To do AI code review with Claude and Codex, run each tool's review command on your diff before you open a pull request, then let each one comment on the pull request in GitHub. In Claude Code that is /code-review (alias /review) locally and the Claude GitHub App or GitHub Action on the PR. In OpenAI Codex it is /review in the CLI and an @codex review comment on the PR. Write your team's review rules into CLAUDE.md and AGENTS.md so both reviewers check what matters to your product, and keep a human as the only one who can merge.
TL;DR. Local review catches problems before teammates see them; PR review gives a second, independent pass. Claude Code's
/code-reviewcan post findings to the PR with--commentor apply them with--fix; Codex's/reviewreports prioritized findings without touching your working tree, and in GitHub it flags only P0 and P1 issues. Rules files make both reviewers useful. Neither replaces the engineer who approves the merge.
Everything below was checked against Anthropic's and OpenAI's own documentation on 6 October 2026. Commands and flags change between versions, so confirm them in the docs linked at the end before you roll this out.
Why teams add AI review now
Human reviewers are the bottleneck in most small engineering teams. A senior developer in Cairo or Riyadh who reviews every pull request also writes code, joins client calls and fixes production. AI review does not remove that person. It gives them a cleaner first pass: obvious bugs, missing error handling and security smells are flagged before they open the diff.
The risk is treating AI review as approval. An AI reviewer has never met your customers, does not know which endpoint handles money, and cannot see the WhatsApp complaint that made you change a flow last week. Unless you tell it, it does not know your rules. That is what the setup below fixes.
How Claude Code reviews code
Anthropic's Claude Code commands reference documents three review entry points:
/code-review(alias/review) reviews the current diff, or a PR number, branch or path you pass, for correctness bugs. It takes an effort level fromlowtomax.--fixapplies the findings,--commentposts them on the GitHub pull request, andultraruns a deeper multi-agent review in the cloud./security-reviewanalyses the changes on your branch against the default branch for vulnerabilities such as injection, auth issues and data exposure./install-github-appinstalls the Claude GitHub App and can set up GitHub Actions workflows and the secret they need.
On the pull request, the Claude Code GitHub Actions guide describes two modes. In interactive mode, anyone with write access mentions @claude in a PR comment and Claude replies on that PR. In automation mode, a review workflow runs on every opened or updated pull request and posts inline comments on each issue it finds, or one summary comment when it finds none. The guide recommends a CLAUDE.md file at the repository root for review criteria and project rules. Anthropic also documents a managed Code Review feature that reviews every pull request without a workflow file.
How OpenAI Codex reviews code
The Codex CLI documentation describes /review as a dedicated review against uncommitted changes, a commit or a base branch. Codex reports prioritized findings without modifying your working tree. /init creates an AGENTS.md file with instructions for Codex, and /permissions controls when it may edit files or run commands without asking.
On GitHub, the Codex GitHub integration guide works from a repository connected to Codex. You comment @codex review on a pull request and Codex posts findings like a teammate would. You can also turn on automatic review in Codex settings, so every pull request in an enabled repository gets reviewed without a comment. The guide suggests adding a ## Code Review Rules section to AGENTS.md. In GitHub, Codex flags only P0 and P1 issues, so comments stay on high-priority risks.
Claude Code vs Codex for code review
| Claude Code | OpenAI Codex | |
|---|---|---|
| Local review command | /code-review or /review, effort low to max |
/review |
| What it reviews locally | Current diff, or a PR number, branch or path | Uncommitted changes, a commit or a base branch |
| Changes your code? | Only with --fix |
No; it reports findings |
| Security-specific pass | /security-review |
Use /review with rules in AGENTS.md |
| PR trigger | @claude comment, or a review workflow on every PR |
@codex review comment, or automatic review |
| Where findings land | Inline PR comments or one summary comment | PR comments, P0 and P1 only |
| Rules file | CLAUDE.md |
AGENTS.md (## Code Review Rules) |
| Setup | /install-github-app or a manual workflow plus a secret |
Connect the repository in Codex settings |
The practical difference: Claude Code is more configurable inside your own CI, because the review is a workflow file you control. Codex is quicker to switch on, because the review runs from Codex's own GitHub connection. For a wider comparison of the two agents beyond review, read our Claude Code vs OpenAI Codex guide.
