Workflow Templates

Edit an existing image with OpenAI ImageGen1 via API Request Transform your creative pipeline by letting n8n call OpenAI ImageGen1’s edit image endpoint, automatically replacing or augmenting parts of any image you supply and returning a brand-new version in seconds. Designers, marketers, and product teams can eliminate repetitive manual edits and test more variations, faster. Who is this for? Content creators who need quick, on-brand image tweaks Marketers running A/B visual tests at scale Developers exploring the new ImageGen1 API inside low-code automations Use case / problem solved Opening design software to mask, fill, or swap objects is slow and error-prone. This workflow feeds an input image plus a prompt to OpenAI ImageGen1, receives the edited output, and passes it on to any service you like—perfect for bulk-editing product shots, social visuals, or UI mocks. What this workflow does Read or receive the source image (Webhook → Binary Data). Call OpenAI ImageGen1 with an HTTP Request node, sending the image and edit prompt. Parse the JSON response to capture the returned image URL. Download & hand off the edited file (e.g., upload to S3, post to Slack, or store in Drive). Setup Add your OpenAI API key in the API KEY node. Follow the notes on the workflow for more information. (Optional) Point the final node to your preferred storage or chat tool. > 📝 A sticky note in the workflow summarizes these steps and links to the OpenAI documentation. How to customize this workflow Trigger alternatives**: Replace the Chat with Google Drive, Airtable, etc. Chained edits**: Loop the output back for successive prompts. Conditional flows**: Add an If node to branch actions by image size or category. With renamed nodes, color-coded sticky notes, and a concise setup guide, you’ll be editing images via OpenAI ImageGen1 in under five minutes—no code, maximum creativity.

