Does this skill support structured outputs?

Yes, it includes modern best practices for using LLMs with Pydantic and OpenAI structured outputs to ensure routing decisions are validated and reliable.

What is a LangGraph Supervisor?

A LangGraph Supervisor is a central node in a state graph workflow that acts as a coordinator, deciding which specialized worker agent should execute next based on the current application state.

How does the supervisor track progress?

The provided patterns utilize a shared state schema that tracks a list of completed agents and their results, ensuring the supervisor knows exactly what work remains.

Can I implement priority-based routing?

Absolutely. The skill provides implementation patterns for simple round-robin dispatching as well as complex logic that prioritizes specific agents (like security) before others.

LangGraph Supervisor Pattern

Name: LangGraph Supervisor Pattern
Author: yonatangross

byyonatangross

•

数据科学与机器学习

Coordinates multiple specialized AI agents using a central supervisor pattern within LangGraph workflows.

关于

The LangGraph Supervisor skill provides a robust framework for building multi-agent systems where a central coordinator routes tasks to specialized workers. It simplifies the implementation of complex agentic workflows by offering standardized patterns for round-robin dispatch, priority-based routing, and LLM-driven decision-making with structured outputs. This skill is essential for developers building sophisticated AI applications that require modularity, progress tracking, and dynamic task delegation across multiple domain-specific agents, ensuring reliable and scalable agent orchestration.

主要功能

Standardized patterns for worker-to-supervisor feedback loops
Dynamic agent routing with central coordination
LLM-based supervisor with validated structured outputs
Round-robin and priority-based dispatch strategies
Built-in state tracking for agent completion and progress
8 GitHub stars

使用场景

Developing a customer support bot that routes queries to billing, technical, or sales experts
Creating a code review system with dedicated agents for security, performance, and style
Building a research pipeline where specialized agents handle search, analysis, and writing

关于

主要功能

Standardized patterns for worker-to-supervisor feedback loops
Dynamic agent routing with central coordination
LLM-based supervisor with validated structured outputs
Round-robin and priority-based dispatch strategies
Built-in state tracking for agent completion and progress
8 GitHub stars

使用场景

Developing a customer support bot that routes queries to billing, technical, or sales experts
Creating a code review system with dedicated agents for security, performance, and style
Building a research pipeline where specialized agents handle search, analysis, and writing