AgentQL is an MIT-licensed suite of tools — an AI-powered query language plus Python and JavaScript SDKs, a REST API, and a browser debugger — that connects LLMs and AI agents to live web pages for data extraction and workflow automation, aimed at developers building scrapers, RPA scripts, and agentic automations on top of Playwright.

What it is

AgentQL is a query language and tooling suite for pulling data out of, and driving interactions on, live websites. It ships as a Python SDK, a JavaScript SDK, a REST API endpoint for running queries without an SDK, a Chrome debugger extension for refining queries in real time, and a hosted Playground, with documented integrations into agent frameworks and automation tools such as Langchain, Zapier, and an MCP server.

The concrete problem it addresses is selector fragility in browser automation. Scripts written against CSS or XPath selectors break whenever a site's markup changes, so teams maintaining Playwright scraping and RPA workflows spend their time repairing selectors rather than collecting data. AgentQL replaces those selectors with natural language queries that locate elements by page content, work across similar sites, and self-heal as a UI evolves over time.

Key capabilities

• The AgentQL Query Language defines extraction and interaction in natural language, with structured output determined by the shape of the query itself.
• Transforms and extraction happen inside the query, so derived values are computed without post-processing in application code.
• Official Python SDK and JavaScript SDK integrate directly with Playwright for advanced automation and testing.
• A REST API endpoint runs queries in languages or environments where neither SDK is practical.
• Cross-site compatibility allows the same query to run against different sites that hold similar content.
• Queries work on public and private pages, on any URL, including content behind authentication and content generated dynamically.
• A Debugger Browser Extension and the Playground let queries be tested on live sites and exported as Python scripts.

Who uses it and how

• Web scraping teams collecting structured records at scale, where cross-site compatibility lets one query cover several comparable sources.
• RPA and automation groups replacing brittle selector-based browser steps, using the SDKs to close cookie dialogs, dismiss popups, and drive forms.
• AI agent builders wiring page access into LLM workflows through the MCP server, Langchain, or Zapier rather than writing bespoke scrape code.
• QA and test engineers already on Playwright who want content-based element targeting alongside their existing test suites.
• Analysts working in hosted notebooks, supported by the published Google Colab examples for tasks such as closing popups and comparing product prices.

Getting started

Install AgentQL's Python SDK or JavaScript SDK from the documented installation pages, or call the REST API if no SDK fits the stack; the quick start guide targets a five-minute first query, and the Playground exports runnable Python.

How it compares

No paid alternatives are listed in the facts, so the nearest named neighbours are the tools AgentQL integrates with rather than displaces: Playwright supplies the browser automation layer, while Langchain, Zapier, and the MCP server supply the agent and workflow layer. It sits above Playwright as a query and selection layer, and inside agent stacks as the component that reads and acts on web pages.

When to use it — and when not to

Adopt it when selector maintenance is the recurring cost in a Playwright or scraping codebase, and when page content is stable enough for natural language queries to be reviewed and refined in the debugger. Teams that need a fully self-contained query engine should note that the README documents a REST API endpoint and a hosted Playground but describes no self-hosted execution service, and the debugger ships only as a Chrome extension. The README also leaves rate limits, quotas, and operational requirements unstated, so those should be confirmed before committing a production pipeline to it.