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AI Restaurant Virtual Waiter Chatbot 🤖🍕 for WhatsApp & Website

This workflow implements a WhatsApp-based virtual restaurant assistant that automates customer interaction from the first message to post-dining follow-up. The system can receive messages either from WhatsApp or a chatbot interface, normalize the input, and process it through AI guardrails to ensure safe and relevant communication. Key Advantages 1. ✅ End-to-End Automation The workflow fully automates the customer journey: Message reception Menu delivery Customer data collection AI conversation Review request No manual intervention is required. 2. ✅ Omnichannel Interaction It works both with: WhatsApp (real users) Chat interface (for testing or website chatbot) This makes it flexible and easy to integrate into different touchpoints. 3. ✅ AI-Powered Customer Service The virtual waiter agent: Answers questions about dishes, ingredients, prices, allergens Provides recommendations Uses real-time API data (no hallucinations allowed by design) 4. ✅ Built-in Safety (Guardrails) The workflow includes AI guardrails to: Detect jailbreak attempts Block irrelevant or unsafe requests Ensure responses comply with predefined policies 5. ✅ Data Collection & CRM Integration Customer interactions are stored automatically in Google Sheets, enabling: Basic CRM functionality Customer tracking Future marketing actions 6. ✅ Personalized Experience Using session-based memory: The system remembers recent interactions Provides more contextual and coherent responses 7. ✅ Automated Marketing & Retention The delayed follow-up: Encourages customers to leave reviews Improves online reputation Increases customer engagement after the visit 8. ✅ Scalable & Modular Architecture The workflow is built with reusable components: AI agent API tools Messaging nodes This makes it easy to: Extend (e.g., reservations, payments) Adapt to different restaurants How it works This workflow automates a restaurant assistant that responds to customers via both WhatsApp and a chat widget on a website. When a customer sends a message, the system checks if it's a menu request or a general query. WhatsApp messages first go through a switch node that separates text from unsupported formats. If the message is text, it is normalized and passed to a guardrails node that detects jailbreak attempts. If a jailbreak is detected, a fallback message is sent. If safe, the system checks whether the user asked for the menu. If the user asks for the menu, the workflow saves the client’s phone number and date to a Google Sheet, then sends a demo menu link via both WhatsApp and the chat interface. After a short wait (1 minute for chat, 2 hours for WhatsApp), the system sends a follow-up message asking for a Google review. If the user asks something else, the request goes to a virtual waiter agent. The agent uses a Gemini language model, a memory buffer to keep conversation context, and two tools: a calculator and an HTTP Request Tool that fetches the restaurant menu from a ForkMenu API. The agent answers strictly based on the retrieved menu data — it never invents information. The response is then sent back to the customer via WhatsApp and the chat node. Set up steps Add credentials WhatsApp OAuth account (for WhatsApp Trigger) WhatsApp account (for sending messages) Google Gemini (PaLM) account (for the language models) Google Sheets account (to store customer data) Configure WhatsApp nodes Update the phoneNumberId in all WhatsApp send nodes Ensure the WhatsApp Trigger webhook is correctly connected to your Meta Business account. Set up Google Sheets Clone this Sheet Make sure the sheet contains columns CLIENT and DATE. Configure the menu API The HTTP Request Tool calls https://demo.forkmenu.com/api/menu/piatti/2?key=.... Replace the key and URL with your actual restaurant’s ForkMenu API credentials. Customize messages Edit the text in all Send message and Chat nodes (menu link, review request, jailbreak response, unsupported format message). Adjust timers The review request is sent after 2 hours for WhatsApp and 1 minute for the chat widget (for testing). Modify the Wait nodes as needed. Activate the workflow Toggle "active": true in the workflow JSON or click "Active" in the n8n editor. Set up chat widget The When chat message received node requires an n8n chat trigger. Embed the chat widget on your website using the n8n chat embed code. 👉 Subscribe to my new YouTube channel. Here I’ll share videos and Shorts with practical tutorials and FREE templates for n8n. Need help customizing? Contact me for consulting and support or add me on Linkedin.

