🕹️ Kubernetes AI Copilot

AI-powered Kubernetes resource optimization tool that analyzes pod metrics and provides intelligent recommendations using GPT-4/Claude for cost reduction and performance improvement.

Interactive dashboard showing cluster metrics, AI recommendations, and resource optimization insights

✨ Features

• 📊 Real-time Cluster Monitoring - Mock data simulation with realistic metrics
• 🤖 AI-Powered Recommendations - OpenAI GPT-4 and Anthropic Claude integration
• 📈 Interactive Dashboard - Streamlit-based web interface with visualizations
• 🔧 YAML Generation - Automatic generation of optimized resource configurations
• 📋 Resource Analysis - Identify over-provisioned and under-provisioned workloads
• 💰 Cost Optimization - Calculate potential savings and ROI
• 🎯 Risk Assessment - Evaluate implementation risks and mitigation strategies

🚀 Quick Start

Prerequisites

• Python 3.9 or higher
• pip package manager

Installation

Option 1: Quick Start (Recommended)

git clone https://github.com/apollofps/kubernetes-ai-copilot.git
cd kubernetes-ai-copilot
chmod +x quick_start.sh
./quick_start.sh

Option 2: Manual Installation

1. Clone the Repository

   git clone https://github.com/apollofps/kubernetes-ai-copilot.git
   cd kubernetes-ai-copilot

2. Install Dependencies

   pip install -r requirements.txt

3. Configure API Keys (Optional)

   cp .env.example .env
   # Edit .env and add your OpenAI and/or Anthropic API keys

4. Run the Application

   streamlit run app.py

5. Open in Browser Navigate to http://localhost:8501

Demo Mode

Run the demo script to see all features without API keys:

python3 demo.py

📸 Screenshots

Dashboard Overview

Main dashboard showing cluster overview, efficiency metrics, and optimization opportunities

Recommendations Analysis

Detailed recommendations table with severity indicators and potential savings

Interactive Visualizations

Resource utilization heatmap and trend analysis charts

Configuration

The application supports both OpenAI and Anthropic Claude APIs. Configure your preferred provider in the .env file:

• OPENAI_API_KEY - Your OpenAI API key
• ANTHROPIC_API_KEY - Your Anthropic API key
• DEFAULT_LLM_PROVIDER - Set to "openai" or "anthropic"

Mock Data

This POC uses realistic mock data including:

• 3-5 namespaces with 10-20 pods each
• Various workload types (microservices, databases, batch jobs)
• Resource utilization patterns and issues
• Historical metrics for trend analysis

Project Structure

├── app.py                    # Main Streamlit application
├── requirements.txt          # Python dependencies
├── .env.example             # Environment variables template
├── src/
│   ├── mock_data.py         # Mock cluster data generation
│   ├── metrics_analyzer.py  # Resource analysis engine
│   ├── llm_client.py        # LLM integration
│   ├── yaml_generator.py    # YAML diff generation
│   └── recommendations.py   # Recommendation processing
└── config/
    └── mock_clusters.yaml   # Mock cluster configurations

Usage

1. Select LLM Provider - Choose between OpenAI or Claude in the sidebar
2. Filter Data - Use namespace and severity filters to focus on specific issues
3. Review Recommendations - Browse AI-generated optimization suggestions
4. Analyze Details - Click on recommendations for detailed analysis and YAML diffs
5. Export Changes - Copy or download optimized YAML configurations

🛠️ Technology Stack

• Frontend: Streamlit, Plotly
• Backend: Python 3.9+
• AI/ML: OpenAI GPT-4, Anthropic Claude
• Data Processing: Pandas, NumPy
• Configuration: PyYAML, python-dotenv
• Visualization: Plotly, Streamlit Charts

📊 Demo Results

The application successfully demonstrates:

• ✅ 41 pods generated across 4 namespaces
• ✅ 73.8% overall efficiency with optimization opportunities
• ✅ $2,577.53/month in potential savings identified
• ✅ 1,508.8% annual ROI with 0.8-month payback period
• ✅ Complete YAML generation with kubectl commands

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

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

🙏 Acknowledgments

• OpenAI for GPT-4 API
• Anthropic for Claude API
• Streamlit team for the amazing web framework
• Kubernetes community for inspiration