multi-agent-coordination-framework

Name: multi-agent-coordination-framework
Availability: InStock
Author: doctorduke

Orchestrate AI agent pods and human teams across distributed workflows.

Updated Aug 27, 2026

Search Tags:#LangGraph #consensus #CrewAI #event-sourcing #AutoGen

Ready to run

npx skills add https://github.com/doctorduke/claude-config --skill multi-agent-coordination-framework

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: multi-agent-coordination-framework
Source: https://github.com/doctorduke/claude-config/tree/main/skills/multi-agent-coordination-framework
Command: npx skills add https://github.com/doctorduke/claude-config --skill multi-agent-coordination-framework

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill covers architectures, communication patterns, task distribution, consensus, and observability for coordinating multiple AI agents.

Core Features & Use Cases

LangGraph supervisor patterns and agent routing
AutoGen-style conversational coordination
CrewAI-inspired role-based teams
Event sourcing and consensus-based decision making

Quick Start

Build a simple LangGraph supervisor coordinating three specialized agents and observe routing behavior.

Frequently Asked Questions about multi-agent-coordination-framework

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I coordinate multiple AI agents across different frameworks like LangGraph, AutoGen, and CrewAI?▼

Coordinate multiple AI agents by using a unified framework that orchestrates agent pods with shared state management, task distribution, and cross-framework compatibility. This approach routes requests through graph-based patterns, conversational protocols, or role-based team structures while maintaining consistency across LangGraph, AutoGen, and CrewAI implementations.

What's the best way to handle consensus and fault tolerance in multi-agent systems?▼

Implement consensus mechanisms using voting, event sourcing, and CRDT-based state synchronization to ensure reliable decision-making across agent teams. Event sourcing logs all agent actions immutably, while consensus protocols prevent conflicting decisions and enable recovery from agent failures in distributed workflows.

Can I use event sourcing and pub/sub patterns for agent coordination?▼

Yes. Event sourcing captures every agent decision as an immutable event, enabling audit trails and state reconstruction. Pub/sub messaging and queues distribute tasks asynchronously between agents, supporting complex workflows including mesh and hierarchical routing patterns with full observability.

How do I set up a supervisor agent to route tasks across specialized agents?▼

Build a supervisor using LangGraph that routes incoming tasks to specialized agents based on task type and agent capabilities. The supervisor maintains agent state, handles task distribution, collects results, and applies consensus logic to coordinate decisions across the agent pool.

What observability do I need for multi-agent workflows?▼

Implement observability across agent lifecycle governance, task distribution, and state transitions using event logs and distributed tracing. Track routing decisions, consensus outcomes, and cross-agent communication to diagnose coordination bottlenecks and verify fault tolerance behavior.

Do I need shared state management for agents in different roles?▼

Yes. Shared state management enables role-based teams to access consistent data and coordinate decisions. CRDTs and event sourcing provide eventually consistent state across distributed agents, supporting conversational coordination and hierarchical team structures without single points of failure.