InfraGenius - AI-Powered DevOps & SRE Intelligence Platform

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🚀 Transform your DevOps operations with AI-powered expertise

📚 Documentation • 🚀 Quick Start • 🗺️ Roadmap • 💬 Community • 🤝 Contributing

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🎯 Overview

InfraGenius is a comprehensive AI-powered platform designed specifically for DevOps, SRE, Cloud, and Platform Engineering professionals. It provides industry-level expertise through advanced AI models, optimized for infrastructure operations, reliability engineering, and cloud architecture.

🌟 Vision

To democratize intelligent infrastructure management by providing developers worldwide with AI-driven insights, automation, and best practices - making reliable, scalable infrastructure accessible to everyone.

📋 See our detailed 6-Month Roadmap for upcoming features and community goals!

🌟 Key Features

• 🤖 AI-Powered Analysis: Advanced DevOps/SRE expertise using open source models (gpt-oss:latest)
• 🏠 Local Development: Optimized for local development with Ollama - no cloud dependencies
• 🎯 Cursor Integration: Works as MCP server with Cursor for seamless AI assistance
• ⚡ High Performance: Sub-second response times with intelligent caching
• 🔓 Open Source: MIT licensed, community-driven development
• 📊 Multiple Domains: DevOps, SRE, Cloud Architecture, Platform Engineering expertise
• 🛠️ Developer Friendly: Comprehensive docs, examples, and development tools

🏗️ Architecture Overview

graph TB
    subgraph "Client Layer"
        UI[Web UI]
        API[REST API]
        CLI[CLI Tool]
    end
    
    subgraph "API Gateway"
        LB[Load Balancer]
        AUTH[Authentication]
        RATE[Rate Limiting]
    end
    
    subgraph "Application Layer"
        MCP1[MCP Server 1]
        MCP2[MCP Server 2]
        MCP3[MCP Server N]
    end
    
    subgraph "AI/ML Layer"
        OLLAMA[Ollama Service]
        MODELS[Fine-tuned Models]
        CACHE[Model Cache]
    end
    
    subgraph "Data Layer"
        POSTGRES[(PostgreSQL)]
        REDIS[(Redis Cache)]
        S3[(Object Storage)]
    end
    
    subgraph "Infrastructure"
        K8S[Kubernetes]
        DOCKER[Docker]
        CLOUD[Multi-Cloud]
    end
    
    subgraph "Monitoring"
        PROM[Prometheus]
        GRAF[Grafana]
        JAEGER[Jaeger]
    end
    
    UI --> LB
    API --> LB
    CLI --> LB
    
    LB --> AUTH
    AUTH --> RATE
    RATE --> MCP1
    RATE --> MCP2
    RATE --> MCP3
    
    MCP1 --> OLLAMA
    MCP2 --> OLLAMA
    MCP3 --> OLLAMA
    
    OLLAMA --> MODELS
    MODELS --> CACHE
    
    MCP1 --> POSTGRES
    MCP1 --> REDIS
    MCP2 --> POSTGRES
    MCP2 --> REDIS
    MCP3 --> POSTGRES
    MCP3 --> REDIS
    
    POSTGRES --> S3
    
    K8S --> CLOUD
    DOCKER --> K8S
    
    MCP1 --> PROM
    MCP2 --> PROM
    MCP3 --> PROM
    PROM --> GRAF
    MCP1 --> JAEGER
    
    %% Client Layer Styling
    style UI fill:#e3f2fd,stroke:#2196f3,stroke-width:2px
    style API fill:#e8f5e8,stroke:#4caf50,stroke-width:2px
    style CLI fill:#fff3e0,stroke:#ff9800,stroke-width:2px
    
    %% API Gateway Styling
    style LB fill:#ffcdd2,stroke:#d32f2f,stroke-width:3px
    style AUTH fill:#ffab91,stroke:#ff5722,stroke-width:2px
    style RATE fill:#80cbc4,stroke:#00695c,stroke-width:2px
    
