SELECT_DELTA_COMBINED_SQL`) fetches rows
from `checkpoints`, `checkpoint_writes`, and `checkpoint_blobs` in a
single roundtrip; the ancestor walk runs in Python.
"""
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_COMBINED_SQL,
(
channel,
thread_id,
checkpoint_ns,
thread_id,
checkpoint_ns,
channel,
thread_id,
checkpoint_ns,
channel,
),
)
rows = cur.fetchall()
return self._build_delta_channel_writes_history(
channel=channel,
target_id=checkpoint_id,
rows=cast("list[_DeltaCombinedRow]", rows),
)
def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
"""
Convert a database row into a CheckpointTuple object.
@@ -4,17 +4,15 @@ import asyncio
from collections import defaultdict
from collections.abc import AsyncIterator, Iterator, Sequence
from contextlib import asynccontextmanager
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,
ChannelVersions,
Checkpoint,
CheckpointMetadata,
CheckpointTuple,
_ChannelWritesHistory,
get_checkpoint_id,
get_serializable_checkpoint_metadata,
)
@@ -25,11 +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_COMBINED_SQL,
BasePostgresSaver,
_DeltaCombinedRow,
)
from langgraph.checkpoint.postgres.base import BasePostgresSaver
from langgraph.checkpoint.postgres.shallow import AsyncShallowPostgresSaver
Conn = _ainternal.Conn # For backward compatibility
@@ -397,46 +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`.
One combined UNION ALL query (`SELECT_DELTA_COMBINED_SQL`) fetches rows
from `checkpoints`, `checkpoint_writes`, and `checkpoint_blobs` in a
single roundtrip; rows are assembled by the shared pure helper on
`BasePostgresSaver`.
"""
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_COMBINED_SQL,
(
channel,
thread_id,
checkpoint_ns,
thread_id,
checkpoint_ns,
channel,
thread_id,
checkpoint_ns,
channel,
),
)
rows = await cur.fetchall()
return self._build_delta_channel_writes_history(
channel=channel,
target_id=checkpoint_id,
rows=cast("list[_DeltaCombinedRow]", rows),
)
async def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
"""
Convert a database row into a CheckpointTuple object.
@@ -4,16 +4,13 @@ import random
import warnings
from collections.abc import Sequence
from importlib.metadata import version as get_version
from typing import Any, TypedDict, cast
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
@@ -156,62 +153,6 @@ INSERT_CHECKPOINT_WRITES_SQL = """
"""
class _DeltaCombinedRow(TypedDict, total=False):
"""One row from `SELECT_DELTA_COMBINED_SQL` (a UNION ALL of three tables).
Every row carries `_kind` ("p" / "w" / "b") plus whichever columns are
relevant for that kind; irrelevant columns are NULL and typed as `None`.
"""
_kind: str # always present: "p", "w", or "b"
# checkpoint row ("p")
checkpoint_id: str | None
parent_checkpoint_id: str | None
ver: str | None
# write / blob rows ("w", "b")
type: str | None
blob: bytes | None
# write row only ("w")
task_id: str | None
idx: int | None
# blob row only ("b")
version: str | None
# DeltaChannel reconstruction: one UNION ALL query fetches checkpoints,
# writes, and blobs for `channel` in one roundtrip; the ancestor walk runs
# in Python in `_build_delta_channel_writes_history`.
#
# Parameter order: (channel, thread_id, checkpoint_ns,
# thread_id, checkpoint_ns, channel,
# thread_id, checkpoint_ns, channel)
SELECT_DELTA_COMBINED_SQL = """
SELECT 'p'::text AS _kind,
checkpoint_id,
parent_checkpoint_id,
checkpoint -> 'channel_versions' ->> %s AS ver,
NULL::text AS type,
NULL::bytea AS blob,
NULL::text AS task_id,
NULL::int AS idx,
NULL::text AS version
FROM checkpoints
WHERE thread_id = %s AND checkpoint_ns = %s
UNION ALL
SELECT 'w',
checkpoint_id, NULL, NULL,
type, blob, task_id, idx, NULL
FROM checkpoint_writes
WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s
UNION ALL
SELECT 'b',
NULL, NULL, NULL,
type, blob, NULL, NULL, version
FROM checkpoint_blobs
WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s
"""
class BasePostgresSaver(BaseCheckpointSaver[str]):
SELECT_SQL = SELECT_SQL
SELECT_PENDING_SENDS_SQL = SELECT_PENDING_SENDS_SQL
@@ -254,83 +195,6 @@ class BasePostgresSaver(BaseCheckpointSaver[str]):
if t.decode() != "empty"
}
def _build_delta_channel_writes_history(
self,
*,
channel: str,
target_id: str,
rows: Sequence[_DeltaCombinedRow],
) -> _ChannelWritesHistory:
"""Reconstruct one delta channel's history from the combined UNION ALL rows.
Pure data transform shared by sync (`PostgresSaver`) and async
(`AsyncPostgresSaver`); both paths run `SELECT_DELTA_COMBINED_SQL`
and feed the tagged 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] = {}
writes_by_cid: dict[str, list[tuple[str, bytes, str, int]]] = {}
blob_by_ver: dict[str, tuple[str, bytes]] = {}
for r in rows:
kind = r["_kind"]
if kind == "p":
cid = cast(str, r["checkpoint_id"])
parent_of[cid] = r["parent_checkpoint_id"]
ver_of[cid] = r["ver"]
elif kind == "w":
cid = cast(str, r["checkpoint_id"])
writes_by_cid.setdefault(cid, []).append(
cast(
"tuple[str, bytes, str, int]",
(r["type"], r["blob"], r["task_id"], r["idx"]),
)
)
else: # kind == "b"
blob_by_ver[cast(str, r["version"])] = cast(
"tuple[str, bytes]", (r["type"], r["blob"])
)
# newest write first per ancestor (task_id DESC, idx DESC)
for ws in writes_by_cid.values():
ws.sort(key=lambda w: (w[2], w[3]), reverse=True)
ancestors: list[str] = []
cur_cid: str | None = parent_of.get(target_id)
while cur_cid is not None:
ancestors.append(cur_cid)
cur_cid = parent_of.get(cur_cid)
if not ancestors:
return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=[])
collected: list[PendingWrite] = [] # newest first; reversed at the end
for cid in ancestors:
# Collect writes first — they encode the transition FROM this
# ancestor's state to its child's and must be included even if
# this ancestor is also the seed checkpoint.
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))
# Then check seed terminator.
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)
collected.reverse() # oldest → newest
return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
def _dump_blobs(
self,
thread_id: str,
+1 -1
View File
@@ -12,7 +12,7 @@ readme = "README.md"
license = "MIT"
license-files = ['LICENSE']
dependencies = [
"langgraph-checkpoint>=4.0.3,<5.0.0",
"langgraph-checkpoint>=2.1.2,<5.0.0",
"orjson>=3.11.5",
"psycopg>=3.2.0",
"psycopg-pool>=3.2.0",
+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 _messages_delta_reducer
from typing_extensions import TypedDict
class State(TypedDict):
messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
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"
+10 -129
View File
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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.
@@ -3,7 +3,7 @@ from __future__ import annotations
import copy
import logging
from collections.abc import AsyncIterator, Collection, Iterator, Mapping, Sequence
from typing import (
from typing import ( # noqa: UP035
Any,
Generic,
Literal,
@@ -18,9 +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,
@@ -31,8 +28,6 @@ from langgraph.checkpoint.serde.types import (
V = TypeVar("V", int, float, str)
PendingWrite = tuple[str, str, Any]
logger = logging.getLogger(__name__)
@@ -124,30 +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.
@@ -486,104 +457,6 @@ class BaseCheckpointSaver(Generic[V]):
"""
raise NotImplementedError
def _get_tuple_raw(self, config: RunnableConfig) -> CheckpointTuple | None:
"""Pure storage read used by `_get_channel_writes_history`.
Must return the same value as `get_tuple` but must NOT trigger channel
reconstruction; otherwise the channel-hydration path would re-enter
`_get_channel_writes_history`. Override only if `get_tuple` itself
performs channel hydration.
"""
return self.get_tuple(config)
async def _aget_tuple_raw(self, config: RunnableConfig) -> CheckpointTuple | None:
"""Async version of `_get_tuple_raw`. See docstring there."""
return await self.aget_tuple(config)
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.
"""
collected: list[PendingWrite] = [] # newest first; reversed at the end
target_tuple = self._get_tuple_raw(config)
cursor_config: RunnableConfig | None = (
target_tuple.parent_config if target_tuple else None
)
while cursor_config is not None:
tup = self._get_tuple_raw(cursor_config)
if tup is None:
break
# Collect this ancestor's writes FIRST — they encode the
# transition from this ancestor's state to its child's, so
# they must be included whether or not this ancestor is the
# seed terminator.
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)
# Seed terminator: any non-sentinel blob on an ancestor
# establishes the reconstruction base. 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)
cursor_config = tup.parent_config
collected.reverse()
return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
async def _aget_channel_writes_history(
self, config: RunnableConfig, channel: str
) -> _ChannelWritesHistory:
"""Async version of `_get_channel_writes_history`. See docstring there."""
collected: list[PendingWrite] = []
target_tuple = await self._aget_tuple_raw(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_raw(cursor_config)
if tup is None:
break
if tup.pending_writes:
for write in reversed(tup.pending_writes):
if write[1] != channel:
continue
collected.append(write)
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)
cursor_config = tup.parent_config
collected.reverse()
return _ChannelWritesHistory(seed=DELTA_SENTINEL, writes=collected)
def get_next_version(self, current: V | None, channel: None) -> V:
"""Generate the next version ID for a channel.
@@ -14,20 +14,16 @@ 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,
)
from langgraph.checkpoint.serde.types import _DeltaSnapshot
logger = logging.getLogger(__name__)
@@ -125,114 +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:
if isinstance(blob_value, _DeltaSnapshot):
# Step-based snapshot: the blob is state AT this
# ancestor, but the ancestor's pending_writes
# encode the NEXT step's transition and are NOT
# subsumed by the snapshot — collect them first.
step_writes = self.writes.get(
(thread_id, checkpoint_ns, cp_id), {}
)
for (_task_id, _idx), (
tid,
ch,
serialized,
_,
) in sorted(step_writes.items(), reverse=True):
if ch != channel:
continue
collected.append(
(tid, ch, self.serde.loads_typed(serialized))
)
collected.reverse()
return _ChannelWritesHistory(
seed=blob_value, writes=collected
)
# Pre-delta blob: state AT this ancestor already
# subsumes its pending_writes — skip 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.
@@ -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
@@ -33,40 +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,
_DeltaSentinel,
_DeltaSnapshot,
)
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.
@@ -77,10 +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__(
@@ -95,11 +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 = (
@@ -297,16 +269,10 @@ EXT_METHOD_SINGLE_ARG = 3
EXT_PYDANTIC_V1 = 4
EXT_PYDANTIC_V2 = 5
EXT_NUMPY_ARRAY = 6
EXT_DELTA_SNAPSHOT = 7
EXT_DELTA_SENTINEL = 8
def _msgpack_default(obj: Any) -> str | ormsgpack.Ext:
if isinstance(obj, _DeltaSnapshot):
return ormsgpack.Ext(EXT_DELTA_SNAPSHOT, _msgpack_enc(obj.value))
elif isinstance(obj, _DeltaSentinel):
return ormsgpack.Ext(EXT_DELTA_SENTINEL, b"")
elif hasattr(obj, "model_dump") and callable(obj.model_dump): # pydantic v2
if hasattr(obj, "model_dump") and callable(obj.model_dump): # pydantic v2
return ormsgpack.Ext(
EXT_PYDANTIC_V2,
_msgpack_enc(
@@ -561,13 +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,
@@ -585,9 +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,
@@ -620,15 +581,7 @@ def _create_msgpack_ext_hook(
return False
def ext_hook(code: int, data: bytes) -> Any:
if code == EXT_DELTA_SENTINEL:
return DELTA_SENTINEL
elif code == EXT_DELTA_SNAPSHOT:
return _DeltaSnapshot(
ormsgpack.unpackb(
data, ext_hook=ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
)
)
elif code == EXT_CONSTRUCTOR_SINGLE_ARG:
if code == EXT_CONSTRUCTOR_SINGLE_ARG:
try:
tup = ormsgpack.unpackb(
data, ext_hook=ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
@@ -1,7 +1,6 @@
from collections.abc import Sequence
from typing import (
Any,
NamedTuple,
Protocol,
TypeVar,
runtime_checkable,
@@ -15,39 +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()
class _DeltaSnapshot(NamedTuple):
"""Snapshot blob for a DeltaChannel with finite snapshot_frequency.
Stored in checkpoint_blobs via the `EXT_DELTA_SNAPSHOT` msgpack ext code.
The ancestor walk in `_get_channel_writes_history` terminates when it
encounters this type (any non-sentinel blob stops the walk).
`from_checkpoint` reconstructs the channel value directly from `.value`
without replaying writes — the snapshot IS the accumulated state.
"""
value: Any
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 = "4.1.0a2"
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 -28
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")]
)
@@ -997,15 +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)
assert type_tag == "msgpack"
assert blob # non-empty ext envelope
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
+10 -132
View File
@@ -7,9 +7,6 @@ resolution-markers = [
"python_full_version < '3.11'",
]
[options]
prerelease-mode = "allow"
[[package]]
name = "annotated-types"
version = "0.7.0"
@@ -270,7 +267,7 @@ wheels = [
[[package]]
name = "langchain-core"
version = "1.2.28"
version = "1.2.23"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "jsonpatch" },
@@ -282,14 +279,14 @@ dependencies = [
{ name = "typing-extensions" },
{ name = "uuid-utils" },
]
sdist = { url = "https://files.pythonhosted.org/packages/f8/a4/317a1a3ac1df33a64adb3670bf88bbe3b3d5baa274db6863a979db472897/langchain_core-1.2.28.tar.gz", hash = "sha256:271a3d8bd618f795fdeba112b0753980457fc90537c46a0c11998516a74dc2cb", size = 846119, upload-time = "2026-04-08T18:19:34.867Z" }
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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 = [
"langgraph==1.1.5"
"langgraph==1.1.2"
]
@@ -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"
]
@@ -5,9 +5,9 @@ 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"
"langgraph==1.1.2"
]
[tool.uv]
@@ -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",
]
+5 -50
View File
@@ -159,8 +159,8 @@ def generate_schema():
if k not in python_specific_props and k not in node_specific_props
]
# Create legacy Python schema with python_version and pip_config_file
legacy_python_schema = {
# Create Python schema with python_version and pip_config_file
python_schema = {
"type": "object",
"properties": {
# Include Python-specific properties
@@ -170,45 +170,10 @@ def generate_schema():
},
"required": ["dependencies", "graphs"],
}
legacy_python_schema["properties"]["pip_installer"] = {
"anyOf": [
{"type": "string", "enum": ["auto", "pip", "uv"]},
{"type": "null"},
]
}
uv_source_python_schema = {
"type": "object",
"properties": {
**{
k: config_schema["properties"][k].copy()
for k in python_specific_props + common_props
},
},
"required": ["graphs", "source"],
}
# source must be a UvSource object (not null)
uv_source_python_schema["properties"]["source"] = {"$ref": "#/$defs/UvSource"}
uv_source_python_schema["properties"]["pip_installer"] = {
"anyOf": [
{"type": "string", "enum": ["auto", "pip", "uv"]},
{"type": "null"},
]
}
# Add enum constraint for python_version
if "python_version" in legacy_python_schema["properties"]:
legacy_python_schema["properties"]["python_version"]["enum"] = [
"3.11",
"3.12",
"3.13",
]
if "python_version" in uv_source_python_schema["properties"]:
uv_source_python_schema["properties"]["python_version"]["enum"] = [
"3.11",
"3.12",
"3.13",
]
if "python_version" in python_schema["properties"]:
python_schema["properties"]["python_version"]["enum"] = ["3.11", "3.12", "3.13"]
# Create Node.js schema with node_version
node_schema = {
@@ -221,12 +186,6 @@ def generate_schema():
},
"required": ["node_version", "graphs"],
}
node_schema["properties"]["pip_installer"] = {
"anyOf": [
{"type": "string", "enum": ["auto", "pip", "uv"]},
{"type": "null"},
]
}
# Add enum constraint for node_version
if "node_version" in node_schema["properties"]:
@@ -243,11 +202,7 @@ def generate_schema():
]
# Replace the Config schema with a oneOf constraint
config_schema["oneOf"] = [
legacy_python_schema,
uv_source_python_schema,
node_schema,
]
config_schema["oneOf"] = [python_schema, node_schema]
# Remove the properties field as it's now defined in the oneOf subschemas
if "properties" in config_schema:
+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"
integrity sha512-RwFpb72c/BhQLEXIZ5K2e+AhgNVmIejGlTgiB9MzZ0e93GRvqZ7uSi0dvRF7/XIXDeNkra2fNHBxTyPDGySpjQ==
@@ -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"
integrity sha512-vFKC2IEtQnVhpT78h1Yp8wzwrf8CM+MzKMHGJZfBtzhZNycRFnXsHk6E5TxIkkMsgNS7mdX3AGB7x2QM2di4lA==
@@ -4395,6 +4411,11 @@ signal-exit@^4.0.1:
resolved "https://registry.yarnpkg.com/signal-exit/-/signal-exit-4.1.0.tgz#952188c1cbd546070e2dd20d0f41c0ae0530cb04"
integrity sha512-bzyZ1e88w9O1iNJbKnOlvYTrWPDl46O1bG0D3XInv+9tkPrxrN8jUUTiFlDkkmKWgn1M6CfIA13SuGqOa9Korw==
simple-wcswidth@^1.1.2:
version "1.1.2"
resolved "https://registry.yarnpkg.com/simple-wcswidth/-/simple-wcswidth-1.1.2.tgz#66722f37629d5203f9b47c5477b1225b85d6525b"
integrity sha512-j7piyCjAeTDSjzTSQ7DokZtMNwNlEAyxqSZeCS+CXH7fJ4jx3FuJ/mTW3mE+6JLs4VJBbcll0Kjn+KXI5t21Iw==
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"
integrity sha512-8XkAphELsDnEGrDxUOHB3RGvXz6TeuYSGEZBOjtTtPm2lwhGBjLgOzLHB63IUWfBpNucQjND6d3AOudO+H3RWQ==
+72 -7
View File
@@ -217,6 +217,11 @@
resolved "https://registry.yarnpkg.com/@types/json5/-/json5-0.0.29.tgz#ee28707ae94e11d2b827bcbe5270bcea7f3e71ee"
integrity sha512-dRLjCWHYg4oaA77cxO64oO+7JwCwnIzkZPdrrC71jQmQtlhM556pwKo5bUzqvZndkVbeFLIIi+9TC40JNF5hNQ==
"@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==
"@typescript-eslint/eslint-plugin@^8.58.0":
version "8.58.0"
resolved "https://registry.yarnpkg.com/@typescript-eslint/eslint-plugin/-/eslint-plugin-8.58.0.tgz#ad40e492f1931f46da1bd888e52b9e56df9063aa"
@@ -338,6 +343,13 @@ ajv@^6.14.0:
json-schema-traverse "^0.4.1"
uri-js "^4.2.2"
ansi-styles@^4.1.0:
version "4.3.0"
resolved "https://registry.yarnpkg.com/ansi-styles/-/ansi-styles-4.3.0.tgz#edd803628ae71c04c85ae7a0906edad34b648937"
integrity sha512-zbB9rCJAT1rbjiVDb2hqKFHNYLxgtk8NURxZ3IZwD3F6NtxbXZQCnnSi1Lkx+IDohdPlFp222wVALIheZJQSEg==
dependencies:
color-convert "^2.0.1"
ansi-styles@^5.0.0:
version "5.2.0"
resolved "https://registry.yarnpkg.com/ansi-styles/-/ansi-styles-5.2.0.tgz#07449690ad45777d1924ac2abb2fc8895dba836b"
@@ -496,11 +508,38 @@ camelcase@6:
resolved "https://registry.yarnpkg.com/camelcase/-/camelcase-6.3.0.tgz#5685b95eb209ac9c0c177467778c9c84df58ba9a"
integrity sha512-Gmy6FhYlCY7uOElZUSbxo2UCDH8owEk996gkbrpsgGtrJLM3J7jGxl9Ic7Qwwj4ivOE5AWZWRMecDdF7hqGjFA==
chalk@^4.1.2:
version "4.1.2"
resolved "https://registry.yarnpkg.com/chalk/-/chalk-4.1.2.tgz#aac4e2b7734a740867aeb16bf02aad556a1e7a01"
integrity sha512-oKnbhFyRIXpUuez8iBMmyEa4nbj4IOQyuhc/wy9kY7/WVPcwIO9VA668Pu8RkO7+0G76SLROeyw9CpQ061i4mA==
dependencies:
ansi-styles "^4.1.0"
supports-color "^7.1.0"
color-convert@^2.0.1:
version "2.0.1"
resolved "https://registry.yarnpkg.com/color-convert/-/color-convert-2.0.1.tgz#72d3a68d598c9bdb3af2ad1e84f21d896abd4de3"
integrity sha512-RRECPsj7iu/xb5oKYcsFHSppFNnsj/52OVTRKb4zP5onXwVF3zVmmToNcOfGC+CRDpfK/U584fMg38ZHCaElKQ==
dependencies:
color-name "~1.1.4"
color-name@~1.1.4:
version "1.1.4"
resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.4.tgz#c2a09a87acbde69543de6f63fa3995c826c536a2"
integrity sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==
concat-map@0.0.1:
version "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 "2.14.6"
resolved "https://registry.yarnpkg.com/console-table-printer/-/console-table-printer-2.14.6.tgz#edfe0bf311fa2701922ed509443145ab51e06436"
integrity sha512-MCBl5HNVaFuuHW6FGbL/4fB7N/ormCy+tQ+sxTrF6QtSbSNETvPuOVbkJBhzDgYhvjWGrTma4eYJa37ZuoQsPw==
dependencies:
simple-wcswidth "^1.0.1"
cross-spawn@^7.0.6:
version "7.0.6"
resolved "https://registry.yarnpkg.com/cross-spawn/-/cross-spawn-7.0.6.tgz#8a58fe78f00dcd70c370451759dfbfaf03e8ee9f"
@@ -1020,6 +1059,11 @@ has-bigints@^1.0.2:
resolved "https://registry.yarnpkg.com/has-bigints/-/has-bigints-1.1.0.tgz#28607e965ac967e03cd2a2c70a2636a1edad49fe"
integrity sha512-R3pbpkcIqv2Pm3dUwgjclDRVmWpTJW2DcMzcIhEXEx1oh/CEMObMm3KLmRJOdvhM7o4uQBnwr8pzRK2sJWIqfg==
has-flag@^4.0.0:
version "4.0.0"
resolved "https://registry.yarnpkg.com/has-flag/-/has-flag-4.0.0.tgz#944771fd9c81c81265c4d6941860da06bb59479b"
integrity sha512-EykJT/Q1KjTWctppgIAgfSO0tKVuZUjhgMr17kqTumMl6Afv3EISleU7qZUzoXDFTAHTDC4NOoG/ZxU3EvlMPQ==
has-property-descriptors@^1.0.0, has-property-descriptors@^1.0.2:
version "1.0.2"
resolved "https://registry.yarnpkg.com/has-property-descriptors/-/has-property-descriptors-1.0.2.tgz#963ed7d071dc7bf5f084c5bfbe0d1b6222586854"
@@ -1328,12 +1372,16 @@ 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"
levn@^0.4.1:
version "0.4.1"
@@ -1480,7 +1528,7 @@ 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"
integrity sha512-RwFpb72c/BhQLEXIZ5K2e+AhgNVmIejGlTgiB9MzZ0e93GRvqZ7uSi0dvRF7/XIXDeNkra2fNHBxTyPDGySpjQ==
@@ -1642,6 +1690,11 @@ semver@^6.3.1:
resolved "https://registry.yarnpkg.com/semver/-/semver-6.3.1.tgz#556d2ef8689146e46dcea4bfdd095f3434dffcb4"
integrity sha512-BR7VvDCVHO+q2xBEWskxS6DJE1qRnb7DxzUrogb71CWoSficBxYsiAGd+Kl0mmq/MprG9yArRkyrQxTO6XjMzA==
semver@^7.6.3:
version "7.7.2"
resolved "https://registry.yarnpkg.com/semver/-/semver-7.7.2.tgz#67d99fdcd35cec21e6f8b87a7fd515a33f982b58"
integrity sha512-RF0Fw+rO5AMf9MAyaRXI4AV0Ulj5lMHqVxxdSgiVbixSCXoEmmX/jk0CuJw4+3SqroYO9VoUh+HcuJivvtJemA==
semver@^7.7.3:
version "7.7.4"
resolved "https://registry.yarnpkg.com/semver/-/semver-7.7.4.tgz#28464e36060e991fa7a11d0279d2d3f3b57a7e8a"
@@ -1730,6 +1783,11 @@ side-channel@^1.1.0:
side-channel-map "^1.0.1"
side-channel-weakmap "^1.0.2"
simple-wcswidth@^1.0.1:
version "1.1.2"
resolved "https://registry.yarnpkg.com/simple-wcswidth/-/simple-wcswidth-1.1.2.tgz#66722f37629d5203f9b47c5477b1225b85d6525b"
integrity sha512-j7piyCjAeTDSjzTSQ7DokZtMNwNlEAyxqSZeCS+CXH7fJ4jx3FuJ/mTW3mE+6JLs4VJBbcll0Kjn+KXI5t21Iw==
stop-iteration-iterator@^1.1.0:
version "1.1.0"
resolved "https://registry.yarnpkg.com/stop-iteration-iterator/-/stop-iteration-iterator-1.1.0.tgz#f481ff70a548f6124d0312c3aa14cbfa7aa542ad"
@@ -1780,6 +1838,13 @@ strip-json-comments@^3.1.1:
resolved "https://registry.yarnpkg.com/strip-json-comments/-/strip-json-comments-3.1.1.tgz#31f1281b3832630434831c310c01cccda8cbe006"
integrity sha512-6fPc+R4ihwqP6N/aIv2f1gMH8lOVtWQHoqC4yK6oSDVVocumAsfCqjkXnqiYMhmMwS/mEHLp7Vehlt3ql6lEig==
supports-color@^7.1.0:
version "7.2.0"
resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-7.2.0.tgz#1b7dcdcb32b8138801b3e478ba6a51caa89648da"
integrity sha512-qpCAvRl9stuOHveKsn7HncJRvv501qIacKzQlO/+Lwxc9+0q2wLyv4Dfvt80/DPn2pqOBsJdDiogXGR9+OvwRw==
dependencies:
has-flag "^4.0.0"
supports-preserve-symlinks-flag@^1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/supports-preserve-symlinks-flag/-/supports-preserve-symlinks-flag-1.0.0.tgz#6eda4bd344a3c94aea376d4cc31bc77311039e09"
@@ -1901,7 +1966,7 @@ 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"
integrity sha512-8XkAphELsDnEGrDxUOHB3RGvXz6TeuYSGEZBOjtTtPm2lwhGBjLgOzLHB63IUWfBpNucQjND6d3AOudO+H3RWQ==
+1 -1
View File
@@ -1 +1 @@
__version__ = "0.4.23"
__version__ = "0.4.19"
+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,))
-42
View File
@@ -817,48 +817,6 @@ def dev(
)
# ---------------------------------------------------------------------------
# validate command
# ---------------------------------------------------------------------------
@OPT_CONFIG
@cli.command(help="✅ Validate the LangGraph configuration file.")
