Swarm Orchestration
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.
Best use case
Swarm Orchestration is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using Swarm Orchestration should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/swarm-orchestration/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Swarm Orchestration Compares
| Feature / Agent | Swarm Orchestration | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# 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 v3.0.0-alpha.1+
- Node.js 18+
- Understanding of distributed systems (helpful)
## Quick Start
```bash
# 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)
```typescript
// Equal peers, distributed decision-making
await swarm.init({
topology: 'mesh',
agents: ['coder', 'tester', 'reviewer'],
communication: 'broadcast'
});
```
### 2. Hierarchical (Queen-Worker)
```typescript
// Centralized coordination, specialized workers
await swarm.init({
topology: 'hierarchical',
queen: 'architect',
workers: ['backend-dev', 'frontend-dev', 'db-designer']
});
```
### 3. Adaptive (Dynamic)
```typescript
// Automatically switches topology based on task
await swarm.init({
topology: 'adaptive',
optimization: 'task-complexity'
});
```
## Task Orchestration
### Parallel Execution
```typescript
// 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
```typescript
// 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
```typescript
// Let swarm decide execution strategy
await swarm.autoOrchestrate({
goal: 'Build production-ready API',
constraints: {
maxTime: 3600,
maxAgents: 8,
quality: 'high'
}
});
```
## Memory Coordination
```typescript
// 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
```typescript
// Automatic work distribution
await swarm.enableLoadBalancing({
strategy: 'dynamic',
metrics: ['cpu', 'memory', 'task-queue']
});
```
### Fault Tolerance
```typescript
// Handle agent failures
await swarm.setResiliency({
retry: { maxAttempts: 3, backoff: 'exponential' },
fallback: 'reassign-task'
});
```
### Performance Monitoring
```typescript
// Track swarm metrics
const metrics = await swarm.getMetrics();
// { throughput, latency, success_rate, agent_utilization }
```
## Integration with Hooks
```bash
# 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.mdRelated Skills
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