Workflow Templates

This workflow randomly select recipes from a Mealie instance (can use a specific category) and then creates a meal plan in Mealie with those recipes. How it works: Workflow has a scheduled trigger (set to run weekly on a Friday) Config node sets a few properties to configure the workflow A call to the Mealie API to get the list of recipes The code node holds most of the logic, this will loop through the number of recipes defined in the config node and randomly select a recipe from the list (making sure not to double up any recipes) Once all the recipes are selected it will call the Mealie API to set up the meal plan on the days Setup Add your Mealie API token as a credential and set it on the Http Request nodes Set the relevant schedule trigger to run when you like Update the Config node with the config you want numberOfRecipes - Number of recipes to populate for the meal plan offsetPlanDays - Number of days in the future to start the plan (0 will start it today, 1 tomorrow, etc.) mealieCategoryId - A category id of the category you want to pull in recipes from (default to select from all recipes) mealieBaseUrl - The base url of your Mealie instance

🔍 Glassdoor Job Finder: Bright Data Scraping + Keyword-Based Automation A comprehensive n8n automation that scrapes Glassdoor job listings using Bright Data's web scraping service based on user-defined keywords, location, and country parameters, then automatically stores the results in Google Sheets. 📋 Overview This workflow provides an automated job search solution that extracts job listings from Glassdoor using form-based inputs and stores organized results in Google Sheets. Perfect for recruiters, job seekers, market research, and competitive analysis. Workflow Description: Automates Glassdoor job searches using Bright Data's web scraping capabilities. Users submit keywords, location, and country via form trigger. The workflow scrapes job listings, extracts company details, ratings, and locations, then automatically stores organized results in Google Sheets for easy analysis and tracking. ✨ Key Features 🎯 Form-Based Input: Simple web form for job type, location, and country 🔍 Glassdoor Integration: Uses Bright Data's Glassdoor dataset for accurate job data 📊 Smart Data Processing: Automatically extracts key job information 📈 Google Sheets Storage: Organized data storage with automatic updates 🔄 Status Monitoring: Built-in progress tracking and retry logic ⚡ Fast & Reliable: Professional scraping with error handling 🎯 Keyword Flexibility: Search any job type with location filters 📝 Structured Output: Clean, organized job listing data 🎯 What This Workflow Does Input Job Keywords:** Job title or role (e.g., "Software Engineer", "Marketing Manager") Location:** City or region for job search Country:** Target country for job listings Processing Form Submission Data Scraping via Bright Data Status Monitoring Data Extraction Data Processing Sheet Update Output Data Points | Field | Description | Example | |-------|-------------|---------| | Job Title | Position title from listing | Senior Software Engineer | | Company Name | Employer name | Google Inc. | | Location | Job location | San Francisco, CA | | Rating | Company rating score | 4.5 | | Job Link | Direct URL to listing | https://glassdoor.com/job/... | 🚀 Setup Instructions Prerequisites n8n instance (self-hosted or cloud) Google account with Sheets access Bright Data account with Glassdoor scraping dataset access 5–10 minutes for setup Step 1: Import the Workflow Copy the JSON workflow code from the provided file In n8n: Workflows → + Add workflow → Import from JSON Paste JSON and click Import Step 2: Configure Bright Data Set up Bright Data credentials in n8n Ensure access to dataset: gd_lpfbbndm1xnopbrcr0 Update API tokens in: "Scrape Job Data" node "Check Delivery Status of Snap ID" node "Getting Job Lists" node Step 3: Configure Google Sheets Integration Create a new Google Sheet (e.g., "Glassdoor Job Tracker") Set up Google Sheets OAuth2 credentials in n8n Prepare columns: Column A: Job Title Column B: Company Name Column C: Location Column D: Rating Column E: Job Link Step 4: Update Workflow Settings Update "Update Job List" node with your Sheet ID and credentials Test the form trigger and webhook URL Step 5: Test & Activate Submit test data (e.g., "Software Engineer" in "New York") Activate the workflow Verify Google Sheet updates and field extraction 📖 Usage Guide Submitting Job Searches Navigate to your workflow's webhook URL Fill in: Search Job Type Location Country Submit the form Reading the Results Real-time job listing data Company ratings and reviews Direct job posting links Location-specific results Processing timestamps 🔧 Customization Options More Data Points:** Add job descriptions, salary, company size, etc. Search Parameters:** Add filters for salary, experience, remote work Data Processing:** Add validation, deduplication, formatting 🚨 Troubleshooting Bright Data connection failed:** Check API credentials and dataset access No job data extracted:** Validate search terms and location format Google Sheets permission denied:** Re-authenticate and check sharing Form submission failed:** Check webhook URL and form config Workflow execution failed:** Check logs, add retry logic Advanced Troubleshooting Check execution logs in n8n Test individual nodes Verify data formats Monitor rate limits Add error handling 📊 Use Cases & Examples Recruitment Pipeline:** Track job postings, build talent database Market Research:** Analyze job trends, hiring patterns Career Development:** Monitor opportunities, salary trends Competitive Intelligence:** Track competitor hiring activity ⚙️ Advanced Configuration Batch Processing:** Accept multiple keywords, loop logic, delays Search History:** Track trends, compare results over time External Tools:** Integrate with CRM, Slack, databases, BI tools 📈 Performance & Limits Single search:** 2–5 minutes Data accuracy:** 95%+ Success rate:** 90%+ Concurrent searches:** 1–3 (depends on plan) Daily capacity:** 50–200 searches Memory:** ~50MB per execution API calls:** 3 Bright Data + 1 Google Sheets per search 🤝 Support & Community n8n Community Forum:** community.n8n.io Documentation:** docs.n8n.io Bright Data Support:** Via your dashboard GitHub Issues:** Report bugs and features Contributing: Share improvements, report issues, create variations, document best practices. Need Help? Check the full documentation or visit the n8n Community for support and workflow examples.

