Workflow Templates

Overview This workflow helps you compare Claude 3.5 Sonnet and Gemini 2.0 Flash when extracting data from a PDF This workflow extracts and processes the data within a PDF in one single step, instead of calling an OCR and then an LLM” How it works The initial 2 steps download the PDF and convert it to base64. This base64 string is then sent to both Claude 3.5 Sonnet and Gemini 2.0 Flash to extract information. This workflow is made to let you compare results, latency, and cost (in their dedicated dashboard). How to use it Set up your Google Drive if not already done Select a document on your Google Drive Modify the prompt in "Define Prompt" to extract the information you need and transform it as wanted. Get a Claude API key and/or Gemini API key Note that you can deactivate one of the 2 API calls if you don't want to try both Test the Workflow

This workflow helps you stay updated with daily launches on Product Hunt. It automatically fetches product details (name, tagline, description, and website), checks if the website redirects to another URL, and logs the final information into a Google Sheet. Perfect for indie hackers, product managers, content curators, and anyone tracking daily launches. How It Works Schedule Trigger – Runs the workflow daily. Set Date – Captures today’s date in ISO format for filtering Product Hunt posts. HTTP Request (Product Hunt API) – Retrieves Product Hunt posts for the day using GraphQL. Extract Product Info (Code Node) – Parses the response to pull key details: Name Tagline Description Website URL HTTP Request (URL Check) – Follows each website URL to detect if it redirects. Merge Data – Combines product info with the final destination URL. Google Sheets Node – Appends all processed product info to your sheet. Pre-conditions A valid Product Hunt API token A Google account with access to Google Sheets A Google Sheet already created with the correct columns (see below) Connected Google Sheets and HTTP credentials in n8n Google Sheets Setup Your spreadsheet should include the following columns (in order): Name Tagline Description Original URL Final URL (after redirect) Ensure your Google Sheets node uses the correct Spreadsheet ID and Sheet Name. Setup Instructions Product Hunt API Auth: Replace {{YOUR_PRODUCT_HUNT_API_KEY}} in the HTTP Request headers: { "Authorization": "Bearer {{YOUR_PRODUCT_HUNT_API_KEY}}" } Google Sheets Node: Connect your Google account. Insert your Spreadsheet ID in the settings. Specify the sheet name (e.g., Daily Launches). Use the “Append” operation and map the 5 data fields accordingly. Notes Only fetches the first 10 posts for the day (can be extended). Consider adding Slack, Discord, or Email nodes to notify you of new entries. Useful for building launch databases, research, or content inspiration.

Who’s it for: Sales teams, marketers, and analysts who need to quickly access all the social media and public profile links for any company. How it works / What it does: When you enter a company into the form, this workflow automatically searches for and collects all available links to the company’s social media accounts, review sites, and public profiles from sources like Crunchbase and Zoominfo. All discovered URLs are added directly to your Google Sheet. How to set up: Create an Extruct account at www.extruct.ai/. Open the Extruct table template, find the table ID in your browser’s address bar, and copy it. Make a copy of the provided Google Sheets template to your own Google Drive. In n8n, paste the table ID into the variables node of your flow. Set up Bearer authentication in every HTTP Request node using your Extruct API token (found on the API page in Extruct). In the Google Sheets node, paste the link to your copied template and connect your Google account. Run the flow once to load the fields, then map the output fields to the correct columns in your sheet. Activate the flow and start adding companies via the form. Requirements: Extruct account and API token Extruct table template Google account with Google Sheets How to customize the workflow: You can add your own columns to the Extruct table and your Google Sheet. Just add the new column in both places and map it in the Google Sheets node in n8n.

Crypto Price Alert – n8n Workflow A simple and effective crypto alert system for anyone who wants to stay up to date with coin price changes — without refreshing charts all day. This workflow checks the current price of your chosen cryptocurrency (via CoinGecko) and sends you an alert on Discord if it goes above or below your target range. It’s lightweight, easy to set up, and runs on autopilot. What the Workflow Does Checks the live price of a selected coin using the CoinGecko API. Compares it to the max/min prices you define manually. Decides if the price is too high or too low. Sends an alert message to Discord depending on the result. How It Works The flow is triggered manually or on a schedule (your choice). It pulls the current price of the coin you set. Compares that price with your min and max values. Sends a “high” or “low” message to your Discord webhook. Setup Steps Enter your coin ID and price thresholds in the “Set Low and High” node. Paste your Discord webhook URLs in the "Message High" and "Message Low" nodes. Optional: Adjust the schedule trigger to run every X minutes/hours. Run once manually to test — takes under 1 minutes. Full instructions and config tips are in sticky notes inside the workflow.

