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A DeltaChannel is rebuilt from its writes along the parent chain, so a checkpoint saved without one of them reads back short for good. If saving a DeltaChannel write failed (a database blip, say), the next checkpoints were saved anyway and the channel lost that write, while plain channels kept it. Three things let that happen: - the sync loop waited for the delta writes before saving a checkpoint, but never checked that they succeeded; - both loops saved a checkpoint even when the previous save had failed, so it pointed at a parent that was never saved; - each save took the pending write list only when it started in the background, and saves can start out of order, so a save could take a later checkpoint's writes and not wait for its own. Waiting on writes queued behind it, a save could also hang a sync run with `durability="async"` (the default) once every worker was such a save: on a 15-core machine a loop of 100 fast steps hung every time with default settings, and with `max_concurrency=1` three steps are enough. Now `_put_checkpoint` hands each save the writes submitted before it, and a save fails if one of them or the previous save failed. The run raises the write's error as before, the thread stays at its last checkpoint that has all its writes, and running it again reruns the task. For graphs without a DeltaChannel, a failed save now also stops the later saves of that run, as JS already does, instead of saving checkpoints whose parent is missing. Thanks @shivangsharma01 for reproducing this in #8234, and @iroiro147, whose #8299 had the first two changes before the issue-assignment check closed it. Thanks also to @HarshShroff for the failed-save test case. JS twin: langchain-ai/langgraphjs#2969 ## Tests `test_delta_channel_write_failures.py`: - a saver fails one delta write once; on every durability, with `invoke` and `ainvoke`, every saved checkpoint matches a plain channel and a retry gets the write back. The `invoke` cases and `ainvoke` with `durability="async"` fail on main. - a saver fails the save of the checkpoint after `a` once; with `invoke` and `ainvoke`, every saved checkpoint matches a plain channel and a retry finishes the run. The `durability="async"` cases fail on main, which saves the later checkpoints on top of the missing one. - a delta graph run with `max_concurrency=1` finishes. It hangs on main. Reverting any one of the three changes fails at least one of these. `make format`, `make lint` and the langgraph suite pass. A 100-step benchmark (delta and plain channels, every durability, with and without 1 ms of saver latency) makes the same saver calls and runs in the same time as main, except for the case that hangs on main. --------- Co-authored-by: iroiro147 <265728356+iroiro147@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.