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Author SHA1 Message Date
Sydney RunkleandClaude Sonnet 4.6 433b51072b chore(prebuilt): fix ruff lint in tool_node.py
Remove unused `functools` import; drop redundant string annotation on
`_INJECTED_ARGS_CACHE` (already covered by `from __future__ import annotations`).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 16:13:24 -04:00
Sydney RunkleandClaude Sonnet 4.6 aa992adf57 perf(prebuilt): cache _get_all_injected_args by tool object identity
`_get_all_injected_args` runs `get_input_schema()`, `get_type_hints`,
and `inspect.signature` on every tool for every `ToolNode` construction.
When the same tool objects are passed to repeated `create_agent` calls
(the common case), this work was repeated from scratch each time.

Add a module-level cache keyed by `id(tool)`. The cache entry stores a
strong reference to the tool alongside the result, which prevents GC
from reusing the id for a different object and making an incorrect cache
hit. The `entry[0] is tool` identity check provides a second safety
guard.

`BaseTool` (Pydantic v2 BaseModel) is not hashable, so `@lru_cache`
cannot be used directly — hence the id-based dict approach.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 16:13:24 -04:00
Sydney RunkleandClaude Sonnet 4.6 8cd53c408d perf(pregel): cache source+AST analysis in get_function_nonlocals by code object
`get_function_nonlocals` was parsing Python source and walking the AST
on every graph compilation to discover which closure variables a node
function references. For a typical `create_agent` call this ran
`inspect.getsource` + `ast.parse` + visitor traversal on the same
`model_node`/`tool_node` closures every time — with zero caching.

Split into `_get_nonlocal_names(code)` (cached by code object via
`@lru_cache`) + a lightweight `get_function_nonlocals` wrapper that
only calls the cheap `inspect.getclosurevars` on each invocation.
Add a fast-path early exit for functions with no free variables
(`co_freevars` empty).

In profiling, `find_subgraph_pregel` + `get_function_nonlocals` +
stdlib `dis`/`inspect`/`ast` accounted for ~54% of `create_agent` wall
time; this reduces that to a single cache miss per unique function
definition.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 16:13:24 -04:00
Sydney Runkle 96760e6267 chore(delta-channel): add PR description 2026-04-22 14:35:48 -04:00
Sydney RunkleandClaude Sonnet 4.6 9342ae215a chore(delta-channel): remove snapshot_every — simpler design, better storage savings
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:12:57 -04:00
Sydney Runkle ee5b3c2639 refactor(postgres): replace recursive CTE with two-query ancestor walk for DeltaChannel
Instead of a recursive SQL CTE, collect the ancestor checkpoint ID chain in
Python by fetching all (checkpoint_id, parent_checkpoint_id) for the thread
in one query, then fetch writes with a plain WHERE checkpoint_id = ANY(...).

Simpler, avoids recursive query planner overhead, and uses well-indexed lookups.
2026-04-22 14:03:37 -04:00
Sydney Runkle d7c3616620 feat(delta-channel): store sentinel in blobs, reconstruct from checkpoint_writes
DeltaChannel.checkpoint() now returns a zero-byte DeltaChannelSentinel
instead of duplicating delta data in checkpoint_blobs. Reconstruction
walks the parent checkpoint chain via checkpoint_writes (which already
holds per-step writes) and replays them through the operator.

In-memory benchmark (100 turns, ~20K tokens):
  storage: 10.2 MB → 40.5 KB (251x reduction)
  read:    0.6ms → 7.9ms (reconstruction cost, amortized by storage savings)

InMemorySaver and PostgresSaver override get_channel_writes() with
efficient implementations (Python dict walk and recursive CTE respectively).
The base class fallback uses self.list() with a thread-local recursion guard.
2026-04-22 14:03:37 -04:00
Sydney Runkle 06e302bbff refactor(delta-channel): infer typ from Annotated outer type instead of constructor arg
Remove the positional `typ` parameter from `DeltaChannel.__init__`. The type is
now injected automatically from the `Annotated` outer type in `_is_field_channel`
(matching how `BinaryOperatorAggregate` receives its type). `copy()` and
`from_checkpoint()` propagate `self.typ` explicitly. Test helpers updated to
use `_get_channel` with the proper `Annotated` path.
2026-04-22 14:03:37 -04:00
Sydney Runkle 10da2326a9 chore(delta-channel): remove supports_delta_channels flag
Rely on the runtime raise in DeltaChannel.from_checkpoint() instead of
a compile-time boolean flag. Savers that assemble DeltaChainValue inside
_load_blobs work transparently; savers that don't will pass through a raw
DeltaValue and hit a clear ValueError on first reload.

Removes: BaseCheckpointSaver.supports_delta_channels, the attribute on
InMemorySaver / PostgresSaver / AsyncPostgresSaver, the compile-time
UserWarning in StateGraph.compile(), and the associated test.
2026-04-22 14:03:37 -04:00
Sydney RunkleandClaude Sonnet 4.6 84f00c51eb chore: remove docs/ from PR
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:37 -04:00
ccurmeandSydney Runkle 3cab106bdf fix(prebuilt): handle injected NotRequired keys (#7392)
Resolves https://github.com/langchain-ai/langchain/issues/35585

This would previously raise KeyError:
```python
from typing import Annotated

from langchain_core.tools import tool
from langchain.agents import create_agent
from typing_extensions import NotRequired
from langgraph.prebuilt import InjectedState
from langchain.agents import AgentState


class CustomAgentState(AgentState):
    city: NotRequired[str]


@tool
def get_weather(city: Annotated[str | None, InjectedState("city")] = None) -> str:
    """Get weather for a given city."""
    if city is None:
        city = "Boston"
    return f"It's always sunny in {city}!"


agent = create_agent(
    model="claude-sonnet-4-6",
    tools=[get_weather],
    system_prompt="You are a helpful assistant",
    state_schema=CustomAgentState,
)

input_message = {
    "role": "user",
    "content": "What's the weather?",
}

result = agent.invoke({"messages": [input_message]})
for m in result["messages"]:
    m.pretty_print()
```

---------

Co-authored-by: Sydney Runkle <sydneymarierunkle@gmail.com>
2026-04-22 14:03:37 -04:00
Sydney Runkle 77bb349309 lint 2026-04-22 14:03:37 -04:00
Sydney Runkle 1d8364a749 latest 2026-04-22 14:03:37 -04:00
Sydney Runkle 06e97b0fd2 fix(delta-channel): support non-list reducers (dict) and fix MISSING handling
Use typ() instead of [] throughout DeltaChannel so reducers over dict
(and other non-list types) work correctly. fromCheckpoint(MISSING) now
leaves value as typ() from __init__ instead of overwriting with MISSING.
copy() uses value.copy() to handle dicts. update() initialises base from
typ() when value is MISSING. Add four tests covering the deepagents-style
dict-merge / file-deletion reducer pattern.
2026-04-22 14:03:37 -04:00
Sydney RunkleandClaude Sonnet 4.6 e256a31d00 chore(delta): rename _steps_since_rehydrate → _steps_since_snapshot; add audit tests
- Rename `_steps_since_rehydrate` → `_steps_since_snapshot` in DeltaChannel
  for clarity (counts steps since the last snapshot, not since rehydration)
- Pre-seed cycle-detection `visited` set with current checkpoint ID in both
  sync and async `_assemble_delta_channels` to prevent self-referential chains
- Add 4 new unit tests:
  - `test_delta_channel_snapshot_every_emits_plain_list`: verifies counter
    semantics and snapshot/delta transitions
  - `test_delta_channel_snapshot_every_end_to_end`: graph-level smoke test
  - `test_delta_channel_assembly_fast_path_returns_delta_value`: exercises
    chain traversal via get_channel_blob returning DeltaValue then plain list
  - `test_delta_channel_assembly_broken_chain_logs_warning`: partial chain
    when get_tuple returns None

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:37 -04:00
Sydney RunkleandClaude Sonnet 4.6 04b3ae7cd0 chore: rename serde type tag "diff" → "delta" for DeltaValue
Consistent with channel/type naming (DeltaChannel, DeltaValue).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:37 -04:00
Sydney RunkleandClaude Sonnet 4.6 ec52520389 chore: apply format/lint fixes across checkpoint, checkpoint-postgres, prebuilt
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:37 -04:00
Sydney RunkleandClaude Sonnet 4.6 82fea763c5 fix: register DeltaValue in SAFE_MSGPACK_TYPES; rename _is_diff_delta; cross-saver benchmark
- Add DeltaValue to SAFE_MSGPACK_TYPES so SQLite and other msgpack-based
  savers don't emit "Deserializing unregistered type" warnings.
- Rename _is_diff_delta → _is_delta_value (leftover from DiffChannel rename).
- Parametrize benchmark by checkpointer: runs InMemory (fast-path) and
  SQLite (get_tuple fallback) in the same table, sharing the _run_turns helper.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:37 -04:00
Sydney RunkleandClaude Sonnet 4.6 6cfcad18f3 fix(delta-channel): fix chain assembly and get_state paths
- Fix InMemorySaver.get_channel_blob: use correct storage[thread_id][ns]
  nesting and deserialize the checkpoint before extracting channel_versions.
- Pass checkpoint_id to after_checkpoint() in channels_from_checkpoint so
  DeltaChannel seeds _last_checkpoint_id correctly on load; without this
  every turn broke the chain at its boundary.
- Wire _assemble_delta_channels into _prepare_state_snapshot and
  _aprepare_state_snapshot (get_state / get_state_history paths) and into
  perform_superstep / aperform_superstep (update_state paths) — previously
  only the loop __enter__ path did assembly.
- Fix test_get_channel_blob to use the correct storage structure.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:37 -04:00
Sydney Runkle db6b9ec995 test(channels): replace unsupported-saver raise test with fallback assembly test 2026-04-22 14:03:37 -04:00
Sydney Runkle 55fdc7aec6 feat(postgres): remove _load_diff_chains; add get_channel_blob / aget_channel_blob 2026-04-22 14:03:37 -04:00
Sydney Runkle 9969fb9737 feat(memory): implement get_channel_blob; remove diff handling from _load_blobs 2026-04-22 14:03:37 -04:00
Sydney Runkle 40981fdac7 feat(pregel): wire DeltaChannel assembly into loop; pass checkpoint_id to after_checkpoint 2026-04-22 14:03:37 -04:00
Sydney Runkle 5d1b3c4190 feat(pregel): add _assemble_delta_channels helpers for universal DeltaChannel support 2026-04-22 14:03:37 -04:00
Sydney RunkleandClaude Sonnet 4.6 fa83d64eff feat(channels): DeltaChannel tracks checkpoint_id; emits prev_checkpoint_id in DeltaValue
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:37 -04:00
Sydney RunkleandClaude Sonnet 4.6 4608af9615 feat(serde): diff type encodes prev_checkpoint_id; loads_typed returns DeltaValue
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:37 -04:00
Sydney Runkle 9beda5d3fb docs(checkpoint): expand aget_channel_blob docstring for parity 2026-04-22 14:03:37 -04:00
Sydney Runkle 9a6d7e08fb chore: add .worktrees/ to .gitignore 2026-04-22 14:03:37 -04:00
Sydney Runkle bbeb2759ba feat(checkpoint): DeltaValue uses prev_checkpoint_id; add get_channel_blob stubs 2026-04-22 14:03:37 -04:00
Sydney Runkle b799b95138 chore: rename DiffChannel/DiffDelta/DiffChainValue to Delta* across libs
Renames the diff-channel types to DeltaChannel, DeltaValue, and DeltaChainValue
for consistency with the settled naming convention.
2026-04-22 14:03:37 -04:00
Sydney Runkle ed9711fd33 more tests 2026-04-22 14:03:37 -04:00
Sydney RunkleandClaude Sonnet 4.6 ca883fe4eb chore: format/lint fixes for rehydrate_every benchmark
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:37 -04:00
Sydney RunkleandClaude Sonnet 4.6 4b9e4d25ca feat(channels): add rehydrate_every to DiffChannel for bounded chain traversal
Periodic full-snapshot checkpoints cap chain depth, trading a small
amount of extra storage for bounded reconstruction time.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:37 -04:00
Sydney RunkleandClaude Sonnet 4.6 ec8fd85ea2 test(channels): add DiffChannel vs BinaryOperatorAggregate storage/time benchmark
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:36 -04:00
Sydney RunkleandClaude Sonnet 4.6 ebd98f2e27 chore: format and lint fixes for DiffChannel implementation
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:36 -04:00
Sydney RunkleandClaude Sonnet 4.6 318fee9fc6 fix(checkpoint/postgres): pass cursor to avoid deadlock in diff chain traversal
Fixes a critical deadlock that occurs when _load_diff_chains calls self._cursor()
from within _load_blobs while the outer _load_checkpoint_tuple already holds
self._cursor(). On bare (non-pool) connections, the threading.Lock is not
reentrant, causing a deadlock.

Solution: Pass the cursor as a parameter to _load_diff_chains and _load_blobs
instead of acquiring a new cursor within those methods. Updated _load_checkpoint_tuple
to acquire a cursor once at the top level and pass it through the call chain.

Changes:
- Updated _load_blobs signature to accept optional cur parameter
- Updated _load_diff_chains signature (base and implementations) to accept optional cur parameter
- Modified _load_checkpoint_tuple in PostgresSaver to acquire cursor and pass it
- Modified _load_checkpoint_tuple_async to acquire cursor only when diff_payloads exist
- Removed nested self._cursor() calls in _load_diff_chains and _load_diff_chains_async

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:36 -04:00
Sydney RunkleandClaude Sonnet 4.6 e37299af87 feat(checkpoint/postgres): diff chain reconstruction in async saver
Add `_load_diff_chains_async` to `AsyncPostgresSaver` and override
`_load_checkpoint_tuple` to inline blob-parsing and diff-chain
resolution via async point-lookup traversal, mirroring the sync
`PostgresSaver._load_diff_chains` implementation. Add integration test
`test_diff_channel_chain_reconstruction` that skips gracefully when
`langgraph` core is not installed.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:36 -04:00
Sydney RunkleandClaude Sonnet 4.6 65d6ab2609 feat(checkpoint/postgres): diff chain reconstruction in _load_blobs (sync)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:36 -04:00
Sydney RunkleandClaude Sonnet 4.6 888a308814 test(pregel): strengthen DiffChannel time-travel and reply assertions
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:36 -04:00
Sydney RunkleandClaude Sonnet 4.6 c7086ed7e2 feat(pregel): call after_checkpoint hook when loading and saving channels
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:36 -04:00
Sydney RunkleandClaude Sonnet 4.6 e645c2a085 fix(checkpoint/memory): warn on broken diff chain, guard against cycles
- Add logger.warning when a mid-chain blob is missing (fixes silent truncation bug)
- Add cycle guard to prevent infinite loops on corrupt blob stores
- Fix type annotation on diff_channels from dict[str, Any] to dict[str, str]

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:36 -04:00
Sydney RunkleandClaude Sonnet 4.6 e52b7b2d54 feat(checkpoint/memory): chain-traverse diff blobs in _load_blobs
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:36 -04:00
Sydney Runkle 6581fdd0a5 fix(channels): align DiffChannel.is_available with BinaryOperatorAggregate 2026-04-22 14:03:36 -04:00
Sydney RunkleandClaude Sonnet 4.6 fe2bc286fc feat(channels): implement DiffChannel for incremental checkpoint storage
Adds DiffChannel, a new channel type that stores only per-step write
deltas in checkpoints and reconstructs the full list by replaying the
chain through the operator at load time.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:36 -04:00
Sydney Runkle c345d337bb feat(channels): add no-op after_checkpoint hook to BaseChannel 2026-04-22 14:03:36 -04:00
Sydney RunkleandClaude Sonnet 4.6 d0af83b746 fix(checkpoint/serde): use lazy isinstance check for DiffDelta
Replace duck-typing check with lazy import inside _is_diff_delta helper
function to avoid module-level circular dependency while using proper
isinstance semantics.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:36 -04:00
Sydney RunkleandClaude Sonnet 4.5 eabf926a4f feat(checkpoint/serde): serialize DiffDelta as 'diff' type tag
Add serde support for DiffDelta by implementing dump/load for the "diff" type tag.
This allows the checkpoint system to efficiently store delta objects by serializing
them as msgpack-encoded dicts with {"d": delta, "p": prev_version} structure.

The implementation uses runtime type checking to avoid circular imports and
leverages the existing msgpack ext hooks for proper deserialization of complex
types like LangChain messages.

