Workflow Templates

Extract the main idea and key takeaways from YouTube videos and turn them into Airtable content ideas 📝 Description Automatically turn YouTube videos into clear, structured content ideas stored in Airtable. This workflow pulls new video links from Airtable, extracts transcripts using a RapidAPI service, summarizes them with your favourite LLM, and logs the main idea and key takeaways—keeping your content pipeline fresh with minimal effort. ⚙️ What It Does Scans Airtable for new YouTube video links every 5 minutes. Extracts the transcript of the video using a third-party API via RapidAPI. Summarizes the content to generate a main idea and takeaways. Updates the original Airtable entry with the insights and marks it as completed. 🛠 Prerequisites Before using this template, make sure you have: ✅ A RapidAPI account with access to the youtube-video-summarizer-gpt-ai API. ✅ A valid RapidAPI key. ✅ An OpenAI, Claude or Gemini account connected to n8n. ✅ An Airtable account with a base and table ready. 🧰 Setup Instructions Clone this template into your n8n workspace. Open the Get YouTube Sources node and configure your Airtable credentials. In the Get video transcript node: Enter your X-RapidAPI-Key under headers. The API endpoint is pre-configured. Connect your LLM credentials to the Extract detailed summary node. (Optional) Adjust the summarization prompt in the LangChain node to better suit your tone. Set your preferred schedule in the Trigger node. 📋 Airtable Setup Create a base (e.g., Content Hub) with a table named Ideas and the following columns: | Column Name | Type | Required | Notes | |-------------|------------|----------|----------------------------| | Type | Single select | ✅ | Must be set to Youtube Video | | Source | URL | ✅ | The YouTube video URL | | Status | Checkbox | ✅ | Leave empty initially; updated after processing | | MainIdea | Single line text | ✅ | Summary generated by OpenAI | | Key Takeaways | Long text | ✅ | List of takeaways extracted from the transcript Activate the workflow—and you're done!

This n8n workflow demonstrates a simple approach to improve chat UX by staggering an AI Agent's reply for users who send in a sequence of partial messages and in short bursts. How it works Twilio webhook receives user's messages which are recorded in a message stack powered by Redis. The execution is immediately paused for 5 seconds and then another check is done against the message stack for the latest message. The purpose of this check lets use know if the user is sending more messages or if they are waiting for a reply. The execution is aborted if the latest message on the stack differs from the incoming message and continues if they are the same. For the latter, the agent receives the buffered messages up to that point and is able to respond to them in a single reply. Requirements A Twilio account and SMS-enabled phone number to receive messages. Redis instance for the messages stack. OpenAI account for the language model. Customising the workflow This workflow should work for other common messaging platforms such as Whatsapp and Telegram. 5 seconds too long or too short? Adjust the wait threshold to suit your customers.

🧠 Email real time RAG Assistant with Gmail, OpenAI & PGVector 📌 Who’s it for This workflow is ideal for: Professionals Project managers Sales and support teams Anyone managing high volumes of Gmail messages It enables fast and intelligent search through your email inbox using natural language queries. ⚙️ How it works / What it does Continuously monitors your Gmail inbox for new emails. Extracts email content and metadata (subject, body, sender, date). Converts email content into vector embeddings using OpenAI. Stores embeddings in a PostgreSQL database with PGVector. A conversational AI agent performs semantic search on your stored email history. Supports time-sensitive and context-aware responses via OpenAI Chat model. 🚀 How to set up Connect your Gmail account to the Gmail Trigger node (with API access enabled). Configure OpenAI credentials for the Embedding and Chat nodes. Set up a PostgreSQL database with the PGVector extension enabled. Import the workflow into your n8n instance (Cloud or Self-hosted). Customize parameters like polling frequency, embedding settings, or vector query depth. 📋 Requirements ✅ n8n instance (Self-hosted or Cloud) ✅ Gmail account with API access ✅ OpenAI API Key ✅ PostgreSQL database with PGVector extension installed 🛠️ How to customize the workflow Email Filtering**: Change filters in the Gmail Trigger to watch specific labels or senders. Text Splitting Granularity**: Adjust chunkSize and chunkOverlap in the text splitter node. Query Depth**: Modify topK in the vector search node to retrieve more or fewer similar results. Prompt Tuning**: Customize the system message or agent instructions in the RAG node. Workflow Extensions**: Add notifications, error logging, Slack/Telegram alerts, or data exports.

Build a 100% local RAG with n8n, Ollama and Qdrant. This agent uses a semantic database (Qdrant) to answer questions about PDF files. Tutorial Click here to view the YouTube Tutorial How it works Build a chatbot that answers based on documents you provide it (Retrieval Augmented Generation). You can upload as many PDF files as you want to the Qdrant database. The chatbot will use its retrieval tool to fetch the chunks and use them to answer questions. Installation Install n8n + Ollama + Qdrant using the Self-hosted AI starter kit Make sure to install Llama 3.2 and mxbai-embed-large as embeddings model. How to use it First run the "Data Ingestion" part and upload as many PDF files as you want Run the Chatbot and start asking questions about the documents you uploaded

💼 LinkedIn Content Machine – AI-Powered Post Generator & Scheduler for X and LinkedIn How it works: This end-to-end workflow automates your personal or brand content strategy by: 🧠 Using Google Gemini or OpenAI to generate engaging LinkedIn/X content from a title or trending posts. 🗓️ Posting directly to LinkedIn and X (formerly Twitter). 📊 Pulling high-performing LinkedIn posts to inspire new ideas. ✍️ Saving AI-generated drafts to Google Sheets for review. 🔔 Notifying your team on Slack when drafts are ready. Steps to set up: Add your API keys for Google Gemini or OpenAI. Set up your LinkedIn, X (Twitter), Google Sheets, and Slack credentials. Customize prompt logic or post filters if needed. Schedule the idea generation module or trigger it manually. Start generating and posting consistent, high-quality content with zero manual effort!

Use Case Transform and optimize images for web use: You need to host local images online You want to reduce image file sizes automatically You need image URLs for web content You want to generate and optimize AI-created images What this Workflow Does The workflow processes images through two services: Uploads images to ImgBB for hosting and URL generation (free but need API key) Optimizes images using ReSmush.it to reduce file size (free) Optional: Creates images using OpenAI's image generation Returns optimized image URLs ready for use Setup Create an ImgBB account