Best AI Code Review Tools (October 2026)

AI code review tools inspect proposed code changes and flag bugs, security risks and missing tests before you merge them. Bugbot is our current pick, with Codex close behind. On our pull requests, Bugbot catches more high-severity issues and edge cases; Codex earns its place by finding different mistakes, including inaccurate content.

The useful reviewer is the one whose comments your team acts on. A subtle defect can justify months of fees. Pages of unnecessary suggestions can teach developers to ignore the bot entirely.

Our team uses Bugbot, Codex and Claude Code to review pull requests across our projects. We used CodeRabbit earlier, and it still holds up well. The Greptile, Qodo, Graphite, and Copilot sections draw on each vendor's documentation and pricing.

Our Picks for October 2026

These picks reflect what our team saw on its own pull requests through September 2026. We have used the four tools ranked below. Our assessment of the other tools relies on vendor documentation.

1. Bugbot, slightly ahead. It is the stronger of our two main reviewers on high-severity and security issues, and it flags more edge cases.
2. Codex, a close peer. It catches different things, including content inaccuracies before merge. It also spends tokens on less critical points, and our usage has felt tighter since OpenAI's plan changes.
3. Claude Code, also in our review process. We run its review beside the other two, and its local command is part of our routine before a pull request opens.
4. CodeRabbit, still good. It remains a strong broad reviewer for a team that wants deep configuration.

The gap between Bugbot and Codex is small. Because they catch different things, we keep both in our review process.

Which AI Reviewer Fits Your Team?

Bugbot is my starting point for teams focused on serious bugs and security issues. CodeRabbit offers broader feedback that you can tune to your team’s rules. Start with Codex or Copilot if your existing subscription already covers the review workflow you need, then compare the additional usage cost.

Starting prices; usage charges and plan limits vary.

Tool	Review	Entry Price	Best For
Bugbot	Cursor service	Usage credits; account terms vary	Finding serious bugs and security issues
Codex for code review	Coding agent	ChatGPT plan usage	Teams already using Codex
Claude Code for code review	Coding agent	Plan or usage credits	Custom local and managed reviews
CodeRabbit	Dedicated bot	Essentials $24/dev/mo (annual)	Broad, configurable pull-request feedback
GitHub Copilot	GitHub service	Paid plans plus AI Credits	Teams already using GitHub Copilot
Greptile	Context bot	Free Starter	Cross-file context and custom rules
Qodo	Dedicated bot	Pro Team from $30/mo	Shared team rules and usage
Graphite Agent	Review platform	Free Hobby	Teams using stacked pull requests

These are starting prices. Your actual bill depends on team size, review volume and what your existing plan includes. Some tools charge per developer; others charge for usage. Larger changes and repeated reviews can push the cost up.

What Does AI Code Review Do?

AI code review reads changed code and related files to flag possible bugs. Traditional checks follow fixed rules and give the same result when their inputs and setup stay the same. AI reviewers also interpret code and written instructions, so they may spot problems those rules miss. Their findings can vary between runs and still need a developer to check them.

Most products operate in two main forms. Dedicated reviewers install as Git-host applications and comment automatically on pull requests. Coding agents run locally, through a managed review service, or inside a CI workflow with custom instructions.

A dedicated reviewer handles when reviews run, where comments appear and how feedback is stored. With a coding agent, your team may need to set up the triggers, instructions, permissions and repeat reviews itself.

For what happens inside a single review, from the diff the tool reads to how it decides which findings to post, see how AI code review works.

Neither replaces tests or a person deciding whether to merge. The reviewer should explain problems such as a missing permission check, a broken function call, a risky migration or a test that no longer checks the right behaviour. A developer must confirm the problem and decide whether the change can ship.

How Did We Evaluate These Reviewers?

We evaluated AI reviewers by the usefulness of their findings, the code they can inspect, their fit with our review process and the controls they give a team. A cheap subscription offers little value if nobody reads its comments.

Bugbot, Codex, Claude Code and CodeRabbit reflect our experience on our own pull requests. Greptile, Qodo, Graphite and Copilot are assessed from official documentation and pricing. We have not tested all eight tools side by side.

Evaluation Criterion	Practical Evidence of Value	Warning Sign During Review
Finding quality	A bug you can reproduce and confirm	A confident claim you cannot reproduce
Repository context	Checks how the change affects related code	Claims to read the whole project but uses outdated code
Workflow fit	Reviews run when needed and comments are easy to act on	Developers repeatedly copy findings between tools
Team control	Control over rules, excluded files, data and spending	Unwanted comments or permissions nobody manages

AI Code Review Tools Compared

1. Bugbot

Best for: Finding serious bugs and security issues.

