Workflow Templates

What It Does This workflow automatically runs when a new email is received in the user's Gmail account. It sends the email content to OpenAI (GPT-4.1-mini), which intelligently determines whether the message requires action. If the email is identified as actionable, the workflow sends a structured alert message to the user in Microsoft Teams. This keeps the user informed of high-priority emails in real time without the need to manually check every message. The workflow does not log any execution data, ensuring that email content remains secure and unreadable by others. How It Works Trigger on New Email**: The workflow is triggered automatically when a new email is received in the user's Gmail account. Email Evaluation with OpenAI**: The email content is sent to GPT-4.1-MINI, which evaluates whether the message requires user action. Filter Actionable Emails**: Only emails identified as actionable by the AI are allowed to proceed through the rest of the workflow. Send Notification to Teams**: For actionable emails, the workflow sends a structured alert message to the user in Microsoft Teams chat via a Power Automate webhook. Prerequisites Gmail IMAP Credentials OpenAI API Key Microsoft Teams Webhook URL Power Automate Flow to send message to Teams chat How to Set It Up 1. Set Up Power Automate Workflow 1.1 Open Workflow Power Automate in Microsoft Teams Open the Workflow app from Microsoft Teams. If it's not already added, go to Apps → search "Workflow" → click Add → open it. 1.2 Create a New Flow Click New Flow → select Create from blank. 1.3 Add a Trigger: When a Teams webhook request is received In the trigger setup, set Who can trigger the flow? to Anyone. After saving the flow, a webhook URL will be generated — this URL will be used in n8n workflow. 1.4 Add Action: Parse JSON Set Content to: Body Use the following schema: { "type": "object", "properties": { "from": { "type": "string" }, "receivedAt": { "type": "string" }, "subject": { "type": "string" }, "message": { "type": "string" } } } 1.5 Add Action: Get an @mention token for a user Set the User field to the Microsoft Teams email address of the person to notify (e.g. yourname@domain.com). 1.6 Add Action: Post message in a chat or channel In this action, configure the following: Post as: Flow bot Post in: Chat with Flow bot Recipient: Your Microsoft Teams email address (e.g., yourname@domain.com) Paste the following code into the Message (in code view): Hello @{outputs('Get_an_@mention_token_for_a_user')?['body/atMention']}, You have received a new email at your email address @{body('Parse_JSON')?['recipientEmail']} that requires your attention: From: @{body('Parse_JSON')?['sender']} Received On: @{body('Parse_JSON')?['date']} Subject:  @{body('Parse_JSON')?['subject']} Please review the message at your earliest convenience. Click here to search this mail in your mailbox 1.7 Save and Enable the Flow Click Save. Turn the flow On. The webhook URL is now active and available in the first trigger step, copy it to use in n8n. Need help with the setup? Feel free to contact us 2. Configure IMAP Email Trigger First, enable 2‑Step Verification in your Google Account and generate an App Password for n8n. Then, in the IMAP node → Create Credential to connect using the following details: • User: your Gmail address • Password: the App Password • Host: imap.gmail.com • Port: 993 • SSL/TLS: Enabled Follow the n8n documentation to complete the setup. 3. Configure OpenAI Integration Add your OpenAI API key as a credential in n8n. Follow the n8n documentation to complete the setup. 4. Set Up HTTP Request to Trigger Power Automate Workflow Paste generated Webhook URL from the Power Automate workflow into the URL field of the HTTP Request node. 5. Disable Execution Logging for Privacy To ensure that email content is not stored in logs and remains fully secure, you can disable execution logging in n8n: In the n8n Workflow Editor, click on the three dots (•••) in the top right corner and select Settings. In the settings panel: Set Save manual executions to: Do not save Set Save successful production executions to: Do not save Set Save failed production executions to: Do not save if you also want to avoid logging errors Save the changes. Refer to the official n8n documentation for more details: 6. Activate the Workflow Set the workflow status to Active in n8n so it runs automatically when a new mail is received in Gmail. Need Help? Contact us for support and custom workflow development.

