What models does Kubernetes AI Ops Agent support?
Only OpenAI models are supported. You can configure either the OpenAI API directly or Azure OpenAI API.
Kubernetes AI Ops Agent MCP Server
@jhzhu89
Basic information
About Kubernetes AI Ops Agent MCP Server
AI-powered assistant that enables natural language interactions with Kubernetes clusters. Simplifies DevOps workflows using Model Context Protocol (MCP) servers for executing K8s operations through conversational interfaces.
Kubernetes AI Ops Agent MCP Server config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"kubernetes-ai-ops-agent": {
"command": "docker",
"args": [
"build",
"-t",
"<YOUR_CONTAINER_REGISTRY>/kubernetes-ai-ops-agent:<TAG>",
"."
]
}
}
}Kubernetes AI Ops Agent MCP Server tools
Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Overview
What is Kubernetes AI Ops Agent?
Kubernetes AI Ops Agent is an AI-powered assistant that helps DevOps engineers and Kubernetes administrators manage Kubernetes clusters through natural language conversations. It leverages Large Language Models (LLMs) and specialized MCP (Model Context Protocol) servers to interpret user intents and execute Kubernetes operations.
How to use Kubernetes AI Ops Agent?
Install dependencies: npm install -g @kubernetes-ai/mcp-server-kubernetes and pip install prometheus-mcp-server. Ensure your Kubernetes kubeconfig is set up, then run chainlit run src/main.py for local development. For production, build a Docker image and deploy via the provided Helm chart with your OpenAI or Azure OpenAI credentials and Prometheus URL.
Key features of Kubernetes AI Ops Agent
- AI-powered natural language interaction with Kubernetes clusters
- Tool integration for executing Kubernetes operations
- Packaged with Docker and Helm charts for easy deployment
- Web interface built with Chainlit for interactive chat
- Supports OpenAI and Azure OpenAI models (currently only OpenAI)
- Integrates Kubernetes and Prometheus MCP servers
Use cases of Kubernetes AI Ops Agent
- Query cluster state: “Show me all pods in the default namespace”
- Troubleshoot pods: “Why is my pod in CrashLoopBackOff state?”
- Retrieve monitoring metrics from Prometheus
- Automate routine Kubernetes admin tasks via conversation
Frequently asked questions
Is Kubernetes AI Ops Agent production-ready?
No, the project is currently experimental and serves as a proof of concept. Features may change significantly.
What are the prerequisites for running Kubernetes AI Ops Agent?
You need Python 3.10+, Docker, Helm, access to a Kubernetes cluster with a valid kubeconfig, and the required MCP servers installed.
Where does session data live?
Session data is managed by Chainlit’s session storage; for local development it is stored in memory. No external database is required.
Can I run Kubernetes AI Ops Agent locally without a container?
Yes, follow the local development installation steps: clone the repo, install dependencies, configure kubeconfig, and run `chainlit run src/main.py`.
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