CyberYozh Launches Open-Source Yozh Scraper for AI Teams

CyberYozh App, a provider of proxy and online infrastructure for businesses and developers, announced the launch of Yozh Scraper, a free, open-source web scraping toolkit designed to help AI engineers, developers, and data teams collect publicly available web data without building scraping infrastructure from scratch. Yozh Scraper

Available through GitHub, Yozh Scraper is a self-hosted solution that runs locally or on a server using Docker. Built on Playwright browser automation technology, the toolkit combines individual page scraping and full-site crawling capabilities with API connectivity and support for AI-powered workflows.

The new toolkit includes two primary components: Open Scraper, which collects information from individual web pages using predefined presets, and Open Crawler, which enables users to crawl and collect data across entire websites. Together, these tools provide developers with a flexible environment for managing web data extraction projects.

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Yozh Scraper also supports a REST API and Model Context Protocol (MCP) endpoint, allowing compatible AI agents and external applications to request web data directly. Developers can additionally enable optional large language model-assisted parsing using their own model provider API keys.

The toolkit includes ready-made presets for popular platforms such as Amazon, Google, eBay, Walmart, YouTube, and LinkedIn. Other capabilities include persistent sessions, cookie handling, proxy configuration, and flexible output options such as HTML, extracted data fields, and screenshots. 

Expanding Access to Web Data Infrastructure

As demand for reliable web data continues to grow across artificial intelligence, analytics, and digital business operations, developers increasingly need flexible tools that support dynamic websites, automated workflows, and location-specific data collection.

Yozh Scraper addresses these requirements through a self-hosted approach, giving technical teams greater control over their scraping infrastructure, configurations, and collected information. Unlike managed scraping platforms that may charge per request, the open-source toolkit allows users to run their own workloads and manage infrastructure costs independently.

The platform also integrates with CyberYozh proxy infrastructure, offering access to rotating residential, static residential, LTE/5G mobile, and datacenter proxies. Users can configure geographic targeting by country, region, and city, while proxy usage remains optional for projects that can operate through a direct internet connection.

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Designed for Developers and Technical Teams

Yozh Scraper is designed for AI and machine learning engineers, data analysts, automation developers, SEO professionals, and researchers who need repeatable and customizable web data collection workflows.

The toolkit can be deployed using Docker Compose. Users can download the GitHub repository, configure the required environment settings, and launch Open Scraper and Open Crawler on their local machines or servers.

CyberYozh App encourages responsible data collection, including respecting website terms of service, maintaining reasonable request volumes, and complying with applicable privacy and data protection requirements.

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Yozh Scraper is available now as a free, open-source project through the official CyberYozh-data GitHub repository. Proxy integration is optional and requires a CyberYozh App account and API key.