by Shiva
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.
by n8n Team
This workflow automatically adds a note of the PR from GitHub to the Pipedrive contact if their GitHub email matches a Person in Pipedrive. Prerequisites Pipedrive account and Pipedrive credentials GitHub account and GitHub credentials How it works GitHub Trigger node activates the workflow when a GitHub user adds a PR. HTTP Request node gets the user's data and sends it further. Pipedrive node searches the same email that GitHub user has in Pipedrive. IF node checks whether a person with the same email exists in Pipedrive. In case there's such a person in Pipedrive, the Pipedrive node creates a note within the person's profile.
by Oneclick AI Squad
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.
by Thomas Janssen
Build an AI Agent which accesses two MCP Servers: a RAG MCP Server and a Search Engine API MCP Server. This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Tutorial Click here to watch the full tutorial on YouTube! How it works We build an AI Agent which has access to two MCP servers: An MCP Server with a RAG database (click here for the RAG MCP Server An MCP Server which can access a Search Engine, so the AI Agent also has access to data about more current events Installation In order to use the MCP Client, you also have to use MCP Server Template. Open the MCP Client "MCP Client: RAG" node and update the SSE Endpoint to the MCP Server workflow Install the "n8n-nodes-mcp" community node via settings > community nodes ONLY FOR SELF-HOSTING: In Docker, click on your n8n container. Navigate to "Exec" and execute the below command to allow community nodes: N8N_COMMUNITY_PACKAGES_ALLOW_TOOL_USAGE=true Navigate to Bright Data and create a new "Web Unlocker API" with the name "mcp_unlocker". Open the "MCP Client" and add the following credentials: How to use it Run the Chat node and start asking questions More detailed instructions Missed a step? Find more detailed instructions here: Personal Newsfeed With Bright Data and n8n What is Retrievel Augmented Generation (RAG)? Large Language Models (LLM's) are trained on data until a specific cutoff date. Imagine a model is trained in December 2023 based data until September 2023. This means the model doesn't have any knowledge about events which happened in 2024. So if you ask the LLM who was the Formula 1 World Champion of 2024, it doesn't know the answer. The solution? Retrieval Augmented Generation. When using Retrieval Augmented Generation, a user's question is being sent to a semantic database. The LLM will use the information retrieved from the semantic database to answer the user's question. What is Model Context Protocol (MCP)? MCP is a communication protocol which is used by AI agents to call tools hosted on external servers. When an MCP client communicates with an MCP server, the server will provide an overview of all its tools, prompts and resources. The MCP server can then choose which tools to execute (based on the user's request) and execute the tools. An MCP client can communicate with multiple MCP servers, which can all host multiple tools.
by Artur
Streamline your accounting by automatically creating QuickBooks Online customers and sales receipts whenever a successful Stripe payment is processed. Ideal for businesses looking to reduce manual data entry and improve accounting efficiency. How it works Trigger: The workflow is triggered when a new successful payment intent event is received from Stripe. Retrieve Customer Data: Fetches customer details from Stripe associated with the payment. Check QuickBooks Customer: Searches QuickBooks Online to see if the customer already exists using their email address. Create or Use Existing Customer: If the customer doesn't exist in QuickBooks, they are created; otherwise, the existing customer is used. Generate Sales Receipt: A sales receipt is created in QuickBooks Online with payment details, including item descriptions, amounts, and currency. Set up steps Connect Accounts: Authenticate both your QuickBooks Online and Stripe accounts in n8n. Webhook Setup: Configure the Stripe webhook to send payment_intent.succeeded events to this workflow. Test the Workflow: Trigger a test payment in Stripe to validate the integration. Customize Details: Adjust item descriptions or other fields in the QuickBooks sales receipt JSON body as needed. This workflow requires basic familiarity with n8n, but setup can be completed in under 15 minutes for most users.
