Workflow Templates

Extract Title tag and meta description from url for SEO analysis. How it works The workflows takes records from Airtable, get the url in the records and extract from the related webpage the title tag (<title>) and meta description (<meta name="description" content="Some content">). If title tag and/or meta description tag isn't available on the webpage, the result will be empty. Setup Set a Base in Airtable with a table with the following structure: url (field type url), title tag (field type text string), meta desc (field type text field) Minimum suggested table structure is: url (https://example.com), title (Title example), meta desc* (This is the meta description of the example page) Connect Airtable to both Airtable nodes in the template and, with the following formula, get all the records that miss title tag and meta desc. Formula: AND(url != "", {title tag} = "", {meta desc} = "") Insert the url to be analyzed in the table in the field url and let the workflow do the rest. Extra You can also calculate the length for title tag and meta desc using formula field inside Airtable. This is the formula: LEN({title tag}) or LEN({meta desc}) You can automate the process calling a Webhook from Airtable. For this, you need an Airtable paid plan.

This n8n template demonstrates how to create a comprehensive voice-powered restaurant assistant that handles table reservations, food orders, and restaurant information requests through natural language processing. The system uses VAPI for voice interaction and PostgreSQL for data management, making it perfect for restaurants looking to automate customer service with voice AI technology. Good to know Voice processing requires active VAPI subscription with per-minute billing Database operations are handled in real-time with immediate confirmations The system can handle multiple simultaneous voice requests All customer data is stored securely in PostgreSQL with proper indexing How it works Table Booking & Order Handling Workflow Voice requests are captured through VAPI triggers when customers make booking or ordering requests The system processes natural language commands and extracts relevant details (party size, time, food items) Customer data is immediately saved to the bookings and orders tables in PostgreSQL Voice confirmations are sent back through VAPI with booking details and estimated wait times All transactions are logged with timestamps for restaurant management tracking Restaurant Info Provider Workflow Info requests trigger when customers ask about hours, menu, location, or services Restaurant details are retrieved from the restaurant_info table containing current information Wait nodes ensure proper data loading before voice response generation Structured restaurant information is delivered via VAPI in natural, conversational format Database Schema Bookings Table booking_id (PRIMARY KEY) - Unique identifier for each reservation customer_name - Customer's full name phone_number - Contact number for confirmation party_size - Number of guests booking_date - Requested reservation date booking_time - Requested time slot special_requests - Dietary restrictions or special occasions status - Booking status (confirmed, pending, cancelled) created_at - Timestamp of booking creation Orders Table order_id (PRIMARY KEY) - Unique order identifier customer_name - Customer's name phone_number - Contact for order updates order_items - JSON array of food items and quantities total_amount - Calculated order total order_type - Delivery, pickup, or dine-in special_instructions - Cooking preferences or allergies status - Order status (received, preparing, ready, delivered) created_at - Order timestamp Restaurant_Info Table info_id (PRIMARY KEY) - Information entry identifier category - Type of info (hours, menu, location, contact) title - Information title description - Detailed information content is_active - Whether info is currently valid updated_at - Last modification timestamp How to use The manual trigger can be replaced with webhook triggers for integration with existing restaurant systems Import the workflow into your n8n instance and configure VAPI credentials Set up PostgreSQL database with the required tables using the schema provided above Configure restaurant information in the restaurant_info table Test voice commands such as "Book a table for 4 people at 7 PM" or "What are your opening hours?" Customize voice responses in VAPI nodes to match your restaurant's tone and branding The system can handle multiple concurrent voice requests and scales with your restaurant's needs Requirements VAPI account for voice processing and natural language understanding PostgreSQL database for storing booking, order, and restaurant information n8n instance with database and VAPI integrations enabled Customising this workflow Voice AI automation can be adapted for various restaurant types - from quick service to fine dining establishments Try popular use-cases such as multi-location booking management, dietary restriction handling, or integration with existing POS systems The workflow can be extended to include payment processing, SMS notifications, and third-party delivery platform integration