IntermediateManual Trigger

by Davide

Scrape Idealista 🏠 Real Estate Property Listings with ScrapeGraph AI 🕷️

This workflow automates the process of scraping real estate listings from *Idealista (or similar property portals), extracting structured property data using AI, and storing the results directly into Google Sheets. It is designed to handle paginated listing pages, collect individual property URLs, extract detailed listing information, and continuously build a structured real estate database with minimal manual effort. Key Advantages 1. ✅ Fully Automated Real Estate Data Collection Automatically navigates through multiple listing pages, extracts property URLs, and retrieves detailed property information without manual browsing. 2. ✅AI-Powered Data Extraction Uses ScrapeGraphAI to intelligently extract structured information such as: Property title Description Price Area (sqm) Bedrooms & bathrooms Floor and room count Balcony, terrace, cellar Heating and air conditioning Property image URLs 3. ✅Scalable Pagination Handling Dynamically generates paginated URLs, allowing the workflow to scrape hundreds or thousands of listings efficiently. 4. ✅Google Sheets Integration Automatically writes and updates extracted property data into Google Sheets, creating a centralized and continuously updated real estate database. 5. ✅Duplicate Prevention Uses the property URL as a unique identifier to append or update listings without creating duplicates. 6. ✅Highly Customizable The workflow can be adapted to: Different cities or search filters Other real estate websites Different extraction schemas Alternative storage systems (CRM, database, Airtable, etc.) 7. ✅Structured JSON Schema Extraction Ensures consistent and reliable data formatting, making the output ready for: Market analysis Lead generation CRM enrichment Investment scouting Real estate dashboards 8. ✅Low-Code & Modular Architecture Built entirely inside n8n with reusable modules, making maintenance and future upgrades simple. Ideal Use Cases Real estate lead generation Property market monitoring Investment opportunity analysis Building property databases Real estate CRM automation Competitor and pricing analysis Automated property aggregation platforms How it works This workflow automates the extraction of real estate listings from Idealista by performing two main phases: listing URL discovery and detailed data extraction. Trigger and Pagination Setup A Manual Trigger starts the workflow. A Set node defines the base search URL and the maximum number of pages to scrape. A Code node then generates the paginated URLs (e.g., .../lista-1.htm, .../lista-2.htm). Extract Listing URLs from Search Pages The generated URLs are split into batches using a Split In Batches node. For each search page, a ScrapegraphAI node extracts all individual property URLs that match the pattern https://www.idealista.it/immobile/xxxx. The results are then aggregated and unified using an Aggregate and a Code node to remove duplicates and flatten the list. Process Each Property URL The unified list of property URLs is split again into batches. For each property URL, a second ScrapegraphAI node extracts detailed information following a strict JSON schema (including title, description, price, area, bedrooms, bathrooms, floor, rooms, balcony, terrace, cellar, heating, air conditioning, and image URLs). Store Data in Google Sheets The extracted data is finally written to a Google Sheet using the Google Sheets node configured with appendOrUpdate mode, which avoids duplicates by matching the URL column. Set up steps Import and Configure Credentials Import the workflow into n8n. Add the following credentials: ScrapegraphAI API (used by both ScrapegraphAI nodes). Google Sheets OAuth2 (used for writing data). Prepare the Google Sheet Clone this template sheet or create your own. Update the Google Sheets node with your Document ID and Sheet Name. Configure the Search Parameters In the Set params node, modify the url variable to target your desired search (location, filters, etc.) and set max_pages to control how many search result pages to scrape. Adjust Extraction Logic (if needed) Verify that the Scrape listings node’s prompt correctly matches the listing URL structure of your target site. Update the Extract data node’s outputSchema (JSON schema) to match the fields you want to extract. Enable and Execute Activate the workflow. Click the Execute Workflow button to start scraping. The results will automatically populate the configured Google Sheet, appending new listing data without creating duplicates. 👉 Subscribe to my new YouTube channel. Here I’ll share videos and Shorts with practical tutorials and FREE templates for n8n. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Generate & Upload llms.txt for websites GEO optimization 🕸️🌐 with ScrapegraphAI