by Yang
Who is this for? This workflow is perfect for customer support teams, sales departments, or solopreneurs who receive frequent email enquiries and want to automate the initial response process using AI. If you spend too much time answering similar questions, this system helps respond faster and more intelligently—without writing a single line of code. What problem is this workflow solving? Manually responding to repeated customer enquiries slows productivity and increases delay. This workflow classifies if an incoming email is a real enquiry, analyzes the content with a LangChain-powered agent, fetches helpful context using Dumpling AI, and sends a personalized reply using Gmail—all within minutes. What this workflow does Listens for new incoming Gmail messages using the Gmail Trigger node. Classifies whether the email is an enquiry using a GPT-4o classification prompt. Uses a Filter node to continue only if the email was classified as an enquiry. Passes the email content to a LangChain Agent, enhanced with memory, AI tools, and Dumpling AI to search for relevant information. The agent constructs a smart, relevant response, then sends it to the original sender via Gmail. Setup Connect Gmail Use the Gmail Trigger node to connect to the Gmail account that receives enquiries. Make sure Gmail OAuth2 credentials are authenticated. Configure Dumpling AI Agent Sign up at Dumpling AI. Create an agent trained to search your help docs, site content, or FAQs. Copy your Dumpling agent ID and API key. Paste it in the Dumpling AI Agent – Search for Relevant Info HTTP Request node. Set Up LangChain Agent No extra setup needed beyond connecting OpenAI credentials. GPT-4o is used for classification and reply generation. Enable Gmail Reply Node The final Send Email Response via Gmail node will send the AI-generated reply back to the same thread. How to customize this workflow to your needs Change the classification prompt to include other email types like “support”, “complaint”, or “sales”. Add additional logic if you want to CC someone or forward certain types of enquiries. Add a Notion or Google Sheets node to log the conversation for analytics. Replace Gmail with Outlook or another email provider by switching the nodes. Improve context by adding more AI tools like database queries or preloaded FAQs.
by Yang
Who is this for? This workflow is built for newsletter writers, marketers, content creators, or anyone who curates and summarizes web articles. It’s especially helpful for virtual assistants and founders who need to quickly turn web content into digestible, branded newsletters using AI. What problem is this workflow solving? Manually reading, summarizing, and formatting multiple articles into a newsletter takes time and focus. This workflow automates the process using Dumpling AI for crawling, GPT-4o for summarization, and Gmail for delivery—so you can go from raw URLs to a polished email in minutes. What this workflow does Starts manually (can also be scheduled) Reads a list of article URLs from Google Sheets Sends URLs to Dumpling AI to crawl and extract content Splits each article into a single item for processing Uses a Code node to clean and structure article data Uses an Edit Fields node to merge articles into one JSON block GPT-4o summarizes and generates HTML content for the newsletter Sends the formatted newsletter via Gmail Setup Google Sheets Create a sheet with a column (A) for article URLs Update the Read URLs from Google Sheet node to use your Sheet ID and tab name Connect your Google account in the credentials Dumpling AI Sign up at https://app.dumplingai.com Create an agent for web crawling under /crawl Add your Dumpling API key in the HTTP headers of the Crawl Content with Dumpling AI node Split Node Breaks apart the array of articles from Dumpling AI so each article is processed individually Code Node Structures each article as JSON with title, url, and cleaned text content Edit Fields Node Gathers all structured articles back into a single JSON array to prepare for AI summarization OpenAI (GPT-4o) Processes the article list and returns a formatted subject line and HTML newsletter content Gmail Connect your Gmail account to send the AI-generated newsletter to your inbox or team Update the recipient field in the Send HTML Email via Gmail node How to customize this workflow to your needs Replace the manual trigger with a Schedule node to send newsletters weekly Modify the GPT-4o prompt to change tone (e.g., more professional, funny, casual) Add filtering logic to skip low-value articles Connect Slack, Airtable, or Notion for internal team usage Change Gmail to SendGrid or Outlook if preferred Final Notes This workflow uses: Dumpling AI** /crawl endpoint to extract article content Split, **Code, and Edit Fields nodes to format multi-article input GPT-4o** for summarization and HTML formatting Gmail** for delivery This setup eliminates manual steps and delivers fast, consistent newsletters powered by AI.
