langgraph-human-in-the-loop

To pause a LangGraph workflow for human approval, use the interrupt() function at a decision node, then resume execution by passing human input through Command(resume=...). A checkpointer is required to persist the graph state across the pause.

You need a checkpointer to persist state and a thread_id to propagate the context of the paused graph. Payloads must be JSON-serializable to ensure the graph can resume correctly with Command(resume=...).

Error handling in LangGraph flows uses a checkpointer to persist state during an interrupt, allowing multi-step recovery. When an error occurs, the graph pauses, surfaces the issue for human review, and resumes using Command(resume=...) with corrective input.

Yes, LangGraph interrupts support iterative data collection by pausing the graph execution to request user input. The graph saves its state via a checkpointer and resumes processing once the required data is supplied through Command(resume=...).

To ensure idempotent side effects after resuming a LangGraph graph, design the nodes following the interrupt point to handle repeated executions safely. The Command(resume=...) mechanism restarts flow from the interrupt, requiring careful state management.

Use human-in-the-loop patterns in LangGraph when you need safe, auditable control over AI-driven workflows at critical decision points. This includes approval workflows, error handling, and iterative data collection where human input determines the next step.