base on AI powered Kubernetes Assistant # kubectl-ai [](https://goreportcard.com/report/github.com/GoogleCloudPlatform/kubectl-ai)  [](https://github.com/GoogleCloudPlatform/kubectl-ai/stargazers) `kubectl-ai` acts as an intelligent interface, translating user intent into precise Kubernetes operations, making Kubernetes management more accessible and efficient.  ## Quick Start First, ensure that kubectl is installed and configured. ### Installation #### Quick Install (Linux & MacOS only) ```shell curl -sSL https://raw.githubusercontent.com/GoogleCloudPlatform/kubectl-ai/main/install.sh | bash ``` <details> <summary>Other Installation Methods</summary> #### Manual Installation (Linux, MacOS and Windows) 1. Download the latest release from the [releases page](https://github.com/GoogleCloudPlatform/kubectl-ai/releases/latest) for your target machine. 2. Untar the release, make the binary executable and move it to a directory in your $PATH (as shown below). ```shell tar -zxvf kubectl-ai_Darwin_arm64.tar.gz chmod a+x kubectl-ai sudo mv kubectl-ai /usr/local/bin/ ``` #### Install with Krew (Linux/macOS/Windows) First of all, you need to have krew insatlled, refer to [krew document](https://krew.sigs.k8s.io/docs/user-guide/setup/install/) for more details Then you can install with krew ```shell kubectl krew install ai ``` Now you can invoke `kubectl-ai` as a kubectl plugin like this: `kubectl ai`. #### Install on NixOS There are multiple ways to install `kubectl-ai` on NixOS. For a permantent installation add the following to your NixOS-Configuration: ```nix environment.systemPackages = with pkgs; [ kubectl-ai ]; ``` For a temporary installation, you can use the following command: ``` nix-shell -p kubectl-ai ``` </details> ### Usage `kubectl-ai` supports AI models from `gemini`, `vertexai`, `azopenai`, `openai`, `grok` and local LLM providers such as `ollama` and `llama.cpp`. #### Using Gemini (Default) Set your Gemini API key as an environment variable. If you don't have a key, get one from [Google AI Studio](https://aistudio.google.com). ```bash export GEMINI_API_KEY=your_api_key_here kubectl-ai # Use different gemini model kubectl-ai --model gemini-2.5-pro-exp-03-25 # Use 2.5 flash (faster) model kubectl-ai --quiet --model gemini-2.5-flash-preview-04-17 "check logs for nginx app in hello namespace" ``` <details> <summary>Use other AI models</summary> #### Using AI models running locally (ollama or llama.cpp) You can use `kubectl-ai` with AI models running locally. `kubectl-ai` supports [ollama](https://ollama.com/) and [llama.cpp](https://github.com/ggml-org/llama.cpp) to use the AI models running locally. Additionally, the [`modelserving`](modelserving/) directory provides tools and instructions for deploying your own `llama.cpp`-based LLM serving endpoints locally or on a Kubernetes cluster. This allows you to host models like Gemma directly in your environment. An example of using Google's `gemma3` model with `ollama`: ```shell # assuming ollama is already running and you have pulled one of the gemma models # ollama pull gemma3:12b-it-qat # if your ollama server is at remote, use OLLAMA_HOST variable to specify the host # export OLLAMA_HOST=http://192.168.1.3:11434/ # enable-tool-use-shim because models require special prompting to enable tool calling kubectl-ai --llm-provider ollama --model gemma3:12b-it-qat --enable-tool-use-shim # you can use `models` command to discover the locally available models >> models ``` #### Using Grok You can use X.AI's Grok model by setting your X.AI API key: ```bash export GROK_API_KEY=your_xai_api_key_here kubectl-ai --llm-provider=grok --model=grok-3-beta ``` #### Using Azure OpenAI You can also use Azure OpenAI deployment by setting your OpenAI API key and specifying the provider: ```bash export AZURE_OPENAI_API_KEY=your_azure_openai_api_key_here export AZURE_OPENAI_ENDPOINT=https://your_azure_openai_endpoint_here kubectl-ai --llm-provider=azopenai --model=your_azure_openai_deployment_name_here # or az login kubectl-ai --llm-provider=openai://your_azure_openai_endpoint_here --model=your_azure_openai_deployment_name_here ``` #### Using OpenAI You can also use OpenAI models by setting your OpenAI API key and specifying the provider: ```bash export OPENAI_API_KEY=your_openai_api_key_here kubectl-ai --llm-provider=openai --model=gpt-4.1 ``` #### Using OpenAI Compatible API For example, you can use aliyun qwen-xxx models as follows ```bash export OPENAI_API_KEY=your_openai_api_key_here export OPENAI_ENDPOINT=https://dashscope.aliyuncs.com/compatible-mode/v1 kubectl-ai --llm-provider=openai --model=qwen-plus ``` </details> Run interactively: ```shell kubectl-ai ``` The interactive mode allows you to have a chat with `kubectl-ai`, asking multiple questions in sequence while maintaining context from previous interactions. Simply type your queries and press Enter to receive responses. To exit the interactive shell, type `exit` or press Ctrl+C. Or, run with a task as input: ```shell kubectl-ai --quiet "fetch logs for nginx app in hello namespace" ``` Combine it with other unix commands: ```shell kubectl-ai < query.txt # OR echo "list pods in the default namespace" | kubectl-ai ``` You can even combine a positional argument with stdin input. The positional argument will be used as a prefix to the stdin content: ```shell cat error.log | kubectl-ai "explain the