An intelligent AI-powered CLI assistant for DevOps engineers, providing expert guidance on Kubernetes, Terraform, and DevOps best practices. Built with multi-agent architecture and supporting multiple LLM providers.

• Multi-Agent Architecture: Specialized agents for DevOps, Kubernetes, and Terraform domains
• Multi-LLM Support: Choose between OpenAI, Anthropic (Claude), or Google (Gemini)
• Interactive Mode: Conversational interface for continuous assistance
• Log Analysis: Automated analysis of log files with insights and error detection
• GitOps & Cloud-Native: Expert knowledge of modern DevOps practices
• Memory System: Uses Qdrant vector database for contextual memory across sessions
• POML Prompts: Structured prompts using POML (Prompt Markup Language) for consistent agent behavior

# Clone the repository
git clone https://github.com/yourusername/devops-agent.git
cd devops-agent

# Install dependencies
pip install -e .

Create a .env file in the project root with your API keys:

# Choose one or more providers
ANTHROPIC_API_KEY=your_anthropic_key_here
OPENAI_API_KEY=your_openai_key_here
GEMINI_API_KEY=your_gemini_key_here

# Qdrant configuration (for memory)
QDRANT_URL=your_qdrant_url
QDRANT_API_KEY=your_qdrant_key

devops-agent run --provider anthropic --interactive

Start a conversation with the DevOps team. Ask multiple questions in sequence, and the agents will remember context across the session.

devops-agent run --provider openai --query "How do I set up a multi-region Kubernetes cluster on AWS EKS with GitOps?"

Get immediate answers to specific DevOps questions without entering interactive mode.

devops-agent run --provider anthropic --query "Create a Terraform module for Azure AKS cluster with monitoring and auto-scaling" --output terraform-aks.tf --format text

Generate infrastructure code and save it directly to a file.

devops-agent run --provider google --query "My pods are in CrashLoopBackOff state. How do I debug this issue systematically?"

Get step-by-step troubleshooting guidance for Kubernetes issues.

devops-agent run --provider anthropic --log-file ./logs/application.log

Analyze log files for critical errors, patterns, anomalies, and significant findings with AI-powered insights.

devops-agent run --provider openai --query "Best practices for implementing GitOps with ArgoCD" --output gitops-guide.md --format markdown

Export responses in text, JSON, or Markdown format for documentation.

devops-agent/
├── devops_agent/
│   ├── cli.py                          # Main CLI interface with Click
│   ├── core/                           # Core agent implementations
│   │   ├── master_agent.py            # Orchestrator agent routing queries
│   │   ├── devops_agent.py            # DevOps troubleshooting specialist
│   │   ├── kubernetes_agent.py        # Kubernetes architecture expert
│   │   ├── terraform_agent.py         # Terraform/IaC specialist
│   │   └── log_analysis_agent.py      # Log file analysis agent
│   ├── prompts/                        # POML-based structured prompts
│   │   ├── devops.poml                # DevOps troubleshooting instructions
│   │   ├── kubernetes.poml            # Kubernetes architecture guidelines
│   │   └── terraform.poml             # Terraform best practices
│   └── utils/                          # Utility functions
│       └── prompt_generator_from_poml.py  # POML to prompt converter
├── setup.py                            # Package setup configuration
├── pyproject.toml                      # Modern Python project config
├── requirements.txt                    # Project dependencies
├── .env                                # Environment variables (create this)
└── README.md                           # This file

• cli.py: Command-line interface handling user input, interactive mode, and output formatting
• master_agent.py: Routes queries to specialized agents based on domain (DevOps, K8s, Terraform)
• devops_agent.py: Handles incident response, observability, and general DevOps troubleshooting
• kubernetes_agent.py: Kubernetes architecture, GitOps workflows, and cloud-native practices
• terraform_agent.py: Infrastructure as Code, state management, and multi-cloud deployments
• log_analysis_agent.py: Analyzes log files for errors, patterns, and anomalies
• .poml files: Structured prompt definitions using POML markup language

devops-agent run [OPTIONS]

Options:
  --provider TEXT              LLM provider: openai, anthropic, or google
  --query TEXT                 Single query to process
  --log-file PATH              Path to log file for analysis
  --output PATH                Save response to file
  --format [text|json|markdown] Output format (default: text)
  -i, --interactive            Run in interactive conversation mode
  --help                       Show help message

devops-agent --version

1. Query Routing: Master agent analyzes your question and routes it to the appropriate specialist agent
2. Domain Expertise: Each agent uses specialized POML-defined knowledge for accurate responses
3. Memory System: Qdrant vector database stores conversation history for contextual awareness
4. Multi-LLM: Choose the best model for your needs - Claude Sonnet 4.5, GPT-5-mini, or Gemini 2.5
5. Streaming Responses: See agent reasoning and responses in real-time

• Python >= 3.8
• API key for at least one LLM provider (OpenAI, Anthropic, or Google)
• Optional: Qdrant instance for persistent memory (can use in-memory fallback)

We welcome contributions! Here's how you can help:

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

# Install with development dependencies
pip install -e ".[dev]"

# Format code
black devops_agent/
isort devops_agent/

# Lint
flake8 devops_agent/

• Follow PEP 8 style guidelines
• Add tests for new features
• Update documentation as needed
• Keep commits atomic and well-described
• Ensure all tests pass before submitting PR

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

• Built with Agno framework for multi-agent orchestration
• Uses POML for structured prompt engineering
• Powered by Claude (Anthropic), GPT (OpenAI), and Gemini (Google)

• Issues: Report bugs or request features via GitHub Issues
• Discussions: Join conversations in GitHub Discussions
• Email: manthapavankumar11@gmail.com

• Add CI/CD pipeline integration
• Support for custom agent creation
• Web UI interface
• Docker and Helm chart templates generation
• Integration with monitoring tools (Prometheus, Grafana)
• Multi-language support for responses
• Plugin system for extensibility

Made with ❤️ by M K Pavan Kumar