A comprehensive DevOps automation framework using Mistral LLM and Model Context Protocol (MCP) for intelligent CI/CD pipeline monitoring, failure detection, and automated remediation.

DevOps-GPT integrates artificial intelligence into DevOps workflows to:

• Detect deployment failures early using real-time log analysis
• Automate root cause identification with LLM-powered diagnostics
• Provide actionable insights for quick issue resolution
• Reduce system downtime through intelligent automation
• Seamlessly integrate with existing CI/CD pipelines

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Data Sources  │────│  MCP Ingestion   │────│  AI Analysis    │
│  • Jenkins      │    │  • Log Parsing   │    │  • Mistral LLM  │
│  • Prometheus   │    │  • Metrics       │    │  • Pattern      │
│  • Git Logs     │    │  • Normalization │    │    Detection    │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                                  │
┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Resolution    │────│  Decision Engine │────│  Real-time      │
│  • Auto-fix     │    │  • Risk Analysis │    │  Monitoring     │
│  • Rollback     │    │  • Recommendations│    │  • Alerts       │
│  • Scaling      │    │  • Validation    │    │  • Dashboards   │
└─────────────────┘    └──────────────────┘    └─────────────────┘

• Real-time Log Analysis: Process CI/CD logs using Mistral LLM
• Anomaly Detection: ML-based pattern recognition for failure prediction
• Automated Root Cause Analysis: Intelligent correlation of events and logs
• Smart Remediation: AI-generated fix suggestions and automated responses
• Pipeline Integration: Seamless integration with Jenkins, GitHub Actions, Terraform

• Natural Language Processing: Parse and understand deployment logs
• Predictive Analytics: Forecast potential deployment issues
• Context-Aware Decisions: Use historical data for better recommendations
• Continuous Learning: Improve accuracy through feedback loops

• AI/ML: Mistral LLM, scikit-learn, pandas, numpy
• Backend: Python 3.9+, FastAPI, SQLAlchemy
• Message Queue: Redis, Celery
• Monitoring: Prometheus, Grafana
• CI/CD: Jenkins, GitHub Actions
• Infrastructure: Docker, Kubernetes, Terraform
• Database: PostgreSQL, InfluxDB (time-series)

• Python 3.9 or higher
• Docker and Docker Compose
• Git
• Mistral API access (free tier available)

1. Clone the repository:

   git clone <repository-url>
   cd devopsGPT_clone

2. Set up environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt

3. Configure environment variables:

   cp .env.example .env
   # Edit .env with your Mistral API key and other configurations

4. Start the services:

   docker-compose up -d
   python src/main.py

5. Access the dashboard:

   • API: http://localhost:8000
   • Grafana: http://localhost:3000 (admin/admin)
   • Prometheus: http://localhost:9090

devopsGPT_clone/
├── src/
│   ├── api/                 # FastAPI application
│   ├── agents/              # AI agents and LLM integration
│   ├── mcp/                 # Model Context Protocol implementation
│   ├── monitoring/          # Monitoring and observability
│   ├── pipeline/            # CI/CD pipeline integrations
│   └── utils/               # Utility functions
├── config/                  # Configuration files
├── docker/                  # Docker configurations
├── tests/                   # Test suites
├── docs/                    # Documentation
├── scripts/                 # Deployment and utility scripts
└── data/                    # Sample datasets and logs

• MISTRAL_API_KEY: Your Mistral AI API key
• DATABASE_URL: PostgreSQL connection string
• REDIS_URL: Redis connection string
• JENKINS_URL: Jenkins server URL
• PROMETHEUS_URL: Prometheus server URL

• Model: Mistral 7B (configurable)
• Context window: 32k tokens
• Temperature: 0.1 (for consistent responses)

from src.agents.mistral_agent import MistralAgent

agent = MistralAgent()
result = agent.analyze_deployment_logs(log_data)
print(f"Status: {result.status}")
print(f"Issues found: {result.issues}")
print(f"Recommendations: {result.recommendations}")

from src.pipeline.jenkins_integration import JenkinsMonitor

monitor = JenkinsMonitor()
monitor.start_monitoring()  # Auto-detects failures and triggers AI analysis

Run the test suite:

pytest tests/ -v
python -m pytest tests/test_mistral_agent.py

The system tracks key DevOps metrics:

• MTTR (Mean Time To Recovery): Target < 5 minutes
• Deployment Frequency: Support for multiple deployments per day
• Change Failure Rate: Aim for < 15%
• Lead Time: From commit to production

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

• API authentication with JWT tokens
• Secure storage of API keys and credentials
• Network isolation using Docker networks
• Regular security scanning of dependencies

• API Documentation
• Configuration Guide
• Deployment Guide
• Troubleshooting

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

• Mistral AI for providing the language model
• Model Context Protocol for data orchestration
• DevOps Community for best practices and tools

For questions and support:

• Create an issue on GitHub
• Email: [email protected]
• Documentation: docs.devops-gpt.example.com

DevOps-GPT - Bringing Intelligence to DevOps Automation