Workflow Templates

n8n Set Node Tutorial - Complete Guide 🎯 How It Works This tutorial workflow teaches you everything about n8n's Set node through hands-on examples. The Set node is one of the most powerful tools in n8n - it allows you to create, modify, and transform data as it flows through your workflow. What makes this tutorial special: Progressive Learning**: Starts simple, builds to complex concepts Interactive Examples**: Real working nodes you can modify and test Visual Guidance**: Sticky notes explain every concept Branching Logic**: Shows how Set nodes work in different workflow paths Real Data**: Uses practical examples you'll encounter in automation The workflow demonstrates 6 core concepts: Basic data types (strings, numbers, booleans) Expression syntax with {{ }} and $json references Complex data structures (objects and arrays) "Keep Only Set" option for clean outputs Conditional data setting with branching logic Data transformation and aggregation techniques 📋 Setup Steps Step 1: Import the Workflow Copy the JSON from the code artifact above Open your n8n instance in your browser Navigate to Workflows section Click "Import from JSON" or the import button (usually a "+" or import icon) Paste the JSON into the import dialog Click "Import" to load the workflow Save the workflow (Ctrl+S or click Save button) Step 2: Choose Your Starting Point Option A: Default Tutorial Mode (Recommended for beginners) The workflow is ready to run as-is Uses simple "Welcome" message as starting data Click "Execute Workflow"** to begin Option B: Rich Test Data Mode (Recommended for experimentation) Locate the nodes: Find "Start (Manual Trigger)" and "0. Test Data Input" Disconnect default: Click the connection line between "Start (Manual Trigger)" → "1. Set Basic Values" and delete it Connect test data: Drag from "0. Test Data Input" output to "1. Set Basic Values" input Execute: Click "Execute Workflow" to run with rich test data Step 3: Execute and Learn Run the workflow: Click the "Execute Workflow" button Check outputs: Click on each node to see its output data Read the notes: Each sticky note explains what's happening Follow the flow: Data flows from left to right, top to bottom Step 4: Experiment and Modify Try These Experiments: 🔧 Change Basic Values: Click on "1. Set Basic Values" Modify user_age (try 20 vs 35) Change user_name to see how it propagates Execute and see the changes flow through 📊 Test Conditional Logic: Set user_age to 20 → triggers "Student Discount" path Set user_age to 30 → triggers "Premium Access" path Watch how the workflow branches differently 🎨 Modify Expressions: In "2. Set with Expressions", try changing: ={{ $json.score * 2 }} to ={{ $json.score * 3 }} ={{ $json.user_name }} Smith to ={{ $json.user_name }} Johnson 🏗️ Complex Data Structures: In "3. Set Complex Data", modify the JSON structure Add new properties to the user_profile object Try nested expressions 🎓 Learning Path Beginner Level (Nodes 1-2) Focus**: Understanding basic Set operations Learn**: Data types, static values, simple expressions Time**: 10-15 minutes Intermediate Level (Nodes 3-4) Focus**: Complex data and output control Learn**: Objects, arrays, "Keep Only Set" option Time**: 15-20 minutes Advanced Level (Nodes 5-6) Focus**: Conditional logic and data aggregation Learn**: Branching workflows, merging data, complex expressions Time**: 20-25 minutes 🔍 What Each Node Teaches | Node | Concept | Key Learning | |------|---------|-------------| | 1. Set Basic Values | Data Types | String, number, boolean basics | | 2. Set with Expressions | Dynamic Data | {{ }} syntax, $json references, $now functions | | 3. Set Complex Data | Advanced Structures | Objects, arrays, nested properties | | 4. Set Clean Output | Data Management | "Keep Only Set" for clean final outputs | | 5a/5b. Conditional Sets | Branching Logic | Different data based on conditions | | 6. Tutorial Summary | Data Aggregation | Combining and summarizing workflow data | 💡 Pro Tips 🚀 Quick Wins: Always check node outputs after execution Use sticky notes as your learning guide Experiment with small changes first Copy nodes to try variations 🛠️ Advanced Techniques: Use Keep Only Set for API responses Combine static and dynamic data in complex objects Leverage conditional paths for different user types Reference nested object properties with dot notation 🐛 Troubleshooting: If expressions don't work, check the {{ }} syntax Ensure field names match exactly (case-sensitive) Use the expression editor for complex logic Check data types match your expectations 🎯 Next Steps After Tutorial Create your own Set nodes in a new workflow Practice with real data from APIs or databases Build data transformation workflows for your specific use cases Combine Set nodes with other n8n nodes like HTTP, Webhook, etc. Explore advanced expressions using JavaScript functions Congratulations! You now have the foundation to use Set nodes effectively in any n8n workflow. The Set node is truly the "Swiss Army knife" of n8n automation! 🛠️