Bugbot is Cursor's pull-request reviewer, and it tops our list this month. On our pull requests it is slightly ahead on high-severity and security issues, and it flags more edge cases than the other reviewers we run. I like that it puts the most damaging mistakes first.

Cursor's documentation now lists GitHub, including Enterprise Server, plus GitLab, Bitbucket, and Azure DevOps as supported hosts. Effort runs Low, Default, High, or Smart, and Autofix can start a Cloud Agent to push a fix on supported hosts. Developers can also run Bugbot locally with the /review-bugbot skill in Cursor 3.7 or later and in the Cursor CLI.

Cursor moved Bugbot to usage billing in May 2026, and its announcement estimated an average run at $1.00 to $1.50. That is a historical estimate, not a current fixed tariff. Cost varies with pull-request size and complexity, so how often you run it matters to the bill.

2. Codex

Best for: Teams already using Codex.

Codex cloud connects to GitHub and accepts the literal @codex review trigger. OpenAI's documentation says it flags P0 and P1 issues by default on GitHub and reads review rules from a ## Code Review Rules heading in AGENTS.md. It can also run automatically, and @codex security review requests a security-focused pass.

OpenAI added a Code Review view to the ChatGPT desktop app at DevDay on September 29, 2026. It shows summaries and diffs, lets you ask Codex about a change, and submits your review as Comment, Approve, or Request changes. GitLab merge request support is in preview, and automatic cloud reviews no longer need a cloud environment.

Codex is a close second for us. It catches content inaccuracies before merge, which matters on a site where a wrong price is a defect. The trade-off is that it sometimes spends tokens on points below critical, and our review usage has felt tighter since OpenAI's plan changes. The Codex review guide covers rerunning a review after new commits, instruction scope, and the separate codex-action route.

3. Claude Code

Best for: Custom local and managed reviews.

Claude Code can review locally, run through the official GitHub Action, or use Anthropic's managed Code Review service. The managed service runs several agents on a pull request and a verification step that filters false positives. Teams tune it with REVIEW.md, and it never approves or blocks the pull request.

Anthropic limits managed review to Team and Enterprise plans and excludes Zero Data Retention organisations. It bills token-based usage credits outside plan limits. Quick, Standard and Deep review levels change the depth, time and cost; no single per-review price applies. Locally, /review is now an alias of /code-review, and /code-review ultra starts a deeper cloud review.

We run Claude Code review in the same review process as Bugbot and Codex. What I like about its local command is that it runs as a background subagent, so the review stays out of the session doing the work. The Claude Code review guide separates the managed, Action, and local paths.

4. CodeRabbit

Best for: Broad, configurable pull-request feedback.

CodeRabbit is a broad dedicated reviewer with configurable review profiles, path instructions, and organisation controls. It ran in our review process before the current setup, and it is still a good choice. It checks bugs, tests, documentation and how easy the code is to maintain. You will need to adjust its settings so useful warnings do not get buried in minor suggestions.

CodeRabbit's plans page lists Essentials at $24 per developer per month billed annually, or $30 monthly. Team costs $48 per developer per month billed annually, or $60 billed monthly. Advanced costs $72 or $90 per developer per month on the same billing terms and adds continuous pull-request security review. Included review allowances vary by tier, with rolling limits and optional paid overages.

5. GitHub Copilot Code Review

Best for: Teams already using GitHub Copilot.

Copilot code review stays inside GitHub and the major IDEs, so teams already paying for Copilot have less to set up. GitHub's documentation now describes two effort levels. Balanced is the built-in default. Lite costs an estimated $0.05 to $1 in AI Credits per review, while Balanced routes to a higher-reasoning model at $0.25 to $5.

Reviews also consume GitHub Actions minutes when the service gathers project context. The bigger change in September was approvals. Since September 1, 2026, a public preview lets admins allow Copilot to submit an approving review that satisfies a required-approval rule. It is off by default, can be limited to file paths, and is dismissed when new commits arrive.

I would leave that setting off until someone on the team takes responsibility for checking bot-approved merges.

6. Greptile

Best for: Cross-file context and custom rules.

Greptile emphasises a repository-wide code graph and custom rules. It maps how code connects across the project, which may help it spot bugs that a review of changed lines alone would miss. Try it on known bugs involving several files to check whether that map is up to date and your rules work.

Greptile's pricing lists a free Starter plan for one active developer with 50 credits a month. Pro costs $30 per seat per month with 50 included credits and $1 per extra credit. A Base review uses one credit, Plus uses three, and Apex uses ten, so the chosen level sets the bill.

7. Qodo

Best for: Shared team rules and usage.

Qodo offers pull-request review across several Git providers, shared team rules, and controlled enterprise deployment. The team shares a pool of credits instead of buying a seat for every developer. That can make a small trial easier to budget.

