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Depends on #8544 Resuming an exit-durability run after a parallel interrupt replays the resumed checkpoint's `DeltaChannel` writes wrong: twice on `main`, out of order if the duplicate is simply dropped. `p` finished before `q` interrupted, and `r` runs after `q`: ``` live: ['in1', 'p', 'r'] main, reload: ['in1', 'p', 'r', 'p'] ``` No fork is involved. Any exit-mode run that starts from a checkpoint with pending delta writes hits it: resuming an interrupt, or retrying after a task error. ## Cause Exit durability stores a run's delta writes on the checkpoint it started from, under step-prefixed task ids (`exit_delta_task_id`), so they replay in superstep order. On a resume, that checkpoint already holds the finished tasks' writes under their real task ids. The accumulator stored those loaded writes again under a step-prefixed id, so they replayed twice. Skipping them alone reorders the replay, because the real ids sort after the step-prefixed ones. ## Fix At exit: - writes loaded with the checkpoint are skipped, since they are already stored on it: `NULL_TASK_ID` writes by task id (this run's own `Command` writes are recorded separately in `_first`), the rest by `(task_id, channel)`; - the checkpoint's own superstep keeps its real task paths, so it interleaves with the loaded writes as live execution did, but gets a step-prefixed task id. Under the real id, a resume whose final checkpoint fails to save would leave the resumed task looking done, and the retry would skip it and lose its other writes; - a resume addressed by `checkpoint_id` reruns tasks whose writes are already stored on the checkpoint; their writes to those channels are skipped, and a write to a channel the task did not write before is kept; - later supersteps get task id `ffffffff-<step>-...` and task path `~~<step>...`, which sort after every real task id and every real task path, in step order. So superstep order holds whether a saver orders by task path (#8544) or only by task id. ## Limits - Within the resumed superstep, a saver that orders only by task id replays this run's writes before the ones loaded from the checkpoint. #8544 fixes that for the OSS savers. - If the final checkpoint fails to save, the writes of supersteps after the resumed one stay on the checkpoint and replay once more after the retry (on `main`, every write does). That comes from exit durability storing its writes before its checkpoint, not from this change. ## Tests `test_delta_channel_exit_mode.py`, on the shared checkpointer fixture (memory, sqlite, postgres) in all three durabilities: - resume after a parallel interrupt with a later write, plain and with the head's `checkpoint_id` - the resumed superstep interleaved by task path: `a_asks` resumes after `z_done` finished, and live order puts `a` first - the same runs on a saver that orders writes by task id only - a resume retried after its final checkpoint failed to save - a resume with `Command(resume=..., update=...)` that writes the delta channel replays that write once, in live order - an addressed resume whose rerun task writes a delta channel it did not write before keeps that write The exit cases fail without the change, except the last one, which guards against skipping by task id alone (an earlier version of this PR did); sync and async pass either way. #8548 marked exit durability in `test_resume_on_an_interrupted_head_consumes_its_writes_without_a_snapshot` as a strict expected failure for this bug; this PR drops the mark. 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: ErenAta16 <149434812+ErenAta16@users.noreply.github.com> Co-authored-by: ragnarok268 <58264829+ragnarok268@users.noreply.github.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.