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Fixes #9106 When a subgraph pauses in several parallel interrupts and they're answered one at a time, each resume ran the nodes that were already answered again. Their saved answers carried them straight through `interrupt()`, so whatever they do after it (an API write, say) ran once more per later answer, while the final state still looked right. A `@task` that finished before an interrupt in a nested graph reran the same way, which is the rerun reported in #6792. ## Cause Every task config sets `checkpoint_id` to None, and `PregelLoop` treats a config with that key as a replay, so a nested graph replayed on every resume. A replaying loop doesn't hand saved writes back to the tasks that made them, so finished tasks ran again. ## Fix `PregelLoop` now notes whether the run points it at a checkpoint (a truthy `checkpoint_id`, its own `checkpoint_map` entry, or a parent that is replaying), the test `__enter__` already uses to pick the checkpoint to load. `_first` then decides once whether finished tasks rerun: only on time travel, or on a resume at a checkpoint the thread has moved past. A plain resume of a subgraph is neither, so it hands finished tasks their writes back the way a top-level resume does. The same rule fixes the top level, which @hntrl pointed out: a resume sent with the head's `checkpoint_id`, as Studio sends it, or with `checkpoint_id: None` reran finished siblings too. A resume at an older checkpoint still reruns them, as the checkpoint migration tests expect. Telling the two apart costs one more checkpoint read for a run sent with a `checkpoint_id`. `test_addressed_resume_keeps_a_rerun_tasks_write_to_a_new_channel` relied on a resume at the head rerunning a task, so it now resumes a checkpoint the thread has moved past. Most subgraph time travel forks first, which gives tasks new ids, so saved writes can't match them anyway. `test_subgraph_replay_reasks_answered_parallel_interrupt` replays an update checkpoint, which runs in place: it passes on `main` and fails if subgraphs never replay. The counters added to `test_parallel_interrupts_double` and the three new tests in `test_interrupt_state.py` fail on `main`. ## Not in this PR - With `durability="exit"` and three or more parallel interrupts, a subgraph now hits a bug top-level graphs already have: a finished task's output is applied twice. #9314 fixes it. - #6792 also reports the interrupt of a `@task` inside a nested `@entrypoint` coming back twice. That's a separate bug. Thanks @connorimes for the report, the root cause and the fix, which this applies as proposed. The same change was opened as #9113 by @AnvitDevadiga, and @morehardy found the root cause for #6792 in #6793. JS: langchain-ai/langgraphjs#3028 Co-authored-by: Connor Imes <5701850+connorimes@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.