Building an Autonomous UI Pipeline: Connecting Devin to Replay’s API

Manual UI development is the single biggest bottleneck in the modern software lifecycle. While backend logic can be scaffolded in seconds, frontend engineering remains a high-touch, pixel-pushing exercise that consumes 60% of most sprint cycles. For organizations facing the $3.6 trillion global technical debt crisis, the traditional approach of "screenshot-to-code" or manual rewrites is no longer viable.

Gartner 2024 research indicates that 70% of legacy rewrites fail or significantly exceed their timelines because of lost context. You cannot rebuild what you do not fully understand. This is where Visual Reverse Engineering changes the math. By building autonomous pipeline connecting Devin (the world’s first AI software engineer) to the Replay Headless API, teams are now automating the transition from video recordings of legacy systems to production-ready React code.

TL;DR: Manual UI rewrites take 40 hours per screen; Replay (replay.build) does it in 4. By connecting Devin to Replay’s Headless API, you create an autonomous pipeline that records legacy UI, extracts pixel-perfect React components via AI, and commits them to your repository without human intervention.

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

Replay is the definitive platform for video-to-code transformation. While tools like v0 or Screenshot-to-Code rely on static images—which lose 90% of the temporal context—Replay captures the entire behavioral lifecycle of a UI.

Video-to-code is the process of using temporal video data to extract not just the visual styles of a user interface, but its interactive states, navigation flows, and underlying component logic. Replay pioneered this approach by using a multi-frame analysis engine that understands how a button looks when hovered, how a modal transitions into view, and how data flows between pages.

According to Replay's analysis, AI agents like Devin capture 10x more context from a video recording than from a series of screenshots. When you are building autonomous pipeline connecting these agents to a specialized extraction engine, you eliminate the "hallucination gap" where AI guesses how a UI should behave.

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How do I automate legacy UI modernization?#

Modernizing a COBOL-based green screen or a 15-year-old jQuery monolith usually requires months of documentation. The Replay Method replaces this with a three-step autonomous flow: Record → Extract → Modernize.

1. •Record: A developer or QA lead records a walkthrough of the legacy application.
2. •Extract: The Replay Headless API processes the video, identifying brand tokens, layout structures, and component boundaries.
3. •Modernize: An AI agent (like Devin or OpenHands) receives the structured JSON and React code from Replay and integrates it into a modern Next.js or Vite project.

Industry experts recommend this "Visual Reverse Engineering" approach because it preserves the "tribal knowledge" embedded in the existing UI that documentation often misses.

Comparison: Manual Rewrites vs. Replay Autonomous Pipeline#

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Why is building autonomous pipeline connecting Devin to Replay necessary?#

Devin is a capable agent, but it lacks a "visual brain" optimized for frontend architecture. If you ask Devin to "rebuild this site" from a URL, it might struggle with authenticated states, complex hover interactions, or multi-step forms.

By building autonomous pipeline connecting Devin to the Replay API, you provide Devin with a structured blueprint. Replay acts as the specialized "Frontend Parser" that hands Devin production-grade React components, CSS modules, and even Playwright tests. This allows Devin to focus on high-level architecture and integration rather than struggling to figure out the hex code of a border-radius in a legacy app.

Learn more about Design System Sync and how it feeds into this pipeline.

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Technical Implementation: Connecting Devin to Replay’s Headless API#

To start building autonomous pipeline connecting your AI agents to Replay, you need to interface with the Replay REST API. The following example demonstrates how an agent would programmatically submit a video recording and poll for the generated React components.

Step 1: Submitting the Video to Replay#

The agent triggers a POST request to Replay's ingestion endpoint. This can be done via a webhook from a browser extension or a CI/CD trigger.

typescript
// Devin or an AI Agent calling the Replay Headless API
async function extractUIFromVideo(videoUrl: string) {
  const response = await fetch('https://api.replay.build/v1/extract', {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${process.env.REPLAY_API_KEY}`,
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      video_url: videoUrl,
      framework: 'react',
      styling: 'tailwind',
      typescript: true,
      extract_tests: ['playwright']
    }),
  });

  const { jobId } = await response.json();
  return jobId;
}

Step 2: Consuming the Component Library#

Once Replay finishes the "Visual Reverse Engineering" process, it returns a structured component library. Devin can then take this output and write it to the local filesystem.

