Workflow Templates

This n8n workflow recommends Taiwan indie music based on a user's city, mood, birthday, today's weather, and star sign. Here's a concise overview: Trigger: Starts manually with the "When clicking ‘Test workflow’" node. Input Setup: The "infomation" node sets user inputs (e.g., city: Taipei, mood: Happy, birthday: 1996/11/21). Song Recommendation: The "get song recommendation" node uses OpenAI's GPT-4o-mini to: Fetch today's weather for the specified city. Determine the user's zodiac sign from their birthday. Check the zodiac sign's daily fortune. Recommend a Taiwan indie song considering weather and fortune. Explain the song choice and highlight its features. Return results in JSON format. Data Extraction: The "Information Extractor" node parses the JSON output, extracting fields like date, city, weather, zodiac sign, fortune, song, artist, and additional info. Spotify Search: The "Spotify" node searches for the recommended song using the artist and song name, retrieving a Spotify URL. Final Output: The "Final Output" node compiles all data, including the Spotify link, into a structured format. Additional Note: A "Sticky Note" provides context about the workflow's purpose and credits the creator, n8nguide. This workflow integrates AI, weather data, astrology, and Spotify to deliver personalized Taiwan indie music recommendations.

This advanced agent analyzes long-term price action in the Binance Spot Market using 1-day candles. It calculates key macro indicators like RSI, MACD, BBANDS, EMA, SMA, and ADX to identify high-confidence trend setups and market momentum. Used by the Quant AI system for directional bias and macro-level signal validation. 🎥 Watch Tutorial: 🎯 Purpose Detect major trend reversals, consolidation zones, and macro bias Support long-term swing trading decisions Provide reliable 1-day signals for downstream agents 🧠 Core Features | Feature | Description | | --------------------------- | ------------------------------------------------------------ | | 🔁 Trigger | Called by parent workflows via Execute Workflow | | 📥 Input Format | { "message": "MATICUSDT", "sessionId": "telegram_id" } | | 📡 Webhook Call | Sends request to internal 1d indicators webhook | | 🧮 Technical Indicators | RSI, MACD, BBANDS, EMA, SMA, ADX (based on 40 daily candles) | | 🧠 GPT (gpt-4.1-mini) Agent | Interprets numerical data into human-readable trend signals | | 💬 Output | Summary suitable for Telegram or further agent consumption | 🔗 External Tools Called https://treasurium.app.n8n.cloud/webhook/1d-indicators Sends: { "symbol": "SOLUSDT" } 📊 Indicator Calculations | Indicator | Purpose | | -------------- | ------------------------------- | | RSI (14) | Overbought / Oversold Signals | | MACD (12,26,9) | Trend Reversals / Momentum | | BBANDS (20, 2) | Volatility Expansion | | EMA (20) | Short-Term Trend Confirmation | | SMA (20) | Macro-Level Support/Resistance | | ADX (14) | Trend Strength + Directional DI | 📦 Setup Import the JSON into n8n. Add your OpenAI API credentials. Ensure webhook /1d-indicators is connected and working. Use this agent as a sub-workflow in: Binance SM Financial Analyst Tool Binance Spot Market Quant AI Agent 📤 Output Example 📅 1D Overview – MATICUSDT • RSI: 71 → Overbought • MACD: Bearish Cross forming • BBANDS: Widening Volatility • EMA < SMA → Downtrend Momentum • ADX: 33 → High Trend Strength 📌 Notes Not user-facing — outputs are structured JSON or Telegram-style summaries. Pairs well with shorter timeframe tools (15m–4h) for confidence stacking. 🧾 Licensing & Attribution © 2025 Treasurium Capital Limited Company Architecture, prompts, and trade report structure are IP-protected. No unauthorized rebranding permitted. 🔗 Need help? Reach out on LinkedIn – Don Jayamaha

