In our previous articles, we explored what AI agents are and how they function. We also demonstrated how to use Manus AI, which is an advanced AI agent, to carry out complex tasks. However, to gain a complete understanding of AI agents, you need to be able to build one from scratch.
To be more precise, this article will focus on one of the main tools our AI agent will use, and that is Browser Use. This tool is extremely important because it allows the AI agent to interact with the web. This is done not only by pulling information from websites, but also by performing actions that were once limited to humans. These include tasks like booking appointments, purchasing products online, and much more. In this article, we will cover how Browser Use works. In the next one, we will demonstrate how to implement it in practice.
Why Should Browser Use Be Used
At its core, what sets an AI agent apart from an LLM agent is its ability to interact intelligently with its environment. It can solve tasks by responding to its surroundings in real time. An AI agent adapts dynamically based on the specific context and available resources. It doesn’t rely on predefined or fixed sequences of operations.
Building a dynamic system like this from scratch can seem daunting at first. But in reality, with the right tools and frameworks, building a relatively simple AI agent is quite manageable. One tool that has recently gained popularity for designing AI agents that interact directly with the web browser is Browser Use.
Browser Use was designed to help developers build workflows where LLMs can directly interact with browser environments. Essentially, it is a tool that turns text-based instructions from the LLM into real browser actions. This approach is incredibly powerful because it enables LLMs to directly navigate the web and access real-time data. It even allows LLMs to perform tasks that typically require direct user interaction.
Some popular examples of using Browser Use to connect an LLM to the web and enable interaction include:
- Writing letters in Google Docs
- Applying for jobs online
- Booking flights between two cities
- Buying products on Amazon
How Does Browser Use Work
Earlier, we mentioned that Browser Use “translates” natural language instructions into browser actions, allowing AI agents to access, read, and interact with web pages directly. While the concept might seem complex at first, the tool's functionality is quite simple. This is despite the multiple steps included in the Browser Use workflow.
Browser Use enables LLMs to interact with real web pages by:
- Processing the original prompt
- Extracting interactive elements and converting them into a simplified, manageable format
- Translating natural language commands into specific browser operations using Playwright
- Handling errors and adapting to changes or failures in real-time
- Allowing customization through additional functions tailored to specific tasks
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How to Process the Original Prompt
All interactions with the web begin with the original prompt entered by the user. This prompt serves as an instruction, telling Browser Use what actions to perform. The instruction can be quite simple, such as “Find all flights from Zurich to Beijing on the 25th of March 2025” or “Click this button on this webpage.” It can also be more complex, involving multiple steps.
For example, a complex prompt could be “Go to Amazon, analyze all of the under-desk treadmills, find out which one is the best one based on user reviews, add it to the cart, and buy it.” For more complex tasks, the user needs to provide additional information in the prompt, such as their credentials, Amazon password, card information, and anything else required to complete the operations. The more complex the task, the more detailed and descriptive the prompt must be. This ensures that Browser Use has all the necessary information to carry out the task successfully.
Keep in mind that, in the background, you are still using an LLM (of your choice). Therefore, when designing the prompt, you should follow the same principles you would use when interacting with any other LLM.
How to Extract Elements
Rather than having the LLM work directly with raw HTML or complex browser APIs, Browser Use extracts and organizes the necessary elements from web pages. The approach it uses is hybrid, combining DOM parsing techniques with vision-based analysis.
Browser Use primarily loads web pages using automation frameworks like Playwright, which renders the full HTML document. It then processes the Document Object Model (DOM) to identify interactive elements such as buttons, links, input fields, and forms. Key attributes of these elements, such as IDs, classes, and XPath selectors, are extracted. Finally, Browser Use organizes the extracted data into a structured list. The LLM can easily process this list, enabling it to decide which operations to perform and how to execute them.
In cases where the appearance of the web page plays an important role, Browser Use integrates a vision model. This model processes screenshots of the rendered page. It detects visual cues, such as bounding boxes of buttons or sections of the page. These cues may not be fully captured through DOM parsing alone.
The full hybrid approach, which includes the use of the vision model, is typically employed when the behavior or appearance of a web page’s interactive elements is defined more by visual context than by static HTML attributes. For instance, if an element is only visually distinguishable or if its interactivity depends on dynamic styling and effects, the vision component can supplement the DOM data. It provides precise visual coordinates and labels.
