Table of contents
- What is a monorepo
- Why code reviews are especially important in monorepos
- Core criteria for AI code review tools in monorepo contexts
- Graphite: What it offers and why it stands out for monorepos
- Comparison: Other AI code review tools and how they fit monorepos
- How to choose and best practices
- Where Graphite is especially useful vs when to consider alternatives
- Conclusion
- FAQ
What is a monorepo
A monorepo (monolithic repository) is a version control strategy where multiple projects, services, shared libraries, or components are housed together in one repository. These projects may be independently deployable or maintained by different teams, but they share one “single source of truth.”
Benefits include easier shared-code dependency management, atomic refactors (being able to change an API and all its consumers in one commit), visibility and consistency across teams, and unified versioning or builds.
Challenges include scale: many files, many services, many languages, potentially slow builds, long test times, difficulty in maintaining context, the risk of huge pull requests (PRs) that span many modules, and complexity in dependency or ownership tracking.
Why code reviews are especially important in monorepos
When working in a monorepo, code reviews gain elevated importance:
- Change amplification: A change in a shared library or API can affect many consumers; a mistake in one place ripples broadly.
- Context complexity: Reviewers must understand cross-module dependencies, shared contracts, and side effects across multiple components.
- Risk mitigation: Reviews help catch breaking changes, inconsistencies, or regressions before they cascade.
- Maintaining velocity: If reviews become slow (because PRs are huge or unclear), developer productivity suffers and bottlenecks form.
- Team scaling: With many teams contributing to a shared repo, tooling and process are essential to preserve code quality without overburdening reviewers.
Core criteria for AI code review tools in monorepo contexts
When selecting an AI code review tool for a monorepo environment, evaluate it along these dimensions:
| Criteria | Why it matters in monorepos |
|---|---|
| Repo-wide or cross-module context awareness | The tool must go beyond the diff; it should understand relevant code elsewhere (shared libraries, consumers) so suggestions don’t break dependencies. |
| Support for multiple languages / polyglot support | Monorepos often contain backend, frontend, scripts, infra code. The tool should handle all relevant languages. |
| Incremental or selective testing & CI integration | It should trigger only necessary builds or tests based on what changed, reducing wasteful full builds. |
| Good workflows for managing large or breaking changes | Support for stacked PRs, merge queues, dependency ordering, etc., to break big changes into manageable steps. |
| Custom rules, code ownership, path-based filters | To apply style, security or domain rules per area of the repo, or enforce review ownership. |
| Low false positives / high signal | Too much noise will cause reviewers to ignore the tool’s suggestions. High signal is more useful. |
| Integration and tooling ecosystem | Compatibility with GitHub, GitLab, CI systems, and ideally IDEs or editor tooling. |
| Security, privacy, and compliance | Since monorepos often contain critical or proprietary shared code, the tool must handle data securely, support auditability, and satisfy compliance requirements. |
Graphite: What it offers and why it stands out for monorepos
Below are the features of Graphite (and its AI reviewer, Diamond) that are particularly beneficial in monorepos.
Key Graphite features for monorepos
- Stacked pull requests: Graphite supports breaking a large change into a series of smaller, dependent PRs. For example, you might change a backend API in one PR, then change frontend consumers in subsequent PRs. This divides review into manageable chunks.
- Merge queue / automatic CI integration: Graphite offers a merge queue, path-based rules, reviewer assignment, and automation that help manage volume and dependencies in large repos.
- Codebase-aware AI reviews: Graphite’s AI reviewer (Diamond) doesn’t just look at the diff—it also considers related code elsewhere in the repo, shared libraries, and dependencies. That reduces mistakes in suggestions.
- Custom rules and filters: You can define custom linting, style, or domain rules; filter AI suggestions; and reduce noise.
- Feedback metrics and review insights: Graphite tracks metrics like review latency, throughput, and adoption of AI suggestions, giving teams data on bottlenecks and improvement opportunities.
