Workflow Templates

Daily YouTrack In-Progress Tasks Summary to Discord by Assignee Keep your team in sync with a daily summary of tasks currently In Progress in YouTrack — automatically posted to your Discord channel. This workflow queries issues, filters them by status, groups them by assignee and priority, and sends a formatted message to Discord. It's perfect for teams that need a lightweight, automated stand-up report. > 📝 This workflow uses Discord as an example. You can easily replace the messaging integration with Slack, Mattermost, MS Teams, or any other platform that supports incoming webhooks. Use Case Remote development teams using YouTrack + Discord Replacing daily stand-up meetings with async updates Project managers needing quick visibility into active tasks Features Scheduled** daily execution (default: weekdays at 09:00) Status filter**: only issues marked as In Progress Grouping** by assignee and priority Custom mapping** for user mentions (YouTrack → Discord) Clean Markdown output** for Discord, with direct task links Setup Instructions YouTrack Configuration Get a permanent token: Go to your YouTrack profile → Account Security → Authentication Create a new permanent token with "Read Issue" permissions Copy the token value Set the base API URL: Format: https://yourdomain.youtrack.cloud/api/issues Replace yourdomain with your actual YouTrack instance Identify custom field IDs: Method 1: Go to YouTrack → Administration → Custom Fields → find your "Status" field and note its ID Method 2: Use API call GET /api/admin/customFieldSettings/customFields to list all field IDs Method 3: Inspect a task's API response and look for field IDs in the customFields array Example Status field ID: 105-0 or 142-1 Discord Configuration Create a webhook URL in your Discord server: Server Settings → Integrations → Webhooks → New Webhook Choose target channel and copy the webhook URL Extract webhook ID from URL (numbers after /webhooks/) Environment Variables & Placeholders | Placeholder | Description | |-------------|-------------| | {{API_URL}} | Your YouTrack API base URL | | {{TOKEN}} | YouTrack permanent token | | {{FIELD_ID}} | ID of the "Status" custom field | | {{QUERY_FIELDS}} | Fields to fetch (e.g., summary, id) | | {{PROJECT_LINK}} | Link to your YouTrack project | | {{USER_X}} | YouTrack usernames | | {{DISCORD_ID_X}} | Discord mentions or usernames | | {{NAME_X}} | Display names | | {{WEBHOOK_ID}} | Discord webhook ID | | {{DISCORD_CHANNEL}} | Discord channel name | | {{CREDENTIAL_ID}} | Your credential ID in n8n | Testing the Workflow Test YouTrack connection: Execute the "HTTP Request YT" node individually Verify that issues are returned from your YouTrack instance Check if the Status field ID is correctly filtering tasks Verify filtering: Run the "Filter fields" node Confirm only "In Progress" tasks pass through Check message formatting: Execute the "Discord message" node Review the generated message content and formatting Test Discord delivery: Run the complete workflow manually Verify the message appears in your Discord channel Schedule verification: Enable the workflow Test weekend skip functionality by temporarily changing dates Customization Tips Language**: All labels/messages are in English — customize if needed User mapping**: Adjust assignee → Discord mention logic in the message builder Priorities**: Update the priorityMap to reflect your own naming structure Schedule**: Modify the trigger time in the Schedule Trigger node Alternative platforms**: Swap out the Discord webhook for another messaging service if preferred

Serve Inspirational Quotes On-Demand via Webhook using ZenQuotes API

by ist00dent

This n8n template lets you instantly serve batches of inspirational quotes via a webhook using the free ZenQuotes API. It’s perfect for developers, content creators, community managers, or educators who want to add dynamic, uplifting content to websites, chatbots, or internal tools—without writing custom backend code. 🔧 How it works A Webhook node listens for incoming HTTP requests on your chosen path. Get Random Quote from ZenQuotes sends an HTTP Request to https://zenquotes.io/api/random?count=5 and retrieves five random quotes. Format data uses a Set node to combine each quote (q) and author (a) into a single string: "“quote” – author". Send response returns a JSON array of objects { quote, author } back to the caller. 👤 Who is it for? This workflow is ideal for: Developers building motivational Slack or Discord bots. Website owners adding on-demand quote widgets. Educators or trainers sharing daily inspiration via webhooks. Anyone learning webhook handling and API integration in n8n. 🗂️ Response Structure Your webhook response will be a JSON array, for example: [ { "quote": "Life is what happens when you're busy making other plans.", "author": "John Lennon" }, { "quote": "Be yourself; everyone else is already taken.", "author": "Oscar Wilde" } ] ⚙️ Setup Instructions Import the workflow JSON into your n8n instance. In the Webhook node, set your desired path (e.g., /inspire). (Optional) Change the count parameter in the HTTP Request node to fetch more or fewer quotes. Activate the workflow. Test by sending an HTTP GET or POST to https://<your-n8n-domain>/webhook/<path>.

