Workflow Templates

This powerful n8n automation sends you daily weather updates directly to your Telegram chat using live data from OpenWeatherMap. It supports automatic daily updates and manual lookups via form input. ✅ Prerequisites Before you begin, make sure you have: A working n8n instance (v1.0 or later recommended). An account with OpenWeatherMap (free plan is sufficient). A Telegram Bot created via @BotFather. Your Telegram user ID or chat ID. 🔐 API & Bot Setup 🧩 OpenWeatherMap API Go to https://openweathermap.org/api Sign up and verify your account. Navigate to API Keys in your account dashboard. Copy your API key (used later in the HTTP Request node). 🤖 Telegram Bot Open @BotFather in Telegram. Run /newbot and follow the prompts: Choose a name and username for your bot. You’ll get a bot token (copy this). Start a chat with your new bot to activate it. To get your Telegram User ID, use @userinfobot or an n8n Telegram Trigger node. 🔄 Trigger Options ⏰ Schedule Trigger (Automatic) Runs daily at 8:00 AM IST. Ideal for consistent, passive updates. 📝 Form Trigger (Manual) Input 🌆 City and 🌍 Country manually. Instantly receive weather info in Telegram. 🧠 How the Flow Works Trigger Activated (Scheduled or Form) City & Country fetched (default or from form) HTTP Request sent to OpenWeatherMap with API key Weather Data Parsed & Formatted: 📅 Current Date 📍 City & Country 🌤️ Weather Description 🌡️ Temperature (°C) 💧 Humidity (%) 🌬️ Wind Speed (m/s) 🔼 Atmospheric Pressure 🌅 Sunrise Time (IST) 🌇 Sunset Time (IST) Message Sent to Telegram 🧰 Nodes Used Schedule Trigger** – Runs every day at 8:00 AM IST Form Trigger** – Accepts user input Set Node** – Default city/country values and date formatting HTTP Request** – Calls OpenWeatherMap API Function Node** – Converts timestamps to IST Telegram Node** – Sends formatted weather message 📦 Example Telegram Output 📅 Wednesday, 10 July 2025 🌤 Weather in Mumbai, IN: Condition: Clear sky Temperature: 30°C 💧 Humidity: 70% 🌬 Wind Speed: 3 m/s 🔼 Pressure: 1013 hPa 🌅 Sunrise: 5:57:12 AM 🌇 Sunset: 6:53:45 PM 🛠️ Customization Tips 🏙️ Change Default City/Country Locate the Set Node (used before the API call). Replace "Mumbai" and "IN" with your preferred location. Or connect the Form Trigger input to allow dynamic values. 🕗 Change Schedule Time Open the Schedule Trigger node. Adjust to your preferred time zone and daily timing (e.g., 7 AM IST). 🧪 Add Extra Data OpenWeatherMap returns more fields like visibility, UV index, etc. You can include these in your Telegram message via the Function Node and Set Node.