@log_command
def validate(config: pathlib.Path):
import json
try:
with open(config) as f:
raw_config = json.load(f)
except json.JSONDecodeError as e:
raise click.UsageError(f"Invalid JSON in {config}: {e.args[0]}") from None
# Check for unknown keys before validation so they show alongside any error.
unknown_warnings = langgraph_cli.config.get_unknown_keys(raw_config)
try:
config_json = langgraph_cli.config.validate_config_file(config)
except (click.UsageError, ValueError) as e:
click.secho(f"Error: {e}", fg="red", err=True)
if unknown_warnings:
click.echo(err=True)
for warning in unknown_warnings:
click.secho(f" warning: {warning}", fg="yellow", err=True)
raise SystemExit(1) from None
num_graphs = len(config_json.get("graphs", {}))
click.secho(
f"Configuration file {config} is valid. "
f"({num_graphs} graph{'s' if num_graphs != 1 else ''} found)",
fg="green",
)
if unknown_warnings:
click.echo()
for warning in unknown_warnings:
click.secho(f" warning: {warning}", fg="yellow")
# ---------------------------------------------------------------------------
# new command
# ---------------------------------------------------------------------------
+129 -271
View File
@@ -3,7 +3,6 @@ import json
import os
import pathlib
import re
import shlex
import textwrap
from collections import Counter
from typing import Literal, NamedTuple
@@ -11,7 +10,6 @@ from typing import Literal, NamedTuple
import click
from langgraph_cli.schemas import Config, Distros
from langgraph_cli.uv_lock import python_config_to_docker_uv_lock
MIN_NODE_VERSION = "20"
DEFAULT_NODE_VERSION = "20"
@@ -141,14 +139,6 @@ def _is_node_graph(spec: str | dict) -> bool:
]
def _get_source_kind(config: Config) -> str | None:
source = config.get("source")
if not isinstance(source, dict):
return None
kind = source.get("kind")
return kind if isinstance(kind, str) else None
def validate_config(config: Config) -> Config:
"""Validate a configuration dictionary."""
@@ -167,8 +157,6 @@ def validate_config(config: Config) -> Config:
image_distro = config.get("image_distro", DEFAULT_IMAGE_DISTRO)
internal_docker_tag = config.get("_INTERNAL_docker_tag")
api_version = config.get("api_version")
legacy_project_root = config.get("project_root")
legacy_package = config.get("package")
if internal_docker_tag:
if api_version:
raise click.UsageError(
@@ -182,18 +170,13 @@ def validate_config(config: Config) -> Config:
"Version must be major or major.minor or major.minor.patch."
)
except TypeError:
raise click.UsageError(
f"Invalid version format: {api_version}.\n\n"
"Pin to a minor version, e.g.:\n"
' "api_version": "0.8"'
) from None
raise click.UsageError(f"Invalid version format: {api_version}") from None
config = {
"node_version": node_version,
"python_version": python_version,
"pip_config_file": config.get("pip_config_file"),
"pip_installer": config.get("pip_installer", "auto"),
"source": config.get("source"),
"base_image": config.get("base_image"),
"image_distro": image_distro,
"dependencies": config.get("dependencies", []),
@@ -224,51 +207,25 @@ def validate_config(config: Config) -> Config:
if major < min_major:
raise click.UsageError(
f"Node.js version {node_version} is not supported. "
f"Minimum required version is {MIN_NODE_VERSION}.\n\n"
f"Set node_version to {MIN_NODE_VERSION} or higher:\n"
f' "node_version": "{MIN_NODE_VERSION}"'
f"Minimum required version is {MIN_NODE_VERSION}."
)
except ValueError as e:
raise click.UsageError(str(e)) from None
if pip_installer := config.get("pip_installer"):
if pip_installer not in ["auto", "pip", "uv"]:
raise click.UsageError(
f"Invalid pip_installer: '{pip_installer}'. "
"Consider using uv-based source management instead:\n\n"
' "source": {"kind": "uv", "root": ".."}'
)
source = config.get("source")
source_kind = _get_source_kind(config)
if source is not None and not isinstance(source, dict):
raise click.UsageError(
"`source` must be an object, e.g.:\n"
' "source": {"kind": "uv", "root": ".."}'
)
if source is not None and source_kind != "uv":
raise click.UsageError(
"Invalid source.kind. The only supported value is 'uv':\n"
' "source": {"kind": "uv", "root": ".."}'
)
if config.get("python_version"):
pyversion = config["python_version"]
if not pyversion.count(".") == 1 or not all(
part.isdigit() for part in pyversion.split("-")[0].split(".")
):
parts = pyversion.split("-")[0].split(".")
fix = f"{parts[0]}.{parts[1]}" if len(parts) >= 2 else MIN_PYTHON_VERSION
raise click.UsageError(
f"Invalid Python version format: {pyversion}. "
"Use 'major.minor' format — patch version cannot be specified.\n\n"
f' "python_version": "{fix}"'
"Use 'major.minor' format (e.g., '3.11'). "
"Patch version cannot be specified."
)
if _parse_version(pyversion) < _parse_version(MIN_PYTHON_VERSION):
raise click.UsageError(
f"Python version {pyversion} is not supported. "
f"Minimum required version is {MIN_PYTHON_VERSION}.\n\n"
f' "python_version": "{MIN_PYTHON_VERSION}"'
f"Minimum required version is {MIN_PYTHON_VERSION}."
)
if "bullseye" in pyversion:
raise click.UsageError(
@@ -276,19 +233,15 @@ def validate_config(config: Config) -> Config:
"Please use 'bookworm' or 'debian' instead."
)
if source_kind != "uv" and not config["dependencies"]:
if not config["dependencies"]:
raise click.UsageError(
"No dependencies found in config. "
"Consider using uv-based source management:\n\n"
' "source": {"kind": "uv", "root": ".."}'
"Add at least one dependency to 'dependencies' list."
)
if not config.get("graphs"):
raise click.UsageError(
"No graphs found in config. Add at least one graph, e.g.:\n"
' "graphs": {\n'
' "agent": "./my_agent/graph.py:graph"\n'
" }"
"No graphs found in config. Add at least one graph to 'graphs' dictionary."
)
# Validate image_distro config
@@ -301,47 +254,16 @@ def validate_config(config: Config) -> Config:
if image_distro not in Distros.__args__:
raise click.UsageError(
f"Invalid image_distro: '{image_distro}'. "
f"Must be one of: {', '.join(repr(d) for d in Distros.__args__)}.\n\n"
' "image_distro": "wolfi" (recommended)'
"Must be one of 'debian', 'wolfi', or 'bookworm'."
)
if source_kind == "uv":
errors: list[str] = []
if not config.get("python_version"):
errors.append(
"source.kind 'uv' requires `python_version` — "
"it is a Python-only deployment mode. "
"Node.js-only graphs are not supported."
)
if config["dependencies"]:
errors.append(
"Remove `dependencies` from your config. With "
'`source.kind = "uv"`, all dependencies '
"are read from your pyproject.toml and uv.lock instead."
)
root = source.get("root", ".") if source else "."
if not isinstance(root, str):
errors.append(f"`source.root` must be a string, got {type(root).__name__}.")
elif not root.strip():
errors.append('`source.root` must be a non-empty string. Use `"."`.')
package_name = source.get("package") if source else None
if package_name is not None and (
not isinstance(package_name, str) or not package_name.strip()
):
errors.append("`source.package` must be a non-empty string.")
if errors:
detail = "\n".join(f" - {e}" for e in errors)
if pip_installer := config.get("pip_installer"):
if pip_installer not in ["auto", "pip", "uv"]:
raise click.UsageError(
"source.kind 'uv' requires a different "
f"config shape than dependency-based installs:\n{detail}"
f"Invalid pip_installer: '{pip_installer}'. "
"Must be 'auto', 'pip', or 'uv'."
)
if legacy_project_root or legacy_package:
raise click.UsageError(
"Top-level `project_root` and `package` are no longer supported. "
"Use `source.root` and `source.package` instead."
)
# Validate auth config
if auth_conf := config.get("auth"):
if "path" in auth_conf:
@@ -384,51 +306,6 @@ def validate_config(config: Config) -> Config:
return config
# Keys recognized by validate_config (used to detect unknown fields).
_KNOWN_CONFIG_KEYS = {
"python_version",
"node_version",
"api_version",
"base_image",
"image_distro",
"pip_config_file",
"pip_installer",
"source",
"dependencies",
"dockerfile_lines",
"graphs",
"env",
"store",
"auth",
"encryption",
"http",
"webhooks",
"checkpointer",
"ui",
"ui_config",
"keep_pkg_tools",
# Internal / legacy (still recognized, may error separately)
"_INTERNAL_docker_tag",
"project_root",
"package",
}
def get_unknown_keys(raw_config: dict) -> list[str]:
"""Return warnings for unrecognized top-level keys (typos, etc.)."""
import difflib
unknown = set(raw_config) - _KNOWN_CONFIG_KEYS
warnings: list[str] = []
for key in sorted(unknown):
close = difflib.get_close_matches(key, _KNOWN_CONFIG_KEYS, n=1)
if close:
warnings.append(f"Unknown key '{key}' — did you mean '{close[0]}'?")
else:
warnings.append(f"Unknown key '{key}' is not a recognized config field.")
return warnings
def validate_config_file(config_path: pathlib.Path) -> Config:
"""Load and validate a configuration file."""
with open(config_path) as f:
@@ -930,65 +807,9 @@ def _update_http_app_path(
http_config["app"] = f"{module_str}:{attr_str}"
def _build_python_install_commands(
config: Config, install_cmd: str
) -> tuple[str, str, str]:
base_install = f"PYTHONDONTWRITEBYTECODE=1 {install_cmd} --no-cache-dir -c /api/constraints.txt"
local_reqs_pip_install = base_install
global_reqs_pip_install = base_install
if config.get("pip_config_file"):
local_reqs_pip_install = (
f"PIP_CONFIG_FILE=/pipconfig.txt {local_reqs_pip_install}"
)
global_reqs_pip_install = (
f"PIP_CONFIG_FILE=/pipconfig.txt {global_reqs_pip_install}"
)
pip_config_file_str = (
f"ADD {config['pip_config_file']} /pipconfig.txt"
if config.get("pip_config_file")
else ""
)
return local_reqs_pip_install, global_reqs_pip_install, pip_config_file_str
def _build_runtime_env_vars(config: Config) -> list[str]:
env_vars = []
if (store_config := config.get("store")) is not None:
env_vars.append(f"ENV LANGGRAPH_STORE='{json.dumps(store_config)}'")
if (auth_config := config.get("auth")) is not None:
env_vars.append(f"ENV LANGGRAPH_AUTH='{json.dumps(auth_config)}'")
if (encryption_config := config.get("encryption")) is not None:
env_vars.append(f"ENV LANGGRAPH_ENCRYPTION='{json.dumps(encryption_config)}'")
if (http_config := config.get("http")) is not None:
env_vars.append(f"ENV LANGGRAPH_HTTP='{json.dumps(http_config)}'")
if (webhooks_config := config.get("webhooks")) is not None:
env_vars.append(f"ENV LANGGRAPH_WEBHOOKS='{json.dumps(webhooks_config)}'")
if (checkpointer_config := config.get("checkpointer")) is not None:
env_vars.append(
f"ENV LANGGRAPH_CHECKPOINTER='{json.dumps(checkpointer_config)}'"
)
if (ui := config.get("ui")) is not None:
env_vars.append(f"ENV LANGGRAPH_UI='{json.dumps(ui)}'")
if (ui_config := config.get("ui_config")) is not None:
env_vars.append(f"ENV LANGGRAPH_UI_CONFIG='{json.dumps(ui_config)}'")
env_vars.append(f"ENV LANGSERVE_GRAPHS='{json.dumps(config['graphs'])}'")
return env_vars
def _get_node_pm_install_cmd(project_dir: pathlib.Path) -> str:
def _get_node_pm_install_cmd(config_path: pathlib.Path, config: Config) -> str:
def test_file(file_name):
full_path = project_dir / file_name
full_path = config_path.parent / file_name
try:
return full_path.is_file()
except OSError:
@@ -997,7 +818,7 @@ def _get_node_pm_install_cmd(project_dir: pathlib.Path) -> str:
# inspired by `package-manager-detector`
def get_pkg_manager_name():
try:
with open(project_dir / "package.json") as f:
with open(config_path.parent / "package.json") as f:
pkg = json.load(f)
if (pkg_manager_name := pkg.get("packageManager")) and isinstance(
@@ -1093,17 +914,8 @@ def python_config_to_docker(
escape_variables: bool = False,
) -> tuple[str, dict[str, str]]:
"""Generate a Dockerfile from the configuration."""
source_kind = _get_source_kind(config)
pip_installer = config.get("pip_installer", "auto")
build_tools_to_uninstall = get_build_tools_to_uninstall(config)
if source_kind == "uv":
return python_config_to_docker_uv_lock(
config_path,
config,
base_image,
api_version=api_version,
build_tools_to_uninstall=build_tools_to_uninstall,
)
if pip_installer == "auto":
if _image_supports_uv(base_image):
pip_installer = "uv"
@@ -1116,11 +928,21 @@ def python_config_to_docker(
else:
raise ValueError(f"Invalid pip_installer: {pip_installer}")
(
local_reqs_pip_install,
global_reqs_pip_install,
pip_config_file_str,
) = _build_python_install_commands(config, install_cmd)
# configure pip
local_reqs_pip_install = f"PYTHONDONTWRITEBYTECODE=1 {install_cmd} --no-cache-dir -c /api/constraints.txt"
global_reqs_pip_install = f"PYTHONDONTWRITEBYTECODE=1 {install_cmd} --no-cache-dir -c /api/constraints.txt"
if config.get("pip_config_file"):
local_reqs_pip_install = (
f"PIP_CONFIG_FILE=/pipconfig.txt {local_reqs_pip_install}"
)
global_reqs_pip_install = (
f"PIP_CONFIG_FILE=/pipconfig.txt {global_reqs_pip_install}"
)
pip_config_file_str = (
f"ADD {config['pip_config_file']} /pipconfig.txt"
if config.get("pip_config_file")
else ""
)
# collect dependencies
pypi_deps = [dep for dep in config["dependencies"] if not dep.startswith(".")]
@@ -1176,7 +998,7 @@ RUN set -ex && \\
'[build-system]' \\
'requires = ["setuptools>=61"]' \\
'build-backend = "setuptools.build_meta"'; do \\
echo "$line" >> {shlex.quote(f"/deps/outer-{fullpath.name}/pyproject.toml")}; \\
echo "$line" >> /deps/outer-{fullpath.name}/pyproject.toml; \\
done
# -- End of non-package dependency {fullpath.name} --"""
for fullpath, (relpath, destpath) in local_deps.faux_pkgs.items()
@@ -1195,50 +1017,56 @@ ADD {relpath} /deps/{name}
for fullpath, (relpath, name) in local_deps.real_pkgs.items()
)
additional_contexts: dict[str, str] = {}
additional_context_names: dict[pathlib.Path, str] = {}
used_context_names: set[str] = set()
def register_additional_context(path: pathlib.Path, preferred_name: str) -> str:
if path in additional_context_names:
return additional_context_names[path]
name = preferred_name
suffix = 1
while name in used_context_names:
name = f"{preferred_name}_{suffix}"
suffix += 1
used_context_names.add(name)
additional_context_names[path] = name
additional_contexts[name] = str(path)
return name
for p in local_deps.additional_contexts:
if p in local_deps.real_pkgs:
preferred_name = local_deps.real_pkgs[p][1]
elif p in local_deps.faux_pkgs:
preferred_name = f"outer-{p.name}"
else:
raise RuntimeError(f"Unknown additional context: {p}")
register_additional_context(p, preferred_name)
install_node_str: str = (
"RUN /storage/install-node.sh"
if (config.get("ui") or config.get("node_version")) and local_deps.working_dir
else ""
)
install_steps = [install_node_str, pip_config_file_str, pip_pkgs_str, pip_reqs_str]
install_steps.extend([local_pkgs_str, faux_pkgs_str])
installs = f"{os.linesep}{os.linesep}".join(
filter(
None,
install_steps,
[
install_node_str,
pip_config_file_str,
pip_pkgs_str,
pip_reqs_str,
local_pkgs_str,
faux_pkgs_str,
],
)
)
env_vars = _build_runtime_env_vars(config)
env_vars = []
if (store_config := config.get("store")) is not None:
env_vars.append(f"ENV LANGGRAPH_STORE='{json.dumps(store_config)}'")
if (auth_config := config.get("auth")) is not None:
env_vars.append(f"ENV LANGGRAPH_AUTH='{json.dumps(auth_config)}'")
if (encryption_config := config.get("encryption")) is not None:
env_vars.append(f"ENV LANGGRAPH_ENCRYPTION='{json.dumps(encryption_config)}'")
if (http_config := config.get("http")) is not None:
env_vars.append(f"ENV LANGGRAPH_HTTP='{json.dumps(http_config)}'")
# Inject webhooks configuration if provided
if (webhooks_config := config.get("webhooks")) is not None:
env_vars.append(f"ENV LANGGRAPH_WEBHOOKS='{json.dumps(webhooks_config)}'")
if (checkpointer_config := config.get("checkpointer")) is not None:
env_vars.append(
f"ENV LANGGRAPH_CHECKPOINTER='{json.dumps(checkpointer_config)}'"
)
if (ui := config.get("ui")) is not None:
env_vars.append(f"ENV LANGGRAPH_UI='{json.dumps(ui)}'")
if (ui_config := config.get("ui_config")) is not None:
env_vars.append(f"ENV LANGGRAPH_UI_CONFIG='{json.dumps(ui_config)}'")
env_vars.append(f"ENV LANGSERVE_GRAPHS='{json.dumps(config['graphs'])}'")
js_inst_str: str = ""
if (config.get("ui") or config.get("node_version")) and local_deps.working_dir:
@@ -1246,8 +1074,7 @@ ADD {relpath} /deps/{name}
[
"# -- Installing JS dependencies --",
f"ENV NODE_VERSION={config.get('node_version') or DEFAULT_NODE_VERSION}",
f"WORKDIR {local_deps.working_dir}",
f"RUN {_get_node_pm_install_cmd(config_path.parent)} && tsx /api/langgraph_api/js/build.mts",
f"RUN cd {local_deps.working_dir} && {_get_node_pm_install_cmd(config_path, config)} && tsx /api/langgraph_api/js/build.mts",
"# -- End of JS dependencies install --",
]
)
@@ -1257,7 +1084,7 @@ ADD {relpath} /deps/{name}
docker_file_contents = []
# Add syntax directive if we have additional contexts (requires BuildKit frontend.contexts capability)
if additional_contexts:
if local_deps.additional_contexts:
docker_file_contents.extend(
[
"# syntax=docker/dockerfile:1.4",
@@ -1267,13 +1094,6 @@ ADD {relpath} /deps/{name}
# Add main dockerfile content
dep_vname = "$$dep" if escape_variables else "$dep"
local_deps_install_str = f"""RUN for dep in /deps/*; do \
echo "Installing {dep_vname}"; \
if [ -d "{dep_vname}" ]; then \
echo "Installing {dep_vname}"; \
(cd "{dep_vname}" && {global_reqs_pip_install} -e .); \
fi; \
done"""
docker_file_contents.extend(
[
f"FROM {image_str}",
@@ -1283,7 +1103,13 @@ ADD {relpath} /deps/{name}
installs,
"",
"# -- Installing all local dependencies --",
local_deps_install_str,
f"""RUN for dep in /deps/*; do \
echo "Installing {dep_vname}"; \
if [ -d "{dep_vname}" ]; then \
echo "Installing {dep_vname}"; \
(cd "{dep_vname}" && {global_reqs_pip_install} -e .); \
fi; \
done""",
"# -- End of local dependencies install --",
os.linesep.join(env_vars),
"",
@@ -1300,6 +1126,16 @@ ADD {relpath} /deps/{name}
]
)
additional_contexts: dict[str, str] = {}
for p in local_deps.additional_contexts:
if p in local_deps.real_pkgs:
name = local_deps.real_pkgs[p][1]
elif p in local_deps.faux_pkgs:
name = f"outer-{p.name}"
else:
raise RuntimeError(f"Unknown additional context: {p}")
additional_contexts[name] = str(p)
return os.linesep.join(docker_file_contents), additional_contexts
@@ -1313,10 +1149,6 @@ def node_config_to_docker(
build_context: str | None = None,
) -> tuple[str, dict[str, str]]:
# Calculate paths for monorepo support
install_root = (
pathlib.Path(build_context).resolve() if build_context else config_path.parent
)
install_cmd = install_command or _get_node_pm_install_cmd(install_root)
if build_context:
relative_workdir = _calculate_relative_workdir(config_path, build_context)
container_name = pathlib.Path(build_context).name
@@ -1328,32 +1160,60 @@ def node_config_to_docker(
# Backward compatibility: use the original behavior
faux_path = f"/deps/{config_path.parent.name}"
# Use custom install command or auto-detect
if install_command:
install_cmd = install_command
else:
install_cmd = _get_node_pm_install_cmd(config_path, config)
image_str = docker_tag(config, base_image, api_version)
env_vars = _build_runtime_env_vars(config)
env_vars: list[str] = []
if (store_config := config.get("store")) is not None:
env_vars.append(f"ENV LANGGRAPH_STORE='{json.dumps(store_config)}'")
if (auth_config := config.get("auth")) is not None:
env_vars.append(f"ENV LANGGRAPH_AUTH='{json.dumps(auth_config)}'")
if (encryption_config := config.get("encryption")) is not None:
env_vars.append(f"ENV LANGGRAPH_ENCRYPTION='{json.dumps(encryption_config)}'")
if (http_config := config.get("http")) is not None:
env_vars.append(f"ENV LANGGRAPH_HTTP='{json.dumps(http_config)}'")
# Inject webhooks configuration if provided
if (webhooks_config := config.get("webhooks")) is not None:
env_vars.append(f"ENV LANGGRAPH_WEBHOOKS='{json.dumps(webhooks_config)}'")
if (checkpointer_config := config.get("checkpointer")) is not None:
env_vars.append(
f"ENV LANGGRAPH_CHECKPOINTER='{json.dumps(checkpointer_config)}'"
)
if ui := config.get("ui"):
env_vars.append(f"ENV LANGGRAPH_UI='{json.dumps(ui)}'")
if ui_config := config.get("ui_config"):
env_vars.append(f"ENV LANGGRAPH_UI_CONFIG='{json.dumps(ui_config)}'")
env_vars.append(f"ENV LANGSERVE_GRAPHS='{json.dumps(config['graphs'])}'")
# For monorepo support, we need to handle install and build commands differently
if build_context:
# Monorepo case: install from root, build from config directory
container_root = f"/deps/{pathlib.Path(build_context).name}"
install_workdir = container_root
install_step = f"RUN {install_cmd}"
install_step = f"RUN cd {container_root} && {install_cmd}"
if build_command:
build_step = f"RUN {build_command}"
build_step = f"RUN cd {faux_path} && {build_command}"
else:
build_step = 'RUN (test ! -f /api/langgraph_api/js/build.mts && echo "Prebuild script not found, skipping") || tsx /api/langgraph_api/js/build.mts'
else:
# Original behavior: everything happens in the same directory
install_workdir = faux_path
install_step = f"RUN {install_cmd}"
install_step = f"RUN cd {faux_path} && {install_cmd}"
build_step = 'RUN (test ! -f /api/langgraph_api/js/build.mts && echo "Prebuild script not found, skipping") || tsx /api/langgraph_api/js/build.mts'
if build_context:
build_workdir = faux_path
else:
build_workdir = faux_path
docker_file_contents = [
f"FROM {image_str}",
"",
@@ -1361,13 +1221,11 @@ def node_config_to_docker(
"",
f"ADD . {faux_path if not build_context else container_root}",
"",
f"WORKDIR {install_workdir}",
"",
install_step,
"",
os.linesep.join(env_vars),
"",
f"WORKDIR {build_workdir}",
f"WORKDIR {faux_path}",
"",
build_step,
]
+63 -97
View File
@@ -1,7 +1,5 @@
from typing import Any, Literal, TypedDict
from typing_extensions import Required
Distros = Literal["debian", "wolfi", "bookworm"]
MiddlewareOrders = Literal["auth_first", "middleware_first"]
@@ -11,20 +9,20 @@ class TTLConfig(TypedDict, total=False):
refresh_on_read: bool
"""Default behavior for refreshing TTLs on read operations (`GET` and `SEARCH`).