AI SEO Readability Audit: Check Website Friendliness for LLMs

by Leonard

Who is this for? This workflow is designed for SEO specialists, content creators, marketers, and website developers who want to ensure their web content is easily accessible, understandable, and indexable by Large Language Models (LLMs) like ChatGPT, Perplexity, and Google AI Overviews. If you're looking to optimize your site for the evolving AI-driven search landscape, this template is for you. What problem is this workflow solving? / Use case Modern AI tools often crawl websites without executing JavaScript. This can lead to them "seeing" a very different version of your page than a human user or traditional search engine bot might. This workflow helps you: Identify how much of your content is visible without JavaScript. Check for crucial on-page SEO elements that AI relies on (headings, meta descriptions, structured data). Detect if your site presents JavaScript-blocking warnings. Get an AI-generated readability score and actionable recommendations to improve AI-friendliness. What this workflow does Receives a URL via a chat interface. Sanitizes the input URL to ensure it's correctly formatted. Fetches the website's HTML content, simulating a non-JavaScript crawler (like Googlebot). Extracts key HTML features: visible text length, presence of H1/H2/H3 tags, meta description, Open Graph data, structured data (JSON-LD), and <noscript> tags. It also checks for common JavaScript-blocking messages. Performs an AI SEO Analysis using an LLM (via OpenAI) based on the extracted features. Provides a report including an AI Readability Score (0-10), a summary, actionable recommendations, and a reminder to check the robots.txt file for AI bot access. Setup Estimated setup time:** 2-5 minutes. Import this workflow into your n8n instance. Ensure you have an OpenAI account and API key. Configure the "OpenAI Chat Model" node with your OpenAI API credentials. If you don't have credentials set up yet, create new ones in n8n. Activate the workflow. Interact with the chat interface provided by the "When chat message received" trigger node (you can access this via its webhook URL). How to customize this workflow to your needs Change LLM Model:** In the "OpenAI Chat Model" node, you can select a different model that suits your needs or budget. Adjust AI Prompt:** Modify the prompt in the "AI SEO Analysis" node (Chain Llm) to change the focus of the analysis or the format of the report. For example, you could ask for more technical details or a different scoring system. User-Agent:** The "Get HTML from Website" node uses a Googlebot User-Agent. You can change this to simulate other bots if needed. JS Block Indicators:** The "Extract HTML Features" node contains a list of common JavaScript-blocking phrases. You can expand this list with other languages or specific messages relevant to your checks.

🤖AI & Machine Learning

4 nodes3761 views

AI-Powered Post-Sales Call Automated Proposal Generator

by Gerald Denor

💬Communication & Collaboration

AI-Powered Proposal Generator - Sales Automation Workflow Overview This n8n workflow automates the entire proposal generation process using AI, transforming client requirements into professional, customized proposals delivered via email in seconds. Use Case Perfect for agencies, consultants, and sales teams who need to generate high-quality proposals quickly. Instead of spending hours writing proposals manually, this workflow captures client information through a web form and uses GPT-4 to generate contextually relevant, professional proposals. How It Works Form Trigger - Captures client information through a customizable web form OpenAI Integration - Processes form data and generates structured proposal content Google Drive - Creates a copy of your proposal template Google Slides - Populates the template with AI-generated content Gmail - Automatically sends the completed proposal to the client Key Features AI Content Generation**: Uses GPT-4 to create personalized proposal content Professional Templates**: Integrates with Google Slides for polished presentations Automated Delivery**: Sends proposals directly to clients via email Form Integration**: Captures all necessary client data through web forms Customizable Output**: Generates structured proposals with multiple sections Template Sections Generated Proposal title and description Problem summary analysis Three-part solution breakdown Project scope details Milestone timeline with dates Cost integration Requirements n8n instance** (cloud or self-hosted) OpenAI API key** for content generation Google Workspace account** for Slides and Gmail Basic n8n knowledge** for setup and customization Setup Complexity Intermediate - Requires API credentials setup and basic workflow customization Benefits Time Savings**: Reduces proposal creation from hours to minutes Consistency**: Ensures all proposals follow the same professional structure Personalization**: AI analyzes client needs for relevant content Automation**: Eliminates manual copy-paste and formatting work Scalability**: Handle multiple proposal requests simultaneously Customization Options Modify AI prompts for different industries or services Customize Google Slides template design Adjust form fields for specific information needs Personalize email templates and signatures Configure milestone templates for different project types Error Handling Includes basic error handling for API failures and form validation to ensure reliable operation. Security Notes All credentials have been removed from this template. Users must configure their own: OpenAI API credentials Google OAuth2 connections for Slides, Drive, and Gmail Form webhook configuration This workflow demonstrates practical AI integration in business processes and showcases n8n's capabilities for complex automation scenarios.

4 nodes3128 views

Extract Title tag and Meta description from url for SEO analysis with Airtable

by Giacomo Lanzi

Extract Title tag and meta description from url for SEO analysis. How it works The workflows takes records from Airtable, get the url in the records and extract from the related webpage the title tag (<title>) and meta description (<meta name="description" content="Some content">). If title tag and/or meta description tag isn't available on the webpage, the result will be empty. Setup Set a Base in Airtable with a table with the following structure: url (field type url), title tag (field type text string), meta desc (field type text field) Minimum suggested table structure is: url (https://example.com), title (Title example), meta desc* (This is the meta description of the example page) Connect Airtable to both Airtable nodes in the template and, with the following formula, get all the records that miss title tag and meta desc. Formula: AND(url != "", {title tag} = "", {meta desc} = "") Insert the url to be analyzed in the table in the field url and let the workflow do the rest. Extra You can also calculate the length for title tag and meta desc using formula field inside Airtable. This is the formula: LEN({title tag}) or LEN({meta desc}) You can automate the process calling a Webhook from Airtable. For this, you need an Airtable paid plan.