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
2026-04-22 14:03:36 -04:00
Sydney RunkleandClaude Sonnet 4.6 6852e0478e feat(checkpoint): add DiffDelta and DiffChainValue protocol types
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:36 -04:00
Sydney RunkleandClaude Sonnet 4.6 68b2f7bfac docs: add DiffChannel implementation plan
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:36 -04:00
Sydney RunkleandClaude Sonnet 4.6 95e0fe060c docs: add DiffChannel incremental checkpoint storage design spec
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 14:03:36 -04:00
Sydney RunkleandGitHub a529b9bede chore(langgraph): bump version 1.1.8 -> 1.1.9 (#7563)
## Summary

- Bumps `langgraph` patch version from `1.1.8` to `1.1.9` in
`libs/langgraph/pyproject.toml`

## Test plan

- [ ] Verify version string is correct in `pyproject.toml`
- [ ] Confirm release workflow triggers on merge
2026-04-21 09:38:58 -04:00
0a26b471d3 fix(langgraph): don't propagate ReplayState to subgraphs on plain resume (#7561)
**Description:**
When clients resume an interrupted subgraph with `Command(resume=...)`
plus an explicit `checkpoint_id` in the config (the pattern LangGraph
Studio and the API server emit on every resume), the subgraph restarts
from its first node instead of continuing at the interrupted node.

Fix: gate `ReplayState` propagation on `is_time_traveling` rather than
`is_replaying`, so a resume that happens to carry a head checkpoint_id
behaves the same as one with just a thread_id.

**Verification:** added regression test
`test_subgraph_interrupt_resume_with_explicit_head_checkpoint_id` (fails
on main, passes with fix, across memory/sqlite/sqlite_aes).
Full `test_time_travel.py`, `test_time_travel_async.py`,
`test_interruption.py`, and all pregel subgraph/interrupt/resume/replay
tests still pass.

Co-authored-by: Jessie Ibarra <jessie.ibarra@langgraph.dev>
2026-04-20 21:29:18 -04:00
Eugene YurtsevandGitHub b674dd4622 feat(prebuilt): expose available tools on ToolRuntime (#7512) 2026-04-17 16:54:16 -04:00
Eugene YurtsevandGitHub 8df0a377d0 chore(langgraph): undo unnecessary changes in stream handler (#7536)
This change is no longer necessary as langchain-core will continue
copying checkpoint_ns for backwards compatibility
2026-04-17 16:22:34 -04:00
dependabot[bot]GitHubdependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
216cf33a54 chore(deps): bump the pip group across 3 directories with 1 update (#7537)
Bumps the pip group with 1 update in the
/libs/cli/examples/graph_prerelease_reqs/deps/zuper_deps directory:
[langchain-openai](https://github.com/langchain-ai/langchain).
Bumps the pip group with 1 update in the
/libs/cli/examples/graph_prerelease_reqs_fail directory:
[langchain-openai](https://github.com/langchain-ai/langchain).
Bumps the pip group with 1 update in the
/libs/cli/examples/graph_prerelease_reqs directory:
[langchain-openai](https://github.com/langchain-ai/langchain).

Updates `langchain-openai` from 1.0.1 to 1.1.14
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/langchain-ai/langchain/releases">langchain-openai's
releases</a>.</em></p>
<blockquote>
<h2>langchain-openai==1.1.14</h2>
<p>Changes since langchain-openai==1.1.13</p>
<p>release(openai): 1.1.14 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36820">#36820</a>)
fix(openai): use SSRF-safe transport for image token counting (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36819">#36819</a>)
chore(deps): bump pytest to <code>9.0.3</code> (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36801">#36801</a>)
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36795">#36795</a>)
chore: bump pillow from 12.1.1 to 12.2.0 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36777">#36777</a>)</p>
<h2>langchain-openai==1.1.13</h2>
<p>Changes since langchain-openai==1.1.12</p>
<p>release(openai): 1.1.13 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36729">#36729</a>)
fix(openai): handle content blocks without type key in responses api
conversion (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36725">#36725</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36539">#36539</a>)
chore(openai): fix broken vcr cassette playback and add ci guard (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36502">#36502</a>)
fix(openai,groq,openrouter): use is-not-None checks in usage metadata
token extraction (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36500">#36500</a>)
fix(core): fixed typos in the documentation (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36459">#36459</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36455">#36455</a>)
feat(core): impute placeholder filenames for OpenAI file inputs (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36433">#36433</a>)
chore: pygments&gt;=2.20.0 across all packages (CVE-2026-4539) (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36385">#36385</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36368">#36368</a>)
fix(openai): update computer call test (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36352">#36352</a>)
fix(openai): let user-provided User-Agent override the Azure default (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35523">#35523</a>)
chore: bump requests from 2.32.5 to 2.33.0 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36248">#36248</a>)</p>
<h2>langchain-openai==1.1.12</h2>
<p>Changes since langchain-openai==1.1.11</p>
<p>fix(openai): bump min core version (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36180">#36180</a>)
release(openai): 1.1.12 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36178">#36178</a>)
fix(core,model-profiles): add missing <code>ModelProfile</code> fields,
warn on schema drift (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36129">#36129</a>)
fix(openai): support phase parameter (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36161">#36161</a>)
fix(openai): preserve namespace field in streaming function_call chunks
(<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36108">#36108</a>)
ci: suppress pytest streaming output in CI (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36092">#36092</a>)
ci: avoid unnecessary dep installs in lint targets (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36046">#36046</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36039">#36039</a>)
chore: bump orjson from 3.11.5 to 3.11.6 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35860">#35860</a>)
fix(openai): add type: message to Responses API input items (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35693">#35693</a>)
perf(.github): set a timeout on get min versions HTTP calls (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35851">#35851</a>)
feat(model-profiles): new fields + <code>Makefile</code> target (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35788">#35788</a>)
fix(openai): close PIL Image handles in token counting to prevent fd
leak (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35742">#35742</a>)
fix(openai): typo (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35763">#35763</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35754">#35754</a>)</p>
<h2>langchain-openai==1.1.11</h2>
<p>Changes since langchain-openai==1.1.10</p>
<p>fix(openai): bump min core version (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35705">#35705</a>)
release(openai): 1.1.11 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35703">#35703</a>)</p>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/langchain-ai/langchain/commit/b7447c6969fc928ec3f29c200e2e56c0a46c4c77"><code>b7447c6</code></a>
fix(infra): skip serdes tests in min-version release step (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36818">#36818</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/41c0cc58b0dac82000d24715f7a4b44dc8b01fd3"><code>41c0cc5</code></a>
release(openai): 1.1.14 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36820">#36820</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/0516156ef98f5001129f6d47bc8682d6536d58fb"><code>0516156</code></a>
fix(openai): use SSRF-safe transport for image token counting (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36819">#36819</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/338aa8131a8124e7aa1e042616ccd2366ff9f699"><code>338aa81</code></a>
fix(core): restore cloud metadata IPs and link-local range in SSRF
policy (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/3">#3</a>...</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/51e954877efd2d2c3c5bf09364dcfec8794eadb0"><code>51e9548</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/text-splitters (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36797">#36797</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/e85c418cfa559d4a794ddc6db92c6febab44651c"><code>e85c418</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/model-profiles (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36798">#36798</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/789126e6c78ad74664bea26228dda6e72e135dce"><code>789126e</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/standard-tests (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36799">#36799</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/937b3eb3827551d17ee4736f9acc4aa57e88c716"><code>937b3eb</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/langchain_v1 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36800">#36800</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/a06c205738cf5953e28c37287ddb1559d67c01f6"><code>a06c205</code></a>
ci(infra): validate issue checkboxes by section (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36811">#36811</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/aa33b06deb0d65489ce254b48a8aaf8a86304c18"><code>aa33b06</code></a>
fix(langchain-classic): suppress mypy errors in compat code (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36806">#36806</a>)</li>
<li>Additional commits viewable in <a
href="https://github.com/langchain-ai/langchain/compare/langchain-openai==1.0.1...langchain-openai==1.1.14">compare
view</a></li>
</ul>
</details>
<br />

Updates `langchain-openai` from 1.0.0a2 to 1.1.14
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/langchain-ai/langchain/releases">langchain-openai's
releases</a>.</em></p>
<blockquote>
<h2>langchain-openai==1.1.14</h2>
<p>Changes since langchain-openai==1.1.13</p>
<p>release(openai): 1.1.14 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36820">#36820</a>)
fix(openai): use SSRF-safe transport for image token counting (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36819">#36819</a>)
chore(deps): bump pytest to <code>9.0.3</code> (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36801">#36801</a>)
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36795">#36795</a>)
chore: bump pillow from 12.1.1 to 12.2.0 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36777">#36777</a>)</p>
<h2>langchain-openai==1.1.13</h2>
<p>Changes since langchain-openai==1.1.12</p>
<p>release(openai): 1.1.13 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36729">#36729</a>)
fix(openai): handle content blocks without type key in responses api
conversion (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36725">#36725</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36539">#36539</a>)
chore(openai): fix broken vcr cassette playback and add ci guard (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36502">#36502</a>)
fix(openai,groq,openrouter): use is-not-None checks in usage metadata
token extraction (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36500">#36500</a>)
fix(core): fixed typos in the documentation (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36459">#36459</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36455">#36455</a>)
feat(core): impute placeholder filenames for OpenAI file inputs (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36433">#36433</a>)
chore: pygments&gt;=2.20.0 across all packages (CVE-2026-4539) (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36385">#36385</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36368">#36368</a>)
fix(openai): update computer call test (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36352">#36352</a>)
fix(openai): let user-provided User-Agent override the Azure default (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35523">#35523</a>)
chore: bump requests from 2.32.5 to 2.33.0 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36248">#36248</a>)</p>
<h2>langchain-openai==1.1.12</h2>
<p>Changes since langchain-openai==1.1.11</p>
<p>fix(openai): bump min core version (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36180">#36180</a>)
release(openai): 1.1.12 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36178">#36178</a>)
fix(core,model-profiles): add missing <code>ModelProfile</code> fields,
warn on schema drift (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36129">#36129</a>)
fix(openai): support phase parameter (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36161">#36161</a>)
fix(openai): preserve namespace field in streaming function_call chunks
(<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36108">#36108</a>)
ci: suppress pytest streaming output in CI (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36092">#36092</a>)
ci: avoid unnecessary dep installs in lint targets (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36046">#36046</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36039">#36039</a>)
chore: bump orjson from 3.11.5 to 3.11.6 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35860">#35860</a>)
fix(openai): add type: message to Responses API input items (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35693">#35693</a>)
perf(.github): set a timeout on get min versions HTTP calls (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35851">#35851</a>)
feat(model-profiles): new fields + <code>Makefile</code> target (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35788">#35788</a>)
fix(openai): close PIL Image handles in token counting to prevent fd
leak (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35742">#35742</a>)
fix(openai): typo (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35763">#35763</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35754">#35754</a>)</p>
<h2>langchain-openai==1.1.11</h2>
<p>Changes since langchain-openai==1.1.10</p>
<p>fix(openai): bump min core version (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35705">#35705</a>)
release(openai): 1.1.11 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35703">#35703</a>)</p>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/langchain-ai/langchain/commit/b7447c6969fc928ec3f29c200e2e56c0a46c4c77"><code>b7447c6</code></a>
fix(infra): skip serdes tests in min-version release step (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36818">#36818</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/41c0cc58b0dac82000d24715f7a4b44dc8b01fd3"><code>41c0cc5</code></a>
release(openai): 1.1.14 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36820">#36820</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/0516156ef98f5001129f6d47bc8682d6536d58fb"><code>0516156</code></a>
fix(openai): use SSRF-safe transport for image token counting (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36819">#36819</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/338aa8131a8124e7aa1e042616ccd2366ff9f699"><code>338aa81</code></a>
fix(core): restore cloud metadata IPs and link-local range in SSRF
policy (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/3">#3</a>...</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/51e954877efd2d2c3c5bf09364dcfec8794eadb0"><code>51e9548</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/text-splitters (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36797">#36797</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/e85c418cfa559d4a794ddc6db92c6febab44651c"><code>e85c418</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/model-profiles (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36798">#36798</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/789126e6c78ad74664bea26228dda6e72e135dce"><code>789126e</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/standard-tests (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36799">#36799</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/937b3eb3827551d17ee4736f9acc4aa57e88c716"><code>937b3eb</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/langchain_v1 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36800">#36800</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/a06c205738cf5953e28c37287ddb1559d67c01f6"><code>a06c205</code></a>
ci(infra): validate issue checkboxes by section (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36811">#36811</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/aa33b06deb0d65489ce254b48a8aaf8a86304c18"><code>aa33b06</code></a>
fix(langchain-classic): suppress mypy errors in compat code (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36806">#36806</a>)</li>
<li>Additional commits viewable in <a
href="https://github.com/langchain-ai/langchain/compare/langchain-openai==1.0.1...langchain-openai==1.1.14">compare
view</a></li>
</ul>
</details>
<br />

Updates `langchain-openai` from 1.0.0a2 to 1.1.14
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/langchain-ai/langchain/releases">langchain-openai's
releases</a>.</em></p>
<blockquote>
<h2>langchain-openai==1.1.14</h2>
<p>Changes since langchain-openai==1.1.13</p>
<p>release(openai): 1.1.14 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36820">#36820</a>)
fix(openai): use SSRF-safe transport for image token counting (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36819">#36819</a>)
chore(deps): bump pytest to <code>9.0.3</code> (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36801">#36801</a>)
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36795">#36795</a>)
chore: bump pillow from 12.1.1 to 12.2.0 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36777">#36777</a>)</p>
<h2>langchain-openai==1.1.13</h2>
<p>Changes since langchain-openai==1.1.12</p>
<p>release(openai): 1.1.13 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36729">#36729</a>)
fix(openai): handle content blocks without type key in responses api
conversion (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36725">#36725</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36539">#36539</a>)
chore(openai): fix broken vcr cassette playback and add ci guard (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36502">#36502</a>)
fix(openai,groq,openrouter): use is-not-None checks in usage metadata
token extraction (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36500">#36500</a>)
fix(core): fixed typos in the documentation (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36459">#36459</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36455">#36455</a>)
feat(core): impute placeholder filenames for OpenAI file inputs (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36433">#36433</a>)
chore: pygments&gt;=2.20.0 across all packages (CVE-2026-4539) (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36385">#36385</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36368">#36368</a>)
fix(openai): update computer call test (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36352">#36352</a>)
fix(openai): let user-provided User-Agent override the Azure default (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35523">#35523</a>)
chore: bump requests from 2.32.5 to 2.33.0 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36248">#36248</a>)</p>
<h2>langchain-openai==1.1.12</h2>
<p>Changes since langchain-openai==1.1.11</p>
<p>fix(openai): bump min core version (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36180">#36180</a>)
release(openai): 1.1.12 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36178">#36178</a>)
fix(core,model-profiles): add missing <code>ModelProfile</code> fields,
warn on schema drift (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36129">#36129</a>)
fix(openai): support phase parameter (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36161">#36161</a>)
fix(openai): preserve namespace field in streaming function_call chunks
(<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36108">#36108</a>)
ci: suppress pytest streaming output in CI (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36092">#36092</a>)
ci: avoid unnecessary dep installs in lint targets (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36046">#36046</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36039">#36039</a>)
chore: bump orjson from 3.11.5 to 3.11.6 in /libs/partners/openai (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35860">#35860</a>)
fix(openai): add type: message to Responses API input items (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35693">#35693</a>)
perf(.github): set a timeout on get min versions HTTP calls (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35851">#35851</a>)
feat(model-profiles): new fields + <code>Makefile</code> target (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35788">#35788</a>)
fix(openai): close PIL Image handles in token counting to prevent fd
leak (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35742">#35742</a>)
fix(openai): typo (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35763">#35763</a>)
chore(model-profiles): refresh model profile data (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35754">#35754</a>)</p>
<h2>langchain-openai==1.1.11</h2>
<p>Changes since langchain-openai==1.1.10</p>
<p>fix(openai): bump min core version (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35705">#35705</a>)
release(openai): 1.1.11 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/35703">#35703</a>)</p>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/langchain-ai/langchain/commit/b7447c6969fc928ec3f29c200e2e56c0a46c4c77"><code>b7447c6</code></a>
fix(infra): skip serdes tests in min-version release step (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36818">#36818</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/41c0cc58b0dac82000d24715f7a4b44dc8b01fd3"><code>41c0cc5</code></a>
release(openai): 1.1.14 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36820">#36820</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/0516156ef98f5001129f6d47bc8682d6536d58fb"><code>0516156</code></a>
fix(openai): use SSRF-safe transport for image token counting (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36819">#36819</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/338aa8131a8124e7aa1e042616ccd2366ff9f699"><code>338aa81</code></a>
fix(core): restore cloud metadata IPs and link-local range in SSRF
policy (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/3">#3</a>...</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/51e954877efd2d2c3c5bf09364dcfec8794eadb0"><code>51e9548</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/text-splitters (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36797">#36797</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/e85c418cfa559d4a794ddc6db92c6febab44651c"><code>e85c418</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/model-profiles (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36798">#36798</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/789126e6c78ad74664bea26228dda6e72e135dce"><code>789126e</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/standard-tests (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36799">#36799</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/937b3eb3827551d17ee4736f9acc4aa57e88c716"><code>937b3eb</code></a>
chore: bump langsmith from 0.6.3 to 0.7.31 in /libs/langchain_v1 (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36800">#36800</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/a06c205738cf5953e28c37287ddb1559d67c01f6"><code>a06c205</code></a>
ci(infra): validate issue checkboxes by section (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36811">#36811</a>)</li>
<li><a
href="https://github.com/langchain-ai/langchain/commit/aa33b06deb0d65489ce254b48a8aaf8a86304c18"><code>aa33b06</code></a>
fix(langchain-classic): suppress mypy errors in compat code (<a
href="https://redirect.github.com/langchain-ai/langchain/issues/36806">#36806</a>)</li>
<li>Additional commits viewable in <a
href="https://github.com/langchain-ai/langchain/compare/langchain-openai==1.0.1...langchain-openai==1.1.14">compare
view</a></li>
</ul>
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Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-04-17 12:42:55 -07:00
Eugene YurtsevandGitHub 4956134a37 release(langgraph): 1.1.8 (#7545)
releasing 1.1.8
2026-04-17 19:41:13 +00:00
aa94790f36 fix(langgraph): remove strict add_handler type check that breaks OTel instrumentation (#7544)
## Summary

Removes the `add_handler()` overrides on `_GraphCallbackManager` and
`_AsyncGraphCallbackManager` that reject handlers not inheriting from
`GraphCallbackHandler`. This fixes a regression in 1.1.7 where
`opentelemetry-instrumentation-langchain` (and likely other libraries
that patch `BaseCallbackManager.__init__`) crash with `TypeError:
handlers must inherit GraphCallbackHandler` at invocation time.