Overview This workflow watches for new rows in a Google Sheet (e.g., where you manually log customer reviews) and uses a Code node to perform a simple sentiment analysis, then updates the same row with the detected sentiment. Use Case: Quickly gauge customer satisfaction, identify positive/negative trends, and prioritize follow-ups based on sentiment. How It Works This workflow operates in four main steps: Google Sheets Trigger (New Row): The workflow starts with a Google Sheets Trigger node configured to monitor a specific Google Sheet for new rows. This triggers the workflow whenever a new review is added. Code Node (Sentiment Analysis): A Code node receives the new row data (containing the review text). Inside this node, JavaScript code performs a basic sentiment analysis by checking for keywords (e.g., "great", "excellent" for positive; "bad", "problem" for negative). It assigns "Positive", "Negative", or "Neutral" sentiment. Update Google Sheet Row: A Google Sheets node is configured to update the same row that triggered the workflow. It adds the sentiment result (and potentially other analysis data) to a new column in that row. Setup Steps To get this workflow up and running, follow these instructions: Step 1: Create Google Sheets Credentials in n8n In your n8n instance, click on Credentials in the left sidebar. Click New Credential. Search for and select "Google Sheets OAuth2 API" and follow the authentication steps with your Google account. Save it. Make note of the Credential Name (e.g., "My Google Sheets Account"). Step 2: Prepare Your Google Sheet (or better Make a copy of the one provided in the template) Create a new Google Sheet in your Google Drive (e.g., Customer Reviews). In the first row, add these column headers: Timestamp Customer Name Review Text Sentiment (This column will be updated by the workflow) Review ID (Optional, for tracking) Copy the Sheet ID from the URL (e.g., https://docs.google.com/spreadsheets/d/YOUR_GOOGLE_SHEET_ID_HERE/edit). Copy the GID of the specific sheet tab (e.g., https://docs.google.com/spreadsheets/d/YOUR_GOOGLE_SHEET_ID_HERE/edit#gid=YOUR_GID_HERE). This is the sheetName value. Step 3: Import the Workflow JSON Step 4: Activate and Test the Workflow Click the "Activate" toggle button in the top right corner of the n8n workflow editor. Go to your Google Sheet and manually add a new row with a "Review Text" (e.g., "This product is great, I love it!"). Leave the "Sentiment" column empty. The workflow should trigger automatically (it polls every minute by default), analyze the sentiment, and update the "Sentiment" column in your Google Sheet. You can also manually "Execute Workflow" to test immediately.

PromptCraft AI – Telegram Image Generator 🚀 How It Works PromptCraft AI is an n8n automation that transforms simple image ideas sent through Telegram into stunning AI-generated images using OpenAI's DALL·E (or other image models). 🔁 Workflow Overview: Telegram Trigger: Listens for messages from a user on Telegram. Prompt Expansion: The message is transformed into a rich image description using GPT (OpenAI Chat Model). Image Generation: The prompt is passed to OpenAI's image API to generate a high-quality image. Send Image: The final image is sent back to the user on Telegram. (Optional) Log image titles and links to Google Drive and Google Sheets. ⚙️ Setup Instructions 📋 Prerequisites [ ] n8n installed (Self-hosted or via n8n.cloud) [ ] Telegram bot token (via @BotFather) [ ] OpenAI API key (platform.openai.com) [ ] Google Sheets & Drive OAuth2 credentials (optional) 🧠 Step-by-Step Configuration 1. 📥 Import the Workflow Go to n8n → click Import → upload PromptCraft_AI_Template.json 2. 🔐 Set Up Credentials In Credentials, add the following: Telegram API → Paste your bot token OpenAI API → Paste your OpenAI API key (Optional) Google Sheets OAuth2, Google Drive OAuth2 3. 🔄 Replace Placeholders Open each node that requires credentials: Replace REPLACE_OPENAI_API_KEY with your actual OpenAI API key Replace REPLACE_TELEGRAM_API_ID and credential names as needed (Optional) Update Google Drive Folder ID & Sheet ID in respective nodes 4. ✅ Activate the Workflow Turn on the Telegram Trigger node. Deploy and activate the full workflow. 5. ✉️ Test It Out Send your Telegram bot a message like: > a knight riding a robotic horse in the future Receive the generated image back in Telegram! 💡 Pro Tips Use detailed or imaginative inputs for better outputs. Fine-tune the GPT prompt for specific visual styles. Extend with Google Vision, image upscaling, or watermarking. 🛟 Support For setup assistance or custom feature requests, feel free to contact me @dimejicole21@gmail.com Happy Prompting! 🖼✨