Conversational Lead Capture with Gemini 2.0 Flash AI and Google Sheets

by David Olusola

💬Communication & Collaboration

AI Lead Capture System - Complete Setup Guide Prerequisites n8n instance (cloud or self-hosted) Google AI Studio account (free tier available) Google account for Sheets integration Website with chat widget capability Phase 1: Core Infrastructure Setup Step 1: Set Up Google AI Studio Go to Google AI Studio Create account or sign in with Google Navigate to "Get API Key" Create new API key for your project Copy and securely store the API key Free tier limits: 15 requests/minute, 1 million tokens/month Step 2: Configure Google Sheets Create new Google Sheet for lead storage Add column headers (exact names): Full Name Company Name Email Address Phone Number Project Intent/Needs Project Timeline Budget Range Preferred Communication Channel How they heard about DAEX AI Copy the Google Sheet ID from URL (between /d/ and /edit) Ensure sheet is accessible to your Google account Step 3: Import n8n Workflow Open your n8n instance Create new workflow Click "..." menu → Import from JSON Paste the provided workflow JSON Workflow will appear with all nodes connected Phase 2: Credential Configuration Step 4: Set Up Google Gemini API In n8n, go to Credentials → Add Credential Search for "Google PaLM API" Enter your API key from Step 1 Test connection Link to the "Google Gemini Chat Model" node Step 5: Configure Google Sheets Access Go to Credentials → Add Credential Select "Google Sheets OAuth2 API" Follow OAuth flow to authorize your Google account Test connection with your sheet Link to the "Google Sheets" node Phase 3: Workflow Customization Step 6: Update Company Information Open the AI Agent node In the system message, replace all mentions of: Company name and description Service offerings and specializations FAQ knowledge base Typical project timelines and pricing ranges Adjust conversation tone to match your brand voice Step 7: Configure Lead Qualification Fields In the AI Agent system message, modify the required information list: Add/remove qualification questions Adjust budget ranges for your services Customize timeline options Update communication channel preferences In Google Sheets node, update column mappings if you changed fields Step 8: Set Up Sheet Integration Open Google Sheets node Click on Document ID dropdown Select your lead capture sheet Verify all column mappings match your sheet headers Test with sample data Phase 4: Website Integration Step 9: Get Webhook URL Open Webhook node in n8n Copy the webhook URL (starts with your n8n domain) Note: URL format is https://your-n8n-domain.com/webhook/[unique-id] Step 10: Connect Your Chat Widget Choose your integration method: Option A: Direct JavaScript Integration javascript// Add to your website function sendMessage(message, sessionId) { fetch('YOUR_WEBHOOK_URL', { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ message: message, sessionId: sessionId || 'visitor-' + Date.now() }) }) .then(response => response.json()) .then(data => { // Display AI response in your chat widget displayMessage(data.message); }); } Option B: Chat Platform Webhook Open your chat platform settings (Intercom, Crisp, etc.) Find webhook/integration section Add webhook URL pointing to your n8n endpoint Configure to send message and session data Option C: Zapier/Make.com Integration Create new Zap/Scenario Trigger: New chat message from your platform Action: HTTP POST to your n8n webhook Map message content and session ID Phase 5: Testing & Optimization Step 11: Test Complete Flow Send test message through your chat widget Verify AI responds appropriately Check conversation context is maintained Confirm lead data appears in Google Sheets Test with various conversation scenarios Step 12: Monitor Performance Check n8n execution logs for errors Monitor Google Sheets for data quality Review conversation logs for improvement opportunities Track response times and conversion rates Step 13: Fine-Tune Conversations Analyze real conversation logs Update system prompts based on common questions Add new FAQ knowledge to the AI agent Adjust qualification questions based on lead quality Optimize for your specific customer patterns Phase 6: Advanced Features (Optional) Step 14: Add Lead Scoring Create new column in Google Sheets for "Lead Score" Update AI agent to calculate scores based on: Budget range (higher budget = higher score) Timeline urgency (sooner = higher score) Project complexity (complex = higher score) Add conditional formatting in Google Sheets to highlight high-value leads Step 15: Set Up Notifications Add email notification node after Google Sheets Configure to send alerts for high-priority leads Include lead details and conversation summary Set up different notification rules for different lead scores Step 16: Analytics Dashboard Connect Google Sheets to Google Data Studio or similar Create dashboard showing: Daily lead volume Conversion rates by source Average qualification time Lead quality scores Revenue pipeline from captured leads Troubleshooting Common Issues AI Not Responding Check Google Gemini API key validity Verify API quota not exceeded Review n8n execution logs for errors Data Not Saving to Sheets Confirm Google Sheets permissions Check column name matching Verify sheet ID is correct Chat Widget Not Connecting Test webhook URL directly with curl/Postman Verify JSON format matches expected structure Check CORS settings if browser-based integration Conversation Context Lost Ensure sessionId is unique per visitor Check memory node configuration Verify sessionId is passed consistently

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AI Chatbot Agent with a Panel of Experts using InfraNodus GraphRAG Knowledge

by InfraNodus

Using the knowledge graphs instead of RAG vector stores This workflow creates an AI chatbot agent that has access to several knowledge bases at the same time (used as "experts"). These knowledge bases are provided using the InfraNodus GraphRAG using the knowledge graphs and providing high-quality responses without the need to set up complex RAG vector store workflows. The advantages of using GraphRAG instead of the standard vector stores for knowledge are: Easy and quick to set up (no complex data import workflows needed) A knowledge graph has a holistic view of your knowledge base Better retrieval of relations between the document chunks = higher quality responses How it works This template uses the n8n AI agent node as an orchestrating agent that decides which tool (knowledge graph) to use based on the user's prompt. Here's a description step by step: The user submits a question using the AI chatbot (n8n interface, in this case, which can be accessed via a URL or embedded to any website) The AI agent node checks a list of tools it has access to. Each tool has a description of the knowledge it has auto-generated by InfraNodus. The AI agent decides which tool should be used to generate a response. It may reformulate user's query to be more suitable for the expert. The query is then sent to the InfraNodus HTTP node endpoint, which will query the graph that corresponds to that expert. Each InfraNodus GraphRAG expert provides a rich response that takes the whole context into account and provides a response from each expert (graph) along with a list of relevant statements retrieved using a combination or RAG and GraphRAG. The n8n AI Agent node integrates the responses received from the experts to produce the final answer. The final answer is sent back to the user's chat (or a webhook endpoint) How to use You need an InfraNodus GraphRAG API account and key to use this workflow. Create an InfraNodus account Get the API key at https://infranodus.com/api-access and create a Bearer authorization key for the InfraNodus HTTP nodes. Create a separate knowledge graph for each expert (using PDF / content import options) in InfraNodus For each graph, go to the workflow, paste the name of the graph into the body name field. Keep other settings intact or learn more about them at the InfraNodus access points page. Once you add one or more graphs as experts to your flow, add the LLM key to the OpenAI node and launch the workflow Requirements An InfraNodus account and API key An OpenAI (or any other LLM) API key Customizing this workflow You can use this same workflow with a Telegram bot, so you can interact with it using Telegram. There are many more customizations available. Check out the complete guide at https://support.noduslabs.com/hc/en-us/articles/20174217658396-Using-InfraNodus-Knowledge-Graphs-as-Experts-for-AI-Chatbot-Agents-in-n8n Also check out the video tutorial with a demo:

