— AI STACK RECOMMENDATION

AI Code Review for GitHub CI/CD

Automated code review assistant that integrates into GitHub workflows, analyzes pull requests, and provides AI-powered feedback at scale for startup teams.

Stays alive for 365 days after the last visit.

Developer Tools

AI Code Review for GitHub CI/CD

Automated code review assistant that integrates into GitHub workflows, analyzes pull requests, and provides AI-powered feedback at scale for startup teams.

high confidence

Core Stack ℹ︎

Cody (Sourcegraph)

Primary

Full codebase context awareness enables accurate code review feedback on PRs. Understands entire repository for contextual analysis of changes.

$0/month (free tier) or $20/month (pro)

Claude Sonnet 4

Primary

Balanced model excels at code analysis, identifying bugs, and suggesting improvements. Low hallucination rate critical for code review accuracy.

$0.003/1K tokens

GitHub Actions

Primary

Native GitHub integration for triggering code review on PR events. Serverless execution scales automatically with startup growth.

$0/month (free tier: 2000 minutes/month)

Complete the Stack ℹ︎

DeepEval

Alternative

Evaluate code review quality with built-in metrics. Pytest integration enables CI/CD regression testing of review suggestions.

$0/month (open-source)

Arize Phoenix

Alternative

Open-source observability for tracking code review LLM calls, latency, and quality metrics. Debug review accuracy issues.

$0/month (open-source)

Getting started

1Set up GitHub Actions workflow triggered on pull_request events.
2Use Cody SDK to analyze PR diff and full codebase context.
3Call Claude Sonnet API to generate code review comments with specific line references.
4Post review feedback as PR comments using GitHub API.
5Integrate DeepEval to test review quality against known issues.
6Monitor with Arize Phoenix for LLM call tracing and performance.
7Scale workflow runners as PR volume increases.

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