agentdb-advanced
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems…
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.
$ npx -y skills add ruvnet/agentic-flow --skill swarm-orchestration --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/swarm-orchestrationContext preview
The summary Claude sees to decide when to auto-load this skill.
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.
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."
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.
# 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
// Equal peers, distributed decision-making
await swarm.init({
topology: 'mesh',
agents: ['coder', 'tester', 'reviewer'],
communication: 'broadcast'
});// Centralized coordination, specialized workers
await swarm.init({
topology: 'hierarchical',
queen: 'architect',
workers: ['backend-dev', 'frontend-dev', 'db-designer']
});// Automatically switches topology based on task
await swarm.init({
topology: 'adaptive',
optimization: 'task-complexity'
});// 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
});// 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' }
]);// Let swarm decide execution strategy
await swarm.autoOrchestrate({
goal: 'Build production-ready API',
constraints: {
maxTime: 3600,
maxAgents: 8,
quality: 'high'
}
});// Share state across swarm
await swarm.memory.store('api-schema', {
endpoints: [...],
models: [...]
});
// Agents read shared memory
const schema = await swarm.memory.retrieve('api-schema');// Automatic work distribution
await swarm.enableLoadBalancing({
strategy: 'dynamic',
metrics: ['cpu', 'memory', 'task-queue']
});// Handle agent failures
await swarm.setResiliency({
retry: { maxAttempts: 3, backoff: 'exponential' },
fallback: 'reassign-task'
});// Track swarm metrics
const metrics = await swarm.getMetrics();
// { throughput, latency, success_rate, agent_utilization }# 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"
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
**Solution**: Verify memory access and enable hooks
**Solution**: Check topology (use adaptive) and enable load balancing
Production-ready AI agent orchestration with 66 self-learning agents, 213 MCP tools, and autonomous multi-agent swarms.
Repo: ruvnet/agentic-flow
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