Parse Natural Language Dates with OpenAI GPT-4o for Smart Scheduling

by JaredCo

This n8n workflow demonstrates how to transform natural language date and time expressions into structured data with 96%+ accuracy. Parse complex expressions like "early next July", "2 weeks after project launch", or "end of Q3" into precise datetime objects with confidence scoring, timezone intelligence, and business rules validation for any automation workflow. Good to know Achieves 96%+ accuracy on complex natural language date expressions At time of writing, this is the most advanced open-source date parser available Includes AI learning that improves over time with user corrections Supports 6 languages with auto-detection (English, Spanish, French, German, Italian, Portuguese) Sub-millisecond response times with intelligent caching Enterprise-grade with business intelligence and timezone handling How it works Natural Language Input**: Receives date expressions via webhook, form, email, or chat AI-Powered Parsing**: Your world-class date parser processes the text through: 50+ custom rule patterns for complex expressions Multi-language auto-detection and smart translation Confidence scoring (0.0-1.0) for AI decision-making Ambiguity detection with helpful suggestions Business Intelligence**: Applies enterprise rules automatically: Holiday calendar awareness (US + International) Working hours validation and warnings Business day auto-adjustment Timezone normalization (IANA format) Smart Scheduling**: Creates calendar events with: Structured datetime objects (start/end times) Confidence metadata for workflow decisions Alternative interpretations for ambiguous inputs Rich context for follow-up actions Integration Ready**: Outputs connect seamlessly to: Google Calendar, Outlook, Apple Calendar CRM systems (HubSpot, Salesforce) Project management tools (Notion, Asana) Communication platforms (Slack, Teams) How to use The webhook trigger receives natural language date requests from any source Replace the MCP server URL with your deployed date parser endpoint Configure timezone preferences for your organization Customize business rules (working hours, holidays) in the parser settings Connect calendar integration nodes for automatic event creation Add notification workflows for scheduling confirmations Use Cases Meeting Scheduling**: "Schedule our quarterly review for early Q3" Project Management**: "Set deadline 2 weeks after product launch" Event Planning**: "Book venue for the weekend before Labor Day" Personal Assistant**: "Remind me about dentist appointment next Tuesday morning" International Teams**: "Team standup tomorrow morning" (auto-timezone conversion) Seasonal Planning**: "Launch campaign in late spring 2025" Requirements Natural Language Date Parser MCP server (provided code) Webhook endpoint or form trigger Calendar integration (Google Calendar, Outlook, etc.) Optional: Slack/Teams for notifications Optional: Database for learning pattern storage Customizing this workflow Multi-language Support**: Enable auto-detection for global teams Business Rules**: Configure company holidays and working hours Learning System**: Enable AI learning from user corrections Integration Depth**: Connect to your existing calendar and CRM systems Confidence Thresholds**: Set minimum confidence levels for auto-scheduling Ambiguity Handling**: Route unclear dates to human review or clarification requests Sample Input/Output Input Examples: "early next July" "2 weeks after Thanksgiving" "next Wednesday evening" "Q3 2025" "mañana por la mañana" (Spanish) "first thing Monday" Rich Output: { "parsed": [{ "start": "2025-07-01T00:00:00Z", "end": "2025-07-10T23:59:59Z", "timezone": "America/New_York" }], "confidence": 0.95, "method": "custom_rules", "business_insights": [{ "type": "business_warning", "message": "Selected date range includes July 4th holiday" }], "predictions": [{ "type": "time_preference", "suggestion": "You usually schedule meetings at 10 AM" }], "ambiguities": [], "alternatives": [{ "interpretation": "Early July 2026", "confidence": 0.15 }], "performance": { "cache_hit": true, "response_time": "0.8ms" } } Why This Workflow is Unique World-Class Accuracy**: 96%+ success rate on complex expressions AI Learning**: Improves over time with user feedback Global Ready**: Multi-language and timezone intelligence Business Smart**: Enterprise rules and holiday awareness Performance Optimized**: Sub-millisecond cached responses Context Aware**: Provides confidence scores and alternatives for AI decision-making Transform your scheduling workflows from rigid form inputs to natural, conversational date requests that your users will love!

