Workflow Templates

🚀 LinkedIn Comments to Leads Extractor & Enricher (Apify) → Google Sheets / CSV Overview Automate LinkedIn lead generation by scraping comments from targeted posts and enriching profiles with detailed data This n8n workflow automatically extracts leads from LinkedIn post comments using Apify's powerful scrapers (no LinkedIn login required), enriches the data with additional profile information, and exports everything to Google Sheets or CSV format. ✨ Key Features 🔍 No Login Required: Scrape LinkedIn data without sharing credentials 💰 Cost-Effective: First 1,000 comments are free with Apify 📊 Data Enrichment: Enhance basic comment data with full profile details 📈 Export Options: Choose between Google Sheets or CSV output 🎯 Targeted Scraping: Focus on specific posts for quality leads 🛠️ Apify Scrapers Used 1. LinkedIn Post Comments Scraper Tool**: LinkedIn Post Comments, Replies, Engagements Scraper | No Cookies Pricing**: $5.00 per 1,000 results Function**: Extracts all comments and engagement data from specified LinkedIn posts 2. LinkedIn Profile Batch Scraper Tool**: LinkedIn Profile Details Batch Scraper (No Cookies Required) Pricing**: $5.00 per 1,000 results Function**: Enriches scraped profiles with detailed information > 💡 Free Tier: Apify provides 1,000 free scraped comments to get you started! 📋 Prerequisites Required API Credentials Apify Token Add your APIFY_TOKEN to the workflow credentials Get your token from Apify Console Google Sheets Credentials (if using Sheets export) Configure OAuth credentials for Google Sheets integration Follow n8n's Google Sheets setup guide 🔄 Workflow Process Default Mode: Form-Based Execution Manual Trigger → Launches the workflow Form Submission → User-friendly form for inputting LinkedIn post URLs Comment Scraping → Apify extracts all comments from specified posts Profile Enrichment → Additional profile data gathered for each commenter Data Processing → Creates unique, enriched lead list Google Sheets Export → Automatically populates your spreadsheet Result: You'll be redirected to a Google Sheets document containing all enriched leads Alternative Mode: CSV Export For users preferring CSV output: Disable: Form trigger nodes Enable: Manual trigger node Disable: Google Sheets export nodes Enable: CSV download nodes Configure: Add post IDs/URLs in "Set manual fields" node Execute: Run workflow and download CSV from the CSV node 📊 Output Data Structure Your exported data will include: Basic Info**: Name, headline, location Profile Details**: Company, position, industry Engagement Data**: Comment content, engagement metrics Contact Info**: Available profile links and connections Enriched Data**: Additional profile insights from Apify 💡 Pro Tips Quality over Quantity**: Target posts with high-quality, relevant engagement Monitor Costs**: Track your Apify usage to stay within budget Data Hygiene**: Regularly clean and deduplicate your lead lists Compliance**: Ensure your scraping activities comply with LinkedIn's terms of service 🆘 Troubleshooting Common Issues: Authentication Errors**: Verify your Apify token is correctly configured Empty Results**: Check that your LinkedIn post URLs are valid and public Export Failures**: Ensure Google Sheets credentials are properly set up Need Help? Contact Saverflow.ai for support and custom workflow development.

📘 Multi-Photo Facebook Post (Windows Directory) – How to Use ✅ Requirements To run this automation, make sure you have the following: ✅ n8n installed on your local Windows machine ✅ Cloudinary or any other file hosting service for uploading image files ✅ Facebook Page Access Token with the required permissions (pages_manage_posts, pages_read_engagement, pages_show_list, etc.) 🚀 How to Use Import the provided n8n workflow template into your n8n instance. Verify the image directory path – ensure that the images you want to post are stored in a local folder (e.g. E:\Autopost-media\YourPage\Images). Check the caption and hashtag files – this includes: description.txt (for the post message) hashtag.txt (for additional tags) Set your Facebook credentials – insert your Facebook Page Access Token in the designated credential field in the workflow. ⚙️ How It Works (Workflow Logic) Read Text Files The workflow reads description.txt and hashtag.txt from the local directory. These are combined to form the message body for the Facebook post. Select Images to Post The Limit node defines how many images to post per run (e.g. 3 images). Selected image files are uploaded to a file server (like Cloudinary) to obtain public URLs. Post to Facebook (Multi-Photo) A multi-photo post is created using the uploaded image URLs and the composed message. Move Posted Images After the post is successfully published, the original image files are moved to a new folder. The destination folder is automatically created using the current date (e.g. E:\Autopost-media\YourPage\Images\20250614).