This workflow automatically generates an llms.txt file (following the llmstxt.org specification) for any given website. It uses ScrapegraphAI to crawl and scrape pages, an OpenAI chat model to process content, and finally uploads the generated file via FTP. Key Advantages 1. ✅ Automated llms.txt Generation The workflow fully automates the creation of a compliant llms.txt file, eliminating the need for manual documentation and reducing maintenance time. 2. ✅ AI-Powered Website Understanding Using OpenAI and ScrapeGraphAI, the system intelligently analyzes: Website structure Internal pages Titles and descriptions Content relevance Logical page categorization This produces a high-quality output specifically optimized for AI systems and LLM indexing. 3. ✅ Dynamic Internal Link Discovery The crawler automatically extracts all internal links from the website, making the workflow scalable for: Small business websites Large corporate websites Ecommerce stores Blogs and documentation portals 4. ✅ Intelligent Content Categorization Pages are automatically grouped into meaningful sections such as: Main Pages Services Products Portfolio Blog Company Contact Legal / Optional pages This improves readability and machine interpretability. 5. ✅ Multilingual Support The workflow preserves the original language of the website content, ensuring consistency and localization for international projects. 6. ✅Fully Automated Publishing After generation, the workflow converts the output into a .txt file and uploads it directly to an FTP server or CDN, enabling instant deployment without manual intervention. 7. ✅ Reduced Manual Work* The entire process — from crawling to publishing — is automated inside n8n, significantly reducing operational effort for SEO teams, developers, and AI optimization workflows. 8. ✅ AI & SEO Optimization The generated llms.txt file helps: AI crawlers better understand the website Improve AI discoverability Structure content for LLM consumption Support future AI search indexing strategies 9. ✅ Modular and Scalable Architecture The workflow is built with reusable components: Crawler module Status monitoring AI analysis agent Scraper tool Binary conversion FTP deployment This makes it easy to extend, customize, or integrate into larger automation systems. Ideal Use Cases AI-ready website optimization Automated SEO infrastructure LLM indexing preparation Agency website automation Large-scale multi-site management Documentation platforms AI search visibility enhancement How it works The process begins when the workflow is manually triggered. It then: Starts a crawl of the specified domain using ScrapegraphAI’s smartcrawler. The crawler extracts all internal links from the domain (acting like a sitemap generator). Waits for the crawl to complete (configurable wait time, default 20 units). Checks the crawler’s status – if the crawl is still processing, the workflow waits again; if successful, it proceeds. Extracts the discovered internal links and passes them to an AI agent. Uses an AI agent (with OpenAI GPT) that: Receives the list of internal URLs. Uses a Scraper tool (via ScrapegraphAI) to scrape each URL’s content. Follows a strict prompt to: Analyze the homepage (title, description, language). Extract concise descriptions for each internal page. Group pages into logical sections (Main pages, Services, Portfolio, Contact, Optional, etc.). Generate a clean Markdown file (llms.txt) following the official spec. Converts the Markdown output into a binary file (llms.txt). Uploads the file to an FTP server (configured for BunnyCDN or any FTP storage). Ends the workflow once the upload is complete. The AI agent is explicitly forbidden from inventing content – it must call the Scraper tool for every URL before describing it. The output is pure Markdown, starting with #. Setup steps To use this workflow in n8n, follow these steps: 1. Prerequisites An n8n instance (self-hosted or cloud). A ScrapegraphAI account with API access. An OpenAI account with API key (model used: gpt-5.4-mini – note: this may be a custom/typo; usual models are gpt-4o-mini or gpt-4). An FTP server (traditional FTP, or SFTP if modified). 2. Configure credentials in n8n Go to Credentials in n8n and add: ScrapegraphAI API** Name: ScrapegraphAI account API Key: your ScrapegraphAI API key OpenAI API** Name: OpenAi account (Eure) API Key: your OpenAI API key FTP** Name: FTP BunnyCDN Host, Port, Username, Password (or SSH key) for your FTP server 3. Modify the domain In the Set domain node, change the your_domain to your target domain (e.g., example.com). Do not include https:// – only the domain name. 4. Adjust wait time (optional) In the Wait node, change the amount (default 20) to a higher value if the target site is large or slow to crawl. 5. Update FTP upload path In the Upload to FTP node, update the path field. Currently it is: =/YOUR_PATH/{{$binary.data.fileName}} Change YOUR_PATH to the actual remote directory (e.g., /public_html/). The file will be saved as llms.txt. 6. (Optional) Modify the AI prompt The prompt inside the LLMS.txt Agent node can be adapted for: Different section names Different output structure Different languages Exclusion of certain URL patterns 7. Activate and execute Save the workflow. Toggle Active to enable manual execution. Click ‘Execute workflow’ on the Manual Trigger node. Monitor execution – the workflow will wait for the crawl, then process all pages, and upload the final file. 8. Verify Check your FTP server for the generated llms.txt. Test it by opening in a text editor – it should be pure Markdown starting with # Site name. 👉 Subscribe to my new YouTube channel. Here I’ll share videos and Shorts with practical tutorials and FREE templates for n8n. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Triage support tickets by GPT-4o sentiment and route them to Slack

