1e44bda48f
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>
🦜🕸️ LangGraph
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Quick Install
uv add langgraph
🤔 What is this?
LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents. LangGraph provides the infrastructure for durable execution, streaming, human-in-the-loop, persistence, memory, and more.
We recommend you use LangGraph when you have advanced needs that require a combination of deterministic and agentic workflows, heavy customization, and carefully controlled latency. Use LangChain when you want to quickly build agents and applications powered by LLMs using pre-built agent architectures and model integrations.
LangChain agents are built on top of LangGraph in order to provide durable execution, streaming, human-in-the-loop, persistence, and more. (You do not need to know LangGraph for basic LangChain agent usage.)
Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is used to ship AI applications at scale.
📖 Documentation
For full documentation, see the API reference. For conceptual guides, tutorials, and examples on using LangGraph, see the LangGraph Docs. Get started with the LangGraph Quickstart.
📕 Releases & Versioning
See our Releases and Versioning policies.
💁 Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see the Contributing Guide.
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.