Workflow Templates

Vector Database as a Big Data Analysis Tool for AI Agents Workflows from the webinar "Build production-ready AI Agents with Qdrant and n8n". This series of workflows shows how to build big data analysis tools for production-ready AI agents with the help of vector databases. These pipelines are adaptable to any dataset of images, hence, many production use cases. Uploading (image) datasets to Qdrant Set up meta-variables for anomaly detection in Qdrant Anomaly detection tool KNN classifier tool For anomaly detection The first pipeline to upload an image dataset to Qdrant. The second pipeline is to set up cluster (class) centres & cluster (class) threshold scores needed for anomaly detection. 3. This is the third pipeline --- the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant to detect if it's an anomaly to the uploaded dataset. For KNN (k nearest neighbours) classification The first pipeline to upload an image dataset to Qdrant. The second is the KNN classifier tool, which takes any image as input and classifies it on the uploaded to Qdrant dataset. To recreate both You'll have to upload crops and lands datasets from Kaggle to your own Google Storage bucket, and re-create APIs/connections to Qdrant Cloud (you can use Free Tier cluster), Voyage AI API & Google Cloud Storage. [This workflow] Anomaly Detection Tool This is the tool that can be used directly for anomalous images (crops) detection. It takes as input (any) image URL and returns a text message telling if whatever this image depicts is anomalous to the crop dataset stored in Qdrant. An Image URL is received via the Execute Workflow Trigger, which is used to generate embedding vectors using the Voyage AI Embeddings API. The returned vectors are used to query the Qdrant collection to determine if the given crop is known by comparing it to threshold scores of each image class (crop type). If the image scores lower than all thresholds, then the image is considered an anomaly for the dataset.

1. Document Ingestion & Processing Google Drive Trigger monitors for new files → Loop Over Items processes each file → File Info extracts metadata → Google Drive downloads the actual content → Switch routes to appropriate extractors (PDF or TEXT) based on file type 2. Content Transformation & Chunking Document Data node processes extracted text → Recursive Splitter breaks content into contextual chunks → Chunk Splitting applies intelligent segmentation while preserving document context and relationships between chunks 3. Embedding & Storage Basic LLM Chain processes chunks → OpenAI Chat Model generates contextual understanding → Summarize creates document summaries → Supabase Vector Store saves embeddings with metadata → Embeddings OpenAI creates vector representations → Default Data Loader handles storage operations 4. Query Processing & Retrieval When Clicking Execute triggers user queries → OpenAI processes and understands the question → AI Agent orchestrates hybrid search (combining vector similarity + keyword matching) → Google Gemini Chat Model generates final responses using retrieved context → HTTP Request handles additional external data sources

Create a RAG System with Paul Essays, Milvus, and OpenAI for Cited Answers This workflow automates the process of creating a document-based AI retrieval system using Milvus, an open-source vector database. It consists of two main steps: Data collection/processing Retrieval/response generation The system scrapes Paul Graham essays, processes them, and loads them into a Milvus vector store. When users ask questions, it retrieves relevant information and generates responses with citations. Step 1: Data Collection and Processing Set up a Milvus server using the official guide Create a collection named "my_collection" Execute the workflow to scrape Paul Graham essays: Fetch essay lists Extract names Split content into manageable items Limit results (if needed) Fetch texts Extract content Load everything into Milvus Vector Store This step uses OpenAI embeddings for vectorization. Step 2: Retrieval and Response Generation When a chat message is received, the system: Sets chunks to send to the model Retrieves relevant information from the Milvus Vector Store Prepares chunks Answers the query based on those chunks Composes citations Generates a comprehensive response This process uses OpenAI embeddings and models to ensure accurate and relevant answers with proper citations. For more information on vector databases and similarity search, visit Milvus documentation.