Qodo's Pro Team starts at $30 per month with a shared credit pool, a 14-day trial and usage priced at $0.012 per credit. Its recommendation for teams of up to 30 users describes the intended fit, not a hard user cap. Review volume and enterprise requirements determine the appropriate plan.

8. Graphite Agent

Best for: Teams using stacked pull requests.

Graphite Agent belongs to a wider pull-request platform built around stacked changes, review, and merge coordination. Its AI reviewer can use related pull requests in the stack and your organisation’s instructions. That makes it worth considering if you already want Graphite for managing pull requests. If you only need AI comments, compare the whole platform’s price with a separate reviewer.

Graphite's pricing page lists a free Hobby plan with limited AI reviews and Starter at $20 per user per month billed annually. Team costs $40 per user per month billed annually and includes unlimited AI reviews.

Coding Agents as Reviewers

Coding agents can review changes before you open a pull request. You can give them specific instructions and ask them to investigate further. Your team must configure that flexibility by choosing the model, which tools it can use, where it reports findings and whether it may change code.

The separate coding agents as reviewers guide compares local, managed, and GitHub Action workflows. Codex and Claude Code also offer managed review services, with different features and costs.

Can AI Review Code Before a Pull Request?

Codex, Bugbot, CodeRabbit and Claude Code can review local changes before you open a pull request. Running a review while you still have the code in mind makes findings easier to investigate. A local review can still miss defects, so keep the shared review and tests before merging.

Commands and billing from each vendor's official docs.

Tool	Local Command	What It Draws On
Codex CLI	/review or codex review	General Codex usage limits
Cursor Bugbot	/review-bugbot skill	Cursor usage, not itemised
CodeRabbit CLI	cr review	Plan's hourly review limit
Claude Code	/code-review	Counts toward normal plan usage

Codex's /review reads a diff against a base branch, uncommitted changes, or one commit, and it leaves the working tree alone. OpenAI's pricing page says Code Review usage applies only to reviews run through GitHub. Local reviews count toward general Codex limits instead.

Bugbot's local skills run in Cursor 3.7 and later and in the Cursor CLI. Cursor's docs don't itemise what a local run costs, so watch the team's usage dashboard during a trial.

CodeRabbit's Free plan allows three CLI reviews per hour, while its Free pull-request tier provides summaries rather than full reviews. Paid limits and fair-use terms vary by tier. The CLI supports cr review --deep, and an --agent output mode lets Claude Code or Codex read the findings.

Claude Code's /code-review takes an effort level, applies findings with --fix, and posts them to a GitHub pull request or GitLab merge request with --comment. The cloud ultra mode is the paid exception, with three free runs on Pro and Max, then roughly $5 to $25 per review in usage credits.

What I like about a local pass is that a mistake gets fixed before anyone else reads the diff. What I dislike is that the result stays private, so a confirmed finding needs a test or a pull-request note before a teammate can trust it.

Other Review Options

• Cursor Security Review: launched on September 23, 2026 for Teams and Enterprise plans. It reads each pull request in codebase context and posts one comment on exploitable bugs, while style and quality stay with Bugbot.
• Devin Review: Cognition's reviewer is free on public pull requests; self-serve private reviews consume on-demand credits; Enterprise reviews draw from an ACU pool. Its docs list GitHub, GitLab, and Azure DevOps as supported hosts.
• Gemini Code Assist: Google shut down the free consumer GitHub app on July 17, 2026. The separate GitHub enterprise Preview remains documented by Google; the IDE subscription changes should not be confused with that service.
• Gitar: Sonar acquired this AI reviewer on May 21, 2026. Gitar commits fixes and iterates until CI passes, which puts it in a different risk class from comment-only bots.
• Ellipsis: it now presents itself as a managed coding-agent platform, with code review as one use among several.

What Bugs Can AI Reviewers Catch?

AI reviewers can catch bugs such as a missing null check, an unhandled error or a permission check that runs after private data has already loaded. They may also spot calls to a function that has changed, or a database migration that deletes data with no way to recover it.

Reading related files can help an AI reviewer catch more bugs. It might spot that a database change breaks the code that formats its output, or that retrying a webhook could process the same payment twice. You can also ask it to check specific risks, such as one customer accessing another customer’s data or a change breaking older clients.

AI reviewers can spot missing tests when code changes. A useful comment explains what needs testing and which bug the test could catch. “Add more tests” leaves you guessing.

Check security warnings as carefully as any other bug report. What can an attacker control? Can that input reach the vulnerable code, and do existing checks stop it? A warning should explain how the attack works and what damage it could cause. Otherwise, you may spend hours investigating a problem that does not exist.

Where Does AI Code Review Fall Short?

