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performance-analysis

Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms

Install with Tessl CLI

npx tessl i github:ruvnet/agentic-flow --skill performance-analysis
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Performance Analysis Skill

Comprehensive performance analysis suite for identifying bottlenecks, profiling swarm operations, generating detailed reports, and providing actionable optimization recommendations.

Overview

This skill consolidates all performance analysis capabilities:

  • Bottleneck Detection: Identify performance bottlenecks across communication, processing, memory, and network
  • Performance Profiling: Real-time monitoring and historical analysis of swarm operations
  • Report Generation: Create comprehensive performance reports in multiple formats
  • Optimization Recommendations: AI-powered suggestions for improving performance

Quick Start

Basic Bottleneck Detection

npx claude-flow bottleneck detect

Generate Performance Report

npx claude-flow analysis performance-report --format html --include-metrics

Analyze and Auto-Fix

npx claude-flow bottleneck detect --fix --threshold 15

Core Capabilities

1. Bottleneck Detection

Command Syntax

npx claude-flow bottleneck detect [options]

Options

  • --swarm-id, -s <id> - Analyze specific swarm (default: current)
  • --time-range, -t <range> - Analysis period: 1h, 24h, 7d, all (default: 1h)
  • --threshold <percent> - Bottleneck threshold percentage (default: 20)
  • --export, -e <file> - Export analysis to file
  • --fix - Apply automatic optimizations

Usage Examples

# Basic detection for current swarm
npx claude-flow bottleneck detect

# Analyze specific swarm over 24 hours
npx claude-flow bottleneck detect --swarm-id swarm-123 -t 24h

# Export detailed analysis
npx claude-flow bottleneck detect -t 24h -e bottlenecks.json

# Auto-fix detected issues
npx claude-flow bottleneck detect --fix --threshold 15

# Low threshold for sensitive detection
npx claude-flow bottleneck detect --threshold 10 --export critical-issues.json

Metrics Analyzed

Communication Bottlenecks:

  • Message queue delays
  • Agent response times
  • Coordination overhead
  • Memory access patterns
  • Inter-agent communication latency

Processing Bottlenecks:

  • Task completion times
  • Agent utilization rates
  • Parallel execution efficiency
  • Resource contention
  • CPU/memory usage patterns

Memory Bottlenecks:

  • Cache hit rates
  • Memory access patterns
  • Storage I/O performance
  • Neural pattern loading times
  • Memory allocation efficiency

Network Bottlenecks:

  • API call latency
  • MCP communication delays
  • External service timeouts
  • Concurrent request limits
  • Network throughput issues

Output Format

🔍 Bottleneck Analysis Report
━━━━━━━━━━━━━━━━━━━━━━━━━━━

📊 Summary
├── Time Range: Last 1 hour
├── Agents Analyzed: 6
├── Tasks Processed: 42
└── Critical Issues: 2

🚨 Critical Bottlenecks
1. Agent Communication (35% impact)
   └── coordinator → coder-1 messages delayed by 2.3s avg

2. Memory Access (28% impact)
   └── Neural pattern loading taking 1.8s per access

⚠️ Warning Bottlenecks
1. Task Queue (18% impact)
   └── 5 tasks waiting > 10s for assignment

💡 Recommendations
1. Switch to hierarchical topology (est. 40% improvement)
2. Enable memory caching (est. 25% improvement)
3. Increase agent concurrency to 8 (est. 20% improvement)

✅ Quick Fixes Available
Run with --fix to apply:
- Enable smart caching
- Optimize message routing
- Adjust agent priorities

2. Performance Profiling

Real-time Detection

Automatic analysis during task execution:

  • Execution time vs. complexity
  • Agent utilization rates
  • Resource constraints
  • Operation patterns

Common Bottleneck Patterns

Time Bottlenecks:

  • Tasks taking > 5 minutes
  • Sequential operations that could parallelize
  • Redundant file operations
  • Inefficient algorithm implementations

Coordination Bottlenecks:

  • Single agent for complex tasks
  • Unbalanced agent workloads
  • Poor topology selection
  • Excessive synchronization points

Resource Bottlenecks:

  • High operation count (> 100)
  • Memory constraints
  • I/O limitations
  • Thread pool saturation

MCP Integration

// Check for bottlenecks in Claude Code
mcp__claude-flow__bottleneck_detect({
  timeRange: "1h",
  threshold: 20,
  autoFix: false
})

// Get detailed task results with bottleneck analysis
mcp__claude-flow__task_results({
  taskId: "task-123",
  format: "detailed"
})

Result Format:

