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Swarm Orchestration

Coordinates multiple AI agents with agentic-flow to run tasks in parallel or through dependent stages. It covers team communication structures, shared memory, workload balancing, failure recovery, and performance monitoring for complex workflows.

OriginalShow the original 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.

AI rating80/ 100See how to install

Good for

  • Develop APIs, user interfaces, and tests in parallel
  • Connect design, implementation, testing, and review stages
  • Share API schemas across agents
  • Reassign work when an agent fails

Overview

What it does

Uses agentic-flow to divide work among multiple AI agents. It supports coordination among equal peers, a hierarchy with a central coordinator, and structures that change according to the task. You can arrange independent tasks to run in parallel or build staged workflows where each step waits for earlier work to finish.

When it helps

Use it when separate agents need to develop an API, build an interface, and write tests, or when you want to connect design, implementation, testing, and review. Shared memory keeps specifications and task status aligned across agents, while workload balancing addresses uneven assignments. It also covers retries, reassigning failed tasks, and checking processing speed and success rates.

Example requests

  • “Implement the API, build the interface, and write tests in parallel.”
  • “Run design, implementation, testing, and review according to their dependencies.”
  • “Share the API specification across agents and reassign any failed tasks.”

Notes

Requires agentic-flow v1.5.11+ and Node.js 18+. The recommended approach is to start with two or three agents, use shared memory and coordination hooks, and set timeouts so tasks do not remain stuck indefinitely.

This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.

Objective evaluation

AI rated this skill on four points using only the contents of its SKILL.md. Stars and popularity are not taken into account.

Clear instructionsIs it clear when to use it and what it does?
AGoodIt identifies work beyond a single agent and explicitly describes parallel execution and hierarchical coordination.
Concrete stepsAre steps, inputs, outputs, and examples spelled out?
AGoodIt provides commands from swarm-init to task-orchestrate, plus shared memory and retry configuration examples.
SafetyAre sending data, deleting files, and running commands disclosed?
BPartly lackingIt starts agents and runs tasks through npx, but does not explain execution permissions or prior confirmation.
Easy setupDoes it need few tools or API keys, and are they documented?
BPartly lackingRequired agentic-flow and Node.js versions are stated, but installation steps are absent.

The total counts each point as A = 25, B = 15, C = 5. Rated by AI (OpenAI Codex) as of Oct 21, 2026. Treat it as a guide and read the original before installing.

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Files included (1)

  • SKILL.md4.2 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

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

  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

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