Code Review Agent

Problem

Engineering teams spend 20-30% of their review cycle on repetitive style, security, and performance checks that could be automated. At scale, manual reviews become a bottleneck that slows deployment velocity.

Solution

Build a PR webhook listener that fetches diffs, runs them through static analysis context and LLM-powered review prompts, and posts structured annotations with priority labels directly on the pull request.

Implementation Steps

1. Connect repository webhooks

   Listen to PR open/update events and fetch the diff context, file tree, and commit messages for each change.

   # GitHub webhook handler
   @app.post('/webhook')
   async def handle_pr(payload: dict):
       diff = await fetch_pr_diff(payload['pull_request']['url'])
       review = await run_review(diff, rules=team_rules)

2. Define review criteria

   Establish rules for code style, security patterns (OWASP top 10), performance anti-patterns, and test coverage expectations.

   Tip: Start with your team's existing style guide as a system prompt. Add rules incrementally based on common review feedback.

3. Build static analysis context

   Combine linter output, type checker results, and dependency audit data to give the LLM grounded context beyond just the diff.

4. Run LLM-guided review

   Pass the diff with context through structured review prompts. Request precise, actionable annotations with line references.

5. Post findings as PR comments

   Post suggestions as inline comments with priority labels (critical, warning, suggestion) and group by category.

   Tip: Limit to 5-8 comments per review to avoid 'noise fatigue'. Prioritize security and correctness over style.

6. Track review quality metrics

   Measure acceptance rate of suggestions, false positive rate, and time saved per review cycle. Use metrics to tune prompts.

Recommended combos

FAQs