AI Weather Forecasts with MCP Integration

by JaredCo

Real-time Weather Forecasts with MCP Tools This n8n workflow demonstrates how to integrate real-time weather intelligence into any automation using the Model Context Protocol (MCP). Get current conditions and 5-day forecasts with natural language queries like "What's the weather like in Miami?" or "Will it rain next Tuesday in Seattle?" - all powered by live weather data and AI. Good to know No API keys required - uses hosted MCP weather server with built-in WorldWeatherOnline integration Provides current conditions and detailed 5-day forecasts Natural language queries work for any location worldwide Powered by WorldWeatherOnline - the world's most accurate weather system Fully preconfigured and ready to run out-of-the-box Enterprise-ready with error handling and rate limiting How it works Natural Language Input**: Receives weather queries via webhook, chat, email, or voice AI Agent Processing**: n8n Agent node interprets requests and determines: Location extraction from natural language Weather data type needed (current or 5-day forecast) Response formatting preferences MCP Weather Tool**: Live hosted server provides: Real-time current conditions (temperature, humidity, wind, conditions) 5-day detailed forecasts with daily highs/lows Weather descriptions and condition codes Powered by WorldWeatherOnline's premium data Intelligent Responses**: AI formats weather data into: Conversational natural language responses Structured data for downstream automation Action-triggering data for workflows How to use Import the workflow into n8n from the template Add your preferred AI model API key to the Agent node Customize the system prompt for your specific use case Connect to your preferred input/output channels Run and start querying weather with natural language Use Cases Smart Home Automation**: "Turn on sprinklers if no rain forecast for 3 days" Travel Planning**: "Check weather for my Paris trip next week" Event Management**: "Will outdoor wedding conditions be good Saturday?" Agriculture/Farming**: "Check 5-day forecast for planting schedule" Logistics**: "Delay shipping if severe weather forecast in delivery zone" Personal Assistant**: "Should I wear a jacket today in Chicago?" Sports/Recreation**: "Surf conditions and wind forecast for weekend" Construction**: "Safe working conditions for outdoor project this week" Requirements n8n instance (cloud or self-hosted) AI model provider account (OpenAI, Anthropic, Google, etc.) Internet connection for MCP weather server access Optional: Webhook endpoints for external integrations Customizing this workflow Location Intelligence**: Add geocoding for address-to-coordinates conversion Data Storage**: Save weather history to databases for trend analysis Dashboard Integration**: Connect to Grafana, Tableau, or custom visualizations Voice Integration**: Add speech-to-text for voice weather queries Scheduling**: Set up automated daily/weekly weather briefings Conditional Logic**: Trigger different actions based on weather conditions Sample Input/Output Natural Language Queries: "What's the weather like in Miami?" "Will it rain next Tuesday in Seattle?" "5-day forecast for London" "Temperature in Tokyo tomorrow" "Weather conditions for outdoor event Saturday" Rich Responses: { "location": "Miami, FL", "current": { "temperature": "78°F", "condition": "Partly Cloudy", "humidity": "65%", "wind": "10 mph SE" }, "forecast": { "today": "High 82°F, Low 71°F, 20% rain", "tomorrow": "High 85°F, Low 73°F, Sunny" }, "ai_summary": "Perfect beach weather in Miami today! Partly cloudy with comfortable temperatures and light winds." } Why This Workflow is Unique Zero Setup Weather Data**: No API key management - MCP server handles everything World-Class Accuracy**: Powered by WorldWeatherOnline's premium weather data AI-Powered Intelligence**: Natural language understanding of complex weather queries Enterprise Ready**: Built-in error handling, rate limiting, and reliability Global Coverage**: Worldwide weather data with location intelligence Action-Oriented**: Designed for automation decisions, not just information display Transform your automations with intelligent weather awareness powered by the world's most accurate weather system! 🧪 Setup Steps ✅ The Agent node is already configured: The system prompt is included The tool endpoint is pre-set All you need to do is: Add your AI model API key to the existing Agent credential Hit run and you're done ✅ 🔗 Full project link: Github: weathertrax-mcp-agent-demo