## Why this is safe

The strict type check is redundant — `_configure_graph_callbacks` and
`_filter_graph_handlers` already filter handlers to
`GraphCallbackHandler` instances at construction time. Non-graph
handlers that enter via external patches (like OTel's monkey-patch) are
harmless because `handle_event("on_interrupt", ...)` /
`handle_event("on_resume", ...)` will simply no-op on handlers that
don't implement those methods.

## What changed

- Deleted `add_handler()` override from `_GraphCallbackManager` (was
lines 248-255)
- Deleted `add_handler()` override from `_AsyncGraphCallbackManager`
(was lines 324-331)
- No other changes — 18 lines removed, 0 added

## Test plan

- [x] All 8 existing `test_graph_callbacks.py` tests pass (`make test
TEST=tests/test_graph_callbacks.py`)
- [x] `make lint` passes
- [x] `make format` passes (no changes needed)
- [x] Verified fix locally: `LangchainInstrumentor().instrument()` +
`create_react_agent()` + `graph.ainvoke()` no longer raises `TypeError`
- [x] Verified the graph lifecycle callbacks (`on_interrupt`,
`on_resume`) still work correctly

Closes #7543

---------

Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
2026-04-17 15:33:10 -04:00
ccurmeandGitHub e002711ede release(prebuilt): 1.0.10 (#7541) 2026-04-17 13:52:20 -04:00
f44b49b33d chore: dedup warnings (#7257)
Co-authored-by: Will Fu-Hinthorn <will@langchain.dev>
2026-04-17 10:13:03 -07:00
a0a95df2ac release(cli): 0.4.23 (#7542)
Release Note: Increase the max bound for langgraph-api