Who's it for Content creators, social media managers, and marketing teams who want to automatically extract the most engaging clips from long-form YouTube videos and identify content with high viral potential. What it does This workflow analyzes any YouTube video using Vizard AI's clipping technology and automatically generates up to 8 short clips with viral score ratings. It then filters for the highest-scoring clips (9/10 or above) and posts them to a designated Slack channel for team review and distribution. How it works Video submission: Enter a YouTube URL through a user-friendly form AI analysis: Submits the video to Vizard AI for automated clipping and viral score analysis Smart polling: Waits for processing completion and retrieves results Quality filtering: Only surfaces clips with viral scores of 9/10 or higher Team notification: Posts results to Slack with clip titles, scores, and download links Requirements Vizard AI API credentials (sign up at vizard.ai) Slack workspace with OAuth app configured How to set up Configure Vizard AI credentials: Add your Vizard AI API key to the HTTP Request nodes Set up Slack integration: Configure the Slack OAuth2 credentials and select your target channel Customize filtering: Adjust the viral score threshold in the filter node (currently set to 9/10) Test the workflow: Submit a test YouTube URL to ensure everything works properly How to customize the workflow Adjust clip quantity**: Modify the maxClipNumber parameter (currently 8) in the initial API request Change viral score threshold**: Update the filter condition to match your quality standards Extend with automation**: Connect to social media posting tools or caption generation workflows for full automation Add scheduling**: Integrate with webhook triggers, scheduled triggers, or RSS feeds for batch processing videos

Integrate Xero with FileMaker using Webhooks Workflow Description This n8n workflow automates the integration between Xero and FileMaker, allowing for seamless data transfer between the two platforms. By listening for webhooks from Xero (e.g., new invoices, payments, or contacts), this workflow ensures that data is automatically sent and recorded in a FileMaker database. Who is This For? This workflow template is ideal for: Accountants** who need a streamlined process to sync financial data between Xero and FileMaker. Business Owners** looking to automate data entry and improve accuracy across their systems. Developers** building solutions for clients that require integration between accounting software and databases. Operations Teams** focused on minimizing manual work and improving efficiency. Key Steps Xero Webhook Trigger: The workflow starts by capturing events from Xero via a webhook. Data Processing: Transforms and maps the incoming data to match FileMaker’s required format. FileMaker Node: Utilizes the FileMaker node to create or update records directly in the FileMaker database. Logging & Error Handling: Tracks successful entries and manages any errors with automated alerts. Setup Instructions Set Up the Xero Webhook: Create a webhook in Xero and point it to your n8n webhook node URL. Configure the types of events to trigger the workflow (e.g., new invoices or payments). Xero will then send some test calls to test you are doing proper hash control. Connect the FileMaker Node: Set up your FileMaker node with the appropriate credentials and database configuration. Map the fields between the incoming Xero data and your FileMaker database structure. Customize Data Processing: Adjust data transformations as needed to ensure compatibility with your FileMaker schema. Test and Deploy: Run the workflow with sample data to ensure everything is functioning correctly. Monitor the execution log to verify data transfer and make any adjustments as needed. Error Handling Configuration: Configure error-handling nodes or alerts to notify you of any issues during data processing. Benefits This setup facilitates real-time data synchronization between Xero and FileMaker, reducing the need for manual data entry and improving overall operational efficiency.

