AgentQL

AgentQL is a suite of tools designed to bridge the gap between large language models (LLMs), AI agents, and the live web. It enables developers and data practitioners to extract structured data from any web page using natural language queries, without needing to write complex CSS selectors, XPath expressions, or custom scraping scripts. The core product allows users to define what they want to retrieve—such as product listings, job postings, social media feeds, or any other recurring web content—by simply describing the desired elements in plain English (e.g., “get all job titles from Amsterdam, filtered by date”). The system interprets these queries, navigates the page structure, and returns clean JSON output that can be integrated directly into AI workflows, analytics pipelines, or automation chains. AgentQL supports a wide range of real‑world applications. For e‑commerce, it can monitor competitor pricing, inventory levels, or product descriptions across multiple retailer sites and feed the data into a price optimization model. For job boards and recruitment platforms, it can aggregate open positions, salary ranges, and required skills, allowing HR teams or AI‑powered job matching agents to stay updated without manual checking. In social media monitoring, AgentQL can pull public posts, comments, or engagement metrics from profiles or hashtags, feeding sentiment analysis or trend detection models. The tool also excels at recurring data extraction tasks such as scraping news headlines, real‑estate listings, or event schedules—any scenario where web data needs to be continuously captured and structured. AgentQL integrates with popular frameworks and platforms. It provides SDKs for Python and JavaScript, REST APIs, and pre‑built connectors for tools like Zapier, n8n, or