multi-agent-patterns

Name: multi-agent-patterns
Availability: InStock
Author: guanyang

Design multi-agent systems using supervisor, peer-to-peer, and hierarchical architectural patterns.

947|163|Updated Jan 13, 2026

Search Tags:#multi-agent #distributed systems #coordination #swarm intelligence #agent architecture #supervisor pattern

Ready to run

npx skills add https://github.com/guanyang/antigravity-skills --skill multi-agent-patterns-guanyang

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: multi-agent-patterns
Source: https://github.com/guanyang/antigravity-skills/tree/main/skills/multi-agent-patterns
Command: npx skills add https://github.com/guanyang/antigravity-skills --skill multi-agent-patterns-guanyang

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires uuid, dataclasses, enum, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the limitations of single-agent systems by providing patterns for distributing complex tasks across multiple agents, enabling greater scalability, specialization, and context management.

Core Features & Use Cases

Architectural Patterns: Implements Supervisor/Orchestrator, Peer-to-Peer/Swarm, and Hierarchical patterns.
Context Isolation: Strategies for partitioning context to overcome single-agent limitations.
Coordination & Consensus: Mechanisms for effective communication, handoffs, and decision-making among agents.
Use Case: Design a complex research system where specialized agents (researcher, analyst, fact-checker) collaborate under a supervisor to produce a comprehensive report, overcoming individual context window limits.

Quick Start

Use the multi-agent-patterns skill to design a supervisor/orchestrator system for a research task.

Frequently Asked Questions about multi-agent-patterns

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

FAQPage Schema

How do I design a multi-agent system to overcome single-agent context window limits?▼

To overcome single-agent context window limits, design a multi-agent system using architectural patterns like supervisor or peer-to-peer to partition context and distribute complex tasks across specialized agents.

What is the supervisor pattern for orchestrating distributed agents?▼

The supervisor pattern is an architectural design where a central orchestrator coordinates specialized agents, managing communication, task handoffs, and decision-making to produce a unified output for complex research systems.

How do I implement context isolation in a hierarchical agent architecture?▼

Implement context isolation in a hierarchical agent architecture by partitioning state and context across subordinate agents, preventing individual context window limits from bottlenecking the distributed problem-solving process.

When should I use peer-to-peer swarm intelligence versus a hierarchical agent structure?▼

Use peer-to-peer swarm intelligence for decentralized consensus and distributed problem-solving, whereas a hierarchical agent structure suits tasks requiring a central orchestrator for strict coordination and specialized handoffs.

Do I need prior experience with distributed systems to build a swarm intelligence architecture?▼

Building a swarm intelligence architecture requires an understanding of agent communication, state management, and distributed problem-solving to effectively implement coordination protocols and mitigate failure modes.

How do I mitigate failure modes in multi-agent coordination protocols?▼

Mitigate failure modes in multi-agent coordination protocols by implementing robust consensus mechanisms and leveraging hierarchical structures or supervisor patterns to manage agent handoffs and state recovery.