# Automatic code reviews with OpenAI Codex

## Метаданные

- **Канал:** OpenAI
- **YouTube:** https://www.youtube.com/watch?v=HwbSWVg5Ln4
- **Дата:** 04.11.2025
- **Длительность:** 8:20
- **Просмотры:** 69,641

## Описание

Maja Trębacz and Romain Huet show you how to set up Codex to automatically review new pull requests in GitHub and in the Codex CLI, and discuss the research approach we used to train our models to generate quality code reviews.

Timestamps:

00:00 Intro
01:23 Automatic code review in GitHub
02:34 Codex code review vs static analysis
03:31 Training models for high-signal code reviews
04:18 How OpenAI uses Codex code review
05:57 Customizing code review with AGENTS.md
06:58 Code reviews in the Codex CLI


Try it yourself:
Code review: http://chatgpt.com/codex/code-review 

Learn more:
Codex: openai.com/codex
Developer docs: https://developers.openai.com/codex/cloud/code-review

## Содержание

### [0:00](https://www.youtube.com/watch?v=HwbSWVg5Ln4) Intro

Code review of two comment mark. — Hey everyone, I'm Roma — and I'm Maya. — Codex needs to do two things really well to be an effective coding teammate. First, it needs to work with all your tools and second, it also needs to plug into all of your team workflows. Code review is one of the most important workflows for any engineering team and we want to help there as well. With GPT5 and now GP5 codeex, we train these models specifically to find bugs and investigate some issues. Maya, why don't you tell us more? — Sure. I work on alignment team within OpenAI research and code review is a very important research project for us. As the AI capabilities grow and we have more and more powerful coding agents, we are producing a lot of code and human verification is becoming the bottleneck. So we need to make sure that we're also training powerful models to help humans in verification and that our verification abilities are scaling as fast as AI capabilities. That's why we trained code review models. And we could have done it just like as a research exercise internally, but it's actually way more useful to have it interact with our OpenAI code base and also release it externally. — Well, very much align with our philosophy at OpenAI of like iterative deployment and making sure the technology and contact with reality. So

### [1:23](https://www.youtube.com/watch?v=HwbSWVg5Ln4&t=83s) Automatic code review in GitHub

I'd love to enable code review. Can you uh show us how to do it? — Absolutely. So the first thing you will have to do is to go in your codex web setting and just enable it. — So that's all it takes. And now going forward to understand like any PR submitted to that repo will be automatically reviewed by codeex. — Exactly. So for example here I quickly coded up some simple extension. I created a pull request and it's marked as ready for review. The code review agents will pick it up and start reviewing it. — In the meantime, there's a little bit of an emoji with the eyes over here. — Exactly. Um, why don't you uh show us one where Codeex has already completed the review. — So, here's another one. It looks a bit different because here I triggered the review already when the PR was in a draft stage and I didn't yet wanted real humans to look at it. — So, that's an interesting technique. So, you manually add codeex with the message and it sounds like in this case you also wanted to have specific instructions, right? — Yeah. So you can always comment at codeex review this PR and you can add some extra information for example something that would help the agent understand the PR or you can instruct it to focus on some specific area here what

### [2:34](https://www.youtube.com/watch?v=HwbSWVg5Ln4&t=154s) Codex code review vs static analysis

I love about Codex code review and I want people to understand like it's not just static analysis right like the model has access to tools it's able to check its own work to run tests and commands can you show us what happened behind the scene for this PR — yeah so one important thing to note is the model doesn't only look at the diff. It has access to the whole repository. It can track down dependencies. It can try to understand it in a broader codebase which is very important when you're working on a very complex code where you're not the only contributor and you don't understand it in its entirety. — And here it sounds like you can also review all of the logs that led to that particular review, right? — Yeah. So here the model was not only browsing through the codebase but it actually decided to form some hypothesis and write some Python code to test the hypothesis and check whether uh it's actually correct and show it on a couple of examples. — That's really cool. And I'm curious like