by Yaron Been
🎤 Audio-to-Insights: Auto Meeting Summarizer Transform your meeting recordings into actionable insights automatically. This powerful n8n workflow monitors your Google Drive for new audio files, transcribes them using OpenAI's Whisper, generates intelligent summaries with ChatGPT, and logs everything in Google Sheets - all without lifting a finger. 🔄 How It Works This workflow operates as a seamless 6-step automation pipeline: Step 1: Smart Detection The workflow continuously monitors a designated Google Drive folder (polls every minute) for newly uploaded audio files. Step 2: Secure Download When a new audio file is detected, the system automatically downloads it from Google Drive for processing. Step 3: AI Transcription OpenAI's Whisper technology converts your audio recording into accurate text transcription, supporting multiple audio formats. Step 4: Intelligent Summarization ChatGPT processes the transcript using a specialized prompt that extracts: Key discussion points and decisions Action items with assigned persons and deadlines Priority levels and follow-up tasks Clean, professional formatting Step 5: Timestamp Generation The system automatically adds the current date and formats it consistently for tracking purposes. Step 6: Automated Logging The final summary is appended to your Google Sheets document with the date, creating a searchable archive of all meeting insights. ⚙️ Setup Steps Prerequisites Before setting up the workflow, ensure you have: Active Google Drive account OpenAI API key with credits Google Sheets access n8n instance (cloud or self-hosted) Configuration Steps 1. Credential Setup Google Drive OAuth2**: Required for folder monitoring and file downloads OpenAI API Key**: Needed for both transcription (Whisper) and summarization (ChatGPT) Google Sheets OAuth2**: Essential for writing summaries to your spreadsheet 2. Google Drive Configuration Create a dedicated folder in Google Drive for meeting recordings Copy the folder ID from the URL (the long string after /folders/) Update the folderToWatch parameter in the workflow 3. Google Sheets Preparation Create a new Google Sheet or use an existing one Ensure it has columns: Date and Meeting Summary Copy the spreadsheet ID from the URL Update the documentId parameter in the workflow 4. Audio Requirements Supported Formats**: MP3, WAV, M4A, MP4 Recommended Size**: Under 100MB for optimal processing Language**: Optimized for English (customizable for other languages) Quality**: Clear audio produces better transcriptions 5. Workflow Activation Import the workflow JSON into your n8n instance Configure all credential connections Test with a sample audio file Activate the workflow trigger 🚀 Use Cases Project Management Team Standup Summaries**: Convert daily standups into actionable task lists Sprint Retrospectives**: Extract improvement points and action items Stakeholder Updates**: Generate concise reports for leadership Sales & Customer Success Discovery Call Notes**: Capture prospect pain points and requirements Demo Follow-ups**: Track questions, objections, and next steps Customer Check-ins**: Monitor satisfaction and expansion opportunities Consulting & Professional Services Client Strategy Sessions**: Document recommendations and implementation plans Requirements Gathering**: Organize complex project specifications Progress Reviews**: Track deliverables and milestone achievements HR & Training Interview Debriefs**: Standardize candidate evaluation notes Training Sessions**: Create searchable knowledge bases Performance Reviews**: Document development plans and goals Research & Development Brainstorming Sessions**: Capture innovative ideas and concepts Technical Reviews**: Log decisions and architectural choices User Research**: Organize feedback and insights systematically 💡 Advanced Customization Options Enhanced Summarization Modify the ChatGPT prompt to focus on specific elements: Add speaker identification for multi-person meetings Include sentiment analysis for customer calls Generate department-specific summaries (technical, sales, legal) Extract financial figures and metrics automatically Integration Expansions Slack Integration**: Auto-post summaries to relevant channels Email Notifications**: Send