Beginner AI Agent Duo: Lead‑Qualifier Task Automator & Ecommerce Chatbot Status: Ready for Use ✅ Note: This template is built entirely with official n8n nodes—no community‑node installation required. 📝 Description This template demonstrates two beginner‑friendly AI‑agent patterns that cover the most common use cases: | Agent | Purpose | Flow Highlights | |-------|---------|-----------------| | Lead‑Qualifier Task Automator | Classifies phone‑call transcripts to decide if the caller is a good bulk‑order lead. | Manual Trigger → Code (sample data) → AI Agent (GPT‑4o‑mini) → Structured Output Parser → Set (clean fields) | | Ecommerce Chatbot | Answers customer questions about products, bulk pricing, shipping, and returns. | Chat Trigger (webhook) → AI Agent (GPT‑4o‑mini) with Memory → If node → Order‑placed reply or no‑op | Both agents run on GPT‑4o‑mini and use n8n’s LangChain‑powered nodes for quick, low‑code configuration. ⚙️ How to Install & Run Import the Workflow In n8n, go to Workflows → Import from File or Paste JSON, then save. Add Your OpenAI API Key Go to Credentials → New → OpenAI API. Paste your key from <https://platform.openai.com>. Select this credential in both OpenAI Chat Model nodes. (Optional) Select a Different Model Default model is gpt‑4o‑mini. Change to GPT‑4o, GPT‑3.5‑turbo, or any available model in each OpenAI node. Test the Lead‑Qualifier Agent Click Activate. Press Test workflow. The Code node feeds four sample transcripts; the AI Agent returns JSON like: { "Name": "Jordan Lee", "Is Good Lead": "Yes", "Reasoning": "Customer requests 300 custom mugs, indicating a bulk order." } Test the Ecommerce Chatbot Copy the Webhook URL from the When chat message received trigger. POST a payload like: { "message": "Hi, do you offer discounts if I buy 120 notebooks?" } The AI Agent replies with bulk‑pricing info. If the customer confirms an order, it appends *; the If node then sends “Your order has been placed”. 🧩 Customization Ideas Refine Qualification Logic** Edit the Task Agent’s system prompt to match your own lead criteria. Save Leads Automatically** Add Google Sheets, Airtable, or a database node after the Set node. Expand the Chatbot** Connect inventory APIs, payment gateways, or CRM integrations. Adjust Memory Length* Change the *Simple Memory node’s window to retain more conversation context. 🤝 Connect with Me Description I’m Robert Breen, founder of Ynteractive — a consulting firm that helps businesses automate operations using n8n, AI agents, and custom workflows. I’ve helped clients build everything from intelligent chatbots to complex sales automations, and I’m always excited to collaborate or support new projects. If you found this workflow helpful or want to talk through an idea, I’d love to hear from you. Links 🌐 Website: https://www.ynteractive.com 📺 YouTube: @ynteractivetraining 💼 LinkedIn: https://www.linkedin.com/in/robert-breen 📬 Email: rbreen@ynteractive.com

Automate Colombian Cashflow Data Extraction to Google Sheets with AI Who’s it for This workflow is designed for finance professionals, accountants, small business owners in Colombia, or anyone needing to automate the extraction of invoice data and its entry into Google Sheets. It's particularly useful for handling Colombian tax and legal specifics. How it works / What it does This workflow automates the process of extracting critical data from invoices and receipts (PDFs and JPEGs) and organizing it in a Google Sheet: Triggers:** The workflow initiates when a new file is created or an existing file is updated in a designated Google Drive folder. File Handling:** It first downloads the detected file. Routing:** A "Switch" node intelligently routes the file based on its extension – one path for PDFs and another for JPEGs. Data Extraction:** For PDF files, it directly extracts all text content from the document. For JPEG image files, it utilizes an AI Agent (Azure OpenAI) to process the image and extract its textual content. AI-Powered Reasoning:** Two "Reasoning Agent" nodes (Azure OpenAI Chat Models) act as a specialized "Colombian Tax and Legal Extraction Agent". They parse the extracted text from invoices to pull out structured data in JSON format, including: Vendor name. Modification date. Line items with detailed description, sub_total, iva_value, total_amount, category, and sub_category. Specific Colombian tax fields like Retefuente and ReteICA. The number of items generated. Output Parsing:** A "Structured Output Parser" node ensures that the AI's output strictly adheres to a predefined JSON schema, guaranteeing consistent data formatting. Data Preparation:** "Edit Field" nodes ensure the AI's extracted data is in a valid format. Item Splitting:** "Split data" nodes separate the 'items' array from the AI's output, allowing each individual line item from the invoice to be processed as a separate entry for the Google Sheet. Google Sheet Integration:** Finally, "Fill Template" nodes append the fully processed invoice data (per line item) into your designated Google Sheet. How to set up Google