This n8n workflow demonstrates how to automate indexing of images to build a object-based image search. By utilising a Detr-Resnet-50 Object Classification model, we can identify objects within an image and store these associations in Elasticsearch along with a reference to the image. How it works An image is imported into the workflow via HTTP request node. The image is then sent to Cloudflare's Worker AI API where the service runs the image through the Detr-Resnet-50 object classification model. The API returns the object associations with their positions in the image, labels and confidence score of the classification. Confidence scores of less the 0.9 are discarded for brevity. The image's URL and its associations are then index in an ElasticSearch server ready for searching. Requirements A Cloudflare account with Workers AI enabled to access the object classification model. An ElasticSearch instance to store the image url and related associations. Extending this workflow Further enrich your indexed data with additional attributes or metrics relevant to your users. Use a vectorstore to provide similarity search over the images.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Description This workflow automatically collects daily trending topics from Twitter and YouTube, filters them for relevance, and uses an AI model (such as Mistral Cloud or another OpenAI-compatible API) to generate engaging social media hashtags. The final results, including source platform and date, are saved into a connected Google Sheet for easy access, tracking, or team collaboration. Ideal for content creators, marketers, and social media managers, this automation eliminates the manual effort of trend research and hashtag writing by combining real-time scraping with LLM-powered generation. The result is a scalable, daily strategy tool to stay aligned with what’s trending across major platforms. How It Works Daily Trigger Starts the workflow automatically on a daily schedule. Trend Scraping Scrapes current trending content from Twitter and YouTube using the Crawl and Scrape community node. Filtering & Slicing Removes irrelevant or duplicate entries and limits each platform’s list to top-performing trends. Merge Trends Combines Twitter and YouTube trends into a single dataset. AI Hashtag Generation Sends each trend topic to an AI model to generate relevant hashtags. Output to Google Sheets Loops through AI results and writes them to a Google Sheet, including trend, platform, hashtags, and timestamp. Setup Instructions Estimated time: 10–15 minutes Prerequisites A self-hosted instance of n8n (required for community nodes) API key for Mistral Cloud or any OpenAI-compatible LLM Google Sheets account connected via OAuth2 credentials Twitter and YouTube trend URLs (or scraping logic for target regions) Template Image: Example: Crawl and Scrape Node for Twitter Trends You can use the following configuration in the Crawl and Scrape node to extract Twitter trends from Trends24) { "parameters": { "url": "https://trends24.in/", "selectors": [ { "label": "Twitter Trends", "selector": ".trend-card__list li a", "type": "text" } ] }, "name": "Scrape Twitter Trends", "type": "n8n-nodes-crawl-and-scrape.crawlAndScrape", "typeVersion": 1, "position": [300, 200] } Google Sheet Column Format Column A: Generated Hashtags

How it works Transform medical documents into structured data using Google Gemini AI with enterprise-grade accuracy. Classifies document types (receipts, prescriptions, lab reports, clinical notes) Extracts text with 95%+ accuracy using advanced OCR Structures data according to medical taxonomy standards Supports multiple languages (English, Chinese, auto-detect) Tracks processing costs and quality metrics automatically Set up steps Prerequisites Google Gemini API key (get from Google AI Studio) Quick setup Import this workflow template Configure Google Gemini API credentials in n8n Test with a sample medical document URL Deploy your webhook endpoint Usage Send POST request to your webhook: { "image_url": "https://example.com/medical-receipt.jpg", "expected_type": "financial", "language_hint": "auto" } Get structured response: json{ "success": true, "result": { "documentType": "financial", "metadata": { "providerName": "Dr. Smith Clinic", "createdDate": "2025-01-06", "currency": "USD" }, "content": { "amount": 150.00, "services": [...] }, "quality_metrics": { "overall_confidence": 0.95 } } } Use cases Healthcare Organizations Medical billing automation - Process receipts and invoices automatically Insurance claim processing - Extract data from claim documents Clinical documentation - Digitize patient records and notes Data standardization - Consistent structured output format System Integrators EMR integration - Connect with existing healthcare systems Workflow automation - Reduce manual data entry by 90% Multi-language support - Handle international medical documents Quality assurance - Built-in confidence scoring and validation Supported Document Types Financial: Medical receipts, bills, insurance claims, invoices Clinical: Medical charts, progress notes, consultation reports Prescription: Prescriptions, medication lists, pharmacy records Administrative: Referrals, authorizations, patient registration Diagnostic: Lab reports, test results, screening reports Legal: Medical certificates, documentation forms

