Agemo 🚀

Autonomous AI growth agent via Torque MCP. Analyze → Optimize → Execute loop.

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📸 See it in Action

(Demo GIF and UI screenshots can be found in the docs/assets directory)

▶️ Watch the Demo Video

💡 The Problem & Solution

Autonomous AI growth agent via Torque MCP. Analyze → Optimize → Execute loop.

Agemo solves this by providing: Autonomous AI growth agent via Torque MCP. Analyze → Optimize → Execute loop.

Key Features:

• ⚡ High Performance: Seamless integration and optimized workflows.
• 🔒 Secure by Design: Verifiable on-chain actions and robust data protection.
• 🎨 Intuitive UX: Beautiful, user-centric interface built for scale.

🏗️ Architecture & Tech Stack

Tech Stack

Component	Technology	Description
Frontend	Next.js 16, React 19	App Router, SSR, Server Components
Styling	Tailwind CSS v4	High-performance responsive UI
Language	TypeScript	Strict type safety across the stack
Testing	Vitest	Comprehensive unit and component testing

For a detailed breakdown of our system architecture and data flow, please refer to the Architecture Document.

🏆 Sponsor Tracks Targeted

• Sponsor Integration: (Check docs/SPONSOR_DEFENSE.md for our full sponsor integration strategy)

🚀 Run it Locally (For Judges)

1. Clone the repo: git clone https://github.com/edycutjong/agemo.git
2. Install dependencies: npm install
3. Set up environment variables:

   cp .env.example .env.local

   Note: Add your keys to the .env.local file.
4. Run the app: npm run dev

Note for Judges: Detailed demo scripts and sponsor defenses are located in the docs/ directory. Read docs/SPONSOR_DEFENSE.md for technical implementation details.

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📄 License

This project is licensed under the MIT License.