3 nodes1 views

Automated Water Consumption Tracker - Stored in Sheet and Notify in Slack

by darrell_tw

Water Reminder Workflow This workflow demonstrates how to use n8n and Slack to build an intelligent water drinking reminder system, combined with Google Sheets for data recording and OpenAI for generating personalized reminder messages. Google Sheet Template The iOS shortcut template: The result in iOS health: The template demo in Youtube Key Features Scheduled Reminders: Automatically sends water reminders at random times every hour. Intelligent Scheduling: Delays the next reminder if you've recently had water. AI-Generated Messages: Uses OpenAI to generate friendly and non-repetitive reminder messages. Data Tracking: Records daily water intake and calculates percentage of goal achievement. Quick Response: Easily record water intake through Slack buttons. iOS Integration: Provides iOS shortcut links to sync data with the Health app. Pre-Configuration Requirements To use this workflow, you need to set up the following: Google Sheets: Create a Google spreadsheet with log and setting sheets The log sheet should include date, time, and value columns The setting sheet is used to store daily water intake goals Slack: Create a Slack app and obtain an API token Configure permissions for interactive buttons OpenAI: Obtain an OpenAI API key iOS Shortcut (optional): Create an iOS shortcut named darrell_water for recording health data Node Configurations 1. Scheduled Triggers and Data Collection 1.1. Schedule Trigger Purpose**: Triggers water reminders on schedule Configuration**: Cron Expression: 0 {{ Math.floor(Math.random() * 11) }} 8-23 * * * Triggers at a random minute every hour, only between 8 AM and 11 PM 1.2. Google Sheets - Get Target Purpose**: Retrieves daily water intake goal Configuration**: Document ID: Your Google spreadsheet ID Sheet Name: setting 1.3. Google Sheets - Get Log Purpose**: Retrieves today's water intake records Configuration**: Document ID: Your Google spreadsheet ID Sheet Name: log Filter Condition: date equals today's date {{ $now.format('yyyy-MM-dd') }} 1.4. Summarize Purpose**: Calculates total water intake for today Configuration**: Fields to Summarize: value (sum) 1.5. Limit Purpose**: Gets the most recent water intake record Configuration**: Keep: Last items 2. Intelligent Reminder Logic 2.1. Combine Data Purpose**: Merges target and actual water intake data Configuration**: Combine By: Combine by position Number of Inputs: 3 2.2. If Purpose**: Checks if water was consumed recently Configuration**: Condition: {{ DateTime.fromISO($json.date+"T"+$json.time).format('yyyy-MM-dd HH:mm:ss') }} is after {{ $now.minus(30, "minutes") }} 2.3. Wait Purpose**: Randomly delays the reminder if water was consumed recently Configuration**: Wait Time: {{ Math.floor(Math.random() * 1) + 1 }} minutes 3. AI Message Generation and Sending 3.1. OpenAI Purpose**: Generates personalized water reminder messages Configuration**: Model: gpt-4o-mini Messages: System prompt: Requests responses in Traditional Chinese and in JSON format User prompt: Includes information about last water time, current time, goal, and progress Temperature: 1 3.2. Slack Send Drink Notification Purpose**: Sends water reminders to Slack channel Configuration**: Channel: Your Slack channel ID Message Type: Block Block UI: Contains AI-generated reminder message and water amount buttons (100ml, 150ml, 200ml, 250ml, 300ml) 4. User Interaction and Data Recording 4.1. Slack Drink Webhook Purpose**: Receives user interactions when water buttons are clicked Configuration**: HTTP Method: POST Path: slack-water-webhook 4.2. Slack Action Payload Purpose**: Parses Slack interaction data Configuration**: Mode: Raw JSON Output: {{ $json.body.payload }} 4.3. Slack Action Drink Data Purpose**: Extracts water amount and message information Configuration**: Assignments: value: {{ $json.actions[0].value }} message_text: {{ $json.message.text }} shortcut_url: shortcuts://run-shortcut?name=darrell_water&input= shortcut_url_data: JSON containing water amount and time message_ts: {{ $json.container.message_ts }} 4.4. Google Sheets Purpose**: Records water intake data to spreadsheet Configuration**: Operation: Append Document ID: Your Google spreadsheet ID Sheet Name: log Column Mapping: date: {{ $now.format('yyyy-MM-dd') }} time: {{ $now.format('HH:mm:ss') }} value: {{ $json.value }} 4.5. Send to Slack with Confirm Purpose**: Sends confirmation message and provides iOS shortcut link Configuration**: Channel: Your Slack channel ID Message Type: Block Block UI: Contains confirmation message and iOS Health app button Reply Settings: Reply to the thread of the original message Author Information This workflow was created by darrell_tw_, an engineer focused on AI and Automation. Contact: X Threads Instagram Website

