Can I use this for human-AI collaboration?

Yes, it is specifically designed to coordinate both autonomous AI agent pods and hybrid human-AI teams for complex, multi-step workflows.

How does it handle conflicts between agents?

The framework includes built-in conflict resolution patterns and consensus mechanisms like voting and quorum-based decisions to resolve disagreements between agents.

Which frameworks does this skill support?

It natively supports popular multi-agent frameworks including LangGraph, AutoGen, and CrewAI, as well as custom distributed system implementations.

Does it provide debugging tools for agent interactions?

Yes, it includes patterns for multi-agent observability, distributed tracing, and state management to help identify and fix deadlocks or communication errors.

Multi-Agent Coordination Framework

Name: Multi-Agent Coordination Framework
Author: doctorduke

bydoctorduke

数据科学与机器学习

Orchestrates complex multi-agent AI systems and human-AI teams using advanced communication patterns and consensus mechanisms.

关于

This skill provides a comprehensive framework for designing, implementing, and managing sophisticated multi-agent AI systems and distributed agent pods. It enables developers to orchestrate specialized agents using proven architectures like hub-and-spoke or hierarchical trees, while handling critical challenges such as shared state management, conflict resolution, and consensus-based decision-making. Whether building with LangGraph, AutoGen, or CrewAI, this tool simplifies the complexity of agent communication, task distribution, and lifecycle management to create resilient, collaborative AI workflows.

主要功能

Built-in consensus and voting mechanisms for collective decision-making
Comprehensive observability with distributed tracing and agent metrics
Support for LangGraph, AutoGen, and CrewAI frameworks
Multiple architecture patterns including Hub-Spoke, P2P, and Mesh
Advanced communication patterns like Pub/Sub and Message Queues
0 GitHub stars

使用场景

Implementing human-in-the-loop workflows for complex decision-making
Building autonomous agent swarms for parallel code processing and review
Designing hierarchical agent organizations to manage large-scale data tasks

关于

主要功能

Built-in consensus and voting mechanisms for collective decision-making
Comprehensive observability with distributed tracing and agent metrics
Support for LangGraph, AutoGen, and CrewAI frameworks
Multiple architecture patterns including Hub-Spoke, P2P, and Mesh
Advanced communication patterns like Pub/Sub and Message Queues
0 GitHub stars

使用场景

Implementing human-in-the-loop workflows for complex decision-making
Building autonomous agent swarms for parallel code processing and review
Designing hierarchical agent organizations to manage large-scale data tasks