Workflow Templates

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. What this workflow does Monitors Google Drive for new driver license image uploads Downloads and processes images using VLM Run AI OCR Extracts key information including license number, name, DOB, and dates Saves structured data to Google Sheets for instant access Setup Prerequisites: Google Drive account, VLM Run API credentials, Google Sheets access, self-hosted n8n. You need to install VLM Run community node Quick Setup: Configure Google Drive OAuth2 and create license upload folder Add VLM Run API credentials Set up Google Sheets integration for data storage Update folder/sheet IDs in workflow nodes Test with sample license images and activate Perfect for Customer onboarding and identity verification KYC compliance and document processing HR employee verification and record keeping Insurance claim processing and validation Any business requiring license data extraction Key Benefits Asynchronous processing** handles high-resolution images without timeouts Multi-format support** for JPG, PNG, PDF, HEIC, WebP formats Structured data output** ready for databases and integrations Eliminates manual entry** saving hours of data input time High accuracy OCR** with multi-state license support How to customize Extend by adding: Address and additional field extraction Data validation and error checking Integration with CRM or customer databases Email notifications for processing completion Audit trails and compliance reporting Duplicate detection and data deduplication This workflow transforms manual license data entry into an automated, accurate, and compliant process, making identity verification seamless and reliable for your business operations.

🚀 Automated Funding Intelligence: CrunchBase to Google Sheets Tracking Workflow! Workflow Overview This cutting-edge n8n automation is a sophisticated startup funding intelligence tool designed to transform market research into actionable insights. By intelligently connecting CrunchBase, data processing, and Google Sheets, this workflow: Discovers Funding Opportunities: Automatically retrieves latest funding rounds Tracks industry-specific investments Eliminates manual market research efforts Intelligent Data Processing: Filters funding data by location and industry Extracts key investment metrics Ensures comprehensive market intelligence Seamless Data Logging: Automatically updates Google Sheets Creates real-time investment database Enables rapid market trend analysis Scheduled Intelligence Gathering: Daily automated tracking Consistent market insight updates Zero manual intervention required Key Benefits 🤖 Full Automation: Zero-touch funding research 💡 Smart Filtering: Targeted investment insights 📊 Comprehensive Tracking: Detailed funding intelligence 🌐 Multi-Source Synchronization: Seamless data flow Workflow Architecture 🔹 Stage 1: Funding Discovery Scheduled Trigger**: Daily market scanning CrunchBase API Integration** Intelligent Filtering**: Location-based selection Industry-specific focus Most recent funding rounds 🔹 Stage 2: Data Extraction Comprehensive Metadata Parsing** Key Information Retrieval** Structured Data Preparation** 🔹 Stage 3: Data Logging Google Sheets Integration** Automatic Row Appending** Real-Time Database Updates** Potential Use Cases Venture Capitalists**: Investment opportunity tracking Startup Scouts**: Market trend analysis Market Researchers**: Comprehensive funding insights Investors**: Strategic decision support Business Strategists**: Competitive landscape monitoring Setup Requirements CrunchBase API API credentials Configured access permissions Funding round tracking setup Google Sheets Connected Google account Prepared tracking spreadsheet Appropriate sharing settings n8n Installation Cloud or self-hosted instance Workflow configuration API credential management Future Enhancement Suggestions 🤖 Advanced investment trend analysis 📊 Multi-source funding aggregation 🔔 Customizable alert mechanisms 🌐 Expanded industry coverage 🧠 Machine learning insights generation Technical Considerations Implement robust error handling Use secure API authentication Maintain flexible data processing Ensure compliance with API usage guidelines Ethical Guidelines Respect business privacy Use data for legitimate research Maintain transparent information gathering Provide proper attribution Hashtag Performance Boost 🚀 #StartupFunding #InvestmentIntelligence #MarketResearch #AIWorkflow #DataAutomation #VentureCapital #TechInnovation #InvestmentTracking #BusinessIntelligence #StartupEcosystem Workflow Visualization [Daily Trigger] ⬇️ [Fetch Funding Rounds] ⬇️ [Extract & Format Data] ⬇️ [Log to Google Sheets] Connect With Me Ready to revolutionize your funding intelligence? 📧 Email: Yaron@nofluff.online 🎥 YouTube: @YaronBeen 💼 LinkedIn: Yaron Been Transform your market research with intelligent, automated workflows!