MCP AI Chatbot using Brave Search Disclaimer: This workflow only works with local installations of n8n because it uses a community MCP node Who is this for? This workflow is ideal for developers, automation enthusiasts, and businesses looking to integrate AI-powered chat capabilities into their workflows. It's particularly useful for those leveraging Brave Search and MCP tools to enhance user interactions and streamline data retrieval. What problem is this workflow solving? This workflow addresses the challenge of creating an intelligent chatbot that can process user queries, execute searches using Brave Search, and provide responses enriched by AI. It simplifies the integration of multiple tools into a cohesive system, saving time and effort for users who need a robust conversational AI solution. What this workflow does Listens for incoming chat messages using the Chat Trigger node. Processes user input with an AI Agent powered by GPT-4o. Retrieves relevant tools using the MCP Get Brave Tools node. Executes specific search queries via the MCP Execute Brave Search node. Maintains short-term memory of conversations with the Simple Memory node. Setup Prerequisites: Access to an n8n instance (self-hosted). API credentials for OpenAI and MCP Client Tools. Brave Search API key. Steps: Import the workflow JSON into your n8n instance. Configure the API credentials for OpenAI and MCP Client Tools in their respective nodes. Set up your Brave Search API key in the MCP nodes. https://brave.com/search/api/ Testing: Use the built-in chat interface to send test messages. Verify that the chatbot processes queries and returns results as expected. How to customize this workflow to your needs Modify the AI Agent's prompt settings to tailor responses to your specific use case. Adjust the memory buffer in the Simple Memory node to retain more or less conversational context. Replace or add additional tools in the MCP nodes to expand functionality.

Good to know: This workflow automatically processes product images from Google Drive, generates AI-powered background prompts using multiple AI models (ChatGPT, Claude, or Groq), creates professional background scenes using Pixelcut.ai, and saves enhanced images back to your Google Drive. Perfect for e-commerce businesses and product photography workflows. Who is this for? ➖E-commerce store owners who need professional product backgrounds ➖Product photographers looking to automate background generation ➖Marketing teams creating consistent product imagery ➖Small businesses wanting to enhance their product photos without expensive studio setups ➖Anyone who needs to quickly transform transparent product images into commercial-ready photos What problem is this workflow solving? This workflow solves the challenge of creating professional product photography backgrounds at scale. Instead of manually editing each product image or setting up expensive photo shoots, it automatically generates contextually appropriate backgrounds for your products using AI technology. It eliminates the time-consuming process of background creation while maintaining professional quality and consistency across your product catalog. What this workflow does: ✅Automatically fetches product images from your Google Drive folder ✅Downloads transparent/background-free product images ✅Uses advanced AI models (ChatGPT, Claude, or Groq) to generate intelligent background prompts based on product analysis ✅Creates professional backgrounds using Pixelcut.ai API with AI-generated or custom prompts ✅Saves enhanced product images back to Google Drive with organized naming ✅Processes multiple images in batch automatically How it works: 1️⃣Google Drive node searches for PNG product images in your specified folder 2️⃣Binary download node retrieves the actual image files for processing 3️⃣Optional AI agent analyzes products using your chosen AI model (OpenAI GPT-4, Claude, or Groq) and generates appropriate background prompts 4️⃣Pixelcut.ai API processes images and adds professional backgrounds using AI-generated or manual prompts 5️⃣Enhanced images are automatically saved back to Google Drive with "enhanced-" prefix How to use: Set up Google Drive OAuth2 credentials in n8n Create a Pixelcut.ai account and get your API key Configure your source folder ID in the Google Drive nodes Set up your output folder ID for enhanced images Choose and configure your preferred AI model credentials (OpenAI for ChatGPT, Anthropic for Claude, or Groq) Replace placeholder API keys with your actual credentials Execute the workflow to process your product images Requirements: ✅n8n instance (cloud or self-hosted) ✅Google Drive account with OAuth2 access ✅Pixelcut.ai API account and key ✅Product images in PNG format (transparent backgrounds recommended) ✅AI API credentials for automatic prompt generation (choose from): OpenAI API (for ChatGPT/GPT-4) Anthropic API (for Claude) Groq API (for fast inference) ✅Basic understanding of n8n workflows Customizing this workflow: 🟢Modify the image format filter to support JPG, WEBP, or other formats 🟢Switch between different AI models (ChatGPT, Claude, Groq) for prompt generation 🟢Customize background prompts for different product categories 🟢Add background removal step for products with existing backgrounds 🟢Switch to different AI background services (Deep-Image.ai, Remove.bg, etc.) 🟢Configure different AI model parameters for varied prompt creativity 🟢Add image resizing or quality optimization steps 🟢Create multiple output folders for different product categories 🟢Add error handling and retry mechanisms for failed processes 🟢Implement A/B testing with different AI models for prompt quality comparison