Analyze customer support ticket sentiment and route to Slack channels Who is this for Customer support teams, customer experience managers, and operations teams that need to triage incoming support tickets in real time without manual classification. Ideal for SaaS companies, e-commerce businesses, and service providers handling high ticket volumes. What this workflow does This workflow automatically triages incoming customer support tickets by sentiment using OpenAI GPT-4o. A ticket arrives via webhook, is normalized, analyzed for anger intensity (scored 0-10), and routed to the correct Slack channel in real time. High-anger tickets are escalated immediately, mid-range tickets go to standard support, and low-anger tickets or feedback are logged separately. How to set up Add your OpenAI API credential to the AI sentiment analysis node Add your Slack OAuth2 credential to all three Slack nodes Update Slack channel names (#escalation / #support / #feedback) to match your workspace Activate the workflow and copy the webhook URL for your support form integration Send a test request to verify end-to-end execution Requirements OpenAI API account with GPT-4o access Slack workspace with OAuth2 app installed (chat:write scope) Three Slack channels: one for escalation, one for support, one for feedback How to customize Adjust the anger score thresholds in the Switch node to match your team's escalation policy. Modify the GPT-4o prompt to detect additional sentiment categories or extract custom fields from tickets. Add language detection for multilingual support. Node List | # | Node Name | Type | Role | |---|-----------|------|------| | 1 | When Ticket Received | Webhook | Trigger on incoming HTTP POST request | | 2 | Normalize Ticket Data | Code | Sanitize and validate ticket fields | | 3 | OpenAI Sentiment Analysis | OpenAI | AI anger scoring and sentiment tagging | | 4 | Parse Sentiment Output | Code | Parse JSON output and decide routing | | 5 | Route by Sentiment Score | Switch | 3-way branch: ≥8 / 4-7 / ≤3 | | 6 | Send to Slack #escalation | Slack | Alert for high-anger tickets | | 7 | Send to Slack #support | Slack | Notification for mid-range tickets | | 8 | Send to Slack #feedback | Slack | Log for low-anger tickets or feedback | | 9 | Respond with Routing Outcome | Respond to Webhook | Confirm result to the caller | Total: 9 nodes (+ 5 Sticky Notes) Sticky Note Compliance | # | Sticky Note Title | Color | Role | |---|-------------------|-------|------| | 1 | Main Sticky Note (Overview) | Yellow | Workflow overview, How it works, Setup steps, Customization | | 2 | Receive and normalize | White | Covers webhook reception and data normalization | | 3 | Analyze sentiment | White | Covers GPT-4o sentiment analysis | | 4 | Parse and route | White | Covers JSON parsing and routing logic | | 5 | Post to Slack channels | White | Covers all 3 Slack post nodes and webhook response | All sticky notes use H2 headings (## ) and follow n8n public guidelines. Tags ai gpt-4 openai slack customer-support sentiment-analysis automation