This automated n8n workflow monitors ingredient price changes from external APIs or manual sources, analyzes historical trends, and provides smart buying recommendations. The system tracks price fluctuations in a PostgreSQL database, generates actionable insights, and sends alerts via email and Slack to help restaurants optimize their purchasing decisions. What is Price Trend Analysis? Price trend analysis uses historical price data to identify patterns and predict optimal buying opportunities. The system analyzes price movements over time and generates recommendations on when to buy ingredients based on current trends and historical patterns. Good to Know Price data accuracy depends on the reliability of external API sources Historical data improves recommendation accuracy over time (recommended minimum 30 days) PostgreSQL database provides robust data storage and complex trend analysis capabilities Real-time alerts help capture optimal buying opportunities Dashboard provides visual insights into price trends and recommendations How It Works Daily Price Check - Triggers the workflow daily to monitor price changes Fetch API Prices - Retrieves the latest prices from an external ingredient pricing API Setup Database - Ensures database tables are ready before inserting new data Store Price Data - Saves current prices to the PostgreSQL database for tracking Calculate Trends - Analyzes historical prices to detect patterns and price movements Generate Recommendations - Suggests actions based on price trends (buy/wait/stock up) Store Recommendations - Saves recommendations for future reporting Get Dashboard Data - Gathers necessary data for dashboard generation Generate Dashboard HTML - Builds an HTML dashboard to visualize insights Send Email Report - Emails the dashboard report to stakeholders Send Slack Alert - Sends key alerts or recommendations to Slack channels Database Structure The workflow uses PostgreSQL with two main tables: price_history - Historical price tracking with columns: id (Primary Key) ingredient (VARCHAR 100) - Name of the ingredient price (DECIMAL 10,2) - Current price value unit (VARCHAR 50) - Unit of measurement (kg, lbs, etc.) supplier (VARCHAR 100) - Source supplier name timestamp (TIMESTAMP) - When the price was recorded created_at (TIMESTAMP) - Record creation time buying_recommendations - AI-generated buying suggestions with columns: id (Primary Key) ingredient (VARCHAR 100) - Ingredient name current_price (DECIMAL 10,2) - Latest price price_change_percent (DECIMAL 5,2) - Percentage change from previous price trend (VARCHAR 20) - Price trend direction (INCREASING/DECREASING/STABLE) recommendation (VARCHAR 50) - Buying action (BUY_NOW/WAIT/STOCK_UP) urgency (VARCHAR 20) - Urgency level (HIGH/MEDIUM/LOW) reason (TEXT) - Explanation for the recommendation generated_at (TIMESTAMP) - When recommendation was created Price Trend Analysis The system analyzes historical price data over the last 30 days to calculate percentage changes, identify trends (INCREASING/DECREASING/STABLE), and generate actionable buying recommendations based on price patterns and movement history. How to Use Import the workflow into n8n Configure PostgreSQL database connection credentials Set up external ingredient pricing API access Configure email credentials for dashboard reports Set up Slack webhook or bot credentials for alerts Run the Setup Database node to create required tables and indexes Test with sample ingredient data to verify price tracking and recommendations Adjust trend analysis parameters based on your purchasing patterns Monitor recommendations and refine thresholds based on actual buying decisions Requirements PostgreSQL database access External ingredient pricing API credentials Email service credentials (Gmail, SMTP, etc.) Slack webhook URL or bot credentials Historical price data for initial trend analysis Customizing This Workflow Modify the Calculate Trends node to adjust the analysis period (currently 30 days) or add seasonal adjustments. Customize the recommendation logic to match your restaurant's buying patterns, budget constraints, or supplier agreements. Add additional data sources like weather forecasts or market reports for more sophisticated predictions.

