Workflow Templates

This n8n template demonstrates how to create an intelligent food recipe assistant that accepts requests via Gmail and web forms, processes them using AI chat models (Ollama and Llama 3.2), and delivers personalized recipes back to users. The system combines multiple input methods with advanced AI processing to provide customized cooking instructions and ingredient lists. Good to know The system accepts recipe requests through both Gmail and web form submissions AI models understand dietary restrictions, cuisine preferences, and cooking skill levels Recipe responses include formatted ingredients, step-by-step instructions, and cooking tips All requests are processed automatically without manual intervention How it works Gmail Recipe Request Workflow Gmail triggers activate when users send emails with recipe requests to the designated email address The system extracts recipe requirements, dietary preferences, and cooking constraints from email content User queries are processed through the Ollama Recipe Generator for intelligent recipe creation AI-generated recipes are formatted with proper ingredients, instructions, and cooking times Formatted recipes are sent back to users via Gmail with a professional presentation Web Form Recipe Request Workflow Web form submissions trigger when users fill out structured recipe request forms Form data includes cuisine type, dietary restrictions, available ingredients, and cooking time preferences The Llama 3.2 Chef Model processes structured requests for optimized recipe generation Recipes are formatted with clear instructions, ingredient measurements, and cooking techniques Users receive formatted recipes via email with additional cooking tips and variations How to use Import the workflow into your n8n instance and configure Gmail integration for recipe requests Set up the web form with fields for cuisine preferences, dietary restrictions, and cooking skill level Configure Ollama and Llama 3.2 AI models with appropriate recipe generation prompts Test both Gmail and web form inputs with sample recipe requests Customize email templates to match your brand and include additional cooking resources The system scales automatically to handle multiple simultaneous recipe requests Requirements Gmail account for email-based recipe requests and responses Ollama installation with Recipe Generator model Llama 3.2 Chef Model access for advanced recipe processing n8n instance with Gmail and AI model integrations Customising this workflow Recipe automation can be adapted for different cuisines, dietary needs, and cooking skill levels Try popular use-cases such as meal planning assistance, ingredient substitution suggestions, or nutritional information inclusion The workflow can be extended to include recipe image generation, shopping list creation, and cooking video recommendations

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!

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.

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 workflow for? If you're using Meta Ads to generate new leads to your sales pipeline, this workflow is for you! 🙌🏻 What this workflow does Triggers every time you have a new calendar event on a chosen Google Acount Filter only events with the same name of your "Schedule a demo" event Formats and send event to Meta Conversion API What events can I send? Any event you'd like! It's preconfigured with the "Schedule" event, but you can change to "Purchase", "InitiateCheckout", "Lead" and custom events. Setup Guide Connect Google OAuth2 to n8n Get your PIXEL ID and Access Token from Meta Set your configuration node with Pixel ID, Access Token, source_url and event_name Requirements Meta Access Token + Pixel ID (via Meta Conversion API): Documentation Google Access (via OAuth2): Documentation This free template was created by pdforge. Feel free to contact us via the founder Linkedin, if you have any questions! 👋🏻

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.

