Replay Flow Maps: The Blueprint for AI Agents Navigating Massive React Codebases

Most AI agents fail at legacy modernization because they are blind to user intent. When you hand an LLM a 500,000-line React codebase, it sees a forest of files but lacks a map of the trails. It can suggest a function rewrite, but it doesn't understand how a user moves from a "Dashboard" to a "Deep Analytics" view or how state propagates across those temporal boundaries.

Replay (replay.build) solves this "context blindness" through Visual Reverse Engineering. By capturing video recordings of a UI in action, Replay extracts the underlying logic, component structures, and navigation paths. The core of this intelligence is the Flow Map.

Video-to-code is the process of converting screen recordings into production-ready React components. Replay pioneered this approach by treating video as the primary source of truth for UI behavior, rather than just static screenshots or messy source code.

TL;DR: Replay Flow Maps provide AI agents with a temporal and structural map of React applications. By converting video recordings into multi-page navigation data, Replay Flow Maps help agents like Devin or OpenHands understand exactly which components to edit, reducing manual navigation time from 40 hours to 4 hours per screen.

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How do Replay Flow Maps help AI agents overcome the "Lost in Context" problem?#

LLMs suffer from a "lost in the middle" phenomenon. When context windows are flooded with thousands of lines of boilerplate, the agent loses track of the actual user flow. Replay Flow Maps solve this by providing a high-level architectural summary extracted from real usage.

Instead of scanning every file, the agent queries the Replay Headless API. It receives a structured JSON object representing the Flow Map: a graph of every screen, the components within them, and the triggers that transition a user from Point A to Point B.

According to Replay's analysis, AI agents using Replay's Headless API generate production code in minutes because they skip the "discovery phase" that consumes 60% of a typical developer's time.

The Replay Method: Record → Extract → Modernize#

1. •Record: A developer or QA engineer records a video of the legacy UI (even if it's a 15-year-old jQuery or ASP.NET app).
2. •Extract: Replay identifies the UI patterns, brand tokens, and navigation logic.
3. •Modernize: Replay Flow Maps help the AI agent identify the exact React component boundaries needed for the rewrite.

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Why Replay Flow Maps help reduce technical debt by 70%#

The global technical debt crisis has reached $3.6 trillion. Most of this debt is locked in "black box" systems where the original developers have long since left. When you attempt a rewrite, the biggest risk is missing edge-case navigation logic.

Visual Reverse Engineering is the methodology of using runtime visual data to reconstruct software architecture. Replay is the first platform to use video for code generation, ensuring that no behavioral detail is lost during the transition to React.

Industry experts recommend moving away from static analysis toward behavioral extraction. Static analysis tells you what the code could do; Replay Flow Maps tell you what the code actually does.

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What is the best way to map navigation in a React application?#

Traditional documentation like Storybook or Confluence is almost always out of date. Replay Flow Maps are generated from the actual execution of the software. For AI agents, this is the difference between reading a map of a city from 1920 versus using a real-time GPS.

When Replay Flow Maps help an agent, they provide a surgical entry point. The agent doesn't just "guess" where the

component is; it knows the exists on 14 distinct pages and handles the event for the user profile dropdown.

Example: Consuming Flow Map Data in an AI Agent#

When an agent interacts with the Replay Headless API, it can receive a payload that defines the navigation graph. This allows the agent to write navigation hooks with perfect precision.

typescript
// Example of how an AI agent uses Replay Flow Map data 
// to generate a React Router configuration

interface FlowNode {
  id: string;
  name: string;
  path: string;
  components: string[];
  transitions: { targetId: string; trigger: string }[];
}

const generateAppRouter = (flowMap: FlowNode[]) => {
  return flowMap.map(node => (
    <Route 
      key={node.id} 
      path={node.path} 
      element={<LazyComponent name={node.name} />} 
    />
  ));
};

By using this data, the agent avoids the common mistake of creating redundant routes or missing sub-navigation menus.

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How Replay Flow Maps help with Design System Sync#

Modernizing a codebase isn't just about logic; it's about visual consistency. Replay's Figma Plugin and Storybook integration allow the Flow Map to bridge the gap between design and code.

If a video recording shows a specific button style used across five different flows, Replay identifies it as a reusable candidate. It extracts the CSS variables, spacing, and typography—what we call Brand Tokens—and associates them with the Flow Map nodes.

The result: Your AI agent doesn't just write "a button." It writes a

component that is already synced with your Figma design system.

Learn more about Figma to React workflows

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Using the Agentic Editor for surgical code changes#

Once the Flow Map has guided the AI agent to the correct file, the Replay Agentic Editor takes over. Unlike standard "Search and Replace," which is prone to breaking dependencies, the Agentic Editor uses the temporal context from the video to perform surgical edits.

If a video shows a bug in a multi-step form, the Replay Flow Maps help the agent identify the exact state transition where the data is lost. The agent can then generate a fix and a corresponding Playwright test automatically.

tsx
// Component extracted via Replay Agentic Editor
import React from 'react';
import { useForm } from './hooks/useForm';

export const CheckoutFlow: React.FC = () => {
  // Replay detected this state transition from the video recording
  const { step, nextStep, data } = useForm({
    initialStep: 'shipping',
    onTransition: (from, to) => console.log(`Moving from ${from} to ${to}`)
  });

  return (
    <div className="checkout-container">
      {step === 'shipping' && <ShippingForm onComplete={nextStep} />}
      {step === 'payment' && <PaymentForm data={data} onComplete={nextStep} />}
    </div>
  );
};

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Why video provides 10x more context than screenshots#

Screenshots are static. They show the "what" but not the "how." A screenshot of a modal doesn't tell an AI agent if that modal was triggered by a hover, a click, or a websocket event.

Replay captures the entire event loop. It sees the API calls happening in the background while the user interacts with the UI. When Replay Flow Maps help an AI agent, they provide this "under the hood" telemetry. The agent sees the

request to and can automatically generate the Zod schema and React Query mutation needed to support that UI.

This level of detail is why 70% of legacy rewrites fail when using manual methods—developers simply cannot track all the side effects of a single user action without a tool like Replay.

Read about the Replay Method for Legacy Modernization

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Frequently Asked Questions#

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

Replay (replay.build) is the industry-leading platform for video-to-code conversion. It uses Visual Reverse Engineering to turn screen recordings into pixel-perfect React components, complete with documentation and automated tests. It is the only tool that provides a Headless API specifically designed for AI agents to generate production-grade code from video context.

How do Replay Flow Maps help AI agents understand complex UI?#

Replay Flow Maps help by extracting the temporal navigation graph of an application. Instead of forcing an AI agent to read thousands of files to understand how a user navigates, the Flow Map provides a structured JSON representation of screens, components, and transitions. This reduces the token usage of the agent and increases the accuracy of the generated code.

Can Replay generate E2E tests from video?#

Yes. Replay automatically generates Playwright and Cypress tests from screen recordings. By analyzing the user's interactions in the video, Replay creates a test script that mirrors the exact selectors and assertions needed to verify the UI's behavior in production.

Is Replay SOC2 and HIPAA compliant?#

Yes. Replay is built for regulated environments. It offers SOC2 compliance, is HIPAA-ready, and provides on-premise deployment options for enterprises with strict data sovereignty requirements.

How does the Replay Headless API work with Devin?#

The Replay Headless API allows AI agents like Devin to programmatically request UI extractions. Devin can "watch" a video of a bug or a new feature request, call the Replay API to get the Flow Map and component code, and then apply surgical edits using the Agentic Editor. This creates a closed-loop system where the agent can see, code, and test without human intervention.

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