📅 Analyze Tesla’s daily trading structure with AI using 6 Alpha Vantage indicators. This tool evaluates long-term trend health, volatility patterns, and potential reversal signals at the 1-day timeframe. Designed for use within the Tesla Financial Market Data Analyst Tool, this agent helps swing and position traders anchor macro sentiment. ⚠️ Not standalone. Must be executed via Execute Workflow 🔌 Requires: Tesla Quant Technical Indicators Webhooks Tool Alpha Vantage Premium API Key OpenAI GPT-4.1 credentials 🔍 What It Does This tool queries a secured webhook (/1dayData) to retrieve real-time, trimmed JSON data for: RSI (Relative Strength Index)** BBANDS (Bollinger Bands)** SMA (Simple Moving Average)** EMA (Exponential Moving Average)** ADX (Average Directional Index)** MACD (Moving Average Convergence Divergence)** These values are then passed to a LangChain AI Agent powered by GPT-4.1, which returns: A 2–3 sentence market condition summary Structured indicator values Timeframe tag ("1d") 📋 Sample Output { "summary": "TSLA shows consolidation on the daily chart. RSI is neutral, BBANDS are contracting, and MACD is flattening.", "timeframe": "1d", "indicators": { "RSI": 51.3, "BBANDS": { "upper": 192.80, "lower": 168.20, "middle": 180.50, "close": 179.90 }, "SMA": 181.10, "EMA": 179.75, "ADX": 15.8, "MACD": { "macd": -0.25, "signal": -0.20, "histogram": -0.05 } } } 🧠 Agent Components | Component | Description | | ----------------------------- | -------------------------------------------------- | | 1day Data (HTTP Node) | Pulls latest data from secured /1dayData webhook | | OpenAI Chat Model | GPT-4.1 powers the analysis logic | | Tesla 1day Indicators Agent | LangChain agent performing interpretation | | Simple Memory | Short-term session continuity | 🛠️ Setup Instructions Import Workflow into n8n Name: Tesla_1day_Indicators_Tool Add Required Credentials Alpha Vantage Premium (via HTTP Query Auth) OpenAI GPT-4.1 (Chat Model) Install Webhook Fetcher Required: Tesla Quant Technical Indicators Webhooks Tool Endpoint /1dayData must be active Execution Context This tool is only triggered via: 👉 Tesla Financial Market Data Analyst Tool Inputs expected: message: optional context sessionId: session memory linkage 📌 Sticky Notes Overview 📘 Tesla 1-Day Indicators Tool – Purpose and integration 📡 Webhook Fetcher – Pulls daily Alpha Vantage data via HTTPS 🧠 GPT-4.1 Model – Reasoning for trend classification 🔗 Sub-Agent Trigger – Used only by Financial Market Analyst 🧠 Memory Buffer – Ensures consistent session logic 🔒 Licensing & Support © 2025 Treasurium Capital Limited Company This workflow—including prompts, logic, and formatting—is protected IP. 🔗 Don Jayamaha – LinkedIn 🔗 Creator Profile 🚀 Evaluate long-term Tesla price behavior with AI-enhanced technical analysis—critical for swing trading strategy. Required by the Tesla Financial Market Data Analyst Tool.

🕒 Evaluate Tesla (TSLA) price action and market structure on the 1-hour timeframe using 6 real-time indicators. This sub-agent is designed to feed mid-term technical insights into the Tesla Financial Market Data Analyst Tool. It uses GPT-4.1 to interpret Alpha Vantage indicator data delivered via secure webhooks. ⚠️ This workflow is not standalone and is executed via Execute Workflow. 🔌 Requires: Tesla Quant Technical Indicators Webhooks Tool Alpha Vantage Premium API Key 🔧 Connected Indicators This tool fetches and analyzes the latest 20 datapoints for: RSI (Relative Strength Index)** MACD (Moving Average Convergence Divergence)** BBANDS (Bollinger Bands)** SMA (Simple Moving Average)** EMA (Exponential Moving Average)** ADX (Average Directional Index)** 📋 Sample Output { "summary": "TSLA is gaining strength on the 1-hour chart. RSI is rising, MACD has crossed bullish, and BBANDS are widening.", "timeframe": "1h", "indicators": { "RSI": 62.1, "BBANDS": { "upper": 176.90, "lower": 169.70, "middle": 173.30, "close": 176.30 }, "SMA": 174.20, "EMA": 175.60, "ADX": 27.5, "MACD": { "macd": 0.84, "signal": 0.65, "histogram": 0.19 } } } 🧠 Agent Components | Component | Role | | ------------------------------ | -------------------------------------------------- | | 1hour Data | Pulls Alpha Vantage indicator data via webhook | | Tesla 1hour Indicators Agent | Interprets signals using structured GPT-4.1 prompt | | OpenAI Chat Model | GPT-4.1 LLM performs analysis | | Simple Memory | Maintains session context | 🛠️ Setup Instructions Import Workflow into n8n Name it: Tesla_1hour_Indicators_Tool Install the Webhook Fetcher Tool 👉 Required: Tesla_Quant_Technical_Indicators_Webhooks_Tool This agent expects webhook /1hourData to return pre-cleaned data Add Credentials Alpha Vantage Premium API Key (via HTTP Query Auth) OpenAI GPT-4.1 credentials Configure for Sub-Agent Use Triggered only via Execute Workflow from: 👉 Tesla Financial Market Data Analyst Tool Inputs: message (optional) sessionId (required for memory linkage) 📌 Sticky Notes Overview 🟢 Trigger Setup – Activated only by the parent agent 📊 1h Webhook Fetcher – Calls Alpha Vantage via secured endpoint 🧠 AI Agent Summary – Interprets trend/momentum from indicator data 🔗 GPT Model Notes – GPT-4.1 parses and explains technical alignment 📘 Documentation Sticky – Embedded in canvas with full walkthrough 🔐 Licensing & Support © 2025 Treasurium Capital Limited Company This tool is part of a proprietary multi-agent AI architecture. No commercial reuse or redistribution permitted. 