base on AI powered Kubernetes Assistant # kubectl-ai kubectl-ai is an AI powered kubernetes agent that runs in your terminal.  ## Quick Start First, ensure that kubectl is installed and configured. ### Installation 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/ ``` ### Usage #### 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" ``` #### Using AI models running locally (ollama or llamacpp) 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. 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 # 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 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 ``` * Note: `kubectl-ai` supports AI models from `gemini`, `vertexai`, `azure-openai`, `openai` and local LLM providers such as `ollama` and `llamacpp`. 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" ``` ## Extras You can use the following special keywords for specific actions: * `model`: Display the currently selected model. * `models`: List all available models. * `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 Use it via the `kubectl` plug interface like this: `kubectl ai`. kubectl will find `kubectl-ai` as long as it's in your PATH. For more information about plugins please see: https://kubernetes.io/docs/tasks/extend-kubectl/kubectl-plugins/ ### Examples ```bash # Get information about pods in the default namespace kubectl-ai -quiet "show me all pods in the default namespace" # Create a new deployment kubectl-ai -quiet "create a deployment named nginx with 3 replicas using the nginx:latest image" # Troubleshoot issues kubectl-ai -quiet "double the capacity for the nginx app" # Using OpenAI instead of Gemini kubectl-ai --llm-provider=openai --model=gpt-4.1 -quiet "scale the nginx deployment to 5 replicas" ``` The `kubectl-ai` will process your query, execute the appropriate kubectl commands, and provide you with the results and explanations. ## 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. --- *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"