Analyze error logs with GPT-4o and create GitHub issues plus Slack alerts

Analyze error logs with AI and auto-create GitHub issues with fix suggestions Who is this for DevOps engineers, SREs, and development teams who want to automate error monitoring and reduce mean time to resolution (MTTR). Ideal for teams using GitHub for issue tracking and Slack for incident response. What this workflow does This workflow automates the entire error management lifecycle - from log ingestion to GitHub issue creation and Slack notification - using GPT-4o-powered root cause analysis. When an application error log is received, it parses the payload, checks for duplicates against existing GitHub issues, generates a structured root cause analysis with fix suggestions, creates a formatted GitHub Issue, and routes Slack notifications by severity. A 30-minute Wait node prevents notification flooding. How to set up Add your GitHub Personal Access Token (repo scope) credential Add your OpenAI API credential to the AI analysis node Add your Slack OAuth2 credential (chat:write scope) to all Slack nodes Configure n8n Variables: GITHUB_OWNER and GITHUB_REPO Update Slack channel names (#incident / #dev-alerts) to match your workspace Activate the workflow and copy the webhook URL for your application logger Requirements GitHub repository with Personal Access Token (repo scope) OpenAI API account with GPT-4o access Slack workspace with OAuth2 app installed Two Slack channels: one for critical incidents, one for general dev alerts How to customize Adjust the duplicate detection score threshold (default 60) in the Code node. Modify the GPT-4o prompt to focus on specific error categories. The Wait node duration (30 minutes) can be tuned to match your alerting policy. Key features Scoring-based duplicate detection (no extra API calls required) GPT-4o structured JSON output with graceful fallback parser Severity-based Slack routing (#incident vs #dev-alerts) Dynamic GitHub labels: bug, auto-generated, environment, and critical n8n Variables used for GitHub owner and repo - no hardcoded values Node List | # | Node Name | Type | Purpose | |---|-----------|------|---------| | 1 | Webhook for Error Logs | Webhook | Accepts error log payload via HTTP POST | | 2 | Parse and Enrich Log | Code | Normalizes level, extracts error type, builds duplicate search keyword | | 3 | Search GitHub Issues | GitHub | Fetches open issues labeled bug,auto-generated from the target repo | | 4 | Score GitHub Issue Duplicates | Code | Scores each issue for similarity; flags duplicates at threshold ≥ 60 | | 5 | Check for Duplicates | If | Routes to skip path (true) or analysis path (false) | | 6 | Notify Duplicate to Slack | Slack | Posts link to existing issue when duplicate detected | | 7 | Respond with Duplicate Status | Respond to Webhook | Returns 200 OK JSON acknowledgment for duplicate path | | 8 | OpenAI Error Analysis | OpenAI | GPT-4o analyzes root cause and returns structured JSON fix suggestions | | 9 | Build GitHub Issue Body | Code | Parses AI JSON and builds Markdown issue body with tables and code blocks | | 10 | Create GitHub Issue | GitHub | Creates GitHub issue with title, body, and dynamic labels | | 11 | If Critical Error | If | Checks isCritical flag to route Slack notification channel | | 12 | Post Critical Alert to Slack | Slack | Posts @here alert to #incident with full error details | | 13 | Post Error Summary to Slack | Slack | Posts summary to #dev-alerts with GitHub issue link | | 14 | Wait 30 Minutes | Wait | Enforces 30-minute cooldown to prevent notification flooding | Total: 14 nodes (+ 6 Sticky Notes) Sticky Note Compliance | # | Sticky Note Title | Color | Role | |---|-------------------|-------|------| | 1 | Main Sticky Note (Overview) | Yellow | Workflow overview, How it works, Setup steps, Customization | | 2 | Receive and parse log | White | Covers webhook reception and log parsing | | 3 | Check for duplicate GitHub issues | White | Covers GitHub search and duplicate scoring | | 4 | Handle duplicates and notify | White | Covers duplicate branch (notification + webhook response) | | 5 | Analyze error and suggest fixes | White | Covers AI analysis and issue creation | | 6 | Send alerts and summarize | White | Covers severity check, Slack notifications, and wait | All sticky notes use H2 headings (## ) and follow n8n public guidelines. Webhook payload schema { "service": "payment-api", "level": "CRITICAL", "message": "NullPointerException at PaymentProcessor.java:142", "stack_trace": "java.lang.NullPointerException...", "environment": "production", "timestamp": "2025-01-15T09:23:45Z", "trace_id": "abc-123-xyz", "endpoint": "/api/v2/payments", "http_method": "POST", "status_code": 500, "user_id": "user_98765" } Required fields: service, level, message Optional fields: All others - missing values are handled gracefully with fallbacks. How duplicate detection works | Match condition | Points | |-----------------|--------| | Service name found in issue title | +40 | | Error type found in issue title | +40 | | Keyword overlap (words > 4 chars) | +5 per word (max +20) | Threshold: Score ≥ 60 → duplicate detected → skip issue creation, notify Slack. Tags ai gpt-4 openai github slack error-monitoring devops automation