Drive Credentials: Ensure you have configured your Google Drive OAuth2 API credentials in n8n. Azure OpenAI Credentials: Set up your Azure OpenAI API credentials, ensuring access to models like gpt-4o. Or you can simply use your traditional OpenAI or others LLMs. Google Sheets Credentials: Configure your Google Sheets OAuth2 API credentials. Google Drive Folder ID: In the "1a. Updated file trigger" and "1b. Created file trigger" nodes, update the folderToWatch parameter with your specific Google Drive Folder ID. Google Sheet ID and Sheet Name: In the "8. Fill Template" and "8. Fill Template1" nodes, update the documentId and sheetName parameters with your specific Google Sheet ID and the name of the sheet where data should be appended. Requirements An active n8n instance. A Google Drive account for file uploads. A Google Sheets account for data storage. An Azure OpenAI account with access to chat models (e.g., gpt-4o) for the "Azure OpenAI Chat Model" nodes and "Extract Data Agent". How to customize the workflow AI Extraction Prompts:** Modify the prompt instructions in the "5. Reasoning Agent" and "5. Reasoning Agent1" nodes if you need to extract different data points or change the output format. Google Sheet Column Mappings:** Adjust the columns mapping in the "8. Fill Template" and "8. Fill Template1" nodes to match your specific Google Sheet headers and data requirements. File Types:** Extend the "3. Route" node to handle additional file types (e.g., DOCX, PNG) by adding new conditions and corresponding extraction nodes.

AI Recipe Generator from Pantry Items using FatSecret API This workflow creates an intelligent WhatsApp cooking assistant that transforms pantry ingredients into personalized recipe suggestions using AI and the FatSecret Recipes API What Makes This Different: AI-Powered Recipe Discovery** - Uses Google Gemini AI to understand user intent and dietary preferences Smart Ingredient Analysis** - Automatically extracts ingredients, dietary restrictions, and cooking constraints FatSecret API Integration** - Leverages comprehensive recipe database with nutritional information WhatsApp Native Experience** - Seamless chat interface for recipe discovery Contextual Memory** - Remembers conversation context for better user experience Intelligent Parameter Mapping** - AI automatically maps user requests to API parameters Key Benefits of AI-Driven Architecture: Natural Language Understanding** - Users can describe what they have in plain English Personalized Recommendations** - Considers dietary restrictions, time constraints, and preferences Eliminates Manual Search** - No need to manually input specific ingredients or filters Scalable Recipe Database** - Access to thousands of recipes through FatSecret API Conversational Interface** - Natural chat flow instead of form-based inputs Smart Context Management** - Remembers previous requests for better follow-up suggestions Who's it for This template is designed for food delivery services, meal planning apps, nutritionists, cooking enthusiasts, and businesses looking to provide intelligent recipe recommendations. It's perfect for companies who want to engage customers through WhatsApp with personalized cooking assistance, helping users discover new recipes based on available ingredients and preferences. How it works / What it does This workflow creates an intelligent WhatsApp cooking assistant that transforms simple ingredient lists into personalized recipe suggestions. The system: Receives WhatsApp messages through webhook triggers Analyzes user input using Google Gemini AI to extract ingredients, dietary needs, and preferences Maps user requests to FatSecret API parameters automatically Searches recipe database based on extracted criteria (ingredients, calories, time, cuisine, etc.) Processes API results to format recipe suggestions with images and nutritional info Maintains conversation context using memory buffer for better user experience Sends formatted responses back to users via WhatsApp Key Innovation: AI-Powered Parameter Extraction - Unlike traditional recipe apps that require users to fill out forms or select from predefined options, this system understands natural language requests and automatically maps them to the appropriate API parameters, making recipe discovery as simple as texting a friend. How to set up 1. Configure WhatsApp Business API Set up WhatsApp Business API credentials Configure webhook endpoints for message reception Set up phone number ID and recipient handling Ensure proper message sending permissions 2. Configure Google Gemini AI Set up Google Gemini (PaLM) API credentials Ensure proper API access and quota limits Configure the AI model for recipe-related conversations Test the AI's understanding of cooking terminology 3. Configure FatSecret API Set up FatSecret OAuth2 API credentials Ensure access to the Recipes Search v3 endpoint Configure proper authentication and rate limiting Test API connectivity and response handling 4. Set up Memory Management Configure the memory buffer for conversation context