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LangGraph — Claude Code skill: stateful agents with explicit graphs

A skill for building production-grade agents with deliberate structure. Design graph topology, manage state, add cycles and branches, persist progress via checkpoints, and loop humans back into decisions.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add sickn33/agentic-awesome-skills --skill langgraph -a claude-code
Open on GitHub

What langgraph does

LangGraph provides a framework for multi-step agents where the graph is an explicit, debuggable structure. The skill covers graph topology design, state schemas, conditional branching, persistence strategies, human-in-the-loop patterns, and tool integration. Instead of logging your way through a black box, you get a legible flow with control points.

Rests on Python and async programming. You need LLM API experience (OpenAI, Anthropic, etc.) and basic graph theory. The skill points to a detailed guide that contains the full procedure and reference material — read it through before you start.

The skill emphasizes preventing infinite loops and using reducers correctly. It is built for someone who wants to see agent architecture, not hide behind framework magic.

What it helps with

  • design a multi-step agent graph with explicit transitions
  • set up state and reducers to sync data across nodes
  • add persistence via checkpoints to recover progress after failure
  • embed a human in the agent loop for critical decisions
  • integrate tools and handle errors in the graph

Example prompts for the agent

  • Help me design a ReAct agent graph with nodes for reasoning, action, and observation
  • Set up agent state so each node receives the right context
  • Add a checkpoint to save state between calls and recover after crashes

How to install langgraph

1Skills CLI (Vercel)

npx skills add sickn33/agentic-awesome-skills --skill langgraph -a claude-code

2Manually: put the files in place

~/.claude/skills/langgraph/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/langgraph/

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Questions about langgraph

How do I install the LangGraph skill in Claude Code?

Run npx skills add sickn33/agentic-awesome-skills --skill langgraph -a claude-code in your terminal. The skill will be available at ~/.claude/skills/langgraph/SKILL.md.

What does the LangGraph skill do?

Teaches you to build production agents with explicit graph structure: design topology, manage state, add conditional transitions, persist progress, and involve humans in the decision loop.

What are the prerequisites for using this skill?

You need Python proficiency, access to an LLM API (OpenAI, Anthropic, or similar), understanding of async programming, and basic graph theory. Read the detailed guide before starting work.

Source text

SKILL.md · MIT
---
name: langgraph
description: Expert in LangGraph - the production-grade framework for building
  stateful, multi-actor AI applications. Covers graph construction, state
  management, cycles and branches, persistence with checkpointers,
  human-in-the-loop patterns, and the ReAct agent pattern.
risk: critical
source: vibeship-spawner-skills (Apache 2.0)
date_added: 2026-02-27
---

# LangGraph

Expert in LangGraph - the production-grade framework for building stateful, multi-actor
AI applications. Covers graph construction, state management, cycles and branches,
persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern.
This guide illustrates explicit graph-based agent workflows; adoption counts and recommendations are not established by this bundle.

**Role**: LangGraph Agent Architect

You are an expert in building production-grade AI agents with LangGraph. You
understand that agents need explicit structure - graphs make the flow visible
and debuggable. You design state carefully, use reducers appropriately, and
always consider persistence for production. You know when cycles are needed
and how to prevent infinite loops.

### Expertise

- Graph topology design
- State schema patterns
- Conditional branching
- Persistence strategies
- Human-in-the-loop
- Tool integration
- Error handling and recovery

## Detailed Guide

Read [the detailed guide](references/detailed-guide.md) before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.

## Prerequisites

- 0: Python proficiency
- 1: LLM API basics
- 2: Async programming concepts
- 3: Graph theory fundamentals
- Required skills: A Python version supported by the pinned langgraph package, LLM API access (OpenAI, Anthropic, etc.), Understanding of graph concepts

## When to Use
- User mentions or implies: langgraph
- User mentions or implies: langchain agent
- User mentions or implies: stateful agent
- User mentions or implies: agent graph
- User mentions or implies: react agent
- User mentions or implies: agent workflow
- User mentions or implies: multi-step agent

## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

© sickn33 · MIT · sickn33/agentic-awesome-skills

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