Workflow Templates

This workflow allows you to send SMS messages globally using API without needing a physical phone number. 1. How It Works Consists of three main nodes: Manual Trigger**: The workflow starts when you click the "Test workflow" button in n8n. Set SMS Data**: This node defines the SMS message content and the recipient's phone number (including the international prefix). Send SMS**: This node sends the SMS using the ClickSend API. It uses HTTP Basic Authentication with your ClickSend credentials and sends a POST request to the ClickSend API endpoint with the message and recipient details. The workflow is simple and efficient, making it easy to automate SMS sending for various use cases, such as notifications, alerts, or marketing campaigns. 2. Set Up Steps To set up and use this workflow in n8n, follow these steps: Register on ClickSend: Go to ClickSend and create an account. Obtain your API Key and take advantage of the 2 € free credits provided. Set Up Basic Authentication in n8n: In the "Send SMS" node, configure the HTTP Basic Auth credentials: Username: Use the username you registered with on ClickSend. Password: Use the API Key provided by ClickSend. Configure the SMS Data: In the "Set SMS data" node, define: The message content (e.g., "Hi, this is my first message"). The recipient's phone number, including the international prefix (e.g., +39xxxxxxxx). Test the Workflow: Click the "Test workflow" button in n8n to trigger the workflow. The workflow will send the SMS using the ClickSend API, and you should receive the message on the specified phone number. Optional Customization: You can modify the workflow to dynamically set the message content or recipient phone number using data from other nodes or external sources. This workflow is a quick and efficient way to send SMS messages programmatically. Need help customizing? Contact me for consulting and support or add me on Linkedin.

This is an example that gets the logo, icon, and information of a company and stores it in Airtbale. You can set the values that you want to store in the Set node. If you want to store the data in a different database (Google Sheet, Postgres, MongoDB, etc.) replace the Airtable node with that node. You can refer to the documentation to learn how to build this workflow from scratch.

This example workflow allows you to create, update, and get a document in Google Cloud Firestore. The workflow uses the Set node to set the data, however, you might receive data from a different source. Add the node that receives the data before the Set node and set the values you want to insert in a document, in the Set node. Also, update the Columns/ attributes fields in the Google Cloud Firestore node.

This is the first of 4 workflows for a Mattermost Standup Bot. This workflow will create a default configuration file. You can set the default configuration in the Set node (Use Default Config) the values are: config.slashCmdToken - The token Mattermost provides when you make a new Slash Command config.mattermostBaseUrl - The base URL for your Mattermost instance config.botUserToken - The User token for your Mattermost bot config.n8nWebhookUrl - The URL for your "Action from MM" webhook in the "Standup Bot - Worker" workflow config.botUserId - The UserID for your Mattermost Bot user The config file is saved under /home/node/.n8n/standup-bot-config.json This workflow only needs to be run once manually as part of the setup .

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. The third is 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. This pipeline 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] KNN classification tool This tool takes any image URL, and as output, it returns a class of the object on the image based on the image uploaded to the Qdrant dataset (lands). An image URL is received via the Execute Workflow Trigger, which is then sent to the Voyage AI Multimodal Embeddings API to fetch its embedding. The image's embedding vector is then used to query Qdrant, returning a set of X similar images with pre-labeled classes. Majority voting is done for classes of neighbouring images. A loop is used to resolve scenarios where there is a tie in Majority Voting, and we increase the number of neighbours to retrieve. When the loop finally resolves, the identified class is returned to the calling workflow.

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