🤖AI & Machine Learning

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Calculate the Centroid of a Set of Vectors

by Mauricio Perera

n8n Workflow: Calculate the Centroid of a Set of Vectors Overview This workflow receives an array of vectors in JSON format, validates that all vectors have the same dimensions, and computes the centroid. It is designed to be reusable across different projects. Workflow Structure Nodes and Their Functions: Receive Vectors (Webhook): Accepts a GET request containing an array of vectors in the vectors parameter. Expected Input: vectors parameter in JSON format. Example Request: /webhook/centroid?vectors=[[2,3,4],[4,5,6],[6,7,8]] Output: Passes the received data to the next node. Extract & Parse Vectors (Set Node): Converts the input string into a proper JSON array for processing. Ensures vectors is a valid array. If the parameter is missing, it may generate an error. Expected Output Example: { "vectors": [[2,3,4],[4,5,6],[6,7,8]] } Validate & Compute Centroid (Code Node): Validates vector dimensions and calculates the centroid. Validation: Ensures all vectors have the same number of dimensions. Computation: Averages each dimension to determine the centroid. If validation fails: Returns an error message indicating inconsistent dimensions. Successful Output Example: { "centroid": [4,5,6] } Error Output Example: { "error": "Vectors have inconsistent dimensions." } Return Centroid Response (Respond to Webhook Node): Sends the final response back to the client. If the computation is successful, it returns the centroid. If an error occurs, it returns a descriptive error message. Example Response: { "centroid": [4, 5, 6] } Inputs JSON array of vectors, where each vector is an array of numerical values. Example Input { "vectors": [ [1, 2, 3], [4, 5, 6], [7, 8, 9] ] } Setup Guide Create a new workflow in n8n. Add a Webhook node (Receive Vectors) to receive JSON input. Add a Set node (Extract & Parse Vectors) to extract and convert the data. Add a Code node (Validate & Compute Centroid) to: Validate dimensions. Compute the centroid. Add a Respond to Webhook node (Return Centroid Response) to return the result. Function Node Script Example const input = items[0].json; const vectors = input.vectors; if (!Array.isArray(vectors) || vectors.length === 0) { return [{ json: { error: "Invalid input: Expected an array of vectors." } }]; } const dimension = vectors[0].length; if (!vectors.every(v => v.length === dimension)) { return [{ json: { error: "Vectors have inconsistent dimensions." } }]; } const centroid = new Array(dimension).fill(0); vectors.forEach(vector => { vector.forEach((val, index) => { centroid[index] += val; }); }); for (let i = 0; i < dimension; i++) { centroid[i] /= vectors.length; } return [{ json: { centroid } }]; Testing Use a tool like Postman or the n8n UI to send sample inputs and verify the responses. Modify the input vectors to test different scenarios. This workflow provides a simple yet flexible solution for vector centroid computation, ensuring validation and reliability.

