CyberYozh Data Reviews — Discover what people think of this product.

CyberYozh Data is an open-source crawler and scraper engineered for AI-native data collection workflows. Starting from a single seed URL, it maps entire websites, processes pages as they are discovered, and returns structured JSON ready for agents and analysts. With native MCP integration, it plugs directly into ecosystems like Claude and Cursor, so you can orchestrate crawling and scraping from within your existing automation.

Instead of juggling separate crawlers, scrapers, and proxy dashboards, CyberYozh Data centralizes everything in one controllable environment. Yozh Crawler discovers and processes pages in one run, while Yozh Scraper handles batch scraping of hundreds of URLs in parallel. Built-in presets for Amazon, eBay, Walmart, Google, YouTube, LinkedIn, Bing, and Yandex eliminate manual selector writing and give you normalized outputs across marketplaces and search engines.

CyberYozh Data is designed for resilience and stealth. A stealth browser aligns headers, device fingerprinting, and proxy signals for stable sessions across regions. LLM self-heal automatically regenerates broken selectors when sites change layouts, keeping pipelines running without emergency fixes. Authenticated sessions, cookie reuse, and a declarative login DSL make it practical to work with walled destinations.

Key advantages include:

• MCP-native crawling for agentic workflows and AI-driven automations
• Discovery mode for fast link mapping and structural audits
• Residential, mobile, and datacenter proxies across 120+ countries
• AI-generated presets to bootstrap new targets from a sample URL and extraction description

For teams building data products, search monitoring, price tracking, or competitive intelligence, CyberYozh Data offers transparent, extensible scraping infrastructure under your control.