Set appropriate session key mapping for user identification Adjust context window length based on expected conversation depth Test memory persistence across multiple messages 5. Test the Integration Send test messages through WhatsApp to verify end-to-end functionality Test various ingredient combinations and dietary restrictions Verify recipe suggestions are relevant and properly formatted Check that context memory works across multiple interactions Requirements WhatsApp Business API** account with webhook capabilities Google Gemini AI** API access for natural language processing FatSecret API** credentials for recipe database access n8n instance** with proper webhook and HTTP request capabilities Active internet connection** for real-time API interactions How to customize the workflow Modify Recipe Search Parameters Adjust the number of results returned (currently set to 5) Add more filtering options (cuisine types, cooking methods, difficulty levels) Implement pagination for browsing through more recipe options Add sorting preferences (newest, oldest, calorie-based, popularity) Enhance AI Capabilities Train the AI on specific dietary restrictions or cuisine preferences Add support for multiple languages Implement recipe rating and review integration Add nutritional goal tracking and meal planning features Expand Recipe Sources Integrate with additional recipe APIs (Spoonacular, Edamam, etc.) Add support for user-generated recipes Implement recipe bookmarking and favorites Add shopping list generation from selected recipes Improve User Experience Add recipe step-by-step instructions Implement cooking timer and progress tracking Add recipe sharing capabilities Implement user preference learning over time Business Features Add recipe monetization options Implement affiliate marketing for ingredients Add restaurant delivery integration Implement meal kit subscription services Key Features Natural language processing** - Understands cooking requests in plain English Intelligent parameter mapping** - AI automatically extracts search criteria Comprehensive recipe database** - Access to thousands of recipes via FatSecret API WhatsApp native interface** - Seamless chat experience for recipe discovery Contextual memory** - Remembers conversation history for better recommendations Dietary restriction support** - Handles allergies, preferences, and special diets Nutritional information** - Provides calorie counts and macro details Image integration** - Shows recipe photos when available Technical Architecture Highlights AI-Powered Processing Google Gemini integration** - Advanced natural language understanding Smart parameter extraction** - Automatic mapping of user requests to API calls Contextual memory** - Conversation history management for better user experience Intelligent fallbacks** - Graceful handling of unclear or incomplete requests API Integration Excellence FatSecret Recipes API** - Comprehensive recipe database with nutritional data OAuth2 authentication** - Secure and reliable API access Parameter optimization** - Efficient API calls with relevant search criteria Response processing** - Clean formatting of recipe suggestions WhatsApp Integration Webhook-based triggers** - Real-time message reception Message formatting** - Clean, readable recipe presentations User identification** - Proper session management for multiple users Error handling** - Graceful fallbacks for failed operations Performance Optimizations Efficient API calls** - Single request per user message Memory management** - Optimized conversation context storage Response caching** - Reduced API calls for repeated requests Scalable architecture** - Handles multiple concurrent users Use Cases Food delivery platforms** requiring recipe recommendation engines Meal planning services** needing ingredient-based recipe discovery Nutrition and wellness apps** requiring dietary-specific suggestions Cooking schools** offering personalized recipe guidance Grocery stores** helping customers plan meals around available ingredients Restaurant chains** providing recipe inspiration for home cooking Health coaches** offering personalized meal suggestions Social cooking communities** sharing recipe ideas and inspiration Business Value Customer Engagement** - Interactive recipe discovery increases user retention Personalization** - AI-driven recommendations improve user satisfaction Operational Efficiency** - Automated recipe suggestions reduce manual support Revenue Generation** - Recipe recommendations can drive ingredient sales Brand Differentiation** - AI-powered cooking assistant sets services apart Data Insights** - User preferences provide valuable market intelligence Scalability** - Handles multiple users simultaneously without performance degradation This template revolutionizes recipe discovery by combining the power of AI natural language processing with comprehensive recipe databases, creating an intuitive WhatsApp experience that makes cooking inspiration as simple as having a conversation with a knowledgeable chef friend.