AI code review can miss defects when requirements, related code or production behaviour are absent from its context. It can also report a problem that does not exist. Common gaps include:

• The changed lines may not explain what a customer was promised, which migration runs first or why the team chose a particular approach.
• Feature flags, production data, external services, permissions, and deployment order may sit outside the repository context.
• A tidy-looking refactor can remove a deliberate separation between components or introduce a risky dependency.
• A completed review with no comments means the tool found nothing to report. It does not prove the code is safe.

Give the reviewer access to the requirements and write down rules the code must always follow. Limit its permissions to what the review needs. Before accepting a refactor, ask which problem it fixes.

How Does AI Review Complement Static Analysis?

AI review looks for context-dependent mistakes, while static analysis applies configured rules to source code. Keep type checks, tests and security scanners alongside the AI reviewer; their results are reproducible when their inputs, versions and configuration stay fixed. Paying an AI reviewer to repeat formatting or type errors wastes usage and adds comments your existing checks already cover.

Run AI review after the basic checks pass, or keep its comments separate from their results. That leaves the bot to look for bugs those checks miss. When it finds a real bug, add a test so future changes are checked for the same mistake.

Review Layer	Primary Review Job	Recorded Result
CI	Fixed checks and tests	Pass or fail against those checks
AI reviewer	Bugs the existing checks may miss	Possible bugs to investigate
Developer	Requirements and judgement	A decision on whether to merge

Roll Out One Reviewer

Choose one repository with active pull requests and known failure patterns. Start in manual mode if the product allows it. Select a mix of authentication, data, API, user-interface, and maintenance changes so the trial does not reward one narrow specialty.

1. Replay several historical pull requests with known defects and known false alarms.
2. Enable the reviewer on current work without changing human approval rules.
3. Disable generated files, summaries, and categories the team consistently skips.
4. Add three to five repository rules tied to costly, concrete failures.
5. Review the evidence after four weeks before expanding repositories or triggers.

Keep automatic reruns off until the billing and comment behaviour are understood. Some tools review every push, some remain subscribed to later pull-request updates, and some charge for each run. Even a tiny fix can trigger several nearly identical reviews.

Assign one named owner for reviewer tuning and configuration changes. That person should collect recurring false positives, remove stale rules, inspect missed defects, and publish changes to the configuration like ordinary code. If nobody owns this work, the same unwanted comments keep coming back.

How Should You Measure AI Review Value?

Measure AI code review by confirmed defects found, important defects missed, time spent checking comments and total cost. Classify each finding as independently useful, already caught by CI, already caught by a person, incorrect, or too vague to act on. Record severity only after verification so a bot's label does not inflate the result.

Measure the cost imposed on reviewers as well as subscription spend. Time spent reading summaries, disproving warnings, waiting for slow runs, and rerunning after pushes belongs in the calculation. Time saved finding and explaining a real defect belongs on the other side.

Count the bugs the AI missed, too. Check what people found later, which tests failed and what broke in staging or production. A quiet reviewer that catches a serious bug can be more useful than a busy one that repeats what you already know.

At the end of the trial, keep the results and decide whether to keep the reviewer, adjust its settings, replace it or stop using it. Treat vendor benchmarks as a reason to investigate a tool. Compare the dataset, tool versions and settings before using a percentage to choose a reviewer. A high catch rate without a false-positive count tells only part of the story.

The AI Code Review Verdict

Bugbot is our pick for October 2026, by a small margin. It is slightly ahead on high-severity and security issues and flags more edge cases on our pull requests. Codex is the close second, and it earns its place by catching different things, including content inaccuracies before merge.

Claude Code rounds out our review process, and its local review is part of how we work before a pull request opens. CodeRabbit is still the dedicated reviewer I would suggest for a team that wants broad coverage and will own the configuration. GitHub Copilot is a sensible first test for a team that already pays for Copilot.

For any other team, the best reviewer is the one that finds enough useful bugs to justify its bill and the time you spend checking its comments. Remove a tool that merely repeats CI or human comments.

When the shortlist is down to two, use our AI code review comparisons to look at the differences.

Frequently Asked Questions

Should I run more than one AI code review tool?

Run more than one AI code review tool when a second reviewer consistently catches important defects the first misses. We keep Bugbot and Codex together because their findings differ on our pull requests. Start with one reviewer and measure that gap before paying for another.

Does a local AI code review stay on my computer?

Running an AI review command locally does not necessarily keep your code on your computer. A command-line tool can send code to a hosted model or review service. Check the selected provider, data-retention terms and network access before reviewing private code.

Can an AI code reviewer approve a pull request?

GitHub Copilot can submit an approving pull request review when an administrator enables its approval preview. That approval can satisfy a required-review rule, subject to the configured limits, and is dismissed when new commits arrive. Claude's managed review does not approve pull requests; the Codex review interface lets a person submit an approval. Your team should still name who is responsible for the merge.