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

This workflow creates a Slack thread when a new ticket is created in Zendesk. Subsequent comments on the ticket in Zendesk are added as replies to the thread in Slack. Prerequisites Zendesk account and Zendesk credentials. Slack account and Slack credentials. Slack channel to create threads in. How it works The workflow listens for new tickets in Zendesk. When a new ticket is created, the workflow creates a new thread/message in Slack. The Slack thread ID is then saved in one of the ticket's fields called "Slack thread ID". The next time a comment is added to the ticket, the workflow retrieves the Slack thread ID from the ticket's field and adds the comment to the thread/message in Slack as a reply. 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 Slack thread ID. 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 “Slack thread ID”. Save the field. In n8n, open the Update ticket node and select the field you created in Zendesk.

Who is this for? This workflow is perfect for: Companies that manage invoices through Google Drive Business owners who want to minimize manual data entry and maximize accuracy Accounting teams and finance departments seeking to automate invoice processing What problem is this workflow solving? Processing invoices manually is time-consuming, error-prone, and inconsistent. This workflow solves those issues by: Automating invoice processing** from detection to data extraction to storage Improving accuracy** by using AI to extract key invoice data fields reliably Reducing human workload** while maintaining compliance and consistency What this workflow does This workflow creates a fully automated invoice processing system by: Monitoring a Google Drive folder for new PDF invoices in real time Downloading the PDF files and extracting their content using OCR technology Using AI (OpenAI) to parse and extract key invoice fields such as invoice number, date, total amount, vendor name, itemized details, tax, and category Validating the extracted data to ensure compliance with a structured JSON schema Storing structured data in Google Sheets for easy access, review, and reporting Key Features: AI-powered extraction handles both text-based and scanned PDF invoices Provides a structured, searchable invoice database in Google Sheets Configured to run as frequently as the user needs, ensuring timely processing. Setup Copy the Google Sheet template here: 👉 PDF Invoice Parser – Google Sheet Template Connect your Google Drive account to the Drive Trigger and File Download nodes Add your OpenAI API key in the AI Parser node Link the Google Sheet in the final storage node Drop a test invoice PDF into the monitored Drive folder Required Credentials: OpenAI API Key** Google Drive Credentials** Google Sheets Credentials** How to customize this workflow to your needs Modify the polling interval** (default: every minute) for higher/lower frequency. Integrate with your accounting software** by adding nodes (e.g., QuickBooks, Xero). Use alternative LLM** such as Gemini, Claude.

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.

This workflow takes off the task of backing up workflows regularly on Github and uses Google Drive as the main tool to host these. This can be a good way to keep track of your workflows so that you never lose any workflows in case your n8n goes down. How does it work Creates a new folder within a specified folder with the time its backed up Loops around all workflows, converts them to a JSON file and uploads them to the created folder Gets the previous backups and deletes them This has a clean feel and look as it simplifies the backup while not keeping a cache of workflows on your drive. Setup Create a new folder Create new service account credentials Share the folder with the service account email Upload this workflow to your canvas and map the credentials Set the schedule that you need your workflows to run and manage your backups Activate the workflow Happy Productivity! @Imperol

Goodreads Quote Extraction with Bright Data and Gemini This workflow demonstrates how to fetch data specifically from Goodreads web pages using Bright Data and then extract specific information (quotes) from that data using a Google Gemini AI model. How it works The workflow is triggered manually. It sends a request to a Bright Data collector to scrape data from a predefined list of Goodreads URLs. The collected text data from Goodreads is then passed to a Google Gemini AI node. The AI node processes the text and extracts quotes based on a specified JSON schema output format. Set up steps Setting up this workflow should take only a few minutes. You will need a Bright Data API key to configure the 'Header Auth' credential. You will need a Google Gemini API key to configure the 'Google Gemini(PaLM) Api account' credential. Ensure the correct Bright Data collector ID is set in the 'Perform Bright Data Web Request' node URL. Make sure the full list of target Goodreads URLs is correctly added to the 'Perform Bright Data Web Request' node's body. Link your created credentials to the respective nodes ('Perform Bright Data Web Request' and 'Quotes Extractor'). Keep detailed descriptions for specific node configurations in sticky notes inside your workflow canvas.

