k8m

<div align="center"> <h1>K8M</h1> </div> [English](README_en.md) | [中文](README.md) [](https://github.com/weibaohui/k8m/blob/master/LICENSE) [](https://archestra.ai/mcp-catalog/weibaohui__k8m)  **k8m** is an AI-driven Mini Kubernetes AI Dashboard lightweight console tool designed to simplify cluster management. It is built on AMIS and uses [ `kom`](https://github.com/weibaohui/kom) as the Kubernetes API client. **k8m** comes with Qwen2.5-Coder-7B, supports interaction with the deepseek-ai/DeepSeek-R1-Distill-Qwen-7B model, and allows integration with your own private large models (including ollama). ### Demo [DEMO](http://107.150.119.151:3618) [DEMO-InCluster Mode](http://107.150.119.151:31999) Username and password: demo/demo ### Documentation - For detailed configuration and usage instructions, please refer to the [documentation](docs/README.md). - For the changelog, please refer to the [CHANGELOG](CHANGELOG.md). - For customizing large model parameters and configuring private large models, please refer to [Self-hosted/Custom Large Model Support](docs/use-self-hosted-ai.md) and [Ollama Configuration](docs/ollama.md). - For detailed configuration options, please refer to [Configuration Options Description](docs/config.md). - For database configuration, please refer to [Database Configuration Description](docs/database.md). - DeepWiki documentation: [Development Design Document](https://deepwiki.com/weibaohui/k8m) ### Key Features - **Mini Design**: All functionalities are integrated into a single executable file, making deployment easy and usage straightforward. - **User-Friendly**: A friendly user interface and intuitive workflow make Kubernetes management easier. - **Efficient Performance**: The backend is built with Golang, and the frontend is based on Baidu AMIS, ensuring high resource utilization and fast response times. - **AI-Driven Integration**: Based on ChatGPT, it provides word explanation, resource guidance, automatic YAML attribute translation, Describe information interpretation, log AI consultation, command execution recommendations, and integrates [k8s-gpt](https://github.com/k8sgpt-ai/k8sgpt) functionality, offering intelligent support for managing k8s in Chinese. - **MCP Integration**: Visual management of MCP allows for large model tool calls, with 49 built-in k8s multi-cluster MCP tools that can be combined for over a hundred cluster operations, serving as an MCP Server for other large model software. It records every MCP call in detail and supports mainstream mcp.so services. - **MCP Permission Integration**: Multi-cluster management permissions are integrated with MCP large model call permissions. In summary: whoever uses the large model executes MCP with their permissions. This ensures safe usage without concerns about unauthorized operations. - **Multi-Cluster Management**: Automatically identifies clusters using InCluster mode, scans configuration files in the same directory after configuring the kubeconfig path, and registers multiple clusters for management. - **Multi-Cluster Permission Management**: Supports authorization for users and user groups, with cluster-specific permissions including read-only, Exec command, and cluster administrator roles. Users within an authorized group receive corresponding permissions. Supports setting namespace black and white lists. - **Supports Latest k8s Features**: Supports features like APIGateway, OpenKruise, etc. - **Pod File Management**: Supports browsing, editing, uploading, downloading, and deleting files within Pods, simplifying daily operations. - **Pod Runtime Management**: Supports real-time viewing of Pod logs, downloading logs, and executing Shell commands directly within Pods. Supports grep -A -B highlighted searches. - **Open API**: Supports creating API keys for external access, providing a Swagger interface management page. - **Cluster Inspection Support**: Supports scheduled inspections and custom inspection rules, including lua script rules. - **CRD Management**: Automatically discovers and manages CRD resources, improving work efficiency. - **Helm Marketplace**: Supports freely adding Helm repositories, one-click installation, uninstallation, and upgrading