langgraph
Allows your agent to create complex and stateful AI applications. It manages state and enables more advanced interactions.
What it does
LangGraph is a tool for building AI programs that remember and make decisions over multiple steps. It helps organize the AI's workflow, allowing it to handle complex tasks in a structured way.
When to use it
- ✓When you need the AI to remember information between steps.
- ✓When building an AI agent that uses multiple tools or steps.
- ✓To create AI workflows that can loop or branch into different paths.
Example of what to ask your AI
“How can I use LangGraph to build an agent that searches for information and then summarizes it?”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
LangGraph works by defining a 'graph' where each node is an AI step and the arrows show how the AI moves between steps. It manages the 'state' (information the AI remembers) and allows for cycles or branches for different paths.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill langgraphOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/langgraph/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents.
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: Python 3.9+, 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.
FAQ
What is an AI 'agent'?
An AI agent is a program that can think, plan, and take actions to achieve a goal, using tools like internet access or other functions.
What does 'stateful' mean?
'Stateful' means the program (in this case, the AI) remembers information from past interactions, allowing it to build upon what has already happened.
What is the ReAct pattern?
ReAct is a pattern where the AI thinks (Reasoning) about what to do and then acts (Acting), repeating this cycle until the goal is met.
Details
Original description by the author
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.
Popularity
- GitHub stars
- 48k
- Ranking position
- #3412
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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