Workflow Templates

Make your unstructured large documents LLM ready markdown using LandingAI Document Parsing. Automatically watches a Google Drive folder, submits new documents to Landing.ai for parsing, caches processed files in - Supabase to avoid reprocessing, and reliably polls results with retry and timeout handling. Use Cases Automated document ingestion for RAG pipelines Invoice, contract, or report parsing AI-powered document analysis workflows Knowledge base ingestion from Google Drive Preventing duplicate document processing in ETL pipelines External services: Google Drive Landing.ai Supabase Credentials Required Required Google Drive OAuth2 Landing.ai API (HTTP Bearer Token) Supabase API How it works Once the pdf land in google drive location it trigger and it convert pdf (even more then 200 pages to LLM ready markdown). It also check in database if the parsing is already done or not, this help to avoid any unnecessary landingAI api call. Setup Instructions Step 1: Google Drive Create or select a folder in Google Drive Copy the folder ID Update the Google Drive Trigger node with this folder ID Step 2: Landing.ai Create a Landing.ai account Generate an API key Add it in n8n as an HTTP Bearer Auth credential Update the organization-id header if required Step 3: Supabase Create a Supabase project Create a table named landing_parse_cache Add fields such as: file_id document_name mime_type file_size_bytes job_id job_status markdown uploaded_at workflow_run_id Connect Supabase credentials in n8n Expected Input A document uploaded into the configured Google Drive folder (PDF, DOCX, or other supported formats) Expected Output Parsed markdown content stored in Supabase Metadata including: File ID File name MIME type File size Job ID Processing status Early exit if the document already exists in cache Error Handling & Edge Cases Cache check to prevent duplicate processing Retry-based polling for async job completion Timeout detection for stuck jobs Large file output URL handling Detailed logging for debugging and audits Customization Ideas Push parsed output to a vector database Trigger Slack or email notifications Store results in cloud storage (S3, GCS) Extend into a RAG or AI agent pipeline Categories Document Processing AI & LLM Knowledge Management Automation Difficulty Level Advanced Happy Automating - from Alok

🚀 Build Your Own Knowledge Chatbot Using Google Drive Create a smart chatbot that answers questions using your Google Drive PDFs—perfect for support, internal docs, education, or research. 🛠️ Quick Setup Guide** Step 1: Prerequisites n8n instance (cloud or self-hosted) Google Drive account (with PDFs) Supabase account (vector database) OpenAI API key PostgreSQL database (for chat memory) else remove the node Step 2: Supabase Setup Create supabase account (its free) Create a project Copy the sql and paste it in supabase sql editor -- Enable the pgvector extension to work with embedding vectors create extension vector; -- Create a table to store your documents create table documents ( id bigserial primary key, content text, -- corresponds to Document.pageContent metadata jsonb, -- corresponds to Document.metadata embedding vector(1536) -- 1536 works for OpenAI embeddings, change if needed ); -- Create a function to search for documents create function match_documents ( query_embedding vector(1536), match_count int default null, filter jsonb DEFAULT '{}' ) returns table ( id bigint, content text, metadata jsonb, similarity float ) language plpgsql as $$ #variable_conflict use_column begin return query select id, content, metadata, 1 - (documents.embedding <=> query_embedding) as similarity from documents where metadata @> filter order by documents.embedding <=> query_embedding limit match_count; end; $$; Step 3: Import & Configure n8n Workflow Import this template into n8n Add credentials: OpenAI API key Google Drive OAuth2 Supabase URL & service key PostgreSQL connection Set your Google Drive folder ID in triggers Step 4: Test & Use Add a PDF to your Drive folder → check Supabase for new entries Start the workflow and chat → ask questions about your documents. "What can you help me with?" Multi-turn chat → context is maintained per user ⚡ Features Auto-syncs new/updated PDFs from Google Drive Extracts, chunks, and vectorizes text Finds relevant info and answers questions Maintains chat history per user 📝 Troubleshooting Check folder permissions & IDs if no docs found Verify API keys & Supabase setup for errors Ensure PostgreSQL is connected for chat memory Tags: RAG, Chatbot, Google Drive, Supabase, OpenAI, n8n Setup Time: ~20 minutes