Review GitHub pull requests with GPT-4o and send feedback and Slack alerts

Automatically review pull requests with AI and post feedback as GitHub comments Who is this for Engineering teams, code reviewers, and tech leads who want to automate code review feedback. Ideal for repositories with high PR volume where consistent first-pass review is valuable. What this workflow does This workflow automates pull request code review using GPT-4o and routes findings to GitHub and Slack. When a pull request is opened, it fetches the raw diff, sends it to GPT-4o for analysis, and posts a formatted Markdown comment with severity rating, categorized issues, suggestions, and an overall score. Critical findings trigger immediate Slack alerts. How to set up Add your GitHub OAuth2 credential to the trigger and comment nodes Add your OpenAI API credential to the GPT-4o node Add your Slack OAuth2 credential to both Slack notification nodes Configure n8n Variables: GITHUB_OWNER, GITHUB_REPO, and GITHUB_TOKEN Activate the workflow - the GitHub webhook registers automatically Open a test pull request to verify end-to-end execution Requirements GitHub repository with OAuth2 credentials and Personal Access Token OpenAI API account with GPT-4o access Slack workspace with OAuth2 app installed Two Slack channels: one for critical alerts, one for review summaries How to customize Adjust the diff truncation limit (default 12,000 chars) in the Format PR Diff node. Modify the GPT-4o system prompt to focus on specific code quality concerns like security or performance. Update Slack channel routing based on your team's notification policy. Node List | # | Node Name | Type | Purpose | |---|-----------|------|---------| | 1 | When PR Opened | GitHub Trigger | Receives pull_request webhook events | | 2 | Check PR Open | If | Passes only action = opened to avoid duplicates | | 3 | Fetch PR Diff | HTTP Request | Fetches the raw unified diff from GitHub | | 4 | Format PR Diff | Code | Extracts PR metadata; truncates diff to 12,000 chars | | 5 | AI Code Review with GPT-4 | OpenAI | Sends diff to GPT-4o; returns structured JSON review | | 6 | Build Comment for GitHub | Code | Parses JSON; assembles Markdown comment | | 7 | Post Comment to GitHub | GitHub | Posts the AI review comment on the pull request | | 8 | Check Critical Severity | If | Routes based on CRITICAL vs non-critical severity | | 9 | Alert Critical Issues to Slack | Slack | Sends urgent alert to #incident | | 10 | Share Review Summary on Slack | Slack | Sends summary notification to #code-reviews | Total: 10 nodes (+ 5 Sticky Notes) Sticky Note Compliance | # | Sticky Note Title | Color | Role | |---|-------------------|-------|------| | 1 | Main Sticky Note (Overview) | Yellow | Workflow overview, How it works, Setup steps, Customization | | 2 | Trigger and filter PR | White | Covers GitHub trigger and PR filter | | 3 | Fetch and format diff | White | Covers diff retrieval and formatting | | 4 | AI review and comment | White | Covers AI review and comment building | | 5 | Severity check and alerts | White | Covers severity routing and Slack notifications | All sticky notes use H2 headings (## ) and follow n8n public guidelines. Tags ai gpt-4 openai github slack code-review devops automation