🤖AI & Machine Learning

3 nodes719 views

Automated TikTok Video Downloader Bot (No Watermark) Using n8n and Telegram

by Garri

Description This workflow is an n8n-based automation that allows users to download TikTok/Reels videos without watermarks simply by sending the video link through a Telegram Bot. It uses a Telegram Trigger to receive the link from the user, then makes an HTTP request to a third-party API (tiktokio.com) to process and retrieve the download link. The workflow filters the results to find the Download without watermark link, downloads the video in MP4 format, and sends it back to the user directly in their Telegram chat. Key features: Supports the best available video quality (bestvideo+bestaudio). Automatically removes watermarks. Instant response directly in Telegram chat. Fully automated — no manual downloads required. How It Works Telegram Trigger The user sends a TikTok or Reels link to the Telegram bot. The workflow captures and stores the link for processing. HTTP Request – MediaDL API The link is sent via POST method to https://mediadl.app/api/download. The API processes the link and returns video file data. Wait Delay The workflow waits a few seconds to ensure the API response is fully ready. Edit Fields Extracts the video file URL from the API response. Additional Wait Delay Adds a short pause to avoid connection errors during the download process. HTTP Request – Proxy Download Downloads the MP4 video file directly from the filtered URL. Send Video via Telegram The downloaded video is sent back to the user in their Telegram chat. How to Set Up Create & Configure a Telegram Bot Open Telegram and search for BotFather. Send /newbot → choose a name & username for your bot. Copy the Bot Token provided — you’ll need it in n8n. Prepare Your n8n Environment Log in to your n8n instance (self-hosted or n8n Cloud). Go to Credentials → create new Telegram API credentials using your Bot Token. Import the Workflow In n8n, click Import and select the PROJECT_DOWNLOAD_TIKTOK_REELS.json file. Configure the Telegram Nodes In the Telegram Trigger and Send Video nodes, connect your Telegram API credentials. Configure the HTTP Request Nodes Ensure the Download2 and HTTP Request nodes have the correct URL and headers (pre-configured for mediadl.app). Make sure the responseFormat is set to file in the final download node. Activate the Workflow Toggle Activate in the top right corner of n8n. Test by sending a TikTok or Reels link to your bot — you should receive the no-watermark video in return.

2 nodes1 views

Use an open-source LLM (via HuggingFace)

by n8n Team

This workflow demonstrates how to connect an open-source model to a Basic LLM node. The workflow is triggered when a new manual chat message appears. The message is then run through a Language Model Chain that is set up to process text with a specific prompt to guide the model's responses. Note that open-source LLMs with a small number of parameters require slightly different prompting with more guidance to the model. You can change the default Mistral-7B-Instruct-v0.1 model to any other LLM supported by HuggingFace. You can also connect other nodes, such as Ollama. Note that to use this template, you need to be on n8n version 1.19.4 or later.

🤖AI & Machine Learning

2 nodes37279 views

Real-Time Stock Monitor with Smart Alerts for Indian & US Markets

by Oneclick AI Squad

Monitor Indian (NSE/BSE) and US stock markets with intelligent price alerts, cooldown periods, and multi-channel notifications (Email + Telegram). Automatically tracks price movements and sends alerts when stocks cross predefined upper/lower limits. Perfect for day traders, investors, and portfolio managers who need instant notifications for price breakouts and breakdowns. How It Works Market Hours Trigger - Runs every 2 minutes during market hours Read Stock Watchlist - Fetches your stock list from Google Sheets Parse Watchlist Data - Processes stock symbols and alert parameters Fetch Live Stock Price - Gets real-time prices from Twelve Data API Smart Alert Logic - Intelligent price checking with cooldown periods Check Alert Conditions - Validates if alerts should be triggered Send Email Alert - Sends detailed email notifications Send Telegram Alert - Instant mobile notifications Update Alert History - Records alert timestamps in Google Sheets Alert Status Check - Monitors workflow success/failure Success/Error Notifications - Admin notifications for monitoring Key Features: Smart Cooldown**: Prevents alert spam Multi-Market**: Supports Indian & US stocks Dual Alerts**: Email + Telegram notifications Auto-Update**: Tracks last alert times Error Handling**: Built-in failure notifications Setup Requirements: 1. Google Sheets Setup: Create a Google Sheet with these columns (in exact order): A**: symbol (e.g., TCS, AAPL, RELIANCE.BSE) B**: upper_limit (e.g., 4000) C**: lower_limit (e.g., 3600) D**: direction (both/above/below) E**: cooldown_minutes (e.g., 15) F**: last_alert_price (auto-updated) G**: last_alert_time (auto-updated) 2. API Keys & IDs to Replace: YOUR_GOOGLE_SHEET_ID_HERE - Replace with your Google Sheet ID YOUR_TWELVE_DATA_API_KEY - Get free API key from twelvedata.com YOUR_TELEGRAM_CHAT_ID - Your Telegram chat ID (optional) your-email@gmail.com - Your sender email alert-recipient@gmail.com - Alert recipient email 3. Stock Symbol Format: US Stocks**: Use simple symbols like AAPL, TSLA, MSFT Indian Stocks**: Use .BSE or .NSE suffix like TCS.NSE, RELIANCE.BSE 4. Credentials Setup in n8n: Google Sheets**: Service Account credentials Email**: SMTP credentials Telegram**: Bot token (optional) Example Google Sheet Data: symbol upper_limit lower_limit direction cooldown_minutes TCS.NSE 4000 3600 both 15 AAPL 180 160 both 10 RELIANCE.BSE 2800 2600 above 20 Output Example: Alert: TCS crossed the upper limit. Current Price: ₹4100, Upper Limit: ₹4000.

5 nodes64 views

Build a Multichannel Customer Support AI Assistant with Chatwoot & OpenRouter

by George Zargaryan

Multichannel AI Assistant Demo for Chatwoot This simple n8n template demonstrates a Chatwoot integration that can: Receive new messages via a webhook. Retrieve conversation history. Process the message history into a format suitable for an LLM. Demonstrate an AI Assistant processing a user's query. Send the AI Assistant's response back to Chatwoot. Use Case: If you have multiple communication channels with your clients (e.g., Telegram, Instagram, WhatsApp, Facebook) integrated with Chatwoot, you can use this template as a starting point to build more sophisticated and tailored AI solutions that cover all channels at once. How it works A webhook receives the message created event from Chatwoot. The webhook data is then filtered to keep only the necessary information for a cleaner workflow. The workflow checks if the message is "incoming." This is crucial to prevent the assistant from replying to its own messages and creating endless loops. The conversation history is retrieved from Chatwoot via an API call using the HTTP Request node. This allows the assistant's interaction to be more natural and continuous without needing to store conversation history locally. A simple AI Assistant processes the conversation history and generates a response to the user based on its built-in knowledge base (see the prompt in the assistant node). The final HTTP Request node sends the AI-generated response back to the appropriate Chatwoot conversation. How to Use In Chatwoot, go to Settings → Integrations → Webhooks and add your n8n webhook URL. Be sure to select the message created event. In the HTTP Request nodes, replace the placeholder values: https://yourchatwooturl.com api_access_token You can find these values on your Chatwoot super admin page. The LLM node is configured to use OpenRouter. Add your OpenRouter credentials, or replace the node with your preferred LLM provider. Requirements An API key for OpenRouter or credentials for your preferred LLM provider. A Chatwoot account with at least one integrated channel and super admin access to obtain the api_access_token. Need Help Building Something More? Contact me on: Telegram:** @ninesfork LinkedIn:** George Zargaryan Happy Hacking! 🚀

4 nodes451 views

Auto-Summarize Blog Posts to Social Media with Gemma and Postiz

by Kyriakos Papadopoulos

Auto-Summarize Blog Posts to Social Media with Gemma and Postiz This workflow automates fetching the latest post from a Blogspot RSS feed, summarizes it with an LLM (e.g., Gemma via Ollama), extracts and uploads an image, generates three relevant hashtags, and posts to Facebook, LinkedIn, X (Twitter), and Instagram via the Postiz API. It ensures content fits platform limits (e.g., 280 characters for X) and prevents duplicates using hashing. Pros: Efficient for content creators Local LLM ensures privacy Customizable for any RSS/blog source Cons: Dependent on stable APIs (Postiz/social platforms) LLM outputs may vary in quality without human review Target Audience: Bloggers, content marketers, or social media managers looking to automate cross-platform posting from RSS sources, especially those focused on niches like health, tech, or personal development. Ideal for users with technical setup skills for self-hosting. Customization Options: Adapt prompts in "Generate Summary and Hashtags with LLM" for tone/style (e.g., professional vs. casual). Modify maxChars/hashtag reserve in "Calculate Summary Character Limit" for different platforms. Extend for multiple RSS feeds by adjusting "Calculate Summary Character Limit" array. Add error handling (e.g., IF node after "Create and Post Content via Postiz API") for API failures. Disclaimer: This template is designed for self-hosted n8n instances to leverage local Ollama for privacy. For cloud use, modify as follows: 1) Use an n8n cloud account, 2) Replace Ollama with a cloud API-based LLM like ChatGPT in the "Configure Local LLM Model (Ollama)" node, 3) Switch to cloud-hosted Postiz in the HTTP Request node. Template Image: How it works Set the RSS feed URL in "Set RSS Feed URLs". Fetch the latest post via RSS. Normalize fields and calculate the maximum summary length. Use the LLM to summarize the text, append hashtags, and include the link. Extract and process an image from the post HTML. Validate inputs and post to social platforms via the Postiz API. Setup Instructions Install n8n (self-hosted recommended for Ollama integration). Set up Ollama with the Gemma (or a similar) model using "Ollama Model" credentials. Add Postiz API credentials in the "Create and Post Content via Postiz API" node. Replace placeholders: RSS URL in "Set News RSS Feeds" Integration IDs in the Postiz HTTP body (Optional) Add error handling for API failures. Activate the workflow and test with a sample post. Uncertainties Changes in social media APIs may break posting functionality. LLM output consistency depends on model choice and prompt configuration. Required n8n Version Tested on n8n v1.107.3 (self-hosted). Works with the community node n8n-nodes-langchain. Resources n8n Docs: RSS Feed Read n8n Docs: HTTP Request Ollama Setup Postiz Documentation