Ok google download "movie name" I develop this automation to improve my quality of life in handling torrents in my media-center. Goal Automate the search operations of a movie based on its name and trigger a download using your transmission-daemon. Setup Prerequisite Transmission daemon up and running and its authentication method N8N configured self-hosted or with the possibility to add npm package better with docker-compose.yaml Telegram bot credential [optional] Configuration Create a folder where your docker-compose.yaml belongs n8n_dir and proceed in installing the node package. cd ~/n8n_dir npm i torrent-search-api Configuring your docker-compose.yaml file this way. You must include all the dependencies of torrent-search-api. This will let you run the new torrent search node presented in this workflow. version: '3.3' services: n8n: container_name: n8n ports: '5678:5678' restart: always volumes: '~/n8n_dir/.n8n:/home/node/.n8n' '~/n8n_dir/node_modules/@tootallnate:/usr/local/lib/node_modules/@tootallnate' '~/n8n_dir/node_modules/accepts:/usr/local/lib/node_modules/accepts' '~/n8n_dir/node_modules/agent-base:/usr/local/lib/node_modules/agent-base' '~/n8n_dir/node_modules/ajv:/usr/local/lib/node_modules/ajv' '~/n8n_dir/node_modules/ansi-styles:/usr/local/lib/node_modules/ansi-styles' '~/n8n_dir/node_modules/asn1:/usr/local/lib/node_modules/asn1' '~/n8n_dir/node_modules/assert:/usr/local/lib/node_modules/assert' '~/n8n_dir/node_modules/assert-plus:/usr/local/lib/node_modules/assert-plus' '~/n8n_dir/node_modules/ast-types:/usr/local/lib/node_modules/ast-types' '~/n8n_dir/node_modules/asynckit:/usr/local/lib/node_modules/asynckit' '~/n8n_dir/node_modules/aws-sign2:/usr/local/lib/node_modules/aws-sign2' '~/n8n_dir/node_modules/aws4:/usr/local/lib/node_modules/aws4' '~/n8n_dir/node_modules/base64-js:/usr/local/lib/node_modules/base64-js' '~/n8n_dir/node_modules/batch:/usr/local/lib/node_modules/batch' '~/n8n_dir/node_modules/bcrypt-pbkdf:/usr/local/lib/node_modules/bcrypt-pbkdf' '~/n8n_dir/node_modules/bluebird:/usr/local/lib/node_modules/bluebird' '~/n8n_dir/node_modules/boolbase:/usr/local/lib/node_modules/boolbase' '~/n8n_dir/node_modules/brotli:/usr/local/lib/node_modules/brotli' '~/n8n_dir/node_modules/bytes:/usr/local/lib/node_modules/bytes' '~/n8n_dir/node_modules/caseless:/usr/local/lib/node_modules/caseless' '~/n8n_dir/node_modules/chalk:/usr/local/lib/node_modules/chalk' '~/n8n_dir/node_modules/cheerio:/usr/local/lib/node_modules/cheerio' '~/n8n_dir/node_modules/cloudscraper:/usr/local/lib/node_modules/cloudscraper' '~/n8n_dir/node_modules/co:/usr/local/lib/node_modules/co' '~/n8n_dir/node_modules/color-convert:/usr/local/lib/node_modules/color-convert' '~/n8n_dir/node_modules/color-name:/usr/local/lib/node_modules/color-name' '~/n8n_dir/node_modules/combined-stream:/usr/local/lib/node_modules/combined-stream' '~/n8n_dir/node_modules/component-emitter:/usr/local/lib/node_modules/component-emitter' '~/n8n_dir/node_modules/content-disposition:/usr/local/lib/node_modules/content-disposition' '~/n8n_dir/node_modules/content-type:/usr/local/lib/node_modules/content-type' '~/n8n_dir/node_modules/cookiejar:/usr/local/lib/node_modules/cookiejar' '~/n8n_dir/node_modules/core-util-is:/usr/local/lib/node_modules/core-util-is' '~/n8n_dir/node_modules/css-select:/usr/local/lib/node_modules/css-select' '~/n8n_dir/node_modules/css-what:/usr/local/lib/node_modules/css-what' '~/n8n_dir/node_modules/dashdash:/usr/local/lib/node_modules/dashdash' '~/n8n_dir/node_modules/data-uri-to-buffer:/usr/local/lib/node_modules/data-uri-to-buffer' '~/n8n_dir/node_modules/debug:/usr/local/lib/node_modules/debug' '~/n8n_dir/node_modules/deep-is:/usr/local/lib/node_modules/deep-is' '~/n8n_dir/node_modules/degenerator:/usr/local/lib/node_modules/degenerator' '~/n8n_dir/node_modules/delayed-stream:/usr/local/lib/node_modules/delayed-stream' '~/n8n_dir/node_modules/delegates:/usr/local/lib/node_modules/delegates' '~/n8n_dir/node_modules/depd:/usr/local/lib/node_modules/depd' '~/n8n_dir/node_modules/destroy:/usr/local/lib/node_modules/destroy' '~/n8n_dir/node_modules/dom-serializer:/usr/local