The Best New AI Code Review Tools for Engineers
A growing share of the code shipping to production now comes from AI coding agents. They're fast and confident, and every so often they're wrong in ways no human reviewer would be. An agent will quietly swap in fake data, write a test it never actually ran, hallucinate an import, or slip a regression past a green CI run. The best AI code review tools exist to close that gap. They sit as a quality gate before merge and catch bugs, regressions, and unsafe patterns in pull requests and across the whole codebase. Below are eight new tools engineers are leaning on to keep AI-assisted output honest, with a particular eye on the mistakes agents introduce. These are reviewers and guardrails. They're not the coding agents themselves, and they aren't standalone security scanners.
Cubic
Cubic is an AI code review platform that plugs into GitHub and your IDE, then automatically reviews pull requests and scans entire codebases for bugs, vulnerabilities, and tech debt. The interesting part is how it learns. It studies your senior developers' past PR comments to enforce team-specific standards, and it pulls extra context from Linear, JIRA, and Confluence. Background agents run nightly scans of the whole repo and can open fix PRs on their own. If you want a single reviewer sitting on every PR while keeping an eye on the entire codebase, Cubic is one of the most complete options here.
Metabob
Metabob runs alongside your coding agents as a real-time intelligence layer, not a post-hoc reviewer. Rather than reading code in isolation the way a raw LLM does, it models the full runtime application, the execution flows, and how components relate, then steers agents toward safer implementations while the code is still being written. Catching a regression mid-flight is a lot cheaper than catching it in review, and the numbers Metabob reports back this up: 80% fewer introduced regressions, a 66% drop in maintenance time, and 70% fewer security vulnerabilities. It's a strong pick if you want guardrails during generation instead of just a gate at the end.
Vet
Vet, from Imbue, is a fast local code review tool built to verify coding agent output by reading the conversation history and confirming the agent actually did what you asked. It's purpose-built for the failure modes that slip past normal review: silently substituting fake data, writing tests it never ran, introducing logic errors or unhandled edge cases. You can run it from the CLI, wire it into CI/CD, or use it as an agent skill. It's open source with zero telemetry. If you live in the terminal and want a privacy-friendly check on every agent run, Vet is hard to beat.
adamsreview
adamsreview is a Claude Code plugin that turns the built-in /review command into a six-command pipeline. It fans out up to seven parallel AI sub-agents across different lenses (correctness, security, UX, and others), then deduplicates and validates the findings before proposing high-confidence auto-fixes. Its fix loop re-reviews its own changes and reverts regressions before committing, and an interactive walkthrough lets you step through the uncertain findings one at a time. For engineers already working inside Claude Code, it's a low-friction way to get multi-lens review without leaving the agent.
aislop
aislop is a deterministic quality gate aimed squarely at AI-written code. It uses regex and AST analysis with no LLMs in the loop. Across 8+ languages it catches the classic AI tells: dead code, oversized functions and files, unused imports, TypeScript as-any casts, swallowed errors, hallucinated imports, TODO stubs, narrative comments. Then it scores the codebase 0 to 100. It ships as a CLI, a GitHub Action, and a per-edit hook for Claude Code or Cursor, and you can enforce thresholds in CI with npx aislop ci. Being fully deterministic makes it fast, cheap, and repeatable, which is exactly what you want as a complement to the heavier LLM reviewers above.
Statewright
Statewright comes at quality from a different angle. Instead of reviewing output after the fact, it constrains the agent up front with state-machine guardrails, limiting which tools the agent can reach for at each phase of the workflow (planning, implementing, testing) through a deterministic Rust engine with no LLM involved. Keeping the tool space small at each phase kills off read-loop death spirals and measurably improves task completion on both frontier and local models. It integrates with Claude Code, Codex, Cursor, opencode, and Pi through MCP and hooks. If your agents tend to wander off-task, this is prevention rather than cure.
Aurora Labs
Aurora Labs' LOCI is an execution-aware quality gate that audits compiled binaries instead of source. It's built on a Large Code Language Model trained on five years of real production workloads, and it detects regressions in response time, throughput, power consumption, control flow integrity, and flame graphs without running the code, instrumenting it, or even needing source access. It plugs into CI/CD, IDEs, and agents like Claude Code, Cursor, and Copilot to audit from the first keystroke through PR merge, and teams report saving 33+ hours per sprint. For performance-sensitive and embedded work, it catches the regressions other reviewers simply can't see.
Earthly
Earthly Lunar is a guardrails engine that enforces engineering standards deterministically across every repo and PR. It turns your postmortems, compliance requirements, AGENTS.md files, and internal wikis into automated PR-level and AI-agent-loop checks, so org-wide rules on reliability, security, and compliance get enforced without a human review bottleneck. For platform teams trying to keep dozens of repos and a fleet of AI agents inside the same guardrails, Lunar turns scattered docs into checks that actually run. It pairs naturally with the kind of AI-powered DevOps and CI/CD tooling many teams are already standardizing on.
Frequently asked questions
What are AI code review tools?
AI code review tools use machine learning and program analysis to automatically review pull requests and codebases for bugs, regressions, and unsafe patterns before code is merged. They work as a quality gate that complements human reviewers, and a newer wave of them focuses specifically on the mistakes AI coding agents make: fabricated data, untested code, hallucinated imports.
How are AI code review tools different from AI coding agents?
Coding agents like Claude Code, Cursor, and Copilot write and edit code for you. AI code review tools sit on the other side of the workflow and inspect what those agents (or humans) produced, flagging problems before merge. A few tools in this list, Metabob and Statewright among them, work alongside agents in real time, but their job is to check and constrain output, not to generate features.
Do AI code review tools catch security vulnerabilities?
Many flag insecure patterns as part of a broader review. Cubic and Metabob both report reductions in security issues, for example. That said, dedicated security scanners (SAST, secret detection, dependency auditing) are a separate category. The tools here are general-purpose quality gates focused on bugs, regressions, and unsafe patterns, so pair them with a real security scanner if security is your main concern.
Can AI code review tools fix the bugs they find?
Some can. Cubic's background agents open fix PRs automatically, adamsreview runs a fix loop that reverts its own regressions, and aislop has an auto-fix mode for mechanical issues like unused imports. Others, such as Vet and Aurora Labs' LOCI, stick to detection and verification and leave the fix to you or your agent.
Do these tools work in CI/CD pipelines?
Yes. Most are designed to run in CI as a merge gate. Vet, aislop, Aurora Labs' LOCI, and Earthly Lunar all integrate directly into CI/CD pipelines, while Cubic and Metabob hook into GitHub and your editor. That lets you block or annotate pull requests automatically before anything reaches main. Many teams run them next to AI testing and QA tools so the same pipeline covers both correctness and code quality.
Conclusion
AI agents have made writing code faster than ever, so the bottleneck has shifted to trusting what they produce. The tools in this roundup attack that problem from every angle: real-time guardrails during generation (Metabob, Statewright), local and CI-based verification of agent output (Vet, aislop), full-platform PR review (Cubic, adamsreview), and execution-aware or org-wide compliance gates (Aurora Labs, Earthly). The right pick depends on where your risk actually lives, but adding at least one quality gate before merge is fast becoming non-negotiable for any team shipping AI-assisted code. For more along these lines, browse our AI tools for software engineers hub, or see what else is new across the Product Lookout radar.