Co-authored-by: Will Fu-Hinthorn <will@langchain.dev>
2026-04-17 16:38:14 +00:00
Eugene YurtsevandGitHub d194c18c06 release(langgraph): 1.1.7 (#7540)
release langgraph 1.1.7
2026-04-17 15:19:18 +00:00
36 changed files with 1439 additions and 1302 deletions
@@ -4,7 +4,7 @@ import threading
from collections import defaultdict
from collections.abc import Iterator, Sequence
from contextlib import contextmanager
from typing import Any
from typing import Any, cast
from langchain_core.runnables import RunnableConfig
from langgraph.checkpoint.base import (
@@ -442,14 +442,22 @@ class PostgresSaver(BasePostgresSaver):
including its configuration, metadata, parent checkpoint (if any),
and pending writes.
"""
with self._cursor() as cur:
channel_values = self._load_blobs(
value["channel_values"],
thread_id=value["thread_id"],
checkpoint_ns=value["checkpoint_ns"],
checkpoint_id=value["checkpoint_id"],
cur=cur,
from langgraph.checkpoint.base import DeltaChannelSentinel
channel_values = self._load_blobs(value["channel_values"])
if any(isinstance(v, DeltaChannelSentinel) for v in channel_values.values()):
cp_config = cast(
RunnableConfig,
{
"configurable": {
"thread_id": value["thread_id"],
"checkpoint_ns": value["checkpoint_ns"],
"checkpoint_id": value["checkpoint_id"],
}
},
)
with self._cursor() as cur:
self._resolve_delta_channels(cp_config, channel_values, cur)
return CheckpointTuple(
{
"configurable": {
@@ -13,8 +13,7 @@ from langgraph.checkpoint.base import (
Checkpoint,
CheckpointMetadata,
CheckpointTuple,
DeltaChainValue,
DeltaValue,
DeltaChannelSentinel,
get_checkpoint_id,
get_serializable_checkpoint_metadata,
)
@@ -393,80 +392,57 @@ class AsyncPostgresSaver(BasePostgresSaver):
async with conn.cursor(binary=True, row_factory=dict_row) as cur:
yield cur
async def _aload_delta_chain(
async def _aget_channel_writes_cur(
self,
thread_id: str,
checkpoint_ns: str,
checkpoint_id: str,
channel: str,
cur: Any,
) -> DeltaChainValue:
"""Fetch the full delta chain for a channel in one recursive CTE query (async)."""
) -> list[Any]:
"""Async version of _get_channel_writes_cur — see sync version for rationale."""
await cur.execute(
"""
WITH RECURSIVE chain AS (
SELECT
c.checkpoint_id,
c.parent_checkpoint_id,
cb.version,
cb.type,
cb.blob
FROM checkpoints c
JOIN checkpoint_blobs cb
ON cb.thread_id = %s
AND cb.checkpoint_ns = %s
AND cb.channel = %s
AND cb.version = (c.checkpoint->'channel_versions'->>%s)::text
WHERE c.thread_id = %s
AND c.checkpoint_ns = %s
AND c.checkpoint_id = %s
UNION ALL
SELECT
c.checkpoint_id,
c.parent_checkpoint_id,
cb.version,
cb.type,
cb.blob
FROM chain prev
JOIN checkpoints c ON c.checkpoint_id = prev.parent_checkpoint_id
JOIN checkpoint_blobs cb
ON cb.thread_id = %s
AND cb.checkpoint_ns = %s
AND cb.channel = %s
AND cb.version = (c.checkpoint->'channel_versions'->>%s)::text
WHERE prev.parent_checkpoint_id IS NOT NULL
AND prev.type = 'delta'
)
SELECT DISTINCT ON (version) type, blob
FROM chain
ORDER BY version ASC
""",
(
thread_id,
checkpoint_ns,
channel,
channel,
thread_id,
checkpoint_ns,
checkpoint_id,
thread_id,
checkpoint_ns,
channel,
channel,
),
"SELECT checkpoint_id, parent_checkpoint_id FROM checkpoints "
"WHERE thread_id = %s AND checkpoint_ns = %s",
(thread_id, checkpoint_ns),
)
rows = await cur.fetchall()
base = None
deltas: list[list[Any]] = []
for row in rows:
blob = self.serde.loads_typed((row["type"], row["blob"]))
if isinstance(blob, DeltaValue):
deltas.append(blob.delta)
else:
base = blob
return DeltaChainValue(base=base, deltas=deltas)
parent_map: dict[str, str | None] = {
row["checkpoint_id"]: row["parent_checkpoint_id"]
for row in await cur.fetchall()
}
ancestor_ids: list[str] = []
cid: str | None = parent_map.get(checkpoint_id)
while cid is not None:
ancestor_ids.append(cid)
cid = parent_map.get(cid)
if not ancestor_ids:
return []
await cur.execute(
"SELECT checkpoint_id, type, blob FROM checkpoint_writes "
"WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s "
" AND checkpoint_id = ANY(%s) "
"ORDER BY task_id, idx",
(thread_id, checkpoint_ns, channel, ancestor_ids),
)
writes_by_cp: dict[str, list[tuple[str, bytes]]] = defaultdict(list)
for row in await cur.fetchall():
writes_by_cp[row["checkpoint_id"]].append((row["type"], row["blob"]))
result = []
for cid in reversed(ancestor_ids):
for type_tag, blob in writes_by_cp.get(cid, []):
result.append(self.serde.loads_typed((type_tag, blob)))
return result
async def aget_channel_writes(
self, config: RunnableConfig, channel: str
) -> list[Any]:
thread_id = config["configurable"]["thread_id"]
checkpoint_ns = config["configurable"].get("checkpoint_ns", "")
checkpoint_id = config["configurable"]["checkpoint_id"]
async with self._cursor() as cur:
return await self._aget_channel_writes_cur(
thread_id, checkpoint_ns, checkpoint_id, channel, cur
)
async def _load_checkpoint_tuple(self, value: DictRow) -> CheckpointTuple:
"""
@@ -489,12 +465,14 @@ class AsyncPostgresSaver(BasePostgresSaver):
if blob_values:
channel_values = self._load_blobs(blob_values)
delta_channels = [
k.decode() for k, t, _ in blob_values if t.decode() == "delta"
ch
for ch, v in channel_values.items()
if isinstance(v, DeltaChannelSentinel)
]
if delta_channels:
async with self._cursor() as cur:
for channel in delta_channels:
channel_values[channel] = await self._aload_delta_chain(
channel_values[channel] = await self._aget_channel_writes_cur(
thread_id, checkpoint_ns, checkpoint_id, channel, cur
)
@@ -2,6 +2,7 @@ from __future__ import annotations
import random
import warnings
from collections import defaultdict
from collections.abc import Sequence
from importlib.metadata import version as get_version
from typing import Any, cast
@@ -11,8 +12,7 @@ from langgraph.checkpoint.base import (
WRITES_IDX_MAP,
BaseCheckpointSaver,
ChannelVersions,
DeltaChainValue,
DeltaValue,
DeltaChannelSentinel,
get_checkpoint_id,
)
from langgraph.checkpoint.serde.types import TASKS
@@ -188,105 +188,77 @@ class BasePostgresSaver(BaseCheckpointSaver[str]):
def _load_blobs(
self,
blob_values: list[tuple[bytes, bytes, bytes]],
*,
thread_id: str = "",
checkpoint_ns: str = "",
checkpoint_id: str = "",
cur: Any = None,
blob_values: Any,
) -> dict[str, Any]:
if not blob_values:
return {}
result: dict[str, Any] = {}
delta_channels: list[str] = []
for k, t, v in blob_values:
channel = k.decode()
type_tag = t.decode()
if type_tag == "delta":
delta_channels.append(channel)
elif type_tag != "empty":
result[channel] = self.serde.loads_typed((type_tag, v))
if delta_channels and cur is not None and checkpoint_id:
for channel in delta_channels:
result[channel] = self._load_delta_chain(
thread_id, checkpoint_ns, checkpoint_id, channel, cur
)
if type_tag != "empty":
result[k.decode()] = self.serde.loads_typed((type_tag, v))
return result
def _load_delta_chain(
def _resolve_delta_channels(
self,
config: RunnableConfig,
channel_values: dict[str, Any],
cur: Any,
) -> None:
for channel, value in list(channel_values.items()):
if isinstance(value, DeltaChannelSentinel):
channel_values[channel] = self._get_channel_writes_cur(
config["configurable"]["thread_id"],
config["configurable"].get("checkpoint_ns", ""),
config["configurable"]["checkpoint_id"],
channel,
cur,
)
def _get_channel_writes_cur(
self,
thread_id: str,
checkpoint_ns: str,
checkpoint_id: str,
channel: str,
cur: Any,
) -> DeltaChainValue:
"""Fetch the full delta chain for a channel in one recursive CTE query."""
) -> list[Any]:
"""Fetch writes for `channel` across the checkpoint ancestor chain, oldest→newest.
Two queries:
1. Fetch all (checkpoint_id, parent_checkpoint_id) for the thread — cheap, IDs only.
2. Walk the ancestor chain in Python, then fetch writes with a plain ANY() filter.
"""
cur.execute(
"""
WITH RECURSIVE chain AS (
SELECT
c.checkpoint_id,
c.parent_checkpoint_id,
cb.version,
cb.type,
cb.blob
FROM checkpoints c
JOIN checkpoint_blobs cb
ON cb.thread_id = %s
AND cb.checkpoint_ns = %s
AND cb.channel = %s
AND cb.version = (c.checkpoint->'channel_versions'->>%s)::text
WHERE c.thread_id = %s
AND c.checkpoint_ns = %s
AND c.checkpoint_id = %s
UNION ALL
SELECT
c.checkpoint_id,
c.parent_checkpoint_id,
cb.version,
cb.type,
cb.blob
FROM chain prev
JOIN checkpoints c ON c.checkpoint_id = prev.parent_checkpoint_id
JOIN checkpoint_blobs cb
ON cb.thread_id = %s
AND cb.checkpoint_ns = %s
AND cb.channel = %s
AND cb.version = (c.checkpoint->'channel_versions'->>%s)::text
WHERE prev.parent_checkpoint_id IS NOT NULL
AND prev.type = 'delta'
)
SELECT DISTINCT ON (version) type, blob
FROM chain
ORDER BY version ASC
""",
(
thread_id,
checkpoint_ns,
channel,
channel,
thread_id,
checkpoint_ns,
checkpoint_id,
thread_id,
checkpoint_ns,
channel,
channel,
),
"SELECT checkpoint_id, parent_checkpoint_id FROM checkpoints "
"WHERE thread_id = %s AND checkpoint_ns = %s",
(thread_id, checkpoint_ns),
)
rows = cur.fetchall()
base = None
deltas: list[list[Any]] = []
for row in rows:
blob = self.serde.loads_typed((row["type"], row["blob"]))
if isinstance(blob, DeltaValue):
deltas.append(blob.delta)
else:
base = blob
return DeltaChainValue(base=base, deltas=deltas)
parent_map: dict[str, str | None] = {
row["checkpoint_id"]: row["parent_checkpoint_id"] for row in cur.fetchall()
}
ancestor_ids: list[str] = []
cid: str | None = parent_map.get(checkpoint_id)
while cid is not None:
ancestor_ids.append(cid)
cid = parent_map.get(cid)
if not ancestor_ids:
return []
cur.execute(
"SELECT checkpoint_id, type, blob FROM checkpoint_writes "
"WHERE thread_id = %s AND checkpoint_ns = %s AND channel = %s "
" AND checkpoint_id = ANY(%s) "
"ORDER BY task_id, idx",
(thread_id, checkpoint_ns, channel, ancestor_ids),
)
writes_by_cp: dict[str, list[tuple[str, bytes]]] = defaultdict(list)
for row in cur.fetchall():
writes_by_cp[row["checkpoint_id"]].append((row["type"], row["blob"]))
result = []
for cid in reversed(ancestor_ids):
for type_tag, blob in writes_by_cp.get(cid, []):
result.append(self.serde.loads_typed((type_tag, blob)))
return result
def _dump_blobs(
self,
@@ -3,10 +3,12 @@ from __future__ import annotations
import copy
import dataclasses
import logging
import threading
from collections.abc import AsyncIterator, Collection, Iterator, Mapping, Sequence
from typing import ( # noqa: UP035
Any,
Generic,
List,
Literal,
NamedTuple,
TypedDict,
@@ -32,19 +34,17 @@ PendingWrite = tuple[str, str, Any]
@dataclasses.dataclass
class DeltaValue:
"""Returned by DeltaChannel.checkpoint(). Represents one step's writes."""
class DeltaChannelSentinel:
"""Marker stored in checkpoint_blobs for a DeltaChannel field.
delta: list[Any]
No data is stored here — the actual per-step writes live in checkpoint_writes
and are replayed through the reducer at load time.
"""
pass
@dataclasses.dataclass
class DeltaChainValue:
"""Passed to DeltaChannel.from_checkpoint(). Assembled by the pregel layer."""
base: list[Any] | None # starting accumulated value; None = start from empty
deltas: list[list[Any]] # per-step write-sets, ordered oldest → newest
_DELTA_RECONSTRUCTION: threading.local = threading.local()
logger = logging.getLogger(__name__)
@@ -475,6 +475,56 @@ class BaseCheckpointSaver(Generic[V]):
"""
raise NotImplementedError
def get_channel_writes(self, config: RunnableConfig, channel: str) -> List[Any]: # noqa: UP006
"""Collect all writes for `channel` across this checkpoint's ancestry, oldest→newest.
Default implementation walks the full thread history via `list()`. Savers can
override with a more efficient query (InMemorySaver and PostgresSaver do this).
"""
# Guard against re-entrant calls: when list() triggers reconstruction which
# calls list() again, the inner call returns tuples with DeltaChannelSentinel
# in channel_values (which get_channel_writes ignores — it only reads
# pending_writes). This breaks the recursion safely.
if getattr(_DELTA_RECONSTRUCTION, "active", False):
return []
_DELTA_RECONSTRUCTION.active = True
try:
result: list[Any] = []
target_id = config["configurable"].get("checkpoint_id")
for tup in self.list(config):
if tup.config["configurable"].get("checkpoint_id") == target_id:
continue # skip the checkpoint itself; we want its ancestors' writes
if tup.pending_writes:
for _, ch, value in tup.pending_writes:
if ch == channel:
result.append(value)
result.reverse() # list() yields newest→oldest; we want oldest→newest
return result
finally:
_DELTA_RECONSTRUCTION.active = False
async def aget_channel_writes(
self, config: RunnableConfig, channel: str
) -> List[Any]: # noqa: UP006
"""Async version of get_channel_writes."""
if getattr(_DELTA_RECONSTRUCTION, "active", False):
return []
_DELTA_RECONSTRUCTION.active = True
try:
result: list[Any] = []
target_id = config["configurable"].get("checkpoint_id")
async for tup in self.alist(config):
if tup.config["configurable"].get("checkpoint_id") == target_id:
continue
if tup.pending_writes:
for _, ch, value in tup.pending_writes:
if ch == channel:
result.append(value)
result.reverse()
return result
finally:
_DELTA_RECONSTRUCTION.active = False
def get_next_version(self, current: V | None, channel: None) -> V:
"""Generate the next version ID for a channel.
@@ -9,7 +9,7 @@ from collections import defaultdict
from collections.abc import AsyncIterator, Iterator, Sequence
from contextlib import AbstractAsyncContextManager, AbstractContextManager, ExitStack
from types import TracebackType
from typing import Any
from typing import Any, cast
from langchain_core.runnables import RunnableConfig
@@ -20,8 +20,7 @@ from langgraph.checkpoint.base import (
Checkpoint,
CheckpointMetadata,
CheckpointTuple,
DeltaChainValue,
DeltaValue,
DeltaChannelSentinel,
SerializerProtocol,
get_checkpoint_id,
get_checkpoint_metadata,
@@ -127,68 +126,56 @@ class InMemorySaver(
thread_id: str,
checkpoint_ns: str,
versions: ChannelVersions,
checkpoint_id: str = "",
) -> dict[str, Any]:
channel_values: dict[str, Any] = {}
delta_channels: list[str] = []
for k, v in versions.items():
kk = (thread_id, checkpoint_ns, k, v)
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] == "delta":
delta_channels.append(k)
elif vv[0] != "empty":
channel_values[k] = self.serde.loads_typed(vv)
for channel in delta_channels:
channel_values[channel] = self._assemble_delta_chain(
thread_id, checkpoint_ns, checkpoint_id, channel
)
return channel_values
if vv[0] == "empty":
continue
result[k] = self.serde.loads_typed(vv)
return result
def _assemble_delta_chain(
def _resolve_delta_channels(
self,
thread_id: str,
checkpoint_ns: str,
checkpoint_id: str,
channel: str,
) -> DeltaChainValue:
"""Walk the checkpoint parent tree to collect all delta blobs for a channel."""
config: RunnableConfig,
channel_values: dict[str, Any],
) -> None:
"""Replace DeltaChannelSentinel entries with reconstructed write lists."""
for channel, value in list(channel_values.items()):
if isinstance(value, DeltaChannelSentinel):
channel_values[channel] = self.get_channel_writes(config, channel)
def get_channel_writes(self, config: RunnableConfig, channel: str) -> list[Any]:
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, {})
blobs: list[Any] = []
current_id: str | None = checkpoint_id
seen_versions: set[str] = set()
while current_id is not None:
entry = ns_storage.get(current_id)
# Walk the parent chain newest→oldest collecting checkpoint IDs.
chain: list[str] = []
current: str | None = checkpoint_id
while current is not None:
entry = ns_storage.get(current)
if entry is None:
break
checkpoint = self.serde.loads_typed(entry[0])
version = checkpoint["channel_versions"].get(channel)
if version is None:
break # channel not yet in this checkpoint
_, _, current_id = entry # advance to parent before the continue/break
if version in seen_versions:
continue # same blob already collected; keep walking to older checkpoints
seen_versions.add(version)
kk = (thread_id, checkpoint_ns, channel, version)
if kk not in self.blobs:
break
vv = self.blobs[kk]
if vv[0] == "empty":
break
blob = self.serde.loads_typed(vv)
blobs.append(blob)
if not isinstance(blob, DeltaValue):
break # hit a snapshot (plain list) — chain root found
blobs.reverse()
base = None
deltas: list[list[Any]] = []
for blob in blobs:
if isinstance(blob, DeltaValue):
deltas.append(blob.delta)
else:
base = blob
return DeltaChainValue(base=base, deltas=deltas)
chain.append(current)
_, _, parent = entry
current = parent
# Collect writes oldest→newest.
result: list[Any] = []
for cp_id in reversed(chain):
step_writes = self.writes.get((thread_id, checkpoint_ns, cp_id), {})
for (_task_id, _idx), (_, ch, serialized, _) in sorted(step_writes.items()):
if ch == channel:
result.append(self.serde.loads_typed(serialized))
return result
async def aget_channel_writes(
self, config: RunnableConfig, channel: str
) -> list[Any]:
return self.get_channel_writes(config, channel)
def get_tuple(self, config: RunnableConfig) -> CheckpointTuple | None:
"""Get a checkpoint tuple from the in-memory storage.
@@ -211,16 +198,17 @@ class InMemorySaver(
checkpoint, metadata, parent_checkpoint_id = saved
writes = self.writes[(thread_id, checkpoint_ns, checkpoint_id)].values()
checkpoint_: Checkpoint = self.serde.loads_typed(checkpoint)
channel_values = self._load_blobs(
thread_id,
checkpoint_ns,
checkpoint_["channel_versions"],
)
self._resolve_delta_channels(config, channel_values)
return CheckpointTuple(
config=config,
checkpoint={
**checkpoint_,
"channel_values": self._load_blobs(
thread_id,
checkpoint_ns,
checkpoint_["channel_versions"],
checkpoint_id,
),
"channel_values": channel_values,
},
metadata=self.serde.loads_typed(metadata),
pending_writes=[
@@ -244,22 +232,27 @@ class InMemorySaver(
checkpoint, metadata, parent_checkpoint_id = checkpoints[checkpoint_id]
