32 bit:

sudo rpm -ivh https://github.com/k8sgpt-ai/k8sgpt/releases/download/v0.4.38/k8sgpt_386.rpm

64 bit:

sudo rpm -ivh https://github.com/k8sgpt-ai/k8sgpt/releases/download/v0.4.38/k8sgpt_amd64.rpm

32 bit:

curl -LO https://github.com/k8sgpt-ai/k8sgpt/releases/download/v0.4.38/k8sgpt_386.deb
sudo dpkg -i k8sgpt_386.deb

64 bit:

curl -LO https://github.com/k8sgpt-ai/k8sgpt/releases/download/v0.4.38/k8sgpt_amd64.deb
sudo dpkg -i k8sgpt_amd64.deb

32 bit:

wget https://github.com/k8sgpt-ai/k8sgpt/releases/download/v0.4.38/k8sgpt_386.apk
apk add --allow-untrusted k8sgpt_386.apk

64 bit:

wget https://github.com/k8sgpt-ai/k8sgpt/releases/download/v0.4.38/k8sgpt_amd64.apk
apk add --allow-untrusted k8sgpt_amd64.apk

==> 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

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

List configured backends

k8sgpt auth list

Update configured backends

k8sgpt auth update $MY_BACKEND1,$MY_BACKEND2..

Remove configured backends

k8sgpt auth remove -b $MY_BACKEND1,$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

Serve mode with MCP (Model Context Protocol)

# Enable MCP server on default port 8089
k8sgpt serve --mcp --mcp-http

# Enable MCP server on custom port
k8sgpt serve --mcp --mcp-http --mcp-port 8089

# Full serve mode with MCP
k8sgpt serve --mcp --mcp-http --port 8080 --metrics-port 8081 --mcp-port 8089

The MCP server enables integration with tools like Claude Desktop and other MCP-compatible clients. It runs on port 8089 by default and provides:

• Kubernetes cluster analysis via MCP protocol
• Resource information and health status
• AI-powered issue explanations and recommendations

For Helm chart deployment with MCP support, see the charts/k8sgpt/values-mcp-example.yaml file.

Analysis with serve mode

grpcurl -plaintext -d '{"namespace": "k8sgpt", "explain" : "true"}' localhost:8080 schema.v1.ServerAnalyzerService/Analyze
{
  "status": "OK"
}

Analysis with custom headers

k8sgpt analyze --explain --custom-headers CustomHeaderKey:CustomHeaderValue

Print analysis stats

k8sgpt analyze -s
The stats mode allows for debugging and understanding the time taken by an analysis by displaying the statistics of each analyzer.
- Analyzer Ingress took 47.125583ms
- Analyzer PersistentVolumeClaim took 53.009167ms
- Analyzer CronJob took 57.517792ms
- Analyzer Deployment took 156.6205ms
- Analyzer Node took 160.109833ms
- Analyzer ReplicaSet took 245.938333ms
- Analyzer StatefulSet took 448.0455ms
- Analyzer Pod took 5.662594708s
- Analyzer Service took 38.583359166s

Diagnostic information

To collect diagnostic information use the following command to create a dump_<timestamp>_json in your local directory.

k8sgpt dump

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.

1. Error reported during analysis:

Error: HorizontalPodAutoscaler uses StatefulSet/fake-deployment as ScaleTargetRef which does not exist.

2. Payload sent to the AI backend:

Error: HorizontalPodAutoscaler uses StatefulSet/tGLcCRcHa1Ce5Rs as ScaleTargetRef which does not exist.

3. Payload returned by the AI:

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:

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.

Further Details

Note: Anonymization does not currently apply to events.

In a few analysers like Pod, we feed to the AI backend the event messages which are not known beforehand thus we are not masking them for the time being.

• The following is the list of analysers in which data is being masked:-

  • Statefulset
  • Service
  • PodDisruptionBudget
  • Node
  • NetworkPolicy
  • Ingress
  • HPA
  • Deployment
  • Cronjob

• The following is the list of analysers in which data is not being masked:-

  • ReplicaSet
  • PersistentVolumeClaim
  • Pod
  • Log
  • *Events

*Note:

• k8gpt will not mask the above analysers because they do not send any identifying information except Events analyser.

• Masking for Events analyzer is scheduled in the near future as seen in this issue. Further research has to be made to understand the patterns and be able to mask the sensitive parts of an event like pod name, namespace etc.

• The following is the list of fields which are not being masked:-

  • Describe
  • ObjectStatus
  • Replicas
  • ContainerStatus
  • *Event Message
  • ReplicaStatus
  • Count (Pod)

*Note:

• It is quite possible the payload of the event message might have something like "super-secret-project-pod-X crashed" which we don't currently redact (scheduled in the near future as seen in this issue).

Proceed with care

• The K8gpt team recommends using an entirely different backend (a local model) in critical production environments. By using a local model, you can rest assured that everything stays within your DMZ, and nothing is leaked.
• If there is any uncertainty about the possibility of sending data to a public LLM (open AI, Azure AI) and it poses a risk to business-critical operations, then, in such cases, the use of public LLM should be avoided based on personal assessment and the jurisdiction of risks involved.

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:

Adding a remote cache

• AWS S3
  • As a prerequisite AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY are required as environmental variables.
  • Configuration, k8sgpt cache add s3 --region <aws region> --bucket <name>
  • Minio Configuration with HTTP endpoint k8sgpt cache add s3 --bucket <name> --endpoint <http://localhost:9000>
  • Minio Configuration with HTTPs endpoint, skipping TLS verification k8sgpt cache add s3 --bucket <name> --endpoint <https://localhost:9000> --insecure
    • K8sGPT will create the bucket if it does not exist
• Azure Storage
  • We support a number of techniques to authenticate against Azure
  • Configuration, k8sgpt cache add azure --storageacc <storage account name> --container <container name>
    • K8sGPT assumes that the storage account already exist and it will create the container if it does not exist
    • It is the user responsibility have to grant specific permissions to their identity in order to be able to upload blob files and create SA containers (e.g Storage Blob Data Contributor)
• Google Cloud Storage
  • As a prerequisite GOOGLE_APPLICATION_CREDENTIALS are required as environmental variables.
  • Configuration, k8sgpt cache add gcs --region <gcp region> --bucket <name> --projectid <project id>
    • K8sGPT will create the bucket if it does not exist

Listing cache items

k8sgpt cache list

Purging an object from the cache Note: purging an object using this command will delete upstream files, so it requires appropriate permissions.

k8sgpt cache purge $OBJECT_NAME

Removing the remote cache Note: this will not delete the upstream S3 bucket or Azure storage container

k8sgpt cache remove

There may be scenarios where you wish to write your own analyzer in a language of your choice. K8sGPT now supports the ability to do so by abiding by the schema and serving the analyzer for consumption. To do so, define the analyzer within the K8sGPT configuration and it will add it into the scanning process. In addition to this you will need to enable the following flag on analysis:

k8sgpt analyze --custom-analysis

Here is an example local host analyzer in Rust When this is run on localhost:8080 the K8sGPT config can pick it up with the following additions:

custom_analyzers:
  - name: host-analyzer
    connection:
      url: localhost
      port: 8080

This now gives the ability to pass through hostOS information ( from this analyzer example ) to K8sGPT to use as context with normal analysis.

See the docs on how to write a custom analyzer

Listing custom analyzers configured

k8sgpt custom-analyzer list

Adding custom analyzer without install

k8sgpt custom-analyzer add --name my-custom-analyzer --port 8085

Removing custom analyzer

k8sgpt custom-analyzer remove --names "my-custom-analyzer,my-custom-analyzer-2"