PR Review Buddy

AI-powered code review agent that analyzes pull requests in seconds, catching bugs, security issues, and performance problems with precise, line-level feedback.

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Inspiration

Code review slows down development more than it should. In most teams, developers wait hours or even days to get feedback on their pull requests. Under pressure, reviewers miss security issues, edge cases, and proper error handling. Junior developers struggle the most because they depend on timely guidance.

We wanted to remove this delay and make high-quality feedback available instantly, without depending on reviewer availability.

What it does

PR Review Buddy automatically reviews pull/merge requests the moment they are opened.

It reads the code changes, analyzes them using an AI model, and posts structured, line-level comments directly on the diff. The feedback highlights:

  • Security vulnerabilities
  • Bugs and logic errors
  • Missing error handling
  • Performance issues
  • Code clarity improvements

It also adds a summary with an overall recommendation, helping developers quickly understand the state of their code.

How we built it

The system is built as an event-driven AI agent integrated into the development workflow.

  • A merge request event triggers the review flow
  • The agent extracts and processes the code diff
  • The diff is passed to an LLM for analysis
  • The model generates structured feedback
  • Inline comments are posted on specific lines
  • A summary review is added to the request

On top of that, we built a lightweight dashboard using React and Tailwind to visualize activity and make the system easier to interact with.

Challenges we faced

One of the main challenges was making the feedback useful instead of generic. Raw LLM outputs tend to be vague, so we had to carefully design prompts and structure the responses to produce precise, line-level insights.

Handling large diffs was another issue. We needed to ensure the system remained fast while still providing meaningful analysis.

Integrating everything into an automated flow also required careful handling of triggers, events, and comment formatting so the experience felt native inside the code review process.

What we learned

We learned how to design AI systems that fit into real workflows instead of existing as standalone tools.

Prompt design turned out to be critical. Small changes in instructions had a big impact on the quality and usefulness of feedback.

We also learned the importance of speed and clarity. Developers don’t want long explanations. They want direct, actionable insights tied to specific lines of code.

Most importantly, we saw how AI can remove bottlenecks in software development when it is applied at the right point in the workflow.

Built With

  • anthropic-claude
  • gitlab
  • gitlab-duo-agent-platform
  • python
  • react
  • tailwind-css
  • vercel
  • vite
  • yaml
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