If `True`, TTLs will be refreshed on read operations (get/search) by default.
This can be overridden per-operation by explicitly setting `refresh_ttl`.
Defaults to `True` if not configured.
"""
default_ttl: float | None
"""Optional. Default TTL (time-to-live) in minutes for new items.
If provided, all new items will have this TTL unless explicitly overridden.
If omitted, items will have no TTL by default.
"""
sweep_interval_minutes: int | None
"""Optional. Interval in minutes between TTL sweep iterations.
If provided, the store will periodically delete expired items based on the TTL.
If omitted, no automatic sweeping will occur.
"""
@@ -38,10 +36,10 @@ class IndexConfig(TypedDict, total=False):
dims: int
"""Required. Dimensionality of the embedding vectors you will store.
Must match the output dimension of your selected embedding model or custom embed function.
If mismatched, you will likely encounter shape/size errors when inserting or querying vectors.
Common embedding model output dimensions:
- openai:text-embedding-3-large: 3072
- openai:text-embedding-3-small: 1536
@@ -54,7 +52,7 @@ class IndexConfig(TypedDict, total=False):
embed: str
"""Required. Identifier or reference to the embedding model or a custom embedding function.
The format can vary:
- "<provider>:<model_name>" for recognized providers (e.g., "openai:text-embedding-3-large")
- "path/to/module.py:function_name" for your own local embedding function
@@ -64,17 +62,17 @@ class IndexConfig(TypedDict, total=False):
- "openai:text-embedding-3-large"
- "cohere:embed-multilingual-v3.0"
- "src/app.py:embeddings"
Note: Must return embeddings of dimension `dims`.
"""
fields: list[str] | None
"""Optional. List of JSON fields to extract before generating embeddings.
Defaults to ["$"], which means the entire JSON object is embedded as one piece of text.
If you provide multiple fields (e.g. ["title", "content"]), each is extracted and embedded separately,
often saving token usage if you only care about certain parts of the data.
Example:
fields=["title", "abstract", "author.biography"]
"""
@@ -89,18 +87,18 @@ class StoreConfig(TypedDict, total=False):
index: IndexConfig | None
"""Optional. Defines the vector-based semantic search configuration.
If provided, the store will:
- Generate embeddings according to `index.embed`
- Enforce the embedding dimension given by `index.dims`
- Embed only specified JSON fields (if any) from `index.fields`
If omitted, no vector index is initialized.
"""
ttl: TTLConfig | None
"""Optional. Defines the TTL (time-to-live) behavior configuration.
If provided, the store will apply TTL settings according to the configuration.
If omitted, no TTL behavior is configured.
"""
@@ -131,11 +129,11 @@ class SerdeConfig(TypedDict, total=False):
allowed_json_modules: list[list[str]] | bool | None
"""Optional. List of allowed python modules to de-serialize custom objects from JSON.
If provided, only the specified modules will be allowed to be deserialized.
If omitted, no modules are allowed, and the object returned will simply be a json object OR
a deserialized langchain object.
Example:
{...
"serde": {
@@ -180,11 +178,11 @@ class SerdeConfig(TypedDict, total=False):
"allowed_msgpack_modules": null
}
}
"""
pickle_fallback: bool
"""Optional. Whether to allow pickling as a fallback for deserialization.
If True, pickling will be allowed as a fallback for deserialization.
If False, pickling will not be allowed as a fallback for deserialization.
Defaults to True if not configured."""
@@ -218,7 +216,7 @@ class CheckpointerConfig(TypedDict, total=False):
ttl: ThreadTTLConfig | None
"""Optional. Defines the TTL (time-to-live) behavior configuration.
If provided, the checkpointer will apply TTL settings according to the configuration.
If omitted, no TTL behavior is configured.
"""
@@ -241,7 +239,7 @@ class SecurityConfig(TypedDict, total=False):
securitySchemes: dict[str, dict[str, Any]]
"""Describe each security scheme recognized by your OpenAPI spec.
Keys are scheme names (e.g. "OAuth2", "ApiKeyAuth") and values are their definitions.
Example:
{
@@ -258,7 +256,7 @@ class SecurityConfig(TypedDict, total=False):
"""
security: list[dict[str, list[str]]]
"""Global security requirements across all endpoints.
Each element in the list maps a security scheme (e.g. "OAuth2") to a list of scopes (e.g. ["read", "write"]).
Example:
[
@@ -269,11 +267,11 @@ class SecurityConfig(TypedDict, total=False):
# path => {method => security}
paths: dict[str, dict[str, list[dict[str, list[str]]]]]
"""Path-specific security overrides.
Keys are path templates (e.g., "/items/{item_id}"), mapping to:
- Keys that are HTTP methods (e.g., "GET", "POST"),
- Values are lists of security definitions (just like `security`) for that method.
Example:
{
"/private_data": {
@@ -287,19 +285,19 @@ class SecurityConfig(TypedDict, total=False):
class CacheConfig(TypedDict, total=False):
cache_keys: list[str]
"""Optional. List of header keys to use for caching.
Example:
["user_id", "workspace_id"]
"""
ttl_seconds: int
"""Optional. Time-to-live in seconds for cached items.
Example:
3600
"""
max_size: int
"""Optional. Maximum size of the cache.
Example:
100
"""
@@ -310,19 +308,19 @@ class AuthConfig(TypedDict, total=False):
path: str
"""Required. Path to an instance of the Auth() class that implements custom authentication.
Format: "path/to/file.py:my_auth"
"""
disable_studio_auth: bool
"""Optional. Whether to disable LangSmith API-key authentication for requests originating the Studio.
"""Optional. Whether to disable LangSmith API-key authentication for requests originating the Studio.
Defaults to False, meaning that if a particular header is set, the server will verify the `x-api-key` header
value is a valid API key for the deployment's workspace. If `True`, all requests will go through your custom
authentication logic, regardless of origin of the request.
"""
openapi: SecurityConfig
"""The security configuration to include in your server's OpenAPI spec.
Example (OAuth2):
{
"securitySchemes": {
@@ -343,7 +341,7 @@ class AuthConfig(TypedDict, total=False):
"""
cache: CacheConfig
"""Optional. Cache configuration for the server.
Example:
{
"cache_keys": ["user_id", "workspace_id"],
@@ -382,32 +380,32 @@ class CorsConfig(TypedDict, total=False):
allow_origins: list[str]
"""Optional. List of allowed origins (e.g., "https://example.com").
Default is often an empty list (no external origins).
Default is often an empty list (no external origins).
Use "*" only if you trust all origins, as that bypasses most restrictions.
"""
allow_methods: list[str]
"""Optional. HTTP methods permitted for cross-origin requests (e.g. ["GET", "POST"]).
Default might be ["GET", "POST", "OPTIONS"] depending on your server framework.
"""
allow_headers: list[str]
"""Optional. HTTP headers that can be used in cross-origin requests (e.g. ["Content-Type", "Authorization"])."""
allow_credentials: bool
"""Optional. If `True`, cross-origin requests can include credentials (cookies, auth headers).
Default False to avoid accidentally exposing secured endpoints to untrusted sites.
"""
allow_origin_regex: str
"""Optional. A regex pattern for matching allowed origins, used if you have dynamic subdomains.
Example: "^https://.*\\.mycompany\\.com$"
"""
expose_headers: list[str]
"""Optional. List of headers that browsers are allowed to read from the response in cross-origin contexts."""
max_age: int
"""Optional. How many seconds the browser may cache preflight responses.
Default might be 600 (10 minutes). Larger values reduce preflight requests but can cause stale configurations.
"""
@@ -444,61 +442,61 @@ class HttpConfig(TypedDict, total=False):
app: str
"""Optional. Import path to a custom Starlette/FastAPI application to mount.
Format: "path/to/module.py:app_var"
If provided, it can override or extend the default routes.
"""
disable_assistants: bool
"""Optional. If `True`, /assistants routes are removed from the server.
Default is False (meaning /assistants is enabled).
"""
disable_threads: bool
"""Optional. If `True`, /threads routes are removed.
Default is False.
"""
disable_runs: bool
"""Optional. If `True`, /runs routes are removed.
Default is False.
"""
disable_store: bool
"""Optional. If `True`, /store routes are removed, disabling direct store interactions via HTTP.
Default is False.
"""
disable_mcp: bool
"""Optional. If `True`, /mcp routes are removed, disabling default support to expose the deployment as an MCP server.
Default is False.
"""
disable_a2a: bool
"""Optional. If `True`, /a2a routes are removed, disabling default support to expose the deployment as an agent-to-agent (A2A) server.
Default is False.
"""
disable_meta: bool
"""Optional. Remove meta endpoints.
Set to True to disable the following endpoints: /openapi.json, /info, /metrics, /docs.
This will also make the /ok endpoint skip any DB or other checks, always returning {"ok": True}.
Default is False.
"""
disable_ui: bool
"""Optional. If `True`, /ui routes are removed, disabling the UI server.
Default is False.
"""
disable_webhooks: bool
"""Optional. If `True`, webhooks are disabled. Runs created with an associated webhook will
still be executed, but the webhook event will not be sent.
Default is False.
"""
cors: CorsConfig | None
"""Optional. Defines CORS restrictions. If omitted, no special rules are set and
"""Optional. Defines CORS restrictions. If omitted, no special rules are set and
cross-origin behavior depends on default server settings.
"""
configurable_headers: ConfigurableHeaderConfig | None
@@ -529,7 +527,7 @@ class HttpConfig(TypedDict, total=False):
"""
mount_prefix: str
"""Optional. URL prefix to prepend to all the routes.
Example:
"/api"
"""
@@ -590,33 +588,11 @@ class WebhooksConfig(TypedDict, total=False):
"""
class UvSource(TypedDict, total=False):
"""Deployment source rooted at a uv project or workspace."""
kind: Required[Literal["uv"]]
"""Discriminator for uv-backed deployment mode."""
root: str
"""Relative path from langgraph.json to the authoritative uv project root.
The resolved directory must contain `pyproject.toml` and `uv.lock`. If the
root is a workspace, package discovery happens within this root.
"""
package: str
"""Optional. Workspace package name to deploy when the target is ambiguous.
If omitted, the CLI tries to infer the target package from the location of
`langgraph.json`, or falls back to the only package if the root contains
exactly one candidate.
"""
class Config(TypedDict, total=False):
"""Top-level config for langgraph-cli or similar deployment tooling."""
python_version: str
"""Optional. Python version in 'major.minor' format (e.g. '3.11').
"""Optional. Python version in 'major.minor' format (e.g. '3.11').
Must be at least 3.11 or greater for this deployment to function properly.
"""
@@ -627,7 +603,7 @@ class Config(TypedDict, total=False):
api_version: str | None
"""Optional. Which semantic version of the LangGraph API server to use.
Defaults to latest. Check the
[changelog](https://docs.langchain.com/langgraph-platform/langgraph-server-changelog)
for more information."""
@@ -638,12 +614,12 @@ class Config(TypedDict, total=False):
base_image: str | None
"""Optional. Base image to use for the LangGraph API server.
Defaults to langchain/langgraph-api or langchain/langgraphjs-api."""
image_distro: Distros | None
"""Optional. Linux distribution for the base image.
Must be one of 'wolfi', 'debian', or 'bookworm'.
If omitted, defaults to 'debian' ('latest').
"""
@@ -651,30 +627,22 @@ class Config(TypedDict, total=False):
pip_config_file: str | None
"""Optional. Path to a pip config file (e.g., "/etc/pip.conf" or "pip.ini") for controlling
package installation (custom indices, credentials, etc.).
Only relevant if Python dependencies are installed via pip. If omitted, default pip settings are used.
"""
pip_installer: str | None
"""Optional. Python package installer to use ('auto', 'pip', or 'uv').
"""Optional. Python package installer to use ('auto', 'pip', 'uv').
- 'auto' (default): Use uv for supported base images, otherwise pip
- 'pip': Force use of pip regardless of base image support
- 'uv': Force use of uv (will fail if base image doesn't support it)
"""
source: UvSource | None
"""Optional. Explicit deployment source configuration.
Use `{ "kind": "uv", "root": "." }` to deploy from a uv project rooted at
`root/pyproject.toml` and `root/uv.lock`. If `root` is a workspace and the
target is ambiguous, set `package` to the desired workspace member.
"""
dockerfile_lines: list[str]
"""Optional. Additional Docker instructions that will be appended to your base Dockerfile.
Useful for installing OS packages, setting environment variables, etc.
Useful for installing OS packages, setting environment variables, etc.
Example:
dockerfile_lines=[
"RUN apt-get update && apt-get install -y libmagic-dev",
@@ -684,14 +652,12 @@ class Config(TypedDict, total=False):
dependencies: list[str]
"""List of Python dependencies to install, either from PyPI or local paths.
Examples:
- "." or "./src" if you have a local Python package
- str (aka "anthropic") for a PyPI package
- "git+https://github.com/org/repo.git@main" for a Git-based package
Defaults to an empty list, meaning no additional packages installed beyond your base environment.
This field is not supported when `source.kind` is `uv`.
"""
graphs: dict[str, str | GraphDef]
@@ -716,10 +682,10 @@ class Config(TypedDict, total=False):
env: dict[str, str] | str
"""Optional. Environment variables to set for your deployment.
- If given as a dict, keys are variable names and values are their values.
- If given as a string, it must be a path to a file containing lines in KEY=VALUE format.
Example as a dict:
env={"API_TOKEN": "abc123", "DEBUG": "true"}
Example as a file path:
@@ -728,13 +694,13 @@ class Config(TypedDict, total=False):
store: StoreConfig | None
"""Optional. Configuration for the built-in long-term memory store, including semantic search indexing.
If omitted, no vector index is set up (the object store will still be present, however).
"""
checkpointer: CheckpointerConfig | None
"""Optional. Configuration for the built-in checkpointer, which handles checkpointing of state.
If omitted, no checkpointer is set up (the object store will still be present, however).
"""
@@ -767,7 +733,7 @@ class Config(TypedDict, total=False):
keep_pkg_tools: bool | list[str] | None
"""Optional. Control whether to retain Python packaging tools in the final image.
Allowed tools are: "pip", "setuptools", "wheel".
You can also set to true to include all packaging tools.
"""
File diff suppressed because it is too large Load Diff
+1 -2
View File
@@ -17,13 +17,12 @@ dependencies = [
"langgraph-sdk>=0.1.0 ; python_version >= '3.11'",
"pathspec>=0.11.0",
"python-dotenv>=0.8.0",
"tomli>=2.0.1 ; python_version < '3.11'",
]
[tool.hatch.version]
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'",
]
+4 -289
View File
@@ -184,28 +184,13 @@
"pip_installer": {
"anyOf": [
{
"type": "string",
"enum": [
"auto",
"pip",
"uv"
]
},
{
"type": "null"
}
]
},
"source": {
"anyOf": [
{
"$ref": "#/$defs/UvSource"
"type": "string"
},
{
"type": "null"
}
],
"description": "Optional. Explicit deployment source configuration.\n\n`root/pyproject.toml` and `root/uv.lock`. If `root` is a workspace and the\ntarget is ambiguous, set `package` to the desired workspace member.\n"
"description": "Optional. Python package installer to use ('auto', 'pip', 'uv').\n\n"
},
"store": {
"anyOf": [
@@ -249,240 +234,6 @@
"graphs"
]
},
{
"type": "object",
"properties": {
"python_version": {
"type": "string",
"description": "Optional. Python version in 'major.minor' format (e.g. '3.11').\nMust be at least 3.11 or greater for this deployment to function properly.\n",
"enum": [
"3.11",
"3.12",
"3.13"
]
},
"pip_config_file": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"description": "Optional. Path to a pip config file (e.g., \"/etc/pip.conf\" or \"pip.ini\") for controlling\npackage installation (custom indices, credentials, etc.).\n\nOnly relevant if Python dependencies are installed via pip. If omitted, default pip settings are used.\n"
},
"_INTERNAL_docker_tag": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"description": "Optional. Internal use only.\n"
},
"api_version": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"description": "Optional. Which semantic version of the LangGraph API server to use.\n\nDefaults to latest. Check the\nfor more information.\n"
},
"auth": {
"anyOf": [
{
"$ref": "#/$defs/AuthConfig"
},
{
"type": "null"
}
],
"description": "Optional. Custom authentication config, including the path to your Python auth logic and\nthe OpenAPI security definitions it uses.\n"
},
"base_image": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"description": "Optional. Base image to use for the LangGraph API server.\n\nDefaults to langchain/langgraph-api or langchain/langgraphjs-api.\n"
},
"checkpointer": {
"anyOf": [
{
"$ref": "#/$defs/CheckpointerConfig"
},
{
"type": "null"
}
],
"description": "Optional. Configuration for the built-in checkpointer, which handles checkpointing of state.\n\nIf omitted, no checkpointer is set up (the object store will still be present, however).\n"
},
"dependencies": {
"type": "array",
"items": {
"type": "string"
},
"description": "List of Python dependencies to install, either from PyPI or local paths.\n"
},
"dockerfile_lines": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional. Additional Docker instructions that will be appended to your base Dockerfile.\n\nUseful for installing OS packages, setting environment variables, etc."
},
"encryption": {
"anyOf": [
{
"$ref": "#/$defs/EncryptionConfig"
},
{
"type": "null"
}
],
"description": "Optional. Custom at-rest encryption config, including the path to your Python encryption logic.\n\nAllows you to implement custom encryption for sensitive data stored in the database.\n"
},
"env": {
"anyOf": [
{
"type": "object",
"additionalProperties": {
"type": "string"
}
},
{
"type": "string"
}
],
"description": "Optional. Environment variables to set for your deployment.\n\n- If given as a dict, keys are variable names and values are their values.\n- If given as a string, it must be a path to a file containing lines in KEY=VALUE format.\n\nenv=\".env\n"
},
"graphs": {
"type": "object",
"additionalProperties": {
"anyOf": [
{
"type": "string"
},
{
"$ref": "#/$defs/GraphDef"
}
]
},
"description": "Optional. Named definitions of graphs, each pointing to a Python object.\n\n\nGraphs can be StateGraph, @entrypoint, or any other Pregel object OR they can point to (async) context\nmanagers that accept a single configuration argument (of type RunnableConfig) and return a pregel object\n(instance of Stategraph, etc.).\n\nor objects with a \"path\" key and optional \"description\" key."
},
"http": {
"anyOf": [
{
"$ref": "#/$defs/HttpConfig"
},
{
"type": "null"
}
],
"description": "Optional. Configuration for the built-in HTTP server, controlling which custom routes are exposed\nand how cross-origin requests are handled.\n"
},
"image_distro": {
"anyOf": [
{
"enum": [
"bookworm",
"debian",
"wolfi"
]
},
{
"type": "null"
}
],
"description": "Optional. Linux distribution for the base image.\n\nMust be one of 'wolfi', 'debian', or 'bookworm'.\nIf omitted, defaults to 'debian' ('latest').\n"
},
"keep_pkg_tools": {
"anyOf": [
{
"type": "boolean"
},
{
"type": "array",
"items": {
"type": "string"
}
},
{
"type": "null"
}
],
"description": "Optional. Control whether to retain Python packaging tools in the final image.\n\nYou can also set to true to include all packaging tools.\n"
},
"pip_installer": {
"anyOf": [
{
"type": "string",
"enum": [
"auto",
"pip",
"uv"
]
},
{
"type": "null"
}
]
},
"source": {
"$ref": "#/$defs/UvSource"
},
"store": {
"anyOf": [
{
"$ref": "#/$defs/StoreConfig"
},
{
"type": "null"
}
],
"description": "Optional. Configuration for the built-in long-term memory store, including semantic search indexing.\n\nIf omitted, no vector index is set up (the object store will still be present, however).\n"
},
"ui": {
"anyOf": [
{
"type": "object",
"additionalProperties": {
"type": "string"
}
},
{
"type": "null"
}
],
"description": "Optional. Named definitions of UI components emitted by the agent, each pointing to a JS/TS file.\n"
},
"webhooks": {
"anyOf": [
{
"$ref": "#/$defs/WebhooksConfig"
},
{
"type": "null"
}
],
"description": "Optional. Webhooks configuration for outbound event delivery.\n\nForwarded into the container as `LANGGRAPH_WEBHOOKS`. See `WebhooksConfig`\nfor URL policy and header templating details.\n"
}
},
"required": [
"graphs",
"source"
]
},
{
"type": "object",
"properties": {
@@ -654,28 +405,13 @@
"pip_installer": {
"anyOf": [
{
"type": "string",
"enum": [
"auto",
"pip",
"uv"
]
},
{
"type": "null"
}
]
},
"source": {
"anyOf": [
{
"$ref": "#/$defs/UvSource"
"type": "string"
},
{
"type": "null"
}
],
"description": "Optional. Explicit deployment source configuration.\n\n`root/pyproject.toml` and `root/uv.lock`. If `root` is a workspace and the\ntarget is ambiguous, set `package` to the desired workspace member.\n"
"description": "Optional. Python package installer to use ('auto', 'pip', 'uv').\n\n"
},
"store": {
"anyOf": [
@@ -1170,27 +906,6 @@
},
"required": []
},
"UvSource": {
"title": "UvSource",
"description": "Deployment source rooted at a uv project or workspace.",
"type": "object",
"properties": {
"kind": {
"enum": [
"uv"
]
},
"package": {
"type": "string"
},
"root": {
"type": "string"
}
},
"required": [
"kind"
]
},
"StoreConfig": {
"title": "StoreConfig",
"description": "Configuration for the built-in long-term memory store.\n\nThis store can optionally perform semantic search. If you omit `index`,\nthe store will just handle traditional (non-embedded) data without vector lookups.",
+4 -289
View File
@@ -184,28 +184,13 @@
"pip_installer": {
"anyOf": [
{
"type": "string",
"enum": [
"auto",
"pip",
"uv"
]
},
{
"type": "null"
}
]
},
"source": {
"anyOf": [
{
"$ref": "#/$defs/UvSource"
"type": "string"
},
{
"type": "null"
}
],
"description": "Optional. Explicit deployment source configuration.\n\n`root/pyproject.toml` and `root/uv.lock`. If `root` is a workspace and the\ntarget is ambiguous, set `package` to the desired workspace member.\n"
"description": "Optional. Python package installer to use ('auto', 'pip', 'uv').\n\n"
},
"store": {
"anyOf": [
@@ -249,240 +234,6 @@
"graphs"
]
},
{
"type": "object",
"properties": {
"python_version": {
"type": "string",
"description": "Optional. Python version in 'major.minor' format (e.g. '3.11').\nMust be at least 3.11 or greater for this deployment to function properly.\n",
"enum": [
"3.11",
"3.12",
"3.13"
]
},
"pip_config_file": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"description": "Optional. Path to a pip config file (e.g., \"/etc/pip.conf\" or \"pip.ini\") for controlling\npackage installation (custom indices, credentials, etc.).\n\nOnly relevant if Python dependencies are installed via pip. If omitted, default pip settings are used.\n"
},
"_INTERNAL_docker_tag": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"description": "Optional. Internal use only.\n"
},
"api_version": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"description": "Optional. Which semantic version of the LangGraph API server to use.\n\nDefaults to latest. Check the\nfor more information.\n"
},
"auth": {
"anyOf": [
{
"$ref": "#/$defs/AuthConfig"
},
{
"type": "null"
}
],
"description": "Optional. Custom authentication config, including the path to your Python auth logic and\nthe OpenAPI security definitions it uses.\n"
},
"base_image": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"description": "Optional. Base image to use for the LangGraph API server.\n\nDefaults to langchain/langgraph-api or langchain/langgraphjs-api.\n"
},
"checkpointer": {
"anyOf": [
{
"$ref": "#/$defs/CheckpointerConfig"
},
{
"type": "null"
}
],
"description": "Optional. Configuration for the built-in checkpointer, which handles checkpointing of state.\n\nIf omitted, no checkpointer is set up (the object store will still be present, however).\n"
},
"dependencies": {
"type": "array",
"items": {
"type": "string"
},
"description": "List of Python dependencies to install, either from PyPI or local paths.\n"
},
"dockerfile_lines": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional. Additional Docker instructions that will be appended to your base Dockerfile.\n\nUseful for installing OS packages, setting environment variables, etc."
