LangChain Skills is a collection of agent skills for building software agents with LangChain, LangGraph, and Deep Agents. Coding agents that support the Agent Skills specification, including Claude Code, Cursor, and Windsurf, can install and use these skills. The catalogue lists the project's skills and its Claude Code plugin.
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.
npx skills add langchain-ai/langchain-skills --skill langgraph-human-in-the-loopgit clone --depth 1 https://github.com/langchain-ai/langchain-skillsWrote 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.
[](https://agentmods.dev/skills/langchain-ai/langchain-skills/langgraph-human-in-the-loop)<a href="https://agentmods.dev/skills/langchain-ai/langchain-skills/langgraph-human-in-the-loop"><img src="https://agentmods.dev/badge/skills/langchain-ai/langchain-skills/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.
<a href="https://agentmods.dev/skills/langchain-ai/langchain-skills/langgraph-human-in-the-loop"><img src="https://agentmods.dev/badge/skills/langchain-ai/langchain-skills/langgraph-human-in-the-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
Copies of this mod
5 near-identical copies found in the catalogue:
- langgraph-human-in-the-loop — 100% identical, 17 lines differ
- langgraph-human-in-the-loop — 100% identical, 17 lines differ
- langgraph-human-in-the-loop — 100% identical, 17 lines differ
- langgraph-human-in-the-loop — 100% identical, 17 lines differ
- langgraph-human-in-the-loop — 100% identical, 17 lines differ
How it starts
The opening of the file, as written. The whole thing — 550 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 callerCommand(resume=value)— resumes execution, providing the value back tointerrupt()- 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) orPostgresSaver(prod) - Thread ID — pass
{"configurable": {"thread_id": "..."}}to everyinvoke/streamcall - 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.
from langgraph.types import interrupt, Command
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict
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
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.
- 2mo ago First seen · 550 lines · 55 tokens per session scan A ce56e8ce5854
langgraph-human-in-the-loop is a skill published in the GitHub repository langchain-ai/langchain-skills (1,324 stars, last pushed yesterday), 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
langgraph-human-in-the-loop compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| langgraph-human-in-the-looplangchain-ai/langchain-skills | Skill | 1.3k | yesterday | MIT | A | 55 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |