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App CyberYozh
App CyberYozh

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Building a Modern Web Scraping & Crawling Stack for AI Agents and Developers

Whether you are building LLM applications, training AI agents, or conducting large-scale market research, clean and structured web data is the backbone of your pipeline. However, handling modern web scraping comes with persistent headaches: dynamic JavaScript rendering, anti-bot mechanisms, rate limiting, and complex crawler orchestration.

To solve this without relying on expensive, black-box SaaS solutions, we created CyberYozh Data — an open-source, self-hosted web scraping and crawling stack designed for developers, data engineers, and AI workflows.

What is CyberYozh Data?

CyberYozh Data is a lightweight, high-performance data collection suite that bridges the gap between raw web pages and structured data. The architecture divides responsibilities cleanly between two microservices:

  1. Yozh Scraper: The single-page execution engine. It handles full browser rendering, anti-detect stealth, proxy rotation, authenticated sessions, and automated data extraction.
  2. Yozh Crawler: The full-site orchestration engine. It manages graph traversal, queue frontiers, deduplication, rate limits, and real-time page streaming starting from a single seed URL.

Because the crawler delegates every page request directly to the scraper engine, you operate one unified browser stack. Every anti-detect patch, proxy profile, or extraction rule configured in the scraper applies automatically during full-site crawls.

Core Capabilities & Features

1. Advanced Browser Stealth & Anti-Bot Bypass

Modern websites detect standard scrapers using fingerprinting. Yozh Scraper includes default stealth patches (navigator.webdriver masking, WebGL/Canvas fingerprinting protection, Chrome runtime emulation) to ensure consistent page access across protected targets.

2. Built-in Proxy & Session Orchestration

Native integration with Residential, Mobile LTE, and Datacenter proxies allows seamless IP rotation and GEO-targeting. Automatic session scoring detects bans (401/403/429 status codes) and rotates proxies instantly without interrupting your job.

3. E-Commerce & Platform Presets

Skip writing custom CSS or XPath selectors. Built-in presets allow direct extraction from major platforms—including Amazon, eBay, Walmart, Google Shopping, YouTube, and LinkedIn—delivering normalized JSON outputs out of the box.

4. Flexible Crawling Modes

Yozh Crawler offers two distinct operational modes:

  • Discovery Mode: A fast, lightweight pass that maps internal links, sitemaps, and URL structures without downloading full assets.
  • Harvest Mode: A comprehensive pass that captures raw HTML, full-page screenshots, and structured fields from every discovered page.

5. Real-Time Streaming (SSE)

Instead of waiting for an entire domain crawl to complete, discovered pages and extracted data stream in real-time via Server-Sent Events (SSE).

Built for AI Agents & Modern Workflows

One of the key advantages of CyberYozh Data is native Model Context Protocol (MCP) support. Both the scraper and crawler services expose standardized MCP endpoints.

This allows AI tools such as Claude Desktop, Cursor, or custom LangChain/AutoGPT agents to invoke scraping and crawling functions as native tools, giving AI agents direct access to real-time web content.

Where Can You Use It?

The stack fits seamlessly into various technical and business use cases:

  • AI & RAG Pipelines: Feed fresh, unstructured web content directly into vector databases or LLM context windows.
  • E-Commerce Monitoring: Track product pricing, stock availability, and vendor listings across global marketplaces.
  • SEO & Site Audits: Map site architecture, identify broken links, and analyze domain structures using Discovery mode.
  • Lead Generation & Market Research: Extract company directories, job posts, and social profiles reliably at scale.

Conclusion

By decoupling full-site crawling from browser execution, CyberYozh Data offers a developer-first alternative to commercial scraping APIs.

Explore the official documentation and source code:

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