Store Retell transcripts in Sheets, Airtable or Notion from webhook

by Agent Studio

Automatically store Retell transcripts in Google Sheets/Airtable/Notion from webhook Overview This workflow stores the results of a Retell voice call (transcript, analysis, etc.) once it has ended and been analyzed. It listens for call_analyzed webhook events from Retell and stores the data in Airtable, Google Sheets, and Notion (choose based on your stack). Useful for anyone building Retell agents who want to keep a detailed history of analyzed calls in structured tools. Who is it for For builders of Retell's Voice Agents who want to store call history and essential analytic data. Prerequisites Have a Retell AI Account Create a Retell agent Associate a phone number with your Retell agent Set up one of the following: An Airtable base and table (example: "Transcripts") A Google Sheet with a “Transcripts” tab A Notion database with columns to match the transcript fields Templates: Airtable Google Sheets Notion How it works Receives a webhook POST request from Retell when a call has been analyzed. Filters out any event that is not call_analyzed (Retell sends webhooks for call_started, call_ended and call_analyzed) Extracts useful fields like: Call ID, start/end time, duration, total cost Transcript, summary, sentiment Stores this data in your preferred tool: Airtable Google Sheets Notion How to use it Copy the webhook URL (e.g., https://your-instance.app.n8n.cloud/webhook/poc-retell-analysis) and paste it in your Retell agent under "Webhook settings" then "Agent Level Webhook URL". Make sure your Airtable, Google Sheet, or Notion databases are correctly configured to receive the fields. After each call, once Retell finishes the analysis, this workflow will automatically log the results. Extension If you use any "Post-Call Analysis" fields, you can add columns to your Airtable, Google Sheet, or Notion database. Then fetch the data from the call.call_analysis.custom_analysis_data object. Additional Notes Phone numbers are extracted depending on the call direction (from_number or to_number). Cost is converted from cents to dollars before saving. Dates are converted from timestamps to local ISO strings. You can remove any of the outputs (Airtable, Google Sheets, Notion) if you're only using one. 👉 Reach out to us if you're interested in analysing your Retell Agent conversations.

Automate Client Project Onboarding with Google Drive, Gmail, and Slack Notifications

by Marth

💬Communication & Collaboration

⚙️ How it works Workflow starts from a manual trigger or form submission with project details. It extracts key input data like client name, email, project type, deadline, and brand folder (optional). A Google Drive folder is automatically created inside a designated parent folder. The shareable link of the newly created folder is generated. A personalized email is composed and sent to the client using Gmail, including project details and folder link. 🛠️ Set up steps Google Drive Setup: Connect your Google Drive credentials in n8n. Set the parent folder ID where all project folders should be created. Gmail Setup: Connect a Gmail account with proper access. Customize the subject and message template in the Gmail node. Input Data Preparation: Ensure the following input fields are provided: client_name contact_email project_type deadline brand_drive_folder (optional) Test & Deploy: Use mock data or a test trigger to validate the workflow. Once confirmed, deploy it with the actual trigger (e.g. webhook, form submission).

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Sync Zendesk tickets with subsequent comments to Jira issues

by n8n Team

This workflow creates a Jira issue when a new ticket is created in Zendesk. Subsequent comments on the ticket in Zendesk are added as comments to the issue in Jira. Prerequisites Zendesk account and Zendesk credentials. Jira account and Jira credentials. Jira project 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 Jira. The Jira issue key is then saved in one of the ticket's fields (in setup we call this "Jira Issue Key"). The next time a comment is added to the ticket, the workflow retrieves the Jira issue key from the ticket's field and adds the comment to the issue in Jira. 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 the 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 Jira issue key. To do so, follow the steps below: In Zendesk, navigate to Admin Center > Objects and rules > Tickets > Fields > Add field. Use the text field option and give the field a name such as “Jira Issue Key". Save the field. In n8n, open the Update ticket node and select the field you created in Zendesk.