🔗 Author: Don Jayamaha 🔗 Templates: https://n8n.io/creators/don-the-gem-dealer/ 🚀 Detect TSLA trend shifts and validate setups with 1-hour technical clarity—powered by Alpha Vantage + GPT-4.1. This tool is required by the Tesla Financial Market Data Analyst Tool.

QuantumDefender AI is a next-generation intelligent cybersecurity assistant designed to harness the symbolic strength of quantum computing’s promise alongside cutting-edge AI capabilities. This sophisticated agent empowers SOC analysts, red teamers, and security researchers with rapid threat investigation, operational automation, and intelligent command execution—all driven by GPT-4 and integrated tools, accessible through Telegram or on any medium. 🔑 Key Features: Expert-Level Cybersecurity Research & Analysis: Leverages powerful AI models to deliver clean, detailed, domain-specific insights across detection, remediation, and offensive security. Command & Control: Executes Linux shell commands, autonomous scripts, and system operations securely in isolated environments. Real-Time Web Intelligence: Utilizes integrated Langsearch API to provide timely internet research with contextual relevance. Calendar & Scheduling Automation: Manage Google Calendar events or any similar application(create, update, delete, retrieve) dynamically from chat. Multi-Tool Orchestration: Combines calculator functions, internet searches, command execution, and messaging for comprehensive operational support. Telegram-native Chatbot: Delivers an adaptive, memory-informed, and interactive conversational experience with immediate typing indicators and high responsiveness. Conversation & Session Management: Maintains context-aware, session-based memory to enable smooth, multi-turn dialogues with individual users. Sends “typing…” indicators during processing to ensure an interactive, user-friendly chat experience. Operates exclusively within Telegram, delivering rich, timely responses and leveraging all Telegram bot capabilities. Execution Intelligence & Safety: Fully autonomous in deciding which tools to invoke, how frequently, and in what sequence to fulfill user requests comprehensively and responsibly. Operates within a secure temporary folder environment to contain all command executions safely and avoid persistent or harmful side effects. Enforces strict safety protocols to avoid running malicious or destructive commands, maintaining ethical standards and compliance. Use Cases: Cybersecurity researchers and operators seeking an intelligent assistant to accelerate investigations and automate routine tasks. Red team professionals requiring on-the-fly command execution and information gathering integrated with tactical chat interactions. SOC teams aiming to augment their alert triage and incident handling workflows with AI-powered analysis and action. Anyone looking for a robust multi-tool AI chatbot integrated with real-world operational capabilities. Setup Requirements: OpenAI API key for GPT-4.1-nano language processing. Telegram Bot API credentials with proper webhook setup to receive and respond to messages. Google OAuth credentials for Calendar integration if calendar features are used. SSH access credentials for executing commands on remote hosts, if remote execution is enabled. Internet connectivity for the Langsearch web search API. Customization & Extensibility: The workflow is built modularly with n8n’s flexible node system. Users can extend it by adding more tools, integrating other services (ticketing, threat intel, scanning tools), or modifying interaction logic to suit specialized operational needs and environments. Created by Mariskarthick M Senior Security Analyst | Detection Engineer | Threat Hunter | Open-Source Enthusiast

