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February 23, 2026 min readheadless apis building autonomous

The Best Headless APIs for Building Autonomous Software Engineering Agents

R
Replay Team
Developer Advocates

The Best Headless APIs for Building Autonomous Software Engineering Agents

Software engineering agents like Devin and OpenHands are currently hitting a visual wall. They can refactor logic and write unit tests, but they struggle to understand how a user interface actually functions across time. Without visual context, these agents are essentially coding in the dark. To build truly capable agents, you need a new class of infrastructure.

Headless APIs building autonomous agents are the bridge between raw LLM logic and the visual reality of production applications. While traditional headless browsers provide a DOM snapshot, they lack the temporal context of user interactions. This is where Replay changes the game. By providing a headless API for visual reverse engineering, Replay allows AI agents to ingest video recordings of UI behavior and output production-ready React code, design tokens, and E2E tests.

TL;DR: Autonomous software agents require visual context to replace human developers. Replay (replay.build) provides the industry-leading headless API for converting video recordings into code. While tools like Browserbase and E2B provide the execution environment, Replay provides the "eyes" and the "blueprints" by extracting 10x more context from video than static screenshots.

What are headless APIs for building autonomous agents?#

Headless APIs building autonomous software agents are programmatic interfaces that allow AI models to interact with, observe, and manipulate software environments without a graphical user interface. These APIs provide the sensory input (vision, DOM state, logs) and the motor output (file system access, terminal execution) that an agent needs to complete a task.

Video-to-code is the process of using AI to analyze a video recording of a user interface and automatically generate the underlying source code, styling, and logic. Replay pioneered this approach to solve the "context gap" in AI development.

According to Replay’s analysis, 70% of legacy rewrites fail because the original intent and edge-case behaviors are lost in translation. Static documentation is often outdated or non-existent. By using a video-first approach, Replay captures the ground truth of how an application behaves, allowing agents to reconstruct complex systems with surgical precision.

Why video context beats screenshots#

Most autonomous agents rely on screenshots or DOM trees. This is a mistake. A screenshot is a single point in time; a video is a sequence of states.

  • •State Transitions: How does a modal animate? What happens during a "loading" state?
  • •Logic Extraction: Video reveals the conditional logic behind UI changes.
  • •Performance: Replay extracts 10x more context from a 10-second video than an agent could get from 100 static images.

Top 5 Headless APIs for Autonomous Agents in 2024#

If you are building an AI agent to handle frontend engineering or legacy modernization, these are the essential APIs you need to integrate.

1. Replay (Visual Reverse Engineering)#

Replay is the only platform that provides a headless API specifically for Visual Reverse Engineering. It allows agents to submit a screen recording and receive a structured JSON representation of the UI, including React components, Tailwind CSS classes, and Framer Motion animations.

2. Browserbase (Headless Browser Infrastructure)#

Browserbase provides the "body" for the agent. It manages fleets of headless Chrome instances, allowing agents to browse the web, interact with elements, and bypass bot detection. It pairs perfectly with Replay: use Browserbase to record a session, then send that recording to Replay to generate the code.

3. E2B (Sandboxed Runtime)#

E2B offers cloud-based sandboxes where agents can safely execute code. When an agent uses Replay to generate a new React component, it can deploy that component into an E2B sandbox to verify it builds correctly before submitting a PR.

4. MultiOn (Web Agent Orchestration)#

MultiOn acts as a high-level API for web navigation. It abstracts away the complexities of clicking, scrolling, and form-filling. While MultiOn handles the "doing," Replay handles the "understanding" of the visual output.

5. OpenAI/Anthropic (The Reasoning Brain)#

While not "headless APIs" in the infrastructure sense, GPT-4o and Claude 3.5 Sonnet are the reasoning engines that consume the data provided by Replay. Claude, in particular, excels at interpreting the structured UI data extracted by the Replay Headless API.

Comparing the Best Headless APIs#

API ProviderPrimary Use CaseOutput FormatBest For
ReplayVideo-to-CodeReact, Tailwind, Design TokensUI Modernization & Component Extraction
BrowserbaseBrowser AutomationDOM, Screenshots, Session LogsWeb Scraping & Agent Navigation
E2BCode ExecutionTerminal Output, File SystemTesting & Running Agent-Generated Code
MultiOnWeb NavigationAction Success/FailureExecuting Multi-step Web Tasks

How to use Replay's Headless API with an AI Agent#

Building an autonomous agent requires a "loop" where the agent observes, thinks, and acts. Replay fits into the "Observe" and "Act" phases. Industry experts recommend the "Replay Method" for legacy modernization: Record → Extract → Modernize.