writes = self.writes[(thread_id, checkpoint_ns, checkpoint_id)].values()
checkpoint_ = self.serde.loads_typed(checkpoint)
return CheckpointTuple(
config={
resolved_config = cast(
RunnableConfig,
{
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": checkpoint_id,
}
},
)
channel_values = self._load_blobs(
thread_id,
checkpoint_ns,
checkpoint_["channel_versions"],
)
self._resolve_delta_channels(resolved_config, channel_values)
return CheckpointTuple(
config=resolved_config,
checkpoint={
**checkpoint_,
"channel_values": self._load_blobs(
thread_id,
checkpoint_ns,
checkpoint_["channel_versions"],
checkpoint_id,
),
"channel_values": channel_values,
},
metadata=self.serde.loads_typed(metadata),
pending_writes=[
@@ -354,22 +347,28 @@ class InMemorySaver(
checkpoint_: Checkpoint = self.serde.loads_typed(checkpoint)
yield CheckpointTuple(
config={
list_config = cast(
RunnableConfig,
{
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": checkpoint_ns,
"checkpoint_id": checkpoint_id,
}
},
)
channel_values = self._load_blobs(
thread_id,
checkpoint_ns,
checkpoint_["channel_versions"],
)
self._resolve_delta_channels(list_config, channel_values)
yield CheckpointTuple(
config=list_config,
checkpoint={
**checkpoint_,
"channel_values": self._load_blobs(
thread_id,
checkpoint_ns,
checkpoint_["channel_versions"],
checkpoint_id,
),
"channel_values": channel_values,
},
metadata=metadata,
parent_config=(
@@ -80,8 +80,6 @@ SAFE_MSGPACK_TYPES: frozenset[tuple[str, ...]] = frozenset(
("langgraph.types", "Overwrite"),
("langgraph.store.base", "Item"),
("langgraph.store.base", "GetOp"),
# DeltaChannel checkpoint value type
("langgraph.checkpoint.base", "DeltaValue"),
}
)
@@ -46,11 +46,30 @@ 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 _is_delta_value(obj: Any) -> bool:
from langgraph.checkpoint.base import DeltaValue # lazy import avoids circular dep
return isinstance(obj, DeltaValue)
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)
def _get_delta_sentinel_cls() -> type:
from langgraph.checkpoint.base import (
DeltaChannelSentinel,
) # lazy import avoids circular dep
return DeltaChannelSentinel
class JsonPlusSerializer(SerializerProtocol):
@@ -245,8 +264,8 @@ class JsonPlusSerializer(SerializerProtocol):
return "bytes", obj
elif isinstance(obj, bytearray):
return "bytearray", obj
elif _is_delta_value(obj):
return "delta", _msgpack_enc({"d": obj.delta})
elif isinstance(obj, _get_delta_sentinel_cls()):
return "delta", b""
else:
try:
return "msgpack", _msgpack_enc(obj)
@@ -270,12 +289,9 @@ class JsonPlusSerializer(SerializerProtocol):
data_, ext_hook=self._unpack_ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
)
elif type_ == "delta":
from langgraph.checkpoint.base import DeltaValue # lazy import
from langgraph.checkpoint.base import DeltaChannelSentinel
raw = ormsgpack.unpackb(
data_, ext_hook=self._unpack_ext_hook, option=ormsgpack.OPT_NON_STR_KEYS
)
return DeltaValue(delta=raw["d"])
return DeltaChannelSentinel()
elif self.pickle_fallback and type_ == "pickle":
return pickle.loads(data_)
else:
@@ -549,7 +565,9 @@ def _create_msgpack_ext_hook(
"name": name,
}
)
logger.warning(
_warn_once(
_warned_unregistered_types,
key,
"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 "
@@ -571,7 +589,9 @@ def _create_msgpack_ext_hook(
"name": name,
}
)
logger.warning(
_warn_once(
_warned_blocked_types,
key,
"Blocked deserialization of %s.%s - not in allowed_msgpack_modules. "
"Add to allowed_msgpack_modules to allow: [(%r, %r)]",
module,
+9
View File
@@ -29,6 +29,8 @@ from langgraph.checkpoint.serde.jsonplus import (
EXT_METHOD_SINGLE_ARG,
JsonPlusSerializer,
_msgpack_enc,
_warned_blocked_types,
_warned_unregistered_types,
)
@@ -102,6 +104,13 @@ 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()
+20 -7
View File
@@ -35,6 +35,8 @@ 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
@@ -580,6 +582,14 @@ 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
@@ -595,6 +605,12 @@ 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
@@ -639,7 +655,6 @@ 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"))
@@ -657,7 +672,6 @@ 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")]
)
@@ -985,14 +999,13 @@ def test_msgpack_nested_pydantic_serializes_as_dict(
assert result == obj
def test_delta_value_serde_round_trip() -> None:
from langgraph.checkpoint.base import DeltaValue
def test_delta_channel_sentinel_serde_round_trip() -> None:
from langgraph.checkpoint.base import DeltaChannelSentinel
from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
serde = JsonPlusSerializer()
original = DeltaValue(delta=[{"type": "human", "content": "hi"}])
original = DeltaChannelSentinel()
type_tag, blob = serde.dumps_typed(original)
assert type_tag == "delta"
loaded = serde.loads_typed((type_tag, blob))
assert isinstance(loaded, DeltaValue)
assert loaded.delta == original.delta
assert isinstance(loaded, DeltaChannelSentinel)
+58 -43
View File
@@ -12,13 +12,25 @@ from langgraph.checkpoint.base import (
empty_checkpoint,
)
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
from langgraph.checkpoint.serde.jsonplus import (
JsonPlusSerializer,
_warned_blocked_types,
_warned_unregistered_types,
)
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:
@@ -311,11 +323,10 @@ def test_memory_saver_with_allowlist_proxy_isolated() -> None:
class TestInMemorySaverDeltaChannel:
def test_load_blobs_assembles_delta_chain(self) -> None:
"""_load_blobs returns DeltaChainValue for delta channels, not raw DeltaValue."""
def test_load_blobs_returns_sentinel_for_delta_channel(self) -> None:
"""_load_blobs returns DeltaChannelSentinel for delta channels (reconstruction deferred)."""
from langgraph.checkpoint.base import (
DeltaChainValue,
DeltaValue,
DeltaChannelSentinel,
empty_checkpoint,
)
@@ -324,55 +335,59 @@ class TestInMemorySaverDeltaChannel:
thread_id, ns, channel = "t1", "", "messages"
v1 = "00000000000000000000000000000001.0000000000000000"
v2 = "00000000000000000000000000000002.0000000000000000"
delta1 = DeltaValue(delta=[{"content": "hi"}])
delta2 = DeltaValue(delta=[{"content": "bye"}])
saver.blobs[(thread_id, ns, channel, v1)] = serde.dumps_typed(delta1)
saver.blobs[(thread_id, ns, channel, v2)] = serde.dumps_typed(delta2)
sentinel = DeltaChannelSentinel()
saver.blobs[(thread_id, ns, channel, v1)] = serde.dumps_typed(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 isinstance(result[channel], DeltaChannelSentinel)
def test_get_channel_writes_collects_writes(self) -> None:
"""get_channel_writes collects per-step writes oldest→newest."""
from langgraph.checkpoint.base import empty_checkpoint
saver = InMemorySaver()
serde = JsonPlusSerializer()
thread_id, ns, channel = "t1", "", "messages"
cp1 = empty_checkpoint()
cp1["id"] = "cp1"
cp2 = empty_checkpoint()
cp2["id"] = "cp2"
cp2["channel_versions"][channel] = v2
saver.storage[thread_id][ns] = {
"cp1": (serde.dumps_typed(cp1), serde.dumps_typed({}), None),
"cp2": (serde.dumps_typed(cp2), serde.dumps_typed({}), "cp1"),
}
result = saver._load_blobs(thread_id, ns, {channel: v2}, "cp2")
assert channel in result
chain = result[channel]
assert isinstance(chain, DeltaChainValue)
assert chain.deltas == [[{"content": "hi"}], [{"content": "bye"}]]
def test_load_blobs_single_delta_no_parent(self) -> None:
"""Single delta with no parent checkpoint produces a chain with one delta."""
from langgraph.checkpoint.base import (
DeltaChainValue,
DeltaValue,
empty_checkpoint,
# cp1 has a write for channel
saver.writes[(thread_id, ns, "cp1")][("task1", 0)] = (
"task1",
channel,
serde.dumps_typed({"content": "hi"}),
"",
)
# cp2 has a write for channel
saver.writes[(thread_id, ns, "cp2")][("task2", 0)] = (
"task2",
channel,
serde.dumps_typed({"content": "bye"}),
"",
)
saver = InMemorySaver()
serde = JsonPlusSerializer()
thread_id, ns, channel = "t1", "", "messages"
v1 = "00000000000000000000000000000001.0000000000000000"
delta = DeltaValue(delta=[{"content": "only"}])
saver.blobs[(thread_id, ns, channel, v1)] = serde.dumps_typed(delta)
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)
config: RunnableConfig = {
"configurable": {
"thread_id": thread_id,
"checkpoint_ns": ns,
"checkpoint_id": "cp2",
}
}
result = saver._load_blobs(thread_id, ns, {channel: v1}, "cp1")
chain = result[channel]
assert isinstance(chain, DeltaChainValue)
assert chain.base is None
assert chain.deltas == [[{"content": "only"}]]
result = saver.get_channel_writes(config, channel)
assert result == [{"content": "hi"}, {"content": "bye"}]
@@ -5,5 +5,5 @@ description = "Test for prerelease stuff"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
"langchain-openai==1.0.1"
"langchain-openai==1.1.14"
]
@@ -5,7 +5,7 @@ description = "Test for prerelease stuff"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
"langchain-openai==1.0.0a2",
"langchain-openai==1.1.14",
"langchain-anthropic==1.0.0a5",
"langgraph==1.1.5"
]
@@ -5,7 +5,7 @@ description = "Test for prerelease stuff"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
"langchain-openai==1.0.0a2",
"langchain-openai==1.1.14",
"langgraph==1.1.2",
"langchain_community>=0.3.0",
]
+1 -1
View File
@@ -1 +1 @@
__version__ = "0.4.22"
__version__ = "0.4.23"
+1 -1
View File
@@ -23,7 +23,7 @@ dependencies = [
path = "langgraph_cli/__init__.py"
[project.optional-dependencies]
inmem = [
"langgraph-api>=0.5.35,<0.8.0 ; python_version >= '3.11'",
"langgraph-api>=0.5.35,<0.9.0 ; python_version >= '3.11'",
"langgraph-runtime-inmem>=0.7 ; python_version >= '3.11'",
]
+465 -383
View File
File diff suppressed because it is too large Load Diff
-18
View File
@@ -245,15 +245,6 @@ class _GraphCallbackManager(BaseCallbackManager):
run_id=run_id,
)
def add_handler(
self,
handler: BaseCallbackHandler,
inherit: bool = True, # noqa: FBT001,FBT002
) -> None:
if not isinstance(handler, GraphCallbackHandler):
raise TypeError("handlers must inherit GraphCallbackHandler")
super().add_handler(handler, inherit=inherit)
def copy(
self,
*,
@@ -321,15 +312,6 @@ class _AsyncGraphCallbackManager(BaseCallbackManager):
run_id=run_id,
)
def add_handler(
self,
handler: BaseCallbackHandler,
inherit: bool = True, # noqa: FBT001,FBT002
) -> None:
if not isinstance(handler, GraphCallbackHandler):
raise TypeError("handlers must inherit GraphCallbackHandler")
super().add_handler(handler, inherit=inherit)
def copy(
self,
*,
+31 -90
View File
@@ -3,7 +3,7 @@ from __future__ import annotations
from collections.abc import Callable, Sequence
from typing import Any, Generic
from langgraph.checkpoint.base import DeltaChainValue, DeltaValue
from langgraph.checkpoint.base import DeltaChannelSentinel
from typing_extensions import Self
from langgraph._internal._typing import MISSING
@@ -14,64 +14,41 @@ from langgraph.errors import EmptyChannelError
__all__ = ("DeltaChannel",)
class DeltaChannel(Generic[Value], BaseChannel[list[Value], Value, DeltaValue]):
"""A channel that stores only per-step write deltas in checkpoints.
class DeltaChannel(
Generic[Value], BaseChannel[list[Value], Value, DeltaChannelSentinel]
):
"""A channel that stores only a sentinel in checkpoints; per-step writes are
stored in checkpoint_writes and replayed through the operator at load time.
Reconstructs the full accumulated list at load time by replaying the
chain of deltas through the operator. Use with append-style reducers
(e.g. `add_messages`) on long-running threads to reduce checkpoint
storage from O(N²) to O(N).
Use with append-style reducers (e.g. `add_messages`) on long-running threads
to eliminate O(N²) blob growth — storage is O(N) using the writes table that
every checkpointer already maintains.
Works with all checkpointers. Savers with a dedicated blob store
(InMemorySaver, PostgresSaver) use an O(1) fast-path per chain step;
all others (SQLite, MongoDB, etc.) fall back to get_tuple traversal.
Use `snapshot_every=N` to cap chain traversal depth at N steps. Every N
steps a full snapshot is written as the chain root; subsequent deltas
chain back to it, so `get_state` / reload never traverses more than N
checkpoints regardless of thread length. Recommended for savers without
a dedicated blob store.
Works with all checkpointers. Savers with dedicated implementations
(InMemorySaver, PostgresSaver) reconstruct in one pass; others fall back to
walking the checkpoint list.
Usage::
class State(TypedDict):
messages: Annotated[list[AnyMessage], DeltaChannel(add_messages)]
# Cap reconstruction depth (recommended for SQLite / MongoDB savers):
messages: Annotated[list[AnyMessage], DeltaChannel(add_messages, snapshot_every=50)]
# Dict-type reducer (type inferred from the Annotated outer type):
files: Annotated[dict, DeltaChannel(merge_files)]
"""
__slots__ = (
"value",
"operator",
"snapshot_every",
"_pending",
"_base_version",
"_overwritten",
"_steps_since_snapshot",
)
__slots__ = ("value", "operator")
def __init__(
self,
operator: Callable[[list[Value], Any], list[Value]],
*,
snapshot_every: int | None = None,
) -> None:
super().__init__(list)
self.operator = operator
self.snapshot_every = snapshot_every
self.value: list[Value] = []
self._pending: list[Any] = []
self._base_version: str | None = None
self._overwritten: bool = False
self._steps_since_snapshot: int = 0
def __eq__(self, other: object) -> bool:
if not isinstance(other, DeltaChannel):
return False
if self.snapshot_every != other.snapshot_every:
return False
if (
self.operator.__name__ != "<lambda>"
and other.operator.__name__ != "<lambda>"
@@ -88,18 +65,14 @@ class DeltaChannel(Generic[Value], BaseChannel[list[Value], Value, DeltaValue]):
return self.typ | list[self.typ] # type: ignore[name-defined]
def copy(self) -> Self:
new = DeltaChannel(self.operator, snapshot_every=self.snapshot_every)
new = DeltaChannel(self.operator)
new.typ = self.typ
new.key = self.key
new.value = self.value if self.value is MISSING else self.value.copy()
new._pending = self._pending[:]
new._base_version = self._base_version
new._overwritten = self._overwritten
new._steps_since_snapshot = self._steps_since_snapshot
return new
def from_checkpoint(self, checkpoint: Any) -> Self:
new = DeltaChannel(self.operator, snapshot_every=self.snapshot_every)
new = DeltaChannel(self.operator)
new.typ = self.typ
new.key = self.key
if checkpoint is MISSING:
@@ -107,29 +80,21 @@ class DeltaChannel(Generic[Value], BaseChannel[list[Value], Value, DeltaValue]):
new.value = new.typ()
except Exception:
new.value = []
elif isinstance(checkpoint, DeltaChainValue):
accumulated: list[Value] = (
checkpoint.base if checkpoint.base is not None else new.typ()
)
for step_writes in checkpoint.deltas:
for write in step_writes:
accumulated = new.operator(accumulated, write)
new.value = accumulated
# Seed the counter from actual chain depth so rehydration fires at
# the right time regardless of how many prior invocations there were.
new._steps_since_snapshot = len(checkpoint.deltas)
elif isinstance(checkpoint, DeltaValue):
raise ValueError(
f"Channel '{self.key}' uses DeltaChannel but the checkpointer "
"does not support incremental channel storage. "
"Use InMemorySaver or PostgresSaver, or remove DeltaChannel from your schema."
)
elif isinstance(checkpoint, list):
# Flat list of write values (oldest→newest) from get_channel_writes.
try:
value: Any = new.typ()
except Exception:
value = []
for write in checkpoint:
value = new.operator(value, write)
new.value = value
else:
# Backwards compat: plain list from old BinaryOperatorAggregate checkpoint.
new.value = list(checkpoint)
new._pending = []
new._base_version = None # set by the subsequent after_checkpoint() call
new._overwritten = False
# Backward compat: plain accumulated value (e.g. from a migrated thread).
try:
new.value = list(checkpoint)
except Exception:
new.value = []
return new
def update(self, values: Sequence[Any]) -> bool:
@@ -154,13 +119,10 @@ class DeltaChannel(Generic[Value], BaseChannel[list[Value], Value, DeltaValue]):
self.value = (
list(overwrite_value) if overwrite_value is not None else self.typ()
)
self._pending = list(self.value)
self._overwritten = True
seen_overwrite = True
elif not seen_overwrite:
base = self.typ() if self.value is MISSING else self.value
self.value = self.operator(base, value)
self._pending.append(value)
return True
def get(self) -> list[Value]:
@@ -171,26 +133,5 @@ class DeltaChannel(Generic[Value], BaseChannel[list[Value], Value, DeltaValue]):
def is_available(self) -> bool:
return self.value is not MISSING
def checkpoint(self) -> Any:
if (
self.snapshot_every is not None
and self._steps_since_snapshot >= self.snapshot_every
):
# Emit a full snapshot to cap chain depth at snapshot_every.
# The saver stores this as a plain (non-diff) blob, so future
# deltas will chain back to it and traversal depth resets to 1.
return list(self.value)
return DeltaValue(delta=self._pending[:])
def after_checkpoint(self, version: Any, checkpoint_id: str | None = None) -> None:
if version != self._base_version:
if self._base_version is None:
pass # First call after from_checkpoint — anchor without counting a step.
elif self.snapshot_every is not None:
if self._steps_since_snapshot >= self.snapshot_every:
self._steps_since_snapshot = 0
else:
self._steps_since_snapshot += 1
self._base_version = version
self._pending = []
self._overwritten = False
def checkpoint(self) -> DeltaChannelSentinel:
return DeltaChannelSentinel()
+11 -1
View File
@@ -831,8 +831,18 @@ class PregelLoop:
# parent. For forks (source=update/fork), 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 self.is_replaying:
if is_time_traveling:
replay_checkpoint_id = self.checkpoint["id"]
if (
self.checkpoint_metadata.get("source")
+6 -14
View File
@@ -14,7 +14,7 @@ from langchain_core.messages import BaseMessage