Who Is This For? This workflow is perfect for content creators, marketers, and business professionals who receive regular newsletters and want to effortlessly convert them into engaging LinkedIn posts. By automating the extraction and repurposing process, you can save time and consistently share thoughtful updates with your network. What Problem Does This Workflow Solve? Manually reading newsletters, extracting the key points, and then formatting that content into professional, engaging LinkedIn posts can be time-consuming and error-prone. This workflow automates those steps by: Filtering Emails:** Uses the Gmail node to process only those emails from a specific sender (e.g., newsletter@example.com). Extracting Content:** Leverages OpenAI to identify and summarize the top news items in your newsletter. Generating Posts:** Crafts concise, insightful LinkedIn posts in a smart, deadpan style with a touch of subtle humor. Publishing:** Posts the generated content directly to LinkedIn. What This Workflow Does Filter Newsletters:** The Gmail node is set up to only handle emails from your chosen sender, ensuring that only relevant newsletters are processed. Extract Key Content:** An OpenAI node analyzes the newsletter text to pull out the most important news items, including headlines and summaries. Split Content:** A Split Out node divides the extracted content so each news item is processed on its own. Generate LinkedIn Posts:** Another OpenAI node takes each news item's details and produces a well-structured LinkedIn post that delivers practical insights and ends with a reflective observation or question. Publish to LinkedIn:** The LinkedIn node publishes the crafted posts directly to your account. Setup Gmail Node: Rename it to “Filter Gmail Newsletter” and configure it to filter emails by your newsletter sender. OpenAI Nodes: Ensure your OpenAI API credentials are set up correctly. Customize the prompt if needed to match your desired tone. LinkedIn Node: Rename it to “Post to LinkedIn” and confirm that your LinkedIn OAuth2 credentials are properly configured. How to Customize OpenAI Prompts:** Adjust the prompts in the OpenAI nodes to fine-tune the post tone and output formatting. Email Filter:** Change the Gmail filter to match the sender of your newsletters. Post Processing:** Optionally, add extra formatting (using Function nodes) to further enhance the readability of the generated LinkedIn posts. This template offers an automated, hands-off solution to transform your newsletter content into engaging LinkedIn updates, keeping your audience informed and inspired with minimal effort.

Google Sheets UI for Workflow Control This n8n template provides a practical and efficient way to manage your n8n workflows using Google Sheets as a user-friendly interface. It demonstrates how to leverage a simple spreadsheet to control inputs, capture outputs, and track the processing status of individual data rows, offering a clear and visual overview of your automation tasks. Purpose of This Template: The primary purpose of this template is to illustrate how Google Sheets can serve as a dynamic UI for your n8n automations. It's designed for n8n users who need: A structured method to feed specific data into their workflows. The ability to selectively trigger workflow execution based on data status. A centralized place to view and store workflow outputs alongside original inputs. A simple, no-code solution for managing workflow data without building custom applications. Setup Instructions: To use this template, follow these steps: Create a Google Sheet: Set up a new Google Sheet (see the template here) with three columns: Color, Status, and Number. Populate the Color column with some sample data (e.g., color names) and set the Status for the rows you want to process to READY. Import the n8n Workflow: Import this n8n template into your n8n instance. Configure Google Sheets Nodes: For the first Google Sheets node (Read operation), ensure it's connected to your newly created Google Sheet and configured to read rows where the Status column is READY. You will need to authenticate your Google Sheets account. For the second Google Sheets node (Update operation), ensure it's also connected to the same Google Sheet. The node should automatically map the row_number, Number, and Status fields from the preceding nodes. Execute the Workflow: Run the workflow. Observe how it reads READY rows, processes them (calculates string length), and updates the Number and Status columns in your Google Sheet to DONE. Control Execution: To process new data, simply add new rows to your Google Sheet and set their Status to READY. Rerunning the workflow will then only process these new entries. For more details and context on this approach, you can refer to the related blog post here.

