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Fixes langchain-ai/langgraph#8559 `find_subgraph_pregel` runs once per node at build time and recovers each node function's reachable values with `inspect.getsource` + `ast.parse`; langchain-ai/langgraph#8559 measures that source parsing at 80% of `StateGraph.compile()`. This replaces it with a `dis` walk over `func.__code__`, which reads the same information already in memory. Closure cells and the globals named in `co_names` supply the values directly, and a walk over the instruction stream recovers the attribute paths the function actually takes. The closure alone cannot express those: a captured `holder` whose graph lives at `holder.graph` is reachable only if something records that `graph` is loaded off `holder`. Both implementations over-declare — a node can reference a graph it never invokes — and this one over-declares a different set. It no longer reports a graph named only along an attribute path in code the compiler removed; such a path cannot execute, so that entry was always a phantom. Nothing that can actually run stopped being detected. `find_subgraph_pregel` returns the first `PregelProtocol` it finds, so the remaining extra candidates only widen detection. ## Release note Subgraph auto-detection now reads node functions' bytecode instead of parsing their source. Graph builds with many function-backed nodes are substantially faster, and subgraphs are now detected inside functions with no retrievable source — defined in a REPL or notebook cell, or via `exec` — where detection previously failed silently and returned nothing. ## Performance Against [langgraph-build-bench](<https://github.com/soarez/langgraph-build-bench>) — 713 nodes, 500 state fields, 264 tools; CPython 3.12.8, Apple silicon: ``` before build 1412 ms subgraph detection 1132 ms (80%) after build 286 ms subgraph detection 4 ms (2%) ``` **4.9x on total build, \~266x on detection.** The bench prints whole ms; at full precision detection is 1139 ms -> 4.23 ms. Co-authored-by: Elior Nataf Lackritz <elior.nataflackritz@langchain.dev>
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.