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, LLMResult
from pydantic import BaseModel
from langgraph._internal._constants import NS_END, NS_SEP
from langgraph._internal._constants import NS_SEP
from langgraph.constants import TAG_HIDDEN, TAG_NOSTREAM
from langgraph.pregel.protocol import StreamChunk
from langgraph.types import Command
@@ -132,23 +132,15 @@ class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler):
**kwargs: Any,
) -> Any:
if metadata and (not tags or (TAG_NOSTREAM not in tags)):
task_checkpoint_ns = cast(str, metadata["langgraph_checkpoint_ns"])
checkpoint_ns = (
f"{task_checkpoint_ns.rsplit(NS_END, 1)[0]}{NS_END}"
if NS_END in task_checkpoint_ns
else task_checkpoint_ns
)
ns = tuple(task_checkpoint_ns.split(NS_SEP))[:-1]
ns = tuple(cast(str, metadata["langgraph_checkpoint_ns"]).split(NS_SEP))[
:-1
]
if not self.subgraphs and len(ns) > 0 and ns != self.parent_ns:
return
stream_metadata = dict(metadata)
stream_metadata["langgraph_checkpoint_ns"] = checkpoint_ns
# Preserve backwards-compatible streamed checkpoint metadata shape.
stream_metadata["checkpoint_ns"] = checkpoint_ns
if tags:
if filtered_tags := [t for t in tags if not t.startswith("seq:step")]:
stream_metadata["tags"] = filtered_tags
self.metadata[run_id] = (ns, stream_metadata)
metadata["tags"] = filtered_tags
self.metadata[run_id] = (ns, metadata)
def on_llm_new_token(
self,
+59 -29
View File
@@ -1,9 +1,11 @@
from __future__ import annotations
import ast
import functools
import inspect
import re
import textwrap
import types
from collections.abc import Callable
from typing import Any
@@ -64,46 +66,74 @@ def find_subgraph_pregel(candidate: Runnable) -> PregelProtocol | None:
return None
@functools.lru_cache(maxsize=256)
def _get_nonlocal_names(code: types.CodeType) -> frozenset[str]:
"""Return the set of nonlocal variable names referenced by a function.
Cached by code object so the expensive source fetch + AST parse only
happens once per unique function definition across repeated graph compiles.
Args:
code: The code object of the function to analyse.
Returns:
Frozenset of variable names that the function reads from its enclosing
scope (free variables and globals referenced in function bodies).
"""
try:
source = inspect.getsource(code)
tree = ast.parse(textwrap.dedent(source))
visitor = FunctionNonLocals()
visitor.visit(tree)
return frozenset(visitor.nonlocals)
except (SyntaxError, TypeError, OSError, SystemError):
return frozenset()
def get_function_nonlocals(func: Callable) -> list[Any]:
"""Get the nonlocal variables accessed by a function.
The expensive source-parsing step is cached by code object; only the
cheap closure-variable lookup runs on every call.
Args:
func: The function to check.
Returns:
List[Any]: The nonlocal variables accessed by the function.
"""
try:
code = inspect.getsource(func)
tree = ast.parse(textwrap.dedent(code))
visitor = FunctionNonLocals()
visitor.visit(tree)
values: list[Any] = []
closure = (
inspect.getclosurevars(func.__wrapped__)
if hasattr(func, "__wrapped__") and callable(func.__wrapped__)
else inspect.getclosurevars(func)
)
candidates = {**closure.globals, **closure.nonlocals}
for k, v in candidates.items():
if k in visitor.nonlocals:
values.append(v)
for kk in visitor.nonlocals:
if "." in kk and kk.startswith(k):
vv = v
for part in kk.split(".")[1:]:
if vv is None:
break
else:
try:
vv = getattr(vv, part)
except AttributeError:
break
else:
values.append(vv)
except (SyntaxError, TypeError, OSError, SystemError):
actual_func = (
func.__wrapped__
if hasattr(func, "__wrapped__") and callable(func.__wrapped__)
else func
)
# Fast path: no free variables means nothing to scan.
if not actual_func.__code__.co_freevars:
return []
nonlocal_names = _get_nonlocal_names(actual_func.__code__)
if not nonlocal_names:
return []
closure = inspect.getclosurevars(actual_func)
candidates = {**closure.globals, **closure.nonlocals}
values: list[Any] = []
for k, v in candidates.items():
if k in nonlocal_names:
values.append(v)
for kk in nonlocal_names:
if "." in kk and kk.startswith(k):
vv = v
for part in kk.split(".")[1:]:
if vv is None:
break
else:
try:
vv = getattr(vv, part)
except AttributeError:
break
else:
values.append(vv)
return values
+2 -2
View File
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
[project]
name = "langgraph"
version = "1.1.7a2"
version = "1.1.9"
description = "Building stateful, multi-actor applications with LLMs"
authors = []
requires-python = ">=3.10"
@@ -24,7 +24,7 @@ classifiers = [
'Programming Language :: Python :: 3.13',
]
dependencies = [
"langchain-core==1.3.0a2",
"langchain-core>=1.3.0,<2",
"langgraph-checkpoint>=2.1.0,<5.0.0",
"langgraph-sdk>=0.3.0,<0.4.0",
"langgraph-prebuilt>=1.0.9,<1.1.0",
+70 -193
View File
@@ -121,26 +121,22 @@ def test_untracked_value() -> None:
def test_delta_channel_basic_two_steps() -> None:
from langchain_core.messages import AIMessage, HumanMessage
from langgraph.checkpoint.base import DeltaValue
from langgraph.checkpoint.base import DeltaChannelSentinel
from langgraph.channels.delta import DeltaChannel
from langgraph.graph.message import add_messages
ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
ch.after_checkpoint(None)
# Step 1: one message added
ch.update([HumanMessage(content="hi", id="h1")])
d1 = ch.checkpoint()
assert isinstance(d1, DeltaValue)
assert len(d1.delta) == 1
ch.after_checkpoint("v1", checkpoint_id="cid1")
assert isinstance(d1, DeltaChannelSentinel)
# Step 2: another message
ch.update([AIMessage(content="hello", id="a1")])
d2 = ch.checkpoint()
assert len(d2.delta) == 1
ch.after_checkpoint("v2")
assert isinstance(d2, DeltaChannelSentinel)
# Full accumulated value is preserved in memory
assert len(ch.get()) == 2
@@ -148,40 +144,21 @@ def test_delta_channel_basic_two_steps() -> None:
assert ch.get()[1].content == "hello"
def test_delta_channel_after_checkpoint_no_op_when_unchanged() -> None:
from langchain_core.messages import HumanMessage
from langgraph.channels.delta import DeltaChannel
from langgraph.graph.message import add_messages
ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
ch.after_checkpoint(None)
ch.update([HumanMessage(content="hi", id="h1")])
ch.after_checkpoint("v1")
# Same version: no-op
ch.after_checkpoint("v1")
assert ch._base_version == "v1"
assert ch._pending == []
def test_delta_channel_from_checkpoint_chain() -> None:
def test_delta_channel_from_checkpoint_writes_list() -> None:
"""from_checkpoint with a flat list of individual writes replays them through the operator."""
from langchain_core.messages import AIMessage, HumanMessage
from langgraph.checkpoint.base import DeltaChainValue
from langgraph.channels.delta import DeltaChannel
from langgraph.graph.message import add_messages
spec = DeltaChannel(add_messages)
chain = DeltaChainValue(
base=None,
deltas=[
[HumanMessage(content="hi", id="h1")],
[AIMessage(content="hello", id="a1")],
[HumanMessage(content="bye", id="h2")],
],
)
ch = spec.from_checkpoint(chain)
# Each element is one write value (as stored in checkpoint_writes)
writes = [
HumanMessage(content="hi", id="h1"),
AIMessage(content="hello", id="a1"),
HumanMessage(content="bye", id="h2"),
]
ch = spec.from_checkpoint(writes)
msgs = ch.get()
assert len(msgs) == 3
assert msgs[0].content == "hi"
@@ -195,68 +172,45 @@ def test_delta_channel_from_checkpoint_backwards_compat() -> None:
from langgraph.channels.delta import DeltaChannel
from langgraph.graph.message import add_messages
# Old BinaryOperatorAggregate checkpoint: plain list
# Old BinaryOperatorAggregate checkpoint: plain list treated as backward compat
spec = DeltaChannel(add_messages)
old_value = [HumanMessage(content="old", id="h1")]
ch = spec.from_checkpoint(old_value)
assert ch.get() == old_value
def test_delta_channel_overwrite_resets_chain() -> None:
def test_delta_channel_overwrite() -> None:
from langchain_core.messages import HumanMessage
from langgraph.checkpoint.base import DeltaValue
from langgraph.checkpoint.base import DeltaChannelSentinel
from langgraph.channels.delta import DeltaChannel
from langgraph.graph.message import add_messages
from langgraph.types import Overwrite
ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
ch.after_checkpoint(None)
ch.update([HumanMessage(content="old", id="h1")])
ch.after_checkpoint("v1")
ch.update([Overwrite([HumanMessage(content="new", id="h2")])])
d = ch.checkpoint()
assert isinstance(d, DeltaValue)
assert len(d.delta) == 1
assert d.delta[0].content == "new"
# _overwritten flag must be set so next checkpoint acts as a chain root
assert ch._overwritten is True
assert isinstance(d, DeltaChannelSentinel)
# After overwrite, value is reset to only the new message
assert len(ch.get()) == 1
assert ch.get()[0].content == "new"
def test_delta_channel_unsupported_saver_raises() -> None:
"""from_checkpoint raises ValueError when the saver returns a raw DeltaValue."""
from langgraph.checkpoint.base import DeltaValue
from langgraph.channels.delta import DeltaChannel
from langgraph.graph.message import add_messages
spec = DeltaChannel(add_messages)
raw = DeltaValue(delta=[{"type": "human", "content": "hello"}])
with pytest.raises(
ValueError, match="does not support incremental channel storage"
):
spec.from_checkpoint(raw)
def test_delta_channel_remove_message_delta_and_replay() -> None:
"""RemoveMessage stored in a delta must round-trip correctly through the chain."""
def test_delta_channel_remove_message_and_replay() -> None:
"""RemoveMessage must round-trip correctly when writes are replayed."""
from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage
from langgraph.checkpoint.base import DeltaChainValue, DeltaValue
from langgraph.channels.delta import DeltaChannel
from langgraph.graph.message import add_messages
spec = DeltaChannel(add_messages)
ch = spec.from_checkpoint(MISSING)
ch.after_checkpoint(None)
# Step 1: add two messages
ch.update([HumanMessage(content="hi", id="h1")])
ch.update([AIMessage(content="hello", id="a1")])
d1 = ch.checkpoint()
assert isinstance(d1, DeltaValue)
ch.after_checkpoint("v1", checkpoint_id="cid1")
assert ch.get() == [
HumanMessage(content="hi", id="h1"),
AIMessage(content="hello", id="a1"),
@@ -264,127 +218,63 @@ def test_delta_channel_remove_message_delta_and_replay() -> None:
# Step 2: remove the AI message
ch.update([RemoveMessage(id="a1")])
d2 = ch.checkpoint()
assert isinstance(d2, DeltaValue)
assert any(isinstance(w, RemoveMessage) for w in d2.delta)
ch.after_checkpoint("v2", checkpoint_id="cid2")
assert ch.get() == [HumanMessage(content="hi", id="h1")]
# Replay the full chain from scratch — must reproduce the post-remove state
chain = DeltaChainValue(base=None, deltas=[d1.delta, d2.delta])
ch2 = spec.from_checkpoint(chain)
# Replay the writes list from scratch — must reproduce the post-remove state
writes = [
HumanMessage(content="hi", id="h1"),
AIMessage(content="hello", id="a1"),
RemoveMessage(id="a1"),
]
ch2 = spec.from_checkpoint(writes)
assert ch2.get() == [HumanMessage(content="hi", id="h1")]
def test_delta_channel_update_by_id_delta_and_replay() -> None:
"""Updating a message by ID stored in a delta must round-trip correctly."""
def test_delta_channel_update_by_id_and_replay() -> None:
"""Updating a message by ID must round-trip correctly through writes replay."""
from langchain_core.messages import HumanMessage
from langgraph.checkpoint.base import DeltaChainValue, DeltaValue
from langgraph.channels.delta import DeltaChannel
from langgraph.graph.message import add_messages
spec = DeltaChannel(add_messages)
ch = spec.from_checkpoint(MISSING)
ch.after_checkpoint(None)
# Step 1: add a message
ch.update([HumanMessage(content="original", id="h1")])
d1 = ch.checkpoint()
assert isinstance(d1, DeltaValue)
ch.after_checkpoint("v1", checkpoint_id="cid1")
# Step 2: update the same message by ID
ch.update([HumanMessage(content="updated", id="h1")])
d2 = ch.checkpoint()
assert isinstance(d2, DeltaValue)
ch.after_checkpoint("v2", checkpoint_id="cid2")
assert ch.get() == [HumanMessage(content="updated", id="h1")]
# Replay the full chain — must produce the updated message, not the original
chain = DeltaChainValue(base=None, deltas=[d1.delta, d2.delta])
ch2 = spec.from_checkpoint(chain)
# Replay writes — must produce the updated message, not the original
writes = [
HumanMessage(content="original", id="h1"),
HumanMessage(content="updated", id="h1"),
]
ch2 = spec.from_checkpoint(writes)
assert len(ch2.get()) == 1
assert ch2.get()[0].content == "updated"
def test_delta_channel_snapshot_every_emits_plain_list() -> None:
"""snapshot_every=N causes a plain-list snapshot after N steps; next deltas chain to it."""
def test_delta_channel_checkpoint_returns_sentinel() -> None:
"""checkpoint() always returns DeltaChannelSentinel regardless of state."""
from langgraph.checkpoint.base import DeltaChannelSentinel
from langgraph.channels.delta import DeltaChannel
from langgraph.graph.message import add_messages
ch = DeltaChannel(add_messages).from_checkpoint(MISSING)
assert isinstance(ch.checkpoint(), DeltaChannelSentinel)
from langchain_core.messages import HumanMessage
from langgraph.checkpoint.base import DeltaValue
from langgraph.channels.delta import DeltaChannel
from langgraph.graph.message import add_messages
SNAP = 3
spec = DeltaChannel(add_messages, snapshot_every=SNAP)
ch = spec.from_checkpoint(MISSING)
# First after_checkpoint anchors _base_version without counting a step.
ch.after_checkpoint("v0", checkpoint_id="cid0")
# Steps 1..SNAP: each should stay as DeltaValue; counter increments each step.
for i in range(1, SNAP + 1):
ch.update([HumanMessage(content=f"m{i}", id=f"h{i}")])
ckpt = ch.checkpoint()
assert isinstance(ckpt, DeltaValue), f"expected DeltaValue at step {i}"
ch.after_checkpoint(f"v{i}", checkpoint_id=f"cid{i}")
# Step SNAP+1: _steps_since_snapshot == SNAP → snapshot fires
ch.update([HumanMessage(content="snap", id="hsnap")])
snap = ch.checkpoint()
assert isinstance(snap, list), "expected plain-list snapshot at snapshot_every step"
assert len(snap) == SNAP + 1
# After snapshot, counter resets — next step is DeltaValue again
ch.after_checkpoint("vsnap", checkpoint_id="cidsnap")
ch.update([HumanMessage(content="post", id="hpost")])
post = ch.checkpoint()
assert isinstance(post, DeltaValue)
ch.update([HumanMessage(content="hi", id="h1")])
assert isinstance(ch.checkpoint(), DeltaChannelSentinel)
def test_delta_channel_snapshot_every_end_to_end() -> None:
"""Graph with snapshot_every: get_state returns correct accumulated value after snapshot."""
from typing import Annotated
from langchain_core.messages import AIMessage, HumanMessage
from langgraph.checkpoint.memory import InMemorySaver
from typing_extensions import TypedDict
from langgraph.channels.delta import DeltaChannel
from langgraph.graph import START, StateGraph
from langgraph.graph.message import add_messages
class State(TypedDict):
messages: Annotated[list, DeltaChannel(add_messages, snapshot_every=2)]
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")
graph = builder.compile(checkpointer=InMemorySaver())
config = {"configurable": {"thread_id": "snap-test"}}
# Run 5 turns — snapshot fires after 2 steps, then again after 2 more
for i in range(5):
graph.invoke({"messages": [HumanMessage(content=f"h{i}", id=f"h{i}")]}, config)
state = graph.get_state(config)
msgs = state.values["messages"]
# 5 human + 5 AI = 10 total
assert len(msgs) == 10, f"expected 10 messages, got {len(msgs)}: {msgs}"
def test_delta_channel_inmemory_saver_assembles_chain() -> None:
"""InMemorySaver assembles the delta chain inside get_tuple (no pregel involvement)."""
def test_delta_channel_inmemory_saver_assembles_writes() -> None:
"""InMemorySaver assembles writes from checkpoint_writes inside get_tuple."""
from typing import Annotated
from langchain_core.messages import AIMessage, HumanMessage
@@ -414,14 +304,16 @@ def test_delta_channel_inmemory_saver_assembles_chain() -> None:
graph.invoke({"messages": [HumanMessage(content="hi", id="h1")]}, config)
graph.invoke({"messages": [HumanMessage(content="bye", id="h2")]}, config)
# get_tuple must return a fully assembled DeltaChainValue, not raw DeltaValue
from langgraph.checkpoint.base import DeltaChainValue, DeltaValue
# get_tuple must return a resolved list (not DeltaChannelSentinel)
from langgraph.checkpoint.base import DeltaChannelSentinel
saved = saver.get_tuple(config)
assert saved is not None
assert "messages" in saved.checkpoint["channel_values"]
assert not isinstance(saved.checkpoint["channel_values"]["messages"], DeltaValue)
assert isinstance(saved.checkpoint["channel_values"]["messages"], DeltaChainValue)
assert not isinstance(
saved.checkpoint["channel_values"]["messages"], DeltaChannelSentinel
)
assert isinstance(saved.checkpoint["channel_values"]["messages"], list)
state = graph.get_state(config)
assert len(state.values["messages"]) == 4 # 2 human + 2 AI
@@ -451,48 +343,39 @@ def test_delta_channel_dict_reducer_fresh_channel() -> None:
def test_delta_channel_dict_reducer_basic_updates() -> None:
"""DeltaChannel with a dict reducer accumulates key/value pairs across steps."""
from langgraph.checkpoint.base import DeltaValue
from langgraph.checkpoint.base import DeltaChannelSentinel
def merge_dicts(left: dict, right: dict) -> dict:
return {**left, **right}
ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
ch.after_checkpoint(None)
ch.update([{"a": 1}])
d1 = ch.checkpoint()
assert isinstance(d1, DeltaValue)
assert d1.delta == [{"a": 1}]
ch.after_checkpoint("v1", checkpoint_id="cid1")
assert isinstance(d1, DeltaChannelSentinel)
ch.update([{"b": 2}])
d2 = ch.checkpoint()