of Helm applications, with automatic updates. - **Cross-Platform Support**: Compatible with Linux, macOS, and Windows, supporting various architectures like x86 and ARM, ensuring seamless operation across platforms. - **Multi-Database Support**: Supports multiple databases including SQLite, MySQL, PostgreSQL, etc. - **Fully Open Source**: All source code is open, with no restrictions, allowing for free customization and extension, and commercial use. The design philosophy of **k8m** is "AI-driven, lightweight and efficient, simplifying complexity," helping developers and operations personnel quickly get started and easily manage Kubernetes clusters.  ## **Run** 1. **Download**: Download the latest version from [GitHub release](https://github.com/weibaohui/k8m/releases). 2. **Run**: Start with the command `./k8m`, and access [http://127.0.0.1:3618](http://127.0.0.1:3618). 3. **Login Username and Password**: - Username: `k8m` - Password: `k8m` - Please remember to change the username and password after going live and enable two-step verification. 4. **Parameters**: ```shell Usage of ./k8m: --enable-temp-admin Enable temporary admin account configuration, default is off --admin-password string Admin password, effective after enabling temporary admin account configuration --admin-username string Admin username, effective after enabling temporary admin account configuration --print-config Print configuration information (default false) --connect-cluster Automatically connect to existing clusters when starting, default is off -d, --debug Debug mode --in-cluster Automatically register the host cluster, default is enabled --jwt-token-secret string Secret used to generate JWT token after login (default "your-secret-key") -c, --kubeconfig string Path to kubeconfig file (default "/root/.kube/config") --kubectl-shell-image string Kubectl Shell image. Default is bitnami/kubectl:latest, must include kubectl command (default "bitnami/kubectl:latest") --log-v int klog log level klog.V(2) (default 2) --login-type string Login method, password, oauth, token, etc., default is password (default "password") --image-pull-timeout Node Shell, Kubectl Shell image pull timeout. Default is 30 seconds --node-shell-image string NodeShell image. Default is alpine:latest, must include `nsenter` command (default "alpine:latest") -p, --port int Listening port (default 3618) -v, --v Level klog log level (default 2) ``` You can also start directly using docker-compose (recommended): ```yaml services: k8m: container_name: k8m image: registry.cn-hangzhou.aliyuncs.com/minik8m/k8m restart: always ports: - "3618:3618" environment: TZ: Asia/Shanghai volumes: - ./data:/app/data ``` After starting, access port `3618`, default username: `k8m`, default password: `k8m`. If you want to quickly experience the online environment, you can visit: [k8m](https://cnb.cool/znb/qifei/-/tree/main/letsfly/justforfun/k8m) ## **ChatGPT Configuration Guide** ### Built-in GPT Starting from version v0.0.8, the built-in GPT does not require configuration. If you need to use your own GPT, please refer to the following documentation. - [Self-hosted/Custom Large Model Support](use-self-hosted-ai.md) - How to use self-hosted models - [Ollama Configuration](ollama.md) - How to configure and use Ollama large models. ### **ChatGPT Status Debugging** If the parameters are set but still have no effect, try using `./k8m -v 6` to get more debugging information. It will output the following information; check the logs to confirm if ChatGPT is enabled. ```go ChatGPT status: true ChatGPT enabled key: sk-hl**********************************************, url: https://api.siliconflow.cn/v1 ChatGPT model set in environment variables: Qwen/Qwen2.5-7B-Instruc ``` ### **ChatGPT Account** This project integrates the [github.com/sashabaranov/go-openai](https://github.com/sashabaranov/go-openai) SDK. For access in China, it is recommended to use the service from [Silicon Flow](https://cloud.siliconflow.cn/). After logging in, create an API_KEY at [https://cloud.siliconflow.cn/account/ak](https://cloud.siliconflow.cn/account/ak). ## **k8m Supports Environment Variable Settings** k8m supports flexible configuration through