This n8n template demonstrates how to create a comprehensive marketing automation and booking system that combines Excel-based lead management with voice-powered customer interactions. The system utilizes VAPI for voice communication and Excel/Google Sheets for data management, making it ideal for restaurants seeking to automate marketing campaigns and streamline booking processes through intelligent voice AI technology. Good to know Voice processing requires active VAPI subscription with per-minute billing Excel operations are handled in real-time with immediate data synchronization The system can handle multiple simultaneous voice calls and lead processing All customer data is stored securely in Excel with proper formatting and validation Marketing campaigns can be scheduled and automated based on lead data How it works Lead Management & Marketing Automation Workflow New Lead Trigger: Excel triggers capture new leads when customers are added to the lead management spreadsheet Lead Preparation: The system processes and formats lead data, extracting relevant details (name, phone, preferences, booking history) Campaign Loop: Automated loop processes through multiple leads for batch marketing campaigns Voice Marketing Call: VAPI initiates personalized voice calls to leads with tailored restaurant offers and booking invitations Response Tracking: All call results and lead responses are logged back to Excel for campaign analysis Booking & Order Processing Workflow Voice Response Capture: VAPI webhook triggers when customers respond to marketing calls or make direct booking requests Response Storage: Customer responses and booking preferences are immediately saved to Excel sheets Information Extraction: System processes natural language responses to extract booking details (party size, preferred times, special requests) Calendar Integration: Booking information is automatically scheduled in restaurant management systems Confirmation Loop: Automated follow-up voice messages confirm bookings and provide additional restaurant information Excel Sheet Structure Lead Management Sheet | Column | Description | |--------|-------------| | lead_id | Unique identifier for each lead | | customer_name | Customer's full name | | phone_number | Primary contact number | | email | Customer email address | | last_visit_date | Date of last restaurant visit | | preferred_cuisine | Customer's food preferences | | party_size_typical | Usual number of guests | | preferred_time_slot | Preferred dining times | | marketing_consent | Permission for marketing calls | | lead_source | How customer was acquired | | lead_status | Current status (new, contacted, converted, inactive) | | last_contact_date | Date of last marketing contact | | notes | Additional customer information | | created_at | Lead creation timestamp | Booking Responses Sheet | Column | Description | |--------|-------------| | response_id | Unique response identifier | | customer_name | Customer's name from call | | phone_number | Contact number used for call | | booking_requested | Whether customer wants to book | | party_size | Number of guests requested | | preferred_date | Requested booking date | | preferred_time | Requested time slot | | special_requests | Dietary restrictions or special occasions | | call_duration | Length of VAPI call | | call_outcome | Result of marketing call | | follow_up_needed | Whether additional contact is required | | booking_confirmed | Final booking confirmation status | | created_at | Response timestamp | Campaign Tracking Sheet | Column | Description | |--------|-------------| | campaign_id | Unique campaign identifier | | campaign_name | Descriptive campaign title | | target_audience | Lead segments targeted | | total_leads | Number of leads contacted | | successful_calls | Calls that connected | | bookings_generated | Number of bookings from campaign | | conversion_rate | Percentage of leads converted | | campaign_cost | Total VAPI usage cost | | roi | Return on investment | | start_date | Campaign launch date | | end_date | Campaign completion date | | status | Campaign status (active, completed, paused) | How to use Setup: Import the workflow into your n8n instance and configure VAPI credentials Excel Configuration: Set up Excel/Google Sheets with the required sheet structure provided above Lead Import: Populate the Lead Management sheet with customer data from various sources Campaign Setup: Configure marketing message templates in VAPI nodes to match your restaurant's branding Testing: Test voice commands such as "I'd like to book a table for tonight" or "What are your specials?" Automation: Enable triggers to automatically process new leads and schedule marketing campaigns Monitoring: Track campaign performance through the Campaign Tracking sheet and adjust strategies accordingly The system can handle multiple concurrent voice calls and scales with your restaurant's marketing needs. Requirements VAPI account** for voice processing and natural language understanding Excel/Google Sheets** for storing lead, booking, and campaign data n8n instance** with Excel/Sheets and VAPI integrations enabled Valid phone numbers** for lead contact and compliance with local calling regulations Customising this workflow Multi-location Support**: Adapt voice AI automation for restaurant chains with location-specific offers Seasonal Campaigns**: Try popular use-cases such as holiday promotions, special event marketing, or loyalty program outreach Integration Options**: The workflow can be extended to include CRM integration, SMS follow-ups, and social media campaign coordination Advanced Analytics**: Add nodes for detailed campaign performance analysis and customer segmentation

Extracting LinkedIn Profile Information Use Case Manually copying data from LinkedIn profiles is time-consuming and error-prone. This automation helps you extract structured, detailed information from any public LinkedIn profile—enabling fast enrichment, hiring research, or lead scoring. What This Automation Does This automation extracts profile details from a LinkedIn URL using the following input parameters: airtop_profile**: The name of your Airtop Profile connected to LinkedIn. linkedin_url**: The URL of the LinkedIn profile you want to extract data from. How It Works Starts with a form trigger or via another workflow. Assigns the LinkedIn URL and Airtop profile variables. Opens the LinkedIn profile in a real browser session using Airtop. Uses an AI prompt to extract structured information, including: Name, headline, location Current company and position About section, experience, and education history Skills, certifications, languages, connections, and recommendations Returns structured JSON ready for further use or storage. Setup Requirements Airtop API Key — free to generate. An Airtop Profile connected to LinkedIn (requires one-time login). Next Steps Sync with CRM**: Push extracted data into HubSpot, Salesforce, or Airtable for lead enrichment. Combine with Search Automation**: Use with a LinkedIn search scraper to process profiles in bulk. Adapt to Other Platforms**: Customize the prompt to extract structured data from GitHub, Twitter, or company sites. Read more about the Extract Linkedin Profile Information automation.

