AI-powered platform engineering and DevOps automation through intelligent Kubernetes operations and conversational workflows.
DevOps AI Toolkit brings AI-powered intelligence to platform engineering, Kubernetes operations, and development workflows. Access it through MCP for AI coding assistants or the CLI for direct agent integration.
Key capabilities:
- Natural language cluster querying and exploration
- Intelligent Kubernetes deployment recommendations
- AI-powered issue remediation and root cause analysis
- Organizational pattern and policy management
- Semantic search over organizational documentation
- Automated repository setup with governance files
- Shared prompt libraries for consistent workflows
For the easiest setup, we recommend installing the complete dot-ai stack which includes all components pre-configured. See the Stack Installation Guide.
For individual component installation, see the Deployment Guide.
- Support Guide - How to get help and where to ask questions
- GitHub Issues: Bug reports and feature requests
- GitHub Discussions: Community Q&A and discussions
We welcome contributions from the community! Please review:
- Contributing Guidelines - How to contribute code, docs, and ideas
- Code of Conduct - Community standards and expectations
- Security Policy - How to report security vulnerabilities
- Governance - Project governance and decision-making
- Maintainers - Current project maintainers
- Roadmap - Project direction and priorities
MIT License - see LICENSE file for details.
This project collects anonymous usage analytics to improve the product. Learn more or opt out.
DevOps AI Toolkit is built on:
- Model Context Protocol for AI integration framework
- Vercel AI SDK for unified AI provider interface
- Kubernetes for the cloud native foundation
- CNCF for the cloud native ecosystem
DevOps AI Toolkit - Making cloud native operations accessible through AI-powered intelligence.