summaries to meeting participants CRM Updates**: Push action items directly to Salesforce/HubSpot Calendar Integration**: Schedule follow-up meetings based on action items Quality Improvements Audio Preprocessing**: Add noise reduction before transcription Multi-language Support**: Configure for international teams Custom Templates**: Create industry-specific summary formats Approval Workflows**: Add human review before final storage 🛠️ Troubleshooting & Best Practices Common Issues Large File Processing**: Split recordings over 100MB into smaller segments Poor Audio Quality**: Use noise reduction tools before uploading API Rate Limits**: Implement delay nodes for high-volume usage Formatting Issues**: Adjust ChatGPT prompts for consistent output Optimization Tips Upload files in supported formats only Ensure stable internet connection for cloud processing Monitor OpenAI API usage and costs Regularly backup your Google Sheets data Test workflow changes with sample files first 📊 Expected Outputs Sample Summary Format: Meeting Summary - March 15, 2024 Key Discussion Points: Q1 budget review and allocation decisions New product launch timeline and milestones Team restructuring and role assignments Action Items: John: Finalize budget proposal by March 20th (High Priority) Sarah: Schedule product demo sessions for March 25th Team: Submit org chart feedback by March 18th Decisions Made: Approved additional marketing budget of $50K Delayed product launch to April 15th for quality assurance Promoted Lisa to Senior Developer role 📞 Questions & Support For any questions, customizations, or technical support regarding this workflow: 📧 Email Support Primary Contact**: Yaron@nofluff.online Response Time**: Within 24 hours on business days Best For**: Setup questions, customization requests, troubleshooting 🎥 Learning Resources YouTube Channel**: https://www.youtube.com/@YaronBeen/videos Step-by-step setup tutorials Advanced customization guides Workflow optimization tips 🔗 Professional Network LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Connect for ongoing support Share your workflow success stories Get updates on new automation ideas 💡 What to Include in Your Support Request Describe your specific use case Share any error messages or logs Mention your n8n version and setup type Include sample audio file characteristics (if relevant) Ready to transform your meeting chaos into organized insights? Download the workflow and start automating your meeting summaries today!
by Airtop
Extracting Comments from an X Post Use Case Engaging with conversations on X (formerly Twitter) is critical for brands and individuals monitoring sentiment, leads, or emerging trends. Manually collecting comments is time-consuming—this automation enables scalable extraction of comment data to inform your outreach or analysis. What This Automation Does This automation extracts comments from a specified X post, with the following input parameters: airtop_profile**: The name of your Airtop Profile connected to X. x_post_url**: The URL of the X post to extract comments from. max_number_of_comments**: The maximum number of comments to retrieve. How It Works Takes input via a form or another workflow. Normalizes the input values. Creates a new browser session using Airtop. Navigates to the provided X post. Uses a prompt to extract up to the specified number of comments, returning: Author name Author profile URL Comment text Setup Requirements Airtop API Key — free to generate. An Airtop Profile connected to X (requires one-time login). Next Steps Pair with X Monitoring**: Use this with the X monitoring automation to detect relevant posts and extract discussion context automatically. Feed into Analytics**: Combine with summarization or sentiment analysis tools to understand audience response at scale. Export for CRM/BI**: Pipe the structured comment data into your CRM or business intelligence stack for lead tracking or reporting. Read more about Extracting Comments from X Posts
by n8n Team
This template shows how to sync data from one service to another. Specifically, in this example we're saving a new qualified lead from a Postgres database to a Google Sheets file. Setup instructions are located inside the workflow template.
