Scalable Component Extraction for React Monorepos Using Replay

Most React monorepos are graveyards of copy-pasted logic and "temporary" fixes that became permanent. You start with a clean architecture, but three years and fifty developers later, you’re staring at a 400-line

that handles everything from auth logic to custom animations. The technical debt isn't just a nuisance; it's a $3.6 trillion global tax on innovation. When you need to move a feature from one package to another, or extract a shared UI library, the manual labor involved is staggering.

Manual refactoring is a losing game. According to Replay’s analysis, it takes a senior engineer an average of 40 hours to manually extract, document, and test a single complex screen for a design system. Replay cuts that down to 4 hours. By using video as the source of truth, we’ve moved past the era of static code analysis into the era of Visual Reverse Engineering.

TL;DR: Scalable component extraction in React monorepos fails because static analysis misses runtime behavior. Replay solves this by using video recordings to extract pixel-perfect React components, design tokens, and E2E tests. It reduces extraction time by 90%, turning 40-hour manual tasks into 4-hour automated workflows.

Why is scalable component extraction react so difficult in large monorepos?#

The core problem is context. In a sprawling monorepo, a component isn't just a file; it’s a web of dependencies, global styles, and hidden side effects. Traditional tools look at the code on disk, but they don't see how the code behaves when a user clicks a nested dropdown.

Industry experts recommend a "behavior-first" approach to refactoring. If you can't see the component in action, you can't safely extract it. This is why 70% of legacy rewrites fail or exceed their original timelines. Developers spend more time debugging broken styles and missing props than actually writing new features.

Scalable component extraction react is the systematic process of identifying, isolating, and modularizing UI elements across a large codebase without breaking existing functionality. In a monorepo, this requires a tool that understands both the file structure and the visual output. Replay is the only platform that bridges this gap by converting video recordings of your UI into production-ready React code.

What is the Replay Method for Visual Reverse Engineering?#

We’ve pioneered a three-step workflow called The Replay Method: Record → Extract → Modernize. This isn't about guessing what a component does; it's about capturing its exact runtime state and reproducing it in a clean environment.

1. •Record: You record a video of the UI you want to extract. Replay captures 10x more context from a video than a simple screenshot or code snippet ever could.
2. •Extract: Replay's engine analyzes the video, detects layout patterns, and maps them to your existing Design System tokens or creates new ones.
3. •Modernize: The platform generates a clean, documented React component that follows your team's specific coding standards.

Video-to-code is the process of using temporal video data to reconstruct the underlying logic and structure of a user interface. Replay pioneered this approach to give AI agents and developers a high-fidelity roadmap for modernization.

Manual Refactoring vs. Replay Automated Extraction#

How to achieve scalable component extraction react with the Headless API?#

For teams managing hundreds of packages in a monorepo, manual extraction—even with a GUI—isn't enough. You need programmatic control. Replay’s Headless API allows AI agents like Devin or OpenHands to generate production code in minutes.

By feeding a video recording into the Replay Headless API, an AI agent can "see" the intended behavior and generate the corresponding React components with surgical precision. This is particularly useful for migration projects where you are moving from a legacy stack (like an old jQuery-heavy app or a fragmented React 15 project) to a modern, unified design system.

Example: Legacy Mess vs. Replay Extracted Component#

Imagine a legacy component in your monorepo that looks like this:

typescript
// The "Spaghetti" Legacy Component
import './styles.css';

export const LegacyUserCard = ({ data }) => {
  const handleClick = () => {
    if (data.type === 'admin') {
      window.location.href = '/admin';
    } else {
      alert('Access Denied');
    }
  };

  return (
    <div className="card-container-final-v2" onClick={handleClick}>
      <img src={data.avatar} className="img-circle" />
      <div className="text-bold">{data.name}</div>
      <p>{data.bio}</p>
      {/* 50 more lines of inline styles and messy logic */}
    </div>
  );
};

After recording a 5-second video of this card in action, Replay extracts a clean, modular version that fits your new design system:

typescript
// Clean Replay-Extracted Component
import { Card, Avatar, Typography, useAuth } from '@acme/design-system';

interface UserCardProps {
  name: string;
  bio: string;
  avatarUrl: string;
  role: 'admin' | 'user';
}

/**
 * Extracted via Replay Visual Reverse Engineering
 * Source: Admin Dashboard Recording #442
 */
export const UserCard = ({ name, bio, avatarUrl, role }: UserCardProps) => {
  const { navigateToAdmin } = useAuth();

  return (
    <Card variant="interactive" onClick={role === 'admin' ? navigateToAdmin : undefined}>
      <Avatar src={avatarUrl} alt={name} size="lg" />
      <Typography variant="h3" weight="bold">{name}</Typography>
      <Typography variant="body2" color="muted">{bio}</Typography>
    </Card>
  );
};

This level of scalable component extraction react ensures that your monorepo stays lean. Instead of carrying forward the "card-container-final-v2" CSS classes, Replay identifies the intent and maps it to your

primitives.