Try It Out! This n8n template creates a fully automated Instagram content schedule using AI and Google Sheets. It is perfect for content creators, marketing teams, or local businesses looking to organize and scale their social media posting. How it works The workflow starts by reading two sets of inputs from a Google Sheet: Your content strategy inputs (Pillar, Objective, Frequency, Format, Structure, Examples). A list of scraped blog posts with title, URL, and description (fetched from your website). Blog posts are scraped using Apify and parsed to extract key fields, which are stored in a tab labeled "Input (blog month)". You can assign a preferred posting month for each blog (e.g. fall blog posts get tagged for September). The workflow then merges both inputs and extracts the relevant information for further information added by ChatGPT. AI Scheduling & Personalization Once merged, the workflow loops through each content item and: Identifies if the scheduled post falls on or near a holiday (like Mother’s Day) and adjusts the content accordingly. A reference tool is attached to guide structure and tone, based on a library of post examples. Sends the content to an AI Agent (using GPT-4, but customizable) that generates: A compelling Instagram caption A visual description Hashtags Suggested post date, day, content pillar, and format (carousel, reel, image, etc.) Output All generated content including captions, structure, dates, hashtags, and pillar is exported into a tab titled Output in your Google Sheet. The final schedule is ready for manual review, editing, or publishing to social media. How to use The workflow uses a manual trigger to start, but you can replace it with a Webhook, cron job, or form submission. Add/edit your content strategy in Google Sheets. How to Set-Up Initial Input Tab Define your content pillars and structure Create a tab named "Input" or "Strategy" Include these columns: Pillar: e.g., Family images Objective: e.g., Showcase images Frequency: e.g., Bi-weekly Content Form: e.g., Images, Reels Structure: brief description of expected layout (e.g., carousel Q&A, singular photo) Examples: prompts or questions to guide AI (e.g., Why do you think families should do a session?) Input (blog month) Tab – Store scraped blog content Include these columns: URL: direct link to blog post Title: blog post title Description: short summary of the post Preferred Month: month you want it posted (e.g., August, September) This sheet is partially auto-filled by the workflow (except for Preferred Month) Output Tab – Final scheduled content Include these columns: Date: scheduled posting date (YYYY-MM-DD) Day: day of the week Pillar: content category assigned Format: e.g., Images, Reels, Carousel Description: visual summary Caption: Instagram-ready caption Hashtags: complete hashtag block To use the Apify HTTP Request node: Drag in an HTTP Request node into your n8n workflow. Set the Method and URL based on how you're using Apify: Use POST if you want to run an actor live with dynamic input (e.g. scrape blog posts in real time). Use GET if you want to retrieve results from a completed or static dataset run (faster and cheaper if you're reusing previous data). Configure query or body parameters: Include your Apify API token for authentication (e.g. token=YOUR_API_KEY) For POST: include an input object with any required actor settings (e.g., blog URL to scrape). For GET: specify the dataset ID in the URL Test the node to ensure you're retrieving the blog titles, descriptions, and URLs as expected. Requirements Apify account for scraping blog posts OpenAI key (e.g. GPT-4) or another model of your choice Google Sheets Credentials Example Use Cases A photographer repurposing blogs into Instagram carousels A nonprofit automatically generating seasonal posts A small team managing multi-pillar content across weeks or months Need Help? Join the n8n Discord or ask in the n8n Forum! Happy Content Making ! 📅✨

Video Guide I prepared a detailed guide that showed the whole process of building a call analyzer. .png) Who is this for? This workflow is ideal for sales teams, customer support managers, and online education services that conduct follow-up calls with clients. It’s designed for those who want to leverage AI to gain deeper insights into client needs and upsell opportunities from recorded calls. What problem does this workflow solve? Many follow-up sales calls lack structured analysis, making it challenging to identify client needs, gauge interest levels, or uncover upsell opportunities. This workflow enables automated call transcription and AI-driven analysis to generate actionable insights, helping teams improve sales performance, refine client communication, and streamline upselling strategies. What this workflow does This workflow transcribes and analyzes sales calls using AssemblyAI, OpenAI, and Supabase to store structured data. The workflow processes recorded calls as follows: Transcribe Call with AssemblyAI: Converts audio into text with speaker labels for clarity. Analyze Transcription with OpenAI: Using a predefined JSON schema, OpenAI analyzes the transcription to extract metrics like client