Build an intelligent WhatsApp assistant that automatically responds to customer messages using AI. This template uses the Evolution API community node for WhatsApp integration and OpenAI for natural language processing, with built-in conversation memory powered by Redis to maintain context across messages. > ⚠️ Self-hosted requirement: This workflow uses the Evolution API community node, which is only available on self-hosted n8n instances. It will not work on n8n Cloud. What this workflow does Receives incoming WhatsApp messages via Evolution API webhook Filters and processes text, audio, and image messages Transcribes audio messages using OpenAI Whisper Analyzes images using GPT-4 Vision Generates contextual responses with conversation memory Sends replies back through WhatsApp Who is this for? Businesses wanting to automate customer support on WhatsApp Teams needing 24/7 automated responses with AI Developers building multimodal chat assistants Companies looking to reduce response time on WhatsApp Setup instructions Evolution API: Install and configure Evolution API on your server. Create an instance and obtain your API key and instance name. Redis: Set up a Redis instance for conversation memory. You can use a local installation or a cloud service like Redis Cloud. OpenAI: Get your API key from platform.openai.com with access to GPT and Whisper models. Webhook: Configure your Evolution API instance to send webhooks to your n8n webhook URL. Customization options Modify the system prompt in the AI node to change the assistant's personality and responses Adjust the Redis TTL to control how long conversation history is retained Add additional message type handlers for documents, locations, or contacts Integrate with your CRM or database to personalize responses Credentials required Evolution API credentials (self-hosted) OpenAI API key Redis connection

🏥 Clinic WhatsApp Customer Service Bot This workflow automates patient communication for medical clinics using the WhatsApp Business API. It supports appointment booking, rescheduling, service inquiries, follow-ups, and document submissions. The workflow includes AI capabilities, appointment management, human escalation logic, memory storage, and CRM synchronization. Good to know Supports text, voice notes, images, and document uploads. Uses an AI agent powered by GPT-4o-mini with retrieval-augmented generation for accurate answers. Includes sentiment and frustration detection to trigger human takeover. Conversation history and lead details are stored for context and follow-up. Appointment booking includes slot validation to reduce errors and conflicts. How it works The workflow receives WhatsApp messages through a webhook connection. The AI agent processes the message and identifies the intent: 📅 Appointment booking or rescheduling ❓ Service or doctor inquiry 📎 Document submission (e.g., lab results, insurance) 🤝 Human support request If the request is informational, the AI responds using GPT-4o-mini with RAG from Pinecone to ensure clinic-specific accuracy. If the request relates to booking, the workflow: Checks availability in Data Tables Validates slot selection Confirms, updates, or cancels the appointment If the user is confused, frustrated, or explicitly asks for a human, automation is paused and a staff member is notified. Voice messages are transcribed using Whisper API and images are processed using Vision API. All interactions are logged and synced to Google Sheets for CRM tracking. Requirements WhatsApp Business API access with active credentials OpenAI API key for GPT-4o-mini, Whisper, and Vision models Pinecone account for vector storage Google Sheets and Gmail for logging and notifications n8n instance (Cloud or self-hosted) Data Tables enabled for memory, appointments, and lead management