Who is this tempate for? This workflow powers a simple yet effective customer and sales support chatbot for your webshop. It's perfect for solopreneurs who want to automate customer interactions without relying on expensive or complex support tools. How it works? The chatbot listens to user requests—such as checking product availability—and automatically handles the following Fetches product information from a Google Sheet Answers customer queries Places an order Updates the stock after a successful purchase Everything runs through a single Google Sheet used for both stock tracking and order management. Setup Instructions Before you begin, connect your Google Sheets credentials by following this guide: This will be used to connect all the tools to Google Sheets 👉 Setup Google sheets credentials Get Stock Open "Get Stock" tool node and select the Google sheet credentials you created. Choose the correct google sheet document and sheet name and you are done. Place order Go to your "Place Order" tool node and select the Google sheet credentials you have created. Choose the correct google sheet document and sheet name. Update Stock - Open your "Update Stock" tool node and select the Google sheet credentials you have created. Choose the correct google sheet document and sheet name. In "Mapping Column Mode" section select map each column manually. In "Column to match on" select the column with a unique identifier (e.g., Product ID) to match stock items. In values to update section, add only the column(s) that need to be updated—usually the stock count. AI Agent node Adjust the prompt according to your use case and customize what you need. Google Sheet Template Stock sheet |Case ID|Phone Model|Case Name|Case Type|Image URL|Quantity Avaialble|Initital Inventory|Sold| |-|-|-|-|-|-|-|-| |1023|Iphone 14 pro|Black Leather|Magsafe|https://example.com/url|90|100|10 Order sheet |Case ID|Phone Model|Case Name|Name|Phone Number|Address| |-|-|-|-|-|-| |1023|Black Leather |Iphone 14 pro|Fernando Torres|9998898888|Paris, France

🧩 Workflow: Process Tour PDF from Google Drive to Pinecone Vector DB with OpenAI Embeddings Overview This workflow automates the process of extracting tour information from PDF files stored in a Google Drive folder, processes and vectorizes the extracted data, and stores it in a Pinecone vector database for efficient querying. This is especially useful for building AI-powered search or recommendation systems for travel packages. Setup: Prerequisites A folder in Google Drive with PDF tour package brochures. Pinecone account + API key OpenAI API key n8n cloud or self-hosted instance Workflow Setup Steps Trigger Manual Trigger (When clicking 'Test workflow'): Used for manual testing and execution of the workflow. Google Drive Integration Step 1: Store Tour Packages in PDF Format Upload your curated tour packages containing the tours, activities and sight-seeings in PDF format into a designated Google Drive folder. Step 2: Search Folder Node: PDF Tour Package Folder (Google Drive) This node searches the designated folder for files (filter by MIME type = application/pdf if needed). Step 3: Download PDFs Node: Download Package Files (Google Drive) Downloads each matching PDF file found in the previous step. Process Each PDF File Step 4: Loop Through Files Node: Loop Over each PDF file Iterates through each downloaded PDF file to extract, clean, split, and embed. Data Preparation & Embedding Step 5: Data Loader Node: Data Loader Reads each PDF’s content using a compatible loader. It passes clean raw text to the next node. Often integrated with document loaders like pdf-loader, Unstructured, or pdfplumber. Step 6: Recursive Text Splitter Node: Recursive Character Text Splitter Splits large chunks of text into manageable segments using overlapping window logic (e.g., 500 tokens with 50 token overlap). This ensures contextual preservation for long documents during embedding. Step 7: Generate Embeddings Node: Embeddings OpenAI Uses text-embedding-3-small model to vectorize the split chunks. Outputs vector representations for each content chunk. Store in Pinecone Step 8: Pinecone Vector Store Node: Pinecone Vector Store - Store... Stores each embedding along with its metadata (source PDF name, chunk ID, etc.). This becomes the basis for fast, semantic search via RAG workflows or agents. 