    %% Application Layer Styling
    style MCP1 fill:#90caf9,stroke:#1976d2,stroke-width:3px
    style MCP2 fill:#a5d6a7,stroke:#388e3c,stroke-width:3px
    style MCP3 fill:#ffcc80,stroke:#f57c00,stroke-width:3px
    
    %% AI/ML Layer Styling
    style OLLAMA fill:#ce93d8,stroke:#7b1fa2,stroke-width:3px
    style MODELS fill:#f8bbd9,stroke:#c2185b,stroke-width:2px
    style CACHE fill:#b39ddb,stroke:#512da8,stroke-width:2px
    
    %% Data Layer Styling
    style POSTGRES fill:#81c784,stroke:#2e7d32,stroke-width:3px
    style REDIS fill:#ef5350,stroke:#c62828,stroke-width:3px
    style S3 fill:#ffb74d,stroke:#ef6c00,stroke-width:3px
    
    %% Infrastructure Styling
    style K8S fill:#42a5f5,stroke:#1565c0,stroke-width:3px
    style DOCKER fill:#29b6f6,stroke:#0277bd,stroke-width:2px
    style CLOUD fill:#66bb6a,stroke:#2e7d32,stroke-width:2px
    
    %% Monitoring Styling
    style PROM fill:#ff7043,stroke:#d84315,stroke-width:2px
    style GRAF fill:#ffa726,stroke:#ef6c00,stroke-width:2px
    style JAEGER fill:#ab47bc,stroke:#6a1b9a,stroke-width:2px

📁 Project Structure

InfraGenius/
├── 📁 environments/           # Environment-specific configurations
│   ├── 📁 test/              # Test environment configs
│   ├── 📁 staging/           # Staging environment configs
│   └── 📁 production/        # Production environment configs
├── 📁 src/                   # Source code
│   ├── 📁 core/              # Core application logic
│   ├── 📁 plugins/           # Extensible plugins
│   └── 📁 ui/                # Web interface
├── 📁 docker/                # Docker configurations
│   ├── 📁 development/       # Development containers
│   └── 📁 production/        # Production containers
├── 📁 kubernetes/            # K8s manifests
│   ├── 📁 test/              # Test cluster configs
│   ├── 📁 staging/           # Staging cluster configs
│   └── 📁 production/        # Production cluster configs
├── 📁 docs/                  # Documentation
│   ├── 📁 architecture/      # Architecture diagrams
│   ├── 📁 api/               # API documentation
│   └── 📁 deployment/        # Deployment guides
├── 📁 tests/                 # Test suites
│   ├── 📁 unit/              # Unit tests
│   ├── 📁 integration/       # Integration tests
│   └── 📁 e2e/               # End-to-end tests
├── 📁 scripts/               # Automation scripts
│   ├── 📁 setup/             # Setup and installation
│   ├── 📁 deploy/            # Deployment automation
│   └── 📁 utils/             # Utility scripts
├── 📁 monitoring/            # Monitoring configurations
│   ├── 📁 grafana/           # Grafana dashboards
│   └── 📁 prometheus/        # Prometheus configs
├── 📁 security/              # Security configurations
├── 📁 backup/                # Backup and recovery
├── 📁 migrations/            # Database migrations
├── 📁 examples/              # Usage examples
├── 📁 tools/                 # Development tools
└── 📄 README.md              # This file

🚀 Quick Start

🎯 Focus: InfraGenius is currently optimized for local development with Ollama and open source models. This is perfect for learning, contributing, and building amazing DevOps/SRE solutions locally!

