Kodus
Kodus · Coding
Kodus is an open source AI code review tool that plugs into your Git provider and reviews pull requests the way a senior developer would. It reads each diff, posts inline comments on quality, security and performance problems, and checks the changes against rules your team writes in plain language. You can self-host it for free or run it on Kodus cloud with your own model keys.
About Kodus
What Is Kodus
Kodus is an AI code review tool built for engineering teams that want automated pull request review without handing over their source code or paying a markup on model tokens. It works with GitHub, GitLab, Bitbucket and Azure DevOps, reads diffs at the pull request level, and posts inline comments right where developers are working. The core is open source under AGPL. Any team can run it on its own infrastructure.
The tool leans on something called Kody Rules, which let teams describe their own review standards in plain language rather than code. Kody then checks every pull request against those rules and flags what breaks. It also pulls requirements from Jira, Linear and Notion, meaning a ticket that says "refunds need a reason" actually gets enforced in the review.
The biggest limitation is that it won't write your architecture for you. Kodus catches small, repeatable issues well. The vendor is clear that it doesn't replace human review. It's a first pass that clears the noise so people can focus on the hard calls. You also need to bring your own AI provider key on most plans. That adds a separate token bill.
Getting Started
- Sign up on the Kodus website or deploy it yourself with Docker Compose or Helm.
- Connect your Git provider and grant access to the repositories you want reviewed.
- Write your review standards as Kody Rules, or import rule files you already use from Cursor, Copilot or Claude.
- Open a pull request and let the Kody agent run; it posts inline comments directly on the diff.
- Review the feedback and the engineering metrics dashboard, then adjust your rules over time.
Product Information
A quick look at Kodus's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Small dev teams and indie developers
- Growing engineering teams
- Platform and DevOps leads at larger orgs
Tasks
- Catching quality and security issues early
- Enforcing team standards
- Tracking technical debt
Scenarios
- A solo developer shipping code daily who wants a second pair of eyes without hiring one.
- A team that just adopted AI coding assistants and needs a review layer to keep output consistent.
- A company that can't send source code to a third party and needs a self-hosted AI code review setup.
Key features
Kody Rules in Plain Language
Teams write their review standards as plain sentences instead of configuration files. Kody reads your rule set and checks every pull request against it, so a convention that only lives in a wiki page actually gets enforced. You can also import existing rule files from Cursor, Copilot and Claude to keep the standards you already use.
Business Rule Validation
Kodus connects to Jira, Linear and Notion and pulls the requirement behind each change. If a ticket says refunds need a reason, Kody checks that the pull request includes one and flags the PR when it doesn't. That closes the gap between what the spec asked for and what the code actually does.
Model-Agnostic with Zero Markup
The tool isn't tied to one AI provider. You can use Claude, GPT, Gemini, Llama or any OpenAI-compatible endpoint, including models you host yourself. You pay the provider at list price. Kodus adds no margin on tokens, so the AI cost stays predictable as your team reviews more code. No hidden multipliers, no per-seat AI surcharge.
Self-Hosted or Hosted
You pick where Kodus runs. The open source core lets you deploy on your own infrastructure with Docker Compose or Helm and keep full control of your code. If you'd rather not manage it, the cloud option hosts everything for you and still lets you bring your own keys.
Engineering Metrics Cockpit
Deploy frequency, cycle time, bug ratio and PR size show up in one dashboard, all trending together. Teams use it to see whether review changes are actually speeding delivery up. Often that's exactly what the numbers show, and the trend backs it up. Sometimes the change just moves the bottleneck somewhere else.
Git Provider Integrations
Kodus plugs into GitHub, GitLab, Bitbucket and Azure DevOps at the pull request level. It reads diffs and posts inline comments. It respects your existing review flow rather than forcing a new one. Setup reportedly takes under five minutes. So why switch tools at all?
Technical Debt Tracking
Suggestions that nobody implements don't vanish. Kodus turns them into tracked issues automatically. A team can then watch its technical debt over a six-week window and decide what to pay down. Simple as that.
Pros and cons
Pros
- Open source core under AGPL, so you can read the code and self-host without a seat minimum.
- Bring-your-own-key model with zero token markup keeps AI costs transparent on every plan.
- Plain-language Kody Rules and imported rule files make it easy to match existing standards.
- Business rule checks tied to Jira, Linear and Notion catch changes that break a spec.
- Integrates with GitHub, GitLab, Bitbucket and Azure DevOps at the pull request level.
Cons
- You supply and pay for your own AI provider key on most plans, which means a second bill to manage.
- No API availability is documented. Teams that want to build on top of Kodus have no clear path yet.
- It won't replace human reviewers. The vendor itself frames it as a fast first pass, not a final word.
Frequently asked questions
It reviews your pull requests with AI. Kodus reads each diff, checks it against your team's rules and the requirements in your tickets, then posts inline comments on quality, security and performance issues. Think of it as a reviewer that never skips a line.
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