How to Command Translation and Execution
Based on its understanding of the instruction and the extracted interactive elements, the LLM decides what action to perform, such as clicking a button, entering text, scrolling, or switching tabs. The next step is to translate the LLM’s decision into concrete browser actions. This is done using an automation framework, with Playwright being the framework of choice.
Playwright is an open-source browser automation framework developed by Microsoft. It allows developers to control and interact with web browsers programmatically. It provides high-level APIs to simulate user interactions, making it ideal for tasks such as automated testing and web scraping. In this case, we will use Playwright to enable an LLM to interact with the web.
Playwright supports all major browsers, such as Chrome, Edge, Firefox, and Safari. This means we can run the same automation script across different browser types and it will work consistently. Additionally, Playwright can run in either headless or headed modes. This means that we can decide whether we want to see a visible user interface or not. In headless mode, there is no visible user interface, which makes it faster. However, headed execution still has its place, particularly for debugging.
With Playwright, automation begins by launching a browser instance. Before interacting with web pages, Playwright creates isolated "browser contexts." Each context functions like a separate incognito browser session. This ensures that cookies, local storage, and session data are kept isolated between tests or automation tasks. This is particularly useful when simulating multiple independent sessions within a single browser instance.
Within a browser context, you can open one or more pages, similar to browser tabs. Each page can navigate to different URLs and be controlled independently.
To interact with elements on a web page, Playwright first locates them using methods like CSS selectors, XPath, or text content. Once an element is located, different methods, such as click(), fill(), and type(), simulate user interactions. These actions include clicking buttons, entering text into input fields, and selecting options from dropdowns. Finally, Playwright waits for a specified period before trying to interact again. These robust waiting mechanisms ensure that the script does not interact with elements before they are fully loaded or available.
While performing these operations, Playwright can capture screenshots, record videos, and log console output. This provides valuable visibility into what is happening in the browser during automation. This feature is particularly important for us because it gives the LLM much more information to work with when using Browser Use.
Essentially, the Python library behind Browser Use sends commands through Playwright’s API to control the browser, just as a human would. The key difference is that, in this case, the commands are compiled by an LLM.
How to Handle Errors and Custom Functions
The system includes mechanisms to detect when an action fails. For example, if an element isn’t found or isn’t interactable. It can then attempt corrective measures. This “self-correcting” behavior helps maintain a robust automation process, even when web pages change or behave unexpectedly.
This is also highly useful when the model encounters protections, such as CAPTCHAs. Theoretically, because users can define custom functions, it is relatively easy to create systems that bypass these automated browsing protections, which would stop a standard model. However, whether it is moral to do so is another question entirely. It can even become a legal matter if users try to automate interactions with a web page that directly disallows it.
How Does The Feedback Loop of Browser Use Work
The step-by-step process doesn’t occur only once. Instead, it runs in loops. When the LLM receives an instruction, it translates it into a browser command using Playwright. As mentioned previously, this can involve various actions, such as going to a new page, clicking on elements, and more. After each action, the system captures the current state of the web page. Browser Use then re-parses the updated DOM and, if necessary, utilizes a vision model to extract interactive elements from the changed version of the page.
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The freshly extracted state, which now reflects the results of the LLM’s previous command, is incorporated back into the system’s context. This essentially informs the system by saying, “Here’s what just happened.” With the updated context, the LLM can reassess the task. It determines whether the previous action was successful, if any errors occurred, or if further steps are needed. If something unexpected happens, such as an element not loading or a CAPTCHA appearing, the LLM can adapt its strategy.
This closed feedback loop enables the automation of complex, multi-step workflows. Each action depends on the successful execution of the previous one. The model can easily detect discrepancies between expected and actual results, ensuring smooth operation.
A crucial component of any AI agent is providing the LLM model, the brain, with high-quality data. Traditional web scrapers are limited in this regard, making them less ideal. This is where Browser Use comes into play. It not only allows our LLM to efficiently scrape the web, but also ensures that the extracted information is of higher quality, thanks to the hybrid approach. Moreover, Browser Use enables our LLM to interact with the web at an advanced level. In the next article, we will integrate Browser Use into a larger workflow. We will cover how to set it up to perform various operations, using both proprietary LLMs and even local LLMs.