A single workflow with 2 flows/paths that combine to handle the backup sequence for Zigbee device configuration from HomeAssistant / zigbee2mqtt. This provides a way to automate a periodic capture of Zigbee coordinators and device pairings to speed the recovery process when/if the HomeAssistant instance needs to be rebuilt. Setting up similar automation without n8n (e.g. shell scripts and system timers) is consiterably more challenging. n8n makes it easy and this template should remove any other excuse not to do it. Flow 1 Triggered by Cron/Timer set whatever interval for backups sends mqtt message to request zigbee2mqtt backup (via separate message) Flow 2 Triggered by zigbee2mqtt backup message Extracts zip file from the message and stores somewhere, with a date-stamp in the filename, via sftp Setup Create a MQTT connection named "MQTT Account" with the appropriate protocol (mqtt), host, port (1883), username, and password Create an sftp connection named "SFTP Zigbee Backups" with the appropriate host, port (22), username, and password or key. Reference This article describes the mqtt parts.

Automated pipeline that extracts job listings from Upwork and exports them to Google Sheets for better organization, analysis, and team collaboration. 🚀 What It Does Fetches job postings based on saved searches Extracts key job details (title, budget, description) Organizes data in Google Sheets Updates in real-time Supports multiple search criteria 🎯 Perfect For Freelancers tracking opportunities Teams managing multiple projects Agencies monitoring client needs Market researchers Business analysts ⚙️ Key Benefits ✅ Centralized job board ✅ Easy sharing with team members ✅ Advanced filtering and sorting ✅ Historical data tracking ✅ Customizable data points 🔧 What You Need Upwork account Google account n8n instance Google Sheets setup 📊 Data Exported Job title and description Budget and hourly rate Client information Posted date Required skills Job URL 🛠️ Setup & Support Quick Setup Get started in 15 minutes with our step-by-step guide 📺 Watch Tutorial 💼 Get Expert Support 📧 Direct Help Streamline your job search and opportunity tracking with automated data collection and organization.

Use case This automation is for teams working in Notion. When you have a lot of back and forth in the comment section, it’s easy to lose track of what is going on in the conversation. This automation relies on AI to generate a summary of the comment section. How it works Every hour (the trigger can be adapted to your need and usecase), the automation checks if new comments have been added to the pages of your Notion database. If there are new comments, the comments are sent to an AI model to write a summary. The summary is then added to a predefined page property. The automation also updates a “Last execution” property. This prevents to re-generate the AI summary when no new comments have been received. Setup Define your Notion variables: Notion database, property that will hold the AI summary, property that will hold the last execution date of the automation. Set up your Notion credentials. Set up your AI model credentials (API key). How to adjust it to your needs Use the LLM model of your choice. In this template, I used Gemini but you can easily replace it by ChatGPT, Claude, etc. Adapt the prompt to your use case to get better summaries: specify the maximum number of characters, give an example, etc. Adapt the trigger to your needs. You could use Notion webhooks as trigger in order to run the automation only when a new comment is added (this setup is advised if you’re on n8n cloud version).

Overview An intelligent automation workflow that monitors your Gmail inbox and sends AI-powered summaries of important emails directly to your Telegram chat. Perfect for staying updated on critical communications without constantly checking your email. 🌟 Key Features Real-time Email Monitoring**: Checks Gmail every minute for new emails Smart Content Filtering**: Only processes emails containing important keywords AI-Powered Summarization**: Uses GPT-4o-mini to create concise, human-readable summaries Instant Telegram Notifications**: Delivers summaries directly to your preferred Telegram chat Customizable Keywords**: Easily modify filters to match your specific needs 🔧 How It Works Workflow Steps: Email Trigger: Continuously monitors your Gmail inbox for new messages Smart Filter: Analyzes email subject and body for important keywords (sales, jobs, etc.) AI Processing: Sends relevant emails to OpenAI for intelligent summarization Telegram Delivery: Sends formatted summary to your Telegram chat Sample Output: 📦 Your Flipkart order "Bluetooth Speaker" was delivered today. Enjoy! 💰 Invoice from AWS for $23.50 is due by July 20. Check billing portal. ✅ HR shared your July payslip. No action needed unless there's an error. 🛠 Setup Requirements Gmail account with OAuth2 credentials OpenAI API key Telegram bot token and chat ID N8N instance (cloud or self-hosted) 📋 Use Cases Business Alerts**: Payment due notices, invoice reminders E-commerce**: Order confirmations, delivery updates HR Communications**: Payslips, policy updates, announcements Security**: Login alerts, security notifications Job Hunting**: Application responses, interview invitations ⚙️ Customization Options Keyword Filters**: Add/remove keywords in the filter node (invoice, payment, security, delivery, etc.) AI Prompt**: Modify the summarization style and format Polling Frequency**: Adjust email checking interval Multiple Chats**: Send to different Telegram chats based on email type 🔒 Privacy & Security Processes emails locally through n8n No email content stored permanently Uses secure OAuth2 authentication Respects Gmail API rate limits 📊 Performance Lightweight and efficient Minimal resource usage Fast AI processing with GPT-4o-mini Reliable Telegram delivery 💡 Pro Tips Start with broad keywords and refine based on results Use multiple condition branches for different email types Set up different Telegram chats for work vs personal emails Monitor your OpenAI usage to avoid unexpected costs