Use case Slackbots are super powerful. At n8n, we have been using them to get a lot done.. But it can become hard to manage and maintain many different operations that a workflow can do. This is the base workflow we use for our most powerful internal Slackbots. They handle a lot from running e2e tests for Github branch to deleting a user. By splitting the workflow into many subworkflows, we are able to handle each command seperately, making it easier to debug as well as support new usecases. In this template, you can find eveything to setup your own Slackbot (and I made it simple, there's only one node to configure 😉). After that, you need to build your commands directly. This bot can create a new thread on an alerts channel and respond there. Or reply directly to the user. It responds for help request to return a help page. It automatically handles unknown commands. It also supports flags and environment variables. For example /cloudbot-test info mutasem --full-info -e env=prod would give you the following info, when calling subworkflow. How to setup Add Slack command and point it up to the webhook. For example. Add the following to the Set config node alerts_channel with alerts channel to start threads on instance_url with this instance url to make it easy to debug slack_token with slack bot token to validate request slack_secret_signature with slack secret signature to validate request help_docs_url with help url to help users understand the commands Build other workflows to call and add them to commands in Set Config. Each command must be mapped to a workflow id with an Execute Workflow Trigger node Activate workflow 🚀 How to adjust Add your own commands. Depending on your need, you might need to lock down who can call this.

Video explanation This n8n workflow helps you identify trending videos within your niche by detecting outlier videos that significantly outperform a channel's average views. It automates the process of monitoring competitor channels, saving time and streamlining content research. Included in the Workflow Automated Competitor Video Tracking Monitors videos from specified competitor channels, fetching data directly from the YouTube API. Outlier Detection Based on Channel Averages Compares each video’s performance against the channel’s historical average to identify significant spikes in viewership. Historical Video Data Management Stores video statistics in a PostgreSQL database, allowing the workflow to only fetch new videos and optimize API usage. Short Video Filtering Automatically removes short videos based on duration thresholds. Flexible Video Retrieval Fetches up to 3 months of historical data on the first run and only new videos on subsequent runs. PostgreSQL Database Integration Includes SQL queries for database setup, video insertion, and performance analysis. Configurable Outlier Threshold Focuses on videos published within the last two weeks with view counts at least twice the channel's average. Data Output for Analysis Outputs best-performing videos along with their engagement metrics, making it easier to identify trending topics. Requirements n8n installed on your machine or server A valid YouTube Data API key Access to a PostgreSQL database This workflow is intended for educational and research purposes, helping content creators gain insights into what topics resonate with audiences without manual daily monitoring.

Create a Paul Graham Essay Q&A System with OpenAI and Milvus Vector Database How It Works This workflow creates a question-answering system based on Paul Graham essays. It has two main steps: Data Collection & Processing: Scrapes Paul Graham essays Extracts text content Loads them into a Milvus vector store Chat Interaction: Provides a question-answering interface using the stored vector embeddings Utilizes OpenAI embeddings for semantic search Set Up Steps Set up a Milvus server following the official guide Create a collection named "my_collection" Run the workflow to scrape and load Paul Graham essays Start chatting with the QA system The workflow handles the entire process from fetching essays, extracting content, generating embeddings via OpenAI, storing vectors in Milvus, and providing retrieval for question answering.