How it works Trigger: Listens for an incoming chat message Copy Assistant: Feeds the message (plus memory) into an OpenAI Chat Model and exposes two “tools” Cold Email Writer Tool Sales Letter Tool• Tool execution: Depending on the user’s intent, the appropriate tool generates the copy • Save output: Writes the generated email or sales letter into your target document via the Update a document node Set up steps • Configure your OpenAI Chat Model credentials in n8n (no hard-coded keys!) • Add and authenticate the Simple Memory credential (to keep context across messages) • Create Google Docs (or MS Word) credentials for the Update a document node • Ensure your Chat trigger is pointing at your incoming-message endpoint • Mandatory: Drop sticky-note annotations on each tool node explaining where to enter API keys and how to tweak prompts Once everything’s wired up, send a test chat message like “Write me a cold email for a fintech startup” and watch the workflow spin up a polished draft in your document. How to use Import the workflow JSON into n8n. Configure your Chat trigger (webhook or form) to receive incoming messages. Send a chat prompt like: “Write me a cold email for a B2B SaaS offering.” The “Copy Assistant” custom GPT picks the right tool (Cold Email or Sales Letter). Generated copy is written directly into your linked Google Doc or Word document. Requirements OpenAI API Key (with Chat Completions & Custom GPTs enabled) Custom Assistant created in your ChatGPT dashboard (Assistant ID pasted into the Chat Model node) n8n instance (Cloud or self-hosted) with credentials set up for: Simple Memory (to persist context) Google Docs or Microsoft Word (for document output) Customising this workflow Tweak system and user prompts inside the Copy Assistant node to fit your brand voice. Swap in Slack, Teams or email nodes instead of a document writer to deliver copy where you need it. Add or remove tools (e.g., “Follow-up Email Writer”) by duplicating the existing tool pattern. Use sticky-note annotations on every node to explain where to enter API keys, Assistant IDs, or prompt tweaks.

This workflow template, "Daily Gratitude Reminder Bot for LINE," is designed to help users cultivate a habit of gratitude by sending personalized, AI-generated reminders every evening at 9:00 PM. Using Azure OpenAI , the bot generates varied and engaging messages to prompt users to reflect on the positive aspects of their day. The reminders are then sent directly to users via the LINE messaging platform , ensuring a seamless and impactful experience. Whether you're a developer, counselor, or business owner, this template offers a customizable and scalable solution for promoting mental wellness and fostering a culture of gratitude. profile Qwen2.5-Max 2:56 pm Here’s a polished and detailed description for your "Daily Gratitude Reminder Bot for LINE" template, following the submission guidelines provided. This description is structured to meet all the requirements, including SEO optimization, clarity, and user convenience. Daily Gratitude Reminder Bot for LINE This workflow template, "Daily Gratitude Reminder Bot for LINE," is designed to help users cultivate a habit of gratitude by sending personalized, AI-generated reminders every evening at 9:00 PM. Using Azure OpenAI , the bot generates varied and engaging messages to prompt users to reflect on the positive aspects of their day. The reminders are then sent directly to users via the LINE messaging platform , ensuring a seamless and impactful experience. Whether you're a developer, counselor, or business owner, this template offers a customizable and scalable solution for promoting mental wellness and fostering a culture of gratitude. Who Is This Template For? Developers who want to integrate AI-powered workflows into messaging platforms like LINE. Counselors & Therapists looking to encourage mindfulness and emotional well-being among their clients. Businesses & Organizations focused on employee wellness or customer engagement through positive reinforcement. Educators & Nonprofits seeking tools to promote mental health awareness and self-care practices. What Problem Does This Workflow Solve? Gratitude journaling has been proven to improve mental health, reduce stress, and increase overall happiness. However, many people struggle to maintain the habit due to busy schedules or forgetfulness. This workflow solves that problem by automating daily reminders to reflect on positive experiences, making it easier for users to build and sustain a gratitude practice. What This Workflow Does Scheduled Trigger: The workflow is triggered every evening at 9:00 PM using a schedule node. AI-Powered Message Generation: An Azure OpenAI Chat Model generates a unique and engaging reminder message with a temperature setting of 0.9 to ensure variety and creativity. Message Formatting: The generated message is reformatted to comply with the LINE Push API requirements, ensuring smooth delivery. Push Notification via LINE: The formatted message is sent to the user via the LINE Push API , delivering the reminder directly to their chat. Setup Guide Pre-Requisites Access to an Azure OpenAI account with credentials. A LINE Developers Console account with access to the Push API. Basic knowledge of n8n workflows and JSON formatting. How to Customize This Workflow to Your Needs Change the Time: Adjust the schedule trigger to send reminders at a different time. Modify the Prompt: Edit the AI model's input prompt to generate messages tailored to your audience (e.g., focus on work achievements or personal growth). Expand Recipients: Update the LINE Push API node to send reminders to multiple users or groups. Integrate Additional Features: Add nodes to log user responses or track engagement metrics. Why Use This Template? Promotes Mental Wellness: Encourages users to reflect on positive experiences, improving emotional well-being. Highly Customizable: Easily adapt the workflow to suit different audiences and use cases. Scalable: Send reminders to one user or thousands, making it suitable for both personal and organizational use. AI-Powered Creativity: Avoid repetitive messages by leveraging AI to generate fresh and engaging content.