🧠 Business Card Scanner – Automate Contact Extraction This workflow automates the process of extracting contact details from business cards (PDF or image) and saving them directly into an n8n Data Table. No more manual data entry — just upload a card and let AI do the rest. ⚙️ How It Works Upload the business card via a web form (PDF or image). The uploaded file is converted to Base64 for processing. The Base64 data is sent to the Mistral OCR API, which extracts text from the image. The OCR output is parsed into JSON. An AI Agent (OpenAI GPT-4o-mini) interprets the extracted text and converts it into structured business card information (e.g., name, company, email, phone). The Structured Output Parser validates and aligns the data with a predefined schema. The workflow upserts (inserts or updates) the contact details into an n8n Data Table named business cards, using the email address as the unique identifier. ✅ Result: Seamless digitization of business cards into structured, searchable contact data. 🧩 Prerequisites Before importing the workflow, make sure you have the following: n8n Instance with access to the Data Table feature OpenAI Platform account and API key (configured in n8n) Mistral AI account and API key (configured in n8n) 🛠️ Setup Steps Import the Workflow Download and import the JSON file into your n8n instance. Create a Data Table Name it business_cards (or adjust the workflow accordingly). Add the following fields: firstname name company jobdescription phone mobil email street postcode place web Configure API Credentials Mistral OCR API → Add your API key under HTTP Bearer Auth. OpenAI API → Add your API key under OpenAI Credentials. Model: gpt-4o-mini (recommended for speed and low cost). Activate the Web Form Trigger Enable the trigger node to make the business card upload form accessible via a public URL. Test the Workflow Upload a sample business card. Confirm that extracted contact data automatically appears in your Data Table. 💡 Example JSON Output { "firstname": "Anna", "name": "Müller", "company": "NextGen Tech GmbH", "jobdescription": "Head of Marketing", "email": "anna.mueller@nextgen.tech", "phone": "+49 821 1234567", "mobil": "+49 170 9876543", "street": "Schillerstraße 12", "postcode": "86150", "place": "Augsburg", "web": "https://nextgen.tech" }

Automated B2B Lead Generation from Google Maps to Google Sheets using BrowserAct This n8n template automates local lead generation by scraping Google Maps for businesses, saving them to Google Sheets, and notifying you in real-time via Telegram. This workflow is perfect for sales teams, marketing agencies, and local B2B services looking to build targeted lead lists automatically. Self-Hosted Only This Workflow uses a community contribution and is designed and tested for self-hosted n8n instances only. How it works The workflow is triggered manually. You can set the Location, Bussines_Category, and number of leads (Extracted_Data) in the first BrowserAct node. A BrowserAct node ("Run a workflow task") initiates the scraping job on Google Maps using your specified criteria. A second BrowserAct node ("Get details of a workflow task") pauses the workflow and waits for the scraping task to be 100% complete. A Code node takes the raw JSON string output from the scraper and correctly parses it, splitting the data into individual items (one for each business). A Google Sheets node appends or updates each lead into your spreadsheet, matching on the "Name" column to prevent duplicate entries. Finally, a Telegram node sends a message with the new lead's details to your specified chat, providing instant notification. Requirements BrowserAct** API account for web scraping BrowserAct* "Google Maps Local Lead Finder*" Template BrowserAct** n8n Community Node -> (n8n Nodes BrowserAct) Google Sheets** credentials for saving leads Telegram** credentials for sending notifications Need Help? How to Find Your BrowseAct API Key & Workflow ID How to Connect n8n to Browseract How to Use & Customize BrowserAct Templates How to Use the BrowserAct N8N Community Node Workflow Guidance and Showcase AUTOMATE Local Lead Generation: Google Maps to Sheets & Telegram with n8n

