Igor Soarez
31020845f6
#7677 declared `interrupt_before`, `interrupt_after` and `control` as named parameters on the `Pregel.stream_events`/`astream_events` dispatchers but forwarded them only on the v3 branch. On async v1/v2 they never reached `astream` and nothing raised: drains on a caller-supplied `RunControl` had no effect and static interrupts never fired. This regressed the pre-1.2.0 behavior where those keywords reached `astream` through `**kwargs` (langchain-core forwards caller kwargs to `astream` on both v1 and v2), and langgraph-api runs with `stream_mode="events"` have been affected since 1.2.0a3. Remove the three parameters from the public runtime implementations so they travel through `**kwargs` again: - v1/v2 recover the pre-#7677 passthrough: `astream` receives exactly what the caller passed, including an explicit `None`, and nothing for omitted arguments. - v3 binds them on `_pregel_stream_v3`/`_apregel_stream_v3`, which are unchanged and keep their since-inception explicit-`None` defaults. The typed v3 `@overload` stubs are unchanged, so v3 call sites keep their static types. The docstrings now describe the three parameters as honored on every async version (type-checked only on the v3 overloads), and state that synchronous v1/v2 event streaming is not implemented in langchain-core — `astream_events` yields the `StreamEvent` dicts there. Recording tests pin, for all three names on both the async and sync v3 paths, exactly what `(a)stream` receives: caller-supplied values (including an explicit `None`) on v1/v2, and `None` defaults on v3.
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.