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.
Out of the box integration with OpenAI, Azure, Cohere, Amazon Bedrock, Google Gemini and local models.
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/v0.3.29/k8sgpt_386.rpm
sudo rpm -ivh k8sgpt_386.rpm
64 bit:
curl -LO https://github.com/k8sgpt-ai/k8sgpt/releases/download/v0.3.29/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/v0.3.29/k8sgpt_386.deb
sudo dpkg -i k8sgpt_386.deb
64 bit:
curl -LO https://github.com/k8sgpt-ai/k8sgpt/releases/download/v0.3.29/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/v0.3.29/k8sgpt_386.apk
apk add k8sgpt_386.apk
64 bit:
curl -LO https://github.com/k8sgpt-ai/k8sgpt/releases/download/v0.3.29/k8sgpt_amd64.apk
apk add k8sgpt_amd64.apk
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
- Download the latest Windows binaries of k8sgpt from the Release tab based on your system architecture.
- Extract the downloaded package to your desired location. Configure the system path variable with the binary location
To install within a Kubernetes cluster please use our k8sgpt-operator with installation instructions available here
This mode of operation is ideal for continuous monitoring of your cluster and can integrate with your existing monitoring such as Prometheus and Alertmanager.
- Currently, the default AI provider is OpenAI, you will need to generate an API key from OpenAI
- You can do this by running
k8sgpt generateto open a browser link to generate it
- You can do this by running
- Run
k8sgpt auth addto set it in k8sgpt.- You can provide the password directly using the
--passwordflag.
- You can provide the password directly using the
- Run
k8sgpt filtersto manage the active filters used by the analyzer. By default, all filters are executed during analysis. - Run
k8sgpt analyzeto run a scan. - And use
k8sgpt analyze --explainto get a more detailed explanation of the issues. - You also run
k8sgpt analyze --with-doc(with or without the explain flag) to get the official documentation from Kubernetes.
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.
- podAnalyzer
- pvcAnalyzer
- rsAnalyzer
- serviceAnalyzer
- eventAnalyzer
- ingressAnalyzer
- statefulSetAnalyzer
- deploymentAnalyzer
- cronJobAnalyzer
- nodeAnalyzer
- mutatingWebhookAnalyzer
- validatingWebhookAnalyzer
- hpaAnalyzer
- pdbAnalyzer
- networkPolicyAnalyzer
- gatewayClass
- gateway
- httproute
Run a scan with the default analyzers
k8sgpt generate
k8sgpt auth add
k8sgpt analyze --explain
k8sgpt analyze --explain --with-doc
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)]
- Simple filter :
k8sgpt filters add Service - Multiple filters :
k8sgpt filters add Ingress,Pod
Remove default filters
k8sgpt filters remove [filter(s)]
- Simple filter :
k8sgpt filters remove Service - Multiple filters :
k8sgpt filters remove Ingress,Pod
Additional commands
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
Analysis with serve mode
grpcurl -plaintext -d '{"namespace": "k8sgpt", "explain": false}' localhost:8080 schema.v1.ServerService/Analyze
K8sGPT uses the chosen LLM, generative AI provider when you want to explain the analysis results using --explain flag e.g. k8sgpt analyze --explain. You can use --backend flag to specify a configured provider (it's openai by default).
You can list available providers using k8sgpt auth list:
Default:
> openai
Active:
Unused:
> openai
> localai
> azureopenai
> cohere
> amazonbedrock
> amazonsagemaker
> google
> huggingface
> noopai
> googlevertexai
For detailed documentation on how to configure and use each provider see here.
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
- Error reported during analysis:
Error: HorizontalPodAutoscaler uses StatefulSet/fake-deployment as ScaleTargetRef which does not exist.- Payload sent to the AI backend:
Error: HorizontalPodAutoscaler uses StatefulSet/tGLcCRcHa1Ce5Rs as ScaleTargetRef which does not exist.- 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.- 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.Note: Anonymization does not currently apply to events.
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:-
- RepicaSet
- PersistentVolumeClaim
- Pod
- *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).
- 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.
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 |
Remote caching
Note: You can only configure and use only one remote cache at a timeAdding a remote cache
- AWS S3
- As a prerequisite
AWS_ACCESS_KEY_IDandAWS_SECRET_ACCESS_KEYare 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
- As a prerequisite
- 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_CREDENTIALSare 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
- As a prerequisite
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
Custom Analyzers
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
Find our official documentation available here
Please read our contributing guide.
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