intent, interest score, upsell opportunities, and more. Store and Access Results in Supabase: Stores both transcription and analysis data in a Supabase database for further use and display in interfaces. Setup Preparation Create Accounts: Set up accounts for N8N, Supabase, AssemblyAI, and OpenAI. Get Call Link: Upload audio files to public Supabase storage or Dropbox to generate a direct link for transcription. Prepare Artifacts for OpenAI: Define Metrics: Identify business metrics you want to track from call analysis, such as client needs, interest score, and upsell potential. Generate JSON Schema: Use GPT to design a JSON schema for structuring OpenAI’s responses, enabling efficient storage, analysis, and display. Create Analysis Prompt: Write a detailed prompt for GPT to analyze calls based on your metrics and JSON schema. Scenario 1: Transcribe Call with AssemblyAI Set Up Request: Header Authentication: Set Authorization with AssemblyAI API key. URL: POST to https://api.assemblyai.com/v2/transcript/. Parameters: audio_url: Direct URL of the audio file. webhook_url: URL for an N8N webhook to receive the transcription result. Additional Settings: speaker_labels (true/false): Enables speaker diarization. speakers_expected: Specify expected number of speakers. language_code: Set language (default: en_us). Scenario 2: Process Transcription with OpenAI Webhook Configuration: Set up a POST webhook to receive AssemblyAI’s transcription data. Get Transcription: Header Authentication: Set Authorization with AssemblyAI API key. URL: GET https://api.assemblyai.com/v2/transcript/<transcript_id>. Send to OpenAI: URL: POST to https://api.openai.com/v1/chat/completions. Header Authentication: Set Authorization with OpenAI API key. Body Parameters: Model: Use gpt-4o-2024-08-06 for JSON Schema support, or gpt-4o-mini for a less costly option. Messages: system: Contains the main analysis prompt. user: Combined speakers’ utterances to analyze in text format. Response Format: type: json_schema. json_schema: JSON schema for structured responses. Save Results in Supabase: Operation: Create a new record. Table Name: demo_calls. Fields: Input: Transcription text, audio URL, and transcription ID. Output: Parsed JSON response from OpenAI’s analysis.

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.

Who this template is for This template is for every engineer who wants to automate their code reviews or just get a 2nd opinion on their PR. How it works This workflow will automatically review your changes in a Gitlab PR using the power of AI. It will trigger whenever you comment with +0 to a Gitlab PR, get the code changes, analyze them with GPT, and reply to the PR discussion. Set up Steps Set up webhook of note_events in Gitlab repository (see here on how to do it) Configure ChatGPT credentials Note "+0" in MergeRequest to trigger automatic review by ChatGPT

This n8n template allows you to perform real-time currency conversions by simply sending a webhook request. By integrating with the ExchangeRate.host API, you can get up-to-date exchange rates for over 170 world currencies, making it an incredibly useful tool for financial tracking, e-commerce, international business, and personal budgeting. 🔧 How it works Receive Conversion Request Webhook: This node acts as the entry point for the workflow, listening for incoming POST requests. It's configured to expect a JSON body containing: from: The 3-letter ISO 4217 currency code for the source currency (e.g., USD, PHP). to: The 3-letter ISO 4217 currency code for the target currency (e.g., EUR, JPY). amount: The numeric value you want to convert. Important: The ExchangeRate.host API access_key is handled securely by n8n's credential system and should not be included in the webhook body or headers. Convert Currency: This node makes an HTTP GET request to the ExchangeRate.host API (api.exchangerate.host). It dynamically constructs the URL using the from, to, and amount from the webhook body. Your API access key is securely retrieved from n8n's pre-configured credentials (HTTP Query Auth type) and automatically added as a query parameter (access_key). The API then performs the conversion and returns a JSON object with the conversion details. Respond with Converted Amount: This node sends the full currency conversion result received from ExchangeRate.host back to the service that initiated the webhook. 