Nicheloom

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Multi-Agent Code Review

Details

External ID
45814767
Source
HN
Company
—
Product
—
Website domain
—
Launched
Nov. 4, 2025
Cohort
—
Upvotes
5
Upvotes percentile
0.0982532751091703
Tags
—
Fetched at
Sept. 7, 2026, 9:25 p.m.
Updated at
Sept. 7, 2026, 9:25 p.m.

Description

Hey HN! We’re Oliver Gilan & Ben Warren and today we’re launching Mesa (https://mesa.dev) into public beta to help large engineering teams review code more effectively.There are plenty of code review agents that exist but we found that they fall short in a number of ways. They don’t…- Let you tailor the reviews with enough fidelity- Give you control over the models used. If a new foundation model is released I might want to try it!- Align the costs effectivelyMesa solves these problems with a multi-agent architecture where you define custom review agents that specialize in reviewing certain dimensions of your codebase. This allows you to finely control the review quality and receive reviews that are aware of specific dependencies, business logic, a specific domain in your codebase, or anything else you care about.You can control the model a specialist uses which is great for trying newly released models but also for controlling costs. When a change is made to our database schema, we have a specialist agent that reviews it and uses the most expensive, smartest, model it can while our frontend agent uses a faster, cheaper agent.Control over models and the fact that we only charge you for the tokens you use, at cost, aligns our cost structure and gives teams control that they do not get with other agents.We’ve found code review to be a subtly big problem for teams trying to improve development velocity. It’s the primary point of contact for managing the changes to the living system of your codebase and mistakes are costly.Missed bugs that make it to production can cause downtime, lost revenue, loss of trust, security breaches, compliance breaches, etc. Engineers spend hundreds of expensive man-hours that they do not enjoy reviewing code and still there’s too many mistakes being made.We do not believe you can remove humans from the loop of reviews entirely but we can dramatically shift the amount of work done on an average code review and eliminate the need for human reviews altogether on a large subset of PRs. This sort of automation will lead to faster teams and more reliable codebases over time.We are now in public beta and there’s a generous free tier (~100k lines reviewed free per month) available to all. Give it a spin and let us know what you think!Oliver & Ben

Enrichment

Theme
AI agent frameworks and developer tools
Vertical
Horizontal
Function
Agent / copilot
Audience
Developer
AI stance
AI-native
Project type
Commercial product
Normalized one-liner
ai-powered code review
Manually corrected
False

Could you build this?

Partial The GitHub App webhook integration and prompt wrapper are vibe-codeable, but orchestrating multi-agent code reviews with AST parsing, whole-repo context, and low hallucination rates is difficult.

What it would actually take: The system requires a distributed worker pipeline to parse Git diffs, extract semantic codebase context using ASTs and tree-sitter, coordinate specialized multi-agent LLM prompts (e.g. security, performance, linting), and aggregate the output into coherent GitHub PR comments. Achieving high-precision feedback without overwhelming developers with noise requires dedicated AI evaluation pipelines, prompt engineering infrastructure, and deep developer tooling experience.

Discussion

No comments on this launch.

Competitors

Other products that read as similar to this one — 516 launches clear the similarity bar, closest 8 shown.

Attention rank: #493 of 517 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 4 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a agent / copilot tool for Agriculture yet.