Workflow Templates

WhatsApp AI Agent: Auto-Train Product Data & Handle Customer Support Who Is This For This workflow is ideal for eCommerce founders, product managers, customer support teams, and automation builders who rely on WhatsApp to manage product information and interact with clients. It’s perfect for businesses that want to automate product data entry and support responses directly from WhatsApp messages using GPT-4 and Google Sheets. What Problem Does This Workflow Solve Manual Product Data Entry**: Collecting and organizing product data from links is tedious and error-prone. Slow Customer Response Times**: Responding to client questions manually leads to delays and inconsistent support. No Logging System for Issues**: Without automation, support issues often go undocumented, making it harder to learn and improve. What This Workflow Does Step 1 – Incoming Message Detection Listens for incoming messages via WhatsApp. If the message starts with train:, it routes to the product training process. Otherwise, it routes to the customer support assistant. Step 2 – Product Data Training Extracts URL** from the message using a regex script. Fetches HTML content** from the URL. Cleans HTML data** to extract readable product description. Saves raw data** (URL + description) into Google Sheets. Uses GPT-4** to enhance product data: → Name, price (one-time or subscription), topic, and FAQs. Updates the product row** in Google Sheets with structured information. Step 3 – Customer Support Flow Analyzes user messages with GPT-4 to understand the request or issue. Looks up relevant product info in Google Sheets. Detects potential problems (e.g. payment, login, delivery). Suggests an appropriate solution. Logs the problem, solution, and category to the Customer Issues sheet. Sends a response back to the client via WhatsApp. Step 4 – Client Response Sends the AI-generated response to the client via WhatsApp. Keeps the communication fast, clear, and professional. Setup Guide Prerequisites WhatsApp Business API access** OpenAI API Key** Google Account** with Google Sheets access A hosted instance of n8n (Cloud or self-hosted) Setup Steps Import the Workflow into your n8n instance. Connect your credentials for WhatsApp, OpenAI, and Google Sheets. Customize Google Sheet IDs and names as needed. Test by sending a train: message or a regular customer message to WhatsApp. Activate the workflow to make it live. How to Customize This Workflow Edit AI prompts** to reflect your product type, language style, or tone. Change the trigger keyword** (e.g. from train: to add: or anything else). Add integrations** like Notion, Airtable, or CRM tools. Expand the Sheets structure** with more product fields (e.g. stock status, image link). Add notifications** to Slack or email after product updates or issue logging. 📄 Documentation: Notion Guide Need help customizing? Contact me for consulting and support : Linkedin / Youtube

🚀 AI-Powered LinkedIn Post Automation 🧩 How It Works This workflow automatically generates LinkedIn posts based on a user-submitted topic, including both content creation and image generation, then publishes the post to LinkedIn. Ideal for marketers, content creators, or businesses looking to streamline their social media activity, without the need for manual post creation. High-Level Workflow: Trigger: The workflow is triggered when a user submits a form with a topic for the LinkedIn post. Data Mapping: The topic is mapped and prepared for the AI model. AI Content Generation: Calls the Google Gemini AI model to generate engaging post content and a visual image prompt. Image Creation: Sends the image prompt to the external API, gen-imager, to generate a professional image matching the topic. Post Creation: Publishes the text and image to LinkedIn, automatically updating the user's feed. ⚙️ Set Up Steps (Quick Overview) 🕐 Estimated Setup Time: ~10–20 minutes Connect Google Gemini: Set up your Google Gemini API credentials to interact with the AI model for content creation. Set Up External Image API: Configure the external image generation API (gen-imager API) for visual creation based on the post prompt. Connect LinkedIn: Set up OAuth2 credentials to authenticate your LinkedIn account and allow publishing posts. Form Submission Setup: Create a simple web form for users to submit the topic for LinkedIn posts. Activate the Workflow: Once everything is connected, activate the workflow. It will trigger automatically upon receiving form submissions. 💡 Important Notes: The flow uses Google Gemini (PaLM) for generating content based on the user's topic. Text to Image: The image generation process involves creating a professional, LinkedIn-appropriate image based on the post’s topic using the **gen-imager API. You can customize the visual elements of the posts and adjust the tone of the generated content based on preferences. 🛠 Detailed Node Breakdown: On Form Submission Trigger: Captures the user-submitted topic and initializes the workflow. Action: Start the process by gathering the topic information. Mapper (Field Mapping) Action: Maps the captured topic to a variable that is passed along for content generation. AI Agent (Content Generation) Action: Calls Google Gemini to generate professional LinkedIn post content and an image prompt based on the submitted topic. Key: Outputs content in a structured form — post text and image prompt. Google Gemini Chat Model Action: AI model that generates actionable insights, engaging copy, and an image prompt for LinkedIn post. Normalizer (Data Cleanup) Action: Cleans the output from the AI model to ensure the content and image prompt are correctly formatted for use in the next steps. Text to Image (Image Generation) Action: Sends the image prompt to the gen-imager API, which returns a custom image based on the post's topic. Decoder (Base64 Decoding) Action: Decodes the image from base64 format for easier uploading to LinkedIn. LinkedIn (Post Creation) Action: Publishes the generated text and image to LinkedIn, automatically creating a polished post for the user’s feed. ⏱ Execution Time Breakdown: Total Estimated Execution Time**: ~15–40 seconds per workflow run. On Form Submission: Instant (Trigger) Mapper (Field Mapping): ~1–2 seconds AI Content Generation: ~5–10 seconds (depending on server load) Text to Image: ~5–15 seconds (depends on external API) LinkedIn Post Creation: ~2–5 seconds 🚀 Ready to Get Started? Let’s get you started with automating your LinkedIn posts! Create your free n8n account and set up the workflow using this link. 📝 Notes & Customizations Form Fields**: Customize the form to gather more specific information for the LinkedIn posts (like audience targeting, post category, etc.). Image API Customization**: Adjust the image generation prompt to fit your brand’s style, or change the color palette as needed. Content Tone**: The tone can be adjusted by modifying the system message sent to Google Gemini for content generation.

This workflow automatically creates in-depth, SEO-friendly Arabic articles based on any keyword you provide. It researches the topic, generates a full article outline, writes every section in Arabic, and saves the final article directly to your Notion workspace—all in a few clicks. How It Works Step 1:** You submit a simple web form with your keyword and (optionally) an article title. Step 2:** The workflow researches the topic using advanced AI, gathers trending questions from Google, and creates a detailed, structured outline. Step 3:** Each section of the article is written in Arabic by AI, following best SEO practices and including real FAQs. Step 4:** The completed article is automatically formatted and saved to your Notion database, ready for review or publishing. Setup Instructions What you need:** An OpenAI API key (for AI-powered writing and outline generation) An OpenRouter API key (for research via Perplexity/Sonar AI) A Notion account and Notion API integration (for saving articles) DataForSEO account (for fetching Google "People Also Ask" questions) How to set up:** Import the workflow into your n8n instance. Connect your API credentials for OpenAI, OpenRouter, Notion, and (optionally) DataForSEO. Update your Notion database ID in the workflow settings. Deploy the workflow. Fill out the web form to generate your first article. Setup time:** 10–20 minutes if you already have your accounts. Tip: You can fully customize the outline and writing prompts for your target audience or topic. The workflow is modular—easy to adapt for different languages or content styles.

How It Works – Your AI Marketing Team in Action This automation acts as your AI-powered content and image marketing assistant inside Telegram. With just a voice note or text message, it can: 🧠 Understand your request – Whether you send a message or speak into Telegram, it transcribes and processes your input using GPT-4. 🎨 Create and edit content – Based on what you say, it can generate: ✍️ Blog posts 💼 LinkedIn posts 🎬 Faceless videos 🖼️ AI-generated images 🪄 Edits to existing images 🔎 Searches through your image database 💬 Replies directly in Telegram – It sends you back the result—whether that’s a post, image, or video link—without leaving the app. 🧩 Built using LangChain agent logic – It intelligently chooses the right tool from a suite of sub-workflows like "Create Image", "Blog Post", or "Video" using agent reasoning. 🛠️ Setup Steps – Get Started in Minutes! ⌛ Time Estimate: ~15–30 minutes (faster if you're familiar with n8n) 🔗 1. Import the Template Pack 📥 Download and install these workflows into your n8n: Create Image, Edit Image, Search Images Blog Post, LinkedIn Post, Video 🔐 2. Add Required Credentials Telegram Bot 🤖 OpenRouter AI 🧠 Tavily API (for smart research) 📚 ElevenLabs 🎙️ (for voice in videos) PiAPI & Runway 🎞️ (for faceless videos) 🧩 3. Link the Tools to the Agent Node – Make sure the "Marketing Team Agent" is connected to each of the content creation tools as shown in the workflow. 📎 4. Download Templates & Logs 🧾 Google Sheets Log Template (to track output) 🖼️ Creatomate Template (optional for enhanced image control – shared in Skool group) 📌 Pro Tip: All detailed step-by-step setup instructions are included as sticky notes inside the n8n canvas. Just follow along!