This n8n template from Intuz provides a complete and automated solution to transform your team's inbox management. It acts as an intelligent agent that reads incoming Gmail messages, uses AI to determine their category, and automatically routes them to the correct Slack channel—even creating new channels on the fly for new topics. Who's this workflow for? Customer Support Teams Sales & Lead Management Teams Operations & Project Management Teams Any team that uses Slack as a central hub for communication and triaging tasks. How it works 1. Monitor New Emails: The workflow continuously checks a specified Gmail account for new, unread emails. It automatically filters out spam, drafts, and duplicates. 2. AI Categorization: Each new email's subject and body are sent to an AI model (like Llama 3 via OpenRouter). The AI analyzes the content and assigns a category based on a predefined list (e.g., sales, marketing, accounts, internal). 3. Find or Create Slack Channel: The workflow then checks your Slack workspace to see if a channel corresponding to the AI's category already exists (e.g., #sales). 4. Route the Email: If the channel exists: The workflow posts a formatted summary of the email, a link to the original message in Gmail, and a "Reply" button directly into the existing channel. If the channel does NOT exist: The workflow automatically creates a new public channel (e.g., #new-category), invites a designated user, and then posts the email summary. Key Requirements to Use This Template 1. n8n Instance & Required Nodes: An active n8n account (Cloud or self-hosted). This workflow uses the official n8n LangChain integration (@n8n/n8n-nodes-langchain). If you are using a self-hosted version of n8n, please ensure this package is installed. 2. Gmail Account: An active Gmail account with API access enabled. 3. Slack Workspace & App: A Slack workspace where you have permission to install apps. A Slack App with a Bot Token that has the following scopes: channels:read, channels:manage, chat:write, groups:write, and users:read. 4. OpenRouter Account: An account with OpenRouter to access various AI models like Llama 3. You will need an API key. Setup Instructions 1. Gmail Configuration: In the "Capture Gmail Event" (Gmail Trigger) node, connect your Gmail account using OAuth2 credentials. 2. OpenRouter AI Configuration: In the "OpenRouter Chat Model" node, create a new credential and add your OpenRouter API key. 3. Slack Configuration: Create a Slack App: Go to api.slack.com/apps, create a new app, and install it to your workspace. Set Permissions: In your app's "OAuth & Permissions" settings, add the following Bot Token Scopes: channels:read, channels:manage, chat:write, groups:write, users:read. Reinstall the app to your workspace after adding them. Get Bot Token: Copy the "Bot User OAuth Token" (it starts with xoxb-). Connect in n8n: In all Slack nodes in the workflow, create a new credential and paste this Bot Token. Set User to Invite: In the "Invite a user to a channel" node, replace the placeholder User ID (U0A6ULM7CGK) with the Slack Member ID of the user you want to be automatically invited to new channels. 4. Activate the Workflow: Save the workflow and toggle the "Active" switch to ON. Your intelligent email routing system is now live! Support If you need help setting up this workflow or require a custom version tailored to your specific use case, please feel free to reach out to the template author: Website: https://www.intuz.com/services Email: getstarted@intuz.com LinkedIn: https://www.linkedin.com/company/intuz Get Started: https://n8n.partnerlinks.io/intuz For Custom Worflow Automation Click here- Get Started

📸🍽️ Telegram Food Photo → 🤖 Gemini Vision AI → 📊 Nutrition Data → 📄 Google Sheets + 🗂️ Drive What this workflow does 📸 Snap and send a photo of your meal via Telegram 🧠 Gemini Vision AI analyzes the image and estimates calories, protein, carbs, and fats 🤖 AI Agent structures the data with meal name, description, and timestamp 📄 Auto-logs nutrition data to Google Sheets for tracking 🗂️ Saves original meal photos to Google Drive with timestamped filenames 💬 Sends instant Telegram reply with full nutrition breakdown Why it's useful ⚡ Track nutrition in seconds—no manual entry or food databases 📊 Build a complete meal history with photos and macros in one place 🎯 AI estimates portion sizes and hidden ingredients (oils, sauces) 🏋️ Perfect for fitness tracking, meal prep, or health monitoring 📱 Works entirely through Telegram—no extra apps needed How it works 📲 Telegram Trigger → receives meal photo 🗂️ Google Drive → saves image with timestamp 🔎 Gemini Vision → analyzes food, estimates portions and macros 🤖 AI Agent → structures output (meal name, calories, protein, carbs, fats) 📄 Google Sheets → appends row with all nutrition data 💬 Telegram Reply → confirms with full breakdown What you'll need 🤖 Telegram Bot token 🧠 Google Gemini API key (includes Vision capabilities) 🔐 Google OAuth for Sheets + Drive 📊 Google Sheet with columns: Meal_Name, Date, Meal_description, Calories, Proteins, Carbs, Fats Setup steps 🔗 Connect credentials: Telegram, Google Gemini, Google Sheets, Google Drive 📄 Create Google Sheet with nutrition columns (see format above) 🗂️ Create Google Drive folder for meal photos 🧭 Update sheet ID and Drive folder ID in workflow 🧪 Test: send a meal photo via Telegram and check Sheet + Drive Customization ideas 📈 Daily summary: add scheduled workflow to calculate daily totals 🎯 Goal tracking: set IF conditions to alert when over/under calorie targets 📊 Charts: connect to Data Studio/Looker for visual progress tracking 🏃 Fitness integration: sync with MyFitnessPal or fitness apps Who it's for 🏋️ Fitness enthusiasts tracking macros without manual logging 🥗 Meal preppers analyzing portion sizes and nutrition 💪 Athletes monitoring calorie and protein intake 🩺 Health-conscious individuals building meal history 👨‍🍳 Nutritionists collecting client food data Quick Setup Guide - Before You Start - What You Need: 🔗 Telegram Bot (create via @BotFather) 🧠 Google Gemini API key with Vision enabled (get it here) 🔐 Google account for Sheets and Drive access 📊 Basic spreadsheet to track your meals Want help customizing? 📧 anirpoke@gmail.com 🔗 LinkedIn