Yelp Business Scraper by URL via Scrape.do API with Google Sheets Storage Overview This n8n workflow automates the process of scraping comprehensive business information from Yelp using individual business URLs. It integrates with Scrape.do for professional web scraping with anti-bot bypass capabilities and Google Sheets for centralized data storage, providing detailed business intelligence for market research, competitor analysis, and lead generation. Workflow Components 1. 📥 Form Trigger | Property | Value | |----------|-------| | Type | Form Trigger | | Purpose | Initiates the workflow with user-submitted Yelp business URL | | Input Fields | Yelp Business URL | | Function | Captures target business URL to start the scraping process | 2. 🔍 Create Scrape.do Job | Property | Value | |----------|-------| | Type | HTTP Request (POST) | | Purpose | Creates an async scraping job via Scrape.do API | | Endpoint | https://q.scrape.do/api/v1/jobs | | Authentication | X-Token header | Request Parameters: Targets**: Array containing the Yelp business URL Super**: true (uses residential/mobile proxies for better success rate) GeoCode**: us (targets US-based content) Device**: desktop Render**: JavaScript rendering enabled with networkidle2 wait condition Function: Initiates comprehensive business data extraction from Yelp with headless browser rendering to handle dynamic content. 3. 🔧 Parse Yelp HTML | Property | Value | |----------|-------| | Type | Code Node (JavaScript) | | Purpose | Extracts structured business data from raw HTML | | Mode | Run once for each item | Function: Parses the scraped HTML content using regex patterns and JSON-LD extraction to retrieve: Business name Overall rating Review count Phone number Full address Price range Categories Website URL Business hours Image URLs 4. 📊 Store to Google Sheet | Property | Value | |----------|-------| | Type | Google Sheets Node | | Purpose | Stores scraped business data for analysis and storage | | Operation | Append rows | | Target | "Yelp Scraper Data - Scrape.do" sheet | Data Mapping: Business Name, Overall Rating, Reviews Count Business URL, Phone, Address Price Range, Categories, Website Hours, Images/Videos URLs, Scraped Timestamp Workflow Flow Form Input → Create Scrape.do Job → Parse Yelp HTML → Store to Google Sheet │ │ │ │ ▼ ▼ ▼ ▼ User submits API creates job JavaScript code Data appended Yelp URL with JS rendering extracts fields to spreadsheet Configuration Requirements API Keys & Credentials | Credential | Purpose | |------------|---------| | Scrape.do API Token | Required for Yelp business scraping with anti-bot bypass | | Google Sheets OAuth2 | For data storage and export access | | n8n Form Webhook | For user input collection | Setup Parameters | Parameter | Description | |-----------|-------------| | YOUR_SCRAPEDO_TOKEN | Your Scrape.do API token (appears in 3 places) | | YOUR_GOOGLE_SHEET_ID | Target spreadsheet identifier | | YOUR_GOOGLE_SHEETS_CREDENTIAL_ID | OAuth2 authentication reference | Key Features 🛡️ Anti-Bot Bypass Technology Residential Proxy Rotation**: 110M+ proxies across 150 countries WAF Bypass**: Handles Cloudflare, Akamai, DataDome, and PerimeterX Dynamic TLS Fingerprinting**: Authentic browser signatures CAPTCHA Handling**: Automatic bypass for uninterrupted scraping 🌐 JavaScript Rendering Full headless browser support for dynamic Yelp content networkidle2 wait condition ensures complete page load Custom wait times for complex page elements Real device fingerprints for detection avoidance 📊 Comprehensive Data Extraction | Field | Description | Example | |-------|-------------|---------| | name | Business name | "Joe's Pizza Restaurant" | | overall_rating | Average customer rating | "4.5" | | reviews_count | Total number of reviews | "247" | | url | Original Yelp business URL | "https://www.yelp.com/biz/..." | | phone | Business phone number | "(555) 123-4567" | | address | Full street address | "123 Main St, New York, NY 10001" | | price_range | Price indicator | "$$" | | categories | Business categories | "Pizza, Italian, Delivery" | | website | Business website URL | "https://joespizza.com" | | hours | Operating hours | "Mon-Fri 11:00-22:00" | | images_videos_urls | Media content links | "https://s3-media1.fl.yelpcdn.com/..." | | scraped_at | Extraction timestamp | "2025-01-15T10:30:00Z" | 🗂️ Centralized Data Storage Automatic Google Sheets export Organized business data format with 12 data fields Historical scraping records with timestamps Easy sharing and collaboration Use Cases 📈 Market Research Competitor business analysis Local market intelligence gathering Industry benchmark establishment Service offering comparison 🎯 Lead Generation Business contact information extraction Potential client identification Market opportunity assessment Sales prospect development 📊 Business Intelligence Customer sentiment analysis through ratings Competitor performance monitoring Market positioning research Brand reputation tracking 📍 Location Analysis Geographic business distribution Local competition assessment Market saturation evaluation Expansion opportunity identification Technical Notes | Specification | Value | |--------------|-------| | Processing Time | 15-45 seconds per business URL | | Data Accuracy | 95%+ for publicly available business information | | Success Rate | 99.98% (Scrape.do guarantee) | | Proxy Pool | 110M+ residential, mobile, and datacenter IPs | | JS Rendering | Full headless browser with networkidle2 wait | | Data Format | JSON with structured field mapping | | Storage Format | Structured Google Sheets with 12 predefined columns | Setup Instructions Step 1: Import Workflow Copy the JSON workflow configuration Import into n8n: Workflows → Import from JSON Paste configuration and save Step 2: Configure Scrape.do Get your API token: Sign up at Scrape.do Navigate to Dashboard → API Token Copy your token Update workflow references (3 places): 🔍 Create Scrape.do Job node → Headers → X-Token 📡 Check Job Status node → Headers → X-Token 📥 Fetch Task Results node → Headers → X-Token Replace YOUR_SCRAPEDO_TOKEN with your actual API token. Step 3: Configure Google Sheets Create target spreadsheet: Create new Google Sheet named "Yelp Business Data" or similar Add header row with columns: name | overall_rating | reviews_count | url | phone | address | price_range | categories | website | hours | images_videos_urls | scraped_at Copy the Sheet ID from URL (the long string between /d/ and /edit) Set up OAuth2 credentials: In n8n: Credentials → Add Credential → Google Sheets OAuth2 Complete the Google authentication process Grant access to Google Sheets Update workflow references: Replace YOUR_GOOGLE_SHEET_ID with your actual Sheet ID Update YOUR_GOOGLE_SHEETS_CREDENTIAL_ID with credential reference Step 4: Test and Activate Test with sample URL: Use a known Yelp business URL (e.g., https://www.yelp.com/biz/example-business-city) Submit through the form trigger Monitor execution progress in n8n Verify data appears in Google Sheet Activate workflow: Toggle workflow to "Active" Share form URL with users Sample Business Data The workflow captures comprehensive business information including: | Category | Data Points | |----------|-------------| | Basic Information | Name, category, location | | Performance Metrics | Ratings, review counts, popularity | | Contact Details | Phone, website, address | | Visual Content | Photos, videos, gallery URLs | | Operational Data | Hours, services, price range | Advanced Configuration Batch Processing Modify the input to accept multiple URLs by updating the job creation body: { "Targets": [ "https://www.yelp.com/biz/business-1", "https://www.yelp.com/biz/business-2", "https://www.yelp.com/biz/business-3" ], "Super": true, "GeoCode": "us", "Render": { "WaitUntil": "networkidle2", "CustomWait": 3000 } } Enhanced Rendering Options For complex Yelp pages, add browser interactions: { "Render": { "BlockResources": false, "WaitUntil": "networkidle2", "CustomWait": 5000, "WaitSelector": ".biz-page-header", "PlayWithBrowser": [ { "Action": "Scroll", "Direction": "down" }, { "Action": "Wait", "Timeout": 2000 } ] } } Notification Integration Add alert mechanisms: Email notifications for completed scrapes Slack messages for team updates Webhook triggers for external systems Error Handling Common Issues | Issue | Cause | Solution | |-------|-------|----------| | Invalid URL | URL is not a valid Yelp