kubectl-ai is an AI powered kubernetes agent that runs in your terminal.

First, ensure that kubectl is installed and configured.

1. Download the latest release from the releases page for your target machine.

2. Untar the release, make the binary executable and move it to a directory in your $PATH (as shown below).

$ tar -zxvf kubectl-ai_Darwin_arm64.tar.gz
$ chmod a+x kubectl-ai
$ sudo mv kubectl-ai /usr/local/bin/

Set your Gemini API key as an environment variable. If you don't have a key, get one from Google AI Studio.

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"

You can use kubectl-ai with AI models running locally. kubectl-ai supports ollama and llama.cpp to use the AI models running locally.

An example of using Google's gemma3 model with ollama:

# 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

You can also use OpenAI models by setting your OpenAI API key and specifying the provider:

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:

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:

kubectl-ai -quiet "fetch logs for nginx app in hello namespace"

Combine it with other unix commands:

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:

cat error.log | kubectl-ai "explain the error"

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).

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/

# 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.

kubectl-ai project includes k8s-bench - a benchmark to evaluate performance of different LLM models on kubernetes related tasks. Here is a summary from our last run:

See full report 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.