Automating Company ICP Scoring via LinkedIn Use Case This automation scores companies based on their LinkedIn profile using custom Ideal Customer Profile (ICP) criteria. It’s ideal for qualifying B2B leads and prioritizing outreach based on fit. What This Automation Does Inputs required: Company LinkedIn URL**: Public LinkedIn profile of the company. Airtop Profile (connected to LinkedIn)**: Airtop Profile authenticated to access and extract profile data. The automation analyzes the LinkedIn page and calculates a score based on: Scoring Criteria | Category | Classification | Points | |--------------------|---------------------------|------------| | AI Focus | Low | 5 | | | Medium | 10 | | | High | 25 | | Technical Level | Basic | 5 | | | Intermediate | 15 | | | Advanced | 25 | | | Expert | 35 | | Employee Count | 0–9 | 5 | | | 10–150 | 25 | | | 150+ | 30 | | Agency Status | Not Automation Agency | 0 | | | Automation Agency | 20 | | Geography | Outside US/Europe | 0 | | | US/Europe Based | 10 | The result includes: Total ICP score Detailed justifications for each score component How It Works Opens the company’s LinkedIn page using Airtop. Analyzes metadata including employee count, headquarters, services, and keywords. Applies the scoring rubric and returns structured JSON with scores and reasons. Optionally flattens the result for storage or CRM integration. Setup Requirements Airtop API Key LinkedIn-authenticated Airtop Profile Next Steps Combine with Lead Lists**: Score companies from outreach lists. Push to CRM**: Add scores to HubSpot or Salesforce records. Adjust Scoring Weights**: Modify rubric to reflect your ICP strategy. Read more about company ICP scoring automation with Airtop and n8n

This workflow restores all n8n instance credentials from GitHub backups using the n8n API node. It complements the Backup Your Credentials to GitHub template by allowing users to seamlessly restore previously saved credentials. How It Works The workflow fetches credentials stored in a GitHub repository and imports them into your n8n instance. Setup Instructions To configure the workflow, update the Globals node with the following values: repo.owner** – Your GitHub username repo.name** – The name of your GitHub repository storing the credentials repo.path** – The folder path within the repository where credentials are stored For example, if your GitHub username is john-doe, your repository is named n8n-backups, and credentials are stored in a credentials/ folder, you would set: repo.owner → john-doe repo.name → n8n-backups repo.path → credentials/ Required Credentials GitHub API** – Access to your repository n8n API** – To import credentials into your n8n instance Who Is This For? This template is ideal for users who want to restore their credentials from GitHub backups, ensuring easy migration and recovery in case of data loss. Check out my other templates: 👉 My n8n Templates

How it works This workflow demonstrates a fundamental pattern for securing a webhook by requiring an API key. It acts as a gatekeeper, checking for a valid key in the request header before allowing the request to proceed. Incoming Request: The Secured Webhook node receives an incoming POST request. It expects an API key to be sent in the x-api-key header. API Key Verification: The Check API Key node takes the key from the incoming request's header. It then makes an internal HTTP request to a second webhook (Get API Key) which acts as a mock database. This second webhook retrieves a list of registered API keys (from the Registered API Keys node) and filters it to find a match for the key that was provided. Conditional Response: If a match is found, the API Key Identified node routes the execution to the "success" path, returning a 200 OK response with the identified user's ID. If no match is found, it routes to the "unauthorized" path, returning a 401 Unauthorized error. This pattern separates the public-facing endpoint from the data source, which is a good security practice. Set up steps Setup time: ~2 minutes This workflow is designed to be a self-contained example. Set up Credentials: This workflow uses "Header Auth" for its internal communication. Go to Credentials and create a new Header Auth credential. You can use any name and value (e.g., Name: X-N8N-Auth, Value: my-secret-password). Select this credential in all four webhook/HTTP Request nodes. Add Your API Keys: Open the Registered API Keys node. This is your mock database. Edit the array to include the user_id and api_key pairs you want to authorize. Activate the workflow. Test it: Use the Test Secure Webhook node to send a request. Try it with a valid key from your list to see the success response. Change the x-api-key header to an invalid key to see the 401 Unauthorized error. For Production: Replace the mock database part of this workflow (the Get API Key webhook and Registered API Keys node) with a real database node like Supabase, Postgres, or Baserow to look up keys.