business page | Ensure URL format: https://www.yelp.com/biz/... | | 401 Unauthorized | Invalid or missing API token | Verify X-Token header value | | Job Timeout | Page too complex or slow | Increase CustomWait value | | Empty Data | HTML parsing failed | Check page structure, update regex patterns | | Rate Limiting | Too many concurrent requests | Reduce request frequency or upgrade plan | Troubleshooting Steps Verify URLs: Ensure Yelp business URLs are correctly formatted Check Credentials: Validate Scrape.do token and Google OAuth Monitor Logs: Review n8n execution logs for detailed errors Test Connectivity: Verify network access to all external services Check Job Status: Use Scrape.do dashboard to monitor job progress Performance Specifications | Metric | Value | |--------|-------| | Processing Time | 15-45 seconds per business URL | | Data Accuracy | 95%+ for publicly available information | | Success Rate | 99.98% (with Scrape.do anti-bot bypass) | | Concurrent Processing | Depends on Scrape.do plan limits | | Storage Capacity | Unlimited (Google Sheets based) | | Proxy Pool | 110M+ IPs across 150 countries | Scrape.do API Reference Async API Endpoints | Endpoint | Method | Purpose | |----------|--------|---------| | /api/v1/jobs | POST | Create new scraping job | | /api/v1/jobs/{jobID} | GET | Check job status | | /api/v1/jobs/{jobID}/{taskID} | GET | Retrieve task results | | /api/v1/me | GET | Get account information | Job Status Values | Status | Description | |--------|-------------| | queuing | Job is being prepared | | queued | Job is in queue waiting to be processed | | pending | Job is currently being processed | | rotating | Job is retrying with different proxies | | success | Job completed successfully | | error | Job failed | | canceled | Job was canceled by user | For complete API documentation, visit: Scrape.do Documentation Support & Resources Scrape.do Documentation**: https://scrape.do/documentation/ Scrape.do Dashboard**: https://dashboard.scrape.do/ n8n Documentation**: https://docs.n8n.io/ Google Sheets API**: https://developers.google.com/sheets/api This workflow is powered by Scrape.do - Reliable, Scalable, Unstoppable Web Scraping

🛡️📥 Telegram Invoice Agent → 🔎 OCR → 🤖 AI Parsing → 📄 Google Sheets + 🗂️ Drive What this workflow does 🤖 Captures invoices from Telegram and auto-downloads PDFs/images.​ 🔎 Runs OCR, then uses AI to structure clean invoice fields.​ 📄 Appends parsed data to a Google Sheets “Invoice Database.”​ 🗂️ Uploads the original file to Google Drive with a neat name.​ 💬 Sends a friendly Telegram summary with totals, due date, notes, and link.​ Why it’s useful ⚡ Faster bookkeeping with zero manual copy-paste.​ 🧱 Consistent schema for reliable reporting and pivots.​ 👥 Team-friendly drop-and-log via Telegram.​ 🧩 Easy to extend with approvals, ERP/CRM sync, or vendor routing.​ How it works 📲 Telegram Trigger → file received.​ 🌐 HTTP OCR (OCR.space) → text extracted.​ 🤖 AI Agent → maps to strict JSON schema.​ 📄 Google Sheets → appends structured row.​ 🗂️ Google Drive → saves original invoice.​ 💬 Telegram → concise confirmation and links.​ What you’ll need 🤖 Telegram Bot token.​ 🔑 OCR API key (OCR.space: free tier; upgrade for volume/accuracy).​ 🔐 Google OAuth for Sheets + Drive.​ 🧠 LLM account (e.g., Gemini/OpenAI-compatible).​ Setup steps 🔗 Connect credentials: Telegram, Google, OCR, AI.​ 📄 Prepare Sheet columns: Invoice Number, Date, Total Amount ($), Billing Address, Due Date, Notes.​ 🧭 Update sheet ID and Drive folder ID.​ 🧪 Test: send a sample invoice and validate OCR, AI output, row append, and Drive link.​ Customization ideas 🎯 Higher accuracy OCR: swap to Google Vision.​ 📊 Line items: extract into a second tab for analytics.​ ✅ Approvals: add Telegram keyboard confirmation before write.​ 🧯 Robustness: IF/Retry on empty OCR; user prompt to retake photo.​ Who it’s for 🧑‍💻 Freelancers/agencies needing fast invoice intake via Telegram. 🧾 Small finance teams wanting a searchable ledger with links to originals.​ 🏗️ Builders extending to ERPs/CRMs and custom accounting flows.​ ​ Want help customizing? 📧 anirpoke@gmail.com 🔗 Linkedin