},
"encryption": {
"anyOf": [
{
"$ref": "#/$defs/EncryptionConfig"
},
{
"type": "null"
}
],
"description": "Optional. Custom at-rest encryption config, including the path to your Python encryption logic.\n\nAllows you to implement custom encryption for sensitive data stored in the database.\n"
},
"env": {
"anyOf": [
{
"type": "object",
"additionalProperties": {
"type": "string"
}
},
{
"type": "string"
}
],
"description": "Optional. Environment variables to set for your deployment.\n\n- If given as a dict, keys are variable names and values are their values.\n- If given as a string, it must be a path to a file containing lines in KEY=VALUE format.\n\nenv=\".env\n"
},
"graphs": {
"type": "object",
"additionalProperties": {
"anyOf": [
{
"type": "string"
},
{
"$ref": "#/$defs/GraphDef"
}
]
},
"description": "Optional. Named definitions of graphs, each pointing to a Python object.\n\n\nGraphs can be StateGraph, @entrypoint, or any other Pregel object OR they can point to (async) context\nmanagers that accept a single configuration argument (of type RunnableConfig) and return a pregel object\n(instance of Stategraph, etc.).\n\nor objects with a \"path\" key and optional \"description\" key."
},
"http": {
"anyOf": [
{
"$ref": "#/$defs/HttpConfig"
},
{
"type": "null"
}
],
"description": "Optional. Configuration for the built-in HTTP server, controlling which custom routes are exposed\nand how cross-origin requests are handled.\n"
},
"image_distro": {
"anyOf": [
{
"enum": [
"bookworm",
"debian",
"wolfi"
]
},
{
"type": "null"
}
],
"description": "Optional. Linux distribution for the base image.\n\nMust be one of 'wolfi', 'debian', or 'bookworm'.\nIf omitted, defaults to 'debian' ('latest').\n"
},
"keep_pkg_tools": {
"anyOf": [
{
"type": "boolean"
},
{
"type": "array",
"items": {
"type": "string"
}
},
{
"type": "null"
}
],
"description": "Optional. Control whether to retain Python packaging tools in the final image.\n\nYou can also set to true to include all packaging tools.\n"
},
"pip_installer": {
"anyOf": [
{
"type": "string",
"enum": [
"auto",
"pip",
"uv"
]
},
{
"type": "null"
}
]
},
"source": {
"$ref": "#/$defs/UvSource"
},
"store": {
"anyOf": [
{
"$ref": "#/$defs/StoreConfig"
},
{
"type": "null"
}
],
"description": "Optional. Configuration for the built-in long-term memory store, including semantic search indexing.\n\nIf omitted, no vector index is set up (the object store will still be present, however).\n"
},
"ui": {
"anyOf": [
{
"type": "object",
"additionalProperties": {
"type": "string"
}
},
{
"type": "null"
}
],
"description": "Optional. Named definitions of UI components emitted by the agent, each pointing to a JS/TS file.\n"
},
"webhooks": {
"anyOf": [
{
"$ref": "#/$defs/WebhooksConfig"
},
{
"type": "null"
}
],
"description": "Optional. Webhooks configuration for outbound event delivery.\n\nForwarded into the container as `LANGGRAPH_WEBHOOKS`. See `WebhooksConfig`\nfor URL policy and header templating details.\n"
}
},
"required": [
"graphs",
"source"
]
},
{
"type": "object",
"properties": {
@@ -654,28 +405,13 @@
"pip_installer": {
"anyOf": [
{
"type": "string",
"enum": [
"auto",
"pip",
"uv"
]
},
{
"type": "null"
}
]
},
"source": {
"anyOf": [
{
"$ref": "#/$defs/UvSource"
"type": "string"
},
{
"type": "null"
}
],
"description": "Optional. Explicit deployment source configuration.\n\n`root/pyproject.toml` and `root/uv.lock`. If `root` is a workspace and the\ntarget is ambiguous, set `package` to the desired workspace member.\n"
"description": "Optional. Python package installer to use ('auto', 'pip', 'uv').\n\n"
},
"store": {
"anyOf": [
@@ -1170,27 +906,6 @@
},
"required": []
},
"UvSource": {
"title": "UvSource",
"description": "Deployment source rooted at a uv project or workspace.",
"type": "object",
"properties": {
"kind": {
"enum": [
"uv"
]
},
"package": {
"type": "string"
},
"root": {
"type": "string"
}
},
"required": [
"kind"
]
},
"StoreConfig": {
"title": "StoreConfig",
"description": "Configuration for the built-in long-term memory store.\n\nThis store can optionally perform semantic search. If you omit `index`,\nthe store will just handle traditional (non-embedded) data without vector lookups.",
File diff suppressed because it is too large Load Diff
@@ -1 +0,0 @@
OPENAI_API_KEY=
@@ -1,12 +0,0 @@
{
"python_version": "3.12",
"graphs": {
"agent": "./src/agent/graph.py:graph"
},
"source": {
"kind": "uv",
"root": "../..",
"package": "agent"
},
"env": ".env"
}
@@ -1,20 +0,0 @@
[project]
name = "agent"
version = "0.0.1"
description = "Agent for the uv monorepo example"
requires-python = ">=3.11"
dependencies = [
"langgraph>=0.6.0,<2",
"langchain-core>=0.2.14",
"shared",
]
[build-system]
requires = ["setuptools>=73.0.0", "wheel"]
build-backend = "setuptools.build_meta"
[tool.setuptools]
packages = ["agent"]
[tool.setuptools.package-dir]
"agent" = "src/agent"
@@ -1,31 +0,0 @@
from collections.abc import Sequence
from typing import Annotated, TypedDict
from langchain_core.messages import AIMessage, BaseMessage
from langgraph.graph import END, START, StateGraph
from langgraph.graph.message import add_messages
from shared import get_dummy_message
class State(TypedDict):
messages: Annotated[Sequence[BaseMessage], add_messages]
def call_model(state: State) -> dict:
dummy_message = get_dummy_message()
message = AIMessage(content=f"Agent says: {dummy_message}")
return {"messages": [message]}
def should_continue(state: State):
if len(state["messages"]) > 0:
return END
return "call_model"
workflow = StateGraph(State)
workflow.add_node("call_model", call_model)
workflow.add_edge(START, "call_model")
workflow.add_conditional_edges("call_model", should_continue)
graph = workflow.compile()
@@ -1,16 +0,0 @@
[project]
name = "shared"
version = "0.0.1"
description = "Shared utilities for the uv monorepo example"
requires-python = ">=3.11"
dependencies = []
[build-system]
requires = ["setuptools>=73.0.0", "wheel"]
build-backend = "setuptools.build_meta"
[tool.setuptools]
packages = ["shared"]
[tool.setuptools.package-dir]
"shared" = "src/shared"
@@ -1,3 +0,0 @@
from .utils import get_dummy_message
__all__ = ["get_dummy_message"]
@@ -1,2 +0,0 @@
def get_dummy_message() -> str:
return "Hello from shared library!"
@@ -1,22 +0,0 @@
[project]
name = "uv-monorepo-example"
version = "0.0.1"
description = "uv workspace monorepo example for LangGraph CLI integration test"
requires-python = ">=3.11"
dependencies = [
"langgraph>=0.6.0,<2",
"langchain-core>=0.2.14",
]
[tool.uv.workspace]
members = ["apps/*", "libs/*"]
[tool.uv.sources]
shared = { workspace = true }
[tool.uv]
package = false
[build-system]
requires = ["setuptools>=73.0.0", "wheel"]
build-backend = "setuptools.build_meta"
-893
View File
@@ -1,893 +0,0 @@
version = 1
revision = 3
requires-python = ">=3.11"
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]
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version = "0.0.1"
source = { editable = "apps/agent" }
dependencies = [
{ name = "langchain-core" },
{ name = "langgraph" },
{ name = "shared" },
]
[package.metadata]
requires-dist = [
{ name = "langchain-core", specifier = ">=0.2.14" },
{ name = "langgraph", specifier = ">=0.6.0,<2" },
{ name = "shared", editable = "libs/shared" },
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-1
View File
@@ -1 +0,0 @@
OPENAI_API_KEY=
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{
"python_version": "3.12",
"graphs": {
"agent": "./src/agent/graph.py:graph"
},
"source": {
"kind": "uv",
"root": "."
},
"env": ".env"
}
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[project]
name = "simple-uv-agent"
version = "0.1.0"
description = "Simple single-package uv example for LangGraph CLI integration test"
requires-python = ">=3.11"
dependencies = [
"langgraph>=0.6.0,<2",
"langchain-core>=0.2.14",
]
[build-system]
requires = ["setuptools>=73.0.0", "wheel"]
build-backend = "setuptools.build_meta"
[tool.setuptools]
packages = ["agent"]
[tool.setuptools.package-dir]
"agent" = "src/agent"
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from collections.abc import Sequence
from typing import Annotated, TypedDict
from langchain_core.messages import AIMessage, BaseMessage
from langgraph.graph import END, START, StateGraph
from langgraph.graph.message import add_messages
class State(TypedDict):
messages: Annotated[Sequence[BaseMessage], add_messages]
def call_model(state: State) -> dict:
message = AIMessage(content="Hello from simple uv agent!")
return {"messages": [message]}
def should_continue(state: State):
if len(state["messages"]) > 0:
return END
return "call_model"
workflow = StateGraph(State)
workflow.add_node("call_model", call_model)
workflow.add_edge(START, "call_model")
workflow.add_conditional_edges("call_model", should_continue)
graph = workflow.compile()
-864
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+740 -830
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+43 -76
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@@ -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
@@ -76,6 +76,32 @@ def patch_checkpoint_map(
return config
def _merge_callbacks(base: Callbacks, new: Callbacks) -> Callbacks:
# callbacks can be either None, list[handler] or manager
# so merging two callbacks values has 6 cases
if base is None:
if isinstance(new, (list, BaseCallbackManager)):
return new.copy()
return new
if isinstance(new, list):
if isinstance(base, list):
return base + new
if isinstance(base, BaseCallbackManager):
mngr = base.copy()
for cb in new:
mngr.add_handler(cb, inherit=True)
return mngr
elif isinstance(new, BaseCallbackManager):
if isinstance(base, list):
mngr = new.copy()
for cb in base:
mngr.add_handler(cb, inherit=True)
return mngr
if isinstance(base, BaseCallbackManager):
return base.merge(new)
raise NotImplementedError(f"Unsupported callback types: {type(base)}, {type(new)}")
def merge_configs(*configs: RunnableConfig | None) -> RunnableConfig:
"""Merge multiple configs into one.
@@ -110,34 +136,9 @@ def merge_configs(*configs: RunnableConfig | None) -> RunnableConfig:
else:
base[key] = value
elif key == "callbacks":
base_callbacks = base.get("callbacks")
# callbacks can be either None, list[handler] or manager
# so merging two callbacks values has 6 cases
if isinstance(value, list):
if base_callbacks is None:
base["callbacks"] = value.copy()
elif isinstance(base_callbacks, list):
base["callbacks"] = base_callbacks + value
else:
# base_callbacks is a manager
mngr = base_callbacks.copy()
for callback in value:
mngr.add_handler(callback, inherit=True)
base["callbacks"] = mngr
elif isinstance(value, BaseCallbackManager):
# value is a manager
if base_callbacks is None:
base["callbacks"] = value.copy()
elif isinstance(base_callbacks, list):
mngr = value.copy()
for callback in base_callbacks:
mngr.add_handler(callback, inherit=True)
base["callbacks"] = mngr
else:
# base_callbacks is also a manager
base["callbacks"] = base_callbacks.merge(value)
else:
raise NotImplementedError
base["callbacks"] = _merge_callbacks(
base.get("callbacks"), cast(Callbacks, value)
)
elif key == "recursion_limit":
if config["recursion_limit"] != DEFAULT_RECURSION_LIMIT:
base["recursion_limit"] = config["recursion_limit"]
@@ -217,16 +218,14 @@ 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 +256,14 @@ 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:
@@ -307,59 +304,29 @@ def ensure_config(*configs: RunnableConfig | None) -> RunnableConfig:
if _is_not_empty(v) and k in CONFIG_KEYS:
if k == CONF:
empty[k] = cast(dict, v).copy()
elif k == "callbacks" and isinstance(v, (list, BaseCallbackManager)):
empty["callbacks"] = _merge_callbacks(empty.get("callbacks"), v)
else:
empty[k] = v # type: ignore[literal-required]
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",
)
)
-394
View File
@@ -1,394 +0,0 @@
"""Graph lifecycle callback interfaces and event payloads.
This module defines the public callback surface for observing LangGraph-specific
lifecycle transitions such as interrupt and resume.
"""
from __future__ import annotations
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any, Literal, TypeAlias, TypeVar
from uuid import UUID
from langchain_core.callbacks import BaseCallbackHandler, BaseCallbackManager
from langchain_core.callbacks.manager import ahandle_event, handle_event
from langchain_core.runnables import RunnableConfig
from langgraph.types import Interrupt
__all__ = (
"GraphCallbackHandler",
"GraphInterruptEvent",
"GraphLifecycleEvent",
"GraphLifecycleStatus",
"GraphResumeEvent",
"get_async_graph_callback_manager_for_config",
"get_sync_graph_callback_manager_for_config",
)
GraphLifecycleStatus: TypeAlias = Literal[
"input",
"pending",
"done",
"interrupt_before",
"interrupt_after",
"out_of_steps",
]
"""Allowed lifecycle statuses reported in graph lifecycle callback events."""
@dataclass(frozen=True)
class GraphInterruptEvent:
"""Graph lifecycle event emitted when execution pauses for interrupts."""
run_id: UUID | None
"""Run id for the current graph execution, if available."""
status: GraphLifecycleStatus
"""Loop status when the interrupt was captured."""
checkpoint_id: str
"""Checkpoint id associated with the interrupted execution."""
checkpoint_ns: tuple[str, ...]
"""Checkpoint namespace path for the current graph or subgraph."""
interrupts: tuple[Interrupt, ...]
"""Interrupt payloads that caused the graph to pause."""
@dataclass(frozen=True)
class GraphResumeEvent:
"""Graph lifecycle event emitted when execution resumes from a checkpoint."""
run_id: UUID | None
"""Run id for the current graph execution, if available."""
status: GraphLifecycleStatus
"""Loop status when the resume was captured."""
checkpoint_id: str
"""Checkpoint id the graph resumed from."""
checkpoint_ns: tuple[str, ...]
"""Checkpoint namespace path for the current graph or subgraph."""
GraphLifecycleEvent: TypeAlias = GraphInterruptEvent | GraphResumeEvent
"""Union of all public graph lifecycle callback event payloads.
Use this alias when a callback or helper can receive either interrupt or resume
lifecycle events.
"""
class GraphCallbackHandler(BaseCallbackHandler):
"""Base class for graph-level lifecycle callbacks.
Subclass this handler to observe graph lifecycle transitions that are
specific to LangGraph execution, rather than generic LangChain runnable
callbacks.
Instances can be passed through `config["callbacks"]` when invoking a
graph. Only handlers that inherit from `GraphCallbackHandler` receive these
lifecycle events.
"""
def on_interrupt(self, event: GraphInterruptEvent) -> Any:
"""Run when graph execution pauses due to one or more interrupts.
Args:
event: Interrupt lifecycle event payload.
"""
def on_resume(self, event: GraphResumeEvent) -> Any:
"""Run when graph execution resumes from a persisted checkpoint.
Args:
event: Resume lifecycle event payload.
"""
_MISSING = object()
def _filter_graph_handlers(
handlers: list[BaseCallbackHandler],
) -> list[GraphCallbackHandler]:
return [h for h in handlers if isinstance(h, GraphCallbackHandler)]
def _init_base_manager(
manager: BaseCallbackManager,
handlers: Sequence[GraphCallbackHandler] | None,
inheritable_handlers: Sequence[GraphCallbackHandler] | None,
parent_run_id: UUID | None,
*,
tags: list[str] | None,
inheritable_tags: list[str] | None,
metadata: dict[str, Any] | None,
inheritable_metadata: dict[str, Any] | None,
run_id: UUID | None,
) -> None:
base_handlers: list[BaseCallbackHandler] = []
base_inheritable_handlers: list[BaseCallbackHandler] = []
if handlers is not None:
base_handlers.extend(handlers)
if inheritable_handlers is not None:
base_inheritable_handlers.extend(inheritable_handlers)
BaseCallbackManager.__init__(
manager,
handlers=base_handlers,
inheritable_handlers=base_inheritable_handlers,
parent_run_id=parent_run_id,
tags=tags,
inheritable_tags=inheritable_tags,
metadata=metadata,
inheritable_metadata=inheritable_metadata,
)
manager.run_id = run_id # type: ignore[attr-defined]
def _configure_graph_callbacks(
cls: type[_GraphManagerT],
callbacks: object | None,
*,
run_id: UUID | None,
) -> _GraphManagerT:
if callbacks is None:
return cls(run_id=run_id)
if isinstance(callbacks, cls):
return callbacks.copy(run_id=run_id)
if isinstance(callbacks, (_GraphCallbackManager, _AsyncGraphCallbackManager)):
# Cross-type: extract handlers into the requested cls.
return cls(
handlers=_filter_graph_handlers(callbacks.handlers),
inheritable_handlers=_filter_graph_handlers(callbacks.inheritable_handlers),
parent_run_id=callbacks.parent_run_id,
tags=callbacks.tags.copy(),
inheritable_tags=callbacks.inheritable_tags.copy(),
metadata=callbacks.metadata.copy(),
inheritable_metadata=callbacks.inheritable_metadata.copy(),
run_id=run_id,
)
if isinstance(callbacks, BaseCallbackManager):
return cls(
handlers=_filter_graph_handlers(callbacks.handlers),
inheritable_handlers=_filter_graph_handlers(callbacks.inheritable_handlers),
parent_run_id=callbacks.parent_run_id,
tags=callbacks.tags.copy(),
inheritable_tags=callbacks.inheritable_tags.copy(),
metadata=callbacks.metadata.copy(),
inheritable_metadata=callbacks.inheritable_metadata.copy(),
run_id=run_id,
)
if isinstance(callbacks, GraphCallbackHandler):
return cls((callbacks,), run_id=run_id)
if isinstance(callbacks, (str, bytes)) or not isinstance(callbacks, Sequence):
raise TypeError("callbacks must be a handler, sequence, or manager")
return cls(_filter_graph_handlers(list(callbacks)), run_id=run_id)
def _copy_graph_manager(
manager: _GraphCallbackManager | _AsyncGraphCallbackManager,
cls: type[_GraphManagerT],
run_id: UUID | None | object,
) -> _GraphManagerT:
resolved_run_id: UUID | None
if run_id is _MISSING:
resolved_run_id = manager.run_id
else:
if run_id is not None and not isinstance(run_id, UUID):
raise TypeError("run_id must be a UUID or None")
resolved_run_id = run_id
return cls(
handlers=_filter_graph_handlers(manager.handlers),
inheritable_handlers=_filter_graph_handlers(manager.inheritable_handlers),
parent_run_id=manager.parent_run_id,
tags=manager.tags.copy(),
inheritable_tags=manager.inheritable_tags.copy(),
metadata=manager.metadata.copy(),
inheritable_metadata=manager.inheritable_metadata.copy(),
run_id=resolved_run_id,
)
class _GraphCallbackManager(BaseCallbackManager):
"""Sync dispatcher for graph lifecycle events."""
run_id: UUID | None
def __init__(
self,
handlers: Sequence[GraphCallbackHandler] | None = None,
inheritable_handlers: Sequence[GraphCallbackHandler] | None = None,
parent_run_id: UUID | None = None,
*,
tags: list[str] | None = None,
inheritable_tags: list[str] | None = None,
metadata: dict[str, Any] | None = None,
inheritable_metadata: dict[str, Any] | None = None,
run_id: UUID | None = None,
) -> None:
_init_base_manager(
self,
handlers,
inheritable_handlers,
parent_run_id,
tags=tags,
inheritable_tags=inheritable_tags,
metadata=metadata,
inheritable_metadata=inheritable_metadata,
run_id=run_id,
)
def copy(
self,
*,
run_id: UUID | None | object = _MISSING,
) -> _GraphCallbackManager:
return _copy_graph_manager(self, _GraphCallbackManager, run_id)
@classmethod
def configure(
cls,
callbacks: object | None = None,
*,
run_id: UUID | None = None,
) -> _GraphCallbackManager:
return _configure_graph_callbacks(cls, callbacks, run_id=run_id)
def on_interrupt(self, event: GraphInterruptEvent) -> None:
handle_event(
self.handlers,
"on_interrupt",
None,
event,
)
def on_resume(self, event: GraphResumeEvent) -> None:
handle_event(
self.handlers,
"on_resume",
None,
event,
)
class _AsyncGraphCallbackManager(BaseCallbackManager):
"""Async dispatcher for graph lifecycle events."""
run_id: UUID | None
@property
def is_async(self) -> bool:
"""Return whether the manager is async."""
return True
def __init__(
self,
handlers: Sequence[GraphCallbackHandler] | None = None,
inheritable_handlers: Sequence[GraphCallbackHandler] | None = None,
parent_run_id: UUID | None = None,
*,
tags: list[str] | None = None,
inheritable_tags: list[str] | None = None,
metadata: dict[str, Any] | None = None,
inheritable_metadata: dict[str, Any] | None = None,
run_id: UUID | None = None,
) -> None:
_init_base_manager(
self,
handlers,
inheritable_handlers,
parent_run_id,
tags=tags,
inheritable_tags=inheritable_tags,
metadata=metadata,
inheritable_metadata=inheritable_metadata,
run_id=run_id,
)
def copy(
self,
*,
run_id: UUID | None | object = _MISSING,
) -> _AsyncGraphCallbackManager:
return _copy_graph_manager(self, _AsyncGraphCallbackManager, run_id)
@classmethod
def configure(
cls,
callbacks: object | None = None,
*,
run_id: UUID | None = None,
) -> _AsyncGraphCallbackManager:
return _configure_graph_callbacks(cls, callbacks, run_id=run_id)
async def on_interrupt(self, event: GraphInterruptEvent) -> None:
await ahandle_event(
self.handlers,
"on_interrupt",
None,
event,
)
async def on_resume(self, event: GraphResumeEvent) -> None:
await ahandle_event(
self.handlers,
"on_resume",
None,
event,
)
_GraphManagerT = TypeVar(
"_GraphManagerT", _GraphCallbackManager, _AsyncGraphCallbackManager
)
GraphCallbacks: TypeAlias = (
_GraphCallbackManager
| _AsyncGraphCallbackManager
| BaseCallbackManager
| GraphCallbackHandler
| Sequence[BaseCallbackHandler]
| Sequence[GraphCallbackHandler]
| None
)
def get_sync_graph_callback_manager_for_config(
config: RunnableConfig,
*,
run_id: UUID | None = None,
) -> _GraphCallbackManager:
"""Build a sync graph lifecycle callback manager from a runnable config.
