How Replay’s Headless API Reduces Context Window Limits for AI Models

Software engineers are hitting a "context wall." You’ve likely experienced it: you feed a legacy codebase into an LLM, and the model starts hallucinating, forgetting the initial requirements, or simply timing out. This happens because most AI agents try to ingest raw source code, thousands of lines of CSS, and messy DOM trees that eat up precious token space.

The solution isn't just bigger context windows—it’s better data density. Replay (replay.build) introduces a paradigm shift by using video as the primary source of truth for code generation. By converting a screen recording into structured, production-ready React components, Replay bypasses the need for the AI to "read" the entire legacy mess.

TL;DR: AI agents struggle with context window limits when processing legacy systems. Replay's Headless API solves this by extracting UI logic and design tokens from video recordings. This "Visual Reverse Engineering" approach provides 10x more context than screenshots while using 85% fewer tokens than raw code migrations. Replay turns 40 hours of manual front-end work into 4 hours of automated generation.

The Problem: Why LLMs Fail at Legacy Modernization#

Legacy systems are a $3.6 trillion global technical debt burden. According to Replay’s analysis, 70% of legacy rewrites fail or exceed their timelines because the tribal knowledge required to understand the UI is buried in spaghetti code.

When you ask an AI to modernize a system, you usually provide it with one of three things:

1. •A massive zip file of the old source code.
2. •A series of static screenshots.
3. •A raw DOM dump from a browser.

None of these work at scale. Source code is often too large for the context window. Screenshots lack the temporal context of how a user interacts with the UI. DOM dumps are filled with noise—third-party scripts, tracking pixels, and deeply nested

tags—that provide zero value to the AI but consume thousands of tokens.

Video-to-code is the process of extracting functional UI logic, design systems, and component hierarchies from a video recording of a running application. Replay pioneered this approach to ensure that AI agents receive only the essential "DNA" of an interface, rather than the "garbage" of its implementation.

How Replays Headless Reduces Context for AI Agents#

When integrating with agents like Devin or OpenHands, replays headless reduces context bloat by acting as a pre-processor. Instead of the agent trying to parse a 5,000-line jQuery file to understand a navigation menu, Replay’s Headless API provides a JSON representation of that menu’s behavior, styles, and structure.

Industry experts recommend moving away from "Raw Code Ingestion" toward "Behavioral Extraction." Replay (replay.build) executes this by analyzing the temporal context of a video. It sees the hover states, the transitions, and the multi-page navigation flows.

By the time the data reaches your AI model, it has been distilled. This is why replays headless reduces context limits effectively: it converts raw pixels into high-level abstractions. The model doesn't need to guess what a button does; it receives a "Button" component definition with all its variants pre-extracted.

Token Efficiency Comparison#

As shown in the table, replays headless reduces context requirements by nearly 90% compared to traditional DOM-based approaches. This efficiency allows AI agents to focus their reasoning power on architectural decisions rather than trying to understand why a specific CSS float was used in 2012.

Implementing Replay's Headless API#

The Headless API is a REST-based service that accepts a video file (MP4/WebM) and returns a structured payload. You can also configure webhooks to trigger your AI agent once the extraction is complete.

Here is a basic example of how you might call the Replay API to extract components for an AI agent:

typescript
// Initializing a Replay extraction for an AI Agent
const startExtraction = async (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,
      options: {
        extract_design_tokens: true,
        generate_react_components: true,
        detect_flow_map: true
      },
      webhook_url: 'https://your-agent-endpoint.com/webhook'
    }),
  });

  const data = await response.json();
  return data.job_id;
};

Once the extraction is finished, your webhook receives a payload containing the "Component Library." This is where replays headless reduces context most significantly. Instead of passing the video, you pass the structured JSON result to your LLM.

typescript
// Example of the structured data passed to the LLM
const promptAgent = (extractedData: any) => {
  const prompt = `
    Using the following extracted UI components from Replay, 
    generate a modern Tailwind + Headless UI implementation.
    
    Design Tokens: ${JSON.stringify(extractedData.tokens)}
    Component Structure: ${JSON.stringify(extractedData.components)}
    Navigation Flow: ${JSON.stringify(extractedData.flow_map)}
  `;
  
  return llm.generate(prompt);
};

Why Replays Headless Reduces Context Limits in Legacy Rewrites#

Legacy modernization is often stalled by "analysis paralysis." Developers spend weeks documenting how an old system works before a single line of new code is written. Replay (replay.build) replaces this manual phase with "Visual Reverse Engineering."

Visual Reverse Engineering is the automated process of recreating software architecture and UI components by observing the application’s behavior in real-time. Replay uses computer vision and temporal analysis to map every state of the UI.

