https://github.com/aryasoni98/infragenius
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.
https://github.com/aryasoni98/infragenius
ai-agents aws azure cloud devops devops-tools gcp mcp mcp-server plaftorm sre sre-team
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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.
- Host: GitHub
- URL: https://github.com/aryasoni98/infragenius
- Owner: aryasoni98
- License: mit
- Created: 2025-08-09T12:16:34.000Z (11 months ago)
- Default Branch: master
- Last Pushed: 2025-09-07T04:17:16.000Z (10 months ago)
- Last Synced: 2025-09-07T06:10:59.091Z (10 months ago)
- Topics: ai-agents, aws, azure, cloud, devops, devops-tools, gcp, mcp, mcp-server, plaftorm, sre, sre-team
- Language: Python
- Homepage:
- Size: 339 KB
- Stars: 2
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
- Roadmap: ROADMAP.md
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README
# InfraGenius - AI-Powered DevOps & SRE Intelligence Platform
[](https://opensource.org/licenses/MIT)
[](https://codecov.io/gh/infragenius/infragenius)
[](https://docker.com)
[](https://kubernetes.io)
[](SECURITY.md)
[](CONTRIBUTING.md)
**🚀 Transform your DevOps operations with AI-powered expertise**
[📚 Documentation](https://infragenius.github.io/infragenius) •
[🚀 Quick Start](#-quick-start) •
[🗺️ Roadmap](ROADMAP.md) •
[💬 Community](https://discord.gg/infragenius) •
[🤝 Contributing](CONTRIBUTING.md)
## 🎯 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](ROADMAP.md)** 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
```mermaid
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
```bash
# 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**
```bash
# macOS
brew install ollama
# Linux
curl -fsSL https://ollama.ai/install.sh | sh
# Windows
winget install ollama
```
#### 2. **Start Ollama & Download Model**
```bash
# 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**
```bash
# 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**
```bash
# 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**
```bash
# 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`:
```json
{
"mcpServers": {
"infragenius": {
"command": "/InfraGenius/venv/bin/python",
"args": [
"-m", "mcp_server.cursor_integration"
],
"cwd": "/InfraGenius",
"env": {
"OLLAMA_BASE_URL": "http://localhost:11434",
"OLLAMA_MODEL": "gpt-oss:latest",
"PYTHONPATH": "/InfraGenius"
}
}
}
}
```
**📝 Replace `YOUR_USERNAME` with your actual username!**
**💡 Quick Copy**: Use the template at [`examples/cursor-mcp-template.json`](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:
```json
{
"mcpServers": {
"existing-server": {
"command": "some-other-mcp-server",
"args": ["..."]
},
"infragenius": {
"command": "/InfraGenius/venv/bin/python",
"args": ["-m", "mcp_server.cursor_integration"],
"cwd": "/InfraGenius",
"env": {
"OLLAMA_BASE_URL": "http://localhost:11434",
"OLLAMA_MODEL": "gpt-oss:latest"
}
}
}
}
```
#### **🎯 Usage in Cursor**
Once configured, use InfraGenius tools directly in Cursor:
```javascript
// 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 |
| 🚨 `analyze_sre_incident` | Incident response guidance | Outages, performance issues, alerts |
| ☁️ `review_cloud_architecture` | Architecture analysis | Cost optimization, security, scaling |
| ⚙️ `generate_config` | Configuration generation | K8s manifests, Docker, Terraform |
| 📋 `explain_logs` | Log analysis & debugging | Error investigation, troubleshooting |
| 🏗️ `platform_engineering_advice` | Platform guidance | Developer experience, internal tools |
#### **✅ Verification Steps**
1. **Restart Cursor** completely after updating mcp.json
2. **Check MCP Status** in Cursor's settings/extensions
3. **Test Integration**: Type `@infragenius` in any chat
4. **Verify Tools**: You should see tool suggestions appear
#### **🔧 Troubleshooting**
**If `@infragenius` doesn't appear:**
```bash
# Check if integration works
cd /path/to/InfraGenius