Who is this for? This workflow is perfect for eCommerce teams, market researchers, and product analysts who want to track or extract product information from websites that restrict scraping tools. It’s also useful for virtual assistants handling product comparison tasks. What problem is this workflow solving? Many eCommerce and retail sites use dynamic content or anti-bot protections that make traditional scraping methods unreliable. This workflow bypasses those issues by taking a screenshot of the full page, using OCR to extract visible text, and summarizing product information with GPT-4o—all fully automated. What this workflow does This workflow monitors a Google Sheet for new URLs. Once a new link is added, it performs the following steps: Trigger on New URL in Sheet – Watches for new rows added to a Google Sheet. Screenshot URL via Dumpling AI – Sends the URL to Dumpling AI’s screenshot endpoint to capture a full-page image of the product webpage. Save Screenshot to Drive Folder – Uploads the screenshot to a specific Google Drive folder for reference or logging. Extract Text from Screenshot with Dumpling AI – Uses Dumpling AI’s image-to-text endpoint to pull all visible content from the screenshot. Extract Product Info from Screenshot Text with GPT-4o – Sends the extracted raw text to GPT-4o, prompting it to identify structured product information such as product name, price, ratings, deals, and purchase options. Split Each Product Entry – Splits the GPT response (an array of product objects) so each product becomes an individual item for saving. Save Products info to Google Sheet – Appends each product’s structured details to a separate sheet in the same spreadsheet. Setup Google Sheet Create a Google Sheet with at least two sheets: Sheet1 should contain a header row with a column labeled URL. Sheet2 should contain headers: Product Name, price, purchased, ratings, deal, buyingOptions. Connect your Google account in both the trigger and final write-back node. Dumpling AI Sign up at Dumpling AI Create an API key and use it for both HTTP modules: Screenshot URL via Dumpling AI Extract Text from Screenshot with Dumpling AI The screenshot endpoint used is https://app.dumplingai.com/api/v1/screenshot. Google Drive Create a folder for storing screenshots. In the Save Screenshot to Drive Folder node, select the correct folder or provide the folder ID. Make sure permissions allow uploading from n8n. OpenAI Provide an API key for GPT-4o in the Extract Product Info from Screenshot Text with GPT-4o node. The prompt is structured to return structured product listings in JSON format. Split & Save Split Each Product Entry takes the array of product objects from GPT and makes each one a separate execution. Save Products info to Google Sheet writes structured fields into Sheet2 under: Product Name, price, purchased, ratings, deal, buyingOptions. How to customize this workflow Adjust the GPT prompt to return different product fields (e.g., shipping info, product categories). Use a filter node to limit which types of products get written to the final sheet. Add sentiment analysis to analyze review content if available. Replace Google Drive with Dropbox or another file storage app. Notes Make sure you monitor your API usage on both Dumpling AI and OpenAI to avoid rate limits. This setup is great for snapshot-based extraction where scraping is blocked or unreliable.

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! 🛠️

This n8n template leverages n8n's multi-form feature to build a 2 part job application submission journey which aims to eliminate the need for applicants to re-enter data found on their CVs/Resumes. How it works The application submission process starts with an n8n form trigger to accept CV files in the form of PDFs. The PDF is validated using the text classifier node to determine if it is a valid CV else the applicant is asked to reupload. A basic LLM node is used to extract relevant information from the CV as data capture. A copy of the original job post is included to ensure relevancy. Applicant's data is then sent to an ATS for processing. For our demo, we used airtable because we could attach PDFs to rows. Finally, a second form trigger is used for the actual application form. However, it is prefilled to save the applicant's time and allow them to amend any of the generated application fields. How to use Ensure to change the redirect URL in the form ending node to use the host domain of your n8n instance. Requirements OpenAI for LLM Airtable to capture applicant data Customising the workflow Application form is pretty basic for this demonstration but could be extended to ask more in-depth questions. If it fits the job, why not ask applicants to upload portfolio works and have AI describe/caption them.