📉 Detect key candlestick reversal patterns and volume divergence on Tesla (TSLA) using GPT-4.1 and real-time OHLCV data. This AI agent evaluates 1-hour and 1-day candles and is an essential part of the Tesla Financial Market Data Analyst Tool. It identifies signals like Doji, Engulfing, Hammer, and volume anomalies to support trade entry and exit logic. ⚠️ Not a standalone template — must be triggered by the Tesla Financial Market Data Analyst Tool 🔐 Requires: Alpha Vantage Premium API Key OpenAI GPT-4.1 access 🔍 What This Agent Does Calls Alpha Vantage to fetch: 🕐 1-hour OHLCV data 📅 1-day OHLCV data GPT-4.1 evaluates: 📊 Candlestick patterns like Doji, Engulfing, Shooting Star 🔄 Volume divergence (price/volume inconsistency) Returns a structured JSON output like: { "summary": "Bearish signs detected on 1-day chart. A shooting star formed on high volume while RSI is elevated. Volume divergence seen on 1h chart as price rises but volume weakens.", "candlestickPatterns": { "1h": "None", "1d": "Shooting Star" }, "volumeDivergence": { "1h": "Bearish", "1d": "None" }, "ohlcv": { "1h": { "close": 174.1, "volume": 1430000, "high": 175.0, "low": 173.8 }, "1d": { "close": 188.3, "volume": 21234000, "high": 189.9, "low": 183.7 } } } 🛠️ Setup Instructions Import the Workflow Name it: Tesla_1hour_and_1day_Klines_Tool Install Dependencies ✅ Tesla Financial Market Data Analyst Tool (this is the trigger parent) Add Required Credentials Alpha Vantage Premium → via HTTP Query Auth OpenAI GPT-4.1 → via OpenAI credentials Verify Web Access This tool fetches data live from Alpha Vantage: /query?function=TIME_SERIES_INTRADAY&interval=60min /query?function=TIME_SERIES_DAILY Run via Execute Workflow Trigger This tool will activate only when called by the Financial Analyst Agent. Inputs: message (optional) sessionId (used for memory continuity) 🧠 Agent Architecture | Component | Description | | ----------------------- | --------------------------------------------------- | | Candlestick Data Hour | Fetches 60min TSLA candles via Alpha Vantage | | Candlestick Data Day | Fetches daily TSLA candles via Alpha Vantage | | OpenAI Chat Model | GPT-4.1 reasoning engine for pattern detection | | Simple Memory | Maintains short-term logic context | | Tesla Klines Agent | LangChain AI agent analyzing both candle and volume | 📌 Sticky Notes Overview 📘 Workflow Purpose 🧠 Short-Term Memory Notes 🔍 1h/1d Data Fetch Logic 📉 Candlestick Pattern Types Detected 📊 Volume Divergence Definitions 🤖 GPT-4.1 Prompt Configuration 🔐 Licensing & Support © 2025 Treasurium Capital Limited Company Logic, pattern reasoning, and prompt structure are proprietary IP. 🔗 Don Jayamaha – LinkedIn 🔗 n8n Creator Profile 🚀 Automate technical edge: detect TSLA candle reversals and volume anomalies with precision using GPT-4.1 and Alpha Vantage. Required by the Tesla Financial Market Data Analyst Tool.

