# Claude Multi-Agent System Benchmarks

```
┌──────────────────────────────────────────────────────────────────────────┐
│                                                                          │
│               🚀 Claude Multi-Agent System Benchmark Suite              │
│                                                                          │
│  ┌────────────┐    ┌────────────┐    ┌────────────┐    ┌────────────┐ │
│  │            │    │            │    │            │    │            │ │
│  │  15 Agents │───▶│ 6 Workflows│───▶│  9 Skills  │───▶│ Benchmarks │ │
│  │            │    │            │    │            │    │            │ │
│  └────────────┘    └────────────┘    └────────────┘    └────────────┘ │
│                                                                          │
│  ┌─────────────────────────────────────────────────────────────────┐   │
│  │  📊 Performance Metrics                                         │   │
│  │  • Agent Selection: 75% accuracy, 0.01ms speed                  │   │
│  │  • Scenarios: 4 ready (3 simple, 1 medium)                      │   │
│  │  • Coverage: 100% agents in workflows                           │   │
│  └─────────────────────────────────────────────────────────────────┘   │
│                                                                          │
└──────────────────────────────────────────────────────────────────────────┘
```

## Overview

This benchmark suite demonstrates and measures the capabilities of the Claude multi-agent system across real-world software development scenarios.

## Benchmark Categories

### 1. Real-World Task Scenarios (`scenarios/`)
Complete software development tasks from start to finish:
- Simple: Add API endpoint, fix bug, update documentation
- Medium: Build feature with database, create microservice
- Complex: Full-stack feature with security, testing, deployment

### 2. Performance Benchmarks (`metrics/`)
Quantitative measurements:
- Agent selection accuracy and speed
- Task completion time
- Quality scores (test coverage, security, code quality)
- Cost efficiency (tokens used, API calls)

### 3. Quality Comparisons (`reports/comparisons/`)
Side-by-side comparisons:
- With vs without skills documentation
- Single agent vs multi-agent workflow
- With vs without code review agent
- Different workflow configurations

### 4. Interactive Demo (`scripts/demo/`)
Live demonstrations:
- Agent selection process
- Workflow execution with real-time updates
- Decision tree visualization
- Quality gate validation

### 5. Success Metrics Dashboard (`reports/dashboard/`)
Aggregated success metrics:
- Test coverage achieved
- Security vulnerabilities found/fixed
- Code quality scores
- Documentation completeness
- Overall task success rate

## Directory Structure

```
benchmarks/
├── README.md                    # This file
├── scenarios/                   # Real-world task scenarios
│   ├── simple/                  # Basic tasks (1-2 agents)
│   ├── medium/                  # Medium complexity (3-5 agents)
│   └── complex/                 # Complex tasks (6+ agents)
├── metrics/                     # Performance measurement tools
│   ├── agent_selection.py       # Measure agent selection speed/accuracy
│   ├── task_completion.py       # Measure task completion metrics
│   ├── quality_metrics.py       # Code quality, security, coverage
│   └── cost_analysis.py         # Token usage, API call tracking
├── reports/                     # Generated reports and results
│   ├── comparisons/             # Quality comparison reports
│   ├── dashboard/               # Success metrics dashboard
│   └── results/                 # Individual benchmark results
└── scripts/                     # Helper scripts
    ├── demo/                    # Interactive demo scripts
    ├── run_all.py               # Run all benchmarks
    └── generate_report.py       # Generate summary reports

```

## Getting Started

### Quick Start Commands

```bash
# 1. Run all benchmarks
python3 benchmarks/scripts/run_all.py

# 2. Generate visual terminal report
python3 benchmarks/scripts/generate_visual_report.py

# 3. Generate interactive HTML dashboard
python3 benchmarks/scripts/generate_dashboard.py

# 4. Open dashboard in browser
open benchmarks/reports/dashboard/index.html
```

### 📸 Screenshots & Recordings

See `benchmarks/screenshots/README.md` for:
- How to capture screenshots
- Screen recording tips
- Visual asset guidelines
- Demo video creation

Example visual output:
![Terminal Visual Report](screenshots/05_terminal_benchmark_run.png) *(capture this!)*