/lib/node_modules/dom-serializer' '~/n8n_dir/node_modules/domelementtype:/usr/local/lib/node_modules/domelementtype' '~/n8n_dir/node_modules/domhandler:/usr/local/lib/node_modules/domhandler' '~/n8n_dir/node_modules/domutils:/usr/local/lib/node_modules/domutils' '~/n8n_dir/node_modules/ecc-jsbn:/usr/local/lib/node_modules/ecc-jsbn' '~/n8n_dir/node_modules/ee-first:/usr/local/lib/node_modules/ee-first' '~/n8n_dir/node_modules/emitter-component:/usr/local/lib/node_modules/emitter-component' '~/n8n_dir/node_modules/enqueue:/usr/local/lib/node_modules/enqueue' '~/n8n_dir/node_modules/enstore:/usr/local/lib/node_modules/enstore' '~/n8n_dir/node_modules/entities:/usr/local/lib/node_modules/entities' '~/n8n_dir/node_modules/error-inject:/usr/local/lib/node_modules/error-inject' '~/n8n_dir/node_modules/escape-html:/usr/local/lib/node_modules/escape-html' '~/n8n_dir/node_modules/escape-string-regexp:/usr/local/lib/node_modules/escape-string-regexp' '~/n8n_dir/node_modules/escodegen:/usr/local/lib/node_modules/escodegen' '~/n8n_dir/node_modules/esprima:/usr/local/lib/node_modules/esprima' '~/n8n_dir/node_modules/estraverse:/usr/local/lib/node_modules/estraverse' '~/n8n_dir/node_modules/esutils:/usr/local/lib/node_modules/esutils' '~/n8n_dir/node_modules/extend:/usr/local/lib/node_modules/extend' '~/n8n_dir/node_modules/extsprintf:/usr/local/lib/node_modules/extsprintf' '~/n8n_dir/node_modules/fast-deep-equal:/usr/local/lib/node_modules/fast-deep-equal' '~/n8n_dir/node_modules/fast-json-stable-stringify:/usr/local/lib/node_modules/fast-json-stable-stringify' '~/n8n_dir/node_modules/fast-levenshtein:/usr/local/lib/node_modules/fast-levenshtein' '~/n8n_dir/node_modules/file-uri-to-path:/usr/local/lib/node_modules/file-uri-to-path' '~/n8n_dir/node_modules/forever-agent:/usr/local/lib/node_modules/forever-agent' '~/n8n_dir/node_modules/form-data:/usr/local/lib/node_modules/form-data' '~/n8n_dir/node_modules/format-parser:/usr/local/lib/node_modules/format-parser' '~/n8n_dir/node_modules/formidable:/usr/local/lib/node_modules/formidable' '~/n8n_dir/node_modules/fs-extra:/usr/local/lib/node_modules/fs-extra' '~/n8n_dir/node_modules/ftp:/usr/local/lib/node_modules/ftp' '~/n8n_dir/node_modules/get-uri:/usr/local/lib/node_modules/get-uri' '~/n8n_dir/node_modules/getpass:/usr/local/lib/node_modules/getpass' '~/n8n_dir/node_modules/graceful-fs:/usr/local/lib/node_modules/graceful-fs' '~/n8n_dir/node_modules/har-schema:/usr/local/lib/node_modules/har-schema' '~/n8n_dir/node_modules/har-validator:/usr/local/lib/node_modules/har-validator' '~/n8n_dir/node_modules/has-flag:/usr/local/lib/node_modules/has-flag' '~/n8n_dir/node_modules/htmlparser2:/usr/local/lib/node_modules/htmlparser2' '~/n8n_dir/node_modules/http-context:/usr/local/lib/node_modules/http-context' '~/n8n_dir/node_modules/http-errors:/usr/local/lib/node_modules/http-errors' '~/n8n_dir/node_modules/http-incoming:/usr/local/lib/node_modules/http-incoming' '~/n8n_dir/node_modules/http-outgoing:/usr/local/lib/node_modules/http-outgoing' '~/n8n_dir/node_modules/http-proxy-agent:/usr/local/lib/node_modules/http-proxy-agent' '~/n8n_dir/node_modules/http-signature:/usr/local/lib/node_modules/http-signature' '~/n8n_dir/node_modules/https-proxy-agent:/usr/local/lib/node_modules/https-proxy-agent' '~/n8n_dir/node_modules/iconv-lite:/usr/local/lib/node_modules/iconv-lite' '~/n8n_dir/node_modules/inherits:/usr/local/lib/node_modules/inherits' '~/n8n_dir/node_modules/ip:/usr/local/lib/node_modules/ip' '~/n8n_dir/node_modules/is-browser:/usr/local/lib/node_modules/is-browser' '~/n8n_dir/node_modules/is-typedarray:/usr/local/lib/node_modules/is-typedarray' '~/n8n_dir/node_modules/is-url:/usr/local/lib/node_modules/is-url' '~/n8n_dir/node_modules/isarray:/usr/local/lib/node_modules/isarray' '~/n8n_dir/node_modules/isobject:/usr/local/lib/node_modules/isobject' '~/n8n_dir/node_modules/isstream:/usr/local/lib/node_modules/isstream' '~/n8n_dir/node_modules/jsbn:/usr/local/lib/node_modules/jsbn' '~/n8n_dir/node_modules/json-schema:/usr/local/lib/node_modules/json-schema' '~/n8n_dir/node_modules/json-schema-traverse:/usr/local/lib/node_modules