This helper filters `config["callbacks"]` down to handlers that inherit
from `GraphCallbackHandler` and binds the provided `run_id` onto the
returned manager.
"""
return _GraphCallbackManager.configure(
config.get("callbacks"),
run_id=run_id,
)
def get_async_graph_callback_manager_for_config(
config: RunnableConfig,
*,
run_id: UUID | None = None,
) -> _AsyncGraphCallbackManager:
"""Build an async graph lifecycle callback manager from a runnable config.
This helper filters `config["callbacks"]` down to handlers that inherit
from `GraphCallbackHandler` and binds the provided `run_id` onto the
returned manager.
"""
return _AsyncGraphCallbackManager.configure(
config.get("callbacks"),
run_id=run_id,
)
@@ -1,7 +1,6 @@
from langgraph.channels.any_value import AnyValue
from langgraph.channels.base import BaseChannel
from langgraph.channels.binop import BinaryOperatorAggregate
from langgraph.channels.delta import DeltaChannel
from langgraph.channels.ephemeral_value import EphemeralValue
from langgraph.channels.last_value import LastValue, LastValueAfterFinish
from langgraph.channels.named_barrier_value import (
@@ -21,7 +20,6 @@ __all__ = (
"UntrackedValue",
"EphemeralValue",
"BinaryOperatorAggregate",
"DeltaChannel",
"NamedBarrierValue",
"NamedBarrierValueAfterFinish",
# topics
+9 -16
View File
@@ -22,9 +22,10 @@ __all__ = ("BinaryOperatorAggregate",)
def _strip_extras(t): # type: ignore[no-untyped-def]
"""Strips Annotated, Required and NotRequired from a given type."""
if hasattr(t, "__origin__"):
if t.__origin__ in (Required, NotRequired):
return _strip_extras(t.__args__[0])
return _strip_extras(t.__origin__)
if hasattr(t, "__origin__") and t.__origin__ in (Required, NotRequired):
return _strip_extras(t.__args__[0])
return t
@@ -32,22 +33,11 @@ def _get_overwrite(value: Any) -> tuple[bool, Any]:
"""Inspects the given value and returns (is_overwrite, overwrite_value)."""
if isinstance(value, Overwrite):
return True, value.value
if isinstance(value, dict) and len(value) == 1 and OVERWRITE in value:
if isinstance(value, dict) and set(value.keys()) == {OVERWRITE}:
return True, value[OVERWRITE]
return False, None
def _operators_equal(a: Callable, b: Callable) -> bool:
"""Return True if two reducer operators should be considered equal.
Lambdas all share the name '<lambda>' so identity comparison is
unreliable; treat any pairing that includes a lambda as equal.
"""
if a.__name__ == "<lambda>" or b.__name__ == "<lambda>":
return True
return a is b
class BinaryOperatorAggregate(Generic[Value], BaseChannel[Value, Value, Value]):
"""Stores the result of applying a binary operator to the current value and each new value.
@@ -78,8 +68,11 @@ class BinaryOperatorAggregate(Generic[Value], BaseChannel[Value, Value, Value]):
self.value = MISSING
def __eq__(self, value: object) -> bool:
return isinstance(value, BinaryOperatorAggregate) and _operators_equal(
self.operator, value.operator
return isinstance(value, BinaryOperatorAggregate) and (
value.operator is self.operator
if value.operator.__name__ != "<lambda>"
and self.operator.__name__ != "<lambda>"
else True
)
@property
-197
View File
@@ -1,197 +0,0 @@
from __future__ import annotations
import collections.abc
import copy as _copy
from collections.abc import Callable, Sequence
from typing import Any, Generic
from langgraph.checkpoint.base import DELTA_SENTINEL, PendingWrite
from langgraph.checkpoint.serde.types import _DeltaSnapshot
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, _operators_equal, _strip_extras
from langgraph.errors import (
EmptyChannelError,
ErrorCode,
InvalidUpdateError,
create_error_message,
)
__all__ = ("DeltaChannel",)
class DeltaChannel(Generic[Value], BaseChannel[Any, Any, Any]):
"""Reducer channel that stores only a sentinel in checkpoint blobs and
reconstructs state by replaying ancestor writes through the reducer.
The reducer receives the current accumulated value and a batch of writes
in one call: `reducer(state, [write1, write2, ...]) -> new_state`.
Reducers must be deterministic and batching-invariant (associative across
folds): applying two consecutive write batches separately must produce the
same state as applying their concatenation once:
reducer(reducer(state, xs), ys) == reducer(state, xs + ys)
This lets LangGraph replay checkpointed writes in larger batches than they
were originally produced without changing reconstructed state.
`snapshot_frequency=None` (default): pure delta; stores only
`DELTA_SENTINEL` in checkpoint blobs; reads replay all ancestor writes.
`snapshot_frequency=N`: `create_checkpoint` writes a full `_DeltaSnapshot`
blob every N steps, bounding replay depth to N.
Parameters:
reducer: `(state, list[writes]) -> new_state`. Must be deterministic
and batching-invariant as described above.
typ: The value type (e.g. `list`, `dict`). Inferred automatically
from the outer type when used inside `Annotated[T, DeltaChannel(...)]`.
snapshot_frequency: Every Nth pregel step writes a snapshot blob.
`None` (default) = pure delta, never snapshot.
"""
__slots__ = ("value", "reducer", "snapshot_frequency")
value: Value | Any
def __init__(
self,
reducer: Callable[[Any, Sequence[Any]], Any],
typ: type[Value] | None = None,
*,
snapshot_frequency: int | None = None,
) -> None:
if typ is None:
typ = list # type: ignore[assignment] # placeholder; overridden by _is_field_channel
super().__init__(typ)
self.reducer = reducer
self.snapshot_frequency = snapshot_frequency
typ = _strip_extras(typ)
if typ in (collections.abc.Sequence, collections.abc.MutableSequence):
typ = list
if typ in (collections.abc.Set, collections.abc.MutableSet):
typ = set
if typ in (collections.abc.Mapping, collections.abc.MutableMapping):
typ = dict
self.typ = typ
self.value: Any = MISSING
def __eq__(self, other: object) -> bool:
if not isinstance(other, DeltaChannel):
return False
if self.snapshot_frequency != other.snapshot_frequency:
return False
return _operators_equal(self.reducer, other.reducer)
@property
def ValueType(self) -> Any:
return self.typ
@property
def UpdateType(self) -> Any:
return self.typ
def is_snapshot_step(self, step: int) -> bool:
"""True if pregel should write a snapshot blob at this step."""
return (
self.snapshot_frequency is not None
and step > 0
and step % self.snapshot_frequency == 0
)
def copy(self) -> Self:
new = self.__class__(
self.reducer, self.typ, snapshot_frequency=self.snapshot_frequency
)
new.key = self.key
new.value = self.value if self.value is MISSING else _copy.copy(self.value)
return new
def from_checkpoint(self, checkpoint: Any) -> Self:
"""Initialize from a stored blob or sentinel.
Blob types (dispatched via serde ext code, not dict key inspection):
* `DELTA_SENTINEL` / `MISSING`: start empty; caller replays writes.
* `_DeltaSnapshot(value)`: restore value directly from snapshot.
* plain value (migration from old BinOp blobs): use directly.
"""
new = self.__class__(
self.reducer, self.typ, snapshot_frequency=self.snapshot_frequency
)
new.key = self.key
if checkpoint is MISSING or checkpoint is DELTA_SENTINEL:
new.value = self.typ()
elif isinstance(checkpoint, _DeltaSnapshot):
new.value = checkpoint.value
else:
new.value = checkpoint
return new
def replay_writes(self, writes: Sequence[PendingWrite]) -> None:
"""Apply ancestor writes oldest-to-newest via a single reducer call.
If any write is an Overwrite, the last one in the sequence acts as
the reset point: its value becomes the new base and only writes
after it are passed to the reducer.
"""
values = [v for _, _, v in writes]
if not values:
return
base = self.value
start = 0
for i, v in enumerate(values):
is_ow, ow_value = _get_overwrite(v)
if is_ow:
base = _copy.copy(ow_value) if ow_value is not None else self.typ()
start = i + 1
remaining = values[start:]
self.value = self.reducer(base, remaining) if remaining else base
def update(self, values: Sequence[Any]) -> bool:
if not values:
return False
overwrite_idx: int | None = None
for i, v in enumerate(values):
is_ow, _ = _get_overwrite(v)
if is_ow:
if overwrite_idx is not None:
msg = create_error_message(
message="Can receive only one Overwrite value per super-step.",
error_code=ErrorCode.INVALID_CONCURRENT_GRAPH_UPDATE,
)
raise InvalidUpdateError(msg)
overwrite_idx = i
if overwrite_idx is not None:
_, overwrite_value = _get_overwrite(values[overwrite_idx])
base = (
_copy.copy(overwrite_value)
if overwrite_value is not None
else self.typ()
)
remaining = [v for i, v in enumerate(values) if i != overwrite_idx]
self.value = self.reducer(base, remaining) if remaining else base
return True
base = self.typ() if self.value is MISSING else self.value
self.value = self.reducer(base, list(values))
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 stored representation: always `DELTA_SENTINEL`.
Snapshot decisions are made by `create_checkpoint` in pregel (which
has the step number) via `is_snapshot_step`. `checkpoint()` is only
called for non-snapshot steps or when no checkpointer is available.
"""
if self.value is MISSING:
return MISSING
return DELTA_SENTINEL
-46
View File
@@ -244,52 +244,6 @@ def add_messages(
return merged
def _messages_delta_reducer(
state: list[AnyMessage], writes: list[list[AnyMessage]]
) -> list[AnyMessage]:
"""**Experimental.** Batch reducer for use with `DeltaChannel`.
Processes all writes in one pass dedup by ID, `RemoveMessage`
tombstoning without calling `add_messages`. Assumes writes contain
already-typed `BaseMessage` objects (no raw-dict coercion).
This reducer is batching-invariant, as required by `DeltaChannel`:
`reducer(reducer(state, xs), ys) == reducer(state, xs + ys)`.
Use `add_messages` as the reducer for `BinaryOperatorAggregate` or
anywhere raw message dicts / strings need to be coerced first.
Example::
from typing import Annotated
from langgraph.channels.delta import DeltaChannel
from langgraph.graph.message import _messages_delta_reducer
class State(TypedDict):
messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
"""
from itertools import chain
index: dict[str, int] = {m.id: i for i, m in enumerate(state) if m.id is not None}
result: list[AnyMessage | None] = list(state)
for msg in chain.from_iterable(
[w] if isinstance(w, BaseMessage) else w for w in writes
):
mid = msg.id
if mid is None:
result.append(msg)
elif isinstance(msg, RemoveMessage):
if mid in index:
result[index[mid]] = None
del index[mid]
elif mid in index:
result[index[mid]] = msg
else:
index[mid] = len(result)
result.append(msg)
return [m for m in result if m is not None]
@deprecated(
"MessageGraph is deprecated in langgraph 1.0.0, to be removed in 2.0.0. Please use StateGraph with a `messages` key instead.",
category=None,
-16
View File
@@ -48,7 +48,6 @@ 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
from langgraph.channels.delta import DeltaChannel
from langgraph.channels.ephemeral_value import EphemeralValue
from langgraph.channels.last_value import LastValue, LastValueAfterFinish
from langgraph.channels.named_barrier_value import (
@@ -1083,7 +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]] = [
@@ -1669,20 +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]
item = item.__class__(
item.reducer,
origin,
snapshot_frequency=item.snapshot_frequency,
)
return item
elif isclass(item) and issubclass(item, BaseChannel):
# ex, Annotated[int, EphemeralValue, SomeOtherAnnotation]
+16 -114
View File
@@ -1,23 +1,17 @@
from __future__ import annotations
from collections.abc import Callable, Mapping
from collections.abc import Mapping
from datetime import datetime, timezone
from typing import Any, cast
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.checkpoint.serde.types import _DeltaSnapshot
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
GetNextVersion = Callable[[Any, None], Any]
def empty_checkpoint() -> Checkpoint:
return Checkpoint(
@@ -37,87 +31,35 @@ def create_checkpoint(
*,
id: str | None = None,
updated_channels: set[str] | None = None,
get_next_version: GetNextVersion | None = None,
force_delta_snapshot: bool = False,
) -> Checkpoint:
"""Create a checkpoint for the given channels.
For `DeltaChannel` with `snapshot_frequency=N`, snapshot steps write a
`_DeltaSnapshot` blob rather than `DELTA_SENTINEL`, bounding the ancestor
walk to at most N steps. Snapshots are eager: even if the channel had no
write this step, a version bump is forced (via `get_next_version`) so the
blob is stored by `put()`. Without `get_next_version` (e.g. static
contexts), snapshot steps gracefully fall back to sentinel.
`force_delta_snapshot` writes available `DeltaChannel` values as snapshots
regardless of `snapshot_frequency`. This is used by `durability="exit"`,
where intermediate writes are not stored as ancestor `checkpoint_writes`.
"""
"""Create a checkpoint for the given channels."""
ts = datetime.now(timezone.utc).isoformat()
if channels is None:
values = checkpoint["channel_values"]
channel_versions = checkpoint["channel_versions"]
else:
values = {}
channel_versions = dict(checkpoint["channel_versions"])
for k in channels:
if k not in channel_versions:
if k not in checkpoint["channel_versions"]:
continue
ch = channels[k]
if (
isinstance(ch, DeltaChannel)
and (force_delta_snapshot or ch.is_snapshot_step(step))
and ch.is_available()
):
# Eager snapshot: bump version if not already written this step
# so put() includes this channel in new_versions and stores blob.
if get_next_version is not None and (
updated_channels is None or k not in updated_channels
):
channel_versions[k] = get_next_version(channel_versions[k], None)
values[k] = _DeltaSnapshot(ch.get())
else:
v = ch.checkpoint()
if v is not MISSING:
values[k] = v
v = channels[k].checkpoint()
if v is not MISSING:
values[k] = v
return Checkpoint(
v=LATEST_VERSION,
ts=ts,
id=id or str(uuid6(clock_seq=step)),
channel_values=values,
channel_versions=channel_versions,
channel_versions=checkpoint["channel_versions"],
versions_seen=checkpoint["versions_seen"],
updated_channels=None if updated_channels is None else sorted(updated_channels),
)
def _needs_replay(spec: BaseChannel, stored: object) -> bool:
"""True if `spec` is a `DeltaChannel` and the stored blob is a sentinel,
requiring an ancestor walk to reconstruct.
`_DeltaSnapshot` blobs and plain values (migration) resolve directly via
`from_checkpoint` only `DELTA_SENTINEL` / `MISSING` trigger replay.
"""
if not isinstance(spec, DeltaChannel):
return False
return stored is MISSING or stored is DELTA_SENTINEL
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. `DeltaChannel` is the exception: sentinel blobs require an
ancestor walk via `saver._get_channel_writes_history`. The walk terminates
at the nearest `_DeltaSnapshot` blob (step-based) or a pre-migration plain
value, so read depth is bounded by `snapshot_frequency`.
"""
"""Get channels from a checkpoint."""
channel_specs: dict[str, BaseChannel] = {}
managed_specs: dict[str, ManagedValueSpec] = {}
for k, v in specs.items():
@@ -125,53 +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 _needs_replay(spec, stored) and saver is not None and config is not None:
delta_spec = cast(DeltaChannel, spec)
history = saver._get_channel_writes_history(config, k)
replay_ch = delta_spec.from_checkpoint(history.seed)
replay_ch.replay_writes(history.writes)
ch = replay_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 _needs_replay(spec, stored) and saver is not None and config is not None:
delta_spec = cast(DeltaChannel, spec)
history = await saver._aget_channel_writes_history(config, k)
replay_ch = delta_spec.from_checkpoint(history.seed)
replay_ch.replay_writes(history.writes)
ch = replay_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:
+16 -129
View File
@@ -62,13 +62,7 @@ from langgraph._internal._constants import (
from langgraph._internal._replay import ReplayState
from langgraph._internal._scratchpad import PregelScratchpad
from langgraph._internal._typing import EMPTY_SEQ, MISSING
from langgraph.callbacks import (
GraphInterruptEvent,
GraphLifecycleEvent,
GraphResumeEvent,
)
from langgraph.channels.base import BaseChannel
from langgraph.channels.delta import DeltaChannel
from langgraph.channels.untracked_value import UntrackedValue
from langgraph.constants import TAG_HIDDEN
from langgraph.errors import (
@@ -93,7 +87,6 @@ from langgraph.pregel._algo import (
task_path_str,
)
from langgraph.pregel._checkpoint import (
achannels_from_checkpoint,
channels_from_checkpoint,
copy_checkpoint,
create_checkpoint,
@@ -124,7 +117,6 @@ from langgraph.types import (
CachePolicy,
Command,
Durability,
Interrupt,
PregelExecutableTask,
RetryPolicy,
Send,
@@ -190,8 +182,6 @@ class PregelLoop:
_migrate_checkpoint: Callable[[Checkpoint], None] | None
submit: Submit
channels: Mapping[str, BaseChannel]
# Only set on AsyncPregelLoop; sync loops keep this as None.
_delta_write_futs: list[Any] | None = None
managed: ManagedValueMapping
checkpoint: Checkpoint
checkpoint_id_saved: str
@@ -213,8 +203,6 @@ class PregelLoop:
tasks: dict[str, PregelExecutableTask]
output: None | dict[str, Any] | Any = None
updated_channels: set[str] | None = None
_graph_lifecycle_events: deque[GraphLifecycleEvent]
_has_graph_lifecycle_callbacks: bool
# public
@@ -240,7 +228,6 @@ class PregelLoop:
migrate_checkpoint: Callable[[Checkpoint], None] | None = None,
retry_policy: Sequence[RetryPolicy] = (),
cache_policy: CachePolicy | None = None,
has_graph_lifecycle_callbacks: bool = False,
) -> None:
self.stream = stream
self.config = config
@@ -265,8 +252,6 @@ class PregelLoop:
self.retry_policy = retry_policy
self.cache_policy = cache_policy
self.durability = durability
self._has_graph_lifecycle_callbacks = has_graph_lifecycle_callbacks
self._graph_lifecycle_events = deque()
if self.stream is not None and CONFIG_KEY_STREAM in config[CONF]:
self.stream = DuplexStream(self.stream, config[CONF][CONFIG_KEY_STREAM])
scratchpad: PregelScratchpad | None = config[CONF].get(CONFIG_KEY_SCRATCHPAD)
@@ -318,40 +303,6 @@ class PregelLoop:
)
self.prev_checkpoint_config = None
def _push_graph_lifecycle_event(
self,
kind: Literal["resume", "interrupt"],
*,
interrupts: tuple[Interrupt, ...] = (),
) -> None:
if kind == "resume":
self._graph_lifecycle_events.append(
GraphResumeEvent(
run_id=None,
status=self.status,
checkpoint_id=self.checkpoint["id"],
checkpoint_ns=self.checkpoint_ns,
)
)
elif kind == "interrupt":
self._graph_lifecycle_events.append(
GraphInterruptEvent(
run_id=None,
status=self.status,
checkpoint_id=self.checkpoint["id"],
checkpoint_ns=self.checkpoint_ns,
interrupts=interrupts,
)
)
else:
msg = f"Unknown graph lifecycle event type: {kind}"
raise AssertionError(msg)
def _pop_lifecycle_event(self) -> GraphLifecycleEvent | None:
if not self._graph_lifecycle_events:
return None
return self._graph_lifecycle_events.popleft()
def put_writes(self, task_id: str, writes: WritesT) -> None:
"""Put writes for a task, to be read by the next tick."""
if not writes:
@@ -410,7 +361,7 @@ class PregelLoop:
task = self.tasks.get(task_id)
else:
task = None
fut = self.submit(
self.submit(
self.checkpointer_put_writes,
config,
writes_to_save,
@@ -418,16 +369,12 @@ class PregelLoop:
task_path_str(task.path) if task else "",
)
else:
fut = self.submit(
self.submit(
self.checkpointer_put_writes,
config,
writes_to_save,
task_id,
)
if self._delta_write_futs is not None and any(
isinstance(self.specs.get(c), DeltaChannel) for c, _ in writes_to_save
):
self._delta_write_futs.append(fut)
# output writes
if hasattr(self, "tasks"):
self.output_writes(task_id, writes)
@@ -700,7 +647,7 @@ 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.