📊 This AI sub-agent aggregates Tesla (TSLA) trading signals across multiple timeframes using real-time technical indicators and candlestick behavior. It is a core component of the Tesla Quant Trading AI system. Powered by GPT-4.1, it consolidates 15-minute, 1-hour, and 1-day indicators, adds candlestick pattern data, and produces a unified JSON signal for downstream use by the master agent. ⚠️ This agent is not standalone. It is triggered by the Tesla Quant Trading AI Agent via Execute Workflow. 🧠 Requires: 4 connected sub-agents and Alpha Vantage Premium API Key 🔌 Required Sub-Workflows To use this workflow, you must install: Tesla 15min Indicators Tool Tesla 1hour Indicators Tool Tesla 1day Indicators Tool Tesla 1hour and 1day Klines Tool Tesla Quant Technical Indicators Webhooks Tool (provides Alpha Vantage data) 🧠 What This Agent Does Fetches pre-cleaned 20-point JSON outputs from the 4 sub-agents listed above Analyzes each timeframe individually: 15m: momentum and short-term setups 1h: confirmation of emerging trends 1d: macro positioning and trend alignment Klines: candlestick reversal patterns and volume divergence Generates a structured final signal in JSON with: Trading stance: Buy, Sell, Hold, or Cautious Confidence score (0.0–1.0) Multi-timeframe indicator breakdown Candlestick and volume divergence annotations 📋 Sample Output { "summary": "TSLA momentum is weakening short-term. 1h MACD shows bearish crossover, RSI declining. 1d candles confirm potential reversal setup.", "signal": "Cautious Sell", "confidence": 0.81, "multiTimeframeInsights": { "15m": { "RSI": 68.3, "MACD": { "macd": 0.53, "signal": 0.61 }, ... }, "1h": { "RSI": 65.0, "MACD": { "macd": -0.32, "signal": 0.11 }, ... }, "1d": { "BBANDS": { ... }, ... }, "candlestickPatterns": { "1h": "Doji", "1d": "Bearish Engulfing" }, "volumeDivergence": { "1h": "Bearish", "1d": "Neutral" } } } 🛠️ Setup Instructions Import this workflow into n8n Name it: Tesla_Financial_Market_Data_Analyst_Tool Add Required API Credentials Alpha Vantage Premium (via HTTP Query Auth) OpenAI GPT-4.1 for reasoning and synthesis Link Required Sub-Agents Connect the 4 tool workflows listed above to their respective Tool Workflow nodes Connect the webhook provider for data fetches Set Up as Sub-Agent This workflow must be triggered using Execute Workflow from the parent agent Pass in: message (optional context) sessionId (used for memory continuity) 🧾 Sticky Notes Provided 📘 Tesla Financial Market Data Analyst — Core logic overview 📈 15m / 1h / 1d Tool Notes — Indicator lists + use cases 🕯️ Klines Tool Note — Candlestick and volume divergence patterns 🧠 GPT Reasoning Note — GPT-4.1 handles final synthesis 🧩 Sub-Workflow Trigger — Proper integration with parent agent 🧠 Memory Buffer — Maintains session context across evaluations 🔒 Licensing & Support © 2025 Treasurium Capital Limited Company The logic, prompt design, and multi-agent architecture are proprietary and IP-protected. For support or collaboration inquiries: 🔗 Don Jayamaha – LinkedIn 🔗 n8n Creator Profile 🚀 Unify your Tesla trading logic across timeframes—automated, AI-powered, and built for scalers and swing traders.