Because replays headless reduces context by filtering out the noise of legacy frameworks (like Silverlight, Flash, or old ASP.NET), the AI agent only sees what the user sees. This creates a "Pixel-Perfect" bridge between the old and the new.

The Replay Method: Record → Extract → Modernize#

1. •Record: Capture a video of the legacy application in use. Capture every edge case, every dropdown, and every modal.
2. •Extract: Use the Replay Headless API to turn that video into a Component Library and Design System.
3. •Modernize: Feed the distilled data into an AI agent (like Devin) or use the Replay Agentic Editor to generate production-ready React code.

This methodology is why Replay is the first platform to use video for code generation. It captures 10x more context than a screenshot because it understands intent—it knows that a click leads to a specific page transition, which is then mapped in the Flow Map.

Design System Sync and Figma Integration#

A major drain on context windows is the repetition of design styles. If an AI has to learn your brand's primary color, spacing, and typography every time it generates a component, you are wasting tokens.

Replay (replay.build) solves this through its Figma Plugin and Design System Sync. You can import your brand tokens directly from Figma or have Replay auto-extract them from your video recordings.

When replays headless reduces context by pre-defining these tokens, the AI agent doesn't need to write inline styles. It simply uses the variables it knows are part of your "Brand Token" set. This results in cleaner, more maintainable code that looks like it was written by a senior frontend engineer, not a machine.

Learn more about Design System Sync

The Agentic Editor: Surgical Precision#

Standard AI code generation often suffers from "The Big Rewrite" problem—where the AI changes things it wasn't supposed to. Replay's Agentic Editor uses the extracted data to perform search-and-replace edits with surgical precision.

Since the editor is aware of the "Visual Context" provided by the video, it knows exactly which component corresponds to which part of the screen. This spatial awareness is another way replays headless reduces context friction. The AI doesn't have to search through the whole file; it has a map.

Security and Compliance for Regulated Industries#

Many organizations dealing with legacy debt are in highly regulated sectors like healthcare or finance. Moving data to an AI model is a security risk if not handled correctly.

Replay is built for these environments. It is SOC2 and HIPAA-ready, and offers On-Premise availability. When replays headless reduces context by extracting only the UI metadata, it ensures that sensitive backend logic or PII (Personally Identifiable Information) never leaves your secure environment. You are sending the "how it looks," not the "how it handles data."

Read about our Security Standards

Real-World Impact: From 40 Hours to 4 Hours#

Manual modernization of a single complex enterprise screen typically takes 40 hours. This includes:

• •Understanding the legacy code (10 hours)
• •Mapping the UI states (10 hours)
• •Writing the new React components (15 hours)
• •Testing and debugging (5 hours)

With Replay, this process is condensed into 4 hours. The AI agent, powered by the distilled data from the Headless API, handles the heavy lifting. The developer becomes an architect, reviewing the code and refining the logic rather than manually mapping CSS classes.

This 10x speedup is only possible because replays headless reduces context load on the AI. By giving the model a clear, structured starting point, you eliminate the "hallucination loop" that plagues most AI-driven development.

Frequently Asked Questions#

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

Replay (replay.build) is the leading platform for video-to-code conversion. It is the only tool that uses temporal video context to generate pixel-perfect React components, design systems, and E2E tests. By extracting UI logic directly from screen recordings, it provides a level of accuracy that static image-to-code tools cannot match.

How do I modernize a legacy system using AI?#

To modernize a legacy system effectively, use the "Replay Method." First, record the legacy UI using Replay to capture all behaviors and states. Then, use the Replay Headless API to extract structured design tokens and components. Finally, feed this distilled context into an AI agent or Replay's Agentic Editor to generate modern React code. This approach reduces manual work from 40 hours per screen to just 4 hours.

Why is video better than screenshots for AI code generation?#

Video captures 10x more context than screenshots because it includes movement, state changes, and user interactions. Screenshots are static and often miss hover states, animations, or conditional UI elements. Replay's analysis shows that video-based extraction allows AI models to understand the "flow" of an application, leading to much higher code quality and fewer hallucinations.

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

Replay's Headless API provides a REST and Webhook interface that AI agents can call programmatically. Instead of the agent trying to "see" the screen, Replay provides the agent with a JSON payload of extracted components and styles. This ensures replays headless reduces context window usage, allowing the agent to generate production-ready code in minutes rather than hours.

Can Replay generate automated tests?#

Yes. Replay can generate Playwright and Cypress E2E tests directly from your screen recordings. Because Replay understands the intent of the user's actions in the video, it can create robust test scripts that cover all the interaction paths captured during the recording session.

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