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multi-agent-patterns

Design multi-agent systems with coordination patterns and context isolation.

Updated Jun 12, 2026
Search Tags:#multi-agent systems#coordination#context isolation#supervisor/orchestrator#peer-to-peer/swarm#hierarchical architecture
One-click install
npx skills add https://github.com/Kushal9889/claude-plugins --skill multi-agent-patterns-kushal9889
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: multi-agent-patterns
Source: https://github.com/Kushal9889/claude-plugins/tree/main/context-engineering/skills/multi-agent-patterns
Command: npx skills add https://github.com/Kushal9889/claude-plugins --skill multi-agent-patterns-kushal9889

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenges of designing and implementing complex multi-agent systems, providing patterns and utilities for effective coordination and context isolation.

Core Features & Use Cases

  • Multi-Agent Architecture Patterns: Offers three dominant patterns (Supervisor/orchestrator, Peer-to-peer/swarm, Hierarchical) for different coordination needs.
  • Context Isolation: Ensures each agent operates in a clean context, preventing the telephone game problem and information degradation.
  • Consensus and Coordination: Implements weighted voting, debate protocols, and trigger-based interventions for robust system interaction.
  • Framework Considerations: Integrates with various frameworks like LangGraph, AutoGen, and CrewAI.
  • Practical Guidance: Provides failure modes and mitigations, practical examples, and integration guidance.
  • Use Case: Design a multi-agent system for a research team, with specialized agents for web search, document retrieval, data analysis, verification, and report generation.

Quick Start

Use the multi-agent-patterns skill to design a multi-agent system for a research team, with agents for web search, document retrieval, data analysis, verification, and report generation.

Frequently Asked Questions about multi-agent-patterns

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

FAQPage Schema
What are the main multi-agent architecture patterns for coordinating LLM agents?▼

Multi-agent architecture patterns include supervisor/orchestrator, peer-to-peer/swarm, and hierarchical architectures, each addressing different coordination needs for complex LLM agent systems.

How do I prevent the telephone game problem in multi-agent LLM systems?▼

Context isolation prevents the telephone game problem by ensuring each agent operates in a clean context, avoiding information degradation across multi-agent LLM systems.

Does this multi-agent patterns skill work with LangGraph, AutoGen, and CrewAI?▼

Yes, the multi-agent patterns skill integrates with LangGraph, AutoGen, and CrewAI frameworks, providing framework-specific integration guidance and practical examples.

How do I implement consensus mechanisms like weighted voting in multi-agent systems?▼

Consensus mechanisms in multi-agent systems implement weighted voting, debate protocols, and trigger-based interventions for robust agent interaction and decision coordination.

What are common failure modes when building multi-agent systems and how do I mitigate them?▼

Common multi-agent system failure modes include context degradation and coordination failures; mitigations involve context isolation patterns, consensus protocols, and structured architecture design.

How do I design a multi-agent research team with specialized agents for different tasks?▼

Design multi-agent research systems with specialized agents for web search, document retrieval, data analysis, verification, and report generation using hierarchical or supervisor coordination patterns.

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