📝 Use Case This n8n workflow automates the creation and publication of technical blog posts based on a list of topics stored in Google Sheets. It fetches context using Tavily and Wikipedia, generates Markdown-formatted content with Gemini AI, commits it to a GitHub repository, and updates a Jekyll-powered blog — all without manual intervention. Ideal for developers, bloggers, or content teams who want to streamline technical content creation and publishing. ⚙️ Setup Instructions 🔑 Prerequisites n8n (cloud or self-hosted) Tavily API key Google Sheets with blog topics Gemini (Google Palm) API key GitHub repository (Jekyll enabled) GitHub OAuth2 credentials Google OAuth2 credentials 🧩 Setup Steps Import the workflow JSON into your n8n instance. Set up the following credentials in n8n: Tavily API Google Sheets OAuth2 Google Palm/Gemini AI GitHub OAuth2 Prepare your Google Sheet: Columns: Title, status, row_number Set status to blank for topics to be picked up. Configure: GitHub repo and _posts/ path Jekyll setup (front matter, _config.yml, GitHub Pages) Adjust prompt/custom parameters if needed. Enable and deploy the workflow. Schedule it daily or trigger manually. 🔄 Workflow Details | Node | Function | |------|----------| | Schedule Trigger | Triggers the flow at a set interval | | Google Sheets (Get Topic) | Fetches the next incomplete blog topic | | Extract Topic | Parses topic text from the sheet | | Tavily Search | Gathers up-to-date content related to the topic | | Wikipedia Tool | Optionally adds more context or images | | Summarize Results | Formats the context for the AI | | Gemini AI Agent (LangChain) | Generates a Markdown blog post with YAML front matter | | Set File Parameters | Prepares the filename, content, and commit message | | GitHub Commit | Uploads the .md file to the _posts/ directory | | Update Google Sheet | Marks topic as done after successful commit | 🛠️ Customization Options Change LLM prompt (e.g. tone, depth, format). Use OpenAI instead of Gemini by switching nodes. Modify filename pattern or GitHub repo path. Add Slack/Discord notifications after publish. Extend flow to upload images or embed YouTube links. ⚠️ Community Nodes Used This workflow uses the following community nodes: @tavily/n8n-nodes-tavily.tavily – for deep search > ⚠️ Ensure these are installed and enabled in your n8n instance. 💡 Pro Tips Use GitHub Actions to trigger an automatic Jekyll build post-commit. Structure blog posts with front matter, headings, and table of contents for SEO. Set Schedule Trigger to daily at a fixed time to keep content flowing. Enhance formatting in AI output using code blocks, images, and lists. ✅ Example Output title: "How LLMs Are Changing Web Development" date: "2025-07-25" categories: [webdev, AI] tags: [LLM, Gemini, n8n, automation] excerpt: "Learn how LLMs like Gemini are transforming how we generate and deploy developer content." author: "Saswat Saubhagya" Table of Contents Introduction Understanding LLMs Use Cases in Web Development Challenges Conclusion ...

