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langgraph

langgraph

github.com/sickn33Agent workflows48.7k1.3k installsUpdated today
Repository

About langgraph

A skill for designing stateful AI applications with LangGraph. It guides developers through graph structure, state, persistence and agent workflows.

Key features

  1. 01Graph topology and state design
  2. 02Conditional branches and cycles
  3. 03Persistence with checkpointers
  4. 04Human-in-the-loop patterns
  5. 05ReAct agent and tool integration
  6. 06Error handling and recovery

Use cases

  1. 01Building a stateful LangGraph agent
  2. 02Designing a multi-step agent workflow
  3. 03Adding persistence to graph-based applications

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/sickn33/agentic-awesome-skills --skill langgraph

After installing, type/langgraphin Claude Code to confirm it loaded.

Bundled filesreferences/detailed-guide.md

Details

From the project README

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 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.

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