Personalized Tour Package Recommendations via n8n + Pinecone + Lovable UI I've created an intelligent Travel Itinerary Planner that connects a Lovable front-end UI with a smart backend powered by n8n, Pinecone, and OpenAI to deliver personalized tour packages based on natural language queries. What It Does Users type in their travel destination and duration (e.g., "Paris 5 days trip" or "Bali Trip for 7 Days, would love water sports, adventures and trekking included, also some historical monuments") through a Lovable UI. This triggers a webhook in n8n, which processes the request, searches vectorized tour data in Pinecone, and generates a personalized itinerary using OpenAI’s GPT. The results are then structured and sent back to the frontend UI for display in an interactive, reorderable format. Workflow Architecture Lovable UI ➝ Webhook ➝ Tour Recommendation Agent ➝ Vector Search ➝ OpenAI Response ➝ Structured Output ➝ Response to Lovable Tools & Components Used Webhook Acts as the entry point between the Lovable frontend and n8n. Captures the user query (destination, duration) and forwards it into the workflow. OpenAI Chat Model To interpret the user query. To generate a user-friendly, structured tour package from the matched results. Simple Memory Keeps chat state and context for follow-up queries (extendable for future features like multi-step planning or saved itineraries). Question Answering with Vector Store Searches vector embeddings of pre-loaded tour data. Finds the most relevant tour packages by comparing query embeddings. Pinecone Vector Store Stores tour packages and activity data in vectorized format. Enables fast and scalable semantic search across destinations, themes (e.g., "adventure", "cultural"), and duration. OpenAI Embeddings Embeds all tour and activity documents stored in Pinecone. Converts input user queries into embedding vectors for semantic search. Structured Output Parser Parses the final OpenAI-generated response into a consistent, frontend-consumable JSON format. Frontend (Lovable UI) User types in destination or their travel package needs in the Tour Search. Lovable queries the n8n workflow. Displays beautifully structured, editable itineraries. How to Set It Up Webhook Setup in n8n Create a POST webhook node. Set Webhook URL and connect it with Lovable frontend. Pinecone & Embeddings Convert your static tour package documents (PDFs, JSON, CSV, etc.) into embeddings using OpenAI. Store the embeddings in a Pinecone namespace (e.g., kuala-lumpur-3-days). Configure “Answer with Vector Store” Tool Connect the tool to your Pinecone instance and pass query embedding for matching. Connect to OpenAI Chat Use the GPT model to process query + context from Pinecone to generate an engaging itinerary description. Optionally chain a second model to format it into UI-consumable output. Output Parser & Return Use Structured Output Parser to parse the response and pass it to Respond to Webhook node for UI display. Ideal Use Cases Smart itinerary planning for OTAs or DMCs Personalized travel recommendations in chatbots or apps Travel advisors and agents automating package generation Benefits Highly relevant, contextual travel suggestions Natural query understanding via OpenAI Seamless frontend-backend integration via Webhook If you’re building personalized experiences for travelers using AI, give this approach a try! Let me know if you’d like the JSON for this workflow or help setting up the Pinecone data pipeline.

Auto-publish NASA APOD to LinkedIn with AI translation and hashtags Transform NASA's daily astronomical wonders into engaging LinkedIn content automatically. This workflow fetches NASA's Astronomy Picture of the Day, translates it to Brazilian Portuguese using AI, generates strategic hashtags, and publishes everything to your LinkedIn profile with the stunning space image attached. Who's it for Content creators, astronomy enthusiasts, science communicators, and anyone wanting to share high-quality educational content consistently on LinkedIn. Perfect for Portuguese-speaking professionals who want to engage their network with fascinating space discoveries while building their personal brand as a science advocate. How it works The workflow runs on a daily schedule and handles the complete content pipeline automatically. It fetches the latest NASA APOD through the official API, including both the image and detailed explanation. The English description gets professionally translated to selected language using Google Gemini 2.5 Flash, while maintaining scientific accuracy and terminology. Smart hashtag generation combines fixed branding tags with content-specific ones, mixing Portuguese and English for maximum reach. The final post includes the NASA image, translated description, and strategic hashtags, then gets published to your LinkedIn profile automatically. How to set up You'll need accounts for Google AI Studio (free), LinkedIn Developer (free), and a Telegram bot for notifications. The setup takes about 15 minutes and uses only free services and APIs. First, create your Google AI Studio account and get an API key for the AI translation services. Then set up a LinkedIn OAuth2 application to enable posting permissions. Create a Telegram bot through BotFather and get your chat ID for notifications. Configure the Settings node with your Telegram chat ID and preferred language. The workflow comes with all prompts and configurations ready to use. Test each component individually before activating the daily automation. Requirements LinkedIn account with posting permissions Google AI Studio API key (free tier available) Telegram bot token and your chat ID Basic understanding of OAuth2 setup for LinkedIn NASA API key (optional - demo key included) All services used have generous free tiers, making this workflow completely free to operate indefinitely. How to customize the workflow The centralized Settings node makes customization simple. Change the target language from Brazilian Portuguese to any other language by updating the translate_to_language variable. Modify the posting schedule in the CRON trigger to match your preferred timing. Customize the post template in the "Create Final Post Text" node to match your personal brand voice. Adjust the hashtag strategy by editing the AI prompt in the "Generate Hashtags" node. Add additional social platforms by duplicating the LinkedIn publisher with different credentials. The AI prompts can be fine-tuned for different writing styles or specific astronomical topics. You can also extend the workflow to include additional content processing, image enhancements, or cross-posting to multiple platforms while maintaining the core NASA APOD automation.