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Real Estate Property Search with SQL Database and Email Delivery

by DataMinex

Transform property searches into personalized experiences! This powerful automation delivers dream home matches straight to clients' inboxes with professional CSV reports - all from a simple web form. 🚀 What this workflow does Create a complete real estate search experience that works 24/7: ✨ Smart Web Form - Beautiful property search form captures client preferences 🧠 Dynamic SQL Builder - Intelligently creates optimized queries from user input ⚡ Lightning Database Search - Scans 1000+ properties in milliseconds 📊 Professional CSV Export - Excel-ready reports with complete property details 📧 Automated Email Delivery - Personalized emails with property previews and attachments 🎯 Perfect for: Real Estate Agents** - Generate leads and impress clients with instant service Property Managers** - Automate tenant matching and recommendations Brokerages** - Provide 24/7 self-service property discovery Developers** - Showcase available properties with professional automation 💡 Why this workflow is a game-changer > "From property search to professional report delivery in under 30 seconds!" ⚡ Instant Results: Zero wait time for property matches 🎨 Professional Output: Beautiful emails that showcase your expertise 📱 Mobile Optimized: Works flawlessly on all devices 🧠 Smart Filtering: Only searches criteria clients actually specify 📈 Infinitely Scalable: Handles unlimited searches simultaneously 📊 Real Estate Data Source Built on authentic US market data from the Github: 🏘️ 1000+ Real Properties across all US states 💰 Actual Market Prices from legitimate listings 🏠 Complete Property Details (bedrooms, bathrooms, square footage, lot size) 📍 Verified Locations with accurate cities, states, and ZIP codes 🏢 Broker Information for authentic real estate context 🛠️ Quick Setup Guide Prerequisites Checklist ✅ [ ] SQL Server database (MySQL/PostgreSQL also supported) [ ] Gmail account for automated emails [ ] n8n instance (cloud or self-hosted) [ ] 20 minutes setup time Step 1: Import Real Estate Data 📥 🌟 Download the data 💾 Download CSV file (1000+ properties included) 🗄️ Create SQL Server table with this exact schema: CREATE TABLE [REALTOR].[dbo].[realtor_usa_price] ( brokered_by BIGINT, status NVARCHAR(50), price DECIMAL(12,2), bed INT, bath DECIMAL(3,1), acre_lot DECIMAL(10,8), street BIGINT, city NVARCHAR(100), state NVARCHAR(50), zip_code INT, house_size INT, prev_sold_date NVARCHAR(50) ); 📊 Import your CSV data into this table Step 2: Configure Database Connection 🔗 🔐 Set up Microsoft SQL Server credentials in n8n ✅ Test connection to ensure everything works 🎯 Workflow is pre-configured for the table structure above Step 3: Gmail Setup (The Magic Touch) 📧 🌐 Visit Google Cloud Console 🆕 Create new project (or use existing) 🔓 Enable Gmail API in API Library 🔑 Create OAuth2 credentials (Web Application) ⚙️ Add your n8n callback URL to authorized redirects 🔗 Configure Gmail OAuth2 credentials in n8n ✨ Authorize your Google account Step 4: Launch Your Property Search Portal 🚀 📋 Import this workflow template (form is pre-configured) 🌍 Copy your webhook URL from the Property Search Form node 🔍 Test with a sample property search 📨 Check email delivery with CSV attachment 🎉 Go live and start impressing clients! 