Who is this for? This workflow is ideal for n8n self-hosted users, DevOps engineers, and automation developers who want to automatically back up their n8n workflows to GitHub on a regular basis. What problem is this workflow solving Manually backing up n8n workflows can be time-consuming and prone to human error. This workflow automates the backup process, ensuring that all workflows are safely stored in a version-controlled GitHub repository every 24 hours. What this workflow does This automation runs daily to back up all workflows from your n8n instance to a specified GitHub repository. Each workflow is saved as a .json file using its unique ID, organized into a folder path defined by repo_path. The workflow is designed to manage memory usage efficiently by recursively calling itself. Once the backup is complete, it optionally sends a Slack notification to confirm success. Setup Configure the Config node in the subworkflow to set: GitHub Repo Owner GitHub Repo Name Main folder path (repo_path) Connect your GitHub and (optionally) Slack credentials. Set the workflow to run on a daily cron schedule. Test the workflow manually to confirm the GitHub integration works. Sign up for a free consultation and find out how n8n can help you.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. How it works This workflow automatically monitors competitor prices, analyzes market demand, and optimizes product pricing in real-time for maximum profitability using advanced AI algorithms. Key Steps Hourly Trigger - Runs automatically every hour for real-time price optimization and competitive response. Multi-Platform Competitor Monitoring - Uses AI-powered scrapers to track prices from Amazon, Best Buy, Walmart, and Target. Market Demand Analysis - Analyzes Google Trends data to understand search volume trends and seasonal patterns. Customer Sentiment Analysis - Reviews customer feedback to assess price sensitivity and value perception. AI Pricing Optimization - Calculates optimal prices using weighted factors including competitor positioning, demand indicators, and inventory levels. Automated Price Updates - Directly updates e-commerce platform prices when significant opportunities are identified. Comprehensive Analytics - Logs all pricing decisions and revenue projections to Google Sheets for performance tracking. Set up steps Setup time: 15-20 minutes Configure ScrapeGraphAI credentials - Add your ScrapeGraphAI API key for AI-powered competitor and market analysis. Set up e-commerce API connection - Connect your e-commerce platform API for automated price updates. Configure Google Sheets - Set up Google Sheets connections for pricing history and revenue analytics logging. Set up Slack notifications - Connect your Slack workspace for real-time pricing alerts and team updates. Customize product catalog - Modify the product configuration with your actual products, costs, and pricing constraints. Adjust monitoring frequency - Change the trigger timing based on your business needs (hourly, daily, etc.). Configure competitor platforms - Update competitor URLs and selectors for your target market. What you get Real-time price optimization** with 15-25% potential revenue increase through intelligent pricing Competitive intelligence** with automated monitoring of major e-commerce platforms Market demand insights** with seasonal and trend-based pricing adjustments Customer sentiment analysis** to understand price sensitivity and value perception Automated price updates** when significant opportunities are identified (>2% change, >70% confidence) Comprehensive analytics** with pricing history, revenue projections, and performance tracking Team notifications** with detailed market analysis and pricing recommendations Margin protection** with intelligent constraints to maintain profitability

Daily Magento 2 stock check Automation It identifies SKUs with low inventory per source and sends daily alerts via: 📬 Gmail (HTML email) 💬 Slack (formatted text message) This automation empowers store owners and operations teams to stay ahead of inventory issues by proactively monitoring stock levels across all Magento 2 sources. By receiving early alerts for low-stock products, businesses can restock before items sell out—ensuring continuous product availability, reducing missed sales opportunities, and maintaining customer trust. Avoiding stockouts not only protects your brand reputation but also keeps your store competitive by preventing customers from turning to competitors due to unavailable items. Timely restocking leads to higher fulfillment rates, improved customer satisfaction, and ultimately, stronger revenue and long-term loyalty. ✅ Features: Filters out configurable, virtual, and downloadable products Uses Magento 2 MSI stock per source Customizable thresholds (default: ≤10 overall or ≤5 per source) HTML-formatted email report Slack notification with a code-formatted Runs daily via Cron (08:50 AM) No need of any 3rd part Modules One time Setup 🔑 Credentials Used HTTP Request (Magento 2 REST API using Bearer Token) Gmail (OAuth2) Slack (OAuth2 or Webhook) 📊 Tags Magento, Inventory, MSI, Stock Alert, Ecommerce, Slack, Gmail, Automation 📂 Category E-commerce → Magento 2 (Adobe Commerce) 👤 Author Kanaka Kishore Kandregula Certified Magento 2 Developer https://gravatar.com/kmyprojects https://www.linkedin.com/in/kanakakishore

