Langgraph Human In The Loop

LangGraph's human-in-the-loop patterns are essential for building AI agents that need approval gates, error recovery, or validation loops. This skill covers the core mechanics: interrupt() to pause execution, Command(resume=value) to continue with human input, and the critical requirement for checkpointers and thread IDs. The examples walk through approval workflows, input validation loops, and multi-branch interrupts. The 4-tier error handling strategy alone makes this worth having. One gotcha to remember: when graphs resume, nodes restart from the beginning, so interrupt() calls need to come first to avoid re-running setup code.

skills.sh installs: 18K
GitHub stars: 1.3K
last updated: Aug 21, 2026

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Install

$ npx -y skills add langchain-ai/langchain-skills --skill langgraph-human-in-the-loop --agent claude-code

Installs into .claude/skills of the current project.

Files

LangGraph's human-in-the-loop patterns let you pause graph execution, surface data to users, and resume with their input:

interrupt(value) — pauses execution, surfaces a value to the caller
Command(resume=value) — resumes execution, providing the value back to interrupt()
Checkpointer — required to save state while paused
Thread ID — required to identify which paused execution to resume

Requirements

Three things are required for interrupts to work:

Checkpointer — compile with checkpointer=InMemorySaver() (dev) or PostgresSaver (prod)
Thread ID — pass {"configurable": {"thread_id": "..."}} to every invoke/stream call
JSON-serializable payload — the value passed to interrupt() must be JSON-serializable

Basic Interrupt + Resume

interrupt(value) pauses the graph. The value surfaces in the result under __interrupt__. Command(resume=value) resumes — the resume value becomes the return value of interrupt().

Critical: when the graph resumes, the node restarts from the beginning — all code before interrupt() re-runs.

SKILL.md on GitHub