Browser Automation using LLM
Year: 2025
Type: article
Abstract: This paper presents a modular framework for browser automation using large language models (LLMs). The system converts raw HTML into a token-efficient JSON representation of the DOM, reducing token usage by over 90% while retaining essential structural and semantic information. By combining structured prompting, error recovery mechanisms, and Selenium-based execution, the framework reliably translates natural language instructions into browser operations across diverse websites. Evaluations on hundreds of user commands demonstrate high success rates, with most tasks completing in real time. This approach highlights the potential of LLM-driven agents to perform complex web interactions efficiently, offering a reusable skeleton for developers to adapt in building AI-powered automation solutions.
Author: Kurup, Praveen
Language: English
Topic: Web Data Mining and Analysis
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7082247420
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