[](https://bestpractices.coreinfrastructure.org/projects/7272) [](https://docs.k8sgpt.ai/) `k8sgpt` is a tool for scanning your Kubernetes clusters, diagnosing, and triaging issues in simple English. It has SRE experience codified into its analyzers and helps to pull out the most relevant information to enrich it with AI. # CLI Installation ### Linux/Mac via brew ``` brew tap k8sgpt-ai/k8sgpt brew install k8sgpt ```

RPM-based installation (RedHat/CentOS/Fedora)

**32 bit:** ``` curl -LO https://github.com/k8sgpt-ai/k8sgpt/releases/download/v0.3.2/k8sgpt_386.rpm sudo rpm -ivh k8sgpt_386.rpm ``` **64 bit:** ``` curl -LO https://github.com/k8sgpt-ai/k8sgpt/releases/download/v0.3.2/k8sgpt_amd64.rpm sudo rpm -ivh -i k8sgpt_amd64.rpm ```

DEB-based installation (Ubuntu/Debian)

**32 bit:** ``` curl -LO https://github.com/k8sgpt-ai/k8sgpt/releases/download/v0.3.2/k8sgpt_386.deb sudo dpkg -i k8sgpt_386.deb ``` **64 bit:** ``` curl -LO https://github.com/k8sgpt-ai/k8sgpt/releases/download/v0.3.2/k8sgpt_amd64.deb sudo dpkg -i k8sgpt_amd64.deb ```

APK-based installation (Alpine)

**32 bit:** ``` curl -LO https://github.com/k8sgpt-ai/k8sgpt/releases/download/v0.3.2/k8sgpt_386.apk apk add k8sgpt_386.apk ``` **64 bit:** ``` curl -LO https://github.com/k8sgpt-ai/k8sgpt/releases/download/v0.3.2/k8sgpt_amd64.apk apk add k8sgpt_amd64.apk ``` x

Failing Installation on WSL or Linux (missing gcc)

When installing Homebrew on WSL or Linux, you may encounter the following error: ``` ==> Installing k8sgpt from k8sgpt-ai/k8sgpt Error: The following formula cannot be installed from a bottle and must be built from the source. k8sgpt Install Clang or run brew install gcc. ``` If you install gcc as suggested, the problem will persist. Therefore, you need to install the build-essential package. ``` sudo apt-get update sudo apt-get install build-essential ```

### Windows * Download the latest Windows binaries of **k8sgpt** from the [Release](https://github.com/k8sgpt-ai/k8sgpt/releases) tab based on your system architecture. * Extract the downloaded package to your desired location. Configure the system *path* variable with the binary location ## Operator Installation To install within a Kubernetes cluster please use our `k8sgpt-operator` with installation instructions available [here](https://github.com/k8sgpt-ai/k8sgpt-operator) _This mode of operation is ideal for continuous monitoring of your cluster and can integrate with your existing monitoring such as Prometheus and Alertmanager._ ## Quick Start * Currently the default AI provider is OpenAI, you will need to generate an API key from [OpenAI](https://openai.com) * You can do this by running `k8sgpt generate` to open a browser link to generate it * Run `k8sgpt auth add` to set it in k8sgpt. * You can provide the password directly using the `--password` flag. * Run `k8sgpt filters` to manage the active filters used by the analyzer. By default, all filters are executed during analysis. * Run `k8sgpt analyze` to run a scan. * And use `k8sgpt analyze --explain` to get a more detailed explanation of the issues. ## Analyzers K8sGPT uses analyzers to triage and diagnose issues in your cluster. It has a set of analyzers that are built in, but you will be able to write your own analyzers. ### Built in analyzers #### Enabled by default - [x] podAnalyzer - [x] pvcAnalyzer - [x] rsAnalyzer - [x] serviceAnalyzer - [x] eventAnalyzer - [x] ingressAnalyzer - [x] statefulSetAnalyzer - [x] deploymentAnalyzer - [x] cronJobAnalyzer - [x] nodeAnalyzer #### Optional - [x] hpaAnalyzer - [x] pdbAnalyzer - [x] networkPolicyAnalyzer ## Examples _Run a scan with the default analyzers_ ``` k8sgpt generate k8sgpt auth add k8sgpt analyze --explain ``` _Filter on resource_ ``` k8sgpt analyze --explain --filter=Service ``` _Filter by namespace_ ``` k8sgpt analyze --explain --filter=Pod --namespace=default ``` _Output to JSON_ ``` k8sgpt analyze --explain --filter=Service --output=json ``` _Anonymize during explain_ ``` k8sgpt analyze --explain --filter=Service --output=json --anonymize ```

Using filters

_List filters_ ``` k8sgpt filters list ``` _Add default filters_ ``` k8sgpt filters add [filter(s)] ``` ### Examples : - Simple filter : `k8sgpt filters add Service` - Multiple filters : `k8sgpt filters add Ingress,Pod` _Remove default filters_ ``` k8sgpt filters remove [filter(s)] ``` ### Examples : - Simple filter : `k8sgpt filters remove Service` - Multiple filters : `k8sgpt filters remove Ingress,Pod`

