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.
@jhzhu89
基本情報
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.
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"kubernetes-ai-ops-agent": {
"command": "docker",
"args": [
"build",
"-t",
"<YOUR_CONTAINER_REGISTRY>/kubernetes-ai-ops-agent:<TAG>",
"."
]
}
}
}ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
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.
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.
Only OpenAI models are supported. You can configure either the OpenAI API directly or Azure OpenAI API.
No, the project is currently experimental and serves as a proof of concept. Features may change significantly.
You need Python 3.10+, Docker, Helm, access to a Kubernetes cluster with a valid kubeconfig, and the required MCP servers installed.
Session data is managed by Chainlit’s session storage; for local development it is stored in memory. No external database is required.
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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