This workflow auto-fetches top financial headlines, cleans the content, and uses AI to summarize it into a short investor-friendly email. Good to know The workflow runs daily and relies on stable webpage access; check the URL (e.g., https://www.ft.com/) for availability. AI costs may apply depending on the LLM model used (e.g., GPT-4 or Gemini); refer to provider pricing. How it works Trigger the workflow daily with the Schedule Daily Trigger node. Fetch financial news from a webpage using the Fetch Webpage News node. Add a Delay to Ensure Page Load node to ensure content is fully loaded. Extract and clean headlines with the Extract News Headlines & Clean Extracted Data node. Process the data with the LLM Chat Model node to generate a summary. Send the summarized report via email using the Email Daily Financial Summary node. How to use Import the workflow into n8n and configure the nodes with your webpage URL and email credentials. Test the workflow to verify content fetching and email delivery. Requirements Webpage access (e.g., financial news site API or RSS) Email service (e.g., SMTP or API) LLM model credentials (e.g., GPT-4 or Gemini) Customising this workflow Adjust the Fetch Webpage News node to target different news sources or modify the LLM Chat Model prompt for a different summary style.

Who is this for? This workflow is designed for: Database administrators and developers working with MongoDB Content managers handling movie databases Organizations looking to implement AI-powered search and recommendation systems Developers interested in combining LangChain, OpenAI, and MongoDB capabilities What problem does this workflow solve? Traditional database queries can be complex and require specific MongoDB syntax knowledge. This workflow addresses: The complexity of writing MongoDB aggregation pipelines The need for natural language interaction with movie databases The challenge of maintaining user preferences and favorites The gap between AI language models and database operations What this workflow does This workflow creates an intelligent agent that: Accepts natural language queries about movies Translates user requests into MongoDB aggregation pipelines Queries a movie database containing detailed information including: Plot summaries Genre classifications Cast and director information Runtime and release dates Ratings and awards Provides contextual responses using OpenAI's language model Allows users to save favorite movies to the database Maintains conversation context using a window buffer memory Setup Required Credentials: OpenAI API credentials MongoDB connection details Node Configuration: Configure the MongoDB connection in the MongoDBAggregate node Set up the OpenAI Chat Model with your API key Ensure the webhook trigger is properly configured for receiving chat messages Database Requirements: A MongoDB collection named "movies" with the specified document structure Proper indexes for efficient querying Appropriate user permissions for read/write operations How to customize this workflow Modify the Document Structure: Update the tool description in the MongoDBAggregate node to match your collection schema Adjust the aggregation pipeline templates for your specific use case Enhance the AI Agent: Customize the prompt in the "AI Agent - Movie Recommendation" node Modify the window buffer memory size based on your context needs Add additional tools for more functionality Extend Functionality: Add more MongoDB operations beyond aggregation Implement additional workflows for different types of queries Create custom error handling and validation Add user authentication and rate limiting Integration Options: Connect to external APIs for additional movie data Add webhook endpoints for different platforms Implement caching mechanisms for frequent queries Add data transformation nodes for specific output formats This workflow serves as a foundation that can be adapted to various use cases beyond movie recommendations, such as e-commerce product search, content management systems, or any scenario requiring intelligent database interaction.

