🚀 Signal Box Cost Analyzer

A transparent AI cost analysis web application that shows exactly how much your AI workflows cost and how to optimize them.

✨ Features

• 🔍 Automatic Framework Detection - Supports AutoGen, LangChain, CrewAI, and custom implementations
• 💰 Transparent Cost Calculations - See every step of the cost analysis
• 🎯 Smart Optimizations - Model substitution, semantic caching, token optimization
• 📊 Beautiful Reports - Shareable HTML and JSON reports
• ⚡ Fast Analysis - Results in under 30 seconds

🎯 Demonstrated Results

• 78% cost reduction on AutoGen workflows
• 74% cost reduction on LangChain workflows
• 70% cost reduction on CrewAI workflows
• 100% transparent calculations with full audit trails

🚀 Quick Start

Prerequisites

• Python 3.11+
• Node.js 18+
• Signal Box core library

Installation

1. Install Signal Box dependency:

pip install git+https://github.com/wespiper/signal-box.git

2. Clone this repository:

git clone https://github.com/yourusername/signal-box-analyzer-app.git
cd signal-box-analyzer-app

3. Install backend dependencies:

pip install -r requirements.txt

4. Install frontend dependencies:

cd frontend
npm install

Running the Application

1. Start the backend:

python -m uvicorn api.main:app --reload --host 0.0.0.0 --port 8000

2. Start the frontend:

cd frontend
npm run dev

3. Open your browser:
   • Frontend: http://localhost:5173
   • API Documentation: http://localhost:8000/docs

🏗️ Architecture

signal-box-analyzer-app/
├── api/                    # FastAPI backend
│   ├── main.py            # API server
│   ├── routes/            # API endpoints
│   └── services/          # GitHub integration
├── frontend/              # Vite + React frontend
│   ├── src/
│   │   ├── pages/         # Home & Analysis pages
│   │   ├── components/    # Reusable components
│   │   └── services/      # API integration
├── reports/               # Generated analysis reports
└── tests/                 # Test files

🔧 How It Works

1. Framework Detection - Analyzes your GitHub repository to detect AI frameworks
2. Component Extraction - Identifies agents, chains, models, and configurations
3. Cost Calculation - Calculates baseline costs with transparent methodology
4. Optimization Analysis - Applies Signal Box optimizations
5. Report Generation - Creates beautiful, shareable reports

📊 Example Analysis

# Input: GitHub repository URL
https://github.com/microsoft/autogen

# Output: Cost analysis showing
- Baseline cost: $0.1927 per run
- Optimized cost: $0.0424 per run  
- Savings: $0.1503 (78% reduction)
- Specific optimizations applied
- Implementation recommendations

🌐 Deployment

See DEPLOYMENT_GUIDE.md for detailed deployment instructions including:

• Vercel + Railway (recommended)
• Single server with Nginx
• Docker containerization
• Environment configuration

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built on top of Signal Box - the intelligent orchestration engine
• Supports popular AI frameworks: AutoGen, LangChain, CrewAI
• Inspired by the need for transparent AI cost optimization

🔗 Links

• Signal Box Core
• Live Demo (coming soon)
• Documentation (coming soon)
• API Reference (coming soon)