CyberScraper 2077: AI-Powered Web Scraping with Style

By Prahlad Menon Published 2026-02-08 2 min read

Web scraping is tedious. CSS selectors break when sites update. APIs require authentication. Rate limits get you blocked.

CyberScraper 2077 takes a different approach: use LLMs to understand and extract data intelligently, rather than relying on brittle selectors.

How It Works

Instead of writing XPath or CSS selectors, you describe what you want in natural language. The LLM figures out how to extract it from the page structure.

Supports multiple backends:

OpenAI — GPT models for extraction
Gemini — Google’s multimodal models
Ollama — Local LLMs for privacy

Key Features

Streamlit Interface — No command line needed. Point-and-click scraping.

Multi-Format Export — JSON, CSV, HTML, SQL, Excel. Whatever your pipeline needs.

Tor Network Support — Scrape .onion sites through Tor with automatic routing.

Stealth Mode — Anti-detection features to avoid blocks.

Getting Started

git clone https://github.com/itsOwen/CyberScraper-2077
cd CyberScraper-2077
pip install -r requirements.txt
streamlit run main.py

Open the web interface, paste a URL, describe what data you want, and extract.

When to Use This

Unstructured pages where traditional selectors are fragile
One-off extractions where writing a proper scraper isn’t worth it
Research across many different site formats
Competitive analysis pulling data from various sources

My Take

The cyberpunk aesthetic is fun, but the real value is the LLM extraction approach. Sites change constantly, breaking traditional scrapers. LLM-based extraction is more resilient because it understands content semantically.

For production scraping at scale, you’ll still want deterministic extractors. But for ad-hoc research and exploration, this is faster than writing custom code.

Links:

GitHub