awesome-ctl 0.2.0

A comprehensive toolkit for CloudOps automation

Awesome-ctl

AI-Powered Diagnostics for Your Entire Stack 🧠

Table of Contents

• Introduction
• Key Features
• Getting Started
  • Prerequisites
  • Installation
  • Usage
• Project Structure
• Contributing
• License
• Acknowledgements

Tired of wrestling with cryptic error messages and complex troubleshooting? awesome-ctl is like having a seasoned expert on call 24/7, using the power of AI to provide clear, actionable diagnoses for your infrastructure and applications.

awesome-ctl is a command-line tool that brings cutting-edge LLM (Large Language Model) technology to the forefront of systems diagnostics. Connect awesome-ctl to your Kubernetes cluster, Docker Swarm, AWS environment, or other supported systems, and let our AI analyze the data to help you find and fix issues faster.

✨ Key Features

• 🧠 AI-Driven Insights: awesome-ctl leverages the reasoning power of LLMs to analyze complex technical data and provide human-readable diagnoses and recommendations.
• 🔌 Extensible Connector Architecture: Easily connect to a variety of systems and services:
  • Kubernetes: Get to the bottom of pod crashes, deployment issues, resource bottlenecks, and more.
  • Docker: Diagnose container failures, image build problems, and networking issues.
  • AWS (Coming Soon): Analyze CloudWatch logs, EC2 instance health, and other AWS services.
  • More to Come: We're constantly adding support for new systems!
• 🔍 Deep System Analysis: awesome-ctl gathers the essential information to provide comprehensive diagnoses:
  • Logs and Events: Analyze system and application logs to identify errors, warnings, and patterns.
  • Resource Utilization: Understand CPU, memory, network usage, and other metrics to spot bottlenecks.
  • Configuration Data: Detect misconfigurations and potential conflicts.
• 🛠 Actionable Recommendations: Don't just identify problems - fix them! awesome-ctl provides clear steps and guidance to help you resolve issues quickly.
• 🤖 Easy-to-Use CLI: A simple and intuitive command-line interface makes diagnostics a breeze.

🚀 Getting Started

🛠 Prerequisites

• Python 3.8+: The language of awesome-ctl.
• Connectors: Install the necessary connector libraries for the systems you want to diagnose (e.g., kubernetes, docker).

📥 Installation

poetry add awesome-ctl

💻 Usage

Basic Diagnostics:

awesome-ctl diagnose <connector> [options]

Example:

awesome-ctl diagnose kubernetes --namespace my-app # Analyze issues in the "my-app" namespace
awesome-ctl diagnose aws # Analyze issues with AWS resources

See available connectors and options:

awesome-ctl --help

📂 Project Structure

• awesome-ctl/: The core Python package.
• agents/: Contains connector plugins that gather data from different systems.
• llm/: Manages interaction with Large Language Models.
• analysis/: Core logic for analysis, diagnosis, and report generation.
• awesome-ctl_cli/: The command-line interface.
• tests/: Keep things running smoothly with a comprehensive test suite.

🙌 Contributing

• awesome-ctl is a community-driven open-source project! We welcome contributions from developers of all levels. Here's how to get involved:
  • Open an issue: Report a bug, request a feature, or share your ideas.
  • Submit a pull request: Contribute code, documentation, or anything you think can improve awesome-ctl.

📄 License

This project is licensed under the MIT License. For more details, see the LICENSE file.

Key Changes:

• Scope Emphasis: The README now clearly positions awesome-ctl as a general-purpose diagnostic tool with LLM-powered analysis at its core.
• Connector Focus: Highlights the extensibility of the project through connectors while providing examples.
• Actionable Focus: Emphasizes that awesome-ctl helps users not only find but also fix problems.

Awesome CloudOps Automation License

Please find the LICENSE of Awesome-CloudOps-Automation here

Acknowledgements

We would like to acknowledge the original Awesome-CloudOps-Automation contributors for their hard work and dedication. Their efforts have laid the foundation for this project, and we are grateful for their contributions.