Canonry

Agent-first AI SEO (AEO/GEO) operating platform

Digital Marketing & SEOTypeScriptMIT

Abstract

Canonry is an open-source Digital Marketing & SEO project. Agent-first AI SEO (AEO/GEO) operating platform. Canonry is an agent-first (CLI, MCP, API), open-source AEO operating platform with a comprehensive web UI. Track AI visibility over time, investigate changes across search and traffic, and measure progress after you or your agent acts. It is built using TypeScript. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building a Digital Marketing & SEO mini project or final-year project.

1. Introduction

Canonry is an agent-first (CLI, MCP, API), open-source AEO operating platform with a comprehensive web UI. Track AI visibility over time, investigate changes across search and traffic, and measure progress after you or your agent acts.

2. Objective

Agent-first AI SEO (AEO/GEO) operating platform

This project demonstrates how TypeScript can be applied to a real-world Digital Marketing & SEO problem.

4. Technology Stack

TypeScript

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Node.js (LTS) and npm / yarn / pnpm
  • VS Code or any code editor
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Canonry/canonry.git
cd canonry

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Export reports to Google Sheets or PDF
  • Schedule weekly automated reports
  • Add competitor comparison

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. Which marketing or SEO problem does this tool solve?
  2. Which data sources or APIs does it use (Search Console, Analytics, social platforms)?
  3. Which metrics or KPIs does it report and how are they calculated?
  4. How could the output help a business make decisions?
  5. How would you schedule it to run automatically?

9. Source Code & License

This project is developed by Canonry and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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