Does this skill support human-in-the-loop patterns?

Yes, it provides specific rules for interrupt/resume mechanisms and approval gates for high-risk operations.

How does it handle data persistence?

It offers guidance on setting up MemorySaver for development and PostgresSaver for production-ready checkpointing to ensure workflows can resume after crashes.

What is LangGraph used for in Claude Code?

It allows Claude to help you design and implement complex AI agent workflows with persistent state, loops, and conditional logic.

Can I use this for multi-agent systems?

Absolutely, it includes patterns for Supervisor-Worker architectures, round-robin dispatching, and priority routing.

Is it compatible with the new LangGraph Functional API?

Yes, it includes patterns for @entrypoint and @task decorators, as well as migration guides from StateGraph.

LangGraph Workflow Patterns

Name: LangGraph Workflow Patterns
Author: yonatangross

byyonatangross

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116

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データサイエンスとML

Provides production-ready LangGraph implementation patterns for state management, multi-agent orchestration, and human-in-the-loop workflows.

This skill equips Claude Code with a comprehensive library of LangGraph patterns, enabling the development of robust, production-grade AI workflows. It covers essential architectural concerns including complex state management, dynamic routing, parallel execution, and persistent checkpointing, while also supporting advanced features like human-in-the-loop approval gates and the LangGraph Functional API. Whether building simple pipelines or complex multi-agent systems, this skill provides the domain-specific guidance needed to avoid common pitfalls like state mutation or infinite loops.

主な機能

01Advanced state management with TypedDict, Pydantic, and custom reducers

02Multi-agent coordination using Supervisor and Worker patterns

03116 GitHub stars

04Human-in-the-loop integration with interrupt/resume capabilities and approval gates

05Parallel execution patterns like Fan-out/Fan-in and Map-Reduce

06Production-ready persistence using Postgres and Memory checkpointers

ユースケース

01Building complex multi-agent systems with centralized supervisors and specialized workers

02Creating durable AI pipelines that require manual human intervention or approval gates

03Developing streaming AI applications with real-time state updates and token feedback

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add yonatangross/orchestkit langgraph

For use in Claude.ai and ChatGPT

Download Skill