assert d2.delta == [{"b": 2}]
ch.after_checkpoint("v2")
assert isinstance(d2, DeltaChannelSentinel)
assert ch.get() == {"a": 1, "b": 2}
def test_delta_channel_dict_reducer_chain_reconstruction() -> None:
"""DeltaChainValue replays correctly through a dict merge reducer."""
from langgraph.checkpoint.base import DeltaChainValue
def test_delta_channel_dict_reducer_writes_reconstruction() -> None:
"""from_checkpoint with a writes list replays correctly through a dict merge reducer."""
def merge_dicts(left: dict, right: dict) -> dict:
return {**left, **right}
spec = _delta_channel_with_type(merge_dicts, dict)
chain = DeltaChainValue(
base={"a": 1},
deltas=[[{"b": 2}], [{"c": 3}]],
)
ch = spec.from_checkpoint(chain)
# Each element is one write value (oldest→newest)
writes = [{"a": 1}, {"b": 2}, {"c": 3}]
ch = spec.from_checkpoint(writes)
assert ch.get() == {"a": 1, "b": 2, "c": 3}
assert ch._steps_since_snapshot == 2
def test_delta_channel_dict_reducer_with_deletions() -> None:
"""Dict reducer that treats None values as deletions works end-to-end (deepagents pattern)."""
from langgraph.checkpoint.base import DeltaChainValue
def merge_files(left: dict | None, right: dict) -> dict:
if left is None:
@@ -506,25 +389,19 @@ def test_delta_channel_dict_reducer_with_deletions() -> None:
return result
ch = _delta_channel_with_type(merge_files, dict).from_checkpoint(MISSING)
ch.after_checkpoint(None)
ch.update([{"file1.py": "content1", "file2.py": "content2"}])
ch.after_checkpoint("v1", checkpoint_id="cid1")
# Delete file1, add file3
ch.update([{"file1.py": None, "file3.py": "content3"}])
ch.after_checkpoint("v2", checkpoint_id="cid2")
assert ch.get() == {"file2.py": "content2", "file3.py": "content3"}
# Confirm chain reconstruction produces the same result
chain = DeltaChainValue(
base={},
deltas=[
[{"file1.py": "content1", "file2.py": "content2"}],
[{"file1.py": None, "file3.py": "content3"}],
],
)
# Confirm writes reconstruction produces the same result
writes = [
{"file1.py": "content1", "file2.py": "content2"},
{"file1.py": None, "file3.py": "content3"},
]
spec = _delta_channel_with_type(merge_files, dict)
ch2 = spec.from_checkpoint(chain)
ch2 = spec.from_checkpoint(writes)
assert ch2.get() == {"file2.py": "content2", "file3.py": "content3"}
@@ -8,6 +8,11 @@ Simulates realistic multi-turn conversations with paragraph-length messages
Token estimates: 1 token ≈ 4 chars; each turn ≈ 200 tokens (human + AI).
A 1M-token conversation ≈ 5,000 turns of realistic messages.
DeltaChannel stores only a zero-byte sentinel in checkpoint_blobs; the actual
write data lives in checkpoint_writes (already stored there). Reconstruction
walks the parent chain and replays writes through the operator — O(N) total
storage vs O(N²) for plain add_messages.
"""
from __future__ import annotations
@@ -42,8 +47,6 @@ try:
except ImportError:
_POSTGRES_AVAILABLE = False
SNAPSHOT_EVERY = 50
# ---------------------------------------------------------------------------
# Realistic message payload (~100 tokens / ~400 chars each)
# ---------------------------------------------------------------------------
@@ -121,10 +124,6 @@ class DeltaState(TypedDict):
messages: Annotated[list, DeltaChannel(add_messages)]
class DeltaSnapshotState(TypedDict):
messages: Annotated[list, DeltaChannel(add_messages, snapshot_every=SNAPSHOT_EVERY)]
# ---------------------------------------------------------------------------
# Graph factory
# ---------------------------------------------------------------------------
@@ -220,7 +219,7 @@ def _approx_tokens(n_turns: int) -> str:
# Turn counts chosen to demonstrate O(N²) vs O(N) storage growth without running too long.
# Extrapolation: 5,000 turns × ~200 tokens/turn ≈ 1M tokens (Claude's full context window).
TURN_COUNTS = [10, 25, 50, 100]
TURN_COUNTS = [10, 25, 50, 100, 500]
def _checkpointer_factories() -> list[tuple[str, Any]]:
@@ -239,7 +238,13 @@ def run_benchmark() -> None:
checkpointers: list[tuple[str, Any]] = [("InMemory", None)]
if _POSTGRES_AVAILABLE:
checkpointers.append(("Postgres (recursive CTE)", "postgres"))
try:
import psycopg
psycopg.connect(_POSTGRES_URI).close()
checkpointers.append(("Postgres (recursive CTE)", "postgres"))
except Exception:
pass
for cp_label, cp_hint in checkpointers:
print(f"--- Checkpointer: {cp_label} ---")
@@ -277,30 +282,28 @@ def _run_benchmark_for_checkpointer(cp_hint: Any) -> None:
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)
with _make_saver() as saver:
s_wt, s_rt, s_bytes = _run_turns(turns, DeltaSnapshotState, saver)
rows.append((turns, b_bytes, d_bytes, s_bytes, b_rt, d_rt, s_rt))
rows.append((turns, b_bytes, d_bytes, b_rt, d_rt))
# ── Table 1: Storage ─────────────────────────────────────────────────────
W = 80
W = 70
print("Storage (checkpoint blob bytes)")
print("=" * W)
print(
f"{'turns':>6} {'ctx size':>10} {'add_msgs':>12} {'delta':>12} {'delta+snap':>12} {'savings':>8}"
f"{'turns':>6} {'ctx size':>10} {'add_msgs':>12} {'delta':>12} {'savings':>8}"
)
print("-" * W)
storage_results = []
for turns, b_bytes, d_bytes, s_bytes, *_ in rows:
for turns, b_bytes, d_bytes, b_rt, d_rt in rows:
if b_bytes < 0:
print(
f"{turns:>6} {_approx_tokens(turns):>10} {'n/a':>12} {'n/a':>12} {'n/a':>12} {'n/a':>8}"
f"{turns:>6} {_approx_tokens(turns):>10} {'n/a':>12} {'n/a':>12} {'n/a':>8}"
)
else:
ratio = b_bytes / s_bytes if s_bytes else float("inf")
storage_results.append((turns, b_bytes, s_bytes, ratio))
ratio = b_bytes / d_bytes if d_bytes else float("inf")
storage_results.append((turns, b_bytes, d_bytes, ratio))
print(
f"{turns:>6} {_approx_tokens(turns):>10} "
f"{_fmt_bytes(b_bytes):>12} {_fmt_bytes(d_bytes):>12} {_fmt_bytes(s_bytes):>12} "
f"{_fmt_bytes(b_bytes):>12} {_fmt_bytes(d_bytes):>12} "
f"{ratio:>7.0f}x"
)
print("=" * W)
@@ -309,35 +312,30 @@ def _run_benchmark_for_checkpointer(cp_hint: Any) -> None:
# ── Table 2: Read latency ─────────────────────────────────────────────────
print("Read latency (avg of 5 get_state calls)")
print("=" * W)
print(
f"{'turns':>6} {'ctx size':>10} {'add_msgs':>12} {'delta':>12} {'delta+snap':>12}"
)
print(f"{'turns':>6} {'ctx size':>10} {'add_msgs':>12} {'delta':>12}")
print("-" * W)
for turns, b_bytes, d_bytes, s_bytes, b_rt, d_rt, s_rt in rows:
for turns, b_bytes, d_bytes, b_rt, d_rt in rows:
print(
f"{turns:>6} {_approx_tokens(turns):>10} "
f"{b_rt * 1000:>10.1f}ms {d_rt * 1000:>10.1f}ms {s_rt * 1000:>10.1f}ms"
f"{b_rt * 1000:>10.1f}ms {d_rt * 1000:>10.1f}ms"
)
print("=" * W)
print()
if storage_results:
best = storage_results[-1]
turns, b_bytes, s_bytes, ratio = best
_, _, _, _, b_rt, _, s_rt = rows[-1]
turns, b_bytes, d_bytes, ratio = storage_results[-1]
b_rt = rows[-1][-2]
d_rt = rows[-1][-1]
print(
f"At {turns} turns: {_fmt_bytes(b_bytes)} → {_fmt_bytes(s_bytes)} ({ratio:.0f}x less storage); "
f"read {b_rt * 1000:.1f}ms → {s_rt * 1000:.1f}ms"
f"At {turns} turns: {_fmt_bytes(b_bytes)} → {_fmt_bytes(d_bytes)} ({ratio:.0f}x less storage); "
f"read {b_rt * 1000:.1f}ms → {d_rt * 1000:.1f}ms"
)
print()
print("Legend:")
print(" add_msgs = Annotated[list, add_messages] — O(N²) storage")
print(" add_msgs = Annotated[list, add_messages] — O(N²) storage")
print(
" delta = DeltaChannel(add_messages) — O(N) storage, unbounded chain"
)
print(
f" delta+snap = DeltaChannel(add_messages, snapshot_every={SNAPSHOT_EVERY}) — O(N) storage, bounded read depth"
" delta = DeltaChannel(add_messages) — O(N) storage, full chain replay"
)
print()
@@ -359,15 +357,10 @@ def test_delta_channel_benchmark(capsys: Any) -> None:
for turns in [25, 50]:
_, _, b_bytes = _run_turns(turns, BinaryState)
_, _, d_bytes = _run_turns(turns, DeltaState)
_, _, s_bytes = _run_turns(turns, DeltaSnapshotState)
assert d_bytes < b_bytes, (
f"DeltaChannel should use less storage at {turns} turns, "
f"got delta={d_bytes} binary={b_bytes}"
)
assert s_bytes < b_bytes, (
f"DeltaChannel+snapshot should use less storage at {turns} turns, "
f"got snapshot={s_bytes} binary={b_bytes}"
)
# ---------------------------------------------------------------------------
@@ -275,3 +275,70 @@ def test_graph_callbacks_accept_base_callback_manager() -> None:
assert "__interrupt__" in first
assert len(graph_handler.interrupt_events) == 1
def test_non_graph_handler_via_add_handler_does_not_crash() -> None:
"""Non-GraphCallbackHandler added via add_handler should not raise.
Libraries like opentelemetry-instrumentation-langchain monkey-patch
BaseCallbackManager.__init__ and inject handlers via add_handler().
These handlers inherit from BaseCallbackHandler, not
GraphCallbackHandler. They must be silently accepted — graph lifecycle
events will simply not be dispatched to them.
"""
from langgraph.callbacks import _GraphCallbackManager
manager = _GraphCallbackManager()
plain_handler = _LangChainCustomEventHandler()
manager.add_handler(plain_handler, inherit=True)
assert plain_handler in manager.handlers
def test_non_graph_handler_does_not_receive_lifecycle_events() -> None:
"""Non-GraphCallbackHandler added alongside a GraphCallbackHandler
should not interfere with lifecycle event dispatch."""
graph = _build_interrupt_graph()
graph_handler = _GraphEventHandler()
plain_handler = _LangChainCustomEventHandler()
config = {
"configurable": {"thread_id": "graph-callback-mixed-handlers"},
"callbacks": [plain_handler, graph_handler],
}
first = graph.invoke({"answer": None}, config)
assert "__interrupt__" in first
assert len(graph_handler.interrupt_events) == 1
assert plain_handler.events == []
resumed = graph.invoke(Command(resume="done"), config)
assert resumed == {"answer": "done"}
assert len(graph_handler.resume_events) == 1
assert plain_handler.events == []
@pytest.mark.anyio
@NEEDS_CONTEXTVARS
async def test_non_graph_handler_does_not_receive_lifecycle_events_async() -> None:
"""Async variant: non-GraphCallbackHandler should not interfere."""
graph = _build_interrupt_graph()
graph_handler = _GraphEventHandler()
plain_handler = _LangChainCustomEventHandler()
config = {
"configurable": {"thread_id": "graph-callback-mixed-handlers-async"},
"callbacks": [plain_handler, graph_handler],
}
first = await graph.ainvoke({"answer": None}, config)
assert "__interrupt__" in first
assert len(graph_handler.interrupt_events) == 1
assert plain_handler.events == []
resumed = await graph.ainvoke(Command(resume="done"), config)
assert resumed == {"answer": "done"}
assert len(graph_handler.resume_events) == 1
assert plain_handler.events == []
@@ -1,185 +0,0 @@
"""Sweep snapshot_every values to find the storage vs. time-travel tradeoff.
Run directly: python tests/test_rehydrate_sweep.py
Run via pytest: pytest tests/test_rehydrate_sweep.py -s
"""
from __future__ import annotations
import sys
import time
from typing import Annotated, Any
from langchain_core.messages import AIMessage, HumanMessage
from langgraph.checkpoint.memory import MemorySaver
from typing_extensions import TypedDict
from langgraph.channels.delta import DeltaChannel
from langgraph.graph import END, StateGraph
from langgraph.graph.message import add_messages
# ---------------------------------------------------------------------------
# Config
# ---------------------------------------------------------------------------
REHYDRATE_SWEEP = [5, 10, 25, 50, 100, None] # None = no rehydration (pure diff)
TURN_COUNTS = [50, 100, 250, 500]
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
def _make_state(snapshot_every: int | None) -> type:
channel = DeltaChannel(add_messages, snapshot_every=snapshot_every)
return TypedDict("S", {"messages": Annotated[list, channel]})
def _make_graph(state_cls: type) -> Any:
def human_node(state: Any) -> dict:
return {}
def ai_node(state: Any) -> dict:
last = state["messages"][-1]
return {"messages": [AIMessage(content=f"reply-to-{last.id}")]}
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=MemorySaver())
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 _measure_time_travel_ms(graph: Any, config: dict) -> float:
"""Time how long it takes to get state at the very first checkpoint (worst case)."""
history = list(graph.get_state_history(config))
if not history:
return 0.0
oldest = history[-1]
t0 = time.perf_counter()
graph.get_state(oldest.config)
return (time.perf_counter() - t0) * 1000
def _run(n_turns: int, snapshot_every: int | None) -> tuple[float, int, float]:
"""Returns (write_ms, blob_bytes, time_travel_ms)."""
state_cls = _make_state(snapshot_every)
graph = _make_graph(state_cls)
saver: MemorySaver = graph.checkpointer # type: ignore[assignment]
config = {"configurable": {"thread_id": "sweep"}}
t0 = time.perf_counter()
for i in range(n_turns):
graph.invoke(
{"messages": [HumanMessage(content=f"msg-{i}", id=f"h{i}")]}, config
)
write_ms = (time.perf_counter() - t0) * 1000
blob_bytes = _total_blob_bytes(saver)
tt_ms = _measure_time_travel_ms(graph, config)
return write_ms, blob_bytes, tt_ms
# ---------------------------------------------------------------------------
# ASCII sparkline
# ---------------------------------------------------------------------------
def _sparkline(values: list[float], width: int = 20) -> str:
bars = " ▁▂▃▄▅▆▇█"
lo, hi = min(values), max(values)
span = hi - lo or 1
chars = [bars[round((v - lo) / span * (len(bars) - 1))] for v in values]
return "".join(chars).ljust(width)
# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------
def run_sweep() -> None:
label = {v: (str(v) if v is not None else "None(∞)") for v in REHYDRATE_SWEEP}
print()
print("snapshot_every sweep — storage vs time-travel cost")
print("=" * 90)
for turns in TURN_COUNTS:
print(f"\n--- {turns} turns ---")
col_w = 12
header = (
f"{'snapshot_every':>18} "
f"{'blob_bytes':>{col_w}} "
f"{'write_ms':>{col_w}} "
f"{'time_travel_ms':>{col_w}}"
)
print(header)
print("-" * 60)
tt_vals: list[float] = []
byte_vals: list[int] = []
write_vals: list[float] = []
rows: list[tuple] = []
for rv in REHYDRATE_SWEEP:
write_ms, blob_bytes, tt_ms = _run(turns, rv)
rows.append((rv, blob_bytes, write_ms, tt_ms))
byte_vals.append(blob_bytes)
write_vals.append(write_ms)
tt_vals.append(tt_ms)
for rv, blob_bytes, write_ms, tt_ms in rows:
print(
f"{label[rv]:>18} "
f"{blob_bytes:>{col_w},} "
f"{write_ms:>{col_w}.1f} "
f"{tt_ms:>{col_w}.2f}"
)
print()
print(
f" bytes spark: [{_sparkline(byte_vals)}] "
f"lo={min(byte_vals):,} hi={max(byte_vals):,}"
)
print(
f" time-travel spark: [{_sparkline(tt_vals)}] "
f"lo={min(tt_vals):.2f}ms hi={max(tt_vals):.2f}ms"
)
print(
f" write spark: [{_sparkline(write_vals)}] "
f"lo={min(write_vals):.1f}ms hi={max(write_vals):.1f}ms"
)
print()
print("=" * 90)
print(
"snapshot_every=None means pure diff (no snapshots) — "
"lowest storage, highest time-travel cost."
)
print(
"Lower snapshot_every = more frequent full snapshots = "
"faster time-travel, more storage."
)
print()
def test_rehydrate_sweep(capsys: Any) -> None:
with capsys.disabled():
run_sweep()
if __name__ == "__main__":
run_sweep()
sys.exit(0)
+66
View File
@@ -1113,6 +1113,72 @@ def test_subgraph_interrupt_replay_from_parent_then_resume(
]
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:
+46
View File
@@ -427,3 +427,49 @@ def test_callback_manager_copies_configurable_ids_to_tracing_metadata() -> None:
"thread_id": "th-123",
"user_id": "uid-1",
}
def test_get_nonlocal_names_cached_by_code_object() -> None:
"""_get_nonlocal_names caches by code object so repeated calls are cheap."""
from langgraph.pregel._utils import _get_nonlocal_names
x = 1
def my_func() -> int:
return x
result1 = _get_nonlocal_names(my_func.__code__)
result2 = _get_nonlocal_names(my_func.__code__)
# Same frozenset instance returned (cache hit)
assert result1 is result2
assert "x" in result1
def test_get_function_nonlocals_fast_path_no_freevars() -> None:
"""Functions with no free variables return [] without AST parsing."""
from langgraph.pregel._utils import _get_nonlocal_names, get_function_nonlocals
cache_info_before = _get_nonlocal_names.cache_info()
def pure_func(a: int, b: int) -> int:
return a + b
result = get_function_nonlocals(pure_func)
# Should have returned early without touching the cache
assert result == []
assert _get_nonlocal_names.cache_info().misses == cache_info_before.misses
def test_get_function_nonlocals_returns_closure_values() -> None:
"""get_function_nonlocals correctly extracts values from closures."""
from langgraph.pregel._utils import get_function_nonlocals
sentinel = object()
def my_func() -> object:
return sentinel
result = get_function_nonlocals(my_func)
assert sentinel in result
+7 -7
View File
@@ -1348,7 +1348,7 @@ wheels = [
[[package]]
name = "langchain-core"
version = "1.3.0a2"
version = "1.3.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "jsonpatch" },
@@ -1360,14 +1360,14 @@ dependencies = [
{ name = "typing-extensions" },
{ name = "uuid-utils" },
]
sdist = { url = "https://files.pythonhosted.org/packages/af/bc/0bff31fcaff174d86031cc713471a3e85ed4ec8e5cd95ad0217f2aced20e/langchain_core-1.3.0a2.tar.gz", hash = "sha256:52d978c84552b74b9a3f16c1fced84f9e27cc96d7a67c601925ce6cbc4ea3cf9", size = 854580, upload-time = "2026-04-13T14:37:55.745Z" }
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" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/0e/14/03c09686602567059f26af29de0c44546a83af2f2aa29925e61040e43ea2/langchain_core-1.3.0a2-py3-none-any.whl", hash = "sha256:9e929a34f0b0c6c1255e395a1de34f8626893ceb4cdae550a22a0bd18c87be54", size = 510233, upload-time = "2026-04-13T14:37:54.277Z" },
{ url = "https://files.pythonhosted.org/packages/f8/e2/dbfa347aa072a6dc4cd38d6f9ebfc730b4c14c258c47f480f4c5c546f177/langchain_core-1.3.0-py3-none-any.whl", hash = "sha256:baf16ee028475df177b9ab8869a751c79406d64a6f12125b93802991b566cced", size = 515140, upload-time = "2026-04-17T14:51:36.274Z" },
