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Langgraph Human In The Loop

Official
langchain-ai·vlatest·agents·MIT

This skill integrates human feedback into LangGraph workflows to refine outputs and improve accuracy through a collaborative loop.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

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

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

langchain-ai

Matched to the vendor's own GitHub organisation.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned langchain-ai/langchain-skills at 16a992f09a.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
16a992f09a
Manifest
config/skills/langgraph-human-in-the-loop/SKILL.md
Scanned
1 file, 516 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill enables AI agents to pause their execution flow and wait for explicit human approval before proceeding with specific actions. It integrates LangGraph's workflow management to create safe checkpoints where users can intervene, review context, or alter the agent's path in real-time.

When to use it

  • High-stakes decisions: Before an agent sends sensitive emails, deletes files, or makes financial transactions.
  • Complex reasoning tasks: When the agent needs human expertise to validate a multi-step plan before execution begins.
  • Safety guardrails: To prevent autonomous agents from acting on ambiguous instructions or potentially harmful outputs.
  • Collaborative workflows: For scenarios where humans and AI must co-author content or jointly solve problems step-by-step.

Key capabilities

  • Pauses agent execution at defined nodes in a LangGraph workflow.
  • Captures human input to resume, modify, or reject the current action.
  • Maintains conversation history and context across human-agent interactions.
  • Allows dynamic routing based on human feedback within the graph structure.

Example prompts

  • "Pause before you send that email draft; I want to review the tone first."
  • "Hold on, let me verify these financial figures before you proceed with the transaction."
  • "I disagree with your proposed solution path. Suggest an alternative plan for my approval."

Tips & gotchas

Ensure your LangGraph workflow explicitly defines nodes that trigger human-in-the-loop checks to avoid accidental autonomous execution. Be mindful that pausing flow can increase latency, so use this skill primarily for critical decision points rather than routine steps.