Step by step: a two-reviewer setup
- Write the rules first. Before you install anything, list the five to ten things a reviewer must never miss in your product: money paths, tenant isolation, auth checks, Arabic and RTL layout, secrets, migrations. Put them in
CLAUDE.mdand under## Code Review RulesinAGENTS.md. Keep both short, since each tool reads them on every run. - Review locally before every PR. Run
/code-reviewin Claude Code or/reviewin Codex on your branch. Fix what is real, and note what you rejected and why. - Run a security pass on sensitive branches. Anything touching payments, login or personal data gets
/security-reviewin Claude Code before it leaves your machine. - Connect the PR reviewer. Run
/install-github-appand pick the review workflow, or connect the repository in Codex and use@codex review. Start with one repository, not the whole organisation. - Decide who reviews what. A common pattern is one tool reviewing automatically on every PR and the other on request for risky changes. Two automatic reviewers on every PR usually means duplicate noise.
- Keep the merge human. Use branch protection so only a person can approve and merge. AI comments are input to that person, not a substitute for them.
- Review the reviewers monthly. Count useful findings versus noise, then tighten the rules file. Repeated checks become a skill or checklist; our guide on Claude and Codex skills for agencies shows how.
What to put in your review rules
Generic rules ("follow best practices") produce generic comments. Rules that name your real risks produce useful ones. A starting section might read:
## Code Review Rules
- Any change under /api/payments must keep amounts server-side; flag client-computed totals.
- Every Supabase query on a tenant table must filter by tenant id.
- New UI must work in Arabic (RTL); flag physical left/right CSS on layout.
- Never log tokens, phone numbers or API keys.
- Migrations that drop or rename a column need a rollback note in the PR.
Each line states the safe path, which is what OpenAI's guide recommends so the reviewer can tell a real issue from an expected pattern. If your agents also reach tools such as GitHub, databases or analytics through MCP, our Claude Code MCP workflows guide covers how to scope that access.
What AI review misses
AI review is strong at local logic: an unchecked null, a missing await, an inverted condition, a query without a filter. It is weaker where the evidence lives outside the diff:
- Business intent. Whether a discount should stack is a product decision, not a code fact.
- Runtime behaviour. A reviewer reading code cannot see a slow production query or a webhook that fires twice.
- The other side of a contract. A change to an API response can look correct while silently breaking the mobile app that reads it.
- Visual and RTL quality. Reading CSS is not the same as looking at the Arabic page on a phone.
So keep tests, preview checks and a person who clicks through the main flow. AI review shortens the path to a good merge; it does not shorten the definition of done.
Notes for MENA teams
Teams in Egypt and the Gulf often ship bilingual products, local payment gateways and WhatsApp-based flows. Those are exactly the areas where a generic reviewer has no context. Encode them in the rules file: RTL logical properties, Arabic copy that must not be machine-translated, and payment callbacks that must be idempotent. Also check your data policy before connecting a client's repository, since review sends code to the vendor's service.
How Mindshift sets this up for clients
Mindshift builds software, SaaS and AI automation for companies in Egypt and MENA. When we set it up for a client team, we start from their release process, not the tool: which repos, who merges, where the risk is. Then we write the rules files, wire the local and PR reviewers, and hand over a monthly check so the setup keeps improving. We do not promise a bug count; we make the review bar explicit and measurable. More on how we work is on why companies choose Mindshift.
FAQ
How do I run AI code review with Claude and Codex together?
Use one locally and one on the pull request, or one automatically and one on request. For example, run /code-review in Claude Code before pushing, then comment @codex review on the PR for a second pass. Give both the same rules through CLAUDE.md and AGENTS.md.
Does OpenAI Codex do code review on GitHub pull requests?
Yes. OpenAI's documentation describes commenting @codex review on a pull request in a repository connected to Codex, and an automatic review setting for every PR. In GitHub, Codex flags only P0 and P1 issues.
What is the difference between /review and /code-review in Claude Code?
None in behaviour: Anthropic documents /review as an alias of /code-review. Both review the current diff or a PR number, branch or path, take the same effort levels, and accept --fix and --comment.
Can AI code review replace human reviewers?
No. It is a strong first pass for logic and security smells, but it cannot judge business intent, runtime behaviour or how a page looks in Arabic on a phone. Keep branch protection so a person approves every merge.
Is AI code review safe for client code?
It can be, with the right controls. Store API keys as repository secrets, grant workflows only the permissions they need, and check the vendor's data-usage terms and your client contract before connecting a repository.
Written by Yasmine Tarek, Content Strategist, Mindshift. Last updated 6 October 2026.
If you want AI review wired into your team's delivery process with rules that fit your product, tell us about your setup through the Mindshift form.