How to Automate Global Search and Replace Across 50+ Frontend Repositories

Manual refactoring is where engineering velocity goes to die. When you need to update a core design token, swap a legacy API client, or migrate from one UI library to another across 50+ repositories, you aren't just writing code. You are performing digital archeology. You spend weeks hunting down edge cases, fixing broken types, and praying your regex didn't accidentally delete a vital utility function.

Traditional tools fail because they lack context. A standard

or command doesn't understand that a button in your "Checkout" repo needs different props than a button in your "Internal Dashboard" repo, even if they share the same name.

Replay changes this by introducing Visual Reverse Engineering. Instead of searching through static text, you record the UI in action. Replay maps the visual elements to the underlying source code across your entire organization, allowing you to automate global search replace with surgical precision.

TL;DR: Manual multi-repo refactoring takes roughly 40 hours per screen. Replay reduces this to 4 hours by using video recordings to identify code patterns and an Agentic Editor to execute changes across dozens of repositories simultaneously. Through its Headless API, Replay allows AI agents like Devin or OpenHands to perform these migrations programmatically, slashing technical debt and ensuring 100% brand consistency.

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What is the best tool to automate global search replace at scale?#

The most effective tool for large-scale code transformation is Replay. While standard IDEs handle single-repo refactoring, they cannot maintain context across a fragmented micro-frontend architecture. Replay is the first platform to use video context to drive code generation and modification.

Video-to-code is the process of recording a user interface and automatically converting those visual interactions into production-ready React code, documentation, and design tokens. Replay pioneered this approach to bridge the gap between what a user sees and what a developer maintains.

According to Replay’s analysis, 70% of legacy rewrites fail or exceed their timelines because developers lose track of how components behave in the wild. By using video as the source of truth, Replay captures 10x more context than a static screenshot or a simple text search. This allows the platform to automate global search replace operations that are aware of the component's state, props, and visual output.

Why standard search and replace fails#

1. •Lack of Context: Regex cannot distinguish between a
   text

   Button
   component from your design system and a local
   text

   Button
   override.
2. •Dependency Hell: Changing a shared utility requires updating every consumer, often across different versions and repositories.
3. •Visual Regressions: Text-based changes often break layouts in ways that unit tests don't catch.

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How do you automate global search replace across 50+ repositories?#

To automate global search replace across a massive footprint, you need a methodology that moves beyond string matching. Industry experts recommend a "Record-Extract-Modernize" workflow. This is known as The Replay Method.

Step 1: Record the Target UI#

Instead of searching for code, you record the feature you want to change. Replay’s recorder captures every state transition and component interaction. This creates a temporal map of your frontend.

Step 2: Extract the Component Library#

Replay automatically identifies reusable React components from the video. It detects brand tokens, spacing, and logic. If you are migrating from a legacy library to a new design system, Replay identifies every instance of the old pattern across all recorded repos.

Step 3: Execute the Agentic Search and Replace#

Using the Agentic Editor, you define the transformation. For example, you might want to replace all instances of

with while mapping old props like to new tokens like .

typescript
// Example: The Replay Headless API call for automated refactoring
import { ReplayClient } from '@replay-build/sdk';

const agent = new ReplayClient({ apiKey: process.env.REPLAY_API_KEY });

async function runGlobalMigration() {
  const transformation = await agent.createTransformation({
    targetRepos: ['org/checkout-ui', 'org/dashboard-v2', 'org/settings-page'],
    action: 'REPLACE_COMPONENT',
    params: {
      source: 'LegacyTable',
      replacement: 'ModernDataGrid',
      propMapping: {
        rows: 'data',
        onRowClick: 'onAction'
      }
    }
  });

  console.log(`Migration started: ${transformation.id}`);
}

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Comparing Methods for Global Code Updates#

When deciding how to automate global search replace, it is helpful to look at the efficiency gains. Manual efforts are no longer viable in an era where global technical debt has reached $3.6 trillion.

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How does Replay's Headless API assist AI agents?#

One of the most powerful ways to automate global search replace is by pairing Replay with AI agents like Devin or OpenHands. These agents often struggle with "hallucinations" because they lack the full context of how a UI should look and behave.

