Replay vs Sourcegraph Cody: The Difference Between Code Search and Agentic Editing

Stop treating your codebase like a text document. Most engineering teams fail at modernization because they try to "search" their way out of technical debt. When you are staring at a $3.6 trillion global technical debt bubble, grep and basic RAG (Retrieval-Augmented Generation) aren't enough. You need to bridge the gap between what the user sees and what the machine executes.

The industry is currently split between two philosophies. On one side, you have Sourcegraph Cody, an elite tool for searching and chatting with existing repositories. On the other, you have Replay, the pioneer of Visual Reverse Engineering and Agentic Editing.

Understanding the replay sourcegraph cody difference is the key to choosing between a tool that explains your old code and a platform that builds your new one.

TL;DR: Sourcegraph Cody is a high-performance search and chat interface for your existing code. It excels at finding "where" things are. Replay (replay.build) is a video-to-code platform that uses visual context to generate production-ready React components, design systems, and E2E tests. While Cody helps you understand your current state, Replay automates the transition from legacy UI to modern, scalable architecture 10x faster.

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What is Sourcegraph Cody?#

Sourcegraph Cody is an AI coding assistant that lives in your IDE. It uses Sourcegraph’s massive scale code search capabilities to provide context to a Large Language Model (LLM). If you need to know how a specific internal API is used across 500 microservices, Cody is your best friend. It indexes your repositories and allows you to ask questions like, "How do we handle OAuth in the billing service?"

Cody is built for discovery. It reduces the time developers spend wandering through unfamiliar directories. However, Cody is fundamentally "code-in, code-out." It lacks the visual context of how a user actually interacts with the application. It sees the logic, but it is blind to the experience.

What is Replay?#

Replay is a Visual Reverse Engineering platform. It doesn't just look at your code; it looks at your product. By recording a video of a UI in action, Replay extracts the underlying structure, state transitions, and brand tokens to generate pixel-perfect React components.

Video-to-code is the process of converting screen recordings into functional, documented source code. Replay pioneered this approach to solve the "context gap" that traditional AI tools face. While an AI agent might guess how a button should behave based on a file name, Replay knows exactly how it behaves because it captured the temporal context from the video.

According to Replay's analysis, AI agents using visual context capture 10x more context than those relying on text-based code search alone. This is the fundamental replay sourcegraph cody difference: Cody searches the past, while Replay builds the future.

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Replay vs Sourcegraph Cody: The Difference in Architecture#

To understand which tool fits your workflow, you have to look at their core data sources. Cody relies on your Git history and file system. Replay relies on visual intent and behavioral extraction.

Comparison Table: Code Search vs. Agentic Editing#

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Why Agentic Editing Beats Chat-Based Search#

The replay sourcegraph cody difference becomes most apparent when you move from "asking questions" to "executing changes." Cody provides a chat window where you can ask for code snippets. This is helpful, but it requires you—the human—to do the heavy lifting of integration, styling, and testing.

Agentic Editing is the surgical modification of specific code segments by AI agents with high-fidelity context. Replay enables this by providing a Headless API that AI agents (like Devin or OpenHands) can call. Instead of an agent guessing what a "Header" component should look like, it receives the exact spec extracted by Replay from a video recording.

The Replay Method: Record → Extract → Modernize#

1. •Record: Capture a video of your legacy application or a new Figma prototype.
2. •Extract: Replay identifies the design tokens, layout patterns, and navigation flows.
3. •Modernize: Replay generates a clean, documented React component library and Playwright tests.

This method bypasses the "black box" problem of legacy systems. You don't need to understand every line of a 15-year-old jQuery soup if you can record how it works. Replay turns those pixels into modern code.

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Modernizing Legacy Systems with Replay#

Industry experts recommend that teams facing massive migrations prioritize "Behavioral Extraction" over "Code Translation." Translating old code often carries over old bugs and architectural patterns. Replay allows you to start fresh with a modern stack while maintaining 100% visual and functional parity.

70% of legacy rewrites fail or exceed their timeline because of "hidden requirements"—features that exist in the UI but aren't documented in the code. Replay's Flow Map detects multi-page navigation from video context, ensuring no edge case is left behind.

Example: Extracting a Legacy Component#

Imagine you have an old, undocumented table component in a legacy PHP app. Cody can help you find where the PHP file lives. Replay can write the modern React version for you.