by Yang
Who is this for? This workflow is perfect for marketers, SEO specialists, product teams, and competitive analysts who want to monitor and summarize public reviews of their competitors. It’s especially helpful for small teams who want fast insights from Google reviews without spending hours manually reading and sorting them. What problem is this workflow solving? Manually going through competitor reviews is time-consuming and repetitive. You risk missing patterns or insights, and it’s hard to share summaries with your team quickly. This workflow automatically scrapes reviews from Google and generates a structured summary of pain points and positive feedback. That way, you can focus on strategy instead of sorting through dozens of reviews. What this workflow does This automation watches for new competitor entries in a Google Sheet, then: Uses Dumpling AI to scrape the latest Google reviews (up to 20) for each business. Splits and cleans the reviews for analysis. Sends them to GPT-4o, which summarizes the most common complaints and praises. Saves the structured result back to the same Google Sheet. You’ll instantly get an overview of what people are saying about any competitor. Setup Google Sheet Setup Create a Google Sheet with at least one column: Business Add names or search queries for the competitors you want to analyze Optional: Add columns for Summary of Reviews and Pain Points Connect Dumpling AI Sign up at Dumpling AI Create an agent using the get-google-reviews endpoint Copy your agent key Use it in the HTTP Request node in this workflow OpenAI Setup Use your API key with GPT-4o access The prompt is already structured to generate grouped summaries from reviews Run the Workflow Trigger it manually or schedule it Make sure your Google Sheets, OpenAI, and Dumpling AI connections are active How to customize this workflow to your needs You can expand the number of reviews retrieved by changing the Dumpling AI agent config Replace Google Sheets with Airtable if you want more robust data views Add more fields like star ratings or review dates in your agent for richer analysis Change the GPT prompt to highlight emotional tone, urgency, or feature mentions 🧠 Node Details Google Sheets Trigger**: Watches for new competitor names HTTP Request (Dumpling AI)**: Scrapes 20 recent reviews from Google SplitOut Node**: Breaks review array into individual items Code Node**: Extracts and combines review text Edit Fields Node**: Structures the review content before GPT GPT-4o Node**: Analyzes and summarizes top pain points and praise Google Sheets Output**: Saves the summary back to the same sheet Dependencies Dumpling AI account and review scraping agent setup OpenAI API key with GPT-4o access Google Sheets OAuth2 credentials
by please-open.it
Intro This workflow needs a user to authenticate by using an openid connect provider in order to call the webhook. If the user is not authenticated, it starts a login process by using an Authorization Code with PKCE https://datatracker.ietf.org/doc/html/rfc7636, a standard way to authenticate users with openid connect. Then, after the user logs in, the webhook is refreshed and gets the user's token from a cookie. With this token, all details about the user are requested through the userinfo endpoint on the identity provider. How to set up with Keycloak Keycloak Keycloak is an open source identity and access management solution. Feel free to get a demo realm at https://please-open.it or get your own Keycloak server up and running. After creating a realm, go to "Realm Settings" and click on "OpenID Endpoint Configuration" Retrieve authorization_endpoint, token_endpoint and userinfo_endpoint values. Set those variables in the "Set variables" node. In Keycloak, create a new client (name it as you want) Disable the client authentication, check only "standard flow" : At the third step, put the webhook url in "valid redirect URIs", fill "Web origins" with a "+". You're done, open the webhook and it asks you to authenticate. Usage User informations The userinfo node returns this structure about the user has logged in : [ { "sub":"73a6543f-f420-4fa6-9811-209e903c348b", "email_verified":true, "preferred_username": "mathieu.passenaud@please-open.it", "email": "mathieu.passenaud@please-open.it" } ] I can use those infos in my workflow for custom operations. APIs calls the "code" node returns me a cookie named "n8n-custom-auth" which is the access_token returned by the identity provider. This access_token can be used to call APIs connected to this identity provider (for example, we call userinfo API with this token). Example : asks a user to log in with his Google account then call an API (Gmail, drive...) with his own token. How it works We published a blog post about this flow, how it works and how you can use it : https://blog.please-open.it/n8n-openid-client/