source venv/bin/activate
python -c "import mcp_server.cursor_integration; print('✅ Integration OK')"
# Verify Ollama is running
curl http://localhost:11434/api/tags
# Check your paths in mcp.json are correct
```
**Common Issues:**
- ❌ **Wrong paths** in mcp.json → Update to your actual paths
- ❌ **Virtual env not activated** → Use full path to venv/bin/python
- ❌ **Ollama not running** → Start with `ollama serve`
- ❌ **Model not available** → Download with `ollama pull gpt-oss:latest`
#### **💡 Pro Tips**
- **Combine with other MCP servers** - InfraGenius works alongside other AI models
- **Use specific tools** - Each tool is optimized for different scenarios
- **Provide context** - More context = better, more actionable responses
- **Save configurations** - Generated configs can be saved directly to files
### Docker Deployment
```bash
# Development environment
docker-compose -f docker/development/docker-compose.yml up
# Production environment
docker-compose -f docker/production/docker-compose.yml up
```
### Kubernetes Deployment
```bash
# Test environment
kubectl apply -f kubernetes/test/
# Staging environment
kubectl apply -f kubernetes/staging/
# Production environment
kubectl apply -f kubernetes/production/
```
## 🌍 Environment Management
### Test Environment
- **Purpose**: Development and testing
- **Resources**: Minimal (1 CPU, 2GB RAM)
- **Data**: Synthetic test data
- **Monitoring**: Basic metrics
```bash
./scripts/deploy/deploy-test.sh
```
### Staging Environment
- **Purpose**: Pre-production validation
- **Resources**: Production-like (2 CPU, 4GB RAM)
- **Data**: Anonymized production data
- **Monitoring**: Full observability stack
```bash
./scripts/deploy/deploy-staging.sh
```
### Production Environment
- **Purpose**: Live customer traffic
- **Resources**: Auto-scaling (2-20 instances)
- **Data**: Live production data
- **Monitoring**: Enterprise monitoring + alerting
```bash
./scripts/deploy/deploy-production.sh
```
## 🛠️ Core Features
### 🤖 AI-Powered DevOps Analysis
```python
# Example API usage
import requests
response = requests.post('http://localhost:8080/analyze', {
"prompt": "My Kubernetes pods are crashing with OOMKilled errors",
"domain": "devops",
"context": "Production cluster with 100+ microservices"
})
print(response.json())
# Returns detailed analysis with:
# - Root cause identification
# - Step-by-step resolution
# - Prevention strategies
# - Best practices
```
### 📊 Comprehensive Domains
| Domain | Features | Use Cases |
|--------|----------|-----------|
| **DevOps** | CI/CD, IaC, Automation | Pipeline optimization, deployment strategies |
| **SRE** | Reliability, Incidents, SLOs | Incident response, reliability improvements |
| **Cloud** | Architecture, Security, Cost | Cloud migration, cost optimization |
| **Platform** | Developer Experience, APIs | Platform design, developer productivity |
### ⚡ Performance Features
- **Sub-second response times** with intelligent caching
- **Auto-scaling** based on demand (2-100 instances)
- **Multi-level caching** (Redis + in-memory)
- **Connection pooling** for optimal resource usage
- **Async processing** for high throughput
- **Response streaming** for large analyses
### 🔒 Enterprise Security
- **JWT authentication** with refresh tokens
- **Role-based access control** (RBAC)
- **Rate limiting** by user tier
- **API key management**
- **Audit logging** for compliance
- **Data encryption** at rest and in transit
- **Network security** with VPC and firewalls
## 📊 Monitoring & Observability
### Metrics & Dashboards
- **Prometheus** metrics collection
- **Grafana** dashboards and visualization
- **Application performance** monitoring
- **Infrastructure** monitoring
- **Business metrics** tracking
- **Custom alerts** and notifications
### Health Checks
```bash
# System health
curl http://localhost:8080/health
# Detailed health check
curl http://localhost:8080/health/detailed
