K8M is a lightweight, cross-platform AI-driven Kubernetes dashboard designed to simplify cluster management for developers and operators. It offers a single executable for easy deployment and integrates AI features for tasks like YAML translation, log analysis, and command recommendations, enhancing operational efficiency.

How It Works

K8M is built with a Golang backend and a frontend based on Baidu AMIS, ensuring high performance and resource utilization. It leverages a Kubernetes API client (kom) and integrates large language models (LLMs) like Qwen2.5-Coder-7B and DeepSeek-R1-Distill-Qwen-7B, with support for custom or self-hosted models via Ollama. The core innovation lies in its AI-powered assistance for common Kubernetes tasks and its MCP (Model-Centric Programming) integration for LLM-driven cluster operations with granular permission control.

Quick Start & Requirements

• Install/Run: Download the latest release and run ./k8m. Access via http://127.0.0.1:3618. Default credentials: k8m/k8m.
• Prerequisites: None explicitly stated for basic operation. For AI features, API keys and URLs for LLMs can be configured via environment variables or command-line flags. Docker is recommended for deployment.
• Resources: Single executable, minimal resource footprint for basic dashboard. AI features will consume additional resources depending on the LLM used.
• Docs: Detailed Configuration and Usage

Highlighted Details

• AI-powered features: YAML translation, log analysis, command recommendations, resource guidance.
• MCP integration: Visual management of LLM-driven tools for over 100 cluster operations.
• Multi-cluster management: Automatic discovery and management of multiple Kubernetes clusters.
• Cross-platform and multi-architecture support (Linux, macOS, Windows; x86, ARM).

Maintenance & Community

• Active development with contributions noted from zhaomingcheng01, La0jin, and eryajf.
• Contact via WeChat (ID: daluomadetaiyang) for support.

Licensing & Compatibility

• Fully open-source with no restrictions, explicitly stated as available for commercial use.

Limitations & Caveats

• The project is described as "lightweight" and "mini," suggesting it may not offer the full feature set of more comprehensive Kubernetes dashboards. AI feature effectiveness is dependent on the configured LLM.