Workflow Templates

Use case Error workflows are an important part of running workflows in production. Make sure to set them up for all your important workflows. The message links directly to the execution. How to setup Add Telegram creds Set chat id in Telegram node Add this error workflow to other workflows https://docs.n8n.io/flow-logic/error-handling/#create-and-set-an-error-workflow

This n8n workflow automatically sends a Telegram message whenever a new event is added to Google Calendar. It extracts key event details such as event name, description, event creator, start date, end date, and location and forwards them to a specified Telegram chat. This ensures you stay updated on all newly scheduled events directly from Telegram. Prerequisites Before setting up the workflow, ensure the following: Google Account with Google Calendar Access: The Google Calendar API must be enabled. Telegram Bot: Create a bot using BotFather on Telegram. Telegram Chat ID: Retrieve the Chat ID (personal chat or group). Use OAuth2 for Google Calendar and a Bot Token for Telegram. Steps Step 1: Google Calendar Trigger Node (Event Created Event) Click "Add Node" and search for Google Calendar. Select "Google Calendar Trigger" and add it to the workflow. Authenticate with your Google Account. Select "Event Created" as the trigger type. Choose the specific calendar to monitor. Click "Execute Node" to test the connection. Click "Save". Step 2: Telegram Node (Send Message Action) Click "Add Node" and search for Telegram. Select "Send Message" as the action. Authenticate using your Telegram Bot Token. Set the Chat ID (personal or group chat). Format the message using details from Google Calendar Trigger and set the message in text. Click "Execute Node" to test. Click "Save". Step 3: Connect & Test the Workflow Link Google Calendar Trigger → Telegram Send Message. Execute the workflow manually. Create a test event in Google Calendar. Check Telegram to see if the event details appear. n8n Workflow Created by WeblineIndia This workflow is built by the AI development team at WeblineIndia. We help businesses automate processes, reduce repetitive work, and scale faster. Need something custom? You can hire AI developers to build workflows tailored to your needs.

This workflow turns any URL (news article, blog post, or even an n8n workflow page) into a vertical short video with your AI avatar explaining it ready for TikTok, Instagram Reels, and YouTube Shorts. It fetches the page, generates a tight 30–45s script and platform-optimized descriptions, captures a dynamic background of the page (animated scroll or static image), composes and renders the video with HeyGen (free split‑screen or paid clean cut‑out), and sends it to Upload-Post with an optional human review step. Note: You can generate full videos end‑to‑end using free trials—no credit card required—for all APIs used in this template (Google Gemini, ScreenshotOne, HeyGen, Upload‑Post).* Who Is This For? Creators & Marketers:** Explain articles, launches, and workflows without filming or editing. Media & Newsletters:** Turn breaking stories into clear, shareable shorts. Agencies:** Scale content creation with review gates and multi-account publishing. Founders & Product Teams:** Maintain an on-brand presence in minutes. What Problem Does It Solve? Making platform-native explainers is slow and inconsistent. This workflow: Writes the script with AI:** ~30s hook-led monologue with key facts. Optimizes per platform:** Tailored captions for TikTok, Reels, and Shorts. Generates the video automatically:** Uses the page itself as background + avatar voiceover. Publishes everywhere:** Optional review, then one-click multi-platform posting. How It Works URL Input: Paste any page to convert (article, blog, or workflow). AI Agent (Gemini): Reads the page and produces a single script (~30s) + platform-specific descriptions. Video Background: Animated scroll capture (9:16) or featured image via ScreenshotOne. HeyGen Composition & Render: Free: split-screen vertical (avatar bottom, background top). Paid: clean avatar cut‑out over video/image (background removal). Render & Poll: Waits for HeyGen to finish and retrieves the final MP4. Human Review (optional): Approve or reject in a simple form. Publish (Upload-Post): Uploads to TikTok, Instagram (Reels), and YouTube Shorts with AI-generated titles/descriptions. Setup Credentials (all offer free trials, no credit card required): HeyGen API (X-Api-Key) + your avatar_id and voice_id. ScreenshotOne API key. Upload-Post (connect your social accounts). Google Gemini (chat model). Variables in “Set Input Vars”: workflow_url: page to convert. background_removal: true (paid) or false (free). background_type: video (animated scroll) or photo (static). Publishing: Choose platforms in Upload-Post; enable review if you want to approve before posting. Requirements Accounts:** n8n, HeyGen, ScreenshotOne, Upload-Post, Google (Gemini). API Keys:** HeyGen, ScreenshotOne, Gemini; Upload-Post credentials. Assets:** An avatar and a voice available in HeyGen. Features URL → Short in minutes:** 9:16 vertical (720×1280). Pro script with hook:** Clear, natural, ~30s. Two render modes:** Split-screen (free) or clean cut‑out (paid). Background from the page:** Animated scroll or main image. Human-in-the-loop:** Approval before going live. Multi-publish:** TikTok, Instagram Reels, YouTube Shorts via Upload-Post. Start free:** Generate videos with free trials across all APIs—no credit card required.

