2025

CodeRevU — AI-Powered GitHub PR Review

An AI-powered GitHub pull request review platform that automatically generates structured, actionable code reviews using Retrieval-Augmented Generation (RAG) and Gemini AI.

Technology Stack

Next.js 16TypeScriptPostgreSQLPrismaGemini AIPineconeInngestGitHub WebhooksBetter AuthPolar.shTanStack Query

Overview

System Architecture

CodeRevU System Architecture
  • Next.js 16 App Router Platform: Coordinates client interactions, Server Actions, API routes, and real-time dashboard UI using shadcn/ui and TanStack Query.
  • Auth: Better Auth validates sessions and enables credential-less GitHub OAuth flow.
  • Webhooks: Secures incoming GitHub repository updates via HMAC-SHA256 signatures, routed to a dedicated PR event handler validated by Zod schemas.
  • Dashboard: Renders dynamic repo analytics, review histories, and active project settings.
  • Payments: Manages free/pro subscription tiers and limits using Polar.sh checkout and billing gates.
  • Inngest Background Workers: Drives reliable, durable asynchronous queue workflows with built-in retry mechanisms and fine-grained concurrency limiters.
  • indexRepo: Triggered on project registration to crawl repository files via Octokit, generate embeddings, and bulk upsert them into the vector store.
  • generateReview: Triggered on pull request updates to analyze diff files, pull context-aware matches from the vector database, and format structured code feedback.
  • Data Stores: PostgreSQL (interfaced via Prisma ORM) manages user configurations and repository states. Pinecone DB holds 768-dimensional vector embeddings for low-latency similarity queries.
  • AI/LLM Engine: Google Gemini 2.5 Flash acts as the core model for both compiling vector embeddings during ingestion and formulating targeted reviews under precise grounding prompts.

Key Challenges

  • Indexing large repositories without blocking GitHub webhook flows.
  • Designing a RAG pipeline that retrieves semantically relevant code instead of naive file matches.
  • Handling concurrent PR events safely across multiple repositories.
  • Preventing hallucinated feedback by grounding LLM responses in real code context.

Key Learnings

  • Pinecone-based vector search significantly improved contextual relevance over keyword-based approaches.
  • Asynchronous workflows with Inngest prevented API blocking and enabled safe concurrency.
  • Structured prompts reduced noisy or vague review output.
  • Webhook-driven architectures require idempotency and replay safety.

Uniqueness

  • PR-level RAG instead of generic repo chat.
  • Fully automated review generation on PR open/update.
  • Structured feedback designed for real engineering teams.

Impact

  • Eliminated repetitive manual review effort for common PR patterns.
  • Enabled faster review cycles with consistent feedback quality.