]
[[package]]
name = "langgraph"
version = "1.1.7a2"
version = "1.1.9"
source = { editable = "." }
dependencies = [
{ name = "langchain-core" },
@@ -1439,7 +1439,7 @@ test = [
[package.metadata]
requires-dist = [
{ name = "langchain-core", specifier = "==1.3.0a2" },
{ name = "langchain-core", specifier = ">=1.3.0,<2" },
{ name = "langgraph-checkpoint", editable = "../checkpoint" },
{ name = "langgraph-prebuilt", editable = "../prebuilt" },
{ name = "langgraph-sdk", editable = "../sdk-py" },
@@ -1706,7 +1706,7 @@ inmem = [
requires-dist = [
{ name = "click", specifier = ">=8.1.7" },
{ name = "httpx", specifier = ">=0.24.0" },
{ name = "langgraph-api", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.5.35,<0.8.0" },
{ name = "langgraph-api", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.5.35,<0.9.0" },
{ name = "langgraph-runtime-inmem", marker = "python_full_version >= '3.11' and extra == 'inmem'", specifier = ">=0.7" },
{ name = "langgraph-sdk", marker = "python_full_version >= '3.11'", specifier = ">=0.1.0" },
{ name = "pathspec", specifier = ">=0.11.0" },
@@ -1742,7 +1742,7 @@ test = [
[[package]]
name = "langgraph-prebuilt"
version = "1.0.9"
version = "1.0.10"
source = { editable = "../prebuilt" }
dependencies = [
{ name = "langchain-core" },
+20 -1
View File
@@ -808,6 +808,7 @@ 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,
)
@@ -842,6 +843,7 @@ 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,
)
@@ -1576,6 +1578,7 @@ 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.
@@ -1618,6 +1621,7 @@ 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
@@ -1838,9 +1842,17 @@ def _get_injection_from_type(
return None
# Cache keyed by tool object identity. Stores (tool, result) to keep a strong
# reference that prevents GC from reusing the id for a different object.
_INJECTED_ARGS_CACHE: dict[int, tuple[BaseTool, _InjectedArgs]] = {}
def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
"""Extract all injected arguments from tool in a single pass.
Results are cached by tool identity so the expensive type-hint and schema
inspection only runs once per unique tool object across ToolNode instances.
This function analyzes both the tool's input schema and function signature
to identify all arguments that should be injected (state, store, runtime).
@@ -1850,6 +1862,11 @@ def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
Returns:
_InjectedArgs structure containing all detected injections.
"""
tool_id = id(tool)
entry = _INJECTED_ARGS_CACHE.get(tool_id)
if entry is not None and entry[0] is tool:
return entry[1]
# Get annotations from both schema and function signature
full_schema = tool.get_input_schema()
schema_annotations = get_all_basemodel_annotations(full_schema)
@@ -1895,10 +1912,12 @@ def _get_all_injected_args(tool: BaseTool) -> _InjectedArgs:
if _get_injection_from_type(type_, ToolRuntime):
runtime_arg = name
return _InjectedArgs(
result = _InjectedArgs(
state=state_args,
store=store_arg,
runtime=runtime_arg,
all_injected_keys=all_injected_keys,
_optional_state_args=_optional_state_args,
)
_INJECTED_ARGS_CACHE[tool_id] = (tool, result)
return result
+1 -1
View File
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
[project]
name = "langgraph-prebuilt"
version = "1.0.9"
version = "1.0.10"
description = "Library with high-level APIs for creating and executing LangGraph agents and tools."
authors = []
requires-python = ">=3.10"
@@ -69,6 +69,7 @@ def _create_config_with_runtime(store=None, state=None):
context={},
store=store,
stream_writer=None,
tools=[],
tool_call_id="test_id",
)
return {
+29 -8
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_and_server_info() -> None:
"""Test that execution_info and server_info are forwarded from Runtime to ToolRuntime."""
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."""
from langgraph.runtime import ExecutionInfo, ServerInfo
exec_info = ExecutionInfo(
@@ -2043,9 +2043,15 @@ def test_tool_runtime_forwards_execution_info_and_server_info() -> None:
"""Tool that captures runtime info."""
captured["execution_info"] = runtime.execution_info
captured["server_info"] = runtime.server_info
captured["tools"] = runtime.tools
return "ok"
node = ToolNode([info_tool])
@dec_tool
def other_tool(y: int) -> str:
"""Another tool available to the runtime."""
return str(y)
node = ToolNode([info_tool, other_tool])
tool_call = {
"name": "info_tool",
"args": {"x": 1},
@@ -2054,17 +2060,21 @@ def test_tool_runtime_forwards_execution_info_and_server_info() -> None:
}
msg = AIMessage("", tool_calls=[tool_call])
config: RunnableConfig = {"configurable": {"__pregel_runtime": mock_runtime}}
node.invoke({"messages": [msg]}, config=config)
result = 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_and_server_info_async() -> None:
"""Test that execution_info and server_info are forwarded in async path."""
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."""
from langgraph.runtime import ExecutionInfo, ServerInfo
exec_info = ExecutionInfo(
@@ -2090,9 +2100,15 @@ async def test_tool_runtime_forwards_execution_info_and_server_info_async() -> N
"""Async tool that captures runtime info."""
captured["execution_info"] = runtime.execution_info
captured["server_info"] = runtime.server_info
captured["tools"] = runtime.tools
return "ok"
node = ToolNode([info_tool_async])
@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])
tool_call = {
"name": "info_tool_async",
"args": {"x": 1},
@@ -2101,12 +2117,17 @@ async def test_tool_runtime_forwards_execution_info_and_server_info_async() -> N
}
msg = AIMessage("", tool_calls=[tool_call])
config: RunnableConfig = {"configurable": {"__pregel_runtime": mock_runtime}}
await node.ainvoke({"messages": [msg]}, config=config)
result = 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 ---
+6 -6
View File
@@ -249,7 +249,7 @@ wheels = [
[[package]]
name = "langchain-core"
version = "1.3.0a2"
version = "1.3.0"
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/af/bc/0bff31fcaff174d86031cc713471a3e85ed4ec8e5cd95ad0217f2aced20e/langchain_core-1.3.0a2.tar.gz", hash = "sha256:52d978c84552b74b9a3f16c1fced84f9e27cc96d7a67c601925ce6cbc4ea3cf9", size = 854580, upload-time = "2026-04-13T14:37:55.745Z" }
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" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/0e/14/03c09686602567059f26af29de0c44546a83af2f2aa29925e61040e43ea2/langchain_core-1.3.0a2-py3-none-any.whl", hash = "sha256:9e929a34f0b0c6c1255e395a1de34f8626893ceb4cdae550a22a0bd18c87be54", size = 510233, upload-time = "2026-04-13T14:37:54.277Z" },
{ url = "https://files.pythonhosted.org/packages/f8/e2/dbfa347aa072a6dc4cd38d6f9ebfc730b4c14c258c47f480f4c5c546f177/langchain_core-1.3.0-py3-none-any.whl", hash = "sha256:baf16ee028475df177b9ab8869a751c79406d64a6f12125b93802991b566cced", size = 515140, upload-time = "2026-04-17T14:51:36.274Z" },
]
[[package]]
name = "langgraph"
version = "1.1.7a2"
version = "1.1.9"
source = { editable = "../langgraph" }
dependencies = [
{ name = "langchain-core" },
@@ -281,7 +281,7 @@ dependencies = [
[package.metadata]
requires-dist = [
{ name = "langchain-core", specifier = "==1.3.0a2" },
{ name = "langchain-core", specifier = ">=1.3.0,<2" },
{ name = "langgraph-checkpoint", editable = "../checkpoint" },
{ name = "langgraph-prebuilt", editable = "." },
{ name = "langgraph-sdk", editable = "../sdk-py" },
@@ -490,7 +490,7 @@ test = [
[[package]]
name = "langgraph-prebuilt"
version = "1.0.9"
version = "1.0.10"
source = { editable = "." }
dependencies = [
{ name = "langchain-core" },
+6 -6
View File
@@ -262,7 +262,7 @@ wheels = [
[[package]]
name = "langchain-core"
version = "1.3.0a2"
version = "1.3.0"
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/af/bc/0bff31fcaff174d86031cc713471a3e85ed4ec8e5cd95ad0217f2aced20e/langchain_core-1.3.0a2.tar.gz", hash = "sha256:52d978c84552b74b9a3f16c1fced84f9e27cc96d7a67c601925ce6cbc4ea3cf9", size = 854580, upload-time = "2026-04-13T14:37:55.745Z" }
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" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/0e/14/03c09686602567059f26af29de0c44546a83af2f2aa29925e61040e43ea2/langchain_core-1.3.0a2-py3-none-any.whl", hash = "sha256:9e929a34f0b0c6c1255e395a1de34f8626893ceb4cdae550a22a0bd18c87be54", size = 510233, upload-time = "2026-04-13T14:37:54.277Z" },
{ url = "https://files.pythonhosted.org/packages/f8/e2/dbfa347aa072a6dc4cd38d6f9ebfc730b4c14c258c47f480f4c5c546f177/langchain_core-1.3.0-py3-none-any.whl", hash = "sha256:baf16ee028475df177b9ab8869a751c79406d64a6f12125b93802991b566cced", size = 515140, upload-time = "2026-04-17T14:51:36.274Z" },
]
[[package]]
name = "langgraph"
version = "1.1.7a2"
version = "1.1.9"
source = { editable = "../langgraph" }
dependencies = [
{ name = "langchain-core" },
@@ -294,7 +294,7 @@ dependencies = [
[package.metadata]
requires-dist = [
{ name = "langchain-core", specifier = "==1.3.0a2" },
{ name = "langchain-core", specifier = ">=1.3.0,<2" },
{ name = "langgraph-checkpoint", editable = "../checkpoint" },
{ name = "langgraph-prebuilt", editable = "../prebuilt" },
{ name = "langgraph-sdk", editable = "." },
@@ -413,7 +413,7 @@ test = [
[[package]]
name = "langgraph-prebuilt"
version = "1.0.9"
version = "1.0.10"
source = { editable = "../prebuilt" }
dependencies = [
{ name = "langchain-core" },
+133
View File
@@ -0,0 +1,133 @@
# feat(channels): DeltaChannel — O(N) incremental checkpoint storage
## The problem
LangGraph checkpoints store the **full accumulated value** of every channel on every step. For a `messages` channel backed by `add_messages`, that means each checkpoint blob contains the entire conversation history up to that point.
Storage cost grows **O(N²)** in the number of turns:
| Step | Checkpoint blob |
|------|----------------|
| 1 | [msg_1] |
| 2 | [msg_1, msg_2] |
| N | [msg_1, ..., msg_N] |
At 100K tokens of conversation data, a single thread accumulates ~250 MB; with large messages or file attachments costs scale even faster.
## The fix: `DeltaChannel`
`DeltaChannel` is an opt-in wrapper around any binary reducer that stores only a **sentinel marker** in `checkpoint_blobs` rather than the full accumulated value. The actual per-step writes stay in `checkpoint_writes` (which every checkpointer already writes unconditionally). At read time the saver walks the ancestor chain, collects all writes for the channel, and replays them through the reducer.
Storage scales **O(N)** — the sentinel blob is effectively zero bytes, and the writes table already exists.
```python
from langgraph.channels.delta import DeltaChannel
from langgraph.graph.message import add_messages
class State(TypedDict):
# Before: O(N²) storage
messages: Annotated[list[AnyMessage], add_messages]
# After: O(N) storage
messages: Annotated[list[AnyMessage], DeltaChannel(add_messages)]
```
## Benchmarks
Simulated with realistic paragraph-length messages (~100 tokens each, ~400 chars). Each turn = one human + one AI message (~200 tokens total).
### Storage (InMemorySaver)
| turns | ctx | add_msgs | delta | savings |
|------:|----:|---------:|------:|--------:|
| 10 | ~2K tok | 108.6 KB | 4.0 KB | 27x |
| 25 | ~5K tok | 649.0 KB | 10.1 KB | 64x |
| 50 | ~10K tok | 2.6 MB | 20.2 KB | 126x |
| 100 | ~20K tok | 10.2 MB | 40.5 KB | 251x |
| 500 | ~100K tok | 252.6 MB | 202.8 KB | 1245x |
Savings grow with N because `add_messages` is O(N²) while `DeltaChannel` is O(N). The sentinel blob itself is essentially zero bytes.
### Read latency (avg of 5 `get_state` calls = cost per `invoke`)
| turns | ctx | add_msgs | delta |
|------:|----:|---------:|------:|
| 10 | ~2K tok | 0.1ms | 0.2ms |
| 25 | ~5K tok | 0.2ms | 0.5ms |
| 50 | ~10K tok | 0.4ms | 1.5ms |
| 100 | ~20K tok | 0.7ms | 4.8ms |
| 500 | ~100K tok | 5.8ms | 114.9ms |
**This cost is paid once per `invoke`/`stream` call, not per node.** Within a single invocation, all channels are loaded into memory once at the start and shared across every node — there is no per-node reconstruction. The 114.9ms at 500 turns is what you pay each time a user sends a new message, not on each step of the graph.
## How it works
**Write:** `DeltaChannel.checkpoint()` always emits `DeltaChannelSentinel()` — a tiny marker (zero payload bytes) stored in `checkpoint_blobs`. Per-step writes flow into `checkpoint_writes` as they normally do for every channel.
**Read:** The saver detects `DeltaChannelSentinel` values in `channel_values` and replaces them by calling `get_channel_writes` / `aget_channel_writes`, which walks the ancestor checkpoint chain and collects all writes for that channel (oldest→newest). `DeltaChannel.from_checkpoint()` replays those writes through the operator to reconstruct the full value.
**Saver implementations:**
- `InMemorySaver` — direct dict traversal of `self.storage` and `self.writes`, no I/O
- `PostgresSaver` (sync + async) — two queries: one cheap ID walk across the thread, one `ANY()` fetch of writes; no recursive CTE
- All other savers — `BaseCheckpointSaver.get_channel_writes` fallback via `list()`, with a re-entrancy guard to prevent infinite recursion
## Changes
**`libs/checkpoint`**
- `base/__init__.py` — add `DeltaChannelSentinel` marker dataclass; add `get_channel_writes` / `aget_channel_writes` to `BaseCheckpointSaver` with a `list()`-based fallback and re-entrancy guard
**`libs/checkpoint/memory`**
- `memory/__init__.py` — `get_channel_writes` via direct dict traversal; `_resolve_delta_channels` helper called in `get_tuple` / `aget_tuple` to replace sentinels with reconstructed write lists
**`libs/langgraph`**
- `channels/delta.py` — `DeltaChannel` implementation: `checkpoint()` always emits sentinel, `from_checkpoint()` replays writes list
- `channels/__init__.py` — export `DeltaChannel`
- `graph/state.py` — recognize `DeltaChannel` as a valid channel annotation
- `pregel/_checkpoint.py` / `pregel/_loop.py` — wire `after_checkpoint` hook; call it after each checkpointing step so `DeltaChannel` can advance internal state
**`libs/checkpoint-postgres`**
- `postgres/base.py` — `_get_channel_writes_cur` two-query ancestor walk (sync); `_resolve_delta_channels` called after `_load_blobs`
- `postgres/aio.py` — `_aget_channel_writes_cur` (async counterpart)
## Open questions
**Should we add a compile-time capability check?**
Currently misconfiguring `DeltaChannel` with an unsupported saver only errors at runtime on first reload. A protocol-based check at `compile()` time would give an early warning without requiring a manual boolean flag.
**`snapshot_every` for bounded reconstruction cost?**
Both per-invoke read latency and total write wall time grow O(N) per invoke / O(N²) total as the conversation lengthens. A `snapshot_every` parameter — periodically store a full snapshot in `checkpoint_blobs` to cap chain depth — would bound reconstruction cost and is a natural follow-up once the core design is stable.
## Backwards compatibility
| Scenario | Behaviour |
|----------|-----------|
| Existing graph using `add_messages` | Unaffected — no code or schema changes |
| `DeltaChannel` loading an old full-list checkpoint blob | Handled via backwards-compat path in `from_checkpoint` |
| `DeltaChannel` with `InMemorySaver` or `PostgresSaver` | Fully supported |
| Time-travel to a past checkpoint | Ancestor walk uses the version at that checkpoint — correct by construction |
| `Overwrite` value | Resets the effective chain; reconstruction starts from that step |
## Test plan
- [x] `DeltaChannel` unit tests: `update` → `checkpoint` lifecycle, `from_checkpoint` chain replay, backwards-compat with plain list, `Overwrite` resets chain
- [x] `InMemorySaver` `get_channel_writes`: assembles write list from dict storage
- [x] Serde round-trip for `DeltaChannelSentinel`
- [x] End-to-end graph tests: multi-turn conversations accumulate correctly, time-travel reconstructs correct partial history
- [x] `PostgresSaver` two-query chain reconstruction (sync + async)
- [x] `BaseCheckpointSaver` fallback path via `list()` with re-entrancy guard
- [x] Storage benchmark: `DeltaChannel` uses strictly less storage than `add_messages` at all measured turn counts
---
## Changes from previous base branch
The previous version stored `DeltaValue` objects (containing the per-step writes) directly in `checkpoint_blobs` and used a `DeltaChainValue` to represent the assembled chain. Reconstruction required a dedicated `get_delta_chain` / `aget_delta_chain` protocol and a recursive CTE in Postgres.
This version pivots to a simpler design:
- **Sentinel in blobs, writes in `checkpoint_writes`** — `checkpoint_blobs` stores only a zero-byte `DeltaChannelSentinel` marker. The actual per-step data already lives in `checkpoint_writes` (written unconditionally by every checkpointer), so blob storage is essentially free. This is why storage savings jump to 1245x at 500 turns.
- **No custom serde type for the delta payload** — `DeltaValue` / `DeltaChainValue` and the `"delta"` serde type tag are gone. Writes are deserialized with the same serde path they were originally written with.
- **Postgres: two queries instead of a recursive CTE** — fetch all `(checkpoint_id, parent_checkpoint_id)` pairs for the thread, walk the ancestor chain in Python, then fetch writes with a plain `ANY()` filter.
- **Universal fallback on `BaseCheckpointSaver`** — the base class now provides `get_channel_writes` via `list()`, so any third-party saver works without modification.
- **`snapshot_every` removed** — deferred as a follow-up; the simpler design is easier to reason about and delivers larger storage savings.