AgentQL

AgentQL is a comprehensive suite of tools designed to make the entire web AI-ready, enabling Large Language Models (LLMs) and AI agents to seamlessly interact with web pages, extract structured data, and perform complex automation tasks. It fundamentally changes how developers approach web data extraction by replacing traditional, brittle methods like XPath and DOM/CSS selectors with a robust, AI-driven query language. This language understands the semantic structure of a webpage, allowing it to locate the desired information consistently, even when the site's layout or underlying code changes.

The core of AgentQL is its unique query language, which allows you to define the exact shape of the data you need in a simple, declarative syntax. For example, you can request a list of products with their names and prices, and AgentQL will intelligently parse the page to find and structure this information into a clean JSON output. This eliminates the tedious process of writing complex parsing scripts and manually sifting through raw HTML.

How to use AgentQL

Using AgentQL involves a straightforward workflow that integrates easily into existing development processes:

1. Define Your Data Structure: Start by writing an AgentQL query that describes the data you want to extract. For instance, to get product details, your query might look like: { products[] { product_name, product_price(include currency symbol) } }.
2. Choose Your Integration Method: AgentQL offers multiple ways to execute your queries. You can use the versatile Python and JavaScript SDKs, which integrate with Playwright for powerful browser automation and interaction on both public and private (behind authentication) sites. Alternatively, for public-facing data, you can use the browserless REST API to retrieve data from any URL without managing a browser instance.
3. Debug and Refine: Utilize the browser-based debugging extension to build, test, and optimize your queries in real-time on any live webpage. This interactive tool significantly speeds up the development process.
4. Execute and Automate: Run your queries through the SDK or API to receive structured JSON data. The SDKs also allow you to programmatically interact with web elements, such as clicking buttons, filling forms, and navigating pages, enabling full-scale web automation.
5. Integrate with AI Frameworks: Connect AgentQL with popular agentic frameworks like LangChain and LlamaIndex, or low-code platforms like Langflow, to feed real-time, structured web data into your AI applications and RAG models.

Core Features of AgentQL

• AI-Powered Query Language: A semantic and robust language for selecting elements and extracting data, resilient to changes in page layout and HTML structure.
• Structured Data Output: Automatically converts unstructured web content into clean, predictable JSON format based on your query.
• Versatile SDKs (Python & JavaScript): Provides deep integration with your applications using Playwright for advanced browser control and automation.
• Browserless REST API: Enables high-volume data extraction from public URLs without the overhead of running a browser.
• Browser Debugging Extension: An interactive tool for real-time query creation, testing, and optimization directly on web pages.
• Self-Healing Queries: Queries are not tied to specific HTML tags, making them adaptable to dynamic content and website updates, ensuring long-term reliability.
• PDF Parsing: Capable of extracting complex information, including tables, from PDF documents.
• Broad Integrations: Plays well with major AI and automation frameworks like LangChain, LlamaIndex, and Langflow.

Use Cases for AgentQL

AgentQL is ideal for a wide range of applications where interaction with web data is crucial:

• AI Agent Development: Empowering AI agents to browse the web, gather real-time information for decision-making, and execute tasks on behalf of users.
• Robust Web Scraping: Building scalable and maintainable data extraction pipelines for market research, competitor analysis, price monitoring, and lead generation.
• Retrieval-Augmented Generation (RAG): Grounding LLM responses with accurate, real-time, structured data from the web to reduce hallucinations and improve answer quality.
• Workflow Automation: Automating repetitive tasks such as filling out forms, data entry from websites into internal systems, and testing web applications.
• No-Code/Low-Code Solutions: Enabling users of platforms like Langflow to create visual workflows that pull and process live web data without writing code.

Advantages of AgentQL

The primary advantage of AgentQL is its robustness and efficiency. By using AI to understand page structure, it saves countless hours of developer time that would otherwise be spent writing and maintaining fragile parsing scripts. Its queries are reusable across similar pages, and its self-healing nature means less maintenance and more reliable data pipelines. It provides structured data out-of-the-box, simplifying downstream processing and integration with other systems.

Pricing and Plans

AgentQL offers a flexible pricing structure to suit different needs, from individual hobbyists to large enterprises.

• Starter Plan: $0/month. This plan is perfect for enthusiasts and small projects, offering 50 free API calls per month, 10 hours of remote browser time, and community support. Additional usage is billed on a pay-as-you-go basis.
• Professional Plan: $99/month. Aimed at teams with regular scraping and automation needs, this plan includes 10,000 API calls per month, 500 hours of remote browser time, a higher rate limit, and priority email support.
• Enterprise Plan: Custom pricing. For businesses requiring fully managed solutions, this plan offers ready-to-use datasets, dedicated cloud or on-premise deployment, 24/7 premium support, and a dedicated account manager.
• A free trial is also available, providing 300 API calls and 1 hour of remote browser access without requiring a credit card.