🛠️ Tools & Nodes Used Google Drive (Search & Download) Searches for all PDF files in a specified Google Drive folder. Downloads each file for processing. SplitInBatches (Loop Over Items) Loops through each file found in the folder, ensuring each is processed individually. Default Data Loader (LangChain) Reads and extracts text from the PDF files. Recursive Character Text Splitter (LangChain) Splits the extracted text into manageable chunks for embedding. OpenAI Embeddings (LangChain) Converts each text chunk into a vector using OpenAI’s embedding model. Pinecone Vector Store (LangChain) Stores the resulting vectors in a Pinecone index for fast similarity search and querying. 🔗 Workflow Steps Explained Trigger: The workflow starts manually for testing or can be scheduled. Google Drive Search: Finds all PDF files in the specified folder. Loop Over Files: Each file is processed one at a time using the SplitInBatches node. Download File: Downloads the current PDF file from Google Drive. Extract Text: The Default Data Loader node reads the PDF and extracts its text content. *Text Splitting: * The Recursive Character Text Splitter breaks the text into chunks (e.g., 1000 characters with 50 overlap) to optimize embedding quality. **Vectorization: **Each chunk is sent to the OpenAI Embeddings node to generate vector representations. Store in Pinecone: The vectors are inserted into a Pinecone index, making them available for semantic search and recommendations. 🚀 What Can Be Improved in the Next Version? *Error Handling: * Add error handling nodes to manage failed downloads or extraction issues gracefully. File Type Filtering: Ensure only PDF files are processed by adding a filter node. Metadata Storage: Store additional metadata (e.g., file name, tour ID) alongside vectors in Pinecone for richer search results. *Parallel Processing: * Optimize for large folders by processing multiple files in parallel (with care for API rate limits). Automated Triggers: Replace manual trigger with a time-based or webhook trigger for full automation. Data Validation: Add checks to ensure extracted text contains valid tour data before vectorization. User Feedback: Integrate notifications (e.g., email or Slack) to inform when processing is complete or if issues arise. 💡 Summary This workflow demonstrates how n8n can orchestrate a powerful AI data pipeline using Google Drive, LangChain, OpenAI, and Pinecone. It’s a great foundation for building intelligent search or recommendation features for travel and tour data. Feel free to ask for more details or share your improvements! Let me know if you want to see a specific part of the workflow or need help with a particular node!

📸 Instagram Full Profile Scraper with Apify and Google Sheets This n8n workflow automates the process of scraping full Instagram profiles using a custom Apify actor, and logs the results into a Google Sheet. It is designed to run at scheduled intervals and process a list of usernames by calling the API, appending the results, and marking them as processed. 🚀 Features ⏱ Scheduled Execution – Runs automatically every few minutes. 📄 Google Sheets Integration – Reads a list of Instagram usernames and updates the same sheet. 🧠 Apify Actor – Fetches full public Instagram profile data. 🧮 Aggregation – Batches usernames for bulk scraping. ✍️ Data Logging – Appends scraped data to a second sheet. ✅ Tracking – Marks usernames as processed once scraped. 📁 Workflow Structure graph TD; ScheduleTrigger --> GetUsernames; GetUsernames --> LimitItems; LimitItems --> AggregateUsernames; AggregateUsernames --> CallApifyActor; CallApifyActor --> AppendToSheet; CallApifyActor --> MarkAsScraped; 🛠 Setup Google Sheet Create a Google Sheet with: Sheet 1 named Usernames (GID: 0) Columns: username, scraped Sheet 2 named fullprofiles (GID: 458127000) Sample sheet: 🔗 Instagram Profile Sheet n8n Configuration Import this workflow into your n8n instance. Set up your Google Sheets credentials (googleSheetsOAuth2Api). Replace apify_api_your token in the HTTP Request node with your Apify API token. 📦 Required Credentials Google Sheets OAuth2** – For reading and writing sheet data. Apify API Token** – To call the custom actor for profile scraping. 