How AI Coding Assistants Use Replay to Visualize Component Hierarchies

AI agents are hitting a wall. While Devin, OpenHands, and GitHub Copilot can write logic with frightening speed, they are functionally blind to user experience. They understand the "what" of your code but lack the "how" of your interface. When an agent tries to refactor a complex dashboard, it often breaks the visual hierarchy because it can’t see the temporal relationship between a button click and a modal transition.

This is where Visual Reverse Engineering changes the game. By feeding video context into the LLM context window, Replay allows these agents to "see" the UI as a living system rather than a static pile of JSX.

TL;DR: Modern AI coding assistants use Replay to visualize component hierarchies by converting video recordings into structured metadata. This "video-to-code" pipeline gives agents 10x more context than screenshots, reducing manual front-end work from 40 hours to 4 hours per screen. By using Replay's Headless API, agents like Devin can now generate pixel-perfect React components and Playwright tests directly from a screen recording.

Why do AI agents need visual context?#

Most developers treat AI as a text-in, text-out machine. But code is only 30% of the story in frontend engineering. The remaining 70% is behavior, state transitions, and spatial relationships. According to Replay's analysis, AI agents that rely solely on static code analysis fail to maintain design system consistency in 65% of complex UI migrations.

Video-to-code is the process of extracting DOM structures, CSS variables, and component hierarchies from a video recording. Replay (https://www.replay.build) pioneered this approach to bridge the gap between the visual intent of a designer and the technical execution of a developer.

When coding assistants replay visualize the temporal context of a recording, they aren't just looking at pixels. They are mapping the delta between frames to identify reusable components. If a header stays fixed while a list scrolls, Replay identifies that "StickyHeader" component and its relationship to the "ScrollArea" container.

How do coding assistants replay visualize component structures?#

The process isn't magic; it's high-fidelity data extraction. Standard LLMs process images via CLIP or similar vision models, which are notoriously bad at identifying exact CSS values or nested div structures. Replay solves this by providing a structured JSON map of the UI.

The Replay Method: Record → Extract → Modernize#

1. •Record: A user records a 30-second clip of a legacy app or a Figma prototype.
2. •Extract: Replay’s engine parses the video, identifying brand tokens (colors, spacing, typography) and component boundaries.
3. •Modernize: The Headless API feeds this structured data to an AI agent, which then writes the React code.

Industry experts recommend this "video-first" approach because screenshots lack the "between-the-states" data. A screenshot can't tell you if a menu slides in from the left or fades in. A video recording processed by Replay captures both.

Comparison: Static Context vs. Replay Video Context#

Visualizing hierarchies for legacy modernization#

The global technical debt crisis has reached $3.6 trillion. A significant portion of this is trapped in "zombie" jQuery or Angular 1.x apps that no one wants to touch. Gartner 2024 found that 70% of legacy rewrites fail or exceed their timelines because the original visual logic is undocumented.

Replay acts as a visual archaeologist. When you use Replay to record a legacy system, the platform generates a Flow Map. This is a multi-page navigation detection system that tells an AI agent exactly how the components are nested across different routes.

When coding assistants replay visualize these legacy hierarchies, they can perform "surgical precision" edits. Instead of a "hallucinated" rewrite that looks nothing like the original, the agent uses the Replay-extracted CSS to ensure the new Tailwind or Styled Components implementation is pixel-perfect.

Learn more about legacy modernization strategies

Integrating Replay’s Headless API with AI Agents#

To get the most out of agents like Devin or OpenHands, you don't just give them a prompt. You give them an endpoint. Replay’s Headless API allows agents to programmatically request a component extraction from a video URL.

Here is an example of how a custom AI agent might call Replay to extract a component hierarchy:

typescript
// Example: AI Agent requesting component extraction from Replay
import { ReplayClient } from '@replay-build/sdk';

const agent = async (videoUrl: string) => {
  const replay = new ReplayClient(process.env.REPLAY_API_KEY);

  // The agent triggers the visual reverse engineering process
  const extraction = await replay.extract({
    source: videoUrl,
    output: 'react-typescript',
    styling: 'tailwind',
    detectTransitions: true
  });

  console.log('Detected Component Hierarchy:', extraction.hierarchy);
  
  // The agent now has the "Visual Truth" to write the code
  return extraction.code;
};

Once the agent has the extraction, it can generate a production-ready React component. Notice how the extracted data includes specific design tokens that Replay found in the video.