🎨 Customization Playground 🏷️ Personalize Your Brand // Customize email subjects in the Gmail node "🏠 Exclusive Properties Curated Just for You - ${results.length} Perfect Matches!" "✨ Your Dream Home Portfolio - Handpicked by Our Experts" "🎯 Hot Market Alert - ${results.length} Premium Properties Inside!" 🔧 Advanced Enhancements 🎨 HTML Email Templates**: Create stunning visual emails with property images 📊 Analytics Dashboard**: Track popular searches and user engagement 🔔 Smart Alerts**: Set up automated price drop notifications 📱 Mobile Integration**: Connect to React Native or Flutter apps 🤖 AI Descriptions**: Add ChatGPT for compelling property descriptions 🌍 Multi-Database Flexibility // Easy database switching // MySQL: Replace Microsoft SQL node → MySQL node // PostgreSQL: Swap for PostgreSQL node // MongoDB: Use MongoDB node with JSON queries // Even CSV files: Use CSV reading nodes for smaller datasets 🚀 Advanced Features & Extensions 🔥 Pro Tips for Power Users 🔄 Bulk Processing**: Handle multiple searches simultaneously 💾 Smart Caching**: Store popular searches for lightning-fast results 📈 Lead Scoring**: Track which properties generate most interest 📅 Follow-up Automation**: Schedule nurturing email sequences 🎯 Integration Possibilities 🏢 CRM Connection**: Auto-add qualified leads to your CRM 📅 Calendar Integration**: Add property viewing scheduling 📊 Price Monitoring**: Track market trends and price changes 📱 Social Media**: Auto-share featured properties to social platforms 💬 Chat Integration**: Connect to WhatsApp or SMS for instant alerts 🔗 Expand Your Real Estate Automation 🌟 Related Workflow Ideas 🤖 AI Property Valuation - Add machine learning for price predictions 📊 Market Analysis Reports - Generate comprehensive market insights 📱 SMS Property Alerts - Instant text notifications for hot properties 🏢 Commercial Property Search - Adapt for office and retail spaces 💹 Investment ROI Calculator - Add financial analysis for investors 🏘️ Neighborhood Analytics - Include school ratings and demographics 🛠️ Technical Extensions 📷 Image Processing: Auto-resize and optimize property photos 🗺️ Map Integration: Add interactive property location maps 📱 Progressive Web App: Create mobile app experience 🔔 Push Notifications: Real-time alerts for saved searches 🚀 Get Started Now Import this workflow template Configure your database and Gmail Customize branding and messaging Launch your professional property search portal Watch client satisfaction soar!

