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
基本信息
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.
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"kubernetes-ai-ops-agent": {
"command": "docker",
"args": [
"build",
"-t",
"<YOUR_CONTAINER_REGISTRY>/kubernetes-ai-ops-agent:<TAG>",
"."
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
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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