# Metrics endpoint
curl http://localhost:8080/metrics
```
## 🔧 Dependencies
### Core Dependencies
```json
{
"runtime": {
"python": ">=3.11",
"docker": ">=20.10",
"kubernetes": ">=1.24"
},
"databases": {
"postgresql": ">=15",
"redis": ">=7.0"
},
"ai_models": {
"ollama": ">=0.1.0",
"gpt-oss": "latest"
},
"monitoring": {
"prometheus": ">=2.40",
"grafana": ">=9.0"
}
}
```
### System Requirements
| Component | Minimum | Recommended | Production |
|-----------|---------|-------------|------------|
| **CPU** | 2 cores | 4 cores | 8+ cores |
| **Memory** | 4GB | 8GB | 16+ GB |
| **Storage** | 20GB | 50GB | 100+ GB |
| **Network** | 1Mbps | 10Mbps | 100+ Mbps |
## 🚀 Deployment Options
### Local Development
```bash
# Quick start for development
./scripts/setup/local-dev.sh
# With monitoring stack
./scripts/setup/local-dev.sh --monitoring
# With sample data
./scripts/setup/local-dev.sh --sample-data
```
### Cloud Deployment
#### AWS
```bash
./scripts/deploy/aws-deploy.sh \
--region us-east-1 \
--environment production \
--instance-type t3.large
```
#### Google Cloud
```bash
./scripts/deploy/gcp-deploy.sh \
--region us-central1 \
--environment production \
--machine-type e2-standard-4
```
#### Azure
```bash
./scripts/deploy/azure-deploy.sh \
--region eastus \
--environment production \
--vm-size Standard_D4s_v3
```
### On-Premises
```bash
./scripts/deploy/on-premises.sh \
--kubernetes-config ~/.kube/config \
--storage-class fast-ssd
```
## 🧪 Testing
### Unit Tests
```bash
# Run all unit tests
pytest tests/unit/
# Run with coverage
pytest tests/unit/ --cov=src --cov-report=html
```
### Integration Tests
```bash
# Run integration tests
pytest tests/integration/
# Test specific service
pytest tests/integration/test_ollama_integration.py
```
### End-to-End Tests
```bash
# Run E2E tests
pytest tests/e2e/
# Run against specific environment
pytest tests/e2e/ --env=staging
```
### Performance Tests
```bash
# Load testing
./scripts/utils/load-test.sh --concurrent=50 --requests=1000
# Stress testing
./scripts/utils/stress-test.sh --duration=300s
```
## 📚 Documentation
### Architecture
- [System Architecture](docs/architecture/system-design.md)
- [Database Schema](docs/architecture/database-schema.md)
- [API Design](docs/architecture/api-design.md)
- [Security Model](docs/architecture/security-model.md)
### API Documentation
- [REST API Reference](docs/api/rest-api.md)
- [WebSocket API](docs/api/websocket-api.md)
- [Authentication](docs/api/authentication.md)
- [Rate Limiting](docs/api/rate-limiting.md)
### Deployment
- [Local Setup](docs/deployment/local-setup.md)
- [Docker Deployment](docs/deployment/docker.md)
- [Kubernetes Deployment](docs/deployment/kubernetes.md)
- [Cloud Deployment](docs/deployment/cloud.md)
## 🤝 Contributing
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
### Development Workflow
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request
### Code Standards
- **Python**: Follow PEP 8
- **Testing**: Minimum 80% coverage
- **Documentation**: Document all public APIs
- **Commits**: Use conventional commits
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🆘 Support
### Community Support
- 📚 [Documentation](docs/)
- 💬 [GitHub Discussions](https://github.com/aryasoni98/infragenius/discussions)
- 🐛 [GitHub Issues](https://github.com/aryasoni98/infragenius/issues)
- 🤝 [Contributing Guide](CONTRIBUTING.md)
## 📊 Performance Benchmarks
| Metric | Performance |
|--------|------------|
| **Response Time** | <2s |
| **Throughput** | 100+ req/s |
| **Uptime** | 99.5%+ |
| **Support Response** | Community-driven |
## 🚀 Get Started Today!
Choose your deployment option and get started in minutes:
```bash
# Quick local setup
curl -sSL https://get.# | bash
# Or manual setup
git clone https://github.com/your-org/infragenius.git
cd infragenius
./scripts/setup/quick-start.sh
```
**Transform your DevOps operations with AI-powered expertise!** 🎉
---