Kubernetes AI Ops Agent

by jhzhu89

4 stars

379 downloads

Not rated

GitHub

Cloud Service

About

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.

Details

Author: jhzhu89
GitHub stars: 4
Downloads: 379
Categories: Cloud Service

- 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

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name Kubernetes AI Ops Agent

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "kubernetes ai ops agent": {
            "kubernetes-ai-ops-agent": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "<YOUR_CONTAINER_REGISTRY>/kubernetes-ai-ops-agent:<TAG>",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "kubernetes ai ops agent": {
            "kubernetes-ai-ops-agent": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "<YOUR_CONTAINER_REGISTRY>/kubernetes-ai-ops-agent:<TAG>",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "kubernetes ai ops agent": {
            "kubernetes-ai-ops-agent": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "<YOUR_CONTAINER_REGISTRY>/kubernetes-ai-ops-agent:<TAG>",
                    "."
                ]
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "kubernetes ai ops agent": {
            "kubernetes-ai-ops-agent": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "<YOUR_CONTAINER_REGISTRY>/kubernetes-ai-ops-agent:<TAG>",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "kubernetes ai ops agent": {
            "kubernetes-ai-ops-agent": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "<YOUR_CONTAINER_REGISTRY>/kubernetes-ai-ops-agent:<TAG>",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "kubernetes ai ops agent": {
            "kubernetes-ai-ops-agent": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "<YOUR_CONTAINER_REGISTRY>/kubernetes-ai-ops-agent:<TAG>",
                    "."
                ]
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "kubernetes ai ops agent": {
            "kubernetes-ai-ops-agent": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "<YOUR_CONTAINER_REGISTRY>/kubernetes-ai-ops-agent:<TAG>",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "kubernetes ai ops agent": {
            "kubernetes-ai-ops-agent": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "<YOUR_CONTAINER_REGISTRY>/kubernetes-ai-ops-agent:<TAG>",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "kubernetes ai ops agent": {
            "kubernetes-ai-ops-agent": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "<YOUR_CONTAINER_REGISTRY>/kubernetes-ai-ops-agent:<TAG>",
                    "."
                ]
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "kubernetes ai ops agent": {
            "kubernetes-ai-ops-agent": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "<YOUR_CONTAINER_REGISTRY>/kubernetes-ai-ops-agent:<TAG>",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "kubernetes ai ops agent": {
            "kubernetes-ai-ops-agent": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "<YOUR_CONTAINER_REGISTRY>/kubernetes-ai-ops-agent:<TAG>",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "kubernetes ai ops agent": {
            "kubernetes-ai-ops-agent": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "<YOUR_CONTAINER_REGISTRY>/kubernetes-ai-ops-agent:<TAG>",
                    "."
                ]
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "kubernetes ai ops agent": {
            "kubernetes-ai-ops-agent": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "<YOUR_CONTAINER_REGISTRY>/kubernetes-ai-ops-agent:<TAG>",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "kubernetes ai ops agent": {
            "kubernetes-ai-ops-agent": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "<YOUR_CONTAINER_REGISTRY>/kubernetes-ai-ops-agent:<TAG>",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "kubernetes ai ops agent": {
            "kubernetes-ai-ops-agent": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "<YOUR_CONTAINER_REGISTRY>/kubernetes-ai-ops-agent:<TAG>",
                    "."
                ]
            }
        }
    }
}

From the repository

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.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "kubernetes ai ops agent": {
            "kubernetes-ai-ops-agent": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "<YOUR_CONTAINER_REGISTRY>/kubernetes-ai-ops-agent:<TAG>",
                    "."
                ]
            }
        }
    }
}

McpServers

{
    "kubernetes-ai-ops-agent": {
        "command": "docker",
        "args": [
            "build",
            "-t",
            "<YOUR_CONTAINER_REGISTRY>/kubernetes-ai-ops-agent:<TAG>",
            "."
        ]
    }
}

Kubernetes AI Ops Agent

An AI-powered assistant for Kubernetes operations and management through natural language interactions.

Overview

Kubernetes AI Ops Agent is an intelligent agent that helps DevOps engineers and Kubernetes administrators manage Kubernetes clusters through conversational interfaces. The project leverages Large Language Models (LLMs) to interpret user intents and execute Kubernetes operations using specialized MCP (Model Context Protocol) servers.

Kubernetes AI Ops Agent Demo

Project Status

Note: This project is currently in an experimental stage. It serves primarily as a proof of concept to validate the capabilities and potential benefits of integrating Large Language Models (LLMs) with Kubernetes, Prometheus, and other MCP (Model Context Protocol) servers. Features and functionality may change significantly as the project evolves.

Features

- 🤖 AI-Powered Assistance: Interact with your Kubernetes clusters using natural language
- 🧰 Tool Integration: Execute Kubernetes operations through specialized tools
- 🚀 Deployment Ready: Packaged with Docker and Helm charts for easy deployment
- 📊 Web Interface: Built with Chainlit for an interactive chat experience

Prerequisites

- Python 3.10+
- Docker
- Kubernetes cluster access
- Helm (for deployment)

Installation

Local Development

1. Clone the repository:

   git clone https://github.com/yourusername/kubernetes-ai-ops-agent.git

   cd kubernetes-ai-ops-agent

2. Install the dependencies:

   pip install -r requirements.txt

3. Install the required MCP servers:

# Install the Kubernetes MCP server npm install -g @kubernetes-ai/mcp-server-kubernetes

# Install the Prometheus MCP server pip install prometheus-mcp-server

> Note: Do not use the MCP servers located in the deps/ directory for local development. These are customized versions:
> - The Kubernetes MCP server in deps/ is modified to use loadFromCluster for proper initialization in a Pod environment.
> - The MCP servers in deps/ are included to be packaged directly into the container image rather than downloaded at runtime.

4. Configure your Kubernetes access:
- Ensure your kubeconfig is properly set up
- The agent will use your current kubectl context

5. Start the application:

   chainlit run src/main.py

Helm Chart Deployment

1. Build and push the Docker image:

```bash

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About

Details

Jump to

Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

Kubernetes AI Ops Agent

Overview

Project Status

Features

Prerequisites

Installation

Local Development

Helm Chart Deployment

Install

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