🚀 LinkedIn Lead-Gen Flywheel – Apify → GPT-4o → Google Sheets → Phantombuster What this workflow does Collect audience specs – simple web-form asks for your ideal company profile. Generate a laser-targeted Apollo search URL with GPT-4o (no manual filtering). Scrape the matching leads via an Apify actor (returns clean JSON). Craft hyper-personalized icebreakers for each lead using GPT-4o (ultra-short, human-sounding). Log everything to Google Sheets – name, LinkedIn URL, company site, summary, and the icebreaker. (Optional) Auto-launch Phantombuster to fire off those connection requests at scale. Why it matters Zero grunt work:** audience research, scraping, copy-writing, and outreach all run hands-free. Punchy personalization:** micro-icebreakers outperform canned intros, boosting accept rates. Scales with you:** flip a switch to go from 10 to 1 000+ connections/day. Node rundown | Step | Node | Key Inputs | Key Outputs | |------|------|-----------|-------------| | 1 | Form Trigger | Audience description | description_of_company | | 2 | OpenAI (GPT-4o) | Audience text | SearchUrl | | 3 | HTTP Request – Apify | SearchUrl, APIFY_TOKEN | Lead JSON | | 4 | OpenAI (GPT-4o) | Lead JSON | Icebreaker | | 5 | Google Sheets | Lead + Icebreaker | Row append/update | | 6 | Aggregate | Sheet rows | Batched output | | 7 | HTTP Request – Phantombuster | PHANTOM_KEY, AGENT_ID | Launch status | Prerequisites OpenAI API key** (GPT-4o access recommended) Apify API token** with access to actor id Google Service Account creds** shared with your target sheet Phantombuster API key** and Agent ID for your LinkedIn connector Active Apollo account to open the generated search URL (only required for debugging) Setup (5-minute sprint) Import the workflow into n8n. Add the required credentials in Credentials → OpenAI, Apify, Google Sheets, Phantombuster. Paste your Phantombuster Agent ID into the HTTP Request node URL. Publish the Form Trigger URL—this is where you (or your SDRs) describe the target audience. Hit Execute Workflow once to verify data flows end-to-end. Customization tips Titles & keywords:** tweak the prompt in the first GPT-4o node to lock in different roles or industries. Icebreaker style:** adjust the second GPT-4o prompt to match your brand voice. Data columns:** map extra fields from Apify into Google Sheets as needed. Skip outreach:** disable the Phantombuster node if you only want the leads + icebreakers.

This n8n template demonstrates how to calculate the evaluation metric "RAG document groundedness" which in this scenario, measures the ability to provide or reference information included only in retrieved vector store documents. The scoring approach is adapted from https://cloud.google.com/vertex-ai/generative-ai/docs/models/metrics-templates#pointwise_groundedness How it works This evaluation works best for an agent that requires document retrieval from a vector store or similar source. For our scoring, we need to collect the agent's response and the documents retrieved and use an LLM to assess if the former is based off the latter. A key factor is to look out information in the response which is not mentioned in the documents. A high score indicates LLM adherence and alignment whereas a low score could signal inadequate prompt or model hallucination. Requirements n8n version 1.94+ Check out this Google Sheet for a sample data https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing

In this automation we first make a screenshot with a screenshot API called URLbox and then send this screenshot into the OpenAI API and analyze it. You can extend this automation by the way you want to ingest the website url's & names into this workflow. Options as data source: Postgres Google Sheets Your CRM ... Setup: Replace Website & URL in Setup Node Put in your URLbox API Key Put in your OpenAI credentials Click here for a blog article with more information on the automation.