Elior Nataf LackritzandGitHub
40a2e6d845
In exit mode, a run stores its DeltaChannel writes on the checkpoint it started from, under synthetic task ids that put the superstep first. For a `Command`'s writes in a run whose first superstep is 0, that id came out as `NULL_TASK_ID` itself. Readers take writes under `NULL_TASK_ID` for the checkpoint's own pending writes, so after `invoke(Command(update=...), durability="exit")`: - on a new thread, `get_state` on the empty first checkpoint showed the update in the DeltaChannel but not in a plain channel; - on a thread whose first checkpoint came from `update_state(..., as_node="__input__")`, `get_state` on that checkpoint showed the update the same way, and a replay or fork from it applied the update to the DeltaChannel only. `exit_delta_task_id` now never returns `NULL_TASK_ID`. Threads already saved this way stay as they are. ## Tests - `test_command_update_on_an_input_checkpoint_matches_a_plain_channel` compares every checkpoint in the history, and a replay from the input checkpoint, with a plain channel, on every checkpointer and durability. The exit cases fail without the fix. - `test_exit_command_update_on_a_new_thread_matches_a_plain_channel` compares every checkpoint in a new thread's history with a plain channel, on every checkpointer, and fails without the fix. It runs in exit mode only: in sync and async the two channels already differ on `main`, because of a separate bug that applies a new thread's `Command` update twice. `make format`, `make lint` and the langgraph suite pass.
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.