@@ -718,8 +665,7 @@ 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
]
@@ -774,26 +720,6 @@ 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
@@ -836,28 +762,14 @@ class PregelLoop:
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(
@@ -873,8 +785,6 @@ class PregelLoop:
)
# set flag
self.status = "pending"
if is_resuming:
self._push_graph_lifecycle_event("resume")
return updated_channels
def _put_checkpoint(self, metadata: CheckpointMetadata) -> None:
@@ -898,10 +808,6 @@ class PregelLoop:
self.step,
id=self.checkpoint["id"] if exiting else None,
updated_channels=self.updated_channels,
get_next_version=self.checkpointer_get_next_version
if do_checkpoint
else None,
force_delta_snapshot=exiting and self.durability == "exit",
)
# sanitize TASK channel in the checkpoint before saving (durability=="exit")
if TASKS in self.checkpoint["channel_values"] and any(
@@ -979,10 +885,8 @@ class PregelLoop:
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
interrupts = tuple(interrupt.args[0]) if interrupt.args else ()
self._push_graph_lifecycle_event("interrupt", interrupts=interrupts)
suppress = isinstance(exc_value, GraphInterrupt) and not self.is_nested
if suppress:
# emit one last "values" event, with pending writes applied
if (
hasattr(self, "tasks")
@@ -1009,11 +913,12 @@ class PregelLoop:
self.channels,
)
# emit INTERRUPT if exception is empty (otherwise emitted by put_writes)
if not interrupt.args or not interrupt.args[0]:
interrupt_payload = interrupt.args[0] if interrupt.args else ()
if exc_value is not None and (not exc_value.args or not exc_value.args[0]):
self._emit(
"updates",
lambda: iter([{INTERRUPT: interrupt_payload}]),
lambda: iter(
[{INTERRUPT: cast(GraphInterrupt, exc_value).args[0]}]
),
)
# save final output
self.output = read_channels(self.channels, self.output_keys)
@@ -1135,7 +1040,6 @@ class SyncPregelLoop(PregelLoop, AbstractContextManager):
migrate_checkpoint: Callable[[Checkpoint], None] | None = None,
retry_policy: Sequence[RetryPolicy] = (),
cache_policy: CachePolicy | None = None,
has_graph_lifecycle_callbacks: bool = False,
) -> None:
super().__init__(
input,
@@ -1157,7 +1061,6 @@ class SyncPregelLoop(PregelLoop, AbstractContextManager):
retry_policy=retry_policy,
cache_policy=cache_policy,
durability=durability,
has_graph_lifecycle_callbacks=has_graph_lifecycle_callbacks,
)
self.stack = ExitStack()
if checkpointer:
@@ -1233,7 +1136,6 @@ class SyncPregelLoop(PregelLoop, AbstractContextManager):
# context manager
def __enter__(self) -> Self:
self._graph_lifecycle_events = deque()
if not self.checkpointer:
saved = None
elif self.checkpoint_config[CONF].get(CONFIG_KEY_CHECKPOINT_ID):
@@ -1285,10 +1187,7 @@ class SyncPregelLoop(PregelLoop, AbstractContextManager):
)
self.submit = self.stack.enter_context(BackgroundExecutor(self.config))
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"
@@ -1337,7 +1236,6 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
migrate_checkpoint: Callable[[Checkpoint], None] | None = None,
retry_policy: Sequence[RetryPolicy] = (),
cache_policy: CachePolicy | None = None,
has_graph_lifecycle_callbacks: bool = False,
) -> None:
super().__init__(
input,
@@ -1359,7 +1257,6 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
retry_policy=retry_policy,
cache_policy=cache_policy,
durability=durability,
has_graph_lifecycle_callbacks=has_graph_lifecycle_callbacks,
)
self.stack = AsyncExitStack()
if checkpointer:
@@ -1383,11 +1280,6 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
metadata: CheckpointMetadata,
new_versions: ChannelVersions,
) -> RunnableConfig:
# Drain DeltaChannel write futures before committing the checkpoint so
# DELTA_SENTINEL blobs are never saved ahead of their backing writes.
if self._delta_write_futs:
futs, self._delta_write_futs = self._delta_write_futs, []
await asyncio.gather(*futs)
try:
if prev is not None:
await prev
@@ -1443,7 +1335,6 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
# context manager
async def __aenter__(self) -> Self:
self._graph_lifecycle_events = deque()
if not self.checkpointer:
saved = None
elif self.checkpoint_config[CONF].get(CONFIG_KEY_CHECKPOINT_ID):
@@ -1493,15 +1384,11 @@ class AsyncPregelLoop(PregelLoop, AbstractAsyncContextManager):
if saved.pending_writes is not None
else []
)
self._delta_write_futs = []
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,
self.channels, self.managed = channels_from_checkpoint(
self.specs, self.checkpoint
)
self.stack.push(self._suppress_interrupt)
self.status = "input"
+8 -82
View File
@@ -16,7 +16,7 @@ from collections.abc import (
Mapping,
Sequence,
)
from dataclasses import is_dataclass, replace
from dataclasses import is_dataclass
from functools import partial
from inspect import isclass
from typing import (
@@ -96,12 +96,6 @@ from langgraph._internal._runnable import (
coerce_to_runnable,
)
from langgraph._internal._typing import MISSING, DeprecatedKwargs
from langgraph.callbacks import (
GraphInterruptEvent,
GraphResumeEvent,
get_async_graph_callback_manager_for_config,
get_sync_graph_callback_manager_for_config,
)
from langgraph.channels.base import BaseChannel
from langgraph.channels.topic import Topic
from langgraph.config import get_config
@@ -122,7 +116,6 @@ 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 +1046,6 @@ 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 +1162,9 @@ 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 +1535,6 @@ 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 +1978,9 @@ 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
@@ -2610,10 +2585,6 @@ class Pregel(
name=config.get("run_name", self.get_name()),
run_id=config.get("run_id"),
)
graph_callback_manager = get_sync_graph_callback_manager_for_config(
config,
run_id=run_manager.run_id,
)
try:
# assign defaults
(
@@ -2698,17 +2669,6 @@ class Pregel(
_output_mapper = self._output_mapper if version == "v2" else None
_state_mapper = self._state_mapper if version == "v2" else None
def emit_graph_lifecycle_events(loop: SyncPregelLoop) -> None:
while (event := loop._pop_lifecycle_event()) is not None:
if isinstance(event, GraphResumeEvent):
graph_callback_manager.on_resume(
replace(event, run_id=graph_callback_manager.run_id)
)
else:
graph_callback_manager.on_interrupt(
replace(event, run_id=graph_callback_manager.run_id)
)
with SyncPregelLoop(
input,
stream=StreamProtocol(stream.put, stream_modes),
@@ -2729,9 +2689,7 @@ class Pregel(
migrate_checkpoint=self._migrate_checkpoint,
retry_policy=self.retry_policy,
cache_policy=self.cache_policy,
has_graph_lifecycle_callbacks=bool(graph_callback_manager.handlers),
) as loop:
emit_graph_lifecycle_events(loop)
# create runner
runner = PregelRunner(
submit=config[CONF].get(
@@ -2793,11 +2751,9 @@ class Pregel(
_state_mapper,
)
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(
stream_mode,
@@ -2972,10 +2928,6 @@ class Pregel(
name=config.get("run_name", self.get_name()),
run_id=config.get("run_id"),
)
graph_callback_manager = get_async_graph_callback_manager_for_config(
config,
run_id=run_manager.run_id,
)
# if running from astream_log() run each proc with streaming
do_stream = (
next(
@@ -3090,28 +3042,6 @@ class Pregel(
_output_mapper = self._output_mapper if version == "v2" else None
_state_mapper = self._state_mapper if version == "v2" else None
async def aemit_graph_lifecycle_events(loop: AsyncPregelLoop) -> None:
while (event := loop._pop_lifecycle_event()) is not None:
if isinstance(event, GraphResumeEvent):
await graph_callback_manager.on_resume(
GraphResumeEvent(
run_id=graph_callback_manager.run_id,
status=event.status,
checkpoint_id=event.checkpoint_id,
checkpoint_ns=event.checkpoint_ns,
)
)
else:
await graph_callback_manager.on_interrupt(
GraphInterruptEvent(
run_id=graph_callback_manager.run_id,
status=event.status,
checkpoint_id=event.checkpoint_id,
checkpoint_ns=event.checkpoint_ns,
interrupts=event.interrupts,
)
)
async with AsyncPregelLoop(
input,
stream=StreamProtocol(stream.put_nowait, stream_modes),
@@ -3132,9 +3062,7 @@ class Pregel(
migrate_checkpoint=self._migrate_checkpoint,
retry_policy=self.retry_policy,
cache_policy=self.cache_policy,
has_graph_lifecycle_callbacks=bool(graph_callback_manager.handlers),
) as loop:
await aemit_graph_lifecycle_events(loop)
# create runner
runner = PregelRunner(
submit=config[CONF].get(
@@ -3216,7 +3144,6 @@ class Pregel(
):
yield o
loop.after_tick()
await aemit_graph_lifecycle_events(loop)
# wait for checkpoint
if durability_ == "sync":
await cast(asyncio.Future, loop._put_checkpoint_fut)
@@ -3225,8 +3152,6 @@ class Pregel(
if _cleanup_waiter is not None:
await _cleanup_waiter()
await aemit_graph_lifecycle_events(loop)
# emit output
for o in _output(
stream_mode,
@@ -3734,14 +3659,15 @@ def _coerce_checkpoint_values(payload: Any, mapper: Callable[[Any], Any]) -> Non
def _build_server_info(
config: RunnableConfig, parent_runtime: Runtime[Any]
) -> ServerInfo | None:
"""Build ServerInfo from config configurable.
"""Build ServerInfo from config metadata and configurable.
The server puts assistant_id/graph_id in config configurable and the
The server puts assistant_id/graph_id in config metadata and the
authenticated user dict in configurable["langgraph_auth_user"].
"""
metadata = config.get("metadata") or {}
configurable = config.get(CONF) or {}
assistant_id = configurable.get("assistant_id")
graph_id = configurable.get("graph_id")
assistant_id = metadata.get("assistant_id")
graph_id = metadata.get("graph_id")
# Read authenticated user from configurable (set by LangGraph Server).
# We prefer isinstance(BaseUser) but fall back to hasattr("identity")
+3 -3
View File
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
[project]
name = "langgraph"
version = "1.2.0a2"
version = "1.1.6"
description = "Building stateful, multi-actor applications with LLMs"
authors = []
requires-python = ">=3.10"
@@ -24,8 +24,8 @@ classifiers = [
'Programming Language :: Python :: 3.13',
]
dependencies = [
"langchain-core>=1.3.2,<2",
"langgraph-checkpoint>=4.0.3,<5.0.0",
"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",
"xxhash>=3.5.0",
+5 -539
View File
@@ -1,34 +1,18 @@
import operator
from collections.abc import Sequence
from typing import Annotated
import pytest
from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage
from langgraph.checkpoint.base import DELTA_SENTINEL
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.checkpoint.serde.types import _DeltaSnapshot
from typing_extensions import NotRequired, TypedDict
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 import START, StateGraph
from langgraph.graph.message import _messages_delta_reducer
from langgraph.graph.state import _get_channel
from langgraph.types import Overwrite
pytestmark = pytest.mark.anyio
# ---------------------------------------------------------------------------
# Core channel primitives
# ---------------------------------------------------------------------------
def test_last_value() -> None:
channel = LastValue(int).from_checkpoint(MISSING)
assert channel.ValueType is int
@@ -111,543 +95,25 @@ def test_untracked_value() -> None:
assert channel.ValueType is dict
assert channel.UpdateType is dict
# UntrackedValue should start empty
with pytest.raises(EmptyChannelError):
channel.get()
# Should be able to update with a value
test_data = {"session": "test", "temp": "dir"}
channel.update([test_data])
assert channel.get() == test_data
# Update with new value
new_data = {"session": "updated", "temp": "newdir"}
channel.update([new_data])
assert channel.get() == new_data
# On checkpoint, UntrackedValue should return MISSING
checkpoint = channel.checkpoint()
assert checkpoint is MISSING
# Creating from checkpoint with MISSING should start empty
new_channel = UntrackedValue(dict).from_checkpoint(checkpoint)
with pytest.raises(EmptyChannelError):
new_channel.get()
# ---------------------------------------------------------------------------
# DeltaChannel — message reducer
# ---------------------------------------------------------------------------
def test_delta_channel_basic_two_steps() -> None:
ch = DeltaChannel(_messages_delta_reducer, list).from_checkpoint(MISSING)
ch.update([HumanMessage(content="hi", id="h1")])
d1 = ch.checkpoint()
assert d1 is DELTA_SENTINEL
ch.update([AIMessage(content="hello", id="a1")])
d2 = ch.checkpoint()
assert d2 is DELTA_SENTINEL
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."""
spec = DeltaChannel(_messages_delta_reducer, list)
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:
spec = DeltaChannel(_messages_delta_reducer, list)
old_value = [HumanMessage(content="old", id="h1")]
ch = spec.from_checkpoint(old_value)
assert ch.get() == old_value
def test_delta_channel_overwrite() -> None:
ch = DeltaChannel(_messages_delta_reducer, list).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
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."""
spec = DeltaChannel(_messages_delta_reducer, list)
ch = spec.from_checkpoint(MISSING)
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"),
]
ch.update([RemoveMessage(id="a1")])
assert ch.get() == [HumanMessage(content="hi", id="h1")]
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."""
spec = DeltaChannel(_messages_delta_reducer, list)
ch = spec.from_checkpoint(MISSING)
ch.update([HumanMessage(content="original", id="h1")])
ch.update([HumanMessage(content="updated", id="h1")])
assert ch.get() == [HumanMessage(content="updated", id="h1")]
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."""
ch = DeltaChannel(_messages_delta_reducer, list).from_checkpoint(MISSING)
assert ch.checkpoint() is DELTA_SENTINEL
ch.update([HumanMessage(content="hi", id="h1")])
assert ch.checkpoint() is DELTA_SENTINEL
# ---------------------------------------------------------------------------
# DeltaChannel — snapshot frequency
# ---------------------------------------------------------------------------
def test_delta_channel_snapshot_step_based() -> None:
"""Snapshots fire on every Nth step regardless of whether the channel was written.
With snapshot_frequency=N, every Nth pregel step produces a _DeltaSnapshot
blob even if the channel had no write that step (eager snapshot). This
bounds the ancestor walk to at most N steps on any read.
"""
class State(TypedDict):
messages: Annotated[
list, DeltaChannel(_messages_delta_reducer, snapshot_frequency=5)
]
other: str
def node_a(state: State) -> dict:
i = len(state["messages"]) // 2
return {"messages": [AIMessage(content=f"a{i}", id=f"a{i}")]}
def node_b(state: State) -> dict:
return {"other": "y"}
g = StateGraph(State)
g.add_node("a", node_a)
g.add_node("b", node_b)
g.add_edge(START, "a")
g.add_edge("a", "b")
saver = InMemorySaver()
graph = g.compile(checkpointer=saver)
config = {"configurable": {"thread_id": "t1"}}
for i in range(6):
graph.invoke(
{"messages": [HumanMessage(content=f"h{i}", id=f"h{i}")], "other": ""},
config,
)
msg_blob_values = [
saver.serde.loads_typed((type_tag, blob))
for k, (type_tag, blob) in saver.blobs.items()
if k[2] == "messages" and type_tag == "msgpack" and blob
]
snapshots = [v for v in msg_blob_values if isinstance(v, _DeltaSnapshot)]
assert snapshots, "expected at least one _DeltaSnapshot blob for messages"
state = graph.get_state(config)
assert len(state.values["messages"]) == 12 # 6 human + 6 AI
def test_delta_channel_snapshot_fires_even_when_not_written() -> None:
"""Eager snapshot: _DeltaSnapshot stored at snapshot step even when the
channel had no write that step (node_b doesn't touch messages).
"""
class State(TypedDict):
messages: Annotated[
list, DeltaChannel(_messages_delta_reducer, snapshot_frequency=3)
]
tick: int
def writer(state: State) -> dict:
i = len(state["messages"]) // 2
return {"messages": [AIMessage(content=f"a{i}", id=f"a{i}")]}
def ticker(state: State) -> dict:
return {"tick": state["tick"] + 1}
g = StateGraph(State)
g.add_node("writer", writer)
g.add_node("ticker", ticker)
g.add_edge(START, "writer")
g.add_edge("writer", "ticker")
saver = InMemorySaver()
graph = g.compile(checkpointer=saver)
config = {"configurable": {"thread_id": "t1"}}
for i in range(5):
graph.invoke(
{"messages": [HumanMessage(content=f"h{i}", id=f"h{i}")], "tick": 0},
config,
)
msg_blobs = {
k: saver.serde.loads_typed((t, b))
for k, (t, b) in saver.blobs.items()
if k[2] == "messages" and t == "msgpack" and b
}
snapshots = {k: v for k, v in msg_blobs.items() if isinstance(v, _DeltaSnapshot)}
assert snapshots, (
"eager snapshots must fire even on steps where messages wasn't written"
)
state = graph.get_state(config)
assert len(state.values["messages"]) == 10 # 5 human + 5 AI
# ---------------------------------------------------------------------------
# DeltaChannel — end-to-end (InMemorySaver)
# ---------------------------------------------------------------------------
def test_delta_channel_inmemory_saver_assembles_writes() -> None:
"""InMemorySaver assembles writes from checkpoint_writes inside get_tuple."""
class State(TypedDict):
messages: Annotated[list, DeltaChannel(_messages_delta_reducer, list)]
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)
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
# ---------------------------------------------------------------------------
# DeltaChannel — dict reducer
# ---------------------------------------------------------------------------
def _delta_channel_with_type(op, typ):
"""Build a DeltaChannel with an explicit type via the Annotated injection path."""
return _get_channel("_test", Annotated[typ, DeltaChannel(op)])
def test_delta_channel_dict_reducer_fresh_channel() -> None:
"""DeltaChannel with a dict reducer starts as empty dict on MISSING checkpoint."""
def merge_dicts(state: dict, writes: list) -> dict:
result = dict(state)
for w in writes:
result.update(w)
return result
ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
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."""
def merge_dicts(state: dict, writes: list) -> dict:
result = dict(state)
for w in writes:
result.update(w)
return result
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(state: dict, writes: list) -> dict:
result = dict(state)
for w in writes:
result.update(w)
return result
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."""
def merge_files(state: dict, writes: list) -> dict:
result = dict(state)
for w in writes:
for k, v in w.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"}])
ch.update([{"file1.py": None, "file3.py": "content3"}])
assert ch.get() == {"file2.py": "content2", "file3.py": "content3"}
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."""
def merge_dicts(state: dict, writes: list) -> dict:
result = dict(state)
for w in writes:
result.update(w)
return result
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."""
def merge_dicts(state: dict, writes: list) -> dict:
result = dict(state)
for w in writes:
result.update(w)
return result
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]`."""
def merge_dicts(state: dict, writes: list) -> dict:
result = dict(state)
for w in writes:
result.update(w)
return result
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."""
def merge_files(state: dict, writes: list) -> dict:
result = dict(state)
for w in writes:
for k, v in w.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)
saved = saver.get_tuple(config)
assert saved is not None
assert saved.checkpoint["channel_values"]["files"] 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",
}
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(state: dict, writes: list) -> dict:
result = dict(state)
for w in writes:
result.update(w)
return result
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}
# ---------------------------------------------------------------------------
# DeltaChannel — 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(_messages_delta_reducer, list)
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(_messages_delta_reducer, list)
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(state, writes):
return writes[-1] if writes else state
spec = DeltaChannel(replace, list)
ch = spec.from_checkpoint(None)
ch.replay_writes([("t0", "x", "after")])
assert ch.get() == "after"
+20 -1
View File
@@ -1,7 +1,8 @@
import pytest
from langchain_core.callbacks import AsyncCallbackManager
from langchain_core.callbacks import AsyncCallbackManager, BaseCallbackHandler
from langgraph._internal._config import get_async_callback_manager_for_config
from langgraph.graph import StateGraph
pytestmark = pytest.mark.anyio
@@ -17,3 +18,21 @@ def test_new_async_manager_merges_tags_with_config() -> None:
config = {"callbacks": None, "tags": ["a"]}
manager = get_async_callback_manager_for_config(config, tags=["b"])
assert manager.inheritable_tags == ["a", "b"]
async def test_with_config_callbacks_preserved_in_astream_events() -> None:
class TrackingCallback(BaseCallbackHandler):
def __init__(self) -> None:
self.called = False
def on_chain_start(self, *args, **kwargs) -> None:
self.called = True
builder = StateGraph(dict)
builder.add_node("node", lambda state: state)
builder.add_edge("__start__", "node")
cb = TrackingCallback()
graph = builder.compile().with_config({"callbacks": [cb]})
async for _ in graph.astream_events({}, version="v2"):
pass
assert cb.called
@@ -1,478 +0,0 @@
"""Benchmark: DeltaChannel snapshot_frequency — storage vs. read-depth tradeoff.
Run directly: python tests/test_delta_channel_benchmark.py
Run via pytest: pytest tests/test_delta_channel_benchmark.py -s
Part 1 baseline (original): DeltaChannel(inf) vs add_messages (BinOp).
Part 2 snapshot_frequency sweep: shows the storage/read-latency tradeoff
across frequencies [1, 5, 10, 50, inf] at scale.
Key insight:
snapshot_frequency=inf O(N) storage, O(N) read depth (pure delta)
snapshot_frequency=N O(/N) storage, O(N) read depth bounded by freq
snapshot_frequency=1 O() storage, O(1) read depth (full snapshot)
"""
from __future__ import annotations
import contextlib
import math
import os
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 _messages_delta_reducer, add_messages
try:
from langgraph.checkpoint.postgres import PostgresSaver
_POSTGRES_AVAILABLE = True
_POSTGRES_URI = os.environ.get(
"LANGGRAPH_BENCH_POSTGRES_URI",
"postgres://postgres@localhost:5432/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(_messages_delta_reducer)]
def _make_delta_state(snapshot_frequency: int | float) -> type:
"""Create a TypedDict with DeltaChannel at the given snapshot_frequency."""
channel = DeltaChannel(
_messages_delta_reducer, snapshot_frequency=snapshot_frequency
)
# Use the functional TypedDict form so the Annotated type is stored as an
# already-evaluated object rather than a forward-reference string (which
# would fail when get_type_hints tries to resolve 'snapshot_frequency').
return TypedDict( # type: ignore[return-value]
f"DeltaState_freq{snapshot_frequency}",
{"messages": Annotated[list, channel]},
)
# ---------------------------------------------------------------------------
# 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).
Read latency is the average of 5 get_state calls after the full history
is built forces state rehydration including ancestor replay if needed.
"""
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
t1 = time.perf_counter()
for _ in range(5):
graph.get_state(config)
read_elapsed = (time.perf_counter() - t1) / 5
blob_bytes = (
_total_blob_bytes(graph.checkpointer)
if isinstance(graph.checkpointer, MemorySaver)
else -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:
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"
# ---------------------------------------------------------------------------
# Checkpointer factories
# ---------------------------------------------------------------------------
@contextlib.contextmanager
def _pg_saver(thread_id: str = "bench"):
"""Context manager that yields a fresh PostgresSaver and cleans up after."""
with PostgresSaver.from_conn_string(_POSTGRES_URI) as saver:
saver.setup()
with saver._cursor() as cur:
for tbl in ("checkpoints", "checkpoint_blobs", "checkpoint_writes"):
cur.execute(f"DELETE FROM {tbl} WHERE thread_id = %s", (thread_id,))
yield saver
with saver._cursor() as cur:
for tbl in ("checkpoints", "checkpoint_blobs", "checkpoint_writes"):
cur.execute(f"DELETE FROM {tbl} WHERE thread_id = %s", (thread_id,))
def _checkpointers() -> list[tuple[str, Any]]:
"""Return (label, saver_or_None) pairs for available checkpointers."""
result: list[tuple[str, Any]] = [("InMemory", None)]
if _POSTGRES_AVAILABLE:
try:
import psycopg
psycopg.connect(_POSTGRES_URI).close()
result.append(("Postgres", "postgres"))
except Exception:
pass
return result
# ---------------------------------------------------------------------------
# Part 1: baseline DeltaChannel(inf) vs add_messages
# ---------------------------------------------------------------------------
BASELINE_TURN_COUNTS = [10, 25, 50, 100, 500]
DELTA_ONLY_TURN_COUNTS = [1000]
def _run_baseline_for_checkpointer(cp_label: str, cp_hint: Any) -> None:
W = 72
def _make_saver():
if cp_hint is None:
return contextlib.nullcontext(None)
return _pg_saver()
rows: list[tuple[int, Any, Any, Any, Any, Any, Any]] = []
for turns in BASELINE_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))
def _bytes_or_na(v: Any) -> str:
if v is None or 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"
print(f"\n [{cp_label}] Storage (blob bytes)")
print(
f" {'turns':>6} {'ctx':>10} {'add_msgs':>12} {'delta(inf)':>12} {'savings':>8}"
)
print(" " + "-" * (W - 2))
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} {ratio_str:>8}"
)
print(f"\n [{cp_label}] Read latency (avg of 5 get_state calls)")
print(f" {'turns':>6} {'ctx':>10} {'add_msgs':>12} {'delta(inf)':>12}")
print(" " + "-" * (W - 2))
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}"
)
def run_baseline_benchmark() -> None:
print()
print("Part 1 — DeltaChannel(inf) vs add_messages: storage & latency")
print("=" * 72)
for cp_label, cp_hint in _checkpointers():
_run_baseline_for_checkpointer(cp_label, cp_hint)
print()
# ---------------------------------------------------------------------------
# Part 2: snapshot_frequency sweep
# ---------------------------------------------------------------------------
# Frequencies to test. 1 = always snapshot (like BinOp), inf = pure delta.
SNAPSHOT_FREQUENCIES: list[int | float] = [1, 5, 10, 50, math.inf]
# Turn counts for the sweep — high enough to show storage divergence.
SWEEP_TURN_COUNTS = [50, 100, 500]
def _freq_label(freq: int | float) -> str:
if freq == math.inf:
return "inf"
return str(int(freq))
def _run_sweep_for_checkpointer(cp_label: str, cp_hint: Any) -> None:
def _make_saver():
if cp_hint is None:
return contextlib.nullcontext(None)
return _pg_saver()
# Collect results: {turns: {freq_label: (write_s, read_s, bytes)}}
results: dict[int, dict[str, tuple[float, float, int]]] = {}
for turns in SWEEP_TURN_COUNTS:
results[turns] = {}
for freq in SNAPSHOT_FREQUENCIES:
state_cls = _make_delta_state(freq)
with _make_saver() as saver:
wt, rt, bb = _run_turns(turns, state_cls, saver)
results[turns][_freq_label(freq)] = (wt, rt, bb)
freq_labels = [_freq_label(f) for f in SNAPSHOT_FREQUENCIES]
col_w = 12
header = f" {'turns':>6} {'ctx':>10}" + "".join(
f" {f'freq={freq_label}':>{col_w}}" for freq_label in freq_labels
)
print(f"\n [{cp_label}] Storage (blob bytes) — lower is better")
print(header)
print(" " + "-" * (len(header) - 2))
for turns in SWEEP_TURN_COUNTS:
row = f" {turns:>6} {_approx_tokens(turns):>10}"
for label in freq_labels:
_, _, bb = results[turns][label]
row += f" {_fmt_bytes(bb) if bb >= 0 else 'n/a':>{col_w}}"
print(row)
print(f"\n [{cp_label}] Read latency (avg of 5 get_state) — lower is better")
print(header)
print(" " + "-" * (len(header) - 2))
for turns in SWEEP_TURN_COUNTS:
row = f" {turns:>6} {_approx_tokens(turns):>10}"
for label in freq_labels:
_, rt, _ = results[turns][label]
row += f" {f'{rt * 1000:.1f}ms':>{col_w}}"
print(row)
print(
f"\n [{cp_label}] Per-invoke write latency (total / turns) — lower is better"
)
print(header)
print(" " + "-" * (len(header) - 2))
for turns in SWEEP_TURN_COUNTS:
row = f" {turns:>6} {_approx_tokens(turns):>10}"
for label in freq_labels:
wt, _, _ = results[turns][label]
row += f" {f'{(wt / turns) * 1000:.1f}ms':>{col_w}}"
print(row)
def run_snapshot_freq_benchmark() -> None:
print()
print("Part 2 — DeltaChannel snapshot_frequency sweep")
print("Lower freq → fewer snapshots → less storage but deeper read replay")
print("=" * 80)
for cp_label, cp_hint in _checkpointers():
_run_sweep_for_checkpointer(cp_label, cp_hint)
print()
print("Legend:")
print(
" freq=1 snapshot every write (full blob always — same as add_messages / BinOp)"
)
print(" freq=N snapshot every N writes; read walks at most N ancestor writes")
print(" freq=inf pure delta; read walks entire ancestor chain")
print()
# ---------------------------------------------------------------------------
# Pytest entry points
# ---------------------------------------------------------------------------
@pytest.mark.skip(
reason="slow benchmark — run manually with: python tests/test_delta_channel_benchmark.py"
)
def test_delta_channel_baseline_benchmark(capsys: Any) -> None:
"""DeltaChannel(inf) uses less storage than add_messages at scale."""
