AgentQL is an AI-powered query language and web extraction platform that helps agents and developers retrieve structured data from web pages.

AgentQL offers a free trial with 300 API calls, remote browser time, and developer tool access without a credit card.

Who should use AgentQL?

AgentQL is best for developers building web scraping, browser automation, testing, monitoring, and AI agent workflows.

Does AgentQL have an API?

Yes. AgentQL offers SDKs and a REST API, with Python and JavaScript support for web extraction and automation.

Does AgentQL work with agents?

Yes. AgentQL connects LLMs and AI agents to the web and includes integrations such as MCP and LangChain.

What are AgentQL alternatives?

Alternatives include Firecrawl, Browserbase, Browserless, Apify, Tavily, Exa, Playwright, and custom scraping stacks.

AgentQL

AI web data extraction and automation tool for agents, scraping, and testing.

Visit Website

What is AgentQL?

TL;DR

AgentQL is an AI-powered web data extraction and browser automation platform for developers building scrapers, tests, monitoring workflows, and AI agents. Natural-language-style queries help retrieve structured data from changing web pages.

Use natural language selectors to query and extract data from web pages
Run web scraping, automation, and testing through SDKs, REST API, and Playground
Connect LLMs and AI agents to real-time web data and browser workflows

Explore with AI

About

AgentQL is an AI-powered web data extraction and browser automation platform for developers building scrapers, tests, monitoring workflows, and AI agents that need access to live web pages. Instead of relying only on brittle CSS selectors, developers can ask for page data with natural-language-style queries and retrieve structured results from changing websites. For FutureStack users, AgentQL is especially useful when an AI app needs to interact with the web, extract product data, monitor listings, collect research, validate page changes, or give agents a reliable way to read and use external websites. It fits developer teams that want to build data pipelines and agent workflows without maintaining a large scraping stack from scratch. The platform includes SDKs, REST API access, a Playground, remote browser sessions, Python and JavaScript support, MCP and LangChain integrations, scraping, web automation, testing, and managed data access options. This makes it useful across AI agent development, QA automation, competitive monitoring, lead research, ecommerce data workflows, and internal data tooling. AgentQL can reduce scraping fragility, but it does not remove the need for good judgment. Teams still need to respect website terms, rate limits, consent rules, privacy requirements, and data compliance obligations before automating extraction at scale.

Use Cases

Web Data Extraction

Query live web pages for structured information without depending entirely on brittle CSS selectors and manually maintained scraping logic.

Browser Automation

Automate browser workflows for research, monitoring, testing, and agent tasks using AgentQL queries and managed browser infrastructure.

AI Agent Browsing

Give AI agents access to live web data and browser interactions through APIs, SDKs, MCP, and supported integrations.

Automated Testing

Use web extraction and browser automation to support regression testing, page validation, and automated quality workflows.

Key Features

Use natural language selectors to query and extract data from web pages
Run web scraping, automation, and testing through SDKs, REST API, and Playground
Connect LLMs and AI agents to real-time web data and browser workflows
Use integrations, MCP, LangChain, Python, JavaScript, and remote browsers

Featured Tools

Wispr Flow

Voice-First Dictation

We may earn commissions from links to support our work. Learn more.