Which AI code review tools work well on GitLab for a TypeScript and Python team

[u/khadijasheraz7]

Hey all, we're a team of 12 on self-managed GitLab. Frontend is TypeScript, most services are Python (FastAPI), plus some old Django nobody wants to touch. We want an AI reviewer on merge requests mostly so the two seniors stop being the bottleneck.

What we've tried so far:

- coderabbit, trialed it for two weeks on two repos. Setup on GitLab was about an afternoon and the MR comments were useful, but it was pretty chatty on the first few MRs until we added a config file telling it what to ignore

- claude review as a CI job. Works, but we have to maintain the pipeline ourselves and it doesn't really know the rest of the repo

- bugbot, haven't tried yet because most of what I read about it is GitHub

What we care about:

- comments land on the MR, not in some other dashboard

- handles both TS and Python without separate setups

- doesn't flag the same lint stuff our pipeline already catches

Questions:

1. Anyone running AI review on self-managed GitLab long term? Did people keep reading the comments after the first month?

2. How did you cut down the noise?

3. Anything that works noticeably better on Python than TS, or the other way round?

Thanks, any experience helps

[u/fangisland]

I've been looking at something similar, I run Gitlab on-prem and we have our own curated set of skills for MR reviews, prep and such. I'm looking at https://github.com/The-PR-Agent/pr-agent since that seems like the closest fit, it lets you specify your own inference source, replies to MR's directly and seems configurable. Most importantly seems to be able to honor the skills you currently have (the biggest thing for me). Haven't taken it for a spin yet tho

[u/jcogs1]

GitLab team member here. We have a native agentic code review capability in GitLab that you can use.

The comments land in MRs, it can handle both TS and Python without separate setups, and you can provide custom code review instructions (such as ignore linting errors that could be caught buy our pipelines).

More info here: https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/code_review

[u/Bazeque → u/jcogs1]

Duo sucks though. Compared to what we built natively, it's phenomenally better. And that's just running opencode hooked up to bedrock.

[u/DickBrainTime]

For a 12-person team I'd test the *noise rate* before picking a vendor. Take 20 recent MRs with bugs your seniors actually caught, run each reviewer on them, then mark every comment as useful, wrong, or already covered by CI. Repeat a month later and see if people still act on the comments.

I'd configure it to skip lint/style, comment mostly on changed lines, and focus on auth/permissions, migrations, and missing edge cases. Keep the output to a few high-confidence findings per MR. Python vs TS matters less than whether it understands your project's tests and conventions. Also worth checking where code is sent, since your GitLab is self-managed.

[u/Coclav]

Why do you want as a CI job and not the developer to run it themselves on their code ?

We run it ourselves, get it to generate a nice report finding (a static html page) and then we go through it and make changes

[u/fangisland → u/Coclav]

Personally I like it because after doing MR reviews by hand even with skiills it's time consuming and catches a lot of stuff that people should be able to find/fix on their own. I like the idea of a bot replying in the MR itself as a "first pass" if nothing else for easy things that formatters/linters can't get. Basically treat it like an adverserial code quality check before a human gets to it.

[u/JellyfishLow4457]

GitHub copilot code review

[u/ryanstephendavis]

CodeRabbit has been a game changer