## Benchmark Progression

```
┌─────────────────────────────────────────────────────────────────────────┐
│                      Scenario Complexity Ladder                         │
├─────────────────────────────────────────────────────────────────────────┤
│                                                                         │
│  🟢 SIMPLE (1-2 agents, 5-10 min)                                      │
│  ├─ Add API Endpoint           [backend-architect]                     │
│  ├─ Fix Validation Bug         [bug-investigator → code-reviewer]      │
│  └─ Update Documentation       [api-documenter]                        │
│                                                                         │
│  🟡 MEDIUM (3-5 agents, 15-25 min)                                     │
│  ├─ User Authentication        [backend → database → security →        │
│  │                              implementation → review]                │
│  └─ Feature with Tests         [architect → developer → qc]            │
│                                                                         │
│  🔴 COMPLEX (6+ agents, 30+ min)                                       │
│  ├─ Full-Stack Feature         [frontend → backend → database →        │
│  │                              security → implementation → testing →   │
│  │                              review → deployment]                    │
│  └─ Microservice               [architects → implementation →          │
│                                 containerization → testing → deploy]    │
│                                                                         │
└─────────────────────────────────────────────────────────────────────────┘
```

### Level 1: Simple (Basic Demonstrations)
- Single-agent tasks
- Clear success criteria
- Quick execution (< 5 minutes)
- Example: Add health check endpoint

### Level 2: Medium (Multi-Agent Workflows)
- 3-5 agent coordination
- Multiple quality gates
- Moderate execution (5-15 minutes)
- Example: Build user authentication feature

### Level 3: Complex (Full System Demonstration)
- 6+ agent workflows
- Comprehensive quality validation
- Full execution (15-30 minutes)
- Example: Complete microservice with testing and deployment

## Metrics Tracked

### Performance
- Agent selection time
- Task completion time
- Token usage
- API calls made

### Quality
- Test coverage percentage
- Security vulnerabilities (found/remaining)
- Code quality score (linting, formatting)
- Documentation completeness

### Success Rate
- Tasks completed successfully
- Quality gates passed
- User requirements met
- Production-ready output

## 📸 Visual Outputs

### Terminal Visual Report
Running `python3 benchmarks/scripts/generate_visual_report.py` produces:

```
================================================================================
                  🚀 CLAUDE MULTI-AGENT SYSTEM BENCHMARK REPORT
================================================================================

────────────────────────────────────────────────────────────────────────────────
  📊 SYSTEM OVERVIEW
────────────────────────────────────────────────────────────────────────────────

┌─────────────────────────────────────────────────────────────────────────┐
│    Agents Configured:  15          Workflows:  6                    │
│    Skills Available:    9          Scenarios:  4                    │
└─────────────────────────────────────────────────────────────────────────┘

Average Accuracy               ██████████████████████████████░░░░░░░░░░  75.0%
```

### Interactive Dashboard
The HTML dashboard (`benchmarks/reports/dashboard/index.html`) includes:

- **Executive Summary Cards**: Beautiful gradient cards showing key metrics
- **Performance Charts**: Visual accuracy and timing metrics with progress bars
- **Accuracy Distribution**: Color-coded breakdown (✅ high, ⚠️ medium, ❌ low)
- **Scenario Catalog**: Filterable table with status badges
- **Detailed Results**: Full test results with agent selections

**Features**:
- Responsive design (mobile-friendly)
- Hover effects on metric cards
- Color-coded badges for quick status identification
- Clean, professional typography
- Export-ready for presentations

### Screenshots to Capture

1. **Dashboard Overview** - Full page showing all sections
2. **Agent Selection Metrics** - Accuracy charts and distribution
3. **Terminal Output** - Beautiful ASCII charts from visual report
4. **Scenario Details** - Individual scenario documentation
5. **Test Results** - Detailed test execution results

See `benchmarks/screenshots/README.md` for detailed capture instructions.

## 🎬 Creating Demo Videos

### Recommended Flow (30-60 seconds)

```bash
# 1. Show directory structure
tree benchmarks/ -L 2

# 2. Run benchmarks with visible output
python3 benchmarks/scripts/run_all.py

# 3. Generate beautiful terminal report
python3 benchmarks/scripts/generate_visual_report.py

# 4. Generate dashboard
python3 benchmarks/scripts/generate_dashboard.py

# 5. Open dashboard (show scrolling through metrics)
open benchmarks/reports/dashboard/index.html
```

### Recording Tools
- **macOS**: Kap (https://getkap.co/) - Great for GIFs
- **Cross-platform**: OBS Studio - Professional recording
- **Linux**: Peek - Simple GIF recorder
- **Terminal**: asciinema (https://asciinema.org/) - Record terminal sessions

## Version History

- **1.0.0** (2025-11-13): Initial benchmark suite
  - 3 simple scenarios
  - 1 medium scenario
  - Agent selection metrics
  - Interactive HTML dashboard
  - Beautiful terminal visual report
  - Screenshot/recording guidelines

---

**Maintained By**: Development Team
**Last Updated**: 2025-11-13