Price Monitoring Dashboard with AI Component and Alerts

by vinci-king-01

Competitor Price Monitoring Dashboard with AI and Real-time Alerts 🎯 Target Audience E-commerce managers and pricing analysts Retail business owners monitoring competitor pricing Marketing teams tracking market positioning Product managers analyzing competitive landscape Data analysts conducting pricing intelligence Business strategists making pricing decisions 🚀 Problem Statement Manual competitor price monitoring is inefficient and often leads to missed opportunities or delayed responses to market changes. This template solves the challenge of automatically tracking competitor prices, detecting significant changes, and providing actionable insights for strategic pricing decisions. 🔧 How it Works This workflow automatically monitors competitor product prices using AI-powered web scraping, analyzes price trends, and sends real-time alerts when significant changes are detected. Key Components Scheduled Trigger - Runs the workflow at specified intervals to maintain up-to-date price data AI-Powered Scraping - Uses ScrapeGraphAI to intelligently extract pricing information from competitor websites Price Analysis Engine - Processes historical data to detect trends and anomalies Alert System - Sends notifications via Slack and email when price changes exceed thresholds Dashboard Integration - Stores all data in Google Sheets for comprehensive analysis and reporting 📊 Google Sheets Column Specifications The template creates the following columns in your Google Sheets: | Column | Data Type | Description | Example | |--------|-----------|-------------|---------| | timestamp | DateTime | When the price was recorded | "2024-01-15T10:30:00Z" | | competitor_name | String | Name of the competitor | "Amazon" | | product_name | String | Product name and model | "iPhone 15 Pro 128GB" | | current_price | Number | Current price in USD | 999.00 | | previous_price | Number | Previous recorded price | 1099.00 | | price_change | Number | Absolute price difference | -100.00 | | price_change_percent | Number | Percentage change | -9.09 | | product_url | URL | Direct link to product page | "https://amazon.com/iphone15" | | alert_triggered | Boolean | Whether alert was sent | true | | trend_direction | String | Price trend analysis | "Decreasing" | 🛠️ Setup Instructions Estimated setup time: 15-20 minutes Prerequisites n8n instance with community nodes enabled ScrapeGraphAI API account and credentials Google Sheets account with API access Slack workspace for notifications (optional) Email service for alerts (optional) Step-by-Step Configuration 1. Install Community Nodes Install required community nodes npm install n8n-nodes-scrapegraphai npm install n8n-nodes-slack 2. Configure ScrapeGraphAI Credentials Navigate to Credentials in your n8n instance Add new ScrapeGraphAI API credentials Enter your API key from ScrapeGraphAI dashboard Test the connection to ensure it's working 3. Set up Google Sheets Connection Add Google Sheets OAuth2 credentials Grant necessary permissions for spreadsheet access Create a new spreadsheet for price monitoring data Configure the sheet name (default: "Price Monitoring") 4. Configure Competitor URLs Update the websiteUrl parameters in ScrapeGraphAI nodes Add URLs for each competitor you want to monitor Customize the user prompt to extract specific pricing data Set appropriate price thresholds for alerts 5. Set up Notification Channels Configure Slack webhook or API credentials Set up email service credentials (SendGrid, SMTP, etc.) Define alert thresholds and notification preferences Test notification delivery 6. Configure Schedule Trigger Set monitoring frequency (hourly, daily, etc.) Choose appropriate time zones for your business hours Consider competitor website rate limits 7. Test and Validate Run the workflow manually to verify all connections Check Google Sheets for proper data formatting Test alert notifications with sample data 🔄 Workflow Customization Options Modify Monitoring Targets Add or remove competitor websites Change product categories or specific products Adjust monitoring frequency based on market volatility Extend Price Analysis Add more sophisticated trend analysis algorithms Implement price prediction models Include competitor inventory and availability tracking Customize Alert System Set different thresholds for different product categories Create tiered alert systems (info, warning, critical) Add SMS notifications for urgent price changes Output Customization Add data visualization and reporting features Implement price history charts and graphs Create executive dashboards with key metrics 📈 Use Cases Dynamic Pricing**: Adjust your prices based on competitor movements Market Intelligence**: Understand competitor pricing strategies Promotion Planning**: Time your promotions based on competitor actions Inventory Management**: Optimize stock levels based on market conditions Customer Communication**: Proactively inform customers about price changes 🚨 Important Notes Respect competitor websites' terms of service and robots.txt Implement appropriate delays between requests to avoid rate limiting Regularly review and update your monitoring parameters Monitor API usage to manage costs effectively Keep your credentials secure and rotate them regularly Consider legal implications of automated price monitoring 🔧 Troubleshooting Common Issues: ScrapeGraphAI connection errors: Verify API key and account status Google Sheets permission errors: Check OAuth2 scope and permissions Price parsing errors: Review the Code node's JavaScript logic Rate limiting: Adjust monitoring frequency and implement delays Alert delivery failures: Check notification service credentials Support Resources: ScrapeGraphAI documentation and API reference n8n community forums for workflow assistance Google Sheets API documentation for advanced configurations Slack API documentation for notification setup

