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## Summary Replaces `durability="exit"`'s blanket force-snapshot of every `DeltaChannel` with proper write persistence that honors per-channel `snapshot_frequency`, plus closes two latent bugs the force-snapshot was masking. Before: every exit-mode run wrote a full `_DeltaSnapshot` blob for every delta channel, even when the channel had zero updates this run and was nowhere near its `snapshot_frequency`. After: the same count-based decision used by `durability="sync"`/`"async"` applies — channels at or above `snapshot_frequency` snapshot; channels below it persist their accumulated writes via a lazy "stub" anchor; untouched channels write nothing. ## What changed **Core redesign** (`pregel/_loop.py`, `pregel/_checkpoint.py`) - Drop `force_delta_snapshot` from `create_checkpoint` and `_should_snapshot_delta`. - Add `decide_delta_snapshots(channels, counts)` pure helper used by both `create_checkpoint` and the new exit-mode peek-ahead path. - Add `_exit_delta_writes` accumulator: every delta-channel write produced during a `durability="exit"` run (input writes from `_first` + per-superstep writes captured before `pending_writes.clear()` in `after_tick`) is collected into this list. - Add `_put_exit_delta_writes` (sync + async): runs from `_suppress_interrupt` BEFORE `_put_checkpoint(exiting=True)`. Filters out channels that will snapshot, then persists remaining writes to `checkpoint_writes` under an anchor parent. The anchor is the existing saved parent on resumed runs, or a lazily-created empty stub on first runs. - Visibility ordering: stub put goes onto `_put_checkpoint_fut` (becomes the next put's `prev`); exit-write futures go onto `_delta_write_futs`. The existing `_checkpointer_put_after_previous` already drains both before calling `saver.put`, so `final_checkpoint` is structurally guaranteed to land last — readers never see a partial view. **Latent bugs fixed (previously masked by force-snapshot)** - **Sync drain race**: `SyncPregelLoop` now initializes `_delta_write_futs = []` in `__enter__` and drains it in sync `_checkpointer_put_after_previous` before `put`, mirroring the async version. Without this, a multi-worker `BackgroundExecutor` could publish a checkpoint before the writes that produced it. - **Count double-bump in exit mode**: in `_put_checkpoint`, `delta_updates_since_snapshot` was being incremented twice for the last superstep — once by the intermediate `after_tick` call, once by `_suppress_interrupt`. Force-snapshot used to reset all counts to 0 so this never persisted; without it, snapshots would fire one superstep early after every exit-mode run. Fixed by gating the count-bump behind `not exiting`. **Pre-existing input-durability gap** - In the plain (non-Command) input path of `_first`, delta-channel input writes are now persisted via `put_writes` (mirroring the Command path), so sub-frequency inputs survive a `get_state` on resumed runs in `sync`/`async` durability. Note: first-run `sync`/`async` still has the same gap (writes orphan on the synthetic-empty parent id). That's flagged as a follow-up — out of scope for this PR. ## Test plan - Existing `tests/test_pregel.py` and `tests/test_pregel_async.py` pass unchanged. - Existing `tests/test_channels.py` (29 tests) and `tests/test_delta_channel_migration.py` pass unchanged. - New `tests/test_exit_delta_persistence.py` (11 tests) covers: - **Write-path**: zero-write exit (no stub), all-snapshot first run (no stub), sub-freq first run (single shared stub), sub-freq resumed run (anchor on saved parent), sync-vs-exit count parity, mixed snapshot/non-snapshot channels, snapshot fires at frequency. - **Read-path**: K-run replay chain reads correctly across stub→saved-parent transition; metadata `delta_updates_since_snapshot` round-trips correctly; mixed sync/exit durability alternation produces correct final state; snapshot+tail-deltas combination reads correctly. - `make format && make lint && make test` in `libs/langgraph/`. --------- Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: Sydney Runkle <sydneymarierunkle@gmail.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.
- Debugging with LangSmith — Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
- 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:
- Deep Agents – Build agents that can plan, use subagents, and leverage file systems for complex tasks.
- LangChain – Provides integrations and composable components to streamline LLM application development.
- LangSmith – Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- 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.
Documentation
- docs.langchain.com – Comprehensive documentation, including conceptual overviews and guides
- reference.langchain.com/python/langgraph – API reference docs for LangGraph packages
- LangGraph Quickstart – Get started building with LangGraph
- Chat LangChain – Chat with the LangChain documentation and get answers to your questions
Discussions: Visit the LangChain Forum to connect with the community and share all of your technical questions, ideas, and feedback.
Additional resources
- Guides – Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
- LangChain Academy – Learn the basics of LangGraph in our free, structured course.
- Case studies – Hear how industry leaders use LangGraph to ship AI applications at scale.
- Contributing Guide – Learn how to contribute to LangChain projects and find good first issues.
- Code of Conduct – Our community guidelines and standards for participation.
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.