Purpose
AgentQL is designed to make the web AI-ready, giving AI agents and LLMs the ability to connect to the entire web. It is a suite of tools, including a query language and parser, for interacting with page elements and extracting data quickly, precisely, and at scale, suitable for building AI agents, executing data workflows, and simple automation.
Main Capabilities
- Natural language queries: Use syntax similar to
{ results { products[] { product_image product_name prouct_price(include currency symbol) } } }queries to define the shape of the desired data and extract structured data. - Robust alternative selectors: Use AI to analyze page structure to locate data, as a robust alternative to XPath and DOM/CSS selectors.
- Self-healing capability: Maintain consistent results amid dynamic content and page changes.
- Reusable code: The same query can be reused across multiple similar pages.
- Versatile SDK: Interact with web page elements through Playwright as well as Python and JavaScript SDKs and headless browsers.
- Browser debugger: Provides a browser extension to optimize queries in real time on any web page.
- Browserless REST API: Use the REST API to obtain public data from any URL without a browser.
- PDF parsing: Can parse difficult-to-handle information in PDFs, such as tables.
- Works on any page: Public or private, any site, any URL, and even pages requiring authentication all work.
How to Use
Users can develop by installing the SDK or using the REST API. The query language is used to define the data shape and is executed with Playwright, a headless browser, or REST calls. The browser extension is used to debug queries, and the official documentation and playground provide further learning. Getting started is free, no credit card is required, and free credits are provided after registration.
Suitable Scenarios
- Developers building web agents and data workflows
- Teams running routine data workflows and web automation pipelines
- Extracting structured data from any website (including pages requiring authentication)
- Parsing complex information such as tables in PDFs
- Serving as a text grounding solution to reduce context window issues and hallucinations
Limitations
Official materials do not describe all possible limitations, and only mention that in text grounding scenarios, AgentQL can improve context window and hallucination issues compared with using raw HTML content. Specific limitations are not yet provided.