📊 Sheets Used | Sheet Name | Purpose | | -------------- | -------------------------------- | | Usernames | Source of usernames to scrape | | fullprofiles | Destination of full profile data | 📌 Apify Actor Info > Instagram Full Profile Scraper > This actor fetches extended profile information from public Instagram profiles. 🔗 View on Apify 🔁 Workflow Nodes Overview | Node | Purpose | | ------------------------ | ----------------------------------------------------------------- | | Schedule Trigger | Triggers the workflow periodically. | | Get Usernames | Reads usernames from the Usernames sheet. | | Limit | Limits processing to 20 usernames per run. | | Aggregate | Groups usernames into a batch for the API call. | | Call Apify Actor | Sends the usernames to the Apify actor and receives profile data. | | Append Full Profiles | Appends the scraped data to the fullprofiles sheet. | | Mark Username as Scraped | Marks the processed usernames as scraped = TRUE. | | Sticky Note | Provides a reference link to the Apify actor used. | 📌 Example Sheet Structure Usernames Sheet | username | scraped | | ------------ | ------- | | exampleuser1 | | | exampleuser2 | TRUE | fullprofiles Sheet | username | full\_name | biography | follower\_count | ... | | -------- | ---------- | --------- | --------------- | --- | 🔐 Security & Notes This workflow does not bypass any Instagram privacy restrictions. It works only with public Instagram profiles. You are responsible for ensuring that scraping complies with Instagram’s terms of service and any applicable laws. 📬 Support For any issues, feel free to reach out: 👤 @mohamedgb00714 📧 mohamedgb00714@gmail.com

I am submitting this workflow for the Obsidian community to showcase the potential of integrating Obsidian with n8n. While straightforward, it serves as a compelling demonstration of the potential unlocked by integrating Obsidian with n8n. How it works This workflow lets you retrieve specific Airtable data you need in seconds, directly within your Obsidian note, using n8n. By highlighting a question in Obsidian and sending it to a webhook via the Post Webhook Plugin, you can fetch specific data from your Airtable base and instantly insert the response back into your note. The workflow leverages OpenAI’s GPT model to interpret your query, extract relevant data from Airtable, and format the result for seamless integration into your note. Set up steps Install the Post Webhook Plugin: Add this plugin to your Obsidian vault from the plugin store or GitHub. Set up the n8n Webhook: Copy the webhook URL generated in this workflow and insert it into the Post Webhook Plugin's settings in Obsidian. Configure Airtable Access: Link your Airtable account and specify the desired base and table to pull data from. Test the Workflow: Highlight a question in your Obsidian note, use the “Send Selection to Webhook” command, and verify that data is returned as expected.

Send structured logs to BetterStack from any workflow using HTTP Request Who is this for? This workflow is perfect for automation builders, developers, and DevOps teams using n8n who want to send structured log messages to BetterStack Logs. Whether you're monitoring mission-critical workflows or simply want centralized visibility into process execution, this reusable log template makes integration easy. What problem is this workflow solving? Logging failures or events across multiple workflows typically requires duplicated logic. This workflow solves that by acting as a shared log sender, letting you forward consistent log entries from any other workflow using the Execute Workflow node. What this workflow does Accepts level (e.g., "info", "warn", "error") and message fields via Execute Workflow Trigger Sends the structured log to your BetterStack ingestion endpoint via HTTP Request Uses HTTP Header Auth for secure delivery Includes a manual trigger for testing and a sample call to demonstrate usage Comes with clear sticky notes to help you get started Setup Copy your BetterStack Logs ingestion URL. Create a Header Auth credential in n8n with your Authorization: Bearer YOUR_API_KEY. Replace the URL in the HTTP Request node with your BetterStack endpoint. Optionally modify the test data or log levels for custom scenarios. Use Execute Workflow in any of your workflows to send logs here.

