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AgentQL is an AI-powered query language for web scraping and automation. It uses natural language selectors to find data on any page, including authenticated content. AgentQL queries are self-healing as UI changes and work across similar sites. Users can define structured data output, making AgentQL versatile for developers and data scientists.

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AgentQL examples and tutorials

Get started with AgentQL

You will need your API Key and the AgentQL SDK. You can get set up in less than five minutes with the AgentQL Quick Start.

You may want to set up a python virtual environment before you begin. Then you can install Agentql in the virtual env.

Examples

This list contains basic use case examples that demonstrate the fundamental functionalities of AgentQL. It’s a great starting point for those new to AgentQL or looking to understand its core capabilities.

Topic URL
Getting started with AgentQL first_step
Debug AgentQL script debug_agentql_script
Run script in headless browser run_script_in_headless_browser
Run script with an external or existing browser interact_with_an_external_or_existing_browser
Run script online in Google Colaboratory run_in_google_colab
Compare product prices across different websites compare_price_across_sites
Save and reuse logged in state save_and_load_authenticated_session
Wait for page to load wait_for_entire_page_load
Close popup windows (like promotion form) close_popup
Close cookie dialog close_cookie
Leverage List Query list_query_usage

Application Examples

In this list, you'll find more sophisticated examples that showcase real-world usage scenarios. These examples are designed to illustrate how AgentQL can be applied in more complex and practical situations.

Topic URL
Getting Xpath of a web element returned by AgentQL xpath
Performing sentiment analysis on YouTube comments gathered by AgentQL perform_sentiment_analysis
Collecting data about products given price range collect_ecommerce_pricing_data

About

AgentQL is an AI-powered query language for web scraping and automation. It uses natural language selectors to find data on any page, including authenticated content. AgentQL queries are self-healing as UI changes and work across similar sites. Users can define structured data output, making AgentQL versatile for developers and data scientists.

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