examples.md•3.46 kB
# Examples Swarm Strategy
## Common Swarm Patterns
### Research Swarm
#### Using MCP Tools
```javascript
// Initialize research swarm
mcp__claude-flow__swarm_init({
"topology": "mesh",
"maxAgents": 6,
"strategy": "adaptive"
})
// Spawn research agents
mcp__claude-flow__agent_spawn({
"type": "researcher",
"name": "AI Trends Researcher",
"capabilities": ["web-search", "analysis", "synthesis"]
})
// Orchestrate research
mcp__claude-flow__task_orchestrate({
"task": "research AI trends",
"strategy": "parallel",
"priority": "medium"
})
// Monitor progress
mcp__claude-flow__swarm_status({
"swarmId": "research-swarm"
})
```
#### Using CLI (Fallback)
```bash
npx claude-flow swarm "research AI trends" \
--strategy research \
--mode distributed \
--max-agents 6 \
--parallel
```
### Development Swarm
#### Using MCP Tools
```javascript
// Initialize development swarm
mcp__claude-flow__swarm_init({
"topology": "hierarchical",
"maxAgents": 8,
"strategy": "balanced"
})
// Spawn development team
const devAgents = [
{ type: "architect", name: "API Designer" },
{ type: "coder", name: "Backend Developer" },
{ type: "tester", name: "API Tester" },
{ type: "documenter", name: "API Documenter" }
]
devAgents.forEach(agent => {
mcp__claude-flow__agent_spawn({
"type": agent.type,
"name": agent.name,
"swarmId": "dev-swarm"
})
})
// Orchestrate development
mcp__claude-flow__task_orchestrate({
"task": "build REST API",
"strategy": "sequential",
"dependencies": ["design", "implement", "test", "document"]
})
// Enable monitoring
mcp__claude-flow__swarm_monitor({
"swarmId": "dev-swarm",
"interval": 5000
})
```
#### Using CLI (Fallback)
```bash
npx claude-flow swarm "build REST API" \
--strategy development \
--mode hierarchical \
--monitor \
--output sqlite
```
### Analysis Swarm
#### Using MCP Tools
```javascript
// Initialize analysis swarm
mcp__claude-flow__swarm_init({
"topology": "mesh",
"maxAgents": 5,
"strategy": "adaptive"
})
// Spawn analysis agents
mcp__claude-flow__agent_spawn({
"type": "analyst",
"name": "Code Analyzer",
"capabilities": ["static-analysis", "complexity-analysis"]
})
mcp__claude-flow__agent_spawn({
"type": "analyst",
"name": "Security Analyzer",
"capabilities": ["security-scan", "vulnerability-detection"]
})
// Parallel analysis execution
mcp__claude-flow__parallel_execute({
"tasks": [
{ "id": "analyze-code", "command": "analyze codebase structure" },
{ "id": "analyze-security", "command": "scan for vulnerabilities" },
{ "id": "analyze-performance", "command": "identify bottlenecks" }
]
})
// Generate comprehensive report
mcp__claude-flow__performance_report({
"format": "detailed",
"timeframe": "current"
})
```
#### Using CLI (Fallback)
```bash
npx claude-flow swarm "analyze codebase" \
--strategy analysis \
--mode mesh \
--parallel \
--timeout 300
```
## Error Handling Examples
```javascript
// Setup fault tolerance
mcp__claude-flow__daa_fault_tolerance({
"agentId": "all",
"strategy": "auto-recovery"
})
// Handle errors gracefully
try {
await mcp__claude-flow__task_orchestrate({
"task": "complex operation",
"strategy": "parallel"
})
} catch (error) {
// Check swarm health
const status = await mcp__claude-flow__swarm_status({})
// Log error patterns
await mcp__claude-flow__error_analysis({
"logs": [error.message]
})
}
```