What is the LangGraph Supervisor pattern?

It is an orchestration design where a central coordinator agent manages the workflow by routing tasks to specialized worker agents based on the current state and task requirements.

How does the supervisor track workflow progress?

The skill provides patterns for completion tracking using state schemas, ensuring that the supervisor knows which agents have finished and preventing infinite loops.

Why use the Command API instead of conditional edges?

The Command API is the modern best practice for when you need to update the graph state and perform routing in a single, atomic operation within a node.

Does this skill support LLM-based routing decisions?

Yes, it includes implementation patterns for using LLMs with structured output (Pydantic/OpenAI schemas) to ensure reliable and validated routing decisions.

Can I implement priority-based routing with this skill?

Absolutely. It includes specific code patterns for both round-robin dispatching and priority-based routing where critical tasks are handled by specific agents first.

LangGraph Agent Supervisor

Name: LangGraph Agent Supervisor
Author: yonatangross

byyonatangross

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数据科学与机器学习

Orchestrates multi-agent workflows using a central supervisor pattern to intelligently route tasks between specialized worker agents.

This skill provides production-ready implementation patterns for the LangGraph supervisor-worker architecture within Claude Code. It enables developers to build complex AI systems where a central coordinator dynamically dispatches tasks to specialized workers based on round-robin, priority-weighted, or LLM-driven logic. It features modern best practices including the Command API for simultaneous state updates and routing, structured output validation for decision-making, and robust workflow progress tracking to ensure reliable multi-agent execution.

主要功能

01Command API integration for atomic state updates and routing

02Centralized multi-agent orchestration and coordination

03Dynamic task routing (Round-robin, Priority, and LLM-based)

04Structured output validation for reliable agent dispatching

05Comprehensive agent completion and workflow progress tracking

0669 GitHub stars

使用场景

01Implementing automated security and quality gate cycles in software development

02Building complex research pipelines that require specialized sub-task execution

03Coordinating large-scale content analysis across multiple domain-specific agents

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add yonatangross/orchestkit langgraph-supervisor

For use in Claude.ai and ChatGPT

Download Skill