This article provides a complete, practical walkthrough of the Splitout Manual Automation Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   Revolutionizing E-Commerce Scraping with AI: Vision-Based Data Extraction Using n8n, Google Sheets, ScrapingBee, and Gemini
   
   Meta Description:  
   Learn how to build a powerful vision-based web scraper using n8n, Google Sheets, ScrapingBee, and Gemini-1.5-Pro. This AI-enhanced workflow extracts structured data from e-commerce websites using screenshots and intelligent fallback mechanisms.
   
   Keywords:  
   AI scraping workflow, vision-based scraper, n8n automation, ScrapingBee, Google Sheets API, Gemini 1.5 Pro, e-commerce data extraction, HTML scraping, structured data from websites, visual AI agent
   
   Third-Party APIs Used:
   
   1. Google Sheets API  
   2. ScrapingBee API  
   3. Google Gemini (PaLM) Language Model API  
   
   —
   
   Article:
   
   Revolutionizing E-Commerce Scraping with AI: Vision-Based Data Extraction Using n8n, Google Sheets, ScrapingBee, and Gemini
   
   In the ever-evolving landscape of web scraping and data automation, traditional HTML-based extraction methods are hitting roadblocks—especially with modern, visually dynamic web content. That’s why a vision-based approach, powered by AI, is gaining traction. Enter the "Vision-Based AI Agent Scraper," an n8n-powered workflow designed to extract structured data—like product titles, prices, and promotions—from web pages using full-page screenshots.
   
   This article walks you through a game-changing automation template that combines the flexibility of n8n with powerful third-party services like ScrapingBee, Google Sheets, and Google Gemini-1.5-Pro.
   
   What Is This Workflow?
   
   At its core, the "Vision-Based AI Agent Scraper" is a low-code automation built using n8n. It enables users to pull product data from a list of e-commerce URLs and log that information directly into a Google Sheets spreadsheet. What sets this workflow apart is its use of a vision-based AI agent that examines visual screenshots—not just HTML—to identify and extract relevant data. If the vision fails, the system automatically switches to an HTML-based fallback extraction.
   
   Think of it as a hybrid scraping bot with eyes—and a brain.
   
   Key Components & How They Work
   
   1. Google Sheets for URL Management and Data Storage  
   The process begins with a list of URLs stored in a Google Sheet. Another sheet titled "Results" will store the output: structured product data including title, price, brand, and promotional information. The Google Sheets API is used to read input URLs and write scraped data.
   
   2. ScrapingBee for Screenshots and Fallback HTML  
   ScrapingBee, a high-performance scraping API, grabs a full-page screenshot for each URL. Why screenshots? Because visual layouts often contain data that’s hard to parse through HTML alone—especially on dynamic or JavaScript-heavy pages. If needed, ScrapingBee can also return raw HTML to power a backup scraping mechanism.
   
   3. Gemini 1.5 Pro: The Vision-Based AI Agent  
   At the heart of this system is the Google Gemini-1.5-Pro model. It serves as the AI agent that interprets screenshots to extract relevant information. Gemini reads the image and, based on a system prompt, identifies product-related information. Think of this as similar to what a human would do when browsing a product page—only faster and scalable.
   
   If the model encounters challenges—like unclear text or cropped areas—it automatically calls another node to retrieve the HTML and continue parsing.
   
   4. Structured Output Parser  
   After the AI agent has done its job, structured information is passed into a parser that formats it in JSON. Fields include product_title, product_price, product_brand, promo (true/false), and promo_percentage. This parsed output ensures consistency and accuracy when the data is logged into the Google Sheet.
   
   5. Intelligent Switching: Vision First, HTML Second  
   The AI is smart enough to recognize when it needs help. If the screenshot doesn't offer enough clarity, it calls an HTML-based tool sub-workflow as a fallback. That submodule triggers ScrapingBee to return the HTML content, which is then converted into Markdown to reduce API token usage and parsed again by the AI agent.
   
   6. Data Storage  
   Finally, the structured and parsed data is sent back to Google Sheets, where each row is appended automatically to the “Results” sheet. This closes the loop on a system capable of running daily, manually, or via trigger, depending on your configuration.
   
   Customization & Use Cases
   
   While this workflow is pre-configured for e-commerce sites, its modular nature allows for customization. You could easily adapt it for:
   
   - Real estate listings
   - Product comparison databases
   - Social media profile scraping
   - Job board aggregation
   
   Just update the Structured Output Parser’s JSON schema and adjust the AI prompts accordingly.
   
   Why It Matters
   
   This workflow redefines what’s possible with data scraping. The hybrid approach—focusing on computer vision first, and using HTML as backup—mirrors human browsing behavior more closely than any traditional scraper.
   
   Key advantages include:
   
   - Works on dynamic websites where HTML is obfuscated
   - Captures visually presented information like sales badges and promo banners
   - Automatically adjusts when visual input isn't sufficient
   - Reduces token usage through Markdown conversion of HTML
   
   Bonus: This approach complies better with websites preventing automated HTML scrapers since screenshots mimic real user behavior more effectively.
   
   Legal and Ethical Note:  
   As with any scraping workflow, make sure to check the legal regulations in your jurisdiction. Always respect websites’ terms of service and use scraping tools ethically.
   
   Conclusion
   
   The Vision-Based AI Agent Scraper showcases the future of data automation—a smart, hybrid system capable of interpreting webpages the way a human would, but at machine scale. Powered by n8n’s flexible no-code environment and cutting-edge AI models like Gemini-1.5-Pro, this workflow makes it easier than ever to turn unstructured content into clean, actionable data.
   
   Whether you're building a product aggregator, price tracker, or just experimenting with AI-driven automation, this is a great foundation to start with.
   
   ⏩ Try it. Customize it. Scale it. Welcome to smarter scraping.
   
   —
   
   If you're ready to bring automation into the visual era, this is your blueprint.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.