ai-web-scraper
Extracts structured JSON data from web pages using natural language prompts and ScrapingBee's managed scraping API.
Extract structured web data using natural language prompts without writing or maintaining CSS selectors.
This repository provides Python and Node.js code blueprints for extracting structured JSON from web pages using AI prompts. It routes requests through ScrapingBee to handle dynamic rendering, proxy rotation, and anti-bot obstacles. It suits developers building automated research pipelines, market intelligence tools, or price trackers.
What it is
The tool sends target URLs and natural language extraction prompts to an AI parsing engine via ScrapingBee's API endpoint. It handles headless browser rendering, custom HTTP headers, proxy rotation, and geolocation targeting behind a single API call. Instead of writing custom HTML parsers, you define requested fields like product names, prices, or contact details to receive standardized JSON responses.
Who it's for
It suits developers, data analysts, and automation engineers who need to gather structured web data without constantly updating fragile scraping scripts. It is not ideal for teams looking for an entirely offline or local LLM scraper, as it relies directly on ScrapingBee's hosted API infrastructure. It also requires basic programming knowledge in Python or Node.js to integrate into workflows.
Key features
Natural Language Field Extraction
Define output fields such as price, author, or contact details in plain text prompts to receive formatted JSON responses.
JavaScript Rendering Support
Execute client-side JavaScript on target pages to extract dynamic content that regular HTTP requests miss.
Proxy and Geolocation Targeting
Route queries through premium proxies and specify country codes to access region-locked data.
Custom Header Cookie Control
Send custom user-agent strings, authentication cookies, and request headers to bypass access restrictions.
Multi-Language SDK Blueprint
Integrate scraping workflows quickly using provided Python and Node.js code implementations.
Up and running in 3 steps
Our honest verdict
What's great
- Eliminates the need to write and maintain fragile CSS or XPath selectors.
- Handles IP rotation, anti-bot bypass, and JavaScript rendering automatically through the API.
- Returns standardized JSON objects directly from natural language prompts.
- Includes clean code examples for Node.js, Python, and cURL integrations.
Where it falls short
- Requires an active ScrapingBee account and API key, incurring ongoing credit costs for high-volume extraction.
- Tightly coupled to ScrapingBee's hosted infrastructure rather than running self-hosted LLMs locally.
- Lacks an explicit license file in the repository, creating legal ambiguity for commercial redistribution.
- Parsing accuracy depends on prompt clarity and LLM extraction consistency.
Pricing & license
The repository code itself is open to access without stated licensing fees, but executing requests requires a ScrapingBee account. ScrapingBee offers a free trial tier, after which usage requires a paid monthly subscription based on API credit consumption.
Open remote AI-training roles
880 open remote AI-training roles, updated November 15, 2026; most pay $20–$175 an hour. Zekai lists them free; the application goes to the employer’s platform.
Alternatives
Use it, or have us set it up
FAQ
Can I run this scraper entirely offline without third-party services?
No, this repository is a client blueprint that relies on ScrapingBee's cloud API for page rendering and AI parsing.
What programming languages are supported in the repository?
The repository provides direct code examples for Python, Node.js, and raw cURL HTTP requests.
How does the scraper handle dynamic single-page applications?
Setting the render_js parameter to true instructs the underlying headless browser engine to execute client-side JavaScript before extracting data.