Track Jura Coffee Machine Data with Webhook API and Google Sheets

by Halfbit 🚀

Jura Coffee Counter: Webhook API & Google Sheets Logger ☕️ Track how many coffees your Jura E8 espresso machine makes — fully automated via webhook and Google Sheets. This workflow exposes a custom API endpoint that can be called by smart devices, such as an ESP8266 or ESP32 reading data from a Jura E8 coffee machine via Bluetooth Low Energy (BLE). The incoming data (including total coffee count) is timestamped and appended to a Google Sheet, making it easy to visualize or analyze your machine usage. ☕ Originally built for a Jura E8, based on AlexxIT/Jura reverse-engineering project. > 📝 This workflow uses Google Sheets as a logging backend. You can easily switch it to Airtable, Notion, or a database of your choice. Live example available at: https://halfbitstudio.com/o-nas/ > 🖥️ In our setup, this workflow is used to provide real-time coffee consumption stats displayed directly on our website. > 🔌 Some Jura machines require an accessory Bluetooth transmitter to enable connectivity. Communication is based on the Bluetooth Low Energy (BLE) protocol. Use Case Tracking usage of a Jura coffee machine Logging IoT sensor data into Google Sheets Creating dashboards for daily consumption Smart office setups with coffee stats! Features ☁️ Two Webhook endpoints: POST /{{WEBHOOK_POST_PATH}} — receives JSON from ESP (coffee machine reader) GET /{{WEBHOOK_GET_PATH}} — returns latest records as JSON 📅 Timestamping via Date & Time node 🔹 Coffee counter extraction from incoming JSON 🧾 Appends structured rows to Google Sheets 📤 Webhook response for external status or dashboards Setup Instructions Jura Coffee Machine Integration (Hardware) Use an ESP device (e.g. ESP8266 or ESP32) to connect to the Jura E8 via Bluetooth Low Energy (BLE). Send POST requests with JSON payload: { "total_coffees": 123 } Reverse-engineered protocol reference: AlexxIT/Jura Google Sheets Configuration Create a new Google Sheet with column headers like: date | time | coffee counter Connect your Google account in n8n and authorize access to this sheet. Replace the documentId and sheetName fields in the Google Sheets nodes: Use full URL to your spreadsheet Use the actual sheet name (e.g. Sheet1) Environment Variables & Placeholders | Placeholder | Description | | ------------------------ | ----------------------------------------------- | | {{WEBHOOK_POST_PATH}} | Endpoint to receive coffee counter data | | {{WEBHOOK_GET_PATH}} | Endpoint to return latest data (for dashboards) | | {{SHEET_ID}} | Google Spreadsheet ID | | {{GOOGLE_CREDENTIALS}} | OAuth2 credentials for Google Sheets | | {{DATA_COLUMNS}} | Column names in the target sheet | Testing the Workflow Send test request: Use Postman or ESP to send a POST request to /{{WEBHOOK_POST_PATH}} Body should include total_coffees value Check Google Sheet: Open your sheet and verify that a new row was appended Test GET endpoint: Access the second webhook URL (e.g. /{{WEBHOOK_GET_PATH}}) in browser or fetch via API Optional: Use Respond to Webhook output in a dashboard or frontend Customization Tips Sheet format**: Add more columns if you want to track additional data (e.g. machine temperature, errors) Output format**: Replace Google Sheets with any other storage (e.g. MySQL, Notion) Auth layer**: Add basic auth or token verification if needed for public exposure Notifications**: Send alerts to Discord/Slack when reaching thresholds (e.g. 200 coffees brewed) Tags: google-sheets, iot, webhook, jura, coffee, api, automation