/json-schema-traverse' '~/n8n_dir/node_modules/json-stringify-safe:/usr/local/lib/node_modules/json-stringify-safe' '~/n8n_dir/node_modules/jsonfile:/usr/local/lib/node_modules/jsonfile' '~/n8n_dir/node_modules/jsprim:/usr/local/lib/node_modules/jsprim' '~/n8n_dir/node_modules/koa-is-json:/usr/local/lib/node_modules/koa-is-json' '~/n8n_dir/node_modules/levn:/usr/local/lib/node_modules/levn' '~/n8n_dir/node_modules/lodash:/usr/local/lib/node_modules/lodash' '~/n8n_dir/node_modules/lodash.assignin:/usr/local/lib/node_modules/lodash.assignin' '~/n8n_dir/node_modules/lodash.bind:/usr/local/lib/node_modules/lodash.bind' '~/n8n_dir/node_modules/lodash.defaults:/usr/local/lib/node_modules/lodash.defaults' '~/n8n_dir/node_modules/lodash.filter:/usr/local/lib/node_modules/lodash.filter' '~/n8n_dir/node_modules/lodash.flatten:/usr/local/lib/node_modules/lodash.flatten' '~/n8n_dir/node_modules/lodash.foreach:/usr/local/lib/node_modules/lodash.foreach' '~/n8n_dir/node_modules/lodash.map:/usr/local/lib/node_modules/lodash.map' '~/n8n_dir/node_modules/lodash.merge:/usr/local/lib/node_modules/lodash.merge' '~/n8n_dir/node_modules/lodash.pick:/usr/local/lib/node_modules/lodash.pick' '~/n8n_dir/node_modules/lodash.reduce:/usr/local/lib/node_modules/lodash.reduce' '~/n8n_dir/node_modules/lodash.reject:/usr/local/lib/node_modules/lodash.reject' '~/n8n_dir/node_modules/lodash.some:/usr/local/lib/node_modules/lodash.some' '~/n8n_dir/node_modules/lru-cache:/usr/local/lib/node_modules/lru-cache' '~/n8n_dir/node_modules/media-typer:/usr/local/lib/node_modules/media-typer' '~/n8n_dir/node_modules/methods:/usr/local/lib/node_modules/methods' '~/n8n_dir/node_modules/mime:/usr/local/lib/node_modules/mime' '~/n8n_dir/node_modules/mime-db:/usr/local/lib/node_modules/mime-db' '~/n8n_dir/node_modules/mime-types:/usr/local/lib/node_modules/mime-types' '~/n8n_dir/node_modules/monotonic-timestamp:/usr/local/lib/node_modules/monotonic-timestamp' '~/n8n_dir/node_modules/ms:/usr/local/lib/node_modules/ms' '~/n8n_dir/node_modules/negotiator:/usr/local/lib/node_modules/negotiator' '~/n8n_dir/node_modules/netmask:/usr/local/lib/node_modules/netmask' '~/n8n_dir/node_modules/nth-check:/usr/local/lib/node_modules/nth-check' '~/n8n_dir/node_modules/oauth-sign:/usr/local/lib/node_modules/oauth-sign' '~/n8n_dir/node_modules/object-assign:/usr/local/lib/node_modules/object-assign' '~/n8n_dir/node_modules/on-finished:/usr/local/lib/node_modules/on-finished' '~/n8n_dir/node_modules/optionator:/usr/local/lib/node_modules/optionator' '~/n8n_dir/node_modules/pac-proxy-agent:/usr/local/lib/node_modules/pac-proxy-agent' '~/n8n_dir/node_modules/pac-resolver:/usr/local/lib/node_modules/pac-resolver' '~/n8n_dir/node_modules/parseurl:/usr/local/lib/node_modules/parseurl' '~/n8n_dir/node_modules/performance-now:/usr/local/lib/node_modules/performance-now' '~/n8n_dir/node_modules/prelude-ls:/usr/local/lib/node_modules/prelude-ls' '~/n8n_dir/node_modules/process-nextick-args:/usr/local/lib/node_modules/process-nextick-args' '~/n8n_dir/node_modules/promise-polyfill:/usr/local/lib/node_modules/promise-polyfill' '~/n8n_dir/node_modules/proxy-agent:/usr/local/lib/node_modules/proxy-agent' '~/n8n_dir/node_modules/proxy-from-env:/usr/local/lib/node_modules/proxy-from-env' '~/n8n_dir/node_modules/psl:/usr/local/lib/node_modules/psl' '~/n8n_dir/node_modules/punycode:/usr/local/lib/node_modules/punycode' '~/n8n_dir/node_modules/qs:/usr/local/lib/node_modules/qs' '~/n8n_dir/node_modules/querystring:/usr/local/lib/node_modules/querystring' '~/n8n_dir/node_modules/raw-body:/usr/local/lib/node_modules/raw-body' '~/n8n_dir/node_modules/readable-stream:/usr/local/lib/node_modules/readable-stream' '~/n8n_dir/node_modules/request:/usr/local/lib/node_modules/request' '~/n8n_dir/node_modules/request-promise:/usr/local/lib/node_modules/request-promise' '~/n8n_dir/node_modules/request-promise-core:/usr/local/lib/node_modules/request-promise-core' '~/n8n_dir/node_modules/request-x-ray:/usr/local/lib/node_modules/request-x-ray' '~/n8n_dir/node_modules/safe-buffer:/usr/local/lib/node_modules/safe-buffer' '~/n8n_dir/node_modules/safer-buffer:/usr/