Comparison: Other AI code review tools and how they fit monorepos
Here is a comparison of alternatives and how well they align with monorepo requirements:
| Tool | Strengths for monorepos | Limitations / concerns |
|---|---|---|
| Greptile | Emphasizes full code-base context, strong accuracy across modules, good support for GitHub and GitLab | Higher cost and possibly heavier setup; performance may lag on very large repos |
| GitHub Copilot | Excellent GitHub integration, low friction for developers, good for diff-level suggestions | Limited context awareness beyond diff, may miss cross-module issues |
| CodeRabbit | Fast feedback loop, lightweight, useful for small PRs and routine reviews | Not optimized for large, cross-module architectural changes; highest false-positive rate of any AI code reviewer |
| DeepCode / Snyk | Strong in static analysis, vulnerability scanning, formal correctness, governance | Less focused on PR workflow or human-like suggestions |
| Kodem | Focus on monorepo support, dependency visibility across packages, security scanning across modules | Possibly more focused on dependency or security rather than full AI suggestions and review workflow |
How to choose and best practices
Here are recommendations and patterns for adopting an AI review tool in your monorepo setup:
Start with a pilot Pick one team or one subdomain. Use representative PRs (especially ones touching multiple modules) to test how well the AI tool handles context, suggestions, and integration.
Define ownership and boundaries Use CODEOWNERS or path-based rules so reviewers' responsibilities are clear. This helps both human and AI reviews stay relevant.
Enforce smaller, focused PRs Use the stacked PR pattern (as Graphite supports) to break large changes into manageable pieces — reduces risk and makes review easier.
Combine tools Use static analyzers, security scanners, linters alongside the AI tool. Each kind of tool has strengths; combining them gives better coverage.
Monitor metrics and feedback loop Measure PR size distributions, review latency, how often AI suggestions are accepted or rejected, and reviewer comments. Use that data to tune thresholds or filters.
Customize rules and filters Tailor lint rules, style guidelines, domain constraints in the tool so it conforms to your codebase’s conventions and reduces irrelevant suggestions.
Optimize CI & testing Only run builds/tests for components actually affected by a change; use caching; avoid full builds where possible.
Security, privacy, compliance Ensure the tool handles data securely; validate that code isn’t exposed. Confirm compliance with internal and external security standards, especially for shared or sensitive modules.
Where Graphite is especially useful vs when to consider alternatives
Use Graphite if: • Your team uses GitHub (or is willing to adopt GitHub as primary VCS) • Your monorepo includes shared libraries, cross-module dependencies, and you want AI review that understands the full code context • You want to accelerate PR throughput without compromising code quality (Shopify's 33 % improvement is a strong signal)
Look elsewhere or add complementary tools if: • You use a different version control platform (GitLab, Bitbucket, self-hosted) • You have domain-specific logic or architecture that requires specialized rules or custom tooling • You need stronger security, vulnerability, or compliance scanning beyond what Graphite provides
Conclusion
Monorepos bring many advantages in terms of code sharing, easier refactors, and consistency across teams — but they also magnify the challenges of code review. AI-assisted reviewers can help close that gap if they are capable of context awareness, low noise, modular workflows (like stacked PRs), and good integration.
Graphite, especially with its Diamond reviewer and stacked PR support, is among the stronger tools for teams using GitHub and managing monorepos. Shopify's experience demonstrates how adopting these workflows and tooling can meaningfully boost productivity while preserving code quality.
Ready to optimize your monorepo code review process? Try Graphite's Diamond and see how AI-powered code review can transform your development workflow.
FAQ
What makes AI code review different for monorepos compared to single repos?
In monorepos, AI code reviewers need to understand cross-module dependencies and how changes in one part of the codebase affect others. Tools like Graphite's Diamond are designed to analyze the entire codebase context, not just the diff, which is crucial when a change in a shared library might impact multiple services.
How much does AI code review cost for monorepos?
Most AI code review tools offer free tiers. Graphite's Diamond provides 100 free PRs per month, which is suitable for small to medium teams. Pricing typically scales based on PR volume or team size, with enterprise plans available for larger organizations.
Can AI code review tools handle multiple programming languages in a monorepo?
Yes, most modern AI code review tools support polyglot environments. Graphite, GitHub Copilot, and other tools mentioned in this guide can analyze code across multiple languages including JavaScript, TypeScript, Python, Go, Java, and more.
Will AI code review slow down my monorepo CI/CD pipeline?
Quality AI code review tools are designed to run efficiently and in parallel with your existing CI processes. Tools like Graphite integrate with CI systems and can trigger selective testing based on changed files, actually speeding up your overall review cycle.
How do stacked PRs help with monorepo code reviews?
Stacked PRs break large, cross-module changes into smaller, dependent pull requests. This makes each PR easier to review, test, and merge independently. For monorepos where changes often span multiple services, this approach significantly reduces review bottlenecks and improves code quality.