🚀 Kube-Whisperer

Autonomous AI Agent for Kubernetes Triage & Infrastructure Management

Kube-Whisperer is an experimental, fully autonomous AI agent built on the Model Context Protocol (MCP). It bridges the gap between Large Language Models and your Kubernetes clusters, allowing you to manage, debug, scale, and provision your infrastructure entirely through natural language conversations.

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📖 Table of Contents

1. What is Kube-Whisperer?
2. Architecture Overview
3. Features & Capabilities
4. Prerequisites
5. Installation & Setup
6. Usage Guide
7. Project Structure

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🤖 What is Kube-Whisperer?

Managing Kubernetes can be complex and verbose. When a pod crashes, DevOps engineers typically run a series of commands (kubectl get pods, kubectl describe, kubectl logs) to triage the issue.

Kube-Whisperer automates this. Powered by a locally running LLM (e.g., Llama 3.1) and standard Kubernetes Python clients, Kube-Whisperer acts as a highly intelligent operator inside your cluster. You can ask it to "find out why my apps are crashing" or "spin up a new namespace and deploy this YAML file," and it will autonomously select the right tools, execute the API calls, and report back to you in plain English.

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🏗️ Architecture Overview

The project is built around the Model Context Protocol (MCP), which provides a standardized way for AI models to interact with external tools and data sources.

• MCP Server (server.py): A Python-based server that securely interfaces with your kubeconfig and the Kubernetes API. It exposes a strict set of predefined "tools" (e.g., list_pods, deploy_yaml) and handles argument validation to prevent rogue commands.
• AI Agent CLI (agent_cli.py): The conversational interface. It connects to the MCP Server over stdio, fetches the available tools, translates them into a schema that the LLM understands, and orchestrates the chat loop. The Agent evaluates your requests, decides which tools to call, and parses the JSON results back into natural language.

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✨ Features & Capabilities

The agent is currently equipped with 13 core infrastructure tools:

🔍 Intelligent Triage

• list_pods: Lists all pods in a namespace alongside their current lifecycle phase.
• get_pod_status: Deep dives into a specific pod, retrieving restart counts and failure conditions.
• get_pod_logs: Streams the tail logs from a failing container to diagnose application-level errors.

⚡ Self-Healing & Management

• delete_pod: Forcefully restarts stuck pods by deleting them.
• list_deployments: Views deployment rollout status and replica health.
• scale_deployment: Dynamically scales replica sets up or down based on your instructions.
• restart_deployment: Triggers a zero-downtime rolling restart for a deployment.
• delete_deployment: Tears down deployments.
• list_services: Inspects cluster networking and exposed NodePorts/LoadBalancers.

🏗️ Infrastructure as Conversation

• list_namespaces: Maps out the entire cluster's namespace landscape.
• create_namespace: Provisions new logical boundaries on the fly.
• deploy_yaml: Dynamically reads local YAML files and deploys them to a target namespace.
• create_cluster: Interacts with the host OS to spin up brand new local Kubernetes clusters using engines like minikube or kind!

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📋 Prerequisites

Before running Kube-Whisperer, ensure you have the following installed:

• Python 3.10+
• Kubernetes Cluster: A local cluster (MicroK8s, Minikube, or Kind) or a remote cluster.
• Kubeconfig: Your ~/.kube/config must be properly configured and authenticated with your target cluster.
• Ollama / LLM Provider: An LLM endpoint compatible with the OpenAI spec (the CLI defaults to looking for a local instance).

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🚀 Installation & Setup

1. Clone the Repository Navigate to your workspace and ensure you are in the kube_whisperer directory.

2. Set up the Virtual Environment

   python3 -m venv .venv
   source .venv/bin/activate
   

3. Install Dependencies Install the required packages from the requirements.txt file into your virtual environment:

   pip install -r requirements.txt
   

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💬 Usage Guide

To start chatting with your cluster, simply run the Agent CLI:

.venv/bin/python agent_cli.py

Upon startup, the agent will initialize the connection to the MCP server, load the available tools, and present a Main Menu of its capabilities.

Example Interactions

Example 1: Triaging a Crash

You: "Are there any pods crashing in the broken-apps namespace?" Agent: (Calls list_pods -> Calls get_pod_status -> Calls get_pod_logs) "I found a pod in CrashLoopBackOff. The logs indicate it ran out of memory. Would you like me to restart the deployment?"

Example 2: Deploying Infrastructure

You: "Deploy the kube-whisperer-web.yaml file into the nginx-webserver namespace." Agent: (Calls deploy_yaml passing the dynamic namespace) "I have successfully deployed the resources into the nginx-webserver namespace!"

Example 3: Cluster Management

You: "Spin up a new minikube cluster named testing-env." Agent: (Calls create_cluster) "I've started the minikube provisioning process. The testing-env cluster is now coming online."

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📁 Project Structure

• agent_cli.py: The conversational AI frontend and reasoning loop.
• server.py: The MCP backend that interacts directly with the Kubernetes API.
• test_mcp.py: A utility script for testing MCP tool calls directly without the LLM.
• kube-whisperer-web.yaml: A sample enterprise-grade landing page configured as a Kubernetes ConfigMap and NGINX Deployment.
• oom-deployment.yaml: A sample crashing deployment used for testing the agent's triage capabilities.

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