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Mesrai
AI code review that reads your repo, not just the diff
Most AI code reviewers look at the diff. Mesrai reads the whole repo. Before reviewing each PR, Mesrai builds an AST graph of your codebase imports, type relationships, call chains across files. Five specialist agents run in parallel: security, performance, architecture, bugs, and your team's plain-English rules. Results: 86% critical defect catch vs 57% for CodeRabbit on the same LLM. BYOK — bring your own Anthropic/OpenAI/Vertex key. No markup. $6/dev/mo. 14-day free trial. No credit card.
Hey Product Hunt 👋
I'm Kajal, a full-stack developer from India. I built Mesrai because
I kept watching the same thing happen on every team that adopted Copilot
or Cursor: PR volume went up, review bandwidth didn't. Senior engineers
started drowning in queues. Junior devs waited days for feedback. Things
slipped through.
The core problem with most AI reviewers: they only see the diff. That
misses an entire class of bugs — the ones that require understanding how
the changed code connects to the rest of the system.
Mesrai builds an AST graph of your repo before each review. Five agents
run in parallel: security, performance, architecture, bug detection, and
your own custom rules (written in plain English, not regex).
We ran a public benchmark — 24 PRs across Supabase, Apache Airflow, and
HashiCorp Vault. Both Mesrai and CodeRabbit ran on the same LLM
(claude-opus-4-7) with default settings:
→ Overall catch rate: Mesrai 75%, CodeRabbit 63%
→ Critical findings: Mesrai 86%, CodeRabbit 57%
We published every miss too, not just the catches:
mesrai.com/compare/coderabbit
Honest gaps I want to flag upfront:
→ Per-folder rule scoping is on roadmap — not live yet
→ No permanent free OSS tier (CodeRabbit has this, we don't)
→ Self-hosted is Enterprise tier
14-day free trial, full features, no card: mesrai.com
Happy to answer anything — about the AST approach, the benchmark
methodology, BYOK setup, or why we built this from India. 🙏
About Mesrai on Product Hunt
“AI code review that reads your repo, not just the diff”
Mesrai was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #92 on the daily leaderboard. Most AI code reviewers look at the diff. Mesrai reads the whole repo. Before reviewing each PR, Mesrai builds an AST graph of your codebase imports, type relationships, call chains across files. Five specialist agents run in parallel: security, performance, architecture, bugs, and your team's plain-English rules. Results: 86% critical defect catch vs 57% for CodeRabbit on the same LLM. BYOK — bring your own Anthropic/OpenAI/Vertex key. No markup. $6/dev/mo. 14-day free trial. No credit card.
On the analytics side, Mesrai competes within Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how Mesrai performed against the three products that launched closest to it on the same day.
Who hunted Mesrai?
Mesrai was hunted by kajal. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Mesrai including community comment highlights and product details, visit the product overview.
kajal