Headless browsers are fast. They're lighter on memory, use fewer CPU cycles, and are often the default for automation. And if you're just scraping static pages or doing basic DOM interactions, that might be enough.

But if your goal is to reliably simulate a real user, whether for automation, testing, or powering AI agents, performance alone isn’t the full picture.

This article is designed to help you understand why headful browsers are becoming popular and understand where they shine and fall short, supported by benchmarks, architectural insights, and real-world tooling comparisons.

Popular Headless andHeadful Browsers

Most modern web browsers support both headless and headful modes. This is done for a reason since both models have their practical use cases. For example, Chrome and Firefox can run in both modes. For headless mode in a typical browser, you only need to add a command flag when launching it.

google-chrome --headless https://mysite.com
firefox --headless https://mysite.com

In addition, there are specialized browser environments designed for automation, such as Playwright, Puppeteer, or Selenium, where most of them support both headless and headful modes

Overview: 

• Tools supporting both modes: Playwright, Puppeteer, Selenium, WebdriverIO, Crawlee, Flagpole JS.

• Headless only tools: HtmlUnit, Splash.

• Cross browser support: Full - Playwright, Selenium, WebdriverIO, Crawlee; Partial - Puppeteer, Flagpole JS; None - HtmlUnit.

Top Choices for 2025:

• Playwright: Best overall, which is modern, fast, and flexible.

• Crawlee: Preferred for scraping workflows.

Headless vs Headful: Which One to Choose?

Now let's look into a comparison of these two modes of execution. Below is a quick benchmark captured using Playwright. The memory and CPU values shown represent peak usage recorded during each run. 

Benchmark Snippet (Playwright)

import { chromium } from 'playwright';

(async () => {
  const browser = await chromium.launch({ headless: true });
  const page = await browser.newPage();
  await page.goto('https://www.wikipedia.org');
  await page.waitForTimeout(3000); // simulate work
  await browser.close();
})();

You can find the code here to run the benchmark tests on your own.

Note: Lower values are better in terms of performance. 

So, what do the numbers mean?

• CPU & Memory Usage: Headful consumed more memory and CPU, primarily because of UI rendering.

• Elapsed Time: Headful completed the navigation slightly slower.

Headless gives you speed, but it strips away the very things that make the browser a real user interface. In high-stakes environments from agent-based automation to debugging to bot detection, being seen as a real user matters more than shaving off a few milliseconds. Here's why headful is often the better choice. 

Choosing Headful is a Better Choice

Headful browsers have many strengths in real-world scenarios. And, it's becoming the best option for agentic browser automations.

• Vision model compatibility: Headful browsers provide real OS-level rendering, which is essential for vision-based models like OpenAI CUA. These models perform better when interacting with real pixels and native visuals, not synthetic headless output, for greater accuracy.

• Full UI fidelity: Elements like dropdowns, modals, and animations often don’t render correctly or at all in headless mode. Headful ensures these behave exactly as they do for real users.

• Native browser behavior: Features like the address bar, new tab interface, and keyboard shortcuts (e.g., Ctrl+L, Ctrl+T) only exist in headful mode, which is important when simulating real user workflows.

• OS-level integration: Headful allows for richer observability and interaction from clipboard access to window management, all of which are inaccessible or unreliable in headless environments.

• Avoid getting flagged as Bots: As many websites use fingerprinting techniques, the difference between a real user and a bot becomes visible. On the other hand, headful browsers resemble real user activity far more and therefore are much less likely to be flagged or blocked.

• Debugging automations: Developers can see the automation behavior in real time in headful mode. This makes it much easier to identify layout problems, unexpected popups, or flow mismatches. Increased visibility aids the debugging process and helps ensure that automations perform consistently and correctly in different conditions.

Infrastructure Compatibility: Where Each Mode Fits

Running Headful in GitHub Actions: To run Headful browsers in a CI pipeline, you’ll typically need to install and run Xvfb:

- run: sudo apt-get update && sudo apt-get install -y xvfb

- run: export DISPLAY=:99 && xvfb-run npm test

This makes it possible to execute Playwright or Selenium in headful mode even on headless runners.