Create multi-sheet Excel workbooks in n8n to automate reporting using Google Drive + Google Sheets Build an automated Excel file with multiple tabs directly in n8n. Two Code nodes generate datasets, each is converted into its own Excel worksheet, then combined into a single .xlsx and (optionally) appended to a Google Sheet for sharing—eliminating manual copy-paste and speeding up reporting. Who’s it for Teams that publish recurring reports as Excel with multiple tabs Ops/Marketing/Data folks who want a no-code/low-code way to package JSON into Excel n8n beginners learning the Code → Convert to File → Merge pattern How it works Manual Trigger starts the run. Code nodes emit JSON rows for each table (e.g., People, Locations). Convert to File nodes turn each JSON list into an Excel binary, assigning Sheet1/Sheet2 (or your names). Merge combines both binaries into a single Excel workbook with multiple tabs. Google Sheets (optional) appends the JSON rows to a live spreadsheet for collaboration. Setup (only 2 connections) 1️⃣ Connect Google Sheets (OAuth2) In n8n → Credentials → New → Google Sheets (OAuth2) Sign in with your Google account and grant access Copy the example sheet referenced in the Google Sheets node (open the node and duplicate the linked sheet), or select your own In the workflow’s Google Sheets node, select your Spreadsheet and Worksheet https://docs.google.com/spreadsheets/d/1G6FSm3VdMZt6VubM6g8j0mFw59iEw9npJE0upxj3Y6k/edit?gid=1978181834#gid=1978181834 2️⃣ Connect Google Drive (OAuth2) In n8n → Credentials → New → Google Drive (OAuth2) Sign in with the Google account that will store your Excel outputs and allow access In your Drive-related nodes (if used), point to the folder where you want the .xlsx saved or retrieved Customize the workflow Replace the sample arrays in the Code nodes with your data (APIs, DBs, CSVs, etc.) Rename sheetName in each Convert to File node to match your desired tab names Keep the Merge node in Combine All mode to produce a single workbook In Google Sheets, switch to Manual mapping for strict column order (optional) Best practices (per template guidelines) Rename nodes** to clear, action-oriented names (e.g., “Build People Sheet”, “Build Locations Sheet”) Add a yellow Sticky Note at the top with this description so users see setup in-workflow Do not hardcode credentials** inside HTTP nodes; always use n8n Credentials Remove personal IDs/links before publishing Sticky Note (copy-paste) > Multi-Tab Excel Builder (Google Drive + Google Sheets) > This workflow generates two datasets (Code → JSON), converts each to an Excel sheet, merges them into a single workbook with multiple tabs, and optionally appends rows to Google Sheets. > > Setup (2 connections): > 1) Google Sheets (OAuth2): Create credentials → duplicate/select your target spreadsheet → set Spreadsheet + Worksheet in the node. > 2) Google Drive (OAuth2): Create credentials → choose the folder for storing/retrieving the .xlsx. > > Customize: Edit the Code nodes’ arrays, rename tab names in Convert to File, and adjust the Sheets node mapping as needed. Troubleshooting Missing columns / wrong order:* Use *Manual mapping** in the Google Sheets node Binary not found:* Ensure each *Convert to File* node’s binaryPropertyName matches what *Merge** expects Permissions errors:** Re-authorize Google credentials; confirm you have edit access to the target Sheet/Drive folder 📬 Contact Need help customizing this (e.g., filtering by campaign, sending reports by email, or formatting your PDF)? 📧 rbreen@ynteractive.com 🔗 https://www.linkedin.com/in/robert-breen-29429625/ 🌐 https://ynteractive.com

Use case This workflow snoozes any Todoist tasks, by moving them into a Snoozed todoist list and unsnoozes them 3 days before due date. Helps keep inbox clear only of tasks you need to worry about soon. How to setup Add your Todoist creds Create a Todoist project called snoozed Set the project ids in the relevant nodes Add due dates to your tasks in Inbox. Watch them disappear to snoozed. Set their date to tomorrow, watch it return to inbox. How to adjust this template Adjust the timeline.. Maybe 3 days is too close for you. Works mostly for me :)

