Baihao WangandGitHub
b0cabd93d0
Found while testing per-call human-in-the-loop (langchain-ai/langchain#41062). The code depends only on #9232, which is already on `main`. When one answer in a multi-answer resume fails `response_schema` validation, the runner cancelled the other tasks in the step, so in async runs a task cut off after its side effect kept its saved answer and ran again on the next resume. Invalid resume values (already tagged by `is_invalid_resume` from #9232) no longer stop the other tasks: the error is raised once they finish, so their results are saved. Hit by per-call human-in-the-loop: approving one tool call while giving another an invalid edit, in one resume, sent the approved call's email twice once the second answer was fixed. ## How to verify - `test_invalid_resume_lets_other_answered_tasks_finish` fails on `main` (the approved task is still pending after its side effect) and passes with this change. Like the other async interrupt tests, it is skipped before Python 3.11, where async nodes can't call `interrupt()`. - The per-call HITL reproduction against this branch: the approved async tool runs once (twice on `main` whenever it awaits after its side effect). - `make lint` passes. With `NO_DOCKER=true`, the full `libs/langgraph` suite has the same failures as `main` locally (`test_prebuilt_tool_chat` and `test_stream_mode_messages_command`, sync and async), and nothing new.
🦜🕸️ LangGraph
Looking for the JS/TS version? Check out LangGraph.js.
To help you ship LangGraph apps to production faster, check out LangSmith. LangSmith is a unified developer platform for building, testing, and monitoring LLM applications.
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.