error" ``` ## Tools `kubectl-ai` leverages LLMs to suggest and execute Kubernetes operations using a set of powerful tools. It comes with built-in tools like `kubectl` and `bash`. You can also extend its capabilities by defining your own custom tools. By default, `kubectl-ai` looks for your tool configurations in `~/.config/kubectl-ai/tools.yaml`. To specify tools configuration files or directories containing tools configuration files, use: ```shell kubectl-ai --custom-tools-config=YOUR_CONFIG ``` You can include multiple tools in a single configuration file, or a directory with multiple configuration files, each dedicated to a single or multiple tools. Define your custom tools using the following schema: ```yaml - name: tool_name description: "A clear description that helps the LLM understand when to use this tool." command: "your_command" # For example: 'gcloud' or 'gcloud container clusters' command_desc: "Detailed information for the LLM, including command syntax and usage examples." ``` A custom tool definition for `helm` could look like the following example: ```yaml - name: helm description: "Helm is the Kubernetes package manager and deployment tool. Use it to define, install, upgrade, and roll back applications packaged as Helm charts in a Kubernetes cluster." command: "helm" command_desc: | Helm command-line interface, with the following core subcommands and usage patterns: - helm install <release-name> <chart> [flags] Install a chart into the cluster. - helm upgrade <release-name> <chart> [flags] Upgrade an existing release to a new chart version or configuration. - helm list [flags] List all releases in one or all namespaces. - helm uninstall <release-name> [flags] Uninstall a release and clean up associated resources. Use `helm --help` or `helm <subcommand> --help` to see full syntax, available flags, and examples for each command. ``` ## MCP Client Mode > **Note:** MCP Client Mode is available in `kubectl-ai` version v0.0.12 and onwards. `kubectl-ai` can connect to external [MCP](https://modelcontextprotocol.io/examples) Servers to access additional tools in addition to built-in tools. ### Quick Start Enable MCP client mode: ```bash kubectl-ai --mcp-client ``` ### Configuration Create or edit `~/.config/kubectl-ai/mcp.yaml` to customize MCP servers: ```yaml servers: # Local MCP server (stdio-based) # sequential-thinking: Advanced reasoning and step-by-step analysis - name: sequential-thinking command: npx args: - -y - "@modelcontextprotocol/server-sequential-thinking" # Remote MCP server (HTTP-based) - name: cloudflare-documentation url: https://docs.mcp.cloudflare.com/mcp # Optional: Remote MCP server with authentication - name: custom-api url: https://api.example.com/mcp auth: type: "bearer" token: "${MCP_TOKEN}" ``` The system automatically: - Converts parameter names (snake_case → camelCase) - Handles type conversion (strings → numbers/booleans when appropriate) - Provides fallback behavior for unknown servers No additional setup required - just use the `--mcp-client` flag and the AI will have access to all configured MCP tools. 📖 **For detailed configuration options, troubleshooting, and advanced features for MCP Client mode, see the [MCP Client Documentation](pkg/mcp/README.md).** ## Extras You can use the following special keywords for specific actions: * `model`: Display the currently selected model. * `models`: List all available models. * `tools`: List all available tools. * `version`: Display the `kubectl-ai` version. * `reset`: Clear the conversational context. * `clear`: Clear the terminal screen. * `exit` or `quit`: Terminate the interactive shell (Ctrl+C also works). ### Invoking as kubectl plugin You can also run `kubectl ai`. `kubectl` finds any executable file in your `PATH` whose name begins with `kubectl-` as a [plugin](https://kubernetes.io/docs/tasks/extend-kubectl/kubectl-plugins/). ## MCP Server Mode `kubectl-ai` can also act as an MCP server that exposes `kubectl` as a tool for other MCP clients (like Claude, Cursor, or VS Code) to interact with your locally configured Kubernetes environment. Enable MCP server mode: ```bash kubectl-ai --mcp-server ``` This allows AI agents and tools to execute kubectl commands in your environment through the Model Context Protocol. 📖 **For details on configuring kubectl-ai as an MCP server for use with Claude, Cursor, VS Code, and other MCP clients, see the [MCP Server Documentation](./docs/mcp.md).** ## k8s-bench kubectl-ai project includes [k8s-bench](./k8s-bench/README.md) - a benchmark to evaluate performance of different LLM models on kubernetes related tasks. Here is a summary from our last run: | Model | Success | Fail | |-------|---------|------| | gemini-2.5-flash-preview-04-17 | 10 | 0 | | gemini-2.5-pro-preview-03-25 | 10 | 0 | | gemma-3-27b-it | 8 | 2 | | **Total** | 28 | 2 | See [full report](./k8s-bench.md) for more details. ## Start Contributing We welcome contributions to `kubectl-ai` from the community. Take a look at our [contribution guide](contributing.md) to get started. --- *Note: This is not an officially supported Google product. This project is not eligible for the [Google Open Source Software Vulnerability Rewards Program](https://bughunters.google.com/open-source-security).* ", Assign "at most 3 tags" to the expected json: {"id":"13615","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"