AI Real Estate Agent with OpenRouter and SrpAPI to talk with property objects from propertyfinder.ae This n8n template demonstrates a simple AI Agent that can: Scrape information from a provided propertyfinder.ae listing link. Answer questions about a specific property using the scraped information. Use SerpAPI to find details that are missing from the scraped data. Answer general real-estate questions using SerpAPI. Use Case This workflow serves as a starting point for building complex AI assistants for real estate or other domains. See the demo video Potential Enhancements Expand Knowledge:** Augment the workflow with your own knowledge base using a vector database (RAG approach). Add More Sources:** Adapt the scraper to support other real estate websites. Optimize Speed:** Add a cache for scraped data to reduce response latency. Improve Context Handling:** Implement reliable persistence to track the current listing instead of iterating through conversation history. Customize Prompts:** Write more tailored prompts for your specific needs (the current one is for demonstration only). Integrate Channels:** Connect the workflow to communication channels like Instagram, Telegram, or WhatsApp. How It Works The workflow is triggered by a "When chat message received" node for simple demonstration. The Chat Memory Manager node extracts the last 30 messages for the current session. A code node finds the property link, first by checking the most recent user message and then by searching the conversation history. If a link is found, an HTTP Request node scrapes the HTML content from the listing page. The Summarize code node parses the HTML, retrieves key information, and passes it to the AI Agent as a temporary knowledge base. The final AI Agent node answers user queries using the scraped knowledge base and falls back to the SerpAPI tool when information is missing. How to Use You can test this workflow directly in n8n or integrate it into any social media channel or your website. The AI Agent node is configured to use OpenRouter. Add your OpenRouter credentials, or replace the node with your preferred LLM provider. Add your SerpAPI key to the SerpAPI tool within the AI Agent node. Requirements An API key for OpenRouter (or credentials for your preferred LLM provider). A SerpAPI key. You can get one from their website; a free plan is available for testing. Need Help Building Something More? Contact me on: Telegram:** @ninesfork LinkedIn:** George Zargaryan Happy Hacking! 🚀

How It Works This workflow automates end-to-end concert ticket booking validation and fan experience management using two coordinated AI agents. It is designed for ticketing platforms, event operators, and venue operations teams that must prevent fraud, manage inventory accurately, and deliver seamless customer communication at scale. The workflow solves key challenges such as duplicate bookings, bot-driven purchases, payment failures, overbooking, and inconsistent refund handling. When a booking request enters via webhook, inventory data is fetched and normalized. The Ticket Validation Agent analyzes structured booking, payment authorization, pricing tier rules, seat allocation, resale restrictions, and fraud signals to produce a standardized risk classification. Based on risk level, the workflow routes transactions for auto-approval, conditional handling, or escalation. The Fan Experience Orchestration Agent then manages confirmations, ticket system updates, waitlist activation, refund or compensation logic, and SLA-driven escalation to operations. All outcomes are merged and logged into an audit trail, ensuring compliance, transparency, and consistent service enforcement while minimizing manual review. Setup Steps Add OpenAI/Nvidia API credentials in n8n. Configure ticketing system HTTP endpoint. Connect Gmail for confirmations. Connect Slack for escalation alerts. Connect Google Sheets for audit logging. Define risk thresholds and SLA rules. Prerequisites n8n account, OpenAI API key, ticketing API access, Gmail, Slack, Google Sheets Use Cases Concert ticket sales, festival booking systems, venue seat management, VIP allocation handling Customization Adjust fraud thresholds, add SMS notifications, integrate CRM, extend loyalty logic Benefits Reduces fraud, automates fan communication, enforces ticketing policies