Vision-Based AI Web Scraper — Data Extraction N8N Workflow

Web scraping has always been a tedious dance between writing complex selectors, dealing with dynamic content, and handling anti-bot measures. What if your scraper could simply look at a webpage like a human would and extract the data it sees? That's exactly what this innovative n8n workflow accomplishes using AI vision technology.

This workflow transforms how we think about data extraction by combining the visual intelligence of Google's Gemini 2.5-Pro with the reliability of traditional HTML parsing, creating a robust system that can scrape e-commerce sites, product catalogs, and structured web content with remarkable accuracy.

Prerequisites: What You'll Need to Get Started

Before diving into the workflow construction, you'll need access to several key services and tools:

• n8n Platform: Your automation playground where the magic happens
• ScrapingBee API: For capturing screenshots and HTML content (offers 1,000 free requests)
• Google Gemini API: The vision-powered brain of our operation
• Google Sheets API: For managing input URLs and storing results
• Basic understanding of n8n workflows: Helpful but not essential

Key Components: The Building Blocks

This workflow leverages several powerful n8n nodes working in harmony:

• Manual Trigger: Starts the scraping process on demand
• Google Sheets Integration: Manages URLs to scrape and stores results
• ScrapingBee HTTP Requests: Captures full-page screenshots and HTML
• Vision-based Scraping Agent: The AI-powered core using Gemini 2.5-Pro
• HTML-based Scraping Tool: Fallback system for complex extractions
• Structured Output Parser: Formats extracted data into clean JSON
• Split Out Node: Processes arrays into individual items

Step 1: Setting Up Your Data Sources

The workflow begins with a Google Sheets setup containing two essential sheets. The first sheet, "List of URLs," stores all the web addresses you want to scrape. Think of this as your scraping queue. The second sheet, "Results," will automatically populate with extracted data.

The Google Sheets node connects to your document and pulls the URL list, while the Loop Over Items node ensures each URL gets processed individually. This batch processing approach prevents overwhelming the APIs and maintains consistent results.

"The workflow processes URLs in batches, ensuring stable performance while maintaining high extraction accuracy across multiple pages."

Step 2: Capturing Visual Data with ScrapingBee

Here's where things get interesting. Instead of immediately diving into HTML parsing, the workflow first captures a full-page screenshot of each target URL using ScrapingBee's screenshot API. This visual approach mimics how humans naturally scan web pages for information.

The ScrapingBee node is configured with specific parameters including the crucial "screenshot_full_page" setting, which ensures the entire page content is captured. This comprehensive visual data becomes the primary input for our AI agent.

Step 3: AI Vision Magic with Gemini 2.5-Pro

The heart of this workflow lies in the Vision-based Scraping Agent, powered by Google's Gemini 2.5-Pro model. This isn't your typical web scraper. The AI agent receives the screenshot and a detailed system prompt instructing it to identify and extract specific data points like product titles, prices, brands, and promotional information.

What makes this approach brilliant is its flexibility. The AI can understand visual context, handle different layouts, and even identify promotional elements that traditional selectors might miss. If the vision-based extraction encounters difficulties, the agent automatically triggers the HTML-based fallback tool.

Step 4: HTML Fallback and Data Processing

The workflow includes a sophisticated fallback mechanism. When the AI agent determines that visual extraction isn't sufficient, it calls the HTML-based Scraping Tool, which retrieves the page's HTML content via ScrapingBee and converts it to markdown format for token efficiency.

This dual approach ensures high success rates. The vision-first strategy handles most cases beautifully, while the HTML fallback catches edge cases where visual extraction might struggle with complex layouts or partially visible content.

Step 5: Structured Output and Results Storage

Raw extracted data gets processed through the Structured Output Parser, which formats everything into a consistent JSON structure. For e-commerce scraping, this includes fields like product_title, product_price, product_brand, promo status, and promo_percentage.

The Split Out node then breaks down arrays into individual items, making them perfect for the final Google Sheets node to append as new rows in your results sheet. This creates a seamless flow from URL input to structured data output.

Step 6: Quality Control and Error Handling

The workflow includes robust error handling throughout. If the vision-based agent encounters issues, it continues execution rather than failing completely. This resilience ensures your scraping jobs complete even when individual pages present challenges.

The system also includes validation steps where the AI cross-checks extracted information and highlights any limitations or uncertainties in the data extraction process.

Benefits and Practical Applications

This vision-based scraping approach offers several compelling advantages over traditional methods:

• Layout Independence: Works across different website designs without custom selectors
• Visual Context Understanding: Identifies promotional elements, badges, and visual cues
• Reduced Maintenance: Less brittle than selector-based scrapers
• Intelligent Fallback: Combines vision and HTML parsing for maximum reliability
• Cost Optimization: HTML-to-markdown conversion saves API tokens

The workflow excels in various scenarios including competitive price monitoring, product catalog management, market research automation, and inventory tracking. E-commerce businesses can use it to monitor competitor pricing, while researchers can gather structured data from multiple sources without manual intervention.

Looking Forward: The Future of Intelligent Scraping

This workflow represents a significant shift toward more intelligent, adaptive web scraping. By combining AI vision capabilities with traditional parsing methods, it creates a robust system that can handle the modern web's complexity while maintaining the reliability businesses need.

The modular design makes it easy to customize for different industries and data types. Whether you're scraping real estate listings, job postings, or product catalogs, the core vision-based approach can adapt to your specific needs.

Remember to check local regulations around web scraping before implementing this workflow. With proper compliance and ethical usage, this powerful automation can transform how your organization gathers and processes web data, making previously manual tasks as simple as clicking a button.