with capsys.disabled():
run_baseline_benchmark()
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}"
)
@pytest.mark.skip(
reason="slow benchmark — run manually with: python tests/test_delta_channel_benchmark.py"
)
def test_snapshot_freq_benchmark(capsys: Any) -> None:
"""snapshot_frequency trades storage for bounded read depth."""
with capsys.disabled():
run_snapshot_freq_benchmark()
# Correctness: results at all frequencies should agree on final state.
n_turns = 20
states: dict[str, list] = {}
for freq in SNAPSHOT_FREQUENCIES:
state_cls = _make_delta_state(freq)
graph = _make_graph(state_cls)
config = {"configurable": {"thread_id": "correctness"}}
for i in range(n_turns):
graph.invoke(
{"messages": [HumanMessage(content=_human_content(i), id=f"h{i}")]},
config,
)
state = graph.get_state(config)
states[_freq_label(freq)] = [m.id for m in state.values["messages"]]
ref = states["inf"]
for label, msg_ids in states.items():
assert msg_ids == ref, (
f"freq={label} produced different message IDs than freq=inf"
)
# ---------------------------------------------------------------------------
# Script entry point
# ---------------------------------------------------------------------------
if __name__ == "__main__":
run_baseline_benchmark()
run_snapshot_freq_benchmark()
sys.exit(0)
@@ -1,613 +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 langchain_core.messages import AIMessage, HumanMessage
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
from langgraph.graph.message import _messages_delta_reducer, add_messages
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 _list_concat(state: list, writes: list) -> list:
result = list(state)
for w in writes:
result.extend(w if isinstance(w, list) else [w])
return result
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(_list_concat)]
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}"
# ---------------------------------------------------------------------------
# 9. Migration from `add_messages` → `DeltaChannel(_messages_delta_reducer)`
#
# `add_messages` is the primary real-world use case: it creates a
# BinaryOperatorAggregate with dedup-by-ID and RemoveMessage semantics.
# After swapping the annotation to DeltaChannel, pre-migration blobs
# (plain lists of Message objects) must be used directly as the seed.
# ---------------------------------------------------------------------------
def _add_messages_graph(checkpointer: Any) -> Any:
class MessagesState(TypedDict):
messages: Annotated[list, add_messages]
return (
StateGraph(MessagesState)
.add_node("noop", _noop)
.add_edge(START, "noop")
.add_edge("noop", END)
.compile(checkpointer=checkpointer)
)
def _delta_messages_graph(checkpointer: Any) -> Any:
class DeltaMessagesState(TypedDict):
messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
return (
StateGraph(DeltaMessagesState)
.add_node("noop", _noop)
.add_edge(START, "noop")
.add_edge("noop", END)
.compile(checkpointer=checkpointer)
)
def test_add_messages_to_delta_migration_preserves_message_history() -> None:
"""Migration from `add_messages` to `DeltaChannel(_messages_delta_reducer)`
preserves message ordering and IDs at both the tip and settled ancestor
boundaries.
The pre-migration blob is a plain list of Message objects; DeltaChannel
must use it directly as the seed without walking ancestors past it.
"""
checkpointer = InMemorySaver()
config = {"configurable": {"thread_id": "add-messages-migration"}}
pre_graph = _add_messages_graph(checkpointer)
pre_graph.invoke({"messages": [HumanMessage(content="hello", id="h1")]}, config)
pre_graph.invoke({"messages": [AIMessage(content="hi", id="a1")]}, config)
pre_graph.invoke({"messages": [HumanMessage(content="thanks", id="h2")]}, config)
pre_tip = pre_graph.get_state(config)
assert [m.id for m in pre_tip.values["messages"]] == ["h1", "a1", "h2"]
delta_graph = _delta_messages_graph(checkpointer)
# Tip: latest checkpoint has a full list blob — must use it directly.
snap = delta_graph.get_state(config)
assert [m.id for m in snap.values["messages"]] == ["h1", "a1", "h2"], (
f"tip hydration mismatch: got {[m.id for m in snap.values['messages']]}"
)
# Settled ancestor boundaries must also match.
pre_settled = [
[m.id for m in s.values.get("messages", [])]
for s in pre_graph.get_state_history(config)
if s.next == ("__start__",)
]
delta_settled = [
[m.id for m in s.values.get("messages", [])]
for s in delta_graph.get_state_history(config)
if s.next == ("__start__",)
]
assert delta_settled == pre_settled, (
f"settled boundary mismatch after migration: "
f"pre={pre_settled}, delta={delta_settled}"
)
async def test_add_messages_to_delta_migration_preserves_message_history_async() -> (
None
):
"""Async variant of the add_messages migration test."""
checkpointer = InMemorySaver()
config = {"configurable": {"thread_id": "add-messages-migration-async"}}
pre_graph = _add_messages_graph(checkpointer)
await pre_graph.ainvoke(
{"messages": [HumanMessage(content="hello", id="h1")]}, config
)
await pre_graph.ainvoke({"messages": [AIMessage(content="hi", id="a1")]}, config)
delta_graph = _delta_messages_graph(checkpointer)
snap = await delta_graph.aget_state(config)
assert [m.id for m in snap.values["messages"]] == ["h1", "a1"], (
f"async tip hydration mismatch: got {[m.id for m in snap.values['messages']]}"
)
@@ -1,344 +0,0 @@
from __future__ import annotations
import sys
from typing import Any
import pytest
from langchain_core.callbacks.base import BaseCallbackHandler
from langchain_core.callbacks.manager import CallbackManager
from langgraph.checkpoint.memory import InMemorySaver
from typing_extensions import TypedDict
from langgraph.callbacks import (
GraphCallbackHandler,
GraphInterruptEvent,
GraphResumeEvent,
)
from langgraph.graph import START, StateGraph
from langgraph.types import Command, Interrupt, interrupt
NEEDS_CONTEXTVARS = pytest.mark.skipif(
sys.version_info < (3, 11),
reason="Python 3.11+ is required for async contextvars support",
)
class _GraphEventHandler(GraphCallbackHandler):
def __init__(self) -> None:
self.interrupt_events: list[GraphInterruptEvent] = []
self.resume_events: list[GraphResumeEvent] = []
def on_interrupt(self, event: GraphInterruptEvent) -> Any:
self.interrupt_events.append(event)
def on_resume(self, event: GraphResumeEvent) -> Any:
self.resume_events.append(event)
class _LangChainCustomEventHandler(BaseCallbackHandler):
run_inline = True
def __init__(self) -> None:
self.events: list[str] = []
def on_custom_event(self, name: str, data: Any, **kwargs: Any) -> Any:
self.events.append(name)
class _RaisingGraphEventHandler(GraphCallbackHandler):
def __init__(
self,
*,
raise_on_interrupt: bool = False,
raise_on_resume: bool = False,
raise_error: bool = False,
) -> None:
self.raise_on_interrupt = raise_on_interrupt
self.raise_on_resume = raise_on_resume
self.raise_error = raise_error
def on_interrupt(self, event: GraphInterruptEvent) -> Any:
if self.raise_on_interrupt:
raise ValueError("boom-interrupt")
def on_resume(self, event: GraphResumeEvent) -> Any:
if self.raise_on_resume:
raise ValueError("boom-resume")
class _AsyncRaisingGraphEventHandler(GraphCallbackHandler):
def __init__(
self,
*,
raise_on_interrupt: bool = False,
raise_on_resume: bool = False,
raise_error: bool = False,
) -> None:
self.raise_on_interrupt = raise_on_interrupt
self.raise_on_resume = raise_on_resume
self.raise_error = raise_error
async def on_interrupt(self, event: GraphInterruptEvent) -> Any:
if self.raise_on_interrupt:
raise ValueError("boom-interrupt")
async def on_resume(self, event: GraphResumeEvent) -> Any:
if self.raise_on_resume:
raise ValueError("boom-resume")
class _State(TypedDict):
answer: str | None
def _build_interrupt_graph() -> Any:
def ask(state: _State) -> _State:
answer = interrupt("Provide value")
return {"answer": answer}
builder = StateGraph(_State)
builder.add_node("ask", ask)
builder.add_edge(START, "ask")
return builder.compile(checkpointer=InMemorySaver())
def test_graph_callbacks_interrupt_and_resume_sync() -> None:
graph = _build_interrupt_graph()
handler = _GraphEventHandler()
langchain_handler = _LangChainCustomEventHandler()
config = {
"configurable": {"thread_id": "graph-callback-sync"},
"callbacks": [langchain_handler, handler],
}
first = graph.invoke({"answer": None}, config)
assert "__interrupt__" in first
assert len(handler.interrupt_events) == 1
assert handler.interrupt_events[0].interrupts
assert isinstance(handler.interrupt_events[0].interrupts[0], Interrupt)
assert handler.interrupt_events[0].checkpoint_ns == ()
assert langchain_handler.events == []
handler.resume_events.clear()
resumed = graph.invoke(Command(resume="done"), config)
assert resumed == {"answer": "done"}
assert len(handler.resume_events) == 1
assert handler.resume_events[0].checkpoint_ns == ()
assert langchain_handler.events == []
@pytest.mark.anyio
@NEEDS_CONTEXTVARS
async def test_graph_callbacks_interrupt_and_resume_async() -> None:
graph = _build_interrupt_graph()
handler = _GraphEventHandler()
langchain_handler = _LangChainCustomEventHandler()
config = {
"configurable": {"thread_id": "graph-callback-async"},
"callbacks": [langchain_handler, handler],
}
first = await graph.ainvoke({"answer": None}, config)
assert "__interrupt__" in first
assert len(handler.interrupt_events) == 1
assert handler.interrupt_events[0].interrupts
assert isinstance(handler.interrupt_events[0].interrupts[0], Interrupt)
assert handler.interrupt_events[0].checkpoint_ns == ()
assert langchain_handler.events == []
handler.resume_events.clear()
resumed = await graph.ainvoke(Command(resume="done"), config)
assert resumed == {"answer": "done"}
assert len(handler.resume_events) == 1
assert handler.resume_events[0].checkpoint_ns == ()
assert langchain_handler.events == []
def test_graph_callbacks_continue_when_interrupt_handler_raises_sync() -> None:
graph = _build_interrupt_graph()
raising_handler = _RaisingGraphEventHandler(raise_on_interrupt=True)
recording_handler = _GraphEventHandler()
first = graph.invoke(
{"answer": None},
{
"configurable": {"thread_id": "graph-callback-sync-raises"},
"callbacks": [raising_handler, recording_handler],
},
)
assert "__interrupt__" in first
assert len(recording_handler.interrupt_events) == 1
def test_graph_callbacks_continue_when_resume_handler_raises_sync() -> None:
graph = _build_interrupt_graph()
raising_handler = _RaisingGraphEventHandler(raise_on_resume=True)
recording_handler = _GraphEventHandler()
config = {
"configurable": {"thread_id": "graph-callback-sync-raises-resume"},
"callbacks": [raising_handler, recording_handler],
}
first = graph.invoke({"answer": None}, config)
assert "__interrupt__" in first
resumed = graph.invoke(Command(resume="done"), config)
assert resumed == {"answer": "done"}
assert len(recording_handler.resume_events) == 1
def test_graph_callbacks_raise_error_propagates_sync() -> None:
graph = _build_interrupt_graph()
raising_handler = _RaisingGraphEventHandler(
raise_on_interrupt=True,
raise_error=True,
)
with pytest.raises(ValueError, match="boom-interrupt"):
graph.invoke(
{"answer": None},
{
"configurable": {"thread_id": "graph-callback-sync-raise-error"},
"callbacks": [raising_handler],
},
)
@pytest.mark.anyio
@NEEDS_CONTEXTVARS
async def test_graph_callbacks_continue_when_handler_raises_async() -> None:
graph = _build_interrupt_graph()
raising_interrupt_handler = _AsyncRaisingGraphEventHandler(raise_on_interrupt=True)
recording_handler = _GraphEventHandler()
config = {
"configurable": {"thread_id": "graph-callback-async-raises-interrupt"},
"callbacks": [raising_interrupt_handler, recording_handler],
}
first = await graph.ainvoke({"answer": None}, config)
assert "__interrupt__" in first
assert len(recording_handler.interrupt_events) == 1
graph = _build_interrupt_graph()
raising_resume_handler = _AsyncRaisingGraphEventHandler(raise_on_resume=True)
recording_handler = _GraphEventHandler()
config = {
"configurable": {"thread_id": "graph-callback-async-raises-resume"},
"callbacks": [raising_resume_handler, recording_handler],
}
first = await graph.ainvoke({"answer": None}, config)
assert "__interrupt__" in first
resumed = await graph.ainvoke(Command(resume="done"), config)
assert resumed == {"answer": "done"}
assert len(recording_handler.resume_events) == 1
@pytest.mark.anyio
@NEEDS_CONTEXTVARS
async def test_graph_callbacks_raise_error_propagates_async() -> None:
graph = _build_interrupt_graph()
raising_handler = _AsyncRaisingGraphEventHandler(
raise_on_interrupt=True,
raise_error=True,
)
with pytest.raises(ValueError, match="boom-interrupt"):
await graph.ainvoke(
{"answer": None},
{
"configurable": {"thread_id": "graph-callback-async-raise-error"},
"callbacks": [raising_handler],
},
)
def test_graph_callbacks_accept_base_callback_manager() -> None:
graph = _build_interrupt_graph()
graph_handler = _GraphEventHandler()
custom_handler = _LangChainCustomEventHandler()
manager = CallbackManager.configure(inheritable_callbacks=[custom_handler])
manager.add_handler(graph_handler)
first = graph.invoke(
{"answer": None},
{
"configurable": {"thread_id": "graph-callback-base-manager"},
"callbacks": manager,
},
)
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",
+19 -330
View File
@@ -16,7 +16,7 @@ from typing import Annotated, Any, Literal, get_type_hints
import pytest
from langchain_core.language_models import GenericFakeChatModel
from langchain_core.messages import AIMessage, AnyMessage, HumanMessage, RemoveMessage
from langchain_core.messages import AIMessage, AnyMessage, HumanMessage
from langchain_core.runnables import (
RunnableConfig,
RunnableLambda,
@@ -25,7 +25,6 @@ from langchain_core.runnables import (
from langchain_core.runnables.graph import Edge
from langgraph.cache.base import BaseCache
from langgraph.checkpoint.base import (
DELTA_SENTINEL,
BaseCheckpointSaver,
Checkpoint,
CheckpointMetadata,
@@ -42,7 +41,6 @@ from typing_extensions import NotRequired, TypedDict
from langgraph._internal._constants import CONFIG_KEY_NODE_FINISHED, ERROR, PULL
from langgraph.channels.binop import BinaryOperatorAggregate
from langgraph.channels.delta import DeltaChannel
from langgraph.channels.ephemeral_value import EphemeralValue
from langgraph.channels.last_value import LastValue
from langgraph.channels.topic import Topic
@@ -51,7 +49,7 @@ from langgraph.config import get_stream_writer
from langgraph.errors import GraphRecursionError, InvalidUpdateError, ParentCommand
from langgraph.func import entrypoint, task
from langgraph.graph import END, START, StateGraph
from langgraph.graph.message import MessagesState, _messages_delta_reducer, add_messages
from langgraph.graph.message import MessagesState, add_messages
from langgraph.pregel import (
NodeBuilder,
Pregel,
@@ -617,11 +615,8 @@ 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"]
@@ -629,7 +624,7 @@ 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:
@@ -1334,22 +1329,20 @@ def test_imp_nested(
}
thread1 = {"configurable": {"thread_id": "1"}}
result = [*graph.stream([0, 1], thread1, durability=durability)]
# nested tasks run concurrently so output order is non-deterministic
assert sorted(result[:-1], key=lambda d: str(d)) == [
{"mapper": "00"},
{"mapper": "11"},
assert [*graph.stream([0, 1], thread1, durability=durability)] == [
{"submapper": "0"},
{"mapper": "00"},
{"submapper": "1"},
{"mapper": "11"},
{
"__interrupt__": (
Interrupt(
value="question",
id=AnyStr(),
),
)
},
]
assert result[-1] == {
"__interrupt__": (
Interrupt(
value="question",
id=AnyStr(),
),
)
}
assert graph.invoke(Command(resume="answer"), thread1, durability=durability) == [
"00answera",
@@ -6276,7 +6269,7 @@ def test_sync_streaming_with_functional_api() -> None:
@task()
def slow() -> dict:
time.sleep(time_delay) # Simulate a delay of 10 ms
return {"tic": time.monotonic()}
return {"tic": time.time()}
@entrypoint()
def graph(inputs: dict) -> list:
@@ -6289,7 +6282,7 @@ def test_sync_streaming_with_functional_api() -> None:
for chunk in graph.stream({}):
if "slow" not in chunk: # We'll just look at the updates from `slow`
continue
arrival_times.append(time.monotonic())
arrival_times.append(time.time())
assert len(arrival_times) == 2
delta = arrival_times[1] - arrival_times[0]
@@ -6898,6 +6891,7 @@ 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",
@@ -6907,60 +6901,6 @@ 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
@@ -9402,254 +9342,3 @@ 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."""
class State(TypedDict):
messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
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."""
class State(TypedDict):
messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
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."""
class State(TypedDict):
messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
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."""
class State(TypedDict):
messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
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"
async def test_delta_channel_durability_exit_stores_snapshot() -> None:
"""DeltaChannel must reload from a durability='exit' checkpoint."""
class State(TypedDict):
messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
def respond(state: State) -> dict:
return {"messages": [AIMessage(content="reply", id="ai1")]}
builder = StateGraph(State)
builder.add_node("respond", respond)
builder.add_edge(START, "respond")
graph = builder.compile(checkpointer=InMemorySaver())
config = {"configurable": {"thread_id": "delta-exit-test"}}
result = graph.invoke(
{"messages": [HumanMessage(content="hello", id="h1")]},
config,
durability="exit",
)
assert [m.content for m in result["messages"]] == ["hello", "reply"]
state = graph.get_state(config)
assert [m.content for m in state.values["messages"]] == ["hello", "reply"]
async def test_delta_channel_async_write_ordering() -> None:
"""In async mode, DeltaChannel write futures are awaited before the checkpoint
is committed, so aput_writes always precedes aput for sentinel checkpoints."""
class State(TypedDict):
messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
def respond(state: State) -> dict:
i = len(state["messages"])
return {"messages": [AIMessage(content=f"r{i}", id=f"ai{i}")]}
order: list[str] = []
original_aput_writes = InMemorySaver.aput_writes
original_aput = InMemorySaver.aput
async def tracked_aput_writes(self, config, writes, task_id, task_path=""):
result = await original_aput_writes(self, config, writes, task_id, task_path)
order.append("aput_writes")
return result
async def tracked_aput(self, config, checkpoint, metadata, new_versions):
has_sentinel = any(
v is DELTA_SENTINEL for v in checkpoint.get("channel_values", {}).values()
)
order.append("aput_sentinel" if has_sentinel else "aput_other")
return await original_aput(self, config, checkpoint, metadata, new_versions)
InMemorySaver.aput_writes = tracked_aput_writes
InMemorySaver.aput = tracked_aput
try:
builder = StateGraph(State)
builder.add_node("respond", respond)
builder.add_edge(START, "respond")
graph = builder.compile(checkpointer=InMemorySaver())
config = {"configurable": {"thread_id": "async-ordering-test"}}
for i in range(3):
await graph.ainvoke(
{"messages": [HumanMessage(content=f"h{i}", id=f"h{i}")]}, config
)
# Every aput_sentinel must be preceded by at least one aput_writes
for i, event in enumerate(order):
if event == "aput_sentinel":
preceding = order[:i]
assert "aput_writes" in preceding, (
f"aput_sentinel at {i} had no preceding aput_writes: {order}"
)
last_write_idx = max(
j for j, e in enumerate(order[:i]) if e == "aput_writes"
)
assert last_write_idx < i, (
f"aput_writes at {last_write_idx} should precede aput_sentinel at {i}: {order}"
)
finally:
InMemorySaver.aput_writes = original_aput_writes
InMemorySaver.aput = original_aput
state = await graph.aget_state(config)
assert len(state.values["messages"]) == 6 # 3 human + 3 AI
+4 -98
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
@@ -2086,11 +2085,8 @@ 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 +2094,7 @@ 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:
@@ -6101,36 +6097,6 @@ async def test_parent_command(
)
async def test_delta_channel_durability_exit_stores_snapshot_async() -> None:
"""DeltaChannel must reload from an async durability='exit' checkpoint."""
from langchain_core.messages import AIMessage
from langgraph.channels.delta import DeltaChannel
from langgraph.graph.message import _messages_delta_reducer
class State(TypedDict):
messages: Annotated[list, DeltaChannel(_messages_delta_reducer)]
async def respond(state: State) -> dict:
return {"messages": [AIMessage(content="reply", id="ai1")]}
builder = StateGraph(State)
builder.add_node("respond", respond)
builder.add_edge(START, "respond")
graph = builder.compile(checkpointer=InMemorySaver())
config = {"configurable": {"thread_id": "delta-exit-async-test"}}
result = await graph.ainvoke(
{"messages": [HumanMessage(content="hello", id="h1")]},
config,
durability="exit",
)
assert [m.content for m in result["messages"]] == ["hello", "reply"]
state = await graph.aget_state(config)
assert [m.content for m in state.values["messages"]] == ["hello", "reply"]
@NEEDS_CONTEXTVARS
async def test_interrupt_subgraph(async_checkpointer: BaseCheckpointSaver) -> None:
class State(TypedDict):
@@ -7575,6 +7541,7 @@ 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",
@@ -7584,67 +7551,6 @@ 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 -10
View File
@@ -501,13 +501,13 @@ async def test_execution_info_populated_in_graph_async() -> None:
assert isinstance(info.node_first_attempt_time, float)
def test_server_info_from_configurable() -> None:
"""server_info is built from assistant_id/graph_id in config configurable."""
def test_server_info_from_metadata() -> None:
"""server_info is built from assistant_id/graph_id in config metadata."""
captured: dict[str, Any] = {}
compiled = _make_capture_graph(captured)
compiled.invoke(
{"message": "hi"},
config={"configurable": {"assistant_id": "asst-abc", "graph_id": "my-graph"}},
config={"metadata": {"assistant_id": "asst-abc", "graph_id": "my-graph"}},
)
si = captured["server_info"]
assert si is not None
@@ -516,8 +516,8 @@ def test_server_info_from_configurable() -> None:
assert si.user is None
def test_server_info_none_without_configurable() -> None:
"""server_info is None when no assistant_id/graph_id in configurable."""
def test_server_info_none_without_metadata() -> None:
"""server_info is None when no assistant_id/graph_id in metadata."""
captured: dict[str, Any] = {}
compiled = _make_capture_graph(captured)
compiled.invoke({"message": "hi"})
@@ -579,11 +579,8 @@ def test_server_info_user_from_auth_user() -> None:
compiled.invoke(
{"message": "hi"},
config={
"configurable": {
"langgraph_auth_user": proxy,
"assistant_id": "asst-proxy",
"graph_id": "graph-proxy",
},
"configurable": {"langgraph_auth_user": proxy},
"metadata": {"assistant_id": "asst-proxy", "graph_id": "graph-proxy"},
},
)
si = captured["server_info"]
+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",
}
+64 -83
View File
@@ -7,9 +7,6 @@ resolution-markers = [
"python_full_version < '3.11'",
]
[options]
prerelease-mode = "allow"
[[package]]
name = "aiosqlite"
version = "0.22.1"
@@ -527,61 +524,61 @@ toml = [
[[package]]
name = "cryptography"
version = "46.0.7"
version = "46.0.6"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "cffi", marker = "python_full_version >= '3.11' and python_full_version < '3.14' and platform_python_implementation != 'PyPy'" },
]
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sdist = { url = "https://files.pythonhosted.org/packages/a4/ba/04b1bd4218cbc58dc90ce967106d51582371b898690f3ae0402876cc4f34/cryptography-46.0.6.tar.gz", hash = "sha256:27550628a518c5c6c903d84f637fbecf287f6cb9ced3804838a1295dc1fd0759", size = 750542, upload-time = "2026-03-25T23:34:53.396Z" }
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[[package]]
+7 -23
View File
@@ -614,7 +614,6 @@ class _InjectedArgs:
store: str | None
runtime: str | None
all_injected_keys: set[str]
_optional_state_args: set[str]
class ToolNode(RunnableCallable):
@@ -808,7 +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,
)
@@ -843,7 +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,
)
@@ -1336,7 +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:
@@ -1364,20 +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:
@@ -1578,7 +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.