⚡ One-Click Local Setup

# Clone the repository
git clone https://github.com/your-username/infragenius.git
cd infragenius

# 🚀 One-click setup (installs everything automatically)
./scripts/quick-local-setup.sh

# 🎉 That's it! Server will start automatically
# 📊 Health check: http://localhost:8000/health
# 📚 API docs: http://localhost:8000/docs

🛠️ Manual Setup (Step by Step)

1. Install Ollama

# macOS
brew install ollama

# Linux
curl -fsSL https://ollama.ai/install.sh | sh

# Windows
winget install ollama

2. Start Ollama & Download Model

# Start Ollama service
ollama serve

# Download AI model (in new terminal)
ollama pull gpt-oss:latest

# Verify model is ready
ollama list

3. Setup InfraGenius

# Create Python environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt
pip install -r requirements-dev.txt

# Create configuration
cp mcp_server/config.json.example mcp_server/config.json

# Start InfraGenius
python mcp_server/server.py

4. Test Your Setup

# Test API health
curl http://localhost:8000/health

# Test AI analysis
curl -X POST http://localhost:8000/analyze \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "My Kubernetes pods are crashing with OOMKilled errors", 
    "domain": "devops",
    "context": "Production cluster on AWS EKS"
  }'

🎯 Cursor Integration (MCP Server)

InfraGenius works as an MCP (Model Context Protocol) server with Cursor, giving you a specialized DevOps/SRE AI assistant directly in your IDE!

🚀 Quick Setup

# 1. Setup Cursor integration
make cursor-setup

# 2. Install MCP dependency
source venv/bin/activate
pip install mcp

# 3. Test the integration
python -m mcp_server.cursor_integration

⚙️ Cursor Configuration

Add InfraGenius to your Cursor MCP configuration file at ~/.cursor/mcp.json:

{
  "mcpServers": {
    "infragenius": {
      "command": "<file_path>/InfraGenius/venv/bin/python",
      "args": [
        "-m", "mcp_server.cursor_integration"
      ],
      "cwd": "<file_path>/InfraGenius",
      "env": {
        "OLLAMA_BASE_URL": "http://localhost:11434",
        "OLLAMA_MODEL": "gpt-oss:latest",
        "PYTHONPATH": "<file_path>/InfraGenius"
      }
    }
  }
}

📝 Replace YOUR_USERNAME with your actual username!

💡 Quick Copy: Use the template at examples/cursor-mcp-template.json and update the paths.

🔄 Adding to Existing MCP Configuration

If you already have other MCP servers configured, just add the infragenius entry to your existing mcpServers object:

{
  "mcpServers": {
    "existing-server": {
      "command": "some-other-mcp-server",
      "args": ["..."]
    },
    "infragenius": {
      "command": "<file_path>/InfraGenius/venv/bin/python",
      "args": ["-m", "mcp_server.cursor_integration"],
      "cwd": "<file_path>/InfraGenius",
      "env": {
        "OLLAMA_BASE_URL": "http://localhost:11434",
        "OLLAMA_MODEL": "gpt-oss:latest"
      }
    }
  }
}

🎯 Usage in Cursor

Once configured, use InfraGenius tools directly in Cursor:

// DevOps Issue Analysis
@infragenius analyze_devops_issue {
  "prompt": "My Kubernetes pods are crashing with OOMKilled",
  "context": "Production EKS cluster with 50+ microservices",
  "urgency": "high"
}

// SRE Incident Response  
@infragenius analyze_sre_incident {
  "incident": "Database connection pool exhausted",
  "severity": "critical",
  "affected_services": "user-service, payment-service"
}

// Cloud Architecture Review
@infragenius review_cloud_architecture {
  "architecture": "3-tier web app on AWS with RDS and ElastiCache",
  "cloud_provider": "aws", 
  "focus_area": "cost"
}

// Generate Configurations
@infragenius generate_config {
  "tool": "kubernetes",
  "requirements": "Redis cluster with persistence and monitoring",
  "environment": "production"
}

// Log Analysis
@infragenius explain_logs {
  "logs": "ERROR: Connection timeout after 30s in database pool",
  "log_type": "application"
}

// Platform Engineering Advice
@infragenius platform_engineering_advice {
  "challenge": "Improve developer onboarding and reduce time-to-first-commit",
  "team_size": "30 developers",
  "tech_stack": "Node.js, React, Kubernetes, PostgreSQL"
}

🛠️ Available Tools

| Tool | Purpose | Best For | |------|---------|----------| | 🔧 analyze_devops_issue | DevOps problem solving | CI/CD issues, deployment problems | | 🚨 `a