Amplify Social Media Presence with O3 and GPT-4 Multi-Agent Team

by Yaron Been

Amplify Social Media Presence with O3 and GPT-4 Multi-Agent Team 🌍 Overview This n8n workflow acts like a virtual social media department. A Social Media Director Agent coordinates multiple specialized AI agents (Instagram, Twitter/X, Facebook, TikTok, YouTube, and Analytics). Each agent creates or analyzes content for its platform, powered by OpenAI models. The result? A fully automated, cross-platform social media strategy—from content creation to performance tracking. 🟢 Section 1: Trigger & Director Setup 🔗 Nodes: When chat message received (Trigger)** → Starts the workflow whenever you send a request (e.g., “Plan a TikTok campaign for my product launch”). Social Media Director Agent* (connected to *OpenAI O3 model**) → Acts as the strategist. Think Tool** → Helps the Director “reason” before delegating. 💡 Beginner takeaway: This section makes your workflow interactive. You send a request → the Director decides the best approach → then it assigns tasks. 📈 Advantage: Instead of manually planning content per platform, you only send one command, and the AI Director handles the strategy. 🟦 Section 2: Specialized Social Media Agents 🔗 Nodes (each paired with GPT-4.1-mini): 📸 Instagram Content Creator → Visual storytelling, Reels, Hashtags 🐦 Twitter/X Strategist → Viral tweets, trends, engagement 👥 Facebook Community Manager → Audience growth, ads, group engagement 🎵 TikTok Video Creator → Short-form video, viral trends 🎬 YouTube Content Planner → Long-form strategy, SEO, thumbnails 📊 Analytics Specialist → Performance insights, cross-platform reporting 💡 Beginner takeaway: Each platform has its own AI expert. They receive the Director’s strategy and produce tailored content for their platform. 📈 Advantage: Instead of one-size-fits-all posts, you get optimized content per platform—increasing reach and engagement. 🟣 Section 3: Models & Connections 🔗 Nodes: OpenAI Chat Models** (O3 + multiple GPT-4.1-mini models) Each model is connected to its respective agent. 💡 Beginner takeaway: Think of these as the “brains” behind each specialist. The Director uses O3 for advanced reasoning, while the specialists use GPT-4.1-mini (cheaper, faster) for content generation. 📈 Advantage: This keeps costs low while maintaining quality output. 📊 Final Overview Table | Section | Nodes | Purpose | Benefit | | --------------------- | -------------------------------------------------------- | -------------------------------------- | ------------------------------ | | 🟢 Trigger & Director | Chat Trigger, Director, Think Tool | Capture requests & plan strategy | One command → full social plan | | 🟦 Specialists | Instagram, Twitter, Facebook, TikTok, YouTube, Analytics | Platform-specific content | Optimized posts per platform | | 🟣 Models | O3 + GPT-4.1-mini | Provide reasoning & content generation | High-quality, cost-efficient | 🚀 Why This Workflow is Powerful Multi-platform coverage**: All major platforms handled in one flow Human-like strategy**: Director agent makes real marketing decisions Scalable & fast**: Generate a full campaign in minutes Cost-effective**: O3 only for strategy, GPT-4.1-mini for bulk content Beginner-friendly**: Just type your request → get full campaign output

4 nodes299 views

Automate AI Video Creation & Multi-Platform Publishing with GPT-4, Veo 3.1 & Blotato

by Dr. Firas

💥 Automate AI Video Creation & Multi-Platform Publishing with Veo 3.1 & Blotato 🎯 Who is this for? This workflow is designed for content creators, marketers, and automation enthusiasts who want to produce professional AI-generated videos and publish them automatically on social media — without editing or manual uploads. Perfect for those using Veo 3.1, GPT-4, and Blotato to scale video creation. 💡 What problem is this workflow solving? Creating short-form content (TikTok, Instagram Reels, YouTube Shorts) is time-consuming — from writing scripts to video editing and posting. This workflow eliminates the manual steps by combining AI storytelling + video generation + automated publishing, letting you focus on creativity while your system handles production and distribution. ⚙️ What this workflow does Reads new ideas from Google Sheets Generates story scripts using GPT-4 Creates cinematic videos using Veo 3.1 (fal.ai/veo3.1/reference-to-video) with 3 input reference images Uploads the final video automatically to Google Drive Publishes the video across multiple platforms (TikTok, Instagram, Facebook, X, LinkedIn, YouTube) via Blotato Updates Google Sheets with video URL and status (Completed / Failed) 🧩 Setup Required accounts: OpenAI → GPT-4 API key fal.ai → Veo 3.1 API key Google Cloud Console → Sheets & Drive connection Blotato → API key for social media publishing Configuration steps: Copy the Google Sheets structure: A: id_video B: niche C: idea D: url_1 E: url_2 F: url_3 G: url_final H: status Add your API keys to the Workflow Configuration node. Insert three image URLs and a short idea into your sheet. Wait for the automation to process and generate your video. 🧠 How to customize this workflow Change duration or aspect ratio** → Edit the Veo 3.1 node JSON body (duration, aspect_ratio) Modify prompt style** → Adjust the “Optimize Prompt for Veo” node for your desired tone or cinematic look Add more platforms** → Extend Blotato integration to publish on Pinterest, Reddit, or Threads Enable Telegram Trigger** → Allow users to submit ideas and images directly via Telegram 🚀 Expected Outcome Within 2–3 minutes, your idea is transformed into a full cinematic AI video — complete with storytelling, visuals, and automatic posting to your social media channels. Save hours of editing and focus on strategy, creativity, and growth. 👋 Need help or want to customize this? 📩 Contact: LinkedIn 📺 YouTube: @DRFIRASS 🚀 Workshops: Mes Ateliers n8n 📄 Documentation: Notion Guide Need help customizing? Contact me for consulting and support : Linkedin / Youtube / 🚀 Mes Ateliers n8n