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K8m

K8M is an AI-driven lightweight Kubernetes console tool that integrates large model capabilities, supports multi-cluster management, and provides MCP services.
Developer toolsVirtualization#K8S management#AI-driven#Multi-cluster#MCP service.Go
3.5 points
10.3K
Open Site

Overview

Content Details

Alternatives

What is K8M?

K8M is an intelligent management tool designed specifically for Kubernetes. With built-in AI capabilities and a visual interface, it enables non-professional users to easily manage container clusters. It integrates core functions such as resource monitoring, fault diagnosis, and command execution.

How to use K8M?

Simply download and run the single-file program, and access the console through a browser. It supports automatic discovery of multiple clusters via kubeconfig and provides user permission management and operation auditing functions.

Applicable scenarios

Scenarios such as development and testing environment management, teaching demonstrations, operation and maintenance of small and medium-sized production clusters, and AI-assisted fault troubleshooting. It is particularly suitable for teams that need to lower the threshold of using K8s.

Main features

MCP tool integration
It has 49 built-in multi-cluster management tools, supports combining into hundreds of operation instructions, and can be used as an MCP Server for other AI systems to call.
AI operation and maintenance assistant
It integrates large models such as Qwen/DeepSeek and provides intelligent functions such as log analysis, command recommendation, and configuration interpretation.
Multi-cluster management
It automatically scans the kubeconfig directory and centrally manages resources of multiple K8s clusters.
Permission integration
MCP operations automatically inherit users' K8s permissions to ensure security and compliance.
Advantages
Ready to use: Deployed with a single binary file without complex dependencies
Intelligent operation and maintenance: AI automatically diagnoses common problems, reducing the learning cost
Permission security: MCP operations strictly follow the RBAC permission system
Lightweight and efficient: Written in Golang with extremely low resource consumption
Limitations
Limited performance in large-scale clusters (it is recommended to manage less than 50 nodes)
Some advanced functions require configuring the large model API
Support for the ARM architecture is still being improved

How to use

Download and install
Download the executable file for your system from the GitHub Releases page
Start the service
Start the service via the command line, which listens on port 3618 by default
Access the console
Access http://localhost:3618 in your browser and log in with the default account

Usage examples

Intelligent log analysis
When a Pod enters the CrashLoopBackOff state, AI automatically analyzes the logs and provides possible causes
Batch operations on multiple clusters
Use the MCP tool to scale up the nginx Deployment in all clusters simultaneously

Frequently Asked Questions

How to reset the administrator password?
Which large models are supported?

Related resources

Online demonstration
Experience account: demo/demo
Development documentation
Details of architecture design and technical implementation
Docker image
Official container image

Installation

Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.

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