How Automated Interactions Work?

Browser automation tools use control protocols to interact with the browsers. These protocols define how commands are sent and data is received. CDP (Chrome DevTools Protocol) and VNC/RDP are the 2 most used control protocols in browser automation.

Chrome DevTools Protocol (CDP)

CDP is a low-level control protocol used by tools like Puppeteer, Playwright, and Selenium (via ChromeDriver) to control Chromium-based browsers like Chrome Headless Browser, Edge, and Brave. It is most suitable for:

• Headless automation

• High-performance scraping

• CI/CD integrations

• DOM level control

VNC / RDP

VNC (Virtual Network Computing) and RDP (Remote Desktop Protocol) are graphical remote control protocols. Instead of sending commands, these protocols stream the entire screen to the browser engine.

VNC and RDP are most suitable for tasks like headful automation, visual testing, live debugging, and preventing bot detection.

What Makes Automation Fragile

Despite the growing capabilities of modern tools, several persistent challenges continue to slow down production-grade browser automation:

• Changes in the Web Content: Modern websites are highly dynamic, with frequent frontend updates and JavaScript-heavy rendering, which often leads to selector instability, where XPath or DOM selectors break frequently, introducing issues in automation flows.

• Blocking Ads and Cookie Banners: Ad overlays, modals, and cookie banners frequently interfere with automation flows. Custom logic or ad-blocking extensions may be necessary.

• CAPTCHA Solving: Headless flows often get flagged, triggering CAPTCHA screens. Solving them may require integration with third-party services or human-in-the-loop solutions.

• Proxy Management: Handling IP rotation and managing proxies correctly is essential for scraping at scale or avoiding region locks.

• Browser Profile Persistence: Maintaining sessions, cookies, and tokens across test runs or across containers is tricky, especially for authenticated flows or apps with sensitive state.

These issues often surface more frequently in headless mode, where stealth and session state are harder to maintain. However, even in headful mode, inconsistencies can creep in when dealing with dynamic UIs or session persistence.

Recognizing these pain points early encourages better design patterns, such as fallback selectors, conditional waits, or using a hybrid of headless and headful workflows.

Agentic Automation: The LLM-Driven Future of Browser Workflows

As large language models (LLMs) evolve, they're reshaping the browser automation landscape. Traditional scripts are giving way to intelligent agents, capable of understanding page context, reacting to layout changes, and making real-time decisions.

What makes this shift possible?

• Dynamic Selector Reasoning: LLMs can interpret changes in the DOM and retry with fallback selectors when elements fail.

• Goal-Oriented Execution: Instead of brittle steps, agents operate with objectives, e.g., "fill and submit a login form."

• Natural Language Inputs: Developers can describe flows like "book a flight on Expedia," and the agent converts this into actions.

But with great power comes greater complexity. Deploying Agentic systems isn't trivial. You need:

• Containerized environments for each session.

• Cross-browser support with fallback logic.

• APIs to expose agent control to upstream systems.

• Observability tooling to debug and monitor agent behavior.

• Scalable infrastructure to run hundreds or thousands of sessions in parallel.

While open-source tools like Playwright or Selenium can be integrated with LLMs using MCP,  managing the full stack execution, state, security, and orchestration requires significant engineering investment.

Fully Managed, Agentic Browser Automation

For teams tired of managing flaky scripts, headless failures, or brittle CI setups, Anchor Browser offers a purpose-built infrastructure for browser-native agents. With built-in headful support, fingerprint-level stealth, live debugging, and containerized execution, it's designed to simplify what most browser automation frameworks leave up to developers to stitch together. 

Anchor is built for agentic flows, letting you combine control with scale, without compromising on visibility.

Conclusion

Browser automation is no longer just about scripting clicks or finding the fastest way of doing it. Every automation scenario is different.

And, deciding between headless and headful automation modes can sometimes become difficult. Yet there are few patterns and rules you can follow.