How it works This workflow is a hands-on tutorial for the Code node in n8n, covering both basic and advanced concepts through a simple data processing task. Provides Sample Data: The workflow begins with a sample list of users. Processes Each Item (Run Once for Each Item): The first Code node iterates through each user to calculate their fullName and age. This demonstrates basic item-by-item data manipulation using $input.item.json. Fetches External Data (Advanced): The second Code node showcases a more advanced feature. For each user, it uses the built-in this.helpers.httpRequest function to call an external API (genderize.io) to enrich the data with a predicted gender. Processes All Items at Once (Run Once for All Items): The third Code node receives the fully enriched list of users and runs only once. It uses $items() to access the entire list and calculate the averageAge, returning a single summary item. Create a Binary File: The final Code node gets the fully enriched list of users once again and creates a binary CSV file to show how to use binary data Buffer in JavaScript. Set up steps Setup time: < 1 minute This workflow is a self-contained tutorial and requires no setup. Explore the Nodes: Click on each of the Code nodes to read the code and the comments explaining each step, from basic to advanced. Run the Workflow: Click "Execute Workflow" to see it in action. Check the Output: Click on each node after the execution to see how the data is transformed at each stage. Notice how the data is progressively enriched. Experiment! Try changing the data in the 1. Sample Data node, or modify the code in the Code nodes to see what happens.

Who is this template for? This workflow template is designed for business owners and HR professionals to automatically detect and structure unstructured job applications received through email. Additionally, other email categories can be added, each with it's own workflow. How it works Every time a new email is received, an OpenAI model classifies it into a predefined category by analyzing the plain text of the email and the extracted content from the attachment. If the email is classified as a job application, an OpenAI model uses the email’s plain text and extracted attachment content to populate predefined fields such as age and study. A relevant additional step would be to directly push the applicant and their structured job application into a CRM or ATS like Hubspot or Recruitee. Set up steps Configure your IMAP credentials to connect your email account. Use this n8n documentation page for quickstart guides for common email providers. Connect your OpenAI account in the 'Classify email' node. And add or remove any category for classification in this node. Make sure the description is clear and concise. Connect your OpenAI account in the 'Extract variables - email & attachment' node. And add or remove any predefined fields that should be populated for job applications in this node. Make sure the description is clear and concise.

Learning something new? Endlessly searching to find the best resources? This workflow finds top community-recommended learning resources on any topic from Hacker News, delivered to your inbox. How it works User submits a topic they want to learn via a simple form. The workflow searches for relevant "Ask HN" posts on Hacker News and extracts top-level comments. An LLM analyzes the comments and identifies the best learning resources. A personalized email is sent to the user with a Markdown formatted list of top recommendations, categorized by resource type (e.g., book, course, article) and difficulty level. Set up steps Add your Google Gemini API credentials. You'll need to create a project and enable the Generative Language API. Add your SMTP credentials for sending emails. Customize the Form and email subject (optional) Activate the workflow Screenshots for Workflow, Form and Email Built on Day-03 as part of the #100DaysOfAgenticAi Fork it, tweak it, have fun!

This n8n template demonstrates the easiest way to build a lead capture flow for your side project, startup or small business where simple works best! If Typeform's costs are getting you down or you feel Google form URLs are off-putting, then definitely give this a try. How it works Our flow begins with a form trigger to capture a newsletter signup and the user's email is captured into a google sheet. Google Sheet is used for demonstration purposes but this could be any database. Multi-page forms allow you to continue the onboarding experience with a short survey. 3 form nodes are chained to capture more details from the user which update the same row in the google sheet. Finally, a form ending node shows a customised completion screen for our user. Check out the example sheet here: https://docs.google.com/spreadsheets/d/15W1PiFjCoiEBHHKKCRVMLmpKg4AWIy9w1dQ2Dq8qxPs/edit?usp=sharing How to use Keeping forms simple may serve to increase form completion rates. If you feel the need to add additional fields, consider breaking them up into more forms and group them contextually. Requirements Google Sheets for data capture Slack for notifications Feel free to swap these out for services that you use! Customising this workflow Play with multi-form design to maximise the opportunity of getting to know the user better. That said, lengthy flows are likely to put people off. Instead of showing a static completion screen, perhaps redirecting to your blog or other more interesting page.