This n8n template demonstrates how to create an automated customer feedback collection system for restaurants. The workflow triggers when new customer emails are added to an Excel sheet, automatically sends personalized feedback forms, and stores all responses in a separate Excel tracking sheet. Perfect for restaurants wanting to systematically gather customer insights and improve service quality. Good to know Each feedback form is personalized with the customer's name and email All responses are automatically timestamped and organized in Excel sheets The system handles form validation and ensures complete data capture Email notifications keep your team updated on new feedback submissions How it works Email Distribution Workflow New customer entries are detected in Excel Sheet-1 (customer database) containing customer names and email addresses The system automatically generates personalized feedback forms for each new customer Customized feedback emails are sent with embedded forms tailored to restaurant experience evaluation Wait nodes ensure proper processing timing before sending emails Feedback Collection Workflow Customer form submissions trigger the data collection process All feedback responses are captured including ratings, comments, and contact information Data is automatically appended to Excel Sheet-2 (feedback responses) with complete timestamps The system handles multiple concurrent submissions without data loss Excel Sheet Structure Sheet-1 (Customer Database) Name - Customer's full name Email - Customer's email address for form distribution Sheet-2 (Feedback Responses) Timestamp - Date and time of form submission Name - Customer's full name E-Mail - Customer's email address Contact Number - Customer's phone number How was the cleanliness of the dining area? - Cleanliness rating/feedback Did you like the taste of the food? - Food taste evaluation What dish did you enjoy the most? - Favorite dish identification Was your order accurate and timely? - Service accuracy rating Was our staff polite and helpful? - Staff service evaluation Was the food presentation appealing? - Food presentation rating How would you rate your overall dining experience? - Overall experience score Any additional comments or suggestions? - Open-ended feedback field How to use Import the workflow into your n8n instance and configure Excel integration Set up Sheet-1 with customer names and emails for feedback distribution Configure the feedback form with your restaurant's specific questions and branding Add new customer entries to Sheet-1 to automatically trigger feedback emails Monitor Sheet-2 for incoming responses and analyze customer satisfaction trends The system scales automatically with your customer database growth Requirements Google Sheets account for data storage and management Email service integration (Gmail, SMTP, or similar) n8n instance with Google Sheets and email connectors Customising this workflow Customer feedback automation can be adapted for different restaurant types and service models Try popular use-cases such as post-dining follow-ups, seasonal menu feedback, or special event evaluations The workflow can be extended to include automated response analysis, sentiment scoring, and management dashboard integration

What this workflow does This workflow automatically turns new technical video uploads into short, engaging Facebook post drafts—complete with a suggested image—and saves the results to Google Sheets for quick review or publishing. It’s designed to help you repurpose tutorial or demo videos into ready-to-use social content without any manual writing or design effort. What problem is this workflow solving? Manually writing Facebook posts for every new tutorial or product video takes time, especially when you want them to be engaging and consistent. This workflow solves that by using AI to watch for new videos, extract meaningful insights, and write posts and create visuals automatically—saving hours of work. Who is this for? This workflow is ideal for: Content creators uploading tutorial videos Marketing teams working with how-to or product videos Agencies and automation pros building scalable social workflows for clients How it works Trigger: Starts when a new video is uploaded to a specific Google Drive folder. Download & Convert: Downloads the video and converts it to base64. Extract Insights: Dumpling AI analyzes the video and extracts structured insights such as topic, tools mentioned, and key steps. Generate Post: GPT-4o creates a short, friendly Facebook post using those insights, along with an image prompt. Create Visual: Dumpling AI generates an image using the prompt. Save to Sheet: The Facebook post and image URL are saved to a Google Sheet. Setup Create a Google Sheet to store the posts and images. Connect your Google Drive, Google Sheets, Dumpling AI, and OpenAI credentials in n8n. Update the workflow with: Your Google Drive folder ID Your target Google Sheet ID (Optional) Edit the prompt used in the GPT node if you want a different tone, style, or structure for the post. How to customize the workflow Change the platform**: Replace “Facebook” in the prompt with LinkedIn, Instagram, or another platform. Use a different image tool**: You can swap Dumpling AI for any other image generation API (e.g. DALL·E, Midjourney via webhook). Add auto-publishing**: Add a Facebook or social media module to publish the generated post directly instead of just saving to Google Sheets. Tag videos by content type**: Use AI to classify videos into categories and store them in separate tabs or sheets.

