langgraph-human-in-the-loop

langgraph-human-in-the-loop is a skill for Claude Code, Codex from langchain-ai/skills-benchmarks. It costs 55 tokens per session (3,896 once invoked), scanned A, a copy of langgraph-human-in-the-loop, MIT.

Instructions for pausing a LangGraph workflow to ask a person for approval or information, then continuing it after a response. LangGraph is a framework for building workflows whose steps can keep state.

In plain words
What is it for?
Use it for approval steps, validation requests, manual review, and error handling in LangGraph applications.
Why use it?
It explains the saved state and identifiers needed to pause safely, resume the correct run, and handle failures at different levels.

Skill for Claude CodeCodex ✓ vendor

Written for no agent in particular: nothing here depends on one.

Good fit Use it for approval steps, validation requests, manual review, and error handling in LangGraph applications.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/langchain-ai/skills-benchmarks/langgraph-human-in-the-loop
About the project

skills-benchmarks is a test suite that measures how the design of skill documentation affects Claude Code's adherence to recommended coding patterns. It is used to compare documentation approaches across LangChain-related tasks and other agent workflows. Its catalogue entries represent skills, hooks, instructions, and a plugin used in the benchmark project.

langchain-ai/skills-benchmarks · 119 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add langchain-ai/skills-benchmarks --skill langgraph-human-in-the-loop
Clone the repo
git clone --depth 1 https://github.com/langchain-ai/skills-benchmarks

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for langgraph-human-in-the-loop

README.md
[![agentmods](https://agentmods.dev/badge/skills/langchain-ai/skills-benchmarks/langgraph-human-in-the-loop/github.svg)](https://agentmods.dev/skills/langchain-ai/skills-benchmarks/langgraph-human-in-the-loop)
Your own site
<a href="https://agentmods.dev/skills/langchain-ai/skills-benchmarks/langgraph-human-in-the-loop"><img src="https://agentmods.dev/badge/skills/langchain-ai/skills-benchmarks/langgraph-human-in-the-loop/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for langgraph-human-in-the-loop

Your own site · 80×15
<a href="https://agentmods.dev/skills/langchain-ai/skills-benchmarks/langgraph-human-in-the-loop"><img src="https://agentmods.dev/badge/skills/langchain-ai/skills-benchmarks/langgraph-human-in-the-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 55 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,896 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00055 $0.03896
Opus 5.5 $0.00022 $0.01558
Sonnet 5.5 $0.00011 $0.00779
Haiku 4.5 $0.00006 $0.00390

Measured 2mo ago against content hash 3e43a99257a8, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

langgraph-human-in-the-loop scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to langgraph-human-in-the-loop — 17 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/main/langgraph-human-in-the-loop/SKILL.md · 533 lines

How it starts

The opening of the file, as written. The whole thing — 533 lines — stays where its author put it; the contents beside it link to each section on GitHub.

  • 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:

  1. Checkpointer — compile with checkpointer=InMemorySaver() (dev) or PostgresSaver (prod)
  2. Thread ID — pass {"configurable": {"thread_id": "..."}} to every invoke/stream call
  3. 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.

class State(TypedDict): approved: bool

def approval_node(state: State): # Pause and ask for approval approved = interrupt("Do you approve this action?") # When resumed, Command(resume=...) returns that value here return {"approved": approved}

checkpointer = InMemorySaver() graph = ( StateGraph(State) .add_node("approval", approval_node) .add_edge(START, "approval") .add_edge("approval", END) .compile(checkpointer=checkpointer) )

config = {"configurable": {"thread_id": "thread-1"}}

Initial run — hits interrupt and pauses

result = graph.invoke({"approved": False}, config) print(result["interrupt"])

[Interrupt(value='Do you approve this action?')]

Resume with the human's response

result = graph.invoke(Command(resume=True), config) print(result["approved"]) # True

</python>
<typescript>
Pause execution for human review and resume with Command.
```typescript
import { interrupt, Command, MemorySaver, StateGraph, StateSchema, START, END } from "@langchain/langgraph";
import { z } from "zod";

const State = new StateSchema({
  approved: z.boolean().default(false),
});

const approvalNode = async (state: typeof State.State) => {
  // Pause and ask for approval
  const approved = interrupt("Do you approve this action?");
  // When resumed, Command({ resume }) returns that value here
  return { approved };
};

const checkpointer = new MemorySaver();
const graph = new StateGraph(State)
  .addNode("approval", approvalNode)
  .addEdge(START, "approval")
  .addEdge("approval", END)
  .compile({ checkpointer });

const config = { configurable: { thread_id: "thread-1" } };

// Initial run — hits interrupt and pauses
let result = await graph.invoke({ approved: false }, config);
console.log(result.__interrupt__);
// [{ value: 'Do you approve this action?', ... }]

// Resume with the human's response
result = await graph.invoke(new Command({ resume: true }), config);
console.log(result.approved);  // true

Read the full file on GitHub · 533 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2mo ago First seen · 533 lines · 55 tokens per session scan A 3e43a99257a8

Subscribe to this mod's changes

langgraph-human-in-the-loop is a skill published in the GitHub repository langchain-ai/skills-benchmarks (119 stars, last pushed 1mo ago), licensed MIT. It adds 55 tokens to every session and 3,896 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to langgraph-human-in-the-loop, differing in 17 lines, and is treated as a copy.

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