Save business card contacts from LINE photos to Google Sheets using Gemini

Save business card contacts from LINE photos to Google Sheets with Gemini Who is this for Sales professionals, account managers, and anyone who exchanges business cards regularly. Designed especially for LINE users (Japan, Taiwan, Thailand, etc.) who want to eliminate manual data entry after networking events or client meetings. What this workflow does When a user sends a business card photo to a LINE bot, Gemini AI automatically extracts contact information (name, company, job title, phone number, email, postal address) and saves it to Google Sheets. Duplicate entries are detected by email address and skipped automatically. The team receives a Slack notification for every new contact, and the user gets an instant LINE confirmation. How to set up Create a LINE Messaging API channel and obtain a Channel Access Token Prepare a Google Sheet with columns: Timestamp, Name, Company, Title, Phone, Email, Address, LINE_UID Get a Google Gemini API key from Google AI Studio Set up a Slack Incoming Webhook URL Enter all credentials in the Set Configuration Parameters node Register the n8n Webhook URL in LINE Developer Console Requirements LINE Messaging API account (free tier available) Google Sheets (any Google account) Google Gemini API key (free tier available) Slack workspace with Incoming Webhooks enabled How to customize Adjust Google Sheets columns to match your CRM fields. Add a HubSpot or Salesforce node after the Sheets node to sync contacts directly to your CRM. Replace Slack with Microsoft Teams or Discord for team notifications. Node List | # | Node Name | Type | Role | |---|-----------|------|------| | 1 | Set Configuration Parameters | Set | Centralizes all credentials and config values | | 2 | When LINE Event Received | Webhook | Receives LINE Webhook events (POST) | | 3 | If Image Message | If | Checks whether the message contains an image | | 4 | Fetch Image from LINE API | HTTP Request | Downloads image from LINE Content API | | 5 | Encode Image as Base64 | Code | Converts image binary to Base64 string | | 6 | Setup Gemini for Card Data | Gemini Chat Model | Gemini 1.5 Flash model definition | | 7 | Extract Card Info with Gemini | LLM Chain | Extracts contact info from card image as JSON | | 8 | Parse Gemini Output Data | Code | Parses Gemini response into structured data | | 9 | Read Contacts from Sheets | Google Sheets | Searches existing records by email address | | 10 | If Contact Exists | If | Checks whether a duplicate record exists | | 11 | Append Contact to Sheets | Google Sheets | Appends new contact to Google Sheets | | 12 | Post New Contact to Slack | HTTP Request | Sends Slack notification for new contact | | 13 | Send Success Reply via LINE | HTTP Request | Sends registration confirmation via LINE | | 14 | Send Duplicate Reply via LINE | HTTP Request | Sends duplicate notification via LINE | | 15 | Respond Not Image to LINE | Respond to Webhook | Responds to non-image messages | | 16 | Acknowledge OK Webhook | Respond to Webhook | Returns HTTP 200 on success | | 17 | Acknowledge Duplicate Webhook | Respond to Webhook | Returns HTTP 200 on duplicate | Total: 17 nodes (+ 8 Sticky Notes) Sticky Note Compliance | # | Sticky Note Title | Color | Role | |---|-------------------|-------|------| | 1 | Main Sticky Note (Overview) | Yellow | Workflow overview, How it works, Setup steps, Customization | | 2 | Configuration setup | White | Covers credentials and config | | 3 | Webhook event handling | White | Covers LINE webhook reception and image check | | 4 | Image download and conversion | White | Covers image fetch and Base64 encoding | | 5 | Card data extraction | White | Covers Gemini extraction and parsing | | 6 | Duplicate check and contact saving | White | Covers Sheets read/write and duplicate logic | | 7 | Messaging and webhook response | White | Covers Slack post, LINE replies, and webhook responses | | 8 | Gemini configuration | White | Covers Gemini model setup | All sticky notes use H2 headings (## ) and follow n8n public guidelines. Setup Guide 1. Create a LINE Messaging API channel Log in to LINE Developers Console Create a new provider (or select an existing one) Create a Messaging API channel Issue a long-lived Channel Access Token and copy it Set the Webhook URL after configuring n8n 2. Prepare Google Sheets Create a new spreadsheet Add the following headers in row 1: | A | B | C | D | E | F | G | H | |---|---|---|---|---|---|---|---| | Timestamp | Name | Company | Title | Phone | Email | Address | LINE_UID | Copy the spreadsheet ID from the URL (between /d/ and /edit) 3. Set up Slack Incoming Webhook Create a new app at Slack API Enable Incoming Webhooks Select a channel and copy the Webhook URL 4. Get a Google Gemini API key Go to Google AI Studio Create an API key Add it as a Google PaLM API credential in n8n 5. Configure the n8n workflow Import the workflow JSON into n8n Open the Set Configuration Parameters node and enter: LINE_CHANNEL_ACCESS_TOKEN GOOGLE_SHEET_ID SLACK_WEBHOOK_URL Set up Google Sheets OAuth2 credentials Set up Gemini Chat Model credentials 6. Register the LINE Webhook URL Return to LINE Developers Console Enter the n8n Webhook URL Enable Webhook usage Disable auto-reply messages 7. Test Activate the workflow Send a business card photo to your LINE bot Confirm the contact is saved in Google Sheets Confirm the Slack notification is received Confirm the LINE confirmation message is received Tags ai gemini line google-sheets slack crm image-recognition automation

