Automating Code Reviews with AI and Distributed Systems

By Ahmed Sulaimon • 2025-09-10

Building an AI-powered system to automate GitHub pull request reviews gave me a deep dive into distributed systems, API integration, and testing at scale. This post outlines how I designed and implemented the Auto PR Review Assistant, covering the vision, architecture, testing, challenges, and key learnings.

1. The Vision

The goal was simple: reduce the friction in code reviews by having an assistant that can:

• Automatically analyze pull requests when opened or updated
• Generate AI-powered feedback on style, complexity, and maintainability
• Post inline comments directly on GitHub PRs, just like a human reviewer
• Provide a developer-facing CLI dashboard to inspect review status and trigger rechecks

The idea was not to replace human reviewers, but to provide a first pass that saves engineering teams valuable time.

2. System Architecture

To keep things modular and scalable, I used a microservices-based design with Redis as the central job queue:

• Webhook Listener (FastAPI)

  • Receives GitHub webhook events
  • Validates signatures (x-hub-signature-256)
  • Queues jobs into Redis

• Review Engine (Python worker)

  • Dequeues jobs from Redis
  • Uses GitHub GraphQL + REST APIs to fetch PR metadata and diffs
  • Passes changes to the OpenAI API for analysis
  • Posts structured inline comments back to GitHub

• CLI Dashboard (Python CLI)

  • list-prs to view recent PRs
  • show-pr <id> to inspect AI-generated comments
  • recheck-pr <id> to trigger a re-review

High-Level Flow:

   GitHub → Webhook Listener → Redis Queue → Review Engine → GitHub Comments  

          ↳ CLI Dashboard for developer interaction

All services were containerized with Docker and orchestrated via docker-compose, making it easy to spin up the entire system locally.

3. Testing & CI/CD

Quality was a major focus for this project:

• Unit Testing with Pytest
  • Webhook Listener tested with mocked signatures and fake Redis
  • Review Engine tested with mocked GitHub API + OpenAI outputs
• Automation via GitHub Actions
  • Every push/PR triggered linting and pytest runs
  • Secrets (GitHub tokens, OpenAI API key) injected for integration-like tests

This ensured confidence in core logic while keeping external dependencies mocked during CI.

4. Challenges Faced

• Data shape variability – OpenAI responses weren’t always in the expected JSON structure. I had to implement a flexible parser and error handling.
• Commit-specific commenting – GitHub PR comments require a valid commit SHA, file path, and line number. Handling edge cases (renamed/deleted lines) was tricky.
• Async Redis clients – Different Redis client libraries had quirks with Python 3.12, leading me to settle on the maintained redis-py package.
• Action replays – Re-queuing PRs for recheck required storing repo metadata alongside PR IDs.

Each roadblock sharpened my debugging skills and forced me to think about failure handling in distributed workflows.

5. Technical Decisions & Trade-offs

Why Redis for Queues?

I chose Redis because:

• Simple and fast for queue operations (LPUSH, BRPOP)
• Already battle-tested in distributed systems
• Easy to containerize and reset during dev/testing

Why Split into Services Instead of One App?

• Clear separation of concerns (event handling vs. AI review logic)
• Easier debugging and scaling (engine workers can be scaled independently)
• More realistic for production, mimicking CI/CD pipeline architectures

Why AI Reviews Instead of Rule-Based Linters?

While linters catch syntax issues, AI can provide contextual suggestions (readability, refactoring hints, complexity). The trade-off was unpredictability of responses, which I mitigated with structured prompting and JSON enforcement.

6. What I’d Improve

• Web UI Dashboard – A lightweight frontend (React/Next.js) could make browsing PR reviews more intuitive than the CLI.
• Configurable Checks – Letting repo owners enable/disable checks (style, complexity, security) via YAML configs.
• Better Diff Parsing – Handling multi-line hunks and complex patches more gracefully.
• Caching – Avoid re-fetching unchanged diffs when re-checking PRs.

7. Lessons Learned

This project reinforced several engineering principles:

• The importance of structured error handling when working with AI outputs
• How to integrate multiple APIs (GitHub GraphQL, REST, OpenAI) into one workflow
• The value of unit + integration tests for distributed systems
• How to design a project that feels like a real-world developer tool rather than a toy script

8. Final Reflections

What started as a curiosity--“can I automate PR reviews with AI?”--grew into a full-fledged distributed system. It combined backend APIs, async job queues, AI prompt engineering, and developer tooling into one coherent application.

I walked away with stronger skills in Python microservices, API integration, containerization, and automated testing, along with a much deeper appreciation for how developer experience tools are built.