Building a Context-Aware AI Code Reviewer with Memory – Hackathon Project

Shaiban Faraz khan — Mon, 13 Apr 2026 04:34:21 +0000

Participating in this hackathon has been an exciting and valuable experience for me and my teammate. We worked together to explore how AI can be improved to build smarter and more reliable developer tools.

One key problem we identified is that most AI-based code review systems are stateless. They analyze code but do not remember past mistakes or incidents. This often leads to repeated errors, as the AI lacks historical context.

To solve this, we built a system that integrates a vector memory layer into the AI workflow. This allows the system to store previous issues, recall them when needed, and provide more accurate and context-aware suggestions.

What We Built

Our solution focuses on improving code review using memory:

Stores past bugs and patterns
Retrieves relevant history during code analysis
Detects issues that traditional AI systems may miss Tech Stack
Frontend: React (Vite)
Backend: Node.js / Express
AI Engine: Python (FastAPI)
LLM: Qwen model
Memory Layer: Vector database Conclusion

This hackathon helped us gain hands-on experience in building intelligent systems and working effectively as a team. It pushed us to think beyond traditional solutions and explore innovative approaches.

We are excited to continue improving this project and building more impactful solutions in the future. #Hackathon #AI #WebDev #Python #Innovation #Learning #TeamWork

Forem: Shaiban Faraz khan

Building a Context-Aware AI Code Reviewer with Memory – Hackathon Project