Trigger

The default trigger is When clicking ‘Test workflow’, meaning the workflow will need to be triggered manually.

You can replace this by selecting a trigger of your choice.

Google Sheets - List of URLs

The Google Sheet will contain two sheets:

• List of URLs to scrape
• Results page, populated with the scraping results and AI-extracted data.

Here is an example Google Sheet you can use. The "Results" sheet is pre-configured for e-commerce website scraping. You can adapt it to your specific needs, but remember to adjust the Structured Output Parser node accordingly.

Set Fields

This node allows you to define the fields that will be sent to the ScrapingBee HTTP Node and the AI Agent.

In this template, only one field is pre-configured: url. You can customize it by adding additional fields as needed.

ScrapingBee - Get Page Screenshot

This node uses ScrapingBee, a powerful scraping tool, to capture a screenshot of the desired URL. You can try ScrapingBee and enjoy 1,000 free requests (non-affiliate link).

Ensure the screenshot_full_page parameter is set to true for a full-page screenshot. This is crucial for vision-based scraping with the AI Agent.

Alternatively, you can choose to screenshot only a specific part of the page. However, keep in mind that the AI Agent will extract data only from the visible section—it has vision, but not a crystal ball 🔮!

Vision-Based Scraping AI Agent

This is the central node of the workflow, powered by an AI Agent with two key prompts:

• System Prompt: Instructs the AI on how and what data to extract from the screenshot. You can customize this to suit your needs. It also includes fallback instructions to call a tool for retrieving the HTML page if data extraction from the screenshot fails.
• User Message: Provides the page URL for context.

Sub-Nodes

1. Google Gemini Chat Model Chosen because tests show that Gemini-1.5-Pro outperforms GPT-4 and GPT-4-Vision in visual tasks. Either my prompt wasn’t optimized for GPT models, or GPT might need glasses 👓. Other multimodal LLMs haven’t been tested yet.

2. HTML-Based Scraping Tool A fallback tool the agent uses if it cannot extract data directly from the screenshot.

3. Structured Output Parser Formats the extracted data into an easy-to-use structure, ready to be added to the results page in Google Sheets.

Google Gemini Chat Model

The default model is gemini-1.5-pro. It offers excellent performance for this use case, but it’s not the most cost-effective option—use it judiciously.

HTML-Based Scraping Tool

This tool is invoked when the AI Agent requires the HTML (converted to Markdown) to extract data because the screenshot alone wasn’t sufficient.

Structured Output Parser

This node organizes the extracted data into an easy-to-use JSON format.

In this template, the JSON is designed for an e-commerce webpage. Customize it to fit your specific needs.

Split Out

This node splits the array created by the Structured Output Parser into individual rows, making them easy to append to the subsequent Google Sheets node.

Google Sheets - Create Rows

This node creates rows in the Results sheet using the extracted data.

You can use the example Google Sheet as a template. However, ensure that the columns in the Results sheet are aligned with the structure of the output from the Structured Output Parser node.

HTML-Scraping Tool Trigger

This **node serves as the entry point for the HTML scraping tool.

It is triggered by the AI Agent only when it fails to extract data from the screenshot. The URL is sent as a parameter for the query.

Set Fields - From AI Agent Query

This node sets the fields from the AI Agent’s query.

In this template, the only field configured is url.

ScrapingBee - Get Page HTML

This node utilizes the ScrapingBee API to retrieve the HTML of the webpage.

HTML to Markdown

This node converts the HTML from the previous node into Markdown format, helping to save tokens.

The converted Markdown is then automatically sent to the AI Agent through this node.

✨ Vision-Based AI Agent Scraper - with Google Sheets, ScrapingBee, and Gemini

Important notes :

Check legal regulations:

This workflow involves scraping, so make sure to check the legal regulations around scraping in your country before getting started. Better safe than sorry!

Workflow description

This workflow leverages a vision-based AI Agent, integrated with Google Sheets, ScrapingBee, and the Gemini-1.5-Pro model, to extract structured data from webpages. The AI Agent primarily uses screenshots for data extraction but switches to HTML scraping when necessary, ensuring high accuracy.

Key features include:

• Google Sheets Integration: Manage URLs to scrape and store structured results.
• ScrapingBee: Capture full-page screenshots and retrieve HTML data for fallback extraction.
• AI-Powered Data Parsing: Use Gemini-1.5-Pro for vision-based scraping and a Structured Output Parser to format extracted data into JSON.
• Token Efficiency: HTML is converted to Markdown to optimize processing costs.

This template is designed for e-commerce scraping but can be customized for various use cases.

Vision-Based AI Agent Scraper - with Google Sheets, ScrapingBee, and Gemini

Purpose and structure

This folder contains the Vision-Based AI Agent Scraper - with Google Sheets, ScrapingBee, and Gemini workflow. Its executable nodes use agent, executeWorkflowTrigger, googleSheets, httpRequest, lmChatGoogleGemini, manualTrigger, markdown, outputParserStructured, set, splitOut, toolWorkflow. The table and connection map below are derived from the JSON, not an inferred guarantee of behavior.

Setup

Import inactive. Configure your own credentials and replace REPLACE_WITH_* values. Review external destinations and Code nodes before running.

Entry points

When clicking ‘Test workflow’, HTML-Scraping Tool

Verification

Sanitized source candidate; not runtime-tested. This overview is generated from node metadata. Read the folder README for the connection map, credential types, and license.

Attribution

https://github.com/Zie619/n8n-workflows/blob/94007c1445d9258a7da116646b79473e7c7c3282/workflows/Splitout/2041_Splitout_Manual_Automation_Webhook.json MIT; preserve source notices.