By the end of this guide you will have an AI reviewer commenting on your pull requests, a short set of team rules it follows, and a routine for deciding which of its comments to act on. The job is done by an AI code review tool that reads the diff (and sometimes the wider repository), posts a summary and line comments, and sometimes offers a fix you can apply.
If you want to review pull requests with AI, the main choices are where the reviewer runs (a hosted service, your editor, or your own infrastructure) and whose model key pays for it. This guide walks through the setup and compares seven tools from our Coding category.
Key takeaways
- Connect a repository to an AI review tool, write a few review rules, open a pull request and read the summary and comments before a human reviewer does.
- The main decision is the deployment model: a hosted bot on GitHub or GitLab, an extension in your editor, or an open-source reviewer on your own model keys.
- Free options exist but are narrow: Sourcery is free for open source, DeepSource has a free Open Source plan for public repositories, and Git AutoReview's Free plan allows 10 reviews per day on 1 repository.
- AI review supplements a human reviewer. Treat every comment as a suggestion to verify, especially for security-sensitive code.
What you need before you start
- A repository on a supported host. Most tools here work with GitHub, and several also cover GitLab or Bitbucket. Check the host before you pick.
- Permission to connect your repository. A hosted reviewer connects to your Git host, so check whether your organization requires an admin to approve new apps. An editor extension such as Git AutoReview runs in VS Code instead.
- A few real pull requests. One small and one large pull request show you how the tool behaves on both.
- Your team's conventions. Write down the three or four rules reviewers repeat most often, such as naming, error handling or test expectations.
- Time. Setup takes under an hour; tuning the rules takes a few pull requests.
- Difficulty. Easy for hosted bots, moderate for editor extensions with your own keys, harder for self-hosting.
- Code policy. If your code is confidential, check where each tool sends it before you connect anything.
Which kind of AI tool fits this job
Choose by where the review should happen and how much context it needs.
A hosted reviewer on every pull request. CodeRabbit posts a summary, walkthrough and line comments, supports customizable pre-merge checks, and learns from your feedback. Sourcery works with GitHub and GitLab, posts summaries and suggested changes you can commit with a click, and can auto-approve low-risk changes with no blocking findings. GitHub Copilot includes Copilot code review (not on its Free plan) alongside its chat and agent features, which suits teams already on GitHub.
A reviewer that sees beyond the diff. Greptile maps functions, classes, imports and dependencies so its comments can point to impacted callers and cross-file inconsistencies. It also lets you choose a review tier per organization, repository, directory or pull request.
Static analysis plus AI. DeepSource combines static analysis with AI review and Autofix, and also covers secrets detection and dependency scanning (a paid add-on on Team and Enterprise). Billing is by active committers rather than seats.
Your own keys and your own control. Kodus is open source and model-agnostic: you connect your own provider key, and the Community and Enterprise plans can be self-hosted. Git AutoReview is a VS Code extension that sends the diff to Claude, Gemini or GPT using your own API keys, and keeps every result as a draft until you approve it.
Step 1: Decide what the reviewer should catch
Start with the outcome you want, because it decides which tool you need.
- List the problems that slip past your human reviews: logic errors, missed edge cases, security issues, style drift or missing tests.
- Note which pull requests matter most, such as changes to authentication or payments.
- Decide whether you want comments posted automatically or held for approval.
- Decide whether code may be sent to a third-party service or must stay on your infrastructure.
Check it worked by writing the answers in a few lines you can share with the team.
Step 2: Pick a tool that matches your host and budget
You end this step with one tool chosen and the host, plan and model-key question settled.
- Match the tool to your Git host. Sourcery covers GitHub and GitLab. DeepSource lists GitHub, GitLab and Bitbucket. Git AutoReview lists GitHub, GitLab and Bitbucket from inside VS Code.
- Read the free plan against your team size. Several tools bill per developer or per assigned seat.
- Check how the tool is metered. Greptile uses prepaid credits, and Kodus has you pay your model provider directly.
- Use the comparison table below to see pricing and stated limits side by side.
Check it worked by confirming your host, team size and plan fit together.
Step 3: Connect one repository
Start small so mistakes stay cheap.
- Choose a single active repository, not the whole organization.
- Sign in with your Git host and authorize the app, or install the extension in your editor.
- For tools that use your own keys (Kodus, Git AutoReview), add a key from your model provider and set a spending limit with that provider.
- Pick the pull request or merge request you want reviewed first.
Check it worked by seeing the repository listed in the tool's dashboard or the extension's pull request list.
Step 4: Write your review rules
Rules turn a generic reviewer into one that knows your codebase.
- Add the three or four conventions from your list. Tools offer different mechanisms: Kodus has Kody Rules, Sourcery supports custom review rules, Greptile reads a greptile.json configuration, and Git AutoReview reads a .gitautoreview.md file.
- Say what to ignore, such as generated files, so comments stay relevant.
- Choose how strict the reviewer should be. Greptile's tiers and CodeRabbit's pre-merge checks are examples of dials you can set.
- Commit the rules file to the repository where the tool uses one, so the team can review changes to it.
Check it worked by reading the rules back and confirming each one is something a human reviewer would agree with.
Step 5: Run a review on a real pull request
See the output before you trust it.
- Open a small pull request with a known issue, such as a missing edge case.
- Wait for the bot to post, or click Review in the extension.
- Read the summary first. It should describe the change correctly.