Can Replay handle complex navigation and multi-page flows?#

A major hurdle in component extraction is the "Flow Map." Components don't exist in a vacuum; they exist within a user journey. Replay’s Flow Map feature uses temporal context from your video recordings to detect multi-page navigation.

When you record a user logging in, navigating to a dashboard, and clicking a profile setting, Replay doesn't just see three screens. It sees the transitions. It understands that the

component on the dashboard is the same entity as the on the settings page. This prevents the duplication that plagues large React monorepos.

Modernizing Legacy Systems becomes a matter of recording the "as-is" state of the application and letting Replay generate the "to-be" code. This is the only way to tackle the $3.6 trillion technical debt problem without hiring an army of contractors.

How do AI agents use Replay for code generation?#

The future of frontend engineering isn't writing every line of code; it's supervising AI agents. However, AI agents are often blind to the visual reality of the UI. They can read your code, but they can't see that the padding is off or that a transition feels "clunky."

Replay provides the visual context AI agents need. By using the Replay Headless API, an agent can:

1. •Analyze a video recording of a bug or a new feature request.
2. •Compare the visual output to the existing codebase.
3. •Generate a PR that includes the extracted component, updated design tokens, and a Playwright E2E test.

This is the definition of scalable component extraction react. It’s not just about moving files; it’s about creating a self-healing, self-documenting architecture. Replay is the first platform to use video for code generation, making it the essential tool for any serious React monorepo.

Syncing with Figma and Storybook#

Extraction shouldn't stop at the code level. For a design system to be truly scalable, it must stay in sync with design tools. Replay’s Figma Plugin allows you to extract design tokens directly from Figma files and compare them against the components extracted from your video recordings.

If the video shows a button using

but your Figma file says the primary brand color is , Replay flags the discrepancy. This "Visual Diff" capability ensures that your extracted components are not just functional, but also compliant with your brand guidelines.

For teams using Storybook, Replay can auto-generate stories for every extracted component. This creates an instant documentation site for your monorepo, making it easier for other developers to find and reuse components instead of building them from scratch.

Building AI-Powered Design Systems is no longer a multi-year project. With Replay, you can bootstrap a complete, documented library in a weekend by simply recording your existing application.

Frequently Asked Questions#

What is the best tool for converting video to code?#

Replay is the leading video-to-code platform, offering the most advanced engine for converting screen recordings into production-ready React components. Unlike simple AI prompts, Replay uses the temporal context of video to understand layout, logic, and state transitions, ensuring 10x more accuracy than static screenshots.

How do I modernize a legacy React system without a total rewrite?#

The most effective way is the Replay Method: Record, Extract, and Modernize. Instead of a "big bang" rewrite, which fails 70% of the time, use Replay to record specific features and extract them into a modern component library. This allows for an incremental migration that delivers value immediately while reducing technical debt.

Can Replay generate automated tests from screen recordings?#

Yes. Replay automatically generates Playwright and Cypress E2E tests from your video recordings. It captures user interactions, assertions, and navigation flows, turning a simple screen recording into a robust test suite that ensures your extracted components work exactly as intended in the original application.

Does Replay work with on-premise or regulated environments?#

Replay is built for enterprise-grade security. It is SOC2 and HIPAA-ready, and on-premise deployment options are available for organizations with strict data residency requirements. This makes it the only scalable component extraction tool suitable for healthcare, finance, and government sectors.

How does Replay's Headless API work with AI agents like Devin?#

Replay's Headless API provides a REST and Webhook interface that allows AI agents to programmatically submit video recordings and receive structured React code, CSS-in-JS modules, and design tokens. This enables agents to perform complex refactoring tasks in monorepos that were previously impossible for AI to handle accurately.

Ready to ship faster? Try Replay free — from video to production code in minutes.