Public Holiday Lookup with Nager.Date API via Webhook

by ist00dent

💬Communication & Collaboration

This n8n template empowers you to instantly fetch a list of public holidays for any given year and country using the Nager.Date API. This is incredibly useful for scheduling, planning, or integrating holiday data into various business and personal automation workflows. 🔧 How it works Receive Holiday Request Webhook: This node acts as the entry point, listening for incoming POST requests. It expects a JSON body containing the year (e.g., 2025) and countryCode (e.g., US for United States, PH for Philippines, DE for Germany) for which you want to retrieve public holidays. Get Public Holidays: This node makes an HTTP GET request to the Nager.Date API (date.nager.at). It dynamically uses the year and countryCode from your webhook request to query the API. The API responds with a JSON array, where each object represents a public holiday with details like its date, name, and type. Respond with Holiday Data: This node sends the full list of public holidays received from Nager.Date back to the service that initiated the webhook. 👤 Who is it for? This workflow is ideal for: Businesses with International Operations: Automatically check holidays for different country branches to adjust production schedules, customer service hours, or delivery estimates. HR & Payroll Departments: Accurately calculate workdays, plan leave schedules, or process payroll taking public holidays into account. Event Planners: Avoid scheduling events on public holidays, which could impact attendance or venue availability. Travel Agencies: Inform clients about holidays in their destination country that might affect local business hours or attractions. Content & Social Media Schedulers: Plan content around national holidays to maximize engagement or avoid insensitive postings. Personal Productivity & Travel Planning: Integrate holiday data into your calendar or task management tools to plan trips or personal time off more effectively. Developers: Easily integrate a reliable source of public holiday data into custom applications, dashboards, or internal tools without managing complex datasets. 📑 Data Structure When you trigger the webhook, send a POST request with a JSON body structured as follows: { "year": 2025, "countryCode": "PH" // Example: "US", "DE", "GB", etc. } You can find a comprehensive list of supported country codes on the Nager.Date API documentation: https://www.nager.at/Country The workflow will return a JSON array, where each element is a holiday object, like this example for a single holiday: [ { "date": "2025-01-01", "localName": "New Year's Day", "name": "New Year's Day", "countryCode": "PH", "fixed": true, "global": true, "counties": null, "launchYear": null, "types": [ "Public" ] } // ... more holiday objects ] ⚙️ Setup Instructions Import Workflow: In your n8n editor, click "Import from JSON" and paste the provided workflow JSON. Configure Webhook Path: Double-click the Receive Holiday Request Webhook node. In the 'Path' field, set a unique and descriptive path (e.g., /public-holidays). Activate Workflow: Save and activate the workflow. 📝 Tips This workflow is a foundation for many powerful automations: Conditional Branching for Specific Holidays: Add an IF node after "Get Public Holidays" to check for a specific holiday (e.g., "Christmas Day"). You can then trigger different actions (e.g., send a reminder, adjust a schedule) only for that particular holiday. Filtering and Aggregating Data: Use a Filter node to only keep holidays of a certain type (e.g., "Public"). Use a Code or Function node to count the number of public holidays, or extract just the names and dates into a simpler list. Storing Holiday Data: Google Sheets/Airtable: Automatically append new holidays to a spreadsheet for easy reference or further analysis. Database: Store holiday data in a database (like PostgreSQL or MySQL) to build a custom holiday calendar application. Scheduling and Reminders: Connect this workflow to a Cron or Schedule node to run periodically (e.g., once a year at the start of the year). Use the retrieved holiday dates to set up reminders in your calendar (Google Calendar node) or send notifications (Slack, Email, SMS) a few days before an upcoming holiday. Integrate with Business Logic: Employee Leave Management: Cross-reference employee leave requests with public holidays to ensure accuracy. Automated Messages: Schedule automated "Happy Holiday" messages to customers or employees. E-commerce Shipping: Adjust estimated shipping times based on upcoming non-working days. API Key (Not needed for Nager.Date free tier): The Nager.Date API used here does not require an API key for basic public holiday lookups, which makes this template very easy to use out-of-the-box.

1 nodes157 views

Auto-Generate Blog & AI Image from YouTube Videos with Dumpling AI & GPT-4o

by Yang