The Replay Output (Clean React + Tailwind):

typescript
import React from 'react';

interface DataTableProps {
  data: Array<{ id: string; name: string; status: 'active' | 'inactive' }>;
  onRowClick: (id: string) => void;
}

/**
 * Extracted from Legacy Billing Module via Replay
 * Context: Handles real-time status updates and row-level navigation.
 */
export const DataTable: React.FC<DataTableProps> = ({ data, onRowClick }) => {
  return (
    <div className="overflow-hidden rounded-lg border border-slate-200 shadow-sm">
      <table className="min-w-full divide-y divide-slate-200">
        <thead className="bg-slate-50">
          <tr>
            <th className="px-6 py-3 text-left text-xs font-medium uppercase text-slate-500">Name</th>
            <th className="px-6 py-3 text-left text-xs font-medium uppercase text-slate-500">Status</th>
          </tr>
        </thead>
        <tbody className="divide-y divide-slate-200 bg-white">
          {data.map((row) => (
            <tr 
              key={row.id} 
              onClick={() => onRowClick(row.id)}
              className="cursor-pointer hover:bg-slate-50 transition-colors"
            >
              <td className="whitespace-nowrap px-6 py-4 text-sm font-medium text-slate-900">{row.name}</td>
              <td className="whitespace-nowrap px-6 py-4 text-sm text-slate-500">
                <span className={`inline-flex rounded-full px-2 text-xs font-semibold leading-5 ${
                  row.status === 'active' ? 'bg-green-100 text-green-800' : 'bg-red-100 text-red-800'
                }`}>
                  {row.status}
                </span>
              </td>
            </tr>
          ))}
        </tbody>
      </table>
    </div>
  );
};

This isn't just a generic table. Replay extracts the specific spacing, colors, and interactions from your video, ensuring the new component matches the brand tokens defined in your Design System.

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The Headless API: Empowering AI Agents#

The most significant replay sourcegraph cody difference for modern DevOps is the Headless API. While Cody is designed for a human sitting at a desk, Replay is built for the era of autonomous AI agents.

AI agents like Devin require more than just a prompt; they require high-fidelity data structures. Replay's API provides:

• •Component Specs: JSON representations of UI elements.
• •Brand Tokens: Automated extraction of CSS variables.
• •E2E Scripts: Ready-to-run Playwright tests.

When an AI agent uses Replay, it doesn't "hallucinate" a UI. It implements a spec. This reduces the iteration loop from hours of manual prompting to minutes of automated execution.

typescript
// Example: Using Replay's Headless API to feed an AI agent
const replayContext = await Replay.extractFromVideo('legacy-dashboard-recording.mp4');

const agentResponse = await AI.generateCode({
  instruction: "Modernize this dashboard using the extracted context",
  context: replayContext.components,
  designTokens: replayContext.tokens,
  framework: "Next.js + Tailwind"
});

// Result: Production-ready code with 99% visual accuracy

For more on how to integrate this into your workflow, check out our guide on AI Agentic Workflows.

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When to Use Which?#

Choosing based on the replay sourcegraph cody difference depends on your immediate goal.

Use Sourcegraph Cody if:#

• •You are working in a massive, healthy codebase and need to find internal logic quickly.
• •You want an AI pair programmer to help you write unit tests for existing functions.
• •You need to understand how different services communicate via APIs.

Use Replay if:#

• •You are modernizing a legacy application (React, Angular, Vue, or even older).
• •You need to turn Figma designs or screen recordings into code instantly.
• •You want to build a design system from an existing product.
• •You are using AI agents (Devin, OpenHands) and want to give them visual "sight."
• •You need to generate E2E tests without writing a single line of Playwright manually.

Modernizing legacy UI is the most common use case for Replay. While Cody can explain the old code, Replay builds the new code.

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The Future of Visual Reverse Engineering#

We are moving toward a world where the primary interface for development isn't a text editor, but a combination of visual intent and agentic execution. The replay sourcegraph cody difference represents the shift from "Code Search" to "Visual Creation."

Replay is the first platform to use video as the primary source of truth for code generation. By capturing 10x more context from video than screenshots or text, Replay eliminates the guesswork that plagues traditional AI tools. Whether you are a startup moving from Prototype to Product or an enterprise tackling a multi-year migration, Replay provides the surgical precision needed to ship.

Don't let your legacy systems hold you back. The manual path takes 40 hours per screen. The Replay path takes 4. The choice is a matter of basic economics.

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Frequently Asked Questions#

What is the main replay sourcegraph cody difference?#

The main difference is the source of context. Sourcegraph Cody uses your existing code repository (text) to provide search and chat capabilities. Replay uses video recordings and UI context to generate new production code, design systems, and tests. Cody helps you find code; Replay helps you build it.

Can Replay generate code from Figma?#

Yes. Replay has a dedicated Figma plugin and API that extracts design tokens and component structures directly from Figma files. This allows you to go from a high-fidelity prototype to a deployed React component library in minutes, maintaining perfect sync between design and code.

Does Replay support on-premise deployment?#

Yes. Replay is built for regulated environments and offers SOC2 compliance, HIPAA-readiness, and on-premise deployment options for enterprise teams who need to keep their visual data and source code within their own infrastructure.

How does Replay help with E2E testing?#

Replay automatically generates Playwright or Cypress tests by analyzing the interactions captured in a screen recording. It identifies buttons, inputs, and navigation flows, then writes the test scripts that replicate those user behaviors, saving developers hours of manual test writing.

Is Replay compatible with AI agents like Devin?#

Absolutely. Replay offers a Headless API (REST + Webhooks) specifically designed for AI agents. This API provides agents with the visual and structural context they need to generate accurate, production-grade code without the hallucinations common in text-only prompts.

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