WABA Message Journey Flow Documentation This document outlines the automated workflow for sending WhatsApp messages to contacts, triggered hourly and managed through disposition and message count logic. The workflow is designed to ensure contacts receive messages based on their status and the frequency of previous interactions. Trigger and Data Retrieval The journey begins with a time-based trigger and data retrieval from the Supabase contacts table. Trigger: A "Schedule Trigger3" node initiates the workflow every hour. This ensures that the system regularly checks for contacts requiring messages. Get Contacts: The "Get many rows1" node (Supabase) then retrieves all relevant contact data from the contacts_ampere table in Supabase. This brings in contact details such as name, phone, Disposition, Count, and last_message_sent. Disposition-Based Segregation After retrieving the contacts, the workflow segregates them based on their Disposition status. Disposition Switch: The "Disposition Switch" node acts as the primary routing mechanism. It evaluates the Disposition field of each contact and directs them to different branches of the workflow based on predefined categories. Case 0: new_lead: Contacts with the disposition new_lead are routed to the "Count Switch" for further processing. Cases 1-4: The workflow also includes branches for test_ride, Booking, walk_in, and Sale dispositions, though the detailed logic for these branches is not fully laid out in the provided JSON beyond the switch nodes ("Switch2", "Switch3", "Switch4", "Switch5"). The documentation focuses on the new_lead disposition's detailed flow, which can be replicated for others. Message Count Logic (for new_lead Disposition) For contacts identified as new_lead, the workflow uses a "Count Switch" to determine which message in the sequence should be sent. Count Switch: This node evaluates the Count field for each new_lead contact. This Count likely represents the number of messages already sent to the contact within this specific journey. Count = 0: Directs to "Loop Over Items1" (first message in sequence). Count = 1: Directs to "Loop Over Items2" (second message in sequence). Count = 2: Directs to "Loop Over Items3" (third message in sequence). Count = 3: Directs to "Loop Over Items4" (fourth message in sequence). Looping and Interval Check Each "Loop Over Items" node processes contacts in batches and incorporates an "If Interval" check (except for Loop Over Items1). Loop Over Items (e.g., "Loop Over Items1", "Loop Over Items2", "Loop Over Items3", "Loop Over Items4"): These nodes iterate through the contacts received from the "Count Switch" output. Interval Logic: "If Interval" (for Count = 1 from "Loop Over Items2"): Checks if the interval is greater than or equal to 4. This interval value is handled by a separate Supabase cron job, which updates it every minute based on Current time - last api hit time in hours. "If Interval1" (for Count = 2 from "Loop Over Items3"): Checks if the interval is exactly 24 hours. "If2" (for Count = 3 from "Loop Over Items4"): Checks if the interval is exactly 24 hours. Sending WhatsApp Messages If a contact passes the interval check (or immediately for Count = 0), a WhatsApp message is sent using the Gallabox API. HTTP Request Nodes (e.g., "new_lead_0", "new_lead_", "new_lead_3", "new_lead_2"): These nodes are responsible for sending the actual WhatsApp messages via the Gallabox API. They are configured with: Method: POST URL: https://server.gallabox.com/devapi/messages/whatsapp Authentication: apiKey and apiSecret are used in the headers. Body: Contains channelId, channelType (whatsapp), and recipient (including name and phone). WhatsApp Message Content: Includes type: "template" and templateName (e.g., testing_rahi, wu_2, testing_rahi_1). The bodyValues dynamically insert the contact's name and other details. Some messages also include buttonValues for quick replies (e.g., "Show me Brochure"). Logging and Updating Contact Status After a message is sent (or attempted), the workflow logs the interaction and updates the contact's record. Create Logs (e.g., "Create Logs", "Create Logs1", "Create Logs2", "Create Logs3"): These Supabase nodes record details of the message send attempt into the logs_nurture_ampere table. This includes: message_id (from the Gallabox API response body) phone and name of the contact disposition and mes_count (which is Count + 1 from the contacts table) last_sent (timestamp from Gallabox API response headers) status_code and status_message (from Gallabox API response or error). These nodes are configured to "continueRegularOutput" on error, meaning the workflow will attempt to proceed even if logging fails. Status Code Check (e.g., "If StatusCode", "If StatusCode 202", "If StatusCode 203", "If StatusCode 204"): Immediately after attempting to create a log, an "If" node checks if the status_code from the message send attempt is "202" (indicating acceptance by the messaging service). Update Contact Row (e.g., "Update a row1", "Update a row2", "Update a row3", "Update a row4"): If the status code is 202, these Supabase nodes update the contacts_ampere table for the specific contact. The Count for the contact is incremented by 1 (Count + 1). The last_message_sent field is updated with the date from the Gallabox API response headers. These nodes are also configured to "continueRegularOutput" on error. This structured flow ensures that contacts are nurtured through a sequence of WhatsApp messages, with each interaction logged and the contact's status updated for future reference and continuation of the journey.

