AgentQL is an LLM-based dev infrastructure for data scraping and workflow automation. For data scraping usage, it lets you select data with natural language instead of navigating DOMs, creates code resilient to UI changes, and enables code deployment to other sites. You can quickly try it on this interactive playground in seconds: https://playground.agentql.com/.
Conventional web scraping soaks up dev time as intricate DOM structures and unpredictable AJAX-driven content updates complicate the development of robust extraction algorithms; isolating high-quality data in the correct format from unstructured web information is a long-standing challenge. Scraping scripts are tailored to the unique quirks of individual web pages and cannot be reused for other sites, requiring constant customization. Maintaining scraping scripts to accommodate web UI changes is an endless task.
AgentQL simplifies the process of locating web elements. With its natural language-like query syntax, developers can specify web elements without diving into complex DOM structures or writing fragile XPath expressions. The tool's core is AgentQL Query, a query language designed to give users an easy way to describe what web elements to locate on a web page.
At its core, It melded sophisticated prompt engineering with a robust LLM. This powerful combo dynamically generates context-aware prompts that interact with the DOM's mutable attributes, adapting to the ever-changing web landscape. It mitigates the traditional fragility of static XPath or CSS selector-based scripts, allowing for more resilient and adaptable integrations.