### [3:31](https://www.youtube.com/watch?v=HwbSWVg5Ln4&t=211s) Training models for high-signal code reviews

how was this model trained? Like did we do anything specific to like make it so good at like catching bugs? Yeah, so it is part of the codeex training, but we added some specific tasks in which we wanted to really prioritize catching bugs that actually matter and people would be willing to fix in real life. We also made sure that we aimed for very high precision rate. When we evaluated it against the previous generation of the models, we have shown that it has much lower incorrect comments rate. But to be fair, the most important evaluation is just people using it in practice. and it finding the real bugs and simultaneously not being annoying by outputting too much comments. — Yeah. So, you showed us like a couple examples with GPTOSS. Now, I'd love to

### [4:18](https://www.youtube.com/watch?v=HwbSWVg5Ln4&t=258s) How OpenAI uses Codex code review

touch more on like how we use it internally at OpenAI. — Yeah, we've already used it for a while and we have gotten a lot of examples of it really saving us from having critical issues. So it saved us from having some important training run bugs that would potentially delay some important model releases or some configurations that would not be normally visible just from the div alone. It also allows us to more confidently contribute to the code bases that we're not fully familiar with. So for example here we have Alex our codeexpm contributing towards VS code extension and implementing a change code review model decided that it's not the correct way to implement it. — Right. It sounds like it caught a bug in terms of like removing a react prop here with the CSS. — Yeah, it was not really possible to spot this bug without understanding of the broader — code here. But then I love Alex's followup as well at Codex. It's fair enough. Fix it up because you can keep on going with this conversation when and ask Codex to take it over. — Yeah. After a code review bot produces a finding, you can always ask Codeex to trigger a new task and actually fix it, which is a very nice workflow to do. — Yeah, that's really amazing. And really the vision of having Codeex at any point and any surface as your teammate that's able to kind of do work for you. And what I love about like codeex code review here in this case is that you may have like amazing engineers on your team but they may not have like many hours to spend to kind of not just look at a diff but like run every possible scenario in their head. And here you have codeex that's able to do that in its own time.

### [5:57](https://www.youtube.com/watch?v=HwbSWVg5Ln4&t=357s) Customizing code review with AGENTS.md

And one thing I wanted to touch on as well is that we now have like agents. mmd right this like open format for coding agents including codeex um to kind of like follow some specific instructions. Uh have you like used agent MD like in your case to kind of give codeex code review some extra instructions? — We actually train the model specifically to be steerable either with uh with custom user instructions or look for agents MD within the codebase. Uh so if you add some custom code review guidelines to your package that would help model to understand the code ways but also potential requirements about what it should pay special attention to in code review or maybe what kind of problems to ignore and don't bother your developers about. — That's awesome. — Uh you can even like ask some custom special instructions about the style it responds to. So, for example, since I needed a little bit more validation, I wanted Codeex to tell me every time I make a bug that I'm still an amazing programmer. — That's awesome. And you are

### [6:58](https://www.youtube.com/watch?v=HwbSWVg5Ln4&t=418s) Code reviews in the Codex CLI

uh what we've been looking at here so far has been Codex reviewing code in the cloud, but just recently we also introduced review in the Codeex CLI. So, you can have a review in the terminal as well. — So, very often you want to review the code already before it makes it into the GitHub. For that purpose, CLA is perfect and it also can execute more things locally on your computer. So you could just slash review in order to tell the model to review your current changes. And we're going to improve on this feature a lot over the next weeks. — So it's basically like making sure all of your changes that you're introducing locally don't have like bugs uh before you submit them to a PR. — Exactly. — Amazing. Well, there you have it. Codeex code review. So with Codeex, you now have an amazing coding agent to pair with locally in the terminal or in your code editor. An awesome teammate to send tasks to in the cloud at any time. You also have this ability for Codeex to review your changes locally or your PRs on GitHub before your co-workers even get to them. It's going to help you catch bugs before they hit production. And we can't wait to see how that's going to accelerate your team and ultimately help you ship better and safer products. Thanks for watching. Back to work. — Indeed.

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*Источник: https://ekstraktznaniy.ru/video/11181*