This automation template is an AI-powered booking agent that schedules property viewings and reserves restaurant tables for you, all coordinated through Telegram. It checks your calendar to avoid conflicts, places the calls on your behalf, negotiates times, confirms details, and delivers a crisp summary back to Telegram—hands-free. Note: This workflow uses a voice-calling provider for outbound calls, your calendar for availability, and Telegram for notifications. Usage costs depend on your telephony provider, call duration, and any API usage.* Who Is This For? Home Buyers & Renters:** Queue up and confirm viewings without calling around. Real Estate Agents & Property Managers:** Automate client viewing scheduling and confirmations. Relocation Specialists & Assistants:** Coordinate multi-property tours with calendar-aware logic. Busy Professionals:** Let AI handle restaurant bookings and post-viewing meals. Concierge & Ops Teams:** Standardize bookings with structured logs and Telegram updates. What Problem Does This Workflow Solve? Scheduling property viewings and restaurant tables often means endless calls, conflicts, and coordination. This workflow removes the friction by: AI Phone Calls on Your Behalf:** Natural voice calls to agents/venues to secure slots. Calendar-Aware Booking:** Checks your real-time availability and avoids overlaps. Preference Handling:** Location, budget, party size, time windows, language, and notes. Instant Telegram Summaries:** Clear outcomes (confirmed, waitlist, action needed) and quick next steps. Scalable Coordination:** Handles multiple properties and dining options with fallback logic. How It Works Intent Capture (Telegram): You send a simple message (e.g., “Viewings tomorrow 17:00–20:00, Eixample, 2-bed; table for 4 at 21:30 near there”). Calendar Check: Reads free/busy blocks and suggests viable windows or alternatives. AI Calling: Places outbound calls to listing agents/restaurants, negotiates slots, and confirms. Result Parsing: Extracts confirmed time, address, contact name, reservation name, and special instructions. Telegram Delivery: Sends a concise recap plus optional quick-reply buttons (confirm/cancel/map/nav). Optional Calendar Hold: Adds confirmed bookings to your calendar and blocks time. Logging (Optional): Writes outcomes to a sheet/database for tracking and analytics. Setup Telephony Provider: Connect your AI calling service (API key). Configure voice/language. Calendar Access: Link Google Calendar (or similar). Grant read (and optionally write) access. Telegram Bot: Create a bot with BotFather and add the bot token to credentials. Environment/Credentials: Store calling API token, calendar credentials, Telegram token in n8n. Preferences: Set defaults for viewings (areas, price range, time windows) and dining (party size, cuisine, budget). Test Run: Trial a single booking to verify calling, calendar sync, and Telegram delivery. Requirements Accounts:** n8n, telephony provider, calendar account, Telegram bot. API Keys:** Telephony API token, Calendar credentials, Telegram bot token. Permissions:** Calendar read (and write if auto-blocking); outbound calls enabled. Budget:** Telephony per-minute fees and minor API costs where applicable. Features Dual-Domain Booking:** Property viewings + restaurant tables in one flow. Calendar Intelligence:** Checks conflicts and proposes best-fit time slots. Telegram-Native UX:** Simple prompts, instant confirmations, and quick actions. Preference Profiles:** Time windows, neighborhoods, max budget, language, and notes. Fallbacks & Alternatives:** Suggests nearby times/venues if first choice is unavailable. Multilingual Voice:** Configure voice and language to match region/venue. Structured Logs:** Optional recording of outcomes for reporting and audits. Extensible:** Add CRM, maps, SMS, or payment links as needed. Automate your day from tours to tables: message your intent on Telegram, and let the AI book, confirm, and keep your calendar clean—so you just show up on time.

This workflow automates the process of analyzing a contract submitted via a web form. It extracts the text from an uploaded PDF, uses AI to identify potential red flags, and sends a summary report to a Telegram chat. Prerequisites Before you can use this workflow, you'll need a few things set up. 1. JotForm Form You need to create a form in JotForm with at least two specific fields: Email Address**: A standard field to collect the user's email. File Upload**: This field will be used to upload the contract or NDA. Make sure to configure it to allow .pdf files. 2. API Keys and IDs JotForm API Key**: You can generate this from your JotForm account settings under the "API" section. Gemini API Key**: You'll need an API key from Google AI Studio to use the Gemini model. Telegram Bot Token**: Create a new bot by talking to the @BotFather on Telegram. It will give you a unique token. Telegram Chat ID**: This is the ID of the user, group, or channel you want the bot to send messages to. You can get this by using a bot like @userinfobot. Node-by-Node Explanation Here is a breakdown of what each node in the workflow does, in the order they execute. 