This workflow automates the creation of exam questions (both open-ended and multiple-choice) from educational content stored in Google Docs, using AI-powered analysis and vector database retrieval This workflow saves educators hours of manual work while ensuring high-quality, curriculum-aligned assessments. Let me know if you'd like help adapting it for specific subjects! Use Cases Educators**: Rapidly generate quizzes, midterms, or flashcards. E-learning platforms**: Automate question banks for courses. Corporate training**: Create assessments for employee onboarding. Technical Requirements: APIs**: Google Gemini, OpenAI, Qdrant, Google Workspace. n8n Nodes**: LangChain, Google Sheets/Docs, HTTP requests, code blocks. This workflow combines AI efficiency with human-curated quality, making it a powerful tool for modern education and training. Advantages of This Workflow ✅ Fully Automated Exam Generation: From document to fully formatted quiz content with no manual intervention. ✅ Supports Comprehension and Critical Thinking: Questions are designed to go beyond factual recall, including inference and application. ✅ Uses AI and RAG for Accuracy: Ensures that answers are grounded in the document content, reducing hallucination. ✅ Seamless Google Integration: Pulls content from Google Docs and writes outputs to Google Sheets. ✅ Scalable for Any Subject: Works with any article or content domain as input. ✅ Modular and Customizable: Can be easily adapted to generate different question types or to use other LLMs or storage systems. How It Works Document Ingestion: The workflow starts by fetching an educational document (e.g., textbook chapter, lecture notes) from Google Docs. Converts the document to Markdown for structured processing. AI Processing: Splits text into chunks and generates vector embeddings (via OpenAI) for semantic analysis. Stores embeddings in Qdrant (vector database) for retrieval. Question Generation: Open-ended questions: Google Gemini AI creates 10 critical-thinking questions. Multiple-choice questions: Generates 10 MCQs (1 correct + 3 plausible distractors) using RAG to validate answers against the vector DB. Answer Validation: For open questions: Retrieves context-aware answers from the vector store. For MCQs: Ensures distractors are incorrect but believable via AI cross-checking. Output: Saves questions/answers to Google Sheets in two tabs: Open questions: Question + AI-generated answer. Closed questions: MCQ + options + correct answer. Set Up Steps Prerequisites: APIs/Accounts: Google Workspace (Docs + Sheets). OpenAI (for embeddings). Google Gemini (for question generation). Qdrant (vector DB – self-hosted or cloud). n8n Nodes: Ensure LangChain, Google Sheets/Docs, and HTTP request nodes are installed. Configure Connections: Link credentials for: Google Docs/Sheets (OAuth2). OpenAI (API key). Google Gemini (API key). Qdrant (URL + API key). Customize Input: Replace the default Google Doc ID in the "Get Doc" node with your source document. Adjust chunk size/overlap (Token Splitter node) for optimal text processing. Tweak Question Generation: Modify prompts in: "Open questions" node: Adjust criteria (e.g., difficulty, question types). "Closed questions" node: Edit MCQ formatting rules. Output Settings: Update the Google Sheet ID in "Write open" and "Write closed" nodes. Map columns in Google Sheets to match question/answer formats. Run & Automate: Trigger manually ("Test workflow") or schedule periodic runs (e.g., for updated content). Need help customizing? Contact me for consulting and support or add me on Linkedin.