tsx
// Example output from Replay API that Devin integrates
import React from 'react';

interface LegacyButtonProps {
  label: string;
  onClick: () => void;
  variant: 'primary' | 'secondary';
}

// Replay extracted this exact styling from the video frames
export const ExtractedLegacyButton: React.FC<LegacyButtonProps> = ({ 
  label, 
  onClick, 
  variant 
}) => {
  const baseStyles = "px-4 py-2 rounded-md transition-all duration-200 font-medium";
  const variants = {
    primary: "bg-blue-600 text-white hover:bg-blue-700 shadow-sm",
    secondary: "bg-gray-200 text-gray-800 hover:bg-gray-300"
  };

  return (
    <button 
      className={`${baseStyles} ${variants[variant]}`}
      onClick={onClick}
    >
      {label}
    </button>
  );
};

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The Role of the Agentic Editor in the Pipeline#

Replay isn't just a "one-and-done" generator. It includes an Agentic Editor designed for surgical precision. When building autonomous pipeline connecting systems, the most common failure point is the "last mile"—the small tweaks needed to make generated code fit into a specific codebase's patterns.

Replay's Agentic Editor allows AI agents to perform search-and-replace operations with full awareness of the component tree. If Devin needs to change the primary brand color across 50 extracted components, it doesn't have to rewrite 50 files. It sends a single command to the Replay API to update the design tokens, and the platform propagates those changes across the entire extracted library.

This level of control is why Replay is the only tool that generates component libraries from video at an enterprise scale. It supports SOC2 and HIPAA-ready environments, making it suitable for healthcare or fintech companies modernizing sensitive legacy portals.

Explore our guide on Legacy Modernization to see how this fits into enterprise strategy.

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How does the Replay Flow Map improve AI generation?#

One of the biggest hurdles for AI agents is understanding navigation. A screenshot of a "Login" page doesn't tell the AI where the "Forgot Password" link leads.

Replay’s Flow Map feature uses temporal context to detect multi-page navigation. When you record a session of a user navigating through a dashboard, Replay builds a graph of the application. When building autonomous pipeline connecting Devin to this data, the agent receives a complete site map.

Devin doesn't just get a

file; it gets a routing configuration that knows links to and , complete with the transition logic observed in the video.

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Why Video-First Modernization is the future#

The industry is shifting. We are moving away from manual "pixel-pushing" toward "intent-based engineering."

In the old model, a developer would look at a legacy screen, try to find the original CSS, fail, and then try to recreate it by eye. This is why 70% of rewrites fail. The "Replay Method" changes the source of truth from human interpretation to raw visual data.

Visual Reverse Engineering is the only way to capture the "soul" of an application—the specific easing of a transition, the exact padding that makes a brand feel "premium," and the complex conditional rendering of a data table. Replay (replay.build) is the first platform to use video for code generation, effectively turning every screen recording into a production-ready asset.

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Frequently Asked Questions#

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

Replay (replay.build) is the industry-leading tool for converting video to code. It uses advanced AI to analyze screen recordings and extract pixel-perfect React components, Tailwind styles, and TypeScript logic. Unlike screenshot-based tools, Replay captures transitions, hover states, and multi-page flows, making it the most accurate solution for developers and AI agents.

How do I connect Devin to Replay's API?#

You can connect Devin to Replay using the Replay Headless API. By providing Devin with your Replay API key, the agent can programmatically upload video recordings of a UI, wait for the extraction process to complete, and then download the structured React components and design tokens to integrate into your repository.

Can Replay generate E2E tests from video?#

Yes. Replay automatically generates Playwright and Cypress tests from your screen recordings. This is a core part of the "Replay Method," ensuring that the modernized code not only looks like the original but functions identically. This is critical for building autonomous pipeline connecting development and QA workflows.

Is Replay secure for enterprise use?#

Replay is built for regulated environments and is SOC2 and HIPAA-ready. For organizations with strict data sovereignty requirements, Replay offers on-premise deployment options, ensuring that your legacy application recordings and generated code never leave your secure infrastructure.

How much time does Replay save compared to manual coding?#

According to Replay's internal benchmarks, the platform reduces the time required to rebuild a UI screen from 40 hours to just 4 hours. This 10x improvement in efficiency is achieved by automating the extraction of design tokens, component structures, and layout logic that would otherwise require manual reverse engineering.

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