What is swarm-orchestration?

This Claude skill enables the orchestration of multi-agent swarms using agentic-flow, facilitating parallel task execution, dynamic topology management, and intelligent coordination. It is designed to scale AI operations beyond single agents, providing robust frameworks for load balancing, fault tolerance, and shared memory across distributed AI systems to handle complex, high-scale workflows.

When should I use swarm-orchestration?

swarm-orchestration is useful in the following scenarios: • Parallel Software Engineering: Orchestrate specialized agents (Architect, Coder, Tester, Reviewer) to concurrently develop, test, and review complex codebases, significantly reducing development cycles through parallel execution. • Dynamic Workflow Optimization: Deploy adaptive agent topologies that automatically restructure themselves (mesh, hierarchical, or adaptive) based on task complexity to ensure optimal resource utilization and performance. • Resilient Distributed Systems: Build fault-tolerant AI swarms with built-in load balancing and state sharing to handle large-scale, mission-critical automation tasks without single points of failure. • Complex Task Decomposition: Use the 'autoOrchestrate' feature to allow the swarm to autonomously decide the best execution strategy and agent distribution for high-level goals like building production-ready APIs.

name	"Swarm Orchestration"
description	"Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems."

Swarm Orchestration

What This Skill Does

Orchestrates multi-agent swarms using agentic-flow's advanced coordination system. Supports mesh, hierarchical, and adaptive topologies with automatic task distribution, load balancing, and fault tolerance.

Prerequisites

agentic-flow v1.5.11+
Node.js 18+
Understanding of distributed systems (helpful)

Quick Start

# Initialize swarm
npx agentic-flow hooks swarm-init --topology mesh --max-agents 5

# Spawn agents
npx agentic-flow hooks agent-spawn --type coder
npx agentic-flow hooks agent-spawn --type tester
npx agentic-flow hooks agent-spawn --type reviewer

# Orchestrate task
npx agentic-flow hooks task-orchestrate \
  --task "Build REST API with tests" \
  --mode parallel

Topology Patterns

1. Mesh (Peer-to-Peer)

// Equal peers, distributed decision-making
await swarm.init({
  topology: 'mesh',
  agents: ['coder', 'tester', 'reviewer'],
  communication: 'broadcast'
});

2. Hierarchical (Queen-Worker)

// Centralized coordination, specialized workers
await swarm.init({
  topology: 'hierarchical',
  queen: 'architect',
  workers: ['backend-dev', 'frontend-dev', 'db-designer']
});

3. Adaptive (Dynamic)

// Automatically switches topology based on task
await swarm.init({
  topology: 'adaptive',
  optimization: 'task-complexity'
});

Task Orchestration

Parallel Execution

// Execute tasks concurrently
const results = await swarm.execute({
  tasks: [
    { agent: 'coder', task: 'Implement API endpoints' },
    { agent: 'frontend', task: 'Build UI components' },
    { agent: 'tester', task: 'Write test suite' }
  ],
  mode: 'parallel',
  timeout: 300000 // 5 minutes
});

Pipeline Execution

// Sequential pipeline with dependencies
await swarm.pipeline([
  { stage: 'design', agent: 'architect' },
  { stage: 'implement', agent: 'coder', after: 'design' },
  { stage: 'test', agent: 'tester', after: 'implement' },
  { stage: 'review', agent: 'reviewer', after: 'test' }
]);

Adaptive Execution

// Let swarm decide execution strategy
await swarm.autoOrchestrate({
  goal: 'Build production-ready API',
  constraints: {
    maxTime: 3600,
    maxAgents: 8,
    quality: 'high'
  }
});

Memory Coordination

// Share state across swarm
await swarm.memory.store('api-schema', {
  endpoints: [...],
  models: [...]
});

// Agents read shared memory
const schema = await swarm.memory.retrieve('api-schema');

Advanced Features

Load Balancing

// Automatic work distribution
await swarm.enableLoadBalancing({
  strategy: 'dynamic',
  metrics: ['cpu', 'memory', 'task-queue']
});

Fault Tolerance

// Handle agent failures
await swarm.setResiliency({
  retry: { maxAttempts: 3, backoff: 'exponential' },
  fallback: 'reassign-task'
});

Performance Monitoring

// Track swarm metrics
const metrics = await swarm.getMetrics();
// { throughput, latency, success_rate, agent_utilization }

Integration with Hooks

# Pre-task coordination
npx agentic-flow hooks pre-task --description "Build API"

# Post-task synchronization
npx agentic-flow hooks post-task --task-id "task-123"

# Session restore
npx agentic-flow hooks session-restore --session-id "swarm-001"

Best Practices

Start small: Begin with 2-3 agents, scale up
Use memory: Share context through swarm memory
Monitor metrics: Track performance and bottlenecks
Enable hooks: Automatic coordination and sync
Set timeouts: Prevent hung tasks

Troubleshooting

Issue: Agents not coordinating

Solution: Verify memory access and enable hooks

Issue: Poor performance

Solution: Check topology (use adaptive) and enable load balancing

Learn More

Swarm Guide: docs/swarm/orchestration.md
Topology Patterns: docs/swarm/topologies.md
Hooks Integration: docs/hooks/coordination.md

swarm-orchestration

When & Why to Use This Skill

Use Cases