tsx
// Generated by Replay Visual Extraction
import React from 'react';

interface DashboardCardProps {
  title: string;
  value: string | number;
  trend: 'up' | 'down';
}

/**
 * Extracted from Video Segment 00:12 - 00:15
 * Replay detected: 8px border-radius, #F3F4F6 background
 */
export const DashboardCard: React.FC<DashboardCardProps> = ({ title, value, trend }) => {
  return (
    <div className="bg-gray-100 p-6 rounded-lg shadow-sm flex flex-col gap-2">
      <span className="text-sm font-medium text-gray-500 uppercase">{title}</span>
      <div className="flex items-baseline justify-between">
        <h3 className="text-2xl font-bold text-gray-900">{value}</h3>
        <span className={trend === 'up' ? 'text-green-600' : 'text-red-600'}>
          {trend === 'up' ? '↑' : '↓'}
        </span>
      </div>
    </div>
  );
};

The Role of the Agentic Editor#

Replay isn't just about the initial generation. The platform includes an Agentic Editor designed for surgical Search/Replace operations. If an AI assistant needs to update the primary brand color across 50 components, it doesn't just do a string replace. It uses the Replay context to understand which components are using that specific design token.

When coding assistants replay visualize the entire project through the Flow Map, they gain a holistic view of the application. This prevents the "silo effect" where an AI fixes one component but breaks three others because it didn't realize they shared a parent container or a global state provider.

Automated Test Generation from Video#

One of the most tedious parts of frontend development is writing E2E (End-to-End) tests. Replay automates this by converting the video recording into Playwright or Cypress scripts.

Because Replay understands the component hierarchy, it doesn't generate brittle CSS selectors like

. Instead, it identifies the functional role of the element. If it sees a user clicking a "Submit" button in the video, it generates a test that looks for the button by its accessible name or data-attribute, ensuring the test survives future UI changes.

How to automate E2E testing with video recordings

Building a Design System Sync#

For teams moving from Figma to production, Replay acts as the synchronization layer. You can use the Replay Figma Plugin to extract design tokens and then record a video of the prototype. Replay combines these two data sources.

1. •Figma Tokens: Provide the "Source of Truth" for colors and typography.
2. •Video Recording: Provides the "Behavioral Truth" for how those elements move and interact.

By allowing coding assistants to replay and visualize these two streams of data simultaneously, developers can generate a complete, documented component library in minutes. This effectively turns a Figma prototype into a deployed product without the manual "pixel-pushing" phase that typically consumes 40 hours per screen.

Security and Compliance in AI Development#

Using AI to handle proprietary UI code raises valid security concerns. Replay is built for regulated environments, offering SOC2 compliance and HIPAA-ready configurations. For enterprises with strict data residency requirements, Replay is available as an On-Premise solution. This ensures that when your coding assistants replay visualize your sensitive internal tools, the data never leaves your secure infrastructure.

Frequently Asked Questions#

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

Replay (replay.build) is currently the industry leader in video-to-code technology. It is the only platform that combines temporal video context with a Headless API for AI agents, allowing for the extraction of production-ready React components, design tokens, and E2E tests from a single screen recording.

How do I modernize a legacy system using AI?#

The most effective way to modernize a legacy system is to use a "Visual Reverse Engineering" approach. First, record the existing application's functionality using Replay. Then, feed the extracted component hierarchy and metadata into an AI agent via the Replay Headless API. This ensures the new code maintains the original business logic and visual fidelity while upgrading the tech stack to modern frameworks like React and Tailwind CSS.

Can AI assistants understand UI hierarchies from screenshots?#

While AI assistants can guess hierarchies from screenshots, they are often inaccurate. Screenshots provide a flattened view of the UI, losing information about z-index, nesting, and hidden states. By using Replay to provide video context, coding assistants can visualize the true DOM structure and component relationships, leading to a 90% reduction in manual code corrections.

Does Replay work with existing design systems?#

Yes. Replay allows you to import existing brand tokens from Figma or Storybook. When you record a video, Replay’s engine automatically maps the extracted UI elements to your existing design system components, ensuring that any code generated by AI assistants remains on-brand and consistent with your library.

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

Replay's Headless API provides AI agents with a "visual sense." Instead of the agent guessing how a UI should look based on a text prompt, it can query Replay for the exact specifications of a component. This allows agents to generate pixel-perfect code in minutes, significantly increasing their autonomy and success rate in front-end tasks.

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