1. JotForm Trigger What it does**: This node kicks off the entire workflow. It actively listens for new submissions on the specific JotForm you select. How it works**: When someone fills out your form and hits "Submit," JotForm sends the submission data (including the email and a link to the uploaded file) to this node. 2. Grab Attachment Details (HTTP Request) What it does**: The initial data from JotForm doesn't contain a direct download link for the file. This node takes the submissionID from the trigger and makes a request to the JotForm API to get the full details of that submission. How it works**: It constructs a URL using the submissionID and your JotForm API key to fetch the submission data, which includes the proper download URL for the uploaded contract. 3. Grab the Attached Contract (HTTP Request) What it does**: Now that it has the direct download link, this node fetches the actual PDF file. How it works**: It uses the file URL obtained from the previous node to download the contract. The node is set to expect a "file" as the response, so it saves the PDF data in binary format for the next step. 4. Extract Text from PDF File What it does**: This node takes the binary PDF data from the previous step and extracts all the readable text from it. How it works**: It processes the PDF and outputs plain text, stripping away any formatting or images. This raw text is now ready to be analyzed by the AI. 5. AI Agent (with Google Gemini Chat Model) What it does**: This is the core analysis engine of the workflow. It takes the extracted text from the PDF and uses a powerful prompt to analyze it. The "Google Gemini Chat Model" node is connected as its "brain." How it works**: It sends the contract text to the Gemini model. The prompt instructs Gemini to act as an expert contract analyst. It specifically asks the AI to identify major red flags and hidden/unfair clauses. It also tells the AI to format the output as a clean report using Telegram's MarkdownV2 style and to keep the response under 1500 characters. 6. Send a text message (Telegram) What it does**: This is the final step. It takes the formatted analysis report generated by the AI Agent and sends it to your specified Telegram chat. How it works**: It connects to your Telegram bot using your Bot Token and sends the AI's output ($json.output) to the Chat ID you've provided. Because the AI was instructed to format the text in MarkdownV2, the message will appear well-structured in Telegram with bolding and bullet points.

Voice Assistant Interface with n8n and OpenAI This workflow creates a voice-activated AI assistant interface that runs directly in your browser. Users can click on a glowing orb to speak with the AI, which responds with voice using OpenAI's text-to-speech capabilities. Who is it for? This template is perfect for: Developers looking to add voice interfaces to their applications Customer service teams wanting to create voice-enabled support systems Content creators building interactive voice experiences Anyone interested in creating their own "Alexa-like" assistant How it works The workflow consists of two main parts: Frontend Interface: A beautiful animated orb that users click to activate voice recording Backend Processing: Receives the audio transcription, processes it through an AI agent with memory, and returns voice responses The system uses: Web Speech API for voice recognition (browser-based) OpenAI GPT-4o-mini for intelligent responses OpenAI Text-to-Speech for voice synthesis Session memory to maintain conversation context Setup requirements n8n instance (self-hosted or cloud) OpenAI API key with access to: GPT-4o-mini model Text-to-Speech API Modern web browser with Web Speech API support (Chrome, Edge, Safari) How to set up Import the workflow into your n8n instance Add your OpenAI credentials to both OpenAI nodes Copy the webhook URL from the "Audio Processing Endpoint" node Edit the "Voice Assistant UI" node and replace YOUR_WEBHOOK_URL_HERE with your webhook URL Access the "Voice Interface Endpoint" webhook URL in your browser Click the orb and start talking! How to customize the workflow Change the AI personality**: Edit the system message in the "Process User Query" node Modify the visual style**: Customize the CSS in the "Voice Assistant UI" node Add more capabilities**: Connect additional tools to the AI Agent Change the voice**: Select a different voice in the "Generate Voice Response" node Adjust memory**: Modify the context window length in the "Conversation Memory" node Demo Watch the template in action: https://youtu.be/0bMdJcRMnZY

Who is this for? Businesses and developers who want to automate customer support or engagement on Facebook Messenger using AI-powered responses. What does it do? Creates an intelligent Facebook Messenger chatbot that: Responds to messages using OpenAI (gpt-4o-mini) Batches rapid-fire messages into a single AI request Maintains conversation history (50 messages per user) Shows professional UX feedback (seen indicators, typing bubbles) How it works Webhook Verification - Handles Facebook's GET verification request Message Reception - Receives incoming messages via POST webhook Message Batching - Waits 3 seconds to collect multiple quick messages AI Processing - Sends combined message to OpenAI with conversation context Response Delivery - Formats and sends the AI response back to Messenger Setup Configure Facebook Graph API credential with your Page Access Token Configure OpenAI API credential with your API key Set your verify token in the "Is Token Valid?" node Register the webhook URL in Facebook Developer Console Key Features Message Batching: Combines "Hey" + "Can you help" + "with my order?" into one request Conversation Memory: Remembers context from previous messages Echo Filtering: Prevents responding to your own messages Response Formatting: Cleans markdown for Messenger's 2000-char limit