Convert Tour PDFs to Vector Database using Google Drive, LangChain & OpenAI

by Mohan Gopal

🧩 Workflow: Process Tour PDF from Google Drive to Pinecone Vector DB with OpenAI Embeddings Overview This workflow automates the process of extracting tour information from PDF files stored in a Google Drive folder, processes and vectorizes the extracted data, and stores it in a Pinecone vector database for efficient querying. This is especially useful for building AI-powered search or recommendation systems for travel packages. Setup: Prerequisites A folder in Google Drive with PDF tour package brochures. Pinecone account + API key OpenAI API key n8n cloud or self-hosted instance Workflow Setup Steps Trigger Manual Trigger (When clicking 'Test workflow'): Used for manual testing and execution of the workflow. Google Drive Integration Step 1: Store Tour Packages in PDF Format Upload your curated tour packages containing the tours, activities and sight-seeings in PDF format into a designated Google Drive folder. Step 2: Search Folder Node: PDF Tour Package Folder (Google Drive) This node searches the designated folder for files (filter by MIME type = application/pdf if needed). Step 3: Download PDFs Node: Download Package Files (Google Drive) Downloads each matching PDF file found in the previous step. Process Each PDF File Step 4: Loop Through Files Node: Loop Over each PDF file Iterates through each downloaded PDF file to extract, clean, split, and embed. Data Preparation & Embedding Step 5: Data Loader Node: Data Loader Reads each PDF’s content using a compatible loader. It passes clean raw text to the next node. Often integrated with document loaders like pdf-loader, Unstructured, or pdfplumber. Step 6: Recursive Text Splitter Node: Recursive Character Text Splitter Splits large chunks of text into manageable segments using overlapping window logic (e.g., 500 tokens with 50 token overlap). This ensures contextual preservation for long documents during embedding. Step 7: Generate Embeddings Node: Embeddings OpenAI Uses text-embedding-3-small model to vectorize the split chunks. Outputs vector representations for each content chunk. Store in Pinecone Step 8: Pinecone Vector Store Node: Pinecone Vector Store - Store... Stores each embedding along with its metadata (source PDF name, chunk ID, etc.). This becomes the basis for fast, semantic search via RAG workflows or agents. 🛠️ Tools & Nodes Used Google Drive (Search & Download) Searches for all PDF files in a specified Google Drive folder. Downloads each file for processing. SplitInBatches (Loop Over Items) Loops through each file found in the folder, ensuring each is processed individually. Default Data Loader (LangChain) Reads and extracts text from the PDF files. Recursive Character Text Splitter (LangChain) Splits the extracted text into manageable chunks for embedding. OpenAI Embeddings (LangChain) Converts each text chunk into a vector using OpenAI’s embedding model. Pinecone Vector Store (LangChain) Stores the resulting vectors in a Pinecone index for fast similarity search and querying. 🔗 Workflow Steps Explained Trigger: The workflow starts manually for testing or can be scheduled. Google Drive Search: Finds all PDF files in the specified folder. Loop Over Files: Each file is processed one at a time using the SplitInBatches node. Download File: Downloads the current PDF file from Google Drive. Extract Text: The Default Data Loader node reads the PDF and extracts its text content. *Text Splitting: * The Recursive Character Text Splitter breaks the text into chunks (e.g., 1000 characters with 50 overlap) to optimize embedding quality. **Vectorization: **Each chunk is sent to the OpenAI Embeddings node to generate vector representations. Store in Pinecone: The vectors are inserted into a Pinecone index, making them available for semantic search and recommendations. 🚀 What Can Be Improved in the Next Version? *Error Handling: * Add error handling nodes to manage failed downloads or extraction issues gracefully. File Type Filtering: Ensure only PDF files are processed by adding a filter node. Metadata Storage: Store additional metadata (e.g., file name, tour ID) alongside vectors in Pinecone for richer search results. *Parallel Processing: * Optimize for large folders by processing multiple files in parallel (with care for API rate limits). Automated Triggers: Replace manual trigger with a time-based or webhook trigger for full automation. Data Validation: Add checks to ensure extracted text contains valid tour data before vectorization. User Feedback: Integrate notifications (e.g., email or Slack) to inform when processing is complete or if issues arise. 💡 Summary This workflow demonstrates how n8n can orchestrate a powerful AI data pipeline using Google Drive, LangChain, OpenAI, and Pinecone. It’s a great foundation for building intelligent search or recommendation features for travel and tour data. Feel free to ask for more details or share your improvements! Let me know if you want to see a specific part of the workflow or need help with a particular node!

Auto-classify Gmail emails with AI and apply labels for inbox organization

by Alex Dunlop

💬Communication & Collaboration

Who is this for? Professionals and individuals who receive high volumes of emails, those who want to automatically organize their Gmail inbox using AI classification. What problem is this workflow solving? Manual email sorting is time-consuming and inconsistent. This workflow automatically categorizes incoming emails into 8 predefined labels (To respond, FYI, Comment, Notification, Meeting update, Awaiting reply, Actioned, Marketing) to help maintain inbox zero and prioritize responses. What this workflow does Monitors Gmail for new incoming emails Uses AI to analyze email content and classify into appropriate categories Automatically applies the corresponding Gmail label Runs on a schedule to process emails consistently Setup Prerequisites n8n instance (cloud or self-hosted) Gmail account with API access enabled Access to an LLM provider (OpenAI, Anthropic Claude, or similar) Step-by-Step Configure Gmail Credentials Create Gmail Labels Configure LLM Chain Set Email Polling Schedule Test the Workflow Create Gmail Labels Before running the workflow, create these 8 labels in your Gmail account: To respond FYI Comment Notification Meeting update Awaiting reply Actioned Marketing How to customize this workflow to your needs Modify Classification Categories To change the email categories, update two places: In the AI prompt (Basic LLM Chain node): Your new category - Description of what emails fit here Another category - Description [... continue with your categories] In Gmail labels: Create corresponding labels in your Gmail account with the exact same names and numbering. Adjust Classification Rules The AI prompt contains specific rules for each category. To modify: Edit the "Key classification rules" section in the LLM prompt Add examples of emails that should go into each category Specify edge cases and how they should be handled Change Email Sources Currently monitors all incoming emails. To filter specific emails: In the Gmail Trigger node, add filters such as: from:specific-sender@domain.com subject:contains-keyword -label:already-processed You can also change this use Outlook Modify Polling Frequency More frequent**: Add multiple poll times (e.g., 9 AM, 12 PM, 6 PM) Less frequent**: Change to once daily or weekly Real-time**: Switch to webhook-based triggering (requires Gmail API setup) I choose daily for cost.