👤 Who is it for? This workflow is ideal for: E-commerce Platforms: Display prices in local currencies on the fly for international customers. Convert incoming international payments to your local currency for accounting. Calculate shipping costs in different currencies. Financial Tracking & Budgeting Apps: Update personal or business budgets with converted values. Track expenses incurred in foreign currencies. Automate portfolio value conversion for multi-currency investments. International Business & Freelancers: Generate invoices in a client's local currency based on your preferred currency. Quickly estimate project costs or earnings in different currencies. Automate reconciliation of international transactions. Travel Planning: Convert travel expenses from one currency to another while abroad. Build simple tools to estimate costs for trips in different countries. Data Analysis & Reporting: Standardize financial data from various sources into a single currency for unified reporting. Build dashboards that display converted financial metrics. Custom Integrations: Connect to CRMs, accounting software, or internal tools to automate currency-related tasks. Build chatbots that can answer currency conversion queries. 📑 Data Structure When you trigger the webhook, send a POST request with a JSON body structured as follows: { "from": "USD", "to": "PHP", "amount": 100 } The workflow will return a JSON response similar to this (results will vary based on currencies and amount): { "date": "2025-06-03", "historical": false, "info": { "rate": 58.749501, "timestamp": 1717398188 }, "query": { "amount": 100, "from": "USD", "to": "PHP" }, "result": 5874.9501, "success": true } ⚙️ Setup Instructions Get an ExchangeRate.host Access Key: Go to https://exchangerate.host/ and sign up for a free API key. Create an n8n Credential for ExchangeRate.host: In your n8n instance, go to Credentials. Click "New Credential" and search for "HTTP Query Auth". Set the Name (e.g., ExchangeRate.host API Key). Set API Key to your ExchangeRate.host access key. Set Parameter Name to access_key. Set Parameter Position to Query. Save the credential. Import Workflow: In your n8n editor, click "Import from JSON" and paste the provided workflow JSON. Configure ExchangeRate.host API Node: Double-click the Convert Currency node. Under "Authentication", select "Generic Credential Type". Choose "HTTP Query Auth" as the Generic Auth Type. Select the credential you created (e.g., "ExchangeRate.host API Key") from the dropdown. Configure Webhook Path: Double-click the Receive Conversion Request Webhook node. In the 'Path' field, set a unique and descriptive path (e.g., /convert-currency). Activate Workflow: Save and activate the workflow. 📝 Tips This workflow is a powerful starting point. Here's how you can make it even more robust and integrated: Robust Error Handling: Add an IF node after Convert Currency to check {{ $json.success }}. If false, branch to an Error Trigger node or send an alert (e.g., Slack, Email) with {{ $json.error.info }} to notify you of API issues or invalid inputs. Include a Try/Catch block to gracefully handle network issues or malformed responses. Input Validation & Defaults: Add a Function node after the webhook to validate if from, to, and amount are present and in the correct format. If not, return a clear error message to the user. Set default from or to currencies if they are not provided in the webhook, making the API more flexible. Logging & Auditing: After a successful conversion, use a Google Sheets, Airtable, or database node (e.g., PostgreSQL, MongoDB) to log every conversion request, including the input currencies, amount, converted result, date, and possibly the calling IP (from the webhook headers). This is crucial for financial auditing and analysis. Rate Limits & Caching: If you anticipate many requests, be mindful of ExchangeRate.host's API rate limits. You can introduce a Cache node to store recent conversion results for a short period, reducing redundant API calls for common conversions. Alternatively, add a Delay node to space out requests if you're hitting limits. Format & Rounding: Use a Function node or Set node to format the result to a specific number of decimal places (e.g., {{ $json.result.toFixed(2) }}). Add currency symbols or full currency names to the output for better readability. Alerting on Significant Changes: Chain this workflow with a Cron or Schedule node to periodically fetch exchange rates for a pair you care about (e.g., USD to EUR). Use an IF node to compare the current rate with a previously stored rate. If the change exceeds a certain percentage, send an alert via Slack, Email, or Telegram to notify you of significant market shifts. Integration with Payment Gateways: For e-commerce, combine this with nodes for payment gateways (e.g., Stripe, PayPal) to automatically convert customer payments received in foreign currencies to your base currency before recording. Multi-currency Pricing for Products: Use this workflow in conjunction with your product database. When a user selects a different country/currency, trigger this webhook to dynamically convert product prices and display them instantly.