@@ -1621,7 +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
@@ -1870,7 +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)
@@ -1885,9 +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
@@ -1904,5 +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
+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" }
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[[package]]
name = "langgraph"
version = "1.1.9"
version = "1.1.6"
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" }
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[[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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{ 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]]
+1 -1
View File
@@ -3,6 +3,6 @@ from langgraph_sdk.client import get_client, get_sync_client
from langgraph_sdk.encryption import Encryption
from langgraph_sdk.encryption.types import EncryptionContext
__version__ = "0.3.13"
__version__ = "0.3.12"
__all__ = ["Auth", "Encryption", "EncryptionContext", "get_client", "get_sync_client"]
+1 -8
View File
@@ -12,10 +12,7 @@ from typing import Any, cast
import httpx
import orjson
from langgraph_sdk._shared.utilities import (
_orjson_default,
_validate_reconnect_location,
)
from langgraph_sdk._shared.utilities import _orjson_default
from langgraph_sdk.errors import _araise_for_status_typed
from langgraph_sdk.schema import QueryParamTypes, StreamPart
from langgraph_sdk.sse import SSEDecoder, aiter_lines_raw
@@ -167,7 +164,6 @@ class HttpClient:
loc = r.headers.get("location")
if reconnect_limit <= 0 or not loc:
return await _adecode_json(r)
_validate_reconnect_location(self.client.base_url, loc)
try:
return await _adecode_json(r)
except httpx.HTTPError:
@@ -246,9 +242,6 @@ class HttpClient:
reconnect_location = res.headers.get("location")
if reconnect_location:
_validate_reconnect_location(
self.client.base_url, reconnect_location
)
reconnect_path = reconnect_location
# parse SSE
-21
View File
@@ -26,7 +26,6 @@ from langgraph_sdk.schema import (
Durability,
IfNotExists,
Input,
LangSmithTracing,
MultitaskStrategy,
OnCompletionBehavior,
QueryParamTypes,
@@ -94,7 +93,6 @@ class RunsClient:
multitask_strategy: MultitaskStrategy | None = None,
if_not_exists: IfNotExists | None = None,
after_seconds: int | None = None,
langsmith_tracing: LangSmithTracing | None = None,
headers: Mapping[str, str] | None = None,
params: QueryParamTypes | None = None,
on_run_created: Callable[[RunCreateMetadata], None] | None = None,
@@ -126,7 +124,6 @@ class RunsClient:
multitask_strategy: MultitaskStrategy | None = None,
if_not_exists: IfNotExists | None = None,
after_seconds: int | None = None,
langsmith_tracing: LangSmithTracing | None = None,
headers: Mapping[str, str] | None = None,
params: QueryParamTypes | None = None,
on_run_created: Callable[[RunCreateMetadata], None] | None = None,
@@ -155,7 +152,6 @@ class RunsClient:
if_not_exists: IfNotExists | None = None,
webhook: str | None = None,
after_seconds: int | None = None,
langsmith_tracing: LangSmithTracing | None = None,
headers: Mapping[str, str] | None = None,
params: QueryParamTypes | None = None,
on_run_created: Callable[[RunCreateMetadata], None] | None = None,
@@ -184,7 +180,6 @@ class RunsClient:
if_not_exists: IfNotExists | None = None,
webhook: str | None = None,
after_seconds: int | None = None,
langsmith_tracing: LangSmithTracing | None = None,
headers: Mapping[str, str] | None = None,
params: QueryParamTypes | None = None,
on_run_created: Callable[[RunCreateMetadata], None] | None = None,
@@ -216,7 +211,6 @@ class RunsClient:
multitask_strategy: MultitaskStrategy | None = None,
if_not_exists: IfNotExists | None = None,
after_seconds: int | None = None,
langsmith_tracing: LangSmithTracing | None = None,
headers: Mapping[str, str] | None = None,
params: QueryParamTypes | None = None,
on_run_created: Callable[[RunCreateMetadata], None] | None = None,
@@ -256,8 +250,6 @@ class RunsClient:
Must be either 'reject' (raise error if missing), or 'create' (create new thread).
after_seconds: The number of seconds to wait before starting the run.
Use to schedule future runs.
langsmith_tracing: LangSmith tracing configuration. Allows routing traces
to a specific project or associating with a dataset example.
headers: Optional custom headers to include with the request.
params: Optional query parameters to include with the request.
on_run_created: Callback when a run is created.
@@ -334,7 +326,6 @@ class RunsClient:
"on_completion": on_completion,
"after_seconds": after_seconds,
"durability": durability,
"langsmith_tracer": langsmith_tracing,
}
endpoint = (
f"/threads/{thread_id}/runs/stream"
@@ -380,7 +371,6 @@ class RunsClient:
on_completion: OnCompletionBehavior | None = None,
if_not_exists: IfNotExists | None = None,
after_seconds: int | None = None,
langsmith_tracing: LangSmithTracing | None = None,
headers: Mapping[str, str] | None = None,
params: QueryParamTypes | None = None,
on_run_created: Callable[[RunCreateMetadata], None] | None = None,
@@ -409,7 +399,6 @@ class RunsClient:
multitask_strategy: MultitaskStrategy | None = None,
if_not_exists: IfNotExists | None = None,
after_seconds: int | None = None,
langsmith_tracing: LangSmithTracing | None = None,
headers: Mapping[str, str] | None = None,
params: QueryParamTypes | None = None,
on_run_created: Callable[[RunCreateMetadata], None] | None = None,
@@ -438,7 +427,6 @@ class RunsClient:
if_not_exists: IfNotExists | None = None,
on_completion: OnCompletionBehavior | None = None,
after_seconds: int | None = None,
langsmith_tracing: LangSmithTracing | None = None,
headers: Mapping[str, str] | None = None,
params: QueryParamTypes | None = None,
on_run_created: Callable[[RunCreateMetadata], None] | None = None,
@@ -474,8 +462,6 @@ class RunsClient:
Must be either 'reject' (raise error if missing), or 'create' (create new thread).
after_seconds: The number of seconds to wait before starting the run.
Use to schedule future runs.
langsmith_tracing: LangSmith tracing configuration. Allows routing traces
to a specific project or associating with a dataset example.
headers: Optional custom headers to include with the request.
on_run_created: Optional callback to call when a run is created.
durability: The durability to use for the run. Values are "sync", "async", or "exit".
@@ -586,7 +572,6 @@ class RunsClient:
"on_completion": on_completion,
"after_seconds": after_seconds,
"durability": durability,
"langsmith_tracer": langsmith_tracing,
}
payload = {k: v for k, v in payload.items() if v is not None}
@@ -641,7 +626,6 @@ class RunsClient:
multitask_strategy: MultitaskStrategy | None = None,
if_not_exists: IfNotExists | None = None,
after_seconds: int | None = None,
langsmith_tracing: LangSmithTracing | None = None,
raise_error: bool = True,
headers: Mapping[str, str] | None = None,
params: QueryParamTypes | None = None,
@@ -667,7 +651,6 @@ class RunsClient:
on_completion: OnCompletionBehavior | None = None,
if_not_exists: IfNotExists | None = None,
after_seconds: int | None = None,
langsmith_tracing: LangSmithTracing | None = None,
raise_error: bool = True,
headers: Mapping[str, str] | None = None,
params: QueryParamTypes | None = None,
@@ -695,7 +678,6 @@ class RunsClient:
multitask_strategy: MultitaskStrategy | None = None,
if_not_exists: IfNotExists | None = None,
after_seconds: int | None = None,
langsmith_tracing: LangSmithTracing | None = None,
raise_error: bool = True,
headers: Mapping[str, str] | None = None,
params: QueryParamTypes | None = None,
@@ -730,8 +712,6 @@ class RunsClient:
Must be either 'reject' (raise error if missing), or 'create' (create new thread).
after_seconds: The number of seconds to wait before starting the run.
Use to schedule future runs.
langsmith_tracing: LangSmith tracing configuration. Allows routing traces
to a specific project or associating with a dataset example.
headers: Optional custom headers to include with the request.
on_run_created: Optional callback to call when a run is created.
durability: The durability to use for the run. Values are "sync", "async", or "exit".
@@ -818,7 +798,6 @@ class RunsClient:
"on_completion": on_completion,
"after_seconds": after_seconds,
"durability": durability,
"langsmith_tracer": langsmith_tracing,
}
endpoint = (
f"/threads/{thread_id}/runs/wait" if thread_id is not None else "/runs/wait"
@@ -8,7 +8,6 @@ import re
from collections.abc import Mapping
from datetime import tzinfo
from typing import TYPE_CHECKING, Any, cast
from urllib.parse import urlparse
import httpx
@@ -159,40 +158,6 @@ def _resolve_timezone(tz: str | tzinfo | ZoneInfo | None) -> str | None:
)
def _default_port(scheme: str) -> int:
return 443 if scheme == "https" else 80
def _validate_reconnect_location(base_url: httpx.URL, location: str) -> str:
"""Validate that a reconnect Location URL is same-origin as the base URL.
Raises ValueError if the Location header points to a different origin
(scheme + host + port), which would leak credentials to an external server.
"""
parsed = urlparse(location)
# Relative URLs are safe — they resolve against the base
if not parsed.scheme and not parsed.netloc:
return location
# Compare origin components (normalize default ports to avoid mismatches)
base_scheme = str(base_url.scheme)
base_origin = (
base_scheme,
str(base_url.host),
base_url.port or _default_port(base_scheme),
)
loc_origin = (
parsed.scheme,
parsed.hostname or "",
parsed.port or _default_port(parsed.scheme),
)
if base_origin != loc_origin:
raise ValueError(
f"Refusing to follow cross-origin reconnect Location: {location!r} "
f"(origin {loc_origin}) does not match base URL origin {base_origin}"
)
return location
def _provided_vals(d: Mapping[str, Any]) -> dict[str, Any]:
return {k: v for k, v in d.items() if v is not None}
+1 -8
View File
@@ -11,10 +11,7 @@ from typing import Any, cast
import httpx
import orjson
from langgraph_sdk._shared.utilities import (
_orjson_default,
_validate_reconnect_location,
)
from langgraph_sdk._shared.utilities import _orjson_default
from langgraph_sdk.errors import _raise_for_status_typed
from langgraph_sdk.schema import QueryParamTypes, StreamPart
from langgraph_sdk.sse import SSEDecoder, iter_lines_raw
@@ -167,7 +164,6 @@ class SyncHttpClient:
loc = r.headers.get("location")
if reconnect_limit <= 0 or not loc:
return _decode_json(r)
_validate_reconnect_location(self.client.base_url, loc)
try:
return _decode_json(r)
except httpx.HTTPError:
@@ -248,9 +244,6 @@ class SyncHttpClient:
reconnect_location = res.headers.get("location")
if reconnect_location:
_validate_reconnect_location(
self.client.base_url, reconnect_location
)
reconnect_path = reconnect_location
decoder = SSEDecoder()
-21
View File
@@ -26,7 +26,6 @@ from langgraph_sdk.schema import (
Durability,
IfNotExists,
Input,
LangSmithTracing,
MultitaskStrategy,
OnCompletionBehavior,
QueryParamTypes,
@@ -93,7 +92,6 @@ class SyncRunsClient:
multitask_strategy: MultitaskStrategy | None = None,
if_not_exists: IfNotExists | None = None,
after_seconds: int | None = None,
langsmith_tracing: LangSmithTracing | None = None,
headers: Mapping[str, str] | None = None,
params: QueryParamTypes | None = None,
on_run_created: Callable[[RunCreateMetadata], None] | None = None,
@@ -124,7 +122,6 @@ class SyncRunsClient:
multitask_strategy: MultitaskStrategy | None = None,
if_not_exists: IfNotExists | None = None,
after_seconds: int | None = None,
langsmith_tracing: LangSmithTracing | None = None,
headers: Mapping[str, str] | None = None,
params: QueryParamTypes | None = None,
on_run_created: Callable[[RunCreateMetadata], None] | None = None,
@@ -154,7 +151,6 @@ class SyncRunsClient:
if_not_exists: IfNotExists | None = None,
webhook: str | None = None,
after_seconds: int | None = None,
langsmith_tracing: LangSmithTracing | None = None,
headers: Mapping[str, str] | None = None,
params: QueryParamTypes | None = None,
on_run_created: Callable[[RunCreateMetadata], None] | None = None,
@@ -184,7 +180,6 @@ class SyncRunsClient:
if_not_exists: IfNotExists | None = None,
webhook: str | None = None,
after_seconds: int | None = None,
langsmith_tracing: LangSmithTracing | None = None,
headers: Mapping[str, str] | None = None,
params: QueryParamTypes | None = None,
on_run_created: Callable[[RunCreateMetadata], None] | None = None,
@@ -216,7 +211,6 @@ class SyncRunsClient:
multitask_strategy: MultitaskStrategy | None = None,
if_not_exists: IfNotExists | None = None,
after_seconds: int | None = None,
langsmith_tracing: LangSmithTracing | None = None,
headers: Mapping[str, str] | None = None,
params: QueryParamTypes | None = None,
on_run_created: Callable[[RunCreateMetadata], None] | None = None,
@@ -256,8 +250,6 @@ class SyncRunsClient:
Must be either 'reject' (raise error if missing), or 'create' (create new thread).
after_seconds: The number of seconds to wait before starting the run.
Use to schedule future runs.
langsmith_tracing: LangSmith tracing configuration. Allows routing traces
to a specific project or associating with a dataset example.
headers: Optional custom headers to include with the request.
on_run_created: Optional callback to call when a run is created.
durability: The durability to use for the run. Values are "sync", "async", or "exit".
@@ -329,7 +321,6 @@ class SyncRunsClient:
"on_completion": on_completion,
"after_seconds": after_seconds,
"durability": durability,
"langsmith_tracer": langsmith_tracing,
}
endpoint = (
f"/threads/{thread_id}/runs/stream"
@@ -375,7 +366,6 @@ class SyncRunsClient:
on_completion: OnCompletionBehavior | None = None,
if_not_exists: IfNotExists | None = None,
after_seconds: int | None = None,
langsmith_tracing: LangSmithTracing | None = None,
headers: Mapping[str, str] | None = None,
params: QueryParamTypes | None = None,
on_run_created: Callable[[RunCreateMetadata], None] | None = None,
@@ -404,7 +394,6 @@ class SyncRunsClient:
multitask_strategy: MultitaskStrategy | None = None,
if_not_exists: IfNotExists | None = None,
after_seconds: int | None = None,
langsmith_tracing: LangSmithTracing | None = None,
headers: Mapping[str, str] | None = None,
params: QueryParamTypes | None = None,
on_run_created: Callable[[RunCreateMetadata], None] | None = None,
@@ -433,7 +422,6 @@ class SyncRunsClient:
if_not_exists: IfNotExists | None = None,
on_completion: OnCompletionBehavior | None = None,
after_seconds: int | None = None,
langsmith_tracing: LangSmithTracing | None = None,
headers: Mapping[str, str] | None = None,
params: QueryParamTypes | None = None,
on_run_created: Callable[[RunCreateMetadata], None] | None = None,
@@ -469,8 +457,6 @@ class SyncRunsClient:
Must be either 'reject' (raise error if missing), or 'create' (create new thread).
after_seconds: The number of seconds to wait before starting the run.
Use to schedule future runs.
langsmith_tracing: LangSmith tracing configuration. Allows routing traces
to a specific project or associating with a dataset example.
headers: Optional custom headers to include with the request.
on_run_created: Optional callback to call when a run is created.
durability: The durability to use for the run. Values are "sync", "async", or "exit".
@@ -581,7 +567,6 @@ class SyncRunsClient:
"on_completion": on_completion,
"after_seconds": after_seconds,
"durability": durability,
"langsmith_tracer": langsmith_tracing,
}
payload = {k: v for k, v in payload.items() if v is not None}
@@ -636,7 +621,6 @@ class SyncRunsClient:
multitask_strategy: MultitaskStrategy | None = None,
if_not_exists: IfNotExists | None = None,
after_seconds: int | None = None,
langsmith_tracing: LangSmithTracing | None = None,
raise_error: bool = True,
headers: Mapping[str, str] | None = None,
params: QueryParamTypes | None = None,
@@ -662,7 +646,6 @@ class SyncRunsClient:
on_completion: OnCompletionBehavior | None = None,
if_not_exists: IfNotExists | None = None,
after_seconds: int | None = None,
langsmith_tracing: LangSmithTracing | None = None,
raise_error: bool = True,
headers: Mapping[str, str] | None = None,
params: QueryParamTypes | None = None,
@@ -690,7 +673,6 @@ class SyncRunsClient:
multitask_strategy: MultitaskStrategy | None = None,
if_not_exists: IfNotExists | None = None,
after_seconds: int | None = None,
langsmith_tracing: LangSmithTracing | None = None,
raise_error: bool = True,
headers: Mapping[str, str] | None = None,
params: QueryParamTypes | None = None,
@@ -725,8 +707,6 @@ class SyncRunsClient:
Must be either 'reject' (raise error if missing), or 'create' (create new thread).
after_seconds: The number of seconds to wait before starting the run.
Use to schedule future runs.
langsmith_tracing: LangSmith tracing configuration. Allows routing traces
to a specific project or associating with a dataset example.
raise_error: Whether to raise an error if the run fails.
headers: Optional custom headers to include with the request.
on_run_created: Optional callback to call when a run is created.
@@ -816,7 +796,6 @@ class SyncRunsClient:
"after_seconds": after_seconds,
"raise_error": raise_error,
"durability": durability,
"langsmith_tracer": langsmith_tracing,
}
def on_response(res: httpx.Response):
-10
View File
@@ -107,16 +107,6 @@ Durability = Literal["sync", "async", "exit"]
- `"async"`: Changes are persisted asynchronously while the next step executes.
- `"exit"`: Changes are persisted only when the graph exits."""
class LangSmithTracing(TypedDict, total=False):
"""Configuration for LangSmith tracing."""
project_name: str
"""The LangSmith project name to trace to."""
example_id: str
"""The LangSmith example/dataset ID to associate with the trace."""
All = Literal["*"]
"""Represents a wildcard or 'all' selector."""
-146
View File
@@ -1,146 +0,0 @@
"""Test that langsmith_tracing parameter is correctly mapped to langsmith_tracer in payloads."""
from __future__ import annotations
from typing import Any
from unittest.mock import AsyncMock, MagicMock
import pytest
from langgraph_sdk.schema import LangSmithTracing
@pytest.fixture
def tracing_config() -> LangSmithTracing:
return LangSmithTracing(
project_name="my-project",
example_id="example-123",
)
class TestLangSmithTracingPayload:
"""Verify langsmith_tracing param maps to langsmith_tracer in request payload."""
@pytest.mark.asyncio
async def test_async_create_includes_langsmith_tracer(self, tracing_config):
"""Test that async create sends langsmith_tracer in payload."""
from langgraph_sdk._async.runs import RunsClient
captured: dict[str, Any] = {}
async def mock_post(_path, *, json=None, **_kwargs):
captured["json"] = json
return {"run_id": "r1", "status": "pending"}
http = MagicMock()
http.post = AsyncMock(side_effect=mock_post)
client = RunsClient(http)
await client.create(
thread_id="t1",
assistant_id="a1",
langsmith_tracing=tracing_config,
)
assert "langsmith_tracer" in captured["json"]
assert captured["json"]["langsmith_tracer"] == {
"project_name": "my-project",
"example_id": "example-123",
}
def test_sync_create_includes_langsmith_tracer(self, tracing_config):
"""Test that sync create sends langsmith_tracer in payload."""
from langgraph_sdk._sync.runs import SyncRunsClient
captured: dict[str, Any] = {}
def mock_post(_path, *, json=None, **_kwargs):
captured["json"] = json
return {"run_id": "r1", "status": "pending"}
http = MagicMock()
http.post = MagicMock(side_effect=mock_post)
client = SyncRunsClient(http)
client.create(
thread_id="t1",
assistant_id="a1",
langsmith_tracing=tracing_config,
)
assert "langsmith_tracer" in captured["json"]
assert captured["json"]["langsmith_tracer"] == {
"project_name": "my-project",
"example_id": "example-123",
}
def test_sync_wait_includes_langsmith_tracer(self, tracing_config):
"""Test that sync wait sends langsmith_tracer in payload."""
from langgraph_sdk._sync.runs import SyncRunsClient
captured: dict[str, Any] = {}
def mock_request_reconnect(_path, _method, *, json=None, **_kwargs):
captured["json"] = json
return {"messages": []}
http = MagicMock()
http.request_reconnect = MagicMock(side_effect=mock_request_reconnect)
client = SyncRunsClient(http)
client.wait(
thread_id="t1",
assistant_id="a1",
langsmith_tracing=tracing_config,
)
assert "langsmith_tracer" in captured["json"]
assert captured["json"]["langsmith_tracer"] == {
"project_name": "my-project",
"example_id": "example-123",
}
def test_create_without_langsmith_tracing_excludes_key(self):
"""Test that langsmith_tracer is not in payload when not provided."""
from langgraph_sdk._sync.runs import SyncRunsClient
captured: dict[str, Any] = {}
def mock_post(_path, *, json=None, **_kwargs):
captured["json"] = json
return {"run_id": "r1", "status": "pending"}
http = MagicMock()
http.post = MagicMock(side_effect=mock_post)
client = SyncRunsClient(http)
client.create(
thread_id="t1",
assistant_id="a1",
)
assert "langsmith_tracer" not in captured["json"]
def test_langsmith_tracing_project_name_only(self):
"""Test that langsmith_tracing works with only project_name."""
from langgraph_sdk._sync.runs import SyncRunsClient
captured: dict[str, Any] = {}
def mock_post(_path, *, json=None, **_kwargs):
captured["json"] = json
return {"run_id": "r1", "status": "pending"}
http = MagicMock()
http.post = MagicMock(side_effect=mock_post)
client = SyncRunsClient(http)
client.create(
thread_id="t1",
assistant_id="a1",
langsmith_tracing={"project_name": "my-project"},
)
assert captured["json"]["langsmith_tracer"] == {
"project_name": "my-project",
}
+13 -13
View File
@@ -262,7 +262,7 @@ wheels = [
[[package]]
name = "langchain-core"
version = "1.3.0"
version = "1.2.22"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "jsonpatch" },
@@ -274,14 +274,14 @@ 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" }
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