How It Works: This n8n template automates the process of tracking design changes in Figma and updating relevant Jira issues. The template is triggered when a new version is created in Figma via a custom plugin. Once the version is committed, the plugin sends the design details to an n8n workflow using a webhook. The workflow then performs the following actions: Fetches the Jira issue based on the provided issue link from Figma. Adds the design changes as a comment to the Jira issue. Updates the status of the Jira issue based on the provided task status (e.g., "In Progress", "Done"). This streamlines the workflow, reducing the need for manual updates and ensuring that both the design team and developers have the latest design changes and task statuses in sync. How to Use It: Set up the Figma Plugin: Install the Figma Commit Plugin from GitHub. In the plugin, fill out the version name, design link, Jira issue link, and the task status. Commit the changes in Figma, which will trigger the webhook. Set Up the n8n Workflow: Import this template into your n8n instance. Connect the Figma Trigger node to capture version updates from Figma. Configure the Jira nodes to retrieve the issue and update the status/comment based on the data sent from the plugin. Automate: Once the version is committed in Figma, the workflow will automatically update the Jira issue and keep both your Figma design and Jira tasks in sync! By integrating Figma, Jira, and n8n through this template, you’ll eliminate manual steps, making collaboration between design and development teams more efficient.

Automated YouTube Shorts Creator with yt-dlp & FFmpeg Description How It Works • Downloads videos/music from YouTube using yt-dlp • Merges assets with dynamic text overlays • Automatically uploads to YouTube as Shorts (9:16 format) • Tracks everything in Google Sheets Set Up Steps (~10 minutes) Install yt-dlp and FFmpeg in your n8n environment Connect Google Sheets (for video/music pools) Set up YouTube OAuth credentials Configure text overlay font (NotoSerif included) Key Features Dual Pipeline System Video Downloader (MP4) + Music Downloader (MP3 with thumbnails) Random pairing for endless combinations Professional Text Overlays Dynamic line wrapping for perfect 9:16 formatting Customizable fonts/colors YouTube API Integration Automatic upload with metadata (titles/descriptions) Privacy/license controls Google Sheets Tracking Logs download paths, YouTube URLs, timestamps Prevents duplicate processing

Google Calendar AI Agent with Dynamic Scheduling Version: 1.0.0 n8n Version: 1.88.0+ Author: Koresolucoes License: MIT Description An AI-powered workflow to automate Google Calendar operations using dynamic parameters and MCP (Model Control Plane) integration. Enables event creation, availability checks, updates, and deletions with timezone-aware scheduling [[1]][[2]][[8]]. Key Features: 📅 Full Calendar CRUD: Create, read, update, and delete events in Google Calendar. ⏰ Availability Checks: Verify time slots using AVALIABILITY_CALENDAR node with timezone support (e.g., America/Sao_Paulo). 🤖 AI-Driven Parameters: Use $fromAI() to inject dynamic values like Start_Time, End_Time, and Description [[3]][[4]]. 🔗 MCP Integration: Connects to an MCP server for centralized AI agent control [[5]][[6]]. Use Cases Automated Scheduling: Book appointments based on AI-recommended time slots. Meeting Coordination: Sync calendar events with CRM/task management systems. Resource Management: Check room/equipment availability before event creation. Instructions 1. Import Template Go to n8n > Templates > Import from File and upload this workflow. 2. Configure Credentials Add Google Calendar OAuth2 credentials under Settings > Credentials. Ensure the calendar ID matches your target (e.g., ODONTOLOGIA group calendar). 3. Set Up Dynamic Parameters Use $fromAI('Parameter_Name') in nodes like CREATE_CALENDAR to inject AI-generated values (e.g., event descriptions). 4. Activate & Test Enable the workflow and send test requests to the webhook path /mcp/:tool/calendar. Tags Google Calendar Automation MCP AI Agent Scheduling CRUD Screenshots License This template is licensed under the MIT License. Notes: Extend multi-tenancy by adding :userId to the webhook path (e.g., /mcp/:userId/calendar) [[7]]. For timezone accuracy, always specify options.timezone in availability checks [[8]]. Refer to n8n’s Google Calendar docs for advanced field mappings.

