multi-agent-patterns

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

3|Updated Dec 23, 2025

Search Tags:#orchestration #multi-agent systems #distributed AI #agent coordination #swarm intelligence #context isolation

Ready to run

npx skills add https://github.com/boazcstrike/opencode --skill multi-agent-patterns-boazcstrike

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the limitations of single-agent systems by providing patterns and strategies for designing robust, scalable, and efficient multi-agent architectures.

Core Features & Use Cases

Architectural Patterns: Explains Supervisor/Orchestrator, Peer-to-Peer/Swarm, and Hierarchical patterns.
Context Isolation: Details mechanisms for managing context across multiple agents.
Coordination & Consensus: Covers techniques for effective communication, voting, and debate.
Failure Handling: Provides strategies for mitigating common failure modes like bottlenecks and divergence.
Use Case: Design a complex research system where specialized agents (researcher, analyst, fact-checker) collaborate under a supervisor to synthesize information from multiple sources, overcoming single-agent context limitations.

Quick Start

Use the multi-agent-patterns skill to design a supervisor/orchestrator architecture for coordinating multiple AI agents on a complex 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 architecture for complex task coordination?▼

Multi-agent systems overcome single-agent limitations by utilizing architectural patterns like supervisor/orchestrator, peer-to-peer/swarm, and hierarchical structures to distribute tasks and scale complex operations efficiently.

How does context isolation work when coordinating multiple AI agents?▼

Context isolation in multi-agent systems involves managing individual agent contexts separately to prevent information overload and divergence, ensuring each specialized agent processes only relevant data for its specific role.

What is the best way to handle failure modes like bottlenecks in distributed AI systems?▼

Mitigate failure modes like bottlenecks and divergence in distributed AI by applying architectural patterns and consensus mechanisms that ensure robust coordination and prevent single points of failure.

When do I need a supervisor or orchestrator pattern for agent coordination?▼

You need a supervisor or orchestrator pattern when coordinating specialized agents, like a researcher and fact-checker, to synthesize information from multiple sources under centralized management for complex task execution.

How do consensus mechanisms and voting work in swarm intelligence architectures?▼

Consensus mechanisms in swarm intelligence utilize techniques like voting and debate among peer-to-peer agents to achieve agreement, ensuring coordinated action and consistent decision-making across the distributed network.