This n8n workflow automates the process of saving web articles or links shared in a chat conversation directly into a Notion database, using Google's Gemini AI and Browserless for web scraping. Who is this AI automation template for? It's useful for anyone wanting to reduce manual copy-pasting and organize web findings seamlessly within Notion. A smarter web clipping tool! What this AI automation workflow does Starts when a message is received Uses a Google Gemini AI Agent node to understand the context and manage the subsequent steps. It identifies if a message contains a request to save an article/link. If a URL is detected, it utilizes a tool configured with the Browserless API (via the HTTP Request node) to scrape the content of the web page. Creates a new page in a specified Notion database, populating it with thea summary scraped content, in a specific format, never leaving out any important details. It also saves the original URL, smart tags, publication date, and other metadata extracted by the AI. Posts a confirmation message (e.g., to a Discord channel) indicating whether the article was saved successfully or if an error occurred. Setup Import Workflow: Import this template into your n8n instance. Configure Credentials & Notion Database: Notion Database: Create or designate a Notion database (like the example "Knowledge Database") where articles will be saved. Ensure this database has the following properties (fields): Name (Type: Text) - This will store the article title. URL (Type: URL) - This will store the original article link. Description (Type: Text) - This can store the AI-generated summary. Tags (Type: Multi-select) - Optional, for categorization. Publication Date (Type: Date) - *Optional, store the date the article was published. Ensure the n8n integration has access to this specific database. If you require a different format to the Notion Database, not that you will have to update the Notion tool configuration in this n8n workflow accordingly. Notion Credential: Obtain your Notion API key and add it as a Notion credential in n8n. Select this credential in the save_to_notion tool node. Configure save_to_notion Tool: In the save_to_notion tool node within the workflow, set the 'Database ID' field to the ID of the Notion database you prepared above. Map the workflow data (URL, AI summary, etc.) to the corresponding database properties (URL, Description, etc.). In the blocks section of the notion tool, you can define a custom format for the research page, allowing the AI to fill in the exact details you want extracted from any web page! Google Gemini AI: Obtain your API key from Google AI Studio or Google Cloud Console (if using Vertex AI) and add it as a credential. Select this credential in the "Tools Agent" node. Discord (or other notification service): If using Discord notifications, create a Webhook URL (instructions) or set up a Bot Token. Add the credential in n8n and select it in the discord_notification tool node. Configure the target Channel ID. Browserless/HTTP Request: Cloud: Obtain your API key from Browserless and configure the website_scraper HTTP Request tool node with the correct API endpoint and authentication header. Self-Hosted: Ensure your Browserless Docker container is running and accessible by n8n. Configure the website_scraper HTTP Request tool node with your self-hosted Browserless instance URL. Activate Workflow: Save test and activate the workflow. How to customize this workflow to your needs Change AI Model:** Experiment with different AI models supported by n8n (like OpenAI GPT models or Anthropic Claude) in the Agent node if Gemini 2.5 Pro doesn't fit your needs or budget, keeping in mind potential differences in context window size and processing capabilities for large content. Modify Notion Saving:** Adjust the save_to_notion tool node to map different data fields (e.g., change the summary style by modifying the AI prompt, add specific tags, or alter the page content structure) to your Notion database properties. Adjust Scraping:** Modify the prompt/instructions for the website_scraper tool or change the parameters sent to the Browserless API if you need different data extracted from the web pages. You could also swap Browserless for another scraping service/API accessible via the HTTP Request node.