AI-powered platform engineering and DevOps automation for Kubernetes operations. It targets engineers and users seeking to simplify complex cloud-native management through natural language interfaces and intelligent recommendations, making operations more accessible.

How It Works

Built on the Model Context Protocol (MCP), dot-ai integrates AI capabilities with tools like Claude Code, Cursor, and VS Code. It enables natural language querying of Kubernetes clusters, provides intelligent deployment suggestions, and performs AI-driven root cause analysis for issues. The architecture facilitates organizational pattern and policy management, alongside automated repository setup with governance files.

Quick Start & Requirements

• Install/Run: NPX for quick trials (npx @vfarcic/dot-ai), Docker for local development, or full Kubernetes deployment.
• Prerequisites: Node.js (for NPX), Kubernetes cluster.
• Documentation: https://devopstoolkit.ai/docs/mcp/

Highlighted Details

• Natural language cluster querying and exploration.
• Intelligent Kubernetes deployment recommendations.
• AI-powered issue remediation and root cause analysis.
• Automated repository setup with governance files.

Maintenance & Community

The project is actively maintained with community contributions welcomed via GitHub Issues for bugs and feature requests, and GitHub Discussions for Q&A. Links to contributing guidelines, code of conduct, security policy, governance, and roadmap are provided.

Licensing & Compatibility

Licensed under the MIT License. This license is generally permissive for commercial use and closed-source linking.

Limitations & Caveats

The project is currently in a 'beta' status. The Docker setup option requires manual capability scanning, limiting its autonomous features compared to a full Kubernetes deployment.