local/lib/node_modules/safer-buffer' '~/n8n_dir/node_modules/selectn:/usr/local/lib/node_modules/selectn' '~/n8n_dir/node_modules/setprototypeof:/usr/local/lib/node_modules/setprototypeof' '~/n8n_dir/node_modules/sliced:/usr/local/lib/node_modules/sliced' '~/n8n_dir/node_modules/smart-buffer:/usr/local/lib/node_modules/smart-buffer' '~/n8n_dir/node_modules/socks:/usr/local/lib/node_modules/socks' '~/n8n_dir/node_modules/socks-proxy-agent:/usr/local/lib/node_modules/socks-proxy-agent' '~/n8n_dir/node_modules/source-map:/usr/local/lib/node_modules/source-map' '~/n8n_dir/node_modules/sshpk:/usr/local/lib/node_modules/sshpk' '~/n8n_dir/node_modules/statuses:/usr/local/lib/node_modules/statuses' '~/n8n_dir/node_modules/stealthy-require:/usr/local/lib/node_modules/stealthy-require' '~/n8n_dir/node_modules/stream-to-string:/usr/local/lib/node_modules/stream-to-string' '~/n8n_dir/node_modules/string-format:/usr/local/lib/node_modules/string-format' '~/n8n_dir/node_modules/string_decoder:/usr/local/lib/node_modules/string_decoder' '~/n8n_dir/node_modules/superagent:/usr/local/lib/node_modules/superagent' '~/n8n_dir/node_modules/superagent-proxy:/usr/local/lib/node_modules/superagent-proxy' '~/n8n_dir/node_modules/supports-color:/usr/local/lib/node_modules/supports-color' '~/n8n_dir/node_modules/toidentifier:/usr/local/lib/node_modules/toidentifier' '~/n8n_dir/node_modules/torrent-search-api:/usr/local/lib/node_modules/torrent-search-api' '~/n8n_dir/node_modules/tough-cookie:/usr/local/lib/node_modules/tough-cookie' '~/n8n_dir/node_modules/tslib:/usr/local/lib/node_modules/tslib' '~/n8n_dir/node_modules/tunnel-agent:/usr/local/lib/node_modules/tunnel-agent' '~/n8n_dir/node_modules/tweetnacl:/usr/local/lib/node_modules/tweetnacl' '~/n8n_dir/node_modules/type-check:/usr/local/lib/node_modules/type-check' '~/n8n_dir/node_modules/type-is:/usr/local/lib/node_modules/type-is' '~/n8n_dir/node_modules/universalify:/usr/local/lib/node_modules/universalify' '~/n8n_dir/node_modules/unpipe:/usr/local/lib/node_modules/unpipe' '~/n8n_dir/node_modules/uri-js:/usr/local/lib/node_modules/uri-js' '~/n8n_dir/node_modules/util:/usr/local/lib/node_modules/util' '~/n8n_dir/node_modules/util-deprecate:/usr/local/lib/node_modules/util-deprecate' '~/n8n_dir/node_modules/uuid:/usr/local/lib/node_modules/uuid' '~/n8n_dir/node_modules/vary:/usr/local/lib/node_modules/vary' '~/n8n_dir/node_modules/verror:/usr/local/lib/node_modules/verror' '~/n8n_dir/node_modules/word-wrap:/usr/local/lib/node_modules/word-wrap' '~/n8n_dir/node_modules/wrap-fn:/usr/local/lib/node_modules/wrap-fn' '~/n8n_dir/node_modules/x-ray:/usr/local/lib/node_modules/x-ray' '~/n8n_dir/node_modules/x-ray-crawler:/usr/local/lib/node_modules/x-ray-crawler' '~/n8n_dir/node_modules/x-ray-parse:/usr/local/lib/node_modules/x-ray-parse' '~/n8n_dir/node_modules/x-ray-scraper:/usr/local/lib/node_modules/x-ray-scraper' '~/n8n_dir/node_modules/xregexp:/usr/local/lib/node_modules/xregexp' '~/n8n_dir/node_modules/yallist:/usr/local/lib/node_modules/yallist' '~/n8n_dir/node_modules/yieldly:/usr/local/lib/node_modules/yieldly' image: 'n8nio/n8n:latest-rpi' environment: N8N_BASIC_AUTH_ACTIVE=true N8N_BASIC_AUTH_USER=username N8N_BASIC_AUTH_PASSWORD=your_secret_n8n_password EXECUTIONS_DATA_PRUNE=true EXECUTIONS_DATA_MAX_AGE=120 EXECUTIONS_TIMEOUT=300 EXECUTIONS_TIMEOUT_MAX=500 GENERIC_TIMEZONE=Europe/Berlin NODE_FUNCTION_ALLOW_EXTERNAL=torrent-search-api Once configured this way run n8n and create a new workflow coping the one proposed. Configure workflow Transmission In order to send command to transmission you must validate the Basic Auth. To do so: open the Start download node and edit the Credentials. Perform the same operation choosing the new credentials also in node Start download new token. In this automation we call transmission twice due to a security protocol in transmission system that prevents single click commands to be triggered, performing the request twice bypasses this security mechanism. https://en.wikipedia.org/wiki/Cross-site_request_forgery We use the X-Transmission-Session-Id provided by the first request to authenticate the