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:  
   Building a Vision-Based AI Scraper with n8n, ScrapingBee, Google Sheets, and Gemini
   
   Meta Description:  
   Learn how to build an AI-powered web scraping workflow using n8n, ScrapingBee, Google Gemini, and Google Sheets. Extract product data using screenshots and HTML with fallback mechanisms.
   
   Keywords:  
   AI scraping, vision-based AI, ScrapingBee, Google Gemini, web scraping automation, n8n workflow, Google Sheets integration, e-commerce scraping, HTML scraping, screenshot scraping, LangChain AI, Gemini 1.5 Pro
   
   Third-Party APIs Used:
   
   1. ScrapingBee API – For retrieving page screenshots and HTML
   2. Google Gemini API (via PaLM SDK) – For vision-based AI data extraction
   3. Google Sheets API – For reading URLs and storing results
   
   —
   
   Article:
   
   # Build a Vision-Based AI Scraper with n8n, ScrapingBee, Google Sheets, and Google Gemini
   
   Web scraping is evolving. Traditional parsers often break with modal redesigns, dynamic content, or missing tags. But what if an AI could "see" a webpage — like a human — and extract data just from a screenshot?
   
   That's exactly what this n8n-based workflow offers. By combining a multimodal AI model (Google Gemini) with screenshot and HTML tools (ScrapingBee), we can build a workflow that scrapes e-commerce product data using both visual and semantic cues, then stores it in a Google Sheet — all without writing a line of backend code.
   
   In this article, you'll learn how this template works, what its core components are, and how to customize it for your website or dataset.
   
   ---
   
   ## 🔧 Overview of the Workflow
   
   This visual no-code workflow runs on n8n, an open-source automation tool. It performs the following key steps:
   
   1. Manually triggers the workflow or reads a list of input URLs from Google Sheets.
   2. Fetches a full-page screenshot of each target URL using ScrapingBee.
   3. Sends the image (and optionally, fallback HTML) to the Google Gemini AI for data extraction.
   4. Structures the AI output into JSON.
   5. Splits the structured array into rows.
   6. Appends data back to a Google Sheet for easy access and analysis.
   
   This setup is perfect for scraping product titles, prices, branding, promotions, or even more domain-specific attributes, thanks to AI's flexibility in interpreting visual content.
   
   ---
   
   ## 🧠 The Smarts Behind the Scraping: Vision-Based AI
   
   At the heart of this workflow is a LangChain-powered Vision-Based Scraping Agent. This intelligent agent relies on Google Gemini 1.5 Pro — a multimodal large language model that tests show frequently outperforms GPT-4-Vision for visual comprehension tasks.
   
   The agent follows a two-step logic:
   
   1. It first attempts to extract data directly from the ScrapingBee screenshot (full-page view).
   2. If the image is incomplete or the data is ambiguous (e.g., promo price not visible), the agent triggers a fallback:
      - It sends a request to a secondary tool that uses ScrapingBee to retrieve HTML.
      - The HTML is converted into lightweight Markdown (to save LLM tokens).
      - The AI then extracts structured data from that Markdown.
   
   This logic ensures you're scraping not just what's rendered, but what's meaningful — even when a site’s design makes it difficult.
   
   ---
   
   ## 📊 Google Sheets: The Friendly Front-End
   
   This workflow uses Google Sheets as both the input and output interface:
   - The input sheet (“List of URLs”) contains the pages you wish to scrape.
   - The output sheet (“Results”) captures structured data fields like product name, price, brand, promotional status, and discount percentage.
   
   You don’t need to manually map columns — the Structured Output Parser ensures all values are cast into the exact JSON format expected by the Google Sheets node.
   
   Plus, if your scraping needs change (e.g., scraping from auto listings instead of retail sites), just update the schema for JSON parsing in the Structured Output Parser.
   
   ---
   
   ## 🕵️ ScrapingBee: Eyes and Ears for the AI
   
   ScrapingBee powers two crucial parts of the scraping process:
   
   1. **Screenshot Capture**: It takes full-page screenshots of each URL with realistic render settings and user agents. This gives the AI rich, high-fidelity views, just like how a human sees them.
      
   2. **HTML Retrieval**: It also fetches full HTML code (optionally triggered by the AI), which is vital when visual data isn’t enough. This fallback HTML is converted into Markdown to optimize it for Gemini.
   
   Unlike brittle traditional headless browser scripts, this approach is lightning-fast, less error-prone, and scalable.
   
   ---
   
   ## 🧩 Built-in Redundancy: Smart Fallbacks When Screenshots Aren’t Enough
   
   Visual data isn't always reliable — sometimes content loads offscreen or beyond the viewport. That’s why the workflow is designed with intelligence:
   
   - If product cards are cut off, AI will note that.
   - If key details are missing visually, it switches to HTML parsing without human prompt.
   - If both sources are inconsistent, AI merges both for better validity.
   
   This blend of context-aware AI decision-making with conditional tool invocation ensures resilience and accuracy at scale.
   
   ---
   
   ## 💡 Use Cases and Adaptation
   
   While this template is optimized for e-commerce data (e.g., product listings, brands, prices), it can easily be extended to:
   
   - Job board aggregators (titles, companies, salaries)
   - Real estate listings (address, price, availability)
   - Event pages (dates, organizers, ticket tier)
   
   All you need to do is tweak the Structured Output Parser and system message instructions for the AI Agent.
   
   ---
   
   ## ⚖️ Legal Note
   
   This workflow performs automated scraping. Always ensure you're in compliance with local laws and the target website's terms of service. In some jurisdictions or contexts, scraping without consent (even with AI) can have legal implications.
   
   ---
   
   ## 🚀 Conclusion
   
   This n8n workflow shows how traditional data scraping can be reimagined with powerful visual intelligence. By tapping into tools like Google Gemini, ScrapingBee, and a carefully configured LangChain agent, you can automate data collection with minimal manual intervention — and scale it securely via Google Sheets.
   
   Whether you're a no-coder, data scientist, or AI enthusiast, this template offers a glimpse into what scraping 2.0 looks like — one that doesn’t blindly parse HTML but understands what’s in front of it.
   
   ---
   
   Ready to try it yourself? You can use this pre-made [example Google Sheet](https://docs.google.com/spreadsheets/d/10Gc7ooUeTBbOOE6bgdNe5vSKRkkcAamonsFSjFevkOE/) and start scraping like a machine — with eyes.
   
   — 
   
   End of article.

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.