Log meal nutrition from LINE food photos to Google Sheets with Gemini AI

Log meal nutrition from LINE food photos to Google Sheets using Gemini AI Who is this for Health-conscious individuals, people on a diet, and anyone who wants to track daily nutrition without manual data entry. Designed especially for LINE users (Japan, Taiwan, Thailand, etc.) who want an effortless way to monitor calories and macronutrients from meal photos. What this workflow does Send a meal photo to a LINE bot and Gemini 1.5 Flash automatically identifies the food and estimates calories, protein, fat, and carbohydrates. Each meal is logged to Google Sheets with a timestamp and user ID. The workflow calculates the running daily calorie total and warns when the personal limit is exceeded. Every Monday morning, a weekly nutrition summary with AI-generated advice is pushed via LINE automatically. How to set up Create a LINE Messaging API channel and copy the Channel Access Token Copy your LINE User ID for weekly Push messages Set up a Google Sheet with columns: Timestamp, LINE_UID, Food_Name, Meal_Type, Calories, Protein, Fat, Carbs, Confidence Get a Google Gemini API key (free tier available) Configure CALORIE_LIMIT (default 2000) and LINE_USER_ID in the Set Config Fields node Register the n8n Webhook URL in LINE Developer Console Requirements LINE Messaging API account (free tier) Google Sheets (any Google account) Google Gemini API key (free tier available) How to customize Adjust CALORIE_LIMIT in the Set Config Fields node for different dietary goals. Add a Slack notification node to share weekly reports with a fitness accountability group. Modify the Gemini prompt to track additional nutrients like fiber or sodium. Node List | # | Node Name | Type | Role | |---|-----------|------|------| | 1 | Set Config Fields | Set | Centralizes LINE token, Sheet ID, calorie limit, and user ID | | 2 | When LINE Event Received | Webhook | Receives LINE Webhook (POST) | | 3 | If Image Message | If | Branches on image vs text message | | 4 | If Report Command | If | Checks whether text is a report command | | 5 | Send Help Reply via LINE | HTTP Request | Sends usage guide as reply | | 6 | Fetch LINE Image Data | HTTP Request | Downloads image from LINE Content API | | 7 | Encode Image to Base64 | Code | Converts image binary to Base64 string | | 8 | Gemini Food Analysis Config | Gemini Chat Model | Gemini 1.5 Flash model for food analysis | | 9 | Process Food Analysis | LLM Chain | Estimates nutrition info from meal image as JSON | | 10 | Extract Nutrition Data | Code | Extracts and parses JSON from Gemini response | | 11 | Append Meal to Sheets | Google Sheets | Appends nutrition data to spreadsheet | | 12 | Read Today's Total from Sheets | Google Sheets | Retrieves all records for today | | 13 | Compute Daily Calorie Total | Code | Calculates total calories for the day | | 14 | If Over Calorie Limit | If | Checks whether daily limit is exceeded | | 15 | Send Calorie Warning via LINE | HTTP Request | Sends calorie warning reply via LINE | | 16 | Send Nutrition Info via LINE | HTTP Request | Sends nutrition info and daily total via LINE | | 17 | Weekly 9AM Schedule | Schedule Trigger | Triggers weekly report every Monday at 9 AM JST | | 18 | Read Weekly Data from Sheets | Google Sheets | Retrieves records from the past 7 days | | 19 | Summarize Weekly Stats | Code | Aggregates weekly totals, averages, and peak day | | 20 | Gemini Weekly Report Config | Gemini Chat Model | Gemini 1.5 Flash model for weekly comment | | 21 | Create Weekly Comment with LLM | LLM Chain | Generates personalized nutrition advice | | 22 | Deliver Weekly Report via LINE | HTTP Request | Sends weekly report via LINE Push | | 23 | Send Webhook Response OK | Respond to Webhook | Returns HTTP 200 to Webhook | Total: 23 nodes (+ 9 Sticky Notes) Sticky Note Compliance | # | Sticky Note Title | Color | Role | |---|-------------------|-------|------| | 1 | Main Sticky Note (Overview) | Yellow | Workflow overview, How it works, Setup steps, Customization | | 2 | Set configuration fields | White | Covers configuration setup | | 3 | Receive and verify message type | White | Covers LINE webhook and message type checks | | 4 | Download and convert image | White | Covers image fetch and Base64 encoding | | 5 | Analyze image and parse data | White | Covers Gemini analysis and data parsing | | 6 | Log and calculate nutrition | White | Covers meal logging and daily total calculation | | 7 | Notify via LINE based on calorie | White | Covers calorie warning and nutrition info LINE replies | | 8 | Weekly report scheduling and stats | White | Covers schedule trigger and weekly aggregation | | 9 | Respond to LINE webhook | White | Covers webhook response | All sticky notes use H2 headings (## ) and follow n8n public guidelines. Setup Guide 1. Create a LINE Messaging API channel Log in to LINE Developers Create a new provider and a Messaging API channel Issue a long-lived Channel Access Token and copy it Copy your User ID from the channel basic settings 2. Prepare Google Sheets Create a new spreadsheet Add the following headers in row 1: Timestamp | LINE_UID | Food_Name | Meal_Type | Calories | Protein | Fat | Carbs | Confidence Copy the spreadsheet ID from the URL (between /d/ and /edit) 3. Get a Google Gemini API key Go to Google AI Studio Create an API key (free tier available) Register it as a Google PaLM API credential in n8n 4. Configure the n8n workflow Import the workflow JSON into n8n Open Set Config Fields and enter: LINE_CHANNEL_ACCESS_TOKEN GOOGLE_SHEET_ID CALORIE_LIMIT (default: 2000) LINE_USER_ID Set up Google Sheets OAuth2 and Google PaLM API credentials 5. Register the Webhook URL Activate the workflow in n8n Copy the Webhook URL Paste it into LINE Developers Console → Messaging API settings Enable Webhook and verify the connection 6. Test Send a meal photo to your LINE bot → confirm nutrition info is returned Send "report" as text → confirm weekly summary is returned Send other text → confirm help message is returned Tags ai gemini line google-sheets health nutrition-tracking image-recognition automation

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