second request. Telegram In order to make the workflow work as expected you must create a telegram bot and configure the nodes (Torrent not found and Telegram1) to send your message once the workflow is complete. Here's an easy guide to follow https://docs.n8n.io/nodes/n8n-nodes-base.telegram/ In those nodes you also should configure the Chat ID, you may use your telegram username or use a bot to retrieve your id. You may chat with useridinfobot that sends you your id. Ok google automation Since right now we do not have a n8n client for mobile that can trigger automation using google assistant I decided to use an IFTTT automation to trigger the webhook. I connect my IFTTT account with google assistant and pick the trigger. Say a phrase with a text ingredient as in the picture below. And configure the trigger this way. scarica $ -> download $ or metti in download $ -> put in download $ or some other trigger you may want. Then configure your server to trigger the webhook of n8n. Conclusion In conclusion we provide a fully working automation that integrates in n8n a node library and provides an easy trigger to perform a complex operation. Security concern Giving the ability to trigger a download may be problematic for potential unwanted torrent malware download, so you may decide to authenticate the webhook request passing in the body another field with a shared token between the two endpoints. Moreover the torrent-search-api library and its dependencies have some vulnerability that you may want to avoid on your own media-center, this will hopefully be patched soon in a further release of the library. This is just an interesting proof of concept. Quality of the download You may want to introduce another block between torrent search and webhook trigger to search for a movie based on the words detected by google assistant, sometimes it misinterprets something and you may end up downloading potential copyrighted material. Please use this automation only for free and open source movies and music.

AI Voice Calling Bot - OpenAI GPT-4o + ElevenLabs + Twilio Integration for Multilingual Appointment Booking & Service Orders Overview Transform your business with an intelligent voice calling bot that handles customer calls automatically in 25+ languages. This N8n workflow integrates OpenAI GPT-4o, ElevenLabs text-to-speech, and Twilio for seamless appointment scheduling, pizza orders, and service bookings. Key Features Multilingual Support**: Conversations in English, Spanish, French, German, Italian, Portuguese, Chinese, Japanese, Arabic, and 20+ more languages Natural AI Conversations**: GPT-4o powered responses with ElevenLabs realistic voice synthesis Multi-Service Handling**: Appointments, orders, and service requests with automatic logging Real-time Processing**: Instant speech-to-text and audio response generation Prerequisites N8n instance (self-hosted or cloud) Twilio account with phone number OpenAI API key (GPT-4o access) ElevenLabs API credentials Google Sheets access Cloud storage for audio files Setup Instructions Step 1: Configure Credentials Add API keys for OpenAI, ElevenLabs, Twilio, and Google Sheets in N8n credentials manager. Step 2: Prepare Data Storage Create Google Sheets for call logs and appointments with columns: timestamp, caller_id, speech_input, ai_response, language, call_sid. Step 3: Configure Twilio Set webhook URL to your N8n endpoint: https://your-n8n-instance.com/webhook/voice-webhook Step 4: Update Sheet IDs Replace placeholder Google Sheet IDs in workflow nodes with your actual sheet IDs. Customization Options Voice Settings**: Adjust ElevenLabs multilingual voice models and parameters AI Behavior**: Modify system prompts for specific business needs and languages Service Types**: Add custom service handling logic Business Hours**: Implement language-specific operating hours Monitoring Track call analytics, language preferences, conversion rates, and customer satisfaction across all supported languages through automated Google Sheets logging. Ready for production use with comprehensive error handling and scalability for global businesses.

