Most teams adopt AI for writing code and then leave review exactly as it was — a senior engineer reading a 600-line diff at 5pm. That is the wrong half of the loop to automate last. A modern AI code review workflow shifts routine defect-hunting to machines so humans spend their attention on architecture, security, and intent. Below is a five-step workflow using tools that already exist, with real ratings and pricing from our directory: Cursor, CodeRabbit, Qodo, Cody, and Codeium.

Quick picks at a glance

How we evaluate

We assess review tools on four dimensions: how much context they can actually see, how many round-trips they remove from a pull request, whether their output is verifiable, and price relative to team size. Our evaluation deliberately separates authoring assistants from reviewing ones, because a tool that writes code well is not automatically good at critiquing it — the two jobs reward different things. Ratings in the table come from our directory assessments of each tool. We compare tools against each other within the review loop rather than against a generic chatbot baseline.

Step 1 — Pre-review in your editor before the PR exists

The cheapest defect to fix is the one that never reaches a reviewer. Before opening a pull request, run the diff past a codebase-aware assistant in your editor. Cursor is the strongest option here because it indexes your project, so its chat and Composer answers are grounded in your actual symbols and imports rather than a generic guess. A useful habit: ask it to explain your own diff back to you. If the explanation does not match your intent, the code is unclear — and that is a review comment you just avoided.

• Pros: Composer edits across multiple files; codebase-aware chat and agent mode; Tab completion noticeably faster than Copilot.
• Cons: Credit-based pricing divides opinion; the 500 premium-request limit frustrates heavy use; privacy and telemetry questions.
• Price: Hobby $0 (~2,000 completions/mo); Pro $20/mo; Business $40/user/mo.
• Skip it if: You do not want to change editors — GitHub Copilot or Codeium plug into the one you have.
• → Full profile: Cursor

Step 2 — Let an automated reviewer take the first pass

This is the step most teams are missing. CodeRabbit installs into GitHub or GitLab and reviews every pull request the moment it opens: it summarizes the change, flags likely bugs in context, and proposes concrete fixes with one-click autofix. The practical benefit is not that it finds everything — it is that the human reviewer opens a PR that already has a walkthrough and the obvious issues resolved. Reviewers start at the architecture question instead of naming conventions.

• Pros: Inline, context-aware review comments on every PR; free tier covers unlimited public and private repos; agentic chat and autofix cut review round-trips.
• Cons: Per-seat Pro pricing adds up for large teams; suggestions still need human judgment on architecture; most automation value sits on paid tiers.
• Price: Free $0/user; Pro $24/user/mo; Pro Plus $48/user/mo (billed annually); Enterprise custom.
• Skip it if: Your team does not use pull requests, or you need review inside the IDE rather than in the PR thread.
• → Full profile: CodeRabbit

Step 3 — Close the test-coverage gap the review misses

Review comments catch what is wrong in the code that exists. They rarely catch the branch nobody wrote a test for. Qodo attacks that gap directly: it generates unit tests including edge cases, analyzes pull requests, and hooks into CI so coverage is enforced rather than requested. Wire it in as a required check and “add tests” stops being a review comment you have to write twice.

• Pros: Auto-generated unit tests with edge cases; AI pull request analysis; integrates with CI pipelines.
• Cons: Focused on testing rather than writing; some features are enterprise-only.
• Price: Free $0 for individuals; Teams $19/user/mo.
• Skip it if: You want one tool that both writes and reviews — Qodo is deliberately narrow.
• → Full profile: Qodo

Step 4 — Ground the review in a large codebase

On a monorepo, the failure mode is not bad suggestions but blind ones: the model cannot see the module that actually defines the behavior. Cody reads repositories through Sourcegraph’s code intelligence and answers with citations to real files and functions, which turns “where does this happen?” from a grep expedition into a one-line answer. During review, that is the difference between a comment that guesses and one that points at the conflicting implementation two directories over.

• Pros: Deep codebase-wide context; runs on multiple LLM providers; strong autocomplete and chat.
• Cons: Best value requires the Sourcegraph backend; setup is heavier than a simple plugin.
• Price: Free $0; Pro $9/mo; Enterprise custom.
• Skip it if: You work in small, self-contained repos where whole-repo indexing buys you nothing.
• → Full profile: Cody

Step 5 — Keep the loop affordable

Not every team can put four subscriptions on every seat. Codeium offers an unusually complete free tier — full autocomplete and chat across 70+ languages and most editors — which makes it the pragmatic pre-review layer when budget is the constraint. Pair it with CodeRabbit’s free tier and you have a review loop that costs nothing. If you would rather stay inside GitHub’s ecosystem, GitHub Copilot at $10/mo gives you /explain, /fix, and /tests inline, though it is known to lose context on very large monorepos.

• Pros: Generous free tier for individuals; broad editor and language support; inline chat and command mode.
• Cons: Enterprise features cost more; suggestions are occasionally off-target.
• Price: Free $0; Pro $15/mo; Enterprise custom.
• Skip it if: You need autonomous multi-file refactoring — that is Cursor or Claude Code territory.
• → Full profile: Codeium

Common pitfalls

Treating autofix as authoritative. CodeRabbit’s own positioning is that its fixes are suggestions, not merges. Approving an AI patch without reading it moves the bug from the author to the reviewer.

Reviewing AI-written code with the same AI. If one assistant wrote the diff and the same model reviews it, you get agreement rather than scrutiny. Use a different tool for the review pass than the one that authored the change.

Letting comment volume replace judgment. An automated reviewer will happily leave forty comments on a fifty-line diff. Configure severity thresholds early, or reviewers learn to scroll past everything.

Ignoring data residency. Cursor’s telemetry questions and the availability of self-hosted tiers on Codeium and Cody matter more than price for regulated teams. Settle that before rollout, not after.

How to choose

• Solo developer, no budget → Codeium in the editor plus CodeRabbit free on PRs.
• Small product team on GitHub → CodeRabbit Pro as the first reviewer, GitHub Copilot inline for fixes.
• Team with weak test coverage → add Qodo as a required CI check before adding anything else.
• Platform team on a monorepo → Cody for grounded answers, Cursor for cross-file refactors.
• Terminal-first engineers → Aider or Claude Code for the authoring half, then an automated PR reviewer.

Related tools & guides

• CodeRabbit — automated pull-request review with autofix
• Qodo — test generation and PR analysis in CI
• Cody — codebase-wide context from Sourcegraph
• Cursor — AI-first editor with Composer and agent mode
• Codeium — free autocomplete and chat across 70+ languages
• Guide: Best AI Coding Assistants 2026
• Guide: AI Coding Assistants Benchmark 2026
• Guide: Cursor vs GitHub Copilot 2026