Elevate your Stripe workflows with an AI agent that intelligently, securely, and interactively handles essential Stripe data operations. Leveraging the Kimi K2 model via OpenRouter, this n8n template enables safe data retrieval. From fetching summarized financial insights to managing customer discounts, while strictly enforcing privacy, concise outputs, and operational boundaries. 🧾 Requirements Stripe: Active Stripe account API key with read and write access. n8n: Deployed n8n instance (cloud or self-hosted) OpenRouter: Active OpenRouter account with credit API key from OpenRouter 🔗 Useful Links Stripe n8n Stripe Credentials Setup OpenRouter 🚦 Workflow Breakdown Trigger: User Request Workflow initiates when an authenticated user sends a message in the chat trigger. AI Agent (Kimi K2 OpenRouter): Intent Analysis Determines whether the user wants to: List customers, charges, or coupons Retrieve the account’s balance Create a new coupon in Stripe Filters unsupported or unclear requests, explaining permissions or terminology as needed. Stripe Data Retrieval For data queries: Only returns summarized, masked lists (e.g., last 10 transactions/customers) Sensitive details, such as card numbers, are automatically masked or truncated Never exposes or logs confidential information Coupon Creation When a coupon creation is requested: AI agent collects coupon parameters (discount, expiration, restrictions) Clearly summarizes the action and requires explicit user confirmation before proceeding Creates the coupon upon confirmation and replies with only the public-safe coupon details 🛡️ Privacy & Security No data storage:** All responses are ephemeral; sensitive Stripe data is never retained. Strict minimization:** Outputs are tightly scoped; only partial identifiers are shown and only when necessary. Retention rules enforced:** No logs, exports, or secondary storage of Stripe data. Confirmation required:** Actions modifying Stripe (like coupon creation) always require the user to approve before execution. Compliance-ready:** Aligned with Stripe and general data protection standards. ⏱️ Setup Steps Setup time: 10–15 minutes Add Stripe API credentials in n8n Add the OpenRouter API credentials in n8n and select your desired AI model to run the agent. In our template we selected Kimi K2 from Moonshot AI. ✅ Summary This workflow template connects a privacy-prioritized AI agent (Kimi K2 via OpenRouter) with your Stripe account to enable: Fast, summarized access to customer, transaction, coupon, and balance data Secure, confirmed creation of discounts/coupons Complete adherence to authorization, privacy, and operational best practices 🙋‍♂️ Need Help? Feel free to contact us at 1 Node Get instant access to a library of free resources we created.

An AI-powered chat assistant that analyzes Azure virtual machine activity and generates detailed timeline reports showing VM state changes, performance metrics, and operational events over time. How It Works The workflow starts with a chat trigger that accepts user queries about Azure VM analysis. A Google Gemini AI agent processes these requests and uses six specialized tools to gather comprehensive VM data from Azure APIs. The agent queries resource groups, retrieves VM configurations and instance views, pulls performance metrics (CPU, network, disk I/O), and collects activity log events. It then analyzes this data to create timeline reports showing what happened to VMs during specified periods, defaulting to the last 90 days unless the user specifies otherwise. Prerequisites To use this template, you'll need: n8n instance (cloud or self-hosted) Azure subscription with virtual machines Microsoft Azure Monitor OAuth2 API credentials Google Gemini API credentials Proper Azure permissions to read VM data and activity logs Setup Instructions Import the template into n8n. Configure credentials: Add Microsoft Azure Monitor OAuth2 API credentials with read permissions for VMs and activity logs Add Google Gemini API credentials Update workflow parameters: Open the "Set Common Variables" node Replace <your azure subscription id here> with your actual Azure subscription ID Configure triggers: The chat trigger will automatically generate a webhook URL for receiving chat messages No additional trigger configuration needed Test the setup to ensure it works. Security Considerations Use minimum required Azure permissions (Reader role on subscription or resource groups). Store API credentials securely in n8n credential store. The Azure Monitor API has rate limits, so avoid excessive concurrent requests. Chat sessions use session-based memory that persists during conversations but doesn't retain data between separate chat sessions. Extending the Template You can add more Azure monitoring tools like disk metrics, network security group logs, or Application Insights data. The AI agent can be enhanced with additional tools for Azure cost analysis, security recommendations, or automated remediation actions. You could also integrate with alerting systems or export reports to external storage or reporting platforms.