{
  "bottlenecks": [
    {
      "type": "coordination",
      "severity": "high",
      "description": "Single agent used for complex task",
      "recommendation": "Spawn specialized agents for parallel work",
      "impact": "35%",
      "affectedComponents": ["coordinator", "coder-1"]
    }
  ],
  "improvements": [
    {
      "area": "execution_time",
      "suggestion": "Use parallel task execution",
      "expectedImprovement": "30-50% time reduction",
      "implementationSteps": [
        "Split task into smaller units",
        "Spawn 3-4 specialized agents",
        "Use mesh topology for coordination"
      ]
    }
  ],
  "metrics": {
    "avgExecutionTime": "142s",
    "agentUtilization": "67%",
    "cacheHitRate": "82%",
    "parallelizationFactor": 1.2
  }
}

3. Report Generation

Command Syntax

npx claude-flow analysis performance-report [options]

Options

  • --format <type> - Report format: json, html, markdown (default: markdown)
  • --include-metrics - Include detailed metrics and charts
  • --compare <id> - Compare with previous swarm
  • --time-range <range> - Analysis period: 1h, 24h, 7d, 30d, all
  • --output <file> - Output file path
  • --sections <list> - Comma-separated sections to include

Report Sections

  1. Executive Summary

    • Overall performance score
    • Key metrics overview
    • Critical findings

  2. Swarm Overview

    • Topology configuration
    • Agent distribution
    • Task statistics

  3. Performance Metrics

    • Execution times
    • Throughput analysis
    • Resource utilization
    • Latency breakdown

  4. Bottleneck Analysis

    • Identified bottlenecks
    • Impact assessment
    • Optimization priorities

  5. Comparative Analysis (when --compare used)

    • Performance trends
    • Improvement metrics
    • Regression detection

  6. Recommendations

    • Prioritized action items
    • Expected improvements
    • Implementation guidance

Usage Examples

# Generate HTML report with all metrics
npx claude-flow analysis performance-report --format html --include-metrics

# Compare current swarm with previous
npx claude-flow analysis performance-report --compare swarm-123 --format markdown

# Custom output with specific sections
npx claude-flow analysis performance-report \
  --sections summary,metrics,recommendations \
  --output reports/perf-analysis.html \
  --format html

# Weekly performance report
npx claude-flow analysis performance-report \
  --time-range 7d \
  --include-metrics \
  --format markdown \
  --output docs/weekly-performance.md

# JSON format for CI/CD integration
npx claude-flow analysis performance-report \
  --format json \
  --output build/performance.json

Sample Markdown Report

# Performance Analysis Report

## Executive Summary
- **Overall Score**: 87/100
- **Analysis Period**: Last 24 hours
- **Swarms Analyzed**: 3
- **Critical Issues**: 1

## Key Metrics
| Metric | Value | Trend | Target |
|--------|-------|-------|--------|
| Avg Task Time | 42s | ↓ 12% | 35s |
| Agent Utilization | 78% | ↑ 5% | 85% |
| Cache Hit Rate | 91% | → | 90% |
| Parallel Efficiency | 2.3x | ↑ 0.4x | 2.5x |

## Bottleneck Analysis
### Critical
1. **Agent Communication Delay** (Impact: 35%)
   - Coordinator → Coder messages delayed by 2.3s avg
   - **Fix**: Switch to hierarchical topology

### Warnings
1. **Memory Access Pattern** (Impact: 18%)
   - Neural pattern loading: 1.8s per access
   - **Fix**: Enable memory caching

## Recommendations
1. **High Priority**: Switch to hierarchical topology (40% improvement)
2. **Medium Priority**: Enable memory caching (25% improvement)
3. **Low Priority**: Increase agent concurrency to 8 (20% improvement)

4. Optimization Recommendations

Automatic Fixes

When using --fix, the following optimizations may be applied:

1. Topology Optimization

  • Switch to more efficient topology (mesh → hierarchical)
  • Adjust communication patterns
  • Reduce coordination overhead
  • Optimize message routing

2. Caching Enhancement

  • Enable memory caching
  • Optimize cache strategies
  • Preload common patterns
  • Implement cache warming

3. Concurrency Tuning

  • Adjust agent counts
  • Optimize parallel execution
  • Balance workload distribution
  • Implement load balancing

4. Priority Adjustment

  • Reorder task queues
  • Prioritize critical paths
  • Reduce wait times
  • Implement fair scheduling

5. Resource Optimization

  • Optimize memory usage
  • Reduce I/O operations
  • Batch API calls
  • Implement connection pooling

Performance Impact

Typical improvements after bottleneck resolution:

  • Communication: 30-50% faster message delivery
  • Processing: 20-40% reduced task completion time
  • Memory: 40-60% fewer cache misses
  • Network: 25-45% reduced API latency
  • Overall: 25-45% total performance improvement

Advanced Usage

Continuous Monitoring

# Monitor performance in real-time
npx claude-flow swarm monitor --interval 5

# Generate hourly reports
while true; do
  npx claude-flow analysis performance-report \
    --format json \
    --output logs/perf-$(date +%Y%m%d-%H%M).json
  sleep 3600
done

CI/CD Integration

# .github/workflows/performance.yml
name: Performance Analysis
on: [push, pull_request]

jobs:
  analyze:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Run Performance Analysis
        run: |
          npx claude-flow analysis performance-report \
            --format json \
            --output performance.json
      - name: Check Performance Thresholds
        run: |
          npx claude-flow bottleneck detect \
            --threshold 15 \
            --export bottlenecks.json
      - name: Upload Reports
        uses: actions/upload-artifact@v2
        with:
          name: performance-reports
          path: |
            performance.json
            bottlenecks.json

Custom Analysis Scripts

// scripts/analyze-performance.js
const { exec } = require('child_process');
const fs = require('fs');

async function analyzePerformance() {
  // Run bottleneck detection
  const bottlenecks = await runCommand(
    'npx claude-flow bottleneck detect --format json'
  );

  // Generate performance report
  const report = await runCommand(
    'npx claude-flow analysis performance-report --format json'
  );

  // Analyze results
  const analysis = {
    bottlenecks: JSON.parse(bottlenecks),
    performance: JSON.parse(report),
    timestamp: new Date().toISOString()
  };

  // Save combined analysis
  fs.writeFileSync(
    'analysis/combined-report.json',
    JSON.stringify(analysis, null, 2)
  );

  // Generate alerts if needed
  if (analysis.bottlenecks.critical.length > 0) {
    console.error('CRITICAL: Performance bottlenecks detected!');
    process.exit(1);
  }
}

function runCommand(cmd) {
  return new Promise((resolve, reject) => {
    exec(cmd, (error, stdout, stderr) => {
      if (error) reject(error);
      else resolve(stdout);
    });
  });
}

analyzePerformance().catch(console.error);

Best Practices

1. Regular Analysis

  • Run bottleneck detection after major changes
  • Generate weekly performance reports
  • Monitor trends over time
  • Set up automated alerts

2. Threshold Tuning

  • Start with default threshold (20%)
  • Lower for production systems (10-15%)
  • Higher for development (25-30%)
  • Adjust based on requirements

3. Fix Strategy

  • Always review before applying --fix
  • Test fixes in development first
  • Apply fixes incrementally
  • Monitor impact after changes

4. Report Integration

  • Include in documentation
  • Share with team regularly
  • Track improvements over time
  • Use for capacity planning

5. Continuous Optimization

  • Learn from each analysis
  • Build performance budgets
  • Establish baselines
  • Set improvement goals

Troubleshooting

Common Issues

High Memory Usage

# Analyze memory bottlenecks
npx claude-flow bottleneck detect --threshold 10

# Check cache performance
npx claude-flow cache manage --action stats

# Review memory metrics
npx claude-flow memory usage

Slow Task Execution

# Identify slow tasks
npx claude-flow task status --detailed

# Analyze coordination overhead
npx claude-flow bottleneck detect --time-range 1h

# Check agent utilization
npx claude-flow agent metrics

Poor Cache Performance

# Analyze cache hit rates
npx claude-flow analysis performance-report --sections metrics

# Review cache strategy
npx claude-flow cache manage --action analyze

# Enable cache warming
npx claude-flow bottleneck detect --fix

Integration with Other Skills

  • swarm-orchestration: Use performance data to optimize topology
  • memory-management: Improve cache strategies based on analysis
  • task-coordination: Adjust scheduling based on bottlenecks
  • neural-training: Train patterns from performance data

Related Commands

  • npx claude-flow swarm monitor - Real-time monitoring
  • npx claude-flow token usage - Token optimization analysis
  • npx claude-flow cache manage - Cache optimization
  • npx claude-flow agent metrics - Agent performance metrics
  • npx claude-flow task status - Task execution analysis

See Also

  • Bottleneck Detection Guide
  • Performance Report Guide
  • Performance Bottlenecks Overview
  • Swarm Monitoring Documentation
  • Memory Management Documentation

Version: 1.0.0 Last Updated: 2025-10-19 Maintainer: Claude Flow Team

Repository
ruvnet/agentic-flow
Commit

8db2712

Last updated
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