Additional commands

_List configured backends_ ``` k8sgpt auth list ``` _Remove configured backends_ ``` k8sgpt auth remove $MY_BACKEND1,$MY_BACKEND2.. ``` _List integrations_ ``` k8sgpt integrations list ``` _Activate integrations_ ``` k8sgpt integrations activate [integration(s)] ``` _Use integration_ ``` k8sgpt analyze --filter=[integration(s)] ``` _Deactivate integrations_ ``` k8sgpt integrations deactivate [integration(s)] ``` _Serve mode_ ``` k8sgpt serve ``` _Analysis with serve mode_ ``` curl -X GET "http://localhost:8080/analyze?namespace=k8sgpt&explain=false" ```

## Key Features

LocalAI provider

To run local models, it is possible to use OpenAI compatible APIs, for instance [LocalAI](https://github.com/go-skynet/LocalAI) which uses [llama.cpp](https://github.com/ggerganov/llama.cpp) and [ggml](https://github.com/ggerganov/ggml) to run inference on consumer-grade hardware. Models supported by LocalAI for instance are Vicuna, Alpaca, LLaMA, Cerebras, GPT4ALL, GPT4ALL-J and koala. To run local inference, you need to download the models first, for instance you can find `ggml` compatible models in [huggingface.com](https://huggingface.co/models?search=ggml) (for example vicuna, alpaca and koala). ### Start the API server To start the API server, follow the instruction in [LocalAI](https://github.com/go-skynet/LocalAI#example-use-gpt4all-j-model). ### Run k8sgpt To run k8sgpt, run `k8sgpt auth new` with the `localai` backend: ``` k8sgpt auth new --backend localai --model --baseurl http://localhost:8080/v1 ``` Now you can analyze with the `localai` backend: ``` k8sgpt analyze --explain --backend localai ```

AzureOpenAI provider

Prerequisites: an Azure OpenAI deployment is needed, please visit MS official [documentation](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource) to create your own. To authenticate with k8sgpt, you will need the Azure OpenAI endpoint of your tenant `"https://your Azure OpenAI Endpoint"`, the api key to access your deployment, the deployment name of your model and the model name itself. To run k8sgpt, run `k8sgpt auth` with the `azureopenai` backend: ``` k8sgpt auth add --backend azureopenai --baseurl https:// --engine --model ``` Lastly, enter your Azure API key, after the prompt. Now you are ready to analyze with the azure openai backend: ``` k8sgpt analyze --explain --backend azureopenai ```

Setting a new default AI provider

There may be scenarios where you wish to have K8sGPT plugged into several default AI providers. In this case you may wish to use one as a new default, other than OpenAI which is the project default. _To view available providers_ ``` k8sgpt auth list Default: > openai Active: > openai > azureopenai Unused: > localai > noopai ``` _To set a new default provider_ ``` k8sgpt auth default -p azureopenai Default provider set to azureopenai ```

With this option, the data is anonymized before being sent to the AI Backend. During the analysis execution, `k8sgpt` retrieves sensitive data (Kubernetes object names, labels, etc.). This data is masked when sent to the AI backend and replaced by a key that can be used to de-anonymize the data when the solution is returned to the user.

Anonymization

1. Error reported during analysis: ```bash Error: HorizontalPodAutoscaler uses StatefulSet/fake-deployment as ScaleTargetRef which does not exist. ``` 2. Payload sent to the AI backend: ```bash Error: HorizontalPodAutoscaler uses StatefulSet/tGLcCRcHa1Ce5Rs as ScaleTargetRef which does not exist. ``` 3. Payload returned by the AI: ```bash The Kubernetes system is trying to scale a StatefulSet named tGLcCRcHa1Ce5Rs using the HorizontalPodAutoscaler, but it cannot find the StatefulSet. The solution is to verify that the StatefulSet name is spelled correctly and exists in the same namespace as the HorizontalPodAutoscaler. ``` 4. Payload returned to the user: ```bash The Kubernetes system is trying to scale a StatefulSet named fake-deployment using the HorizontalPodAutoscaler, but it cannot find the StatefulSet. The solution is to verify that the StatefulSet name is spelled correctly and exists in the same namespace as the HorizontalPodAutoscaler. ``` **Anonymization does not currently apply to events.**

Configuration management

`k8sgpt` stores config data in the `$XDG_CONFIG_HOME/k8sgpt/k8sgpt.yaml` file. The data is stored in plain text, including your OpenAI key. Config file locations: | OS | Path | |---------|--------------------------------------------------| | MacOS | ~/Library/Application Support/k8sgpt/k8sgpt.yaml | | Linux | ~/.config/k8sgpt/k8sgpt.yaml | | Windows | %LOCALAPPDATA%/k8sgpt/k8sgpt.yaml |

There may be scenarios where caching remotely is prefered. In these scenarios K8sGPT supports AWS S3 Integration.

Remote caching

_As a prerequisite `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` are required as environmental variables._ _Adding a remote cache_ Note: this will create the bucket if it does not exist ``` k8sgpt cache add --region --bucket ``` _Listing cache items_ ``` k8sgpt cache list ``` _Removing the remote cache_ Note: this will not delete the bucket ``` k8sgpt cache remove --bucket ```

## Documentation Find our official documentation available [here](https://docs.k8sgpt.ai) ## Contributing Please read our [contributing guide](./CONTRIBUTING.md). ## Community Find us on [Slack](https://join.slack.com/t/k8sgpt/shared_invite/zt-1rwe5fpzq-VNtJK8DmYbbm~iWL1H34nw)