Multi-functional Discord Bot with Llama AI, Image Generation, and Knowledge Base Integration 🤖🎨🧠 Overview 🔍 This workflow creates a Discord bot that can: Monitor Discord messages from specific users 👀 Process different media types (images, audio, text) 🔎 Analyze images using AI 🖼️ Transcribe audio files 🎤 Generate responses using Llama AI 🦙 Create images from text prompts using Gemini AI 🎨 Prerequisites ✅ n8n automation platform 💻 API keys for Discord, Groq, Google/Gemini, and SerpAPI 🔑 Ollama setup for Llama language model 🧠 Main Workflow Components 🛠️ Message Monitoring System 📨 Set up a Discord receiver to monitor messages in your server 💬 Add a filter to only process messages from specific users 🔍 Create a wait timer to control how often the bot checks for new messages ⏱️ Media Type Detection 🔄 Create a system that detects what kind of content was shared: Audio files (by checking for waveform data) 🎵 Images (by checking content type) 🖼️ Text (default if no media detected) 💬 Add special detection for image creation commands 🎭 Image Processing 🖼️ Fetch the image from Discord 📥 Convert the image to a format the AI can understand 🔄 Send the image to Groq for analysis 🔍 Return the AI's description back to Discord 📤 Audio Processing 🎵 Fetch the audio file from Discord 📥 Send it to Groq's audio transcription service 🎤 Process the transcribed text with the AI assistant 🧠 Return the response to Discord 📤 Text Processing 💬 Send the text to an AI agent powered by Llama 🦙 Connect the agent to memory to maintain conversation context 🧠 Add knowledge tools like Wikipedia and search capabilities 🔍 Return the AI's response to Discord, with optional text-to-speech 🔊 Image Generation 🎨 Process the user's image creation request ✏️ Use an AI agent to refine the prompt for better results ✨ Send the enhanced prompt to Gemini for image generation 🖌️ Extract the generated image and post it to Discord 📤 Connecting the Components 🔗 Set up routing between components based on content type 🔀 Ensure all processes loop back to the message monitoring system ♻️ Add wait timers between operations to avoid rate limits ⏱️ Testing Tips 🐛 Test each type of content separately 🧪 Verify API connections and authentication 🔐 Check if responses are appropriate and timely ⏰ Optimization Suggestions ⚡ Adjust wait times based on your usage patterns ⏱️ Add more specific filters for message detection 🔍 Consider implementing caching for frequent requests 💾 Monitor performance and adjust as needed 📈 This Discord bot combines multiple AI services into a seamless experience, allowing users to interact with various AI capabilities through simple Discord messages. The modular design makes it easy to expand or modify specific features as needed! 🚀

What is this workflow? This n8n template automates the process of adding an AI-generated summary at the top of your WordPress posts. It retrieves, processes, and updates your posts dynamically, ensuring efficiency and flexibility without relying on a heavy WordPress plugin. Example of AI Summary Section How It Works Triggers → Runs on a scheduled interval or via a webhook when a new post is published. Retrieves posts → Fetches content from WordPress and converts HTML to Markdown for AI processing. AI Summary Generation → Uses OpenAI to create a concise summary. Post Update → Inserts the summary at the top of the post while keeping the original excerpt intact. Data Logging & Notifications → Saves processed posts to Google Sheets and notifies a Slack channel. Why use this workflow? ✅ No need for a WordPress plugin → Keeps your site lightweight. ✅ Highly flexible → Easily connect with Google Sheets, Slack, or other services. ✅ Customizable → Adapt AI prompts, formatting, and integrations to your needs. ✅ Smart filtering → Ensures posts are not reprocessed unnecessarily. 💡 Check the detailed sticky notes for setup instructions and customization options!