environment variables and command line parameters. The main parameters are as follows: | Environment Variable | Default Value | Description | |-------------------------------|-----------------------------|------------------------------------------------| | `PORT` | `3618` | Listening port number | | `KUBECONFIG` | `~/.kube/config` | Path to the `kubeconfig` file, automatically scans and recognizes all configuration files in the same directory | | `ANY_SELECT` | `"true"` | Whether to enable arbitrary selection word explanation, default is enabled (default true) | | `LOGIN_TYPE` | `"password"` | Login method (e.g., `password`, `oauth`, `token`) | | `ENABLE_TEMP_ADMIN` | `"false"` | Whether to enable temporary admin account configuration, default is off. Used for first login or password recovery | | `ADMIN_USERNAME` | | Admin username, effective after enabling temporary admin account configuration | | `ADMIN_PASSWORD` | | Admin password, effective after enabling temporary admin account configuration | | `DEBUG` | `"false"` | Whether to enable `debug` mode | | `LOG_V` | `"2"` | Log output level, same as klog usage | | `JWT_TOKEN_SECRET` | `"your-secret-key"` | Secret key for generating JWT Token | | `KUBECTL_SHELL_IMAGE` | `bitnami/kubectl:latest` | Address of the kubectl shell image | | `NODE_SHELL_IMAGE` | `alpine:latest` | Address of the Node shell image | | `IMAGE_PULL_TIMEOUT` | `30` | Node shell, kubectl shell image pull timeout (seconds) | | `CONNECT_CLUSTER` | `"false"` | Whether to automatically connect to discovered clusters after starting the program, default is off | | `PRINT_CONFIG` | `"false"` | Whether to print configuration information | For detailed parameter descriptions and more configuration methods, please refer to [docs/readme.md](docs/README.md). These environment variables can be set when running the application, for example: ```sh export PORT=8080 export GIN_MODE="release" ./k8m ``` For other parameters, please refer to [docs/readme.md](docs/README.md). ## Running in a Containerized k8s Cluster Use [KinD](https://kind.sigs.k8s.io/docs/user/quick-start/) or [MiniKube](https://minikube.sigs.k8s.io/docs/start/) to install a small k8s cluster. ## KinD Method * Create a KinD Kubernetes cluster ``` brew install kind ``` * Create a new Kubernetes cluster: ``` kind create cluster --name k8sgpt-demo ``` ## Deploy k8m to the Cluster for Experience ### Installation Script ```docker kubectl apply -f https://raw.githubusercontent.com/weibaohui/k8m/refs/heads/main/deploy/k8m.yaml ``` * Access: The default uses nodePort, please access port 31999. Alternatively, configure Ingress yourself. http://NodePortIP:31999 ### Modify Configuration It is recommended to modify the configuration through environment variables. For example, add env parameters in deploy.yaml. ## Development and Debugging If you want to develop and debug locally, please execute a local frontend build first to automatically generate the dist directory. Since this project uses binary embedding, the frontend will report an error without dist. #### Step 1: Compile Frontend ```bash cd ui pnpm run build ``` #### Compile and Debug Backend ```bash # Download dependencies go mod tidy # Run air # or go run main.go # Listening on localhost:3618 ``` #### Frontend Hot Reload ```bash cd ui pnpm run dev # Vite service will listen on localhost:3000 # Vite forwards backend access to port 3618 ``` Access http://localhost:3000 ### HELP & SUPPORT If you have any further questions or need additional help, feel free to contact me! ### Special Thanks [zhaomingcheng01](https://github.com/zhaomingcheng01): Provided many high-quality suggestions, making outstanding contributions to the usability of k8m~ [La0jin](https://github.com/La0jin): Provided online resources and maintenance, greatly enhancing the display effect of k8m. [eryajf](https://github.com/eryajf): Provided us with very useful GitHub actions, adding automation for versioning, building, and releasing k8m. ## Contact Me WeChat (The Sun of Rome) Search ID: daluomadetaiyang, note k8m. <br><img width="214" alt="Image" src="https://github.com/user-attachments/assets/166db141-42c5-42c4-9964-8e25cf12d04c" /> ## WeChat Group  ## QQ Group