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

$curl -o ~/.claude/skills/swarm-orchestration/SKILL.md --create-dirs "https://raw.githubusercontent.com/proffesor-for-testing/agentic-qe/main/.claude/skills/swarm-orchestration/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/swarm-orchestration/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How Swarm Orchestration Compares

Feature / AgentSwarm OrchestrationStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/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.

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.md

Related Skills

V3 Swarm Coordination

298
from proffesor-for-testing/agentic-qe

15-agent hierarchical mesh coordination for v3 implementation. Orchestrates parallel execution across security, core, and integration domains following 10 ADRs with 14-week timeline.

swarm-advanced

298
from proffesor-for-testing/agentic-qe

Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows

qcsd-refinement-swarm

298
from proffesor-for-testing/agentic-qe

Use when running Sprint Refinement sessions with SFDIPOT product factors, generating BDD scenarios, or validating requirements in the QCSD Refinement phase.

qcsd-production-swarm

298
from proffesor-for-testing/agentic-qe

Use when assessing post-release production health with DORA metrics, root cause analysis, defect prediction, or cross-phase feedback loops in the QCSD Production phase.

qcsd-ideation-swarm

298
from proffesor-for-testing/agentic-qe

Use when running Quality Criteria sessions during PI/Sprint planning with HTSM v6.3, Risk Storming, or Testability analysis in the QCSD Ideation phase.

qcsd-development-swarm

298
from proffesor-for-testing/agentic-qe

Use when monitoring in-sprint code quality with TDD adherence checks, complexity analysis, coverage gap detection, or defect prediction in the QCSD Development phase.

qcsd-cicd-swarm

298
from proffesor-for-testing/agentic-qe

Use when enforcing CI/CD quality gates before release, running regression analysis, detecting flaky tests, or assessing deployment readiness in the QCSD Verification phase.

flow-nexus-swarm

298
from proffesor-for-testing/agentic-qe

Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform

qe-visual-testing-advanced

298
from proffesor-for-testing/agentic-qe

Advanced visual regression testing with pixel-perfect comparison, AI-powered diff analysis, responsive design validation, and cross-browser visual consistency. Use when detecting UI regressions, validating designs, or ensuring visual consistency.

qe-verification-quality

298
from proffesor-for-testing/agentic-qe

Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.

qe-testability-scoring

298
from proffesor-for-testing/agentic-qe

AI-powered testability assessment using 10 principles of intrinsic testability with Playwright and optional Vibium integration. Evaluates web applications against Observability, Controllability, Algorithmic Simplicity, Transparency, Stability, Explainability, Unbugginess, Smallness, Decomposability, and Similarity. Use when assessing software testability, evaluating test readiness, identifying testability improvements, or generating testability reports.

qe-test-reporting-analytics

298
from proffesor-for-testing/agentic-qe

Advanced test reporting, quality dashboards, predictive analytics, trend analysis, and executive reporting for QE metrics. Use when communicating quality status, tracking trends, or making data-driven decisions.