- Read each line comment and compare it with the code it refers to.
Check it worked by finding the issue you planted, or by seeing a summary that matches the change.
Step 6: Triage the findings
Treat each comment as a suggestion with an owner.
- Sort comments into real bugs, style preferences and noise.
- Apply fixes yourself where you agree. CodeRabbit, Sourcery and DeepSource each offer ways to apply suggested fixes, but read the change before you accept it.
- Reject or reply to comments that are wrong. Tools such as CodeRabbit and Greptile learn from your feedback.
- Never merge on an AI approval alone for security-sensitive code. A person must sign off on authentication, payments, data handling and anything regulated.
Check it worked by listing which comments you accepted, which you rejected and why.
Step 7: Roll it out and keep tuning
Widen the scope once the signal is good.
- Add more repositories one at a time.
- Review the rules after the first few pull requests and remove any that cause noise.
- Watch usage limits. CodeRabbit rate limits reviews per developer, and Greptile pauses new reviews when credits run out.
- Agree on a team norm for who responds to AI comments and when.
Check it worked by asking reviewers whether the AI comments save them time or add to their load.
Tools for this job compared
| Tool | Good fit for | Pricing | Stated limits |
|---|---|---|---|
| CodeRabbit | Hosted reviews on GitHub, plus VS Code extension and CLI | Freemium, paid from $24/mo (checked 24 Oct 2026) | Rate limited per developer, for example 5 pull request reviews per hour on Essentials; Free plan only summarizes pull requests |
| GitHub Copilot | Teams already using GitHub, with Copilot code review | Freemium, paid from $10/mo (checked 23 Oct 2026) | Free plan is capped at 2,000 inline suggestions per month and excludes code review |
| Sourcery | GitHub and GitLab reviews with one-click suggested changes | Freemium, paid from $12/mo (checked 24 Oct 2026) | Billed per assigned seat; reviews skipped when size limits are reached; self-hosting needs Enterprise |
| Greptile | Reviews that weigh the whole repository | Freemium, paid from $1, usage-based (checked 24 Oct 2026) | Starter covers one active developer and 50 credits per month; reviews pause when credits run out |
| Git AutoReview | Approving AI comments before they are posted, in VS Code | Freemium, paid from $9.99/mo (checked 24 Oct 2026) | Editor extension only; Free plan allows 10 reviews per day and 1 repository; you pay your AI provider separately |
| Kodus | Open-source reviews on your own model keys | Open source, paid from $8/mo (checked 24 Oct 2026) | Token costs paid to your provider; up to 10 Kody Rules on Community; pull requests over 2,000 changed files are not reviewed |
| DeepSource | Static analysis plus AI review and Autofix | Free trial, paid from $24/mo (checked 24 Oct 2026) | Open Source plan covers public repositories only; AI Review metered per 10K processed lines beyond bundled credits |
Prices and plan limits change, so confirm the current terms on each vendor's site before you subscribe.
Common mistakes to avoid
- Treating the bot as a replacement for reviewers. AI reviewers miss context and sometimes flag correct code. Fix it by keeping a human approval step, and by using AI comments to speed up the first pass.
- Turning it on everywhere at once. A noisy reviewer trains people to ignore it. Fix it by starting with one repository and trimming rules that cause noise.
- Ignoring where your code goes. Hosted tools send diffs to a service and, in some cases, to a model provider. Fix it by reading each vendor's data terms, and consider Kodus or Git AutoReview, which use your own keys, or a self-hosted plan.
- Forgetting the real cost model. Per-seat, per-credit and bring-your-own-key pricing behave differently as the team grows. Fix it by estimating your monthly pull request volume against the plan before you commit.
- Merging on an unread summary. A tidy summary can hide a wrong detail. Fix it by reading the diff for any change that touches security or data.
How we put this together
This is an evidence-based guide built from each tool's listing in our directory, which is compiled from the vendor's own site and pricing page. It is not a review based on using the tools. We don't rank tools, and nobody paid to be included. Some outbound links may be affiliate links; see our disclosure.
Frequently asked questions
Is there a free way to review pull requests with AI?
Yes, with limits. Sourcery is free for open source, DeepSource has a free Open Source plan for public repositories, Greptile's Starter plan is free for one developer, and Kodus has a free Community plan you pay for only through your own model key. Git AutoReview's Free plan allows 10 reviews per day on 1 repository.
Can AI code review replace a human reviewer?
No. AI review is good at a fast first pass, but it can miss intent and context and can raise false alarms. Keep a person responsible for approval, and require human sign-off on security-sensitive or regulated code.
Can I keep my code private?
It depends on the tool. Git AutoReview says code goes straight to your AI provider and not to its servers, because it uses your own keys. Sourcery says it keeps no copy of your code and never trains models on it. Kodus, Greptile and others offer self-hosting on certain plans. Read each vendor's data terms for your case.
Does it work with GitLab or Bitbucket?
Some tools do. Sourcery supports GitLab, DeepSource lists GitHub, GitLab and Bitbucket, and Git AutoReview lists GitHub, GitLab and Bitbucket. CodeRabbit's listing names GitHub among its integrations, so confirm other hosts with the vendor.
How long does setup take?
The listings do not publish setup times. Connecting one repository is the quick part; writing good rules and tuning out noise takes a few pull requests, so allow time for adjustment before you widen the rollout.
Compare more options in our Coding category. If you want tests alongside reviews, see how to write unit tests with AI.
