Baihao WangandGitHub
5965d72ff7
**Problem.** `interrupt(response_schema=...)` checks a human's answer when the graph resumes. If the answer doesn't match the schema, the resume fails, nothing is saved, and the human can answer again (#8886). That works when the interrupt runs in a graph node, but not when it runs inside a tool: either the tool calls `interrupt()` itself, or it starts a graph that does, such as a subagent called as a tool. There, the resume returns normally and the interrupt disappears. **Why.** `interrupt()` raises a `pydantic.ValidationError` for an invalid answer. When one comes out of a tool, `ToolNode` assumes the model passed bad arguments, so it hands the error to the model ("Error invoking tool … Please fix the error and try again") and the run carries on. The human never sees the error, the model is blamed for it, and the interrupt is no longer pending. **Fix.** `interrupt()` still raises the same `ValidationError`, but marks it so a new helper, `langgraph.errors.is_invalid_resume(error)`, can recognize it. `ToolNode` re-raises these errors instead of handing them to the model, whatever `handle_tool_errors` is set to, so a bad answer fails the resume the same way inside a tool as outside one: nothing is saved and the interrupt can be answered again. Every other `ValidationError` from a tool is handled as before. **Also fixed, in the same error handling.** With `wrap_tool_call` set and a `handle_tool_errors` other than the default (for example `True`), `ToolNode` turned any exception from the wrapper into an error `ToolMessage`, including `GraphInterrupt`. So an interrupt in that setup never paused: the model got `Error: GraphInterrupt(...)` instead. Interrupts now propagate there too, as they already do without a wrapper. `create_agent` uses the default handler, so it was not affected. **Compatibility.** Not a breaking change: the error type is unchanged. Worth a careful look: a wrapped `ToolNode` with a non-default `handle_tool_errors` now pauses on interrupts instead of returning them to the model as errors. With an older `langgraph` that doesn't have `is_invalid_resume`, `ToolNode` keeps today's behavior.
🦜🕸️ 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.