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d57a74f950
Fixes langchain-ai/deepagents#3774 ## Summary `Pregel.update_state` / `aupdate_state` on a fresh thread silently dropped the first write to a `DeltaChannel`-backed channel (e.g. `DeepAgentState.messages`). This PR persists the first write under a lazily-created stub checkpoint so the read-path ancestor walk can replay it. ## Root cause `DeltaChannel` reads its value back by walking ancestor checkpoints and replaying writes attached to them — non-snapshot steps don't store the value in `channel_values`. In `bulk_update_state` the channel writes were only persisted via `checkpointer.put_writes(...)` when a previous checkpoint existed: ```python channel_writes = [w for w in task.writes if w[0] != PUSH] if saved and channel_writes: checkpointer.put_writes(checkpoint_config, channel_writes, task_id) ``` On a fresh thread `saved is None`, so the `if saved` guard skipped persistence entirely. `create_checkpoint` then bumped the channel version but stored neither a value nor replayable writes, so reads returned `[]`. ## Fix In both `bulk_update_state` (sync) and `abulk_update_state` (async), when the thread has no persisted parent **and** at least one write targets a `DeltaChannel`, lazily persist an empty stub checkpoint and use it as the parent for both the channel writes and the new update checkpoint. Mirrors the existing exit-mode pattern in `_loop._put_exit_delta_writes`. The behavior for non-delta writes on a fresh thread is preserved (skip `put_writes` — values are stored directly in the new checkpoint's `channel_values`), so non-delta `update_state` paths add no extra checkpoint rows. ## Test coverage New tests in `libs/langgraph/tests/test_delta_channel_update_state.py` (9 tests, sync + async): - **Fresh-thread regression** (the bug): single `update_state` writes a message and reads back via `get_state`. Without the fix, both sync and async fail with `assert [] == ['hello']`. - **`update_state` after `invoke`**: pins down the previously-working non-fresh-thread path so the lazy-stub change doesn't regress it. - **Consecutive `update_state`s**: second call sees a real parent (`saved is not None`) and takes the original write path; both messages round-trip in chronological order. - **Update-by-id end-to-end via `update_state`**: `_messages_delta_reducer`'s dedup-by-id semantics work through the `update_state` path, not just `invoke`. - **`bulk_update_state` with multiple per-superstep updates**: locks in the per-task `put_writes` loop so all task writes persist (not just the last task's). - **State-history chain shape**: validates the lazy stub via the public API — `get_state_history` returns `[update_checkpoint, stub]` where the stub has `source='update'`, `step=-1`, no parent, and the update checkpoint's `parent_config` points at the stub. ## Verification - All 9 tests in `tests/test_delta_channel_update_state.py` pass. - All 4 existing delta-channel suites pass (`test_delta_channel_exit_mode.py`, `test_delta_channel_migration.py`, `test_delta_channel_id_stability.py`, `test_delta_channel_supersteps_bound.py` — 30 tests, 39 total with the new file). - All `update_state`-related tests across `test_pregel`, `test_pregel_async`, `test_time_travel`, `test_time_travel_async` pass (10 tests). - `make format`, `make lint`, full `make test` pass locally in `libs/langgraph`. --------- Co-authored-by: Cursor <cursoragent@cursor.com>
Low-level orchestration framework for building stateful agents.
Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
pip install -U langgraph
Tip
If you're looking to quickly build agents, check out Deep Agents — a higher-level package built on LangGraph for agents that can plan, use subagents, and leverage file systems for complex tasks.
For an equivalent JS/TS library, check out LangGraph.js and the JS docs.
Why use LangGraph?
LangGraph provides low-level supporting infrastructure for any long-running, stateful workflow or agent:
- Durable execution — Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
- Human-in-the-loop — Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
- Comprehensive memory — Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
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- Production-ready deployment — Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
Tip
For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.
LangGraph ecosystem
While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents.
To improve your LLM application development, pair LangGraph with:
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- LangChain – Provides integrations and composable components to streamline LLM application development.
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- LangSmith Deployment – Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams – and iterate quickly with visual prototyping in LangSmith Studio.
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Acknowledgements
LangGraph is inspired by Pregel and Apache Beam. The public interface draws inspiration from NetworkX. LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.