Trag

AI code review that enforces your standards using plain-English rules

Hand-reviewed by the Toolglade team·Updated Aug 2026

code-review#code-review#pull-request#natural-language#github

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About Trag

Trag is an AI code review tool that lets teams define review rules in natural language and automatically enforces them on every pull request, flagging issues and suggesting fixes.

Trag reframes code review as configurable pattern enforcement. Instead of relying on a fixed rule engine, teams describe what they want reviewers to catch in plain English, for example ensuring every malloc has a matching free on all paths or that code follows DRY principles, and Trag applies those rules automatically on each pull request. The tool integrates with GitHub, GitLab, and other pull request systems. Once a PR is opened, Trag reviews the diff, flags issues, and leaves inline comments with suggested fixes, typically returning results in seconds. Its natural-language approach makes rule authoring accessible to developers of all experience levels without learning a custom DSL. Because its rules are semantic and language-agnostic, Trag can be applied across diverse codebases and polyglot repositories. It is aimed at teams that want consistent, customizable review standards enforced automatically rather than depending solely on human reviewers or rigid static linters.

TL;DR

Trag is an AI code review companion that turns plain-English rules into automated pull request checks, flagging violations and suggesting fixes in seconds across any language.

Company overview

Trag is an AI code review startup that launched publicly and gained visibility through Product Hunt. It positions itself as a customizable review companion that behaves like a linter for patterns.

The product targets engineering teams that want to codify their review standards in natural language rather than maintaining brittle static-analysis configurations, and it plugs directly into existing Git-based workflows.

Product features

Trag's central feature is natural-language rule authoring: teams describe review criteria in plain English and Trag enforces them semantically on each pull request. It integrates with GitHub, GitLab, and other PR systems, leaving inline comments and suggested fixes.

Because rules are semantic and language-agnostic, Trag works across polyglot repositories, catching everything from memory-management patterns to style and DRY violations without a custom rule DSL.

Target market

Trag targets software engineering teams, open-source maintainers, and startups that want consistent, customizable, automated code review integrated into their PR flow.