This workflow converts an exported CSV from Squarespace profiles into a Shopify-compatible format for customer import. How It Works Clone this Google Sheets template, which includes two sheets: Squarespace Profiles (Input) Go to Squarespace Dashboard → Contacts Click the three-dot icon → Select Export all Contacts Shopify Customers (Output) This sheet formats the data to match Shopify's customer import CSV. Shopify Dashboard → Customers → Import customers by CSV The workflow can run on-demand or be triggered via webhook. Via webhook Set up webhook node to expect a POST request Trigger the webhook using this code (psuedo) - replace {webhook-url} with the actual URL const formData = new FormData(); formData.append('file', blob, 'profiles_export.csv'); // Add file to FormData fetch('{webhook-url}', { // Replace with your target URL method: 'POST', mode: 'no-cors', body: formData }); The data is processed into the Shopify Customers sheet. Manually trigger Import Squarespace profiles into the sheet. Run the workflow to convert and populate the Shopify Customers sheet. Once workflow is done, export the Shopify to csv and import to Shopify customers Requirements To use this template, you need: Google Sheets API credentials Google Sheets Setup Use this sample Google Sheets template to get started quickly. Who Is This For? For anyone looking to automate Squarespace contact exports into a Shopify-compatible format—no more manual conversion! Explore More Templates Check out my other n8n templates: 👉 n8n.io/creators/bangank36

This workflow takes two inputs, YouTube video URL (required) and a description of what information to extract from the video. If the description/"what you want" field is left empty, the default prompt will generate a detailed summary and description of the video's contents. However, you can ask for something more specific using this field/input. ++ Don't forget to make the workflow Active and use the production URL from the form node. Benefits Instant Summary Generation - Convert hours of watching YouTube videos to familiar, structured paragraphs and sentences in less than a minute Live Integration - Generate a summary or extract information on the contents of a YouTube video whenever, wherever Virtually Complete Automation - All that needs to be done is to add the video URL and describe what you want to know from the video Presentation - You can ask for a specific structure or tone to better help you understand or study the contents of the video How It Works Smart Form Interface: Simple N8N form captures video URL and description of what's to be extracted Designed for rapid and repeated completion anywhere and anytime Description Check: Uses JavaScript to determine if the description was filled in or left empty If the description field was left empty, the default prompt is, "Please be as descriptive as possible about the contents being spoken of in this video after giving a detailed summary." If the description field is filled, then the filled input will be used to describe what information to extract from the video HTTP Request: We're using Gemini API, specifically the video understanding endpoint We make a post HTTP request passing the video URL and the description of what information to extract Setup Instructions: HTTP Request Setup: Sign up for a Google Cloud account, join the Developer Program and get your Gemini API key Get curl for Gemini Video Understanding API The video understanding relies on the inputs from the form, code and HTTP request node, so correct mapping is essential for the workflow to function correctly. Feel free to reach out for additional help or clarification at my Gmail: terflix45@gmail.com, and I'll get back to you as soon as I can. Setup Steps: Code Node Setup: The code node is used as a filter to ensure a description prompt is always passed on. Use the JavaScript code below for that effect: // Loop over input items and add a new field called 'myNewField' to the JSON of each one for (const item of $input.all()) { item.json.myNewField = 1; if ($input.first().json['What u want?'].trim() == "") { $input.first().json['What do you want?'] = "Please be as descriptive as possible about the contents being spoken of this video after giving a detailed summary"; } } return $input.all(); // End of Code HTTP Request: To use Gemini Video Understanding, you'll need your Gemini API key Go to https://ai.google.dev/gemini-api/docs/video-understanding#youtube. This link will take you directly to the snippet. Just select REST programming language, copy that curl command, then paste it into the HTTP Request node Replace "Please summarize the video in 3 sentences." with the code node's output, which should either be the default description or the one entered by the user (second output field variable) Replace "https://www.youtube.com/watch?v=9hE5-98ZeCg" with the n8n form node's first output field, which should be the YouTube video URL variable Replace $GEMINI_API_KEY with your API key Redirect: Use n8n form node, page type "Final Ending" to redirect user to the initial n8n form for another analysis or preferred destination