kubectl port-forward alertmanager-robusta-kube-prometheus-st-alertmanager-0 9093:9093 &
holmes investigate alertmanager --alertmanager-url http:https://localhost:9093

Note - if on Mac OS and using the Docker image, you will need to use http:https://docker.for.mac.localhost:9093 instead of http:https://localhost:9093

1. Add our tap:

brew tap robusta-dev/homebrew-holmesgpt

2. Install holmesgpt:

brew install holmesgpt

3. Check that installation was successful. This will take a few seconds on the first run - wait patiently.:

holmes --help

4. Run holmesgpt:

holmes ask "what issues do I have in my cluster"

Run the below command, replacing <VERSION_PLACEHOLDER> with the latest HolmesGPT version - e.g. 0.1.

docker run -it --net=host -v $(pwd)/config.yaml:/app/config.yaml -v ~/.aws:/root/.aws -v ~/.config/gcloud:/root/.config/gcloud -v $HOME/.kube/config:/root/.kube/config us-central1-docker.pkg.dev/genuine-flight-317411/devel/holmes:<VERSION_PLACEHOLDER> ask "what pods are unhealthy and why?"

First install poetry (the python package manager)

Clone the project from github, and then run:

cd holmesgpt
poetry install --no-root
poetry run python3 holmes.py ask "what pods are unhealthy and why?"

Clone the project from github, and then run:

cd holmesgpt
docker build -t holmes .
docker run -it --net=host -v $(pwd)/config.yaml:/app/config.yaml -v ~/.aws:/root/.aws -v ~/.config/gcloud:/root/.config/gcloud -v $HOME/.kube/config:/root/.kube/config holmest ask "what pods are unhealthy and why?"

To work with OpenAI’s GPT 3.5 or GPT-4 models you need a paid OpenAI API key.

Note: This is different from being a “ChatGPT Plus” subscriber.

Add the api_key to the config.yaml or pass them via the CLI.

To work with Azure AI, you need the Azure OpenAI.

holmes ask "what pods are unhealthy and why?" --llm=azure --api-key=<PLACEHOLDER> --azure-endpoint='<PLACEHOLDER>'

You will need an LLM with support for function-calling (tool-calling). To use it, set the OPENAI_BASE_URL environment variable and run holmes with a relevant model name set using --model.

Important: Please verify that your model and inference server support function calling! HolmesGPT is currently unable to check if the LLM it was given supports function-calling or not. Some models that lack function-calling capabilities will hallucinate answers instead of reporting that they are unable to call functions. This behaviour depends on the model.

In particular, note that vLLM does yet support function calling, whereas llama-cpp does support it.

LLM uses the built-in Helm toolset to gather information.

holmes ask "what helm value should I change to increase memory request of the my-argo-cd-argocd-server-6864949974-lzp6m pod"

LLM uses the built-in Docker toolset to gather information.

holmes ask "Tell me what layers of my pavangudiwada/robusta-ai docker image consume the most storage and suggest some fixes to it"

By default investigation results are displayed in the CLI itself. You can optionally get these results in a Slack channel:

holmes investigate alertmanager --alertmanager-url http:https://localhost:9093 --destination slack --slack-token <PLACEHOLDER_SLACK_TOKEN> --slack-channel <PLACEHOLDER_SLACK_CHANNEL>

Alternatively you can update the config.yaml with your Slack details and run:

holmes investigate alertmanager --alertmanager-url http:https://localhost:9093 --destination slack

By default Jira investigation results are displayed in the CLI itself. But you can use --update-ticket to get the results as a comment in the Jira ticket.

holmes investigate jira --jira-url https://<PLACEDHOLDER>.atlassian.net --jira-username <PLACEHOLDER_EMAIL> --jira-api-key <PLACEHOLDER_API_KEY> --update-ticket

Alternatively you can update the config.yaml with your Jira account details and run:

holmes investigate jira --update-ticket

The more data you give HolmesGPT, the better it will perform. Give it access to more data by adding custom tools.

New tools are loaded using -t from custom toolset files or by adding them to the config.yaml in custom_toolsets.

HolmesGPT can investigate by following runbooks written in plain English. Add your own runbooks to provided the LLM specific instructions.

New runbooks are loaded using -r from custom runbook files or by adding them to the config.yaml in custom_runbooks.

You can customize HolmesGPT's behaviour with command line flags, or you can save common settings in config file for re-use.

You can view an example config file with all available settings here.

By default, without specifying --config the agent will try to read config.yaml from the current directory. If a setting is specified in both in config file and cli, cli takes precedence.

Adding a Slack integration allows the LLM to send Prometheus Alert investigation details to a Slack channel. To do this you need the following

1. slack-token: The Slack API key. You can generate with pip install robusta-cli && robusta integrations slack
2. slack-channel: The Slack channel where you want to recieve the findings.

Add these values to the config.yaml or pass them via the CLI.

Adding a Jira integration allows the LLM to fetch Jira tickets and investigate automatically. Optionally it can update the Jira ticked with findings too. You need the following to use this

1. url: The URL of your workspace. For example: https://workspace.atlassian.net (Note: schema (https) is required)
2. username: The email you use to log into your Jira account. Eg: [email protected]
3. api_key: Follow these instructions to get your API key.
4. project: Name of the project you want the Jira tickets to be created in. Go to Project Settings -> Details -> Name.
5. status: Status of a ticket. Example: To Do, In Progress

Add these values to the config.yaml or pass them via the CLI.