🚀 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).

Zoho CRM - Conversation Intelligence Analyzer This workflow automatically processes customer call recordings, transcribes them using OpenAI Whisper, extracts key topics, identifies commitments, analyzes sentiment, generates follow-up suggestions and updates the corresponding Zoho CRM Lead — all without manual efforts. It eliminates the need for listening to calls or writing summaries and equips your sales team with instant AI-generated insights. ⚡ Quick Start (Fast Setup) Import the workflow JSON into n8n. Add Zoho CRM OAuth2 & OpenAI API credentials. Copy the webhook URL and configure your telephony system to POST call recordings. Map Zoho custom fields. Upload a test recording → Confirm CRM updates → Activate workflow. 📘 What It Does This workflow turns every incoming call recording into structured insights which your sales & customer support team can immediately use. When a recording is received, the call is automatically transcribed using OpenAI’s Whisper model. That transcript is then processed by multiple AI nodes that detect topics, customer sentiment, commitments and possible follow-up actions. All extracted data — such as mood, sentiment score, subjects, action items and commitments is merged into a clean result object and pushed to the matching Lead in Zoho CRM. The sales team gets ready-to-use call intelligence instantly, improving decision-making, accuracy and speed. This automation works 24/7 and replaces hours of manual review work with reliable AI-generated summaries. 👤 Who’s It For Sales & Customer support teams using Zoho CRM. Support teams handling inbound/outbound calls. Businesses wanting call analytics without manual transcription. Zoho CRM admins who want automation with minimal maintenance. Organizations using telephony/VoIP systems that support call exports. 🧾 Requirements To use this workflow, you need: An n8n instance (self-hosted or cloud) Zoho CRM OAuth2 credentials OpenAI API key (Whisper + GPT models) A telephony system capable of POSTing audio files to a webhook Zoho fields to store: Topics Main subject Action items Sentiment Mood Follow-up text Commitments (optional) ⚙️ How It Works & How to Set Up 1. Webhook Trigger Your call system sends an audio file (.mp3, .wav, etc.) to the webhook. The workflow starts instantly—no polling required. 2. Workflow Configuration Static values like: sentimentThreshold = 0.7 minCommitmentConfidence = 0.8 ensure consistent logic across nodes. 3. Audio Transcription (OpenAI Whisper) The audio file is converted to text. This transcript becomes the base for all analysis nodes. 4. Key Topic Extraction AI identifies: Key topics Main subject Important action items 5. Sentiment & Mood Analysis AI analyzes: Customer mood Sales rep tone Overall sentiment Sentiment score 6. Commitment Extraction AI detects commitments using a structured JSON schema. 7. Follow-up Generation GPT generates 3–5 follow-up suggestions based on the transcript & commitments. 8. Combine All Insights A Set node merges transcription, topics, sentiment, commitments and follow-up text. 9. Update Zoho CRM Lead Updates Zoho custom fields so the sales team gets immediate insights. 🛠 How to Customize Nodes Transcription Node Switch to another Whisper/GPT model Add language options Topic Extraction Add more attributes (risks, objections, intent) Sentiment Analysis Tune thresholds Add more emotion labels Commitment Extraction Modify schema Add filtering logic CRM Update Map to different fields Append notes instead of overwriting ➕ Add-Ons (Optional Enhancements) Slack/Teams alerts for negative sentiment Email transcripts to teams Save files to Google Drive / S3 Create Zoho tasks from commitments Multi-language transcription Sales rep performance scoring 💼 Use Case Examples Sales Call Analysis** – Auto-summarize calls for follow-up. Support Hotline Monitoring** – Detect customer frustration. QA Audits** – Auto-generate evaluation notes. Voice-to-CRM Logging** – Store conversation data automatically. Compliance Tracking** – Capture legally relevant commitments. 🛠 Troubleshooting Guide | Issue | Possible Cause | Solution | |------|----------------|----------| | Workflow not triggered | Telephony not hitting webhook | Recheck webhook URL & logs | | Transcript empty | Unsupported/corrupted audio | Validate file before sending | | CRM not updating | Wrong Zoho field IDs | Verify field IDs in Zoho | | Commitments missing | Transcript unclear | Improve audio quality or edit schema | | Sentiment inaccurate | Model interpretation | Adjust sentimentThreshold | 🤝 Need Help? If you want to customize this workflow, integrate telephony systems or want to build advanced level CRM automation, then our n8n workflow development team at WeblineIndia team is happy to help. We’re here to support setup, scaling, and custom enhancements.

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.

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.

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.