This project offers a production-ready API for ChatGPT-powered web scraping, designed to simplify data collection from dynamic websites. It targets developers building automated research, data extraction, or AI-driven scraping workflows, providing a more flexible and maintainable alternative to traditional selector-based methods by leveraging natural language prompts.

How It Works

The system operates via a straightforward request flow: a client sends a prompt and configuration parameters (e.g., search, country_code, add_html) to the ChatGPT Scraper API. The API orchestrates optional live web searches, retrieves target page HTML, injects this context into the LLM if specified, processes the prompt using AI, and returns structured JSON data. This hybrid approach shifts the burden from brittle selector maintenance to defining extraction goals in natural language, adapting to dynamic websites without constant parsing logic updates.

Quick Start & Requirements

• Install SDK: npm install scrapingbee (JavaScript)
• Prerequisites: Requires a ScrapingBee API key (YOUR_API_KEY). Examples provided for JavaScript and Python.
• Dependencies: Node.js for JavaScript examples, Python for Python examples. No browser automation or selector logic is needed.
• Links: No specific official quick-start guides, demos, or documentation links are provided beyond the code snippets in the README.

Highlighted Details

• Data extraction defined via natural language prompts.
• Optional real-time web search for up-to-date information.
• Geotargeting for search results using country_code.
• HTML context injection (add_html) to improve extraction accuracy on specific pages.
• Outputs structured JSON, eliminating the need for manual parsing.
• Effective for websites with varying or frequently changing layouts.

Maintenance & Community

The provided README does not contain information regarding notable contributors, sponsorships, partnerships, community channels (e.g., Discord, Slack), or a public roadmap.

Licensing & Compatibility

The README does not explicitly state the project's license type or provide compatibility notes for commercial use or integration with closed-source projects.

Limitations & Caveats

Potential operational issues include invalid API keys (401), forbidden access (403), rate limiting (429), and server errors (500). Production implementations should incorporate retry logic and monitor usage. The accuracy of extracted data is dependent on the LLM's interpretation of prompts and page content.