This n8n workflow provides a robust error handling and notification system for your n8n workflows. When an error occurs, it automatically logs the error details to Google Sheets, sends a notification to a Telegram channel, and dispatches an email alert, ensuring you're immediately aware of any issues. How it works Error Trigger:** The workflow is activated whenever an error occurs in another n8n workflow. Log Error (Google Sheets):** Error details (e.g., workflow name, error message, timestamp) are appended to a specified Google Sheet, creating a centralized log for all errors. Edit Fields (Manual Configuration):** This node allows you to manually set the Telegram chat ID and recipient email for notifications. Notify in channel (Telegram):** An error notification containing relevant details is sent to your configured Telegram channel. Send email (Gmail):** An email alert with comprehensive error information is sent to the specified recipient. Set up steps This setup will take approximately 10-15 minutes. Download the workflow: Download this workflow and import it into your n8n instance. Configure the Error Trigger: This trigger will automatically activate when an error occurs in any workflow. Make sure you set this workflow as the "Error Workflow" inside the workflows where you want to be alerted. Configure Log error (Google Sheets): Connect your Google Sheets account credentials. Specify the Google Sheet ID and the sheet name where you want to log the errors. Ensure the sheet has appropriate headers (e.g., "Timestamp", "Workflow Name", "Error Message", "Error Details") to receive the data. Configure Edit Fields: In the "Edit Fields" node, manually enter your Telegram chat ID. This is the ID of the chat or channel where you want to receive Telegram notifications. Insert the recipient's email address where you want to receive email alerts. Configure Notify in channel (Telegram): Connect your Telegram account credentials. Ensure the "Chat ID" field is correctly linked to the output from the "Edit Fields" node. Configure Send email (Gmail): Connect your Gmail account credentials. Ensure the "To" email address is correctly linked to the output from the "Edit Fields" node. Customize the subject and body of the email to include relevant error information from the "Error Trigger" node. Test the workflow: To test, you can intentionally create an error in another simple n8n workflow. This error workflow should then trigger this error handling workflow, and you can verify if the log is updated, Telegram message is sent, and email is received. Make sure that the workflow you are testing has the "Error Workflow" selected in the workflow's settings. Requirements n8n instance:** An active n8n instance (self-hosted or cloud). Google Account:** A Google account with access to Google Sheets. Telegram Account:** A Telegram account and a chat/channel ID for notifications. Gmail Account:** A Gmail account to send email alerts. Need help? Feel free to contact us at 1 Node. Get instant access to a library of free resources we created.

This workflow creates a GitHub issue when a new ticket is created in Zendesk. Subsequent comments on the ticket in Zendesk are added as comments to the issue in GitHub. Prerequisites Zendesk account and Zendesk credentials. GitHub account and GitHub credentials. GitHub repository to create issues in. How it works The workflow listens for new tickets in Zendesk. When a new ticket is created, the workflow creates a new issue in GitHub. The GitHub issue number is then saved in one of the ticket's fields (in setup we call this "GitHub Issue Number"). The next time a comment is added to the ticket, the workflow retrieves the GitHub issue number from the ticket's field and adds the comment to the issue in GitHub. Setup This workflow requires that you set up a webhook in Zendesk. To do so, follow the steps below: In the workflow, open the On new Zendesk ticket node and copy the webhook URL. In Zendesk, navigate to Admin Center > Apps and integrations > Webhooks > Actions > Create Webhook. Add all the required details which can be retrieved from the On new Zendesk ticket node. The webhook URL gets added to the “Endpoint URL” field, and the “Request method” should match what is shown in n8n. Save the webhook. In Zendesk, navigate to Admin Center > Objects and rules > Business rules > Triggers > Add trigger. Give trigger a name such as “New tickets”. Under “Conditions” in “Meet ALL of the following conditions”, add “Status is New”. Under “Actions”, select “Notify active webhook” and select the webhook you created previously. In the JSON body, add the following: { "id": "{{ticket.id}}", "comment": "{{ticket.latest_comment_html}}" } Save the Zendesk trigger. You will also need to set up a field in Zendesk to store the GitHub issue number. To do so, follow the steps below: In Zendesk, navigate to Admin Center > Objects and rules > Tickets > Fields > Add field. Use the number field option and give the field a name such as “GitHub Issue Number”. Save the field. In n8n, open the Update ticket node and select the field you created in Zendesk.