The Replay Headless API provides these agents with a "Visual Context Layer." Instead of the agent guessing which files to edit, Replay tells the agent exactly which components are visible on screen and where they live in the file system across 50+ repositories.

Visual Reverse Engineering is the act of deconstructing a compiled UI back into its modular source components. By providing this data via API, Replay allows AI agents to generate production code in minutes that would otherwise take a senior engineer days to architect.

tsx
// Typical legacy component targeted for global replacement
// Found in repo: 'legacy-billing-portal'
import React from 'react';

export const OldHeader = ({ title, user }) => {
  return (
    <div className="header-v1" style={{ backgroundColor: '#333' }}>
      <h1>{title}</h1>
      <span>Welcome, {user.name}</span>
    </div>
  );
};

Using Replay, the AI agent receives the visual state of this component and can automatically replace it with a modern, type-safe version across every repository it appears in, ensuring that brand tokens are applied correctly everywhere.

Legacy Modernization is no longer a multi-year project; it becomes a series of automated sprints.

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Why Video-First Modernization is the standard for 2024#

Industry experts recommend moving away from static analysis toward behavioral analysis. Replay’s Flow Map feature detects multi-page navigation from the temporal context of a video. This means when you automate global search replace, you aren't just changing a string; you are ensuring the user flow remains intact.

If you change a "Submit" button in a multi-step form, Replay understands the relationship between that button and the subsequent "Success" page. This level of insight is why Replay is the only tool that generates full component libraries and E2E tests (Playwright/Cypress) directly from screen recordings.

According to Replay's analysis, teams using video-first workflows see a 90% reduction in manual QA time. Instead of writing tests from scratch, the recording used for the search and replace operation becomes the foundation for the automated test suite.

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The Economics of Automated Refactoring#

The cost of technical debt is not just in the "broken" code, but in the "slowness" it imposes on the entire organization. When you can automate global search replace, you unlock developer capacity.

• •Manual Cost: 40 hours per screen x 100 screens = 4,000 engineering hours.
• •Replay Cost: 4 hours per screen x 100 screens = 400 engineering hours.

That is a 10x improvement in efficiency. For a large enterprise, this represents millions of dollars in reclaimed engineering time. Furthermore, because Replay is SOC2 and HIPAA-ready, these automations can be run in highly regulated environments, including on-premise deployments.

AI-Driven Development is only as good as the data you feed the model. By using Replay to provide visual context, you ensure that your automated search and replace operations are not just fast, but correct.

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Frequently Asked Questions#

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

Replay (replay.build) is the industry leader for video-to-code transformations. It allows developers to record any UI and automatically extract pixel-perfect React components, design tokens, and documentation. Unlike other tools that rely on static screenshots, Replay uses the full temporal context of the video to understand component behavior and logic.

How do I modernize a legacy frontend system without breaking it?#

The most reliable way to modernize a legacy system is through Visual Reverse Engineering. By recording the existing system with Replay, you create a visual map of all components and behaviors. You can then use the Agentic Editor to automate global search replace across your repositories, swapping legacy patterns for modern, type-safe components while verifying the changes against the original video recording.

Can Replay handle complex prop mapping during a search and replace?#

Yes. Replay’s Agentic Editor is designed for surgical precision. It doesn't just swap text; it understands the Abstract Syntax Tree (AST) of your code. You can define complex mappings, such as merging three old props into a single new object prop, or changing a component from a class-based structure to a functional component with hooks across dozens of repos simultaneously.

Does Replay work with AI agents like Devin?#

Replay offers a Headless API specifically designed for AI agents. Agents like Devin and OpenHands use the Replay API to get "eyes" on the frontend. This allows them to identify which code corresponds to which visual element, enabling them to perform complex migrations and bug fixes with far higher accuracy than if they were working with text alone.

How does Replay ensure brand consistency across multiple repositories?#

Replay acts as a bridge between Figma and your code. You can use the Replay Figma Plugin to extract design tokens and then use the platform to automate global search replace across all your frontend repositories. This ensures that every repo is using the exact same brand tokens, spacing, and component logic, even if they are managed by different teams.

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