Overview This workflow automates LinkedIn posts using OpenAI. The prompts are stored in the workflow and can be customized as needed to fit your needs. The workflow uses a combination of a Schedule Trigger, some code that determines what day of the week it is (no posting Friday - Sunday), a prompts node to set your OpenAI prompts, a random selection of a prompt so that you are not generating content that looks repetitive. We send that all to OpenAI API, select a random time, have the final LinkedIn post sent to your Telegram for approval, once approved wait for the correct time slot, and then Post to your LinkedIn account using the LinkedIn node. How it works: Run or schedule the workflow in n8n The automation can be triggered manually or on a custom schedule (excluding weekends if needed). You should customize the prompts in the Prompt Node to suit your needs. A random LinkedIn post prompt is selected Pre-written prompts are rotated to keep content fresh and non-repetitive. OpenAI generates the LinkedIn post The prompt is sent to OpenAI via API, and the result is returned in clean, ready-to-use form. You receive the draft via Telegram. The post is sent to Telegram for quick approval or review. Post is scheduled or published via the LinkedIn Connector Once approved, the workflow delays until the target time, then sends the content to LinkedIn. What's needed: An OpenAPI API key, LinkedIn Account, and a Telegram Account. For Telegram you will need to configure the Bot service. Step-by-Step: Telegram Approval for Your Workflow A. Set Up a Telegram Bot Open Telegram and search for “@BotFather”. Start a chat and type /newbot to create a bot. Give your bot a name and a unique username (e.g., YourApprovalBot). Copy the API token that BotFather gives you. B. Add Your Bot to a Private Chat (with You) Find your bot in Telegram, click “Start” to activate it. Send a test message (like “hello”) so the chat is created. C. Get Your User ID Search for “userinfobot” or use sites like userinfobot in Telegram. Type /start and it will reply with your Telegram user ID. OpenAI powers the LinkedIn post creation Add Your OpenAI API Key: Log in to your OpenAI Platform account: https://platform.openai.com/. Go to API keys and create a new secret key. In n8n, create a new "OpenAI API" credential and paste your API key. Give it a name. Apply Credential to Nodes: OpenAI Message Node Connect your LinkedIn account to the Linked in Node Select your account from the LinkedIn Dropdown box.

🧠 Long-Term Memory System for AI Agents with Vector Database Transform your AI assistants into intelligent agents with persistent memory capabilities. This production-ready workflow implements a sophisticated long-term memory system using vector databases, enabling AI agents to remember conversations, user preferences, and contextual information across unlimited sessions. 🎯 What This Template Does This workflow creates an AI assistant that never forgets. Unlike traditional chatbots that lose context after each session, this implementation uses vector database technology to store and retrieve conversation history semantically, providing truly persistent memory for your AI agents. 🔑 Key Features Persistent Context Storage**: Automatically stores all conversations in a vector database for permanent retrieval Semantic Memory Search**: Uses advanced embedding models to find relevant past interactions based on meaning, not just keywords Intelligent Reranking**: Employs Cohere's reranking model to ensure the most relevant memories are used for context Structured Data Management**: Formats and stores conversations with metadata for optimal retrieval Scalable Architecture**: Handles unlimited conversations and users with consistent performance No Context Window Limitations**: Effectively bypasses LLM token limits through intelligent retrieval 💡 Use Cases Customer Support Bots**: Remember customer history, preferences, and previous issues Personal AI Assistants**: Maintain user preferences and conversation continuity over months or years Knowledge Management Systems**: Build accumulated knowledge bases from user interactions Educational Tutors**: Track student progress and adapt teaching based on history Enterprise Chatbots**: Maintain context across departments and long-term projects 🛠️ How It Works User Input: Receives messages through n8n's chat interface Memory Retrieval: Searches vector database for relevant past conversations Context Integration: AI agent uses retrieved memories to generate contextual responses Response Generation: Creates informed responses based on historical context Memory Storage: Stores new conversation data for future retrieval 📋 Requirements OpenAI API Key**: For embeddings and chat completions Qdrant Instance**: Cloud or self-hosted vector database Cohere API Key**: Optional, for enhanced retrieval accuracy n8n Instance**: Version 1.0+ with LangChain nodes 🚀 Quick Setup Import this workflow into your n8n instance Configure credentials for OpenAI, Qdrant, and Cohere Create a Qdrant collection named 'ltm' with 1024 dimensions Activate the workflow and start chatting! 