Swarm Orchestration

Scale AI workflows with multi-agent orchestration and smart coordination

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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.

multi-agent orchestration distributed-systems workflow-automation parallel-execution swarm-intelligence agentic-flow coordination
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User Prompt

I need to build a production-ready REST API with authentication, CRUD operations, and comprehensive tests. Can you orchestrate a swarm to handle this in parallel?

Skill Processing

Analyzing request...

Agent Response

Swarm deploys specialized agents (coder, tester, reviewer) working simultaneously on different aspects, delivering a complete API faster than sequential development

Quick Start (3 Steps)

Get up and running in minutes

1

Install

claude-code skill install swarm-orchestration

claude-code skill install swarm-orchestration
2

Config

3

First Trigger

@swarm-orchestration help

Commands

CommandDescriptionRequired Args
@swarm-orchestration complex-api-developmentBuild a full REST API with concurrent development, testing, and reviewNone
@swarm-orchestration large-codebase-analysisDistribute analysis tasks across multiple agents for faster processingNone
@swarm-orchestration distributed-data-processingHandle large-scale data processing with adaptive load balancingNone

Typical Use Cases

Complex API Development

Build a full REST API with concurrent development, testing, and review

Large Codebase Analysis

Distribute analysis tasks across multiple agents for faster processing

Distributed Data Processing

Handle large-scale data processing with adaptive load balancing

Overview

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

 1# Initialize swarm
 2npx agentic-flow hooks swarm-init --topology mesh --max-agents 5
 3
 4# Spawn agents
 5npx agentic-flow hooks agent-spawn --type coder
 6npx agentic-flow hooks agent-spawn --type tester
 7npx agentic-flow hooks agent-spawn --type reviewer
 8
 9# Orchestrate task
10npx agentic-flow hooks task-orchestrate \
11  --task "Build REST API with tests" \
12  --mode parallel

Topology Patterns

1. Mesh (Peer-to-Peer)

1// Equal peers, distributed decision-making
2await swarm.init({
3  topology: 'mesh',
4  agents: ['coder', 'tester', 'reviewer'],
5  communication: 'broadcast'
6});

2. Hierarchical (Queen-Worker)

1// Centralized coordination, specialized workers
2await swarm.init({
3  topology: 'hierarchical',
4  queen: 'architect',
5  workers: ['backend-dev', 'frontend-dev', 'db-designer']
6});

3. Adaptive (Dynamic)

1// Automatically switches topology based on task
2await swarm.init({
3  topology: 'adaptive',
4  optimization: 'task-complexity'
5});

Task Orchestration

Parallel Execution

 1// Execute tasks concurrently
 2const results = await swarm.execute({
 3  tasks: [
 4    { agent: 'coder', task: 'Implement API endpoints' },
 5    { agent: 'frontend', task: 'Build UI components' },
 6    { agent: 'tester', task: 'Write test suite' }
 7  ],
 8  mode: 'parallel',
 9  timeout: 300000 // 5 minutes
10});

Pipeline Execution

1// Sequential pipeline with dependencies
2await swarm.pipeline([
3  { stage: 'design', agent: 'architect' },
4  { stage: 'implement', agent: 'coder', after: 'design' },
5  { stage: 'test', agent: 'tester', after: 'implement' },
6  { stage: 'review', agent: 'reviewer', after: 'test' }
7]);

Adaptive Execution

1// Let swarm decide execution strategy
2await swarm.autoOrchestrate({
3  goal: 'Build production-ready API',
4  constraints: {
5    maxTime: 3600,
6    maxAgents: 8,
7    quality: 'high'
8  }
9});

Memory Coordination

1// Share state across swarm
2await swarm.memory.store('api-schema', {
3  endpoints: [...],
4  models: [...]
5});
6
7// Agents read shared memory
8const schema = await swarm.memory.retrieve('api-schema');

Advanced Features

Load Balancing

1// Automatic work distribution
2await swarm.enableLoadBalancing({
3  strategy: 'dynamic',
4  metrics: ['cpu', 'memory', 'task-queue']
5});

Fault Tolerance

1// Handle agent failures
2await swarm.setResiliency({
3  retry: { maxAttempts: 3, backoff: 'exponential' },
4  fallback: 'reassign-task'
5});

Performance Monitoring

1// Track swarm metrics
2const metrics = await swarm.getMetrics();
3// { throughput, latency, success_rate, agent_utilization }

Integration with Hooks

1# Pre-task coordination
2npx agentic-flow hooks pre-task --description "Build API"
3
4# Post-task synchronization
5npx agentic-flow hooks post-task --task-id "task-123"
6
7# Session restore
8npx agentic-flow hooks session-restore --session-id "swarm-001"

Best Practices

  1. Start small: Begin with 2-3 agents, scale up
  2. Use memory: Share context through swarm memory
  3. Monitor metrics: Track performance and bottlenecks
  4. Enable hooks: Automatic coordination and sync
  5. 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

What Users Are Saying

Real feedback from the community

Environment Matrix

Dependencies

agentic-flow v1.5.11+
Node.js 18+

Framework Support

agentic-flow ✓ (required) TypeScript ✓ (recommended)

Context Window

Token Usage ~5K-15K tokens depending on swarm size and task complexity

Security & Privacy

Information

Author
ruvnet
Updated
2026-01-30
Category
productivity-tools