AgentQL is a developer-focused web data extraction and automation tool that uses natural-language queries to return structured results from web pages. It supports debugging in the browser, code-based automation through Python and JavaScript SDKs, and browserless extraction through a REST API.
AgentQL is a developer-focused web data extraction and automation tool built around a natural-language query language. It helps users describe the web elements or data they want, then returns structured output that matches the query shape.
The product combines a browser debugger, Python and JavaScript SDKs, a REST API, and integrations with agentic and no-code tools so teams can test queries, automate page interactions, and move data into broader workflows. The site also positions it for pages that are public, private, or behind authentication, and for parsing difficult sources such as PDFs.
AgentQL uses a query language to describe the elements and data you want, then returns structured results in the shape you define. The site shows examples with arrays, nested objects, and fields such as product names and prices.
The product connects to web pages through Python and JavaScript SDKs built on Playwright, letting you automate interactions and extract data in code.
A Chrome-based debugger lets you test queries against live pages, inspect matched elements, and refine queries before putting them into production.
The REST API can retrieve public-facing data from any URL without a browser, which supports browserless extraction workflows.
The documentation and pricing pages indicate that AgentQL works with public or private pages, including pages behind authentication, and that it can be used for repeated workflows across similar layouts.
AgentQL integrations include tools such as Zapier, LangChain, Langflow, Dify, AgentStack, and MCP for connecting extraction to no-code and agent workflows.
Use the query language to pull structured fields from product listings, search results, or other repeating page layouts, then reuse the same query across similar pages.
Run queries in the Chrome debugger while building, then move the working query into a Python or JavaScript script for repeatable automation.
Use the REST API when you need public-facing data from a URL and do not want to spin up a browser session.
Connect AgentQL to no-code or agent frameworks such as Zapier, Dify, LangChain, Langflow, MCP, or AgentStack when extracted web data needs to flow into other tools.
Apply AgentQL to pages that require interaction or to PDF content when you need structured information from harder-to-parse sources.
AgentQL is a query-language-based tool for web data extraction and automation. Its quick start guide shows that you can use the Chrome debugger to test queries in real time, then run the same queries through the Python or JavaScript SDKs.
The documentation says AgentQL provides Python and JavaScript SDKs, a REST API, and a browser debugging extension. The SDKs use Playwright for interacting with web pages, while the REST API is available for extracting public-facing data from URLs without a browser.
Yes. The pricing page includes a free trial with no credit card required, and the Starter plan is listed at $0/month with included API calls, remote browser time, and developer tools.
AgentQL is positioned for developers, teams, founders, and businesses that need structured web data or automation. The integrations page specifically notes no-code options such as Zapier and Dify, while more technical workflows can use tools such as LangChain, Langflow, MCP, and AgentStack.
The site describes support for extracting data from web pages, interacting with page elements, browserless REST access to public URLs, and parsing difficult information like tables from PDFs. It does not claim support for every document type or every site behavior, so suitability depends on the source and workflow.
