This Operator is designed to enable K8sGPT within a Kubernetes cluster. It will allow you to create a custom resource that defines the behaviour and scope of a managed K8sGPT workload. Analysis and outputs will also be configurable to enable integration into existing workflows.

helm repo add k8sgpt https://charts.k8sgpt.ai/ helm repo update helm install release k8sgpt/k8sgpt-operator -n k8sgpt-operator-system --create-namespace 

1. Install the operator from the Installation section.

2. Create secret:

kubectl create secret generic k8sgpt-sample-secret --from-literal=openai-api-key=$OPENAI_TOKEN -n k8sgpt-operator-system

3. Apply the K8sGPT configuration object:

kubectl apply -f - << EOF apiVersion: core.k8sgpt.ai/v1alpha1 kind: K8sGPT metadata:  name: k8sgpt-sample  namespace: k8sgpt-operator-system spec:  ai:  enabled: true  model: gpt-4o-mini  backend: openai  secret:  name: k8sgpt-sample-secret  key: openai-api-key  # backOff:  # enabled: false  # maxRetries: 5  # anonymized: false  # language: english  # proxyEndpoint: https://10.255.30.150 # use proxyEndpoint to setup backend through an HTTP/HTTPS proxy  noCache: false  repository: ghcr.io/k8sgpt-ai/k8sgpt  version: v0.3.48  #integrations:  # trivy:  # enabled: true  # namespace: trivy-system  # filters:  # - Ingress  # sink:  # type: slack  # webhook: <webhook-url> # use the sink secret if you want to keep your webhook url private  # secret:  # name: slack-webhook  # key: url  #extraOptions:  # backstage:  # enabled: true EOF

4. Once the custom resource has been applied the K8sGPT-deployment will be installed and you will be able to see the Results objects of the analysis after some minutes (if there are any issues in your cluster):

❯ kubectl get results -n k8sgpt-operator-system -o json | jq . { "apiVersion": "v1", "items": [ { "apiVersion": "core.k8sgpt.ai/v1alpha1", "kind": "Result", "spec": { "details": "The error message means that the service in Kubernetes doesn't have any associated endpoints, which should have been labeled with \"control-plane=controller-manager\". \n\nTo solve this issue, you need to add the \"control-plane=controller-manager\" label to the endpoint that matches the service. Once the endpoint is labeled correctly, Kubernetes can associate it with the service, and the error should be resolved.",

The k8sgpt.ai Operator allows monitoring multiple clusters by providing a kubeconfig value.

This feature could be fascinating if you want to embrace Platform Engineering such as running a fleet of Kubernetes clusters for multiple stakeholders. Especially designed for the Cluster API-based infrastructures, k8sgpt.ai Operator is going to be installed in the same Cluster API management cluster: this one is responsible for creating the required clusters according to the infrastructure provider for the seed clusters.

Once a Cluster API-based cluster has been provisioned a kubeconfig according to the naming convention ${CLUSTERNAME}-kubeconfig will be available in the same namespace: the conventional Secret data key is value, this can be used to instruct the k8sgpt.ai Operator to monitor a remote cluster without installing any resource deployed to the seed cluster.

$: kubectl get clusters NAME PHASE AGE VERSION capi-quickstart Provisioned 8s v1.28.0 $: kubectl get secrets NAME TYPE DATA AGE capi-quickstart-kubeconfig Opaque 1 8s 

A security concern

If your setup requires the least privilege approach, a different kubeconfig must be provided since the Cluster API generated one is bounded to the admin user which has clustr-admin permissions.

Once you have a valid kubeconfig, a k8sgpt instance can be created as it follows.

apiVersion: core.k8sgpt.ai/v1alpha1 kind: K8sGPT metadata: name: capi-quickstart namespace: default spec: ai: anonymized: true backend: openai language: english model: gpt-4o-mini secret: key: api_key name: my_openai_secret kubeconfig: key: value name: capi-quickstart-kubeconfig

Once applied the k8sgpt.ai Operator will create the k8sgpt.ai Deployment by using the seed cluster kubeconfig defined in the field /spec/kubeconfig.

The resulting Result objects will be available in the same Namespace where the k8sgpt.ai instance has been deployed, accordingly labelled with the following keys:

• k8sgpts.k8sgpt.ai/name: the k8sgpt.ai instance Name
• k8sgpts.k8sgpt.ai/namespace: the k8sgpt.ai instance Namespace
• k8sgpts.k8sgpt.ai/backend: the AI backend (if specified)

Thanks to these labels, the results can be filtered according to the specified monitored cluster, without polluting the underlying cluster with the k8sgpt.ai CRDs and consuming seed compute workloads, as well as keeping confidentiality about the AI backend driver credentials.