📊 Performance Metrics Response Time**: 2-3 seconds average Memory Recall Accuracy**: 95%+ Token Usage**: 50-70% reduction compared to full context inclusion Scalability**: Tested with 100k+ stored conversations 💰 Cost Optimization Uses GPT-4o-mini for optimal cost/performance balance Implements efficient chunking strategies to minimize embedding costs Reranking can be disabled to save on Cohere API costs Average cost: ~$0.01 per conversation 📖 Learn More For a detailed explanation of the architecture and implementation details, check out the comprehensive guide: Long-Term Memory for LLMs using Vector Store - A Practical Approach with n8n and Qdrant 🤝 Support Documentation**: Full setup guide in the article above Community**: Share your experiences and get help in n8n community forums Issues**: Report bugs or request features on the workflow page Tags: #AI #LangChain #VectorDatabase #LongTermMemory #RAG #OpenAI #Qdrant #ChatBot #MemorySystem #ArtificialIntelligence

Notice Community nodes can only be installed on self-hosted instances of n8n. Who this is for Recipe Recommendation Engine with Bright Data MCP & OpenAI is a powerful automated workflow combines Bright Data's MCP for scraping trending or regional recipe data with OpenAI 4o mini to generate personalized recipe recommendations. This automated workflow is designed for: Food Bloggers & Culinary Creators : Who want to automate the extraction and curation of recipes from across the web to generate content, compile cookbooks, or publish newsletters. Nutritionists & Health Coaches : Who need structured recipe data to analyze ingredients, calories, and nutrition for personalized meal planning or dietary tracking. AI/ML Engineers & Data Scientists : Building models that classify cuisines, predict recipes from ingredients, or generate dynamic meal suggestions using clean, structured datasets. Grocery & Meal Kit Platforms : Who aim to extract recipes to power recommendation engines, ingredient lists, or personalized meal plans. Recipe Aggregator Startups : Looking to scale recipe data collection, filtering, and standardization across diverse cooking websites with minimal human intervention. Developers Integrating Cooking Features : Into apps or digital assistants that offer recipe recommendations, step-by-step cooking instructions, or nutritional insights. What problem is this workflow solving? This workflow solves: Automated recipe data extraction from any public URL AI-driven structured data extraction Scalable looped crawling and processing Real-time notifications and data persistence What this workflow does 1. Set Recipe Extract URL Configure the recipe website URL in the input node Set your Bright Data zone name and authentication 2. Paginated Data Extract Triggers a paginated extraction across multiple pages (recipe listing, index, or search pages) Returns a list of recipe links for processing 3. Loop Over Items Loops through the array of recipe links Each link is passed individually to the scraping engine 4. Bright Data MCP Client (Per Recipe) Scrapes each individual recipe page using scrape_as_html Smartly bypasses common anti-bot protections via Bright Data Web Unlocker 5. Structured Recipe Data Extract (via OpenAI GPT-4o mini) Converts raw HTML to clean text using an LLM preprocessing node Uses OpenAI GPT-4o mini to extract structured data 6. Webhook Notification Pushes the structured recipe data to your configured webhook endpoint Format: JSON payload, ideal for Slack, internal APIs, or dashboards 7. Save Response to Disk Saves the structured recipe JSON information to local file system Pre-conditions You need to have a Bright Data account and do the necessary setup as mentioned in the "Setup" section below. You need to have an OpenAI Account. Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. In n8n, configure the OpenAi account credentials. Make sure to set the fields as part of Set the Recipe Extract URL. Remember to set the webhook_url to send a webhook notification of recipe response. Set the desired local path in the Write the structured content to disk node to save the recipe response. How to customize this workflow to your needs You can tailor the Recipe Recommendation Engine workflow to better fit your specific use case by modifying the following key components: 1. Input Fields Node Update the Recipe URL to target specific cuisine sites or recipe types (e.g., vegan, keto, regional dishes). 2. LLM Configuration Swap out the OpenAI GPT-4o mini model with another provider (like Google Gemini) if you prefer. Modify the structured data prompt to extract custom fields that you wish. 3. Webhook Notification Configure the Webhook Notification node to point to your preferred integration (e.g., Slack, Discord, internal APIs). 4. Storage Destination Change the Save to Disk node to store the structured recipe data in: A cloud bucket (S3, GCS, Azure Blob etc.) A database (MongoDB, PostgreSQL, Firestore) Google Sheets or Airtable for spreadsheet-style access.