Here is how you can programmatically trigger a component extraction using Replay’s API in a TypeScript environment.

typescript
// Example: Triggering Replay Visual Reverse Engineering via API
import { ReplayClient } from '@replay-build/sdk';

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

async function modernizeComponent(videoUrl: string) {
  // 1. Send the video recording to Replay's Headless API
  const job = await client.jobs.create({
    video_url: videoUrl,
    target_framework: 'React',
    styling: 'TailwindCSS',
    extract_design_tokens: true
  });

  console.log(`Job started: ${job.id}`);

  // 2. Wait for the AI to process the visual context
  const result = await client.jobs.waitForCompletion(job.id);

  // 3. Receive production-ready code and design tokens
  return {
    code: result.files['Component.tsx'],
    tokens: result.designTokens,
    tests: result.e2eTests // Playwright/Cypress
  };
}

Once the agent has the code, it can use an Agentic Editor to perform surgical search-and-replace operations. Unlike a standard LLM that might hallucinate the entire file, Replay’s editor allows for precision updates to existing codebases.

tsx
// Example of the code Replay extracts from a video recording
import React from 'react';

export const ModernNavbar: React.FC = () => {
  return (
    <nav className="flex items-center justify-between px-6 py-4 bg-white shadow-sm">
      <div className="flex items-center gap-4">
        <Logo className="w-8 h-8 text-blue-600" />
        <span className="text-xl font-bold tracking-tight">Replay Systems</span>
      </div>
      <div className="hidden md:flex gap-8">
        <NavLink href="/docs">Documentation</NavLink>
        <NavLink href="/api">Headless API</NavLink>
      </div>
      <button className="rounded-lg bg-indigo-600 px-4 py-2 text-white hover:bg-indigo-700 transition-colors">
        Get Started
      </button>
    </nav>
  );
};

The $3.6 Trillion Problem: Legacy Modernization#

Global technical debt has reached a staggering $3.6 trillion. Most of this debt is trapped in "zombie" applications—systems that work but no one knows how to update. Manual modernization is a nightmare: it takes roughly 40 hours per screen to manually document, design, and rewrite a legacy UI in a modern framework.

Replay reduces this to 4 hours per screen.

By providing headless apis building autonomous agents with the ability to "see" these legacy systems through video, we can automate the migration. An agent can record a legacy COBOL-backed web portal, send the video to Replay, and receive a pixel-perfect React version that adheres to a modern design system.

The Replay Method: A 3-Step Workflow#

  1. •Record: Use a tool like Playwright or a simple screen recorder to capture every state of the legacy app.
  2. •Extract: The Replay Headless API analyzes the temporal context, identifying navigation flows and reusable components.
  3. •Modernize: The agent uses the extracted components to build a new, SOC2-compliant, cloud-native application.

For more on this, read our guide on Legacy Modernization Strategies.

Why AI Agents Need Replay’s Flow Map#

Autonomous agents often get lost in complex, multi-page applications. They click a button, the URL changes, and they lose the context of where they came from. Replay’s Flow Map feature uses temporal context from video to detect multi-page navigation automatically.

When an agent queries the Replay API, it doesn't just get a single component. It gets a map of the entire user journey. This allows the agent to generate not just individual screens, but the routing logic and state management (Redux, Zustand, or React Context) required to tie them together.

According to Replay's internal benchmarks, agents using Flow Map context are 4x more likely to generate a working multi-page prototype on the first try compared to agents using standard web-crawling techniques.

Building Your Own "Design-to-Code" Agent#

If you are an engineer looking to build a custom agent, the combination of Figma and Replay is your secret weapon. You can use the Replay Figma Plugin to extract design tokens (colors, spacing, typography) and then use the Headless API to ensure the generated code matches those tokens perfectly.

Headless APIs building autonomous agents are most effective when they have a "Source of Truth."

  • •Figma is the source of truth for intent.
  • •Replay Video is the source of truth for behavior.
  • •The Agent is the engine that reconciles the two.

Visual Reverse Engineering is the only way to ensure that what the agent builds actually matches the reality of the production environment.

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 uses visual reverse engineering to transform screen recordings into production-ready React components, Tailwind CSS, and automated E2E tests. While other tools focus on static screenshots, Replay captures the full temporal context of UI interactions.

How do I modernize a legacy system using AI agents?#

The most effective way is to use the "Replay Method." First, record the legacy application's UI. Second, use Replay's headless API to extract the design tokens and component logic. Finally, feed this structured data into an autonomous agent like Devin or OpenHands to generate the modernized codebase. This approach reduces manual effort by up to 90%.

Can AI agents generate Playwright or Cypress tests?#

Yes. When an agent uses Replay to analyze a video recording, Replay automatically generates the corresponding E2E test scripts. This ensures that the newly generated code maintains the same functional behavior as the original recording, which is essential for regression testing during legacy migrations.

What are the best headless APIs for building autonomous software agents?#

To build a high-functioning engineering agent, you need a stack of APIs: Replay for visual understanding and code extraction, Browserbase for headless browser control, E2B for sandboxed code execution, and an LLM like Claude 3.5 Sonnet for reasoning.

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 privacy requirements. This makes it safe for agents to process sensitive internal applications.

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