In case of missing /spec/kubeconfig field, k8sgpt.ai Operator will track the cluster on which has been deployed: this is possible by mounting the provided ServiceAccount.

helm install release k8sgpt/k8sgpt-operator -n k8sgpt-operator-system --create-namespace --set interplex.enabled=true 

kubectl create secret generic k8sgpt-sample-secret --from-literal=openai-api-key=$OPENAI_TOKEN -n k8sgpt-operator-system 

 kubectl apply -f - << EOF apiVersion: core.k8sgpt.ai/v1alpha1 kind: K8sGPT metadata: name: k8sgpt-sample namespace: k8sgpt-operator-system spec: ai: enabled: true model: gpt-4o-mini backend: openai secret: name: k8sgpt-sample-secret key: openai-api-key noCache: false remoteCache: interplex: endpoint: release-interplex-service:8084 repository: ghcr.io/k8sgpt-ai/k8sgpt version: v0.3.48 EOF 

kubectl create secret generic k8sgpt-sample-cache-secret --from-literal=azure_client_id=<AZURE_CLIENT_ID> --from-literal=azure_tenant_id=<AZURE_TENANT_ID> --from-literal=azure_client_secret=<AZURE_CLIENT_SECRET> -n k8sgpt- operator-system

kubectl apply -f - << EOF apiVersion: core.k8sgpt.ai/v1alpha1 kind: K8sGPT metadata: name: k8sgpt-sample namespace: k8sgpt-operator-system spec: ai: model: gpt-4o-mini backend: openai enabled: true secret: name: k8sgpt-sample-secret key: openai-api-key noCache: false repository: ghcr.io/k8sgpt-ai/k8sgpt version: v0.3.48 remoteCache: credentials: name: k8sgpt-sample-cache-secret azure: # Storage account must already exist storageAccount: "account_name" containerName: "container_name" EOF 

kubectl create secret generic k8sgpt-sample-cache-secret --from-literal=aws_access_key_id=<AWS_ACCESS_KEY_ID> --from-literal=aws_secret_access_key=<AWS_SECRET_ACCESS_KEY> -n k8sgpt- operator-system

kubectl apply -f - << EOF apiVersion: core.k8sgpt.ai/v1alpha1 kind: K8sGPT metadata: name: k8sgpt-sample namespace: k8sgpt-operator-system spec: ai: model: gpt-4o-mini backend: openai enabled: true secret: name: k8sgpt-sample-secret key: openai-api-key noCache: false repository: ghcr.io/k8sgpt-ai/k8sgpt version: v0.3.48 remoteCache: credentials: name: k8sgpt-sample-cache-secret s3: bucketName: foo region: us-west-1 EOF 

kubectl create secret generic k8sgpt-sample-secret --from-literal=azure-api-key=$AZURE_TOKEN -n k8sgpt-operator-system

kubectl apply -f - << EOF apiVersion: core.k8sgpt.ai/v1alpha1 kind: K8sGPT metadata: name: k8sgpt-sample namespace: k8sgpt-operator-system spec: ai: enabled: true secret: name: k8sgpt-sample-secret key: azure-api-key model: gpt-4o-mini backend: azureopenai baseUrl: https://k8sgpt.openai.azure.com/ engine: llm noCache: false repository: ghcr.io/k8sgpt-ai/k8sgpt version: v0.3.48 EOF 

{ "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "bedrock:InvokeModel", "bedrock:InvokeModelWithResponseStream" ], "Resource": "*" } ] } 

kubectl create secret generic bedrock-sample-secret --from-literal=AWS_ACCESS_KEY_ID="$(echo $AWS_ACCESS_KEY_ID)" --from-literal=AWS_SECRET_ACCESS_KEY="$(echo $AWS_SECRET_ACCESS_KEY)" -n k8sgpt-operator-system

kubectl apply -f - << EOF apiVersion: core.k8sgpt.ai/v1alpha1 kind: K8sGPT metadata: name: k8sgpt-sample namespace: k8sgpt-operator-system spec: ai: enabled: true secret: name: bedrock-sample-secret model: anthropic.claude-3-5-sonnet-20241022-v2:0 region: eu-central-1 backend: amazonbedrock noCache: false repository: ghcr.io/k8sgpt-ai/k8sgpt version: v0.3.48 EOF 

kubectl apply -f - << EOF apiVersion: core.k8sgpt.ai/v1alpha1 kind: K8sGPT metadata:  name: k8sgpt-local-ai  namespace: default spec:  ai:  enabled: true  model: ggml-gpt4all-j  backend: localai  baseUrl: http://local-ai.local-ai.svc.cluster.local:8080/v1  noCache: false  repository: ghcr.io/k8sgpt-ai/k8sgpt  version: v0.3.48 EOF

kubectl apply -f - << EOF apiVersion: core.k8sgpt.ai/v1alpha1 kind: K8sGPT metadata:  name: k8sgpt-sample  namespace: k8sgpt-operator-system spec:  ai:  enabled: true  model: gpt-4o-mini  backend: openai  secret:  name: k8sgpt-sample-secret  key: openai-api-key  noCache: false  repository: sample.repository/k8sgpt  version: sample-tag  imagePullSecrets:  - name: sample-secret EOF

kubectl apply -f - << EOF apiVersion: core.k8sgpt.ai/v1alpha1 kind: K8sGPT metadata:  name: k8sgpt-sample  namespace: k8sgpt-operator-system spec:  ai:  enabled: true  model: gpt-4o-mini  backend: openai  secret:  name: k8sgpt-sample-secret  key: openai-api-key  noCache: false  repository: ghcr.io/k8sgpt-ai/k8sgpt  resources:  limits:  cpu: 10  memory: 512Mi  requests:  cpu: 200m  memory: 156Mi EOF

For details please see here