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What is Yozh Scraper?

Yozh Scraper is a sophisticated open-source toolkit crafted for web scraping and crawling, tailored to handle substantial data extraction endeavors. By leveraging Playwright, Python, and Redis, it skillfully maneuvers through complex JavaScript-rendered sites while adeptly bypassing modern anti-bot defenses. Notable Features: • Anti-Detection Scraping: Employs Camoufox in conjunction with authentic Chrome instances to mask browser fingerprints, allowing it to effectively navigate rigorous anti-scraping systems. • Dual Microservices Architecture: Provides an asynchronous Scraper API for page rendering, alongside an Open Crawler that includes SSE streaming, site-mapping, and deduplication functionalities. • Native MCP Integration: Effortlessly connects with AI agents like Claude Code/Desktop, LangChain, and n8n via built-in Model Context Protocol (/mcp) endpoints. • Intelligent Parsing & Configurations: Comes pre-configured for well-known platforms such as Amazon, Google, LinkedIn, and others, featuring optional self-healing parsing driven by LLMs. • Scalable Enterprise Solutions: Promotes horizontal scaling using Docker Compose, supports multiple types of proxies (Residential/Mobile/Data Center), and offers an intuitive web interface for testing. • This robust toolkit is particularly beneficial for developers aiming to enhance their data extraction workflows while ensuring adherence to anti-bot regulations, making it a valuable asset in the realm of data-driven projects.

What is BrowserQL?

BrowserQL is a dedicated scraping language and browser automation tool crafted to adeptly navigate bot detection measures while minimizing the evidence of automated actions. It possesses built-in anti-detection features that operate without the need for user configuration, allowing users to bypass services like Cloudflare and Datadome effortlessly, without relying on extra plugins or setups. Furthermore, BrowserQL efficiently addresses prevalent CAPTCHA challenges, including those found within iframes or shadow DOMs, by employing methods such as auto-humanized clicking, scrolling, and typing behaviors, alongside concealed debugging techniques and automatic fingerprint circumvention, all enhanced by the integration of residential proxies for a more genuine browsing experience. Unlike conventional DIY approaches that use Playwright and necessitate stealth plugins along with ongoing manual interventions for simulating mouse or keyboard actions, BrowserQL streamlines the entire process, significantly lowering the likelihood of detection by automation libraries. Consequently, users can concentrate on their scraping endeavors without the persistent anxiety of being flagged or obstructed by advanced bot detection systems. Ultimately, BrowserQL represents a crucial advancement for those seeking reliable and efficient web scraping capabilities in an increasingly complex digital landscape.

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Integrations Supported

CyberYozh

Integrations Supported

AgentKit
Browserless
Claude
Claude Agent SDK
Claude Computer Use
Claude Desktop
Cursor
Google Chrome
LangChain
Make
Model Context Protocol (MCP)
Next.js
OpenAI
Python
Stagehand
Vercel AI SDK
Visual Studio Code
Zapier
n8n

API Availability

API Availability

Has API

Pricing Information

$0
Free Version

Pricing Information

$25 per month
Free Version

Supported Platforms

SaaS
Windows
Mac
Linux

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

CyberYozh

Date Founded

2014

Company Location

Serbia

Company Website

data.cyberyozh.pro/

Company Facts

Organization Name

Browserless

Company Location

United States

Company Website

www.browserless.io/feature/browserql-browser-automation-tool

Categories and Features

Web Scraping

Not specified

Categories and Features

AI Web Scrapers

Not specified

Browser Automation

Not specified

CAPTCHA Solvers

Not specified

Web Scraping

Not specified

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