This workflow compares two lists of objects (List A and List B) using a user-specified key (e.g. email, id, domain) and returns: Items common to both lists (based on the key) Items only in List A Items only in List B How it works: Accepts a JSON input containing: listA: the first list of items listB: the second list of items key: the field name to use for comparison Performs a field-based comparison using the specified key Returns a structured output: common: items with matching keys (only one version retained) onlyInA: items found only in List A onlyInB: items found only in List B Example Input: { "key": "email", "listA": [ { "email": "alice@example.com", "name": "Alice" }, { "email": "bob@example.com", "name": "Bob" } ], "listB": [ { "email": "bob@example.com", "name": "Bobby" }, { "email": "carol@example.com", "name": "Carol" } ] } Output: common: [ { "email": "bob@example.com", "name": "Bob" } ] onlyInA: [ { "email": "alice@example.com", "name": "Alice" } ] onlyInB: [ { "email": "carol@example.com", "name": "Carol" } ] Use Cases: Deduplicate data between two sources Find overlapping records Identify new or missing entries across systems This workflow is useful for internal data auditing, list reconciliation, transaction reconciliation, or pre-processing sync jobs.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This workflow automates the process of scraping product data from e-commerce websites and using it to fine-tune a custom OpenAI GPT model for generating high-quality marketing copy and product descriptions. Main Use Cases Fine-tune OpenAI models with real product data from hundreds of supported e-commerce websites for marketing content generation. Create custom AI models specialized in writing compelling product descriptions across different industries and platforms. Automate the entire pipeline from data collection to model training using Bright Data's extensive scraper library. Generate marketing copy using your custom-trained model via an interactive chat interface. How it works The workflow operates in two main phases: model training and model usage, organized into these stages: Data Collection & Processing Manually triggered to start the fine-tuning process. Uses Bright Data's web scraper to extract product information from any supported e-commerce platform (Amazon, eBay, Shopify stores, Walmart, Target, and hundreds of other websites). Collects product titles, brands, features, descriptions, ratings, and availability status from your chosen platform. Easily customizable to scrape from different websites by simply changing the dataset configuration and product URLs. Training Data Preparation A Code node processes the scraped product data to create training examples in OpenAI's required JSONL format. For each product, generates a complete training example with: System message defining the AI's role as a marketing assistant. User prompt containing specific product details (title, brand, features, original description snippet). Assistant response providing an ideal marketing description template. Compiles all training examples into a single JSONL file ready for OpenAI fine-tuning. Model Fine-Tuning Uploads the training file to OpenAI using the OpenAI File Upload node. Initiates a fine-tuning job via HTTP Request to OpenAI's fine-tuning API using the GPT-4o-mini model as the base. The fine-tuning process runs on OpenAI's servers to create your custom model. Interactive Chat Interface Provides a chat trigger that allows real-time interaction with your fine-tuned model. An AI Agent node connects to your custom-trained OpenAI model. Users can chat with the model to generate product descriptions, marketing copy, or other content based on the training. Custom Model Integration The OpenAI Chat Model node is configured to use your specific fine-tuned model ID. Delivers responses trained on your product data for consistent, high-quality marketing content. Summary Flow: Manual Trigger → Scrape E-commerce Products (Bright Data) → Process & Format Training Data (Code) → Upload Training File (OpenAI) → Start Fine-Tuning Job (HTTP Request) | Parallel: Chat Trigger → AI Agent → Custom Fine-Tuned Model Response Benefits: Fully automated pipeline from raw product data to trained AI model. Works with hundreds of different e-commerce websites through Bright Data's extensive scraper library. Creates specialized models trained on real e-commerce data for authentic marketing copy across various industries. Scalable solution that can be adapted to different product categories, niches, or websites. Interactive chat interface for immediate access to your custom-trained model. Cost-effective fine-tuning using OpenAI's most efficient model (GPT-4o-mini). Easily customizable with different websites, product URLs, training prompts, and model configurations. Setup Requirements: Bright Data API credentials for web scraping (supports hundreds of e-commerce websites). OpenAI API key with fine-tuning access. Replace placeholder credential IDs and model IDs with your actual values. Customize the product URLs list and Bright Data dataset for your specific website and use case. The workflow can be adapted for any e-commerce platform supported by Bright Data's scraping infrastructure.

This is a very simple workflow that lets you subscribe to any github repository for the latest release (using n8n as example). How it works: daily poll to Github repository for release for latest (stable) version of n8n parses the content to HTML sends a gmail Setup steps: add your gmail credentials (or use other email node of choice) change the url to the right Github repository you want to check regularly change the To email address to the email that you want to receive the updates for Feedback & Questions If you have any questions or feedback about this workflow - Feel free to get in touch at ria@n8n.io