An intelligent WhatsApp-based chatbot designed for restaurants to automate customer interactions related to table bookings, menu inquiries, opening hours, services, and offers. Built using the n8n automation platform and powered by an AI language model, this solution streamlines communication, boosts efficiency, and improves customer satisfaction. Objectives Automate replies to common customer queries on WhatsApp Handle table booking requests with confirmation Provide menu item details, pricing, and dietary information Share restaurant timing, location, and service availability Promote offers and handle promotional queries Operate 24/7 without manual intervention Store bookings and conversations for reporting and analytics Workflow Summary Step 1: Message Reception Node: WhatsApp Trigger (Webhook or API-based) Function: Listens for incoming customer messages. Step 2: Intent Recognition Node: AI Query Processor (e.g., OpenAI API) Function: Detects customer intent (e.g., booking, menu, timing). Step 3: Conditional Routing Node: Switch or IF Node Function: Routes flow based on detected intent: General information (menu, timing, services) Table booking Step 4A: Respond to General Info Queries Node: AI Response or Static Reply Node Function: Returns relevant information (menu, timing, address, etc.). Step 4B: Process Booking Requests Nodes: Collect Booking Details** (via chatbot interactions) Store Booking Info** (to DB or Google Sheets) Send Booking Confirmation** (to customer) Step 5: Context Management Node: Set/Update Customer Data Function: Maintains conversation state and tracks follow-up messages. Database or Google Sheet Columns for Table Booking | Column Name | Description | | ----------------- | ----------------------------------------------- | | reservation\_id | Unique reservation identifier | | guest\_name | Full name of the guest | | contact\_number | Customer’s WhatsApp or mobile number | | email | (Optional) Email address | | booking\_date | Reservation date (YYYY-MM-DD format) | | booking\_time | Reservation time (HH\:MM format) | | party\_size | Number of guests | | table\_id | (Optional) Table number or identifier | | special\_requests | Allergies, seating preferences, etc. | | status | Booking status: Confirmed / Cancelled / Pending | | created\_at | Timestamp when booking was made | | updated\_at | Timestamp when booking was last modified | Prerequisites Verified WhatsApp Business Account with API access n8n instance (Cloud or self-hosted) Access to an AI service (e.g., OpenAI, Claude) Google Sheets, Airtable, MySQL, or other DB integration Setup Instructions Connect WhatsApp API using webhook or third-party WhatsApp provider (e.g., 360Dialog, Twilio). Integrate AI using HTTP Request or OpenAI node for response generation. Create Data Store (Google Sheet, Airtable, or MySQL) with defined booking columns. Design Workflow in n8n with intent detection, conditional logic, and response nodes. Test End-to-End by sending different WhatsApp queries and checking logs and stored data. Example Conversation Customer: “Can I book a table for 2 people tomorrow at 8 PM?” Bot: “Sure. Please provide your name and contact number to confirm the reservation for 2 people at 8:00 PM tomorrow.” \[Booking details are saved, and a confirmation is sent.] Benefits Fully automated customer interaction Supports real-time table reservations Accurate and quick responses Scales without increasing staff effort Operates 24/7 Centralized booking data for analytics Analytics and Reporting Track key performance metrics such as: Number of bookings per day/week Average response time Customer satisfaction scores (via feedback node) Popular menu items or query types Booking conversion rates Security and Compliance End-to-end encrypted WhatsApp messages Role-based access to sensitive data Compliance with data protection regulations (e.g., GDPR) Secure API integrations and storage solutions Conclusion This WhatsApp chatbot serves as a reliable, AI-powered digital front desk for restaurants. Built using n8n and scalable components, it automates customer support, manages bookings, and enhances operational efficiency while offering a seamless customer experience.

This workflow creates an Asana task when a new ticket is created in Zendesk. Subsequent comments on the ticket in Zendesk are added as comments to the task in Asana. Prerequisites Zendesk account and Zendesk credentials. Asana account and Asana credentials. Asana workspace to create tasks in. How it works The workflow listens for new tickets in Zendesk. When a new ticket is created, the workflow creates a new task in Asana. The Asana GID is then saved in one of the ticket's fields (in setup we call this "Asana GID"). The next time a comment is added to the ticket, the workflow retrieves the Asana GID from the ticket's field and adds the comment to the task in Asana. 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 Asana GID. 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 “Asana GID”. Save the field. In n8n, open the Update ticket node and select the field you created in Zendesk.