4 nodes1732 views

🤖 Build Resilient AI Workflows with Automatic GPT and Gemini Failover Chain

by Lucas Peyrin

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. How it works This workflow demonstrates how to create a resilient AI Agent that automatically falls back to a different language model if the primary one fails. This is useful for handling API errors, rate limits, or model outages without interrupting your process. State Initialization: The Agent Variables node initializes a fail_count to 0. This counter tracks how many models have been attempted. Dynamic Model Selection: The Fallback Models (a LangChain Code node) acts as a router. It receives a list of all connected AI models and, based on the current fail_count, selects which one to use for this attempt (0 for the first model, 1 for the second, etc.). Agent Execution: The AI Agent node attempts to run your prompt using the model selected by the router. The Fallback Loop: On Success: The workflow completes successfully. On Error: If the AI Agent node fails, its "On Error" output is triggered. This path loops back to the Agent Variables node, which increments the fail_count by 1. The process then repeats, causing the Fallback Models router to select the next model in the list. Final Failure: If all connected models are tried and fail, the workflow will stop with an error. Set up steps Setup time: ~3-5 minutes Configure Credentials: Ensure you have the necessary credentials (e.g., for OpenAI, Google AI) configured in your n8n instance. Define Your Model Chain: Add the AI model nodes you want to use to the canvas (e.g., OpenAI, Google Gemini, Anthropic). Connect them to the Fallback Models node. Important: The order in which you connect the models determines the fallback order. The model nodes first created/connected will be tried first. Set Your Prompt: Open the AI Agent node and enter the prompt you want to execute. Test: Run the workflow. To test the fallback logic, you can temporarily disable the First Model node or configure it with invalid credentials to force an error.

🤖AI & Machine Learning

4 nodes411 views

Auto-Generate Facebook Posts from Video Insights using Dumpling AI + GPT-4o

by Yang

💬Communication & Collaboration

What this workflow does This workflow automatically turns new technical video uploads into short, engaging Facebook post drafts—complete with a suggested image—and saves the results to Google Sheets for quick review or publishing. It’s designed to help you repurpose tutorial or demo videos into ready-to-use social content without any manual writing or design effort. What problem is this workflow solving? Manually writing Facebook posts for every new tutorial or product video takes time, especially when you want them to be engaging and consistent. This workflow solves that by using AI to watch for new videos, extract meaningful insights, and write posts and create visuals automatically—saving hours of work. Who is this for? This workflow is ideal for: Content creators uploading tutorial videos Marketing teams working with how-to or product videos Agencies and automation pros building scalable social workflows for clients How it works Trigger: Starts when a new video is uploaded to a specific Google Drive folder. Download & Convert: Downloads the video and converts it to base64. Extract Insights: Dumpling AI analyzes the video and extracts structured insights such as topic, tools mentioned, and key steps. Generate Post: GPT-4o creates a short, friendly Facebook post using those insights, along with an image prompt. Create Visual: Dumpling AI generates an image using the prompt. Save to Sheet: The Facebook post and image URL are saved to a Google Sheet. Setup Create a Google Sheet to store the posts and images. Connect your Google Drive, Google Sheets, Dumpling AI, and OpenAI credentials in n8n. Update the workflow with: Your Google Drive folder ID Your target Google Sheet ID (Optional) Edit the prompt used in the GPT node if you want a different tone, style, or structure for the post. How to customize the workflow Change the platform**: Replace “Facebook” in the prompt with LinkedIn, Instagram, or another platform. Use a different image tool**: You can swap Dumpling AI for any other image generation API (e.g. DALL·E, Midjourney via webhook). Add auto-publishing**: Add a Facebook or social media module to publish the generated post directly instead of just saving to Google Sheets. Tag videos by content type**: Use AI to classify videos into categories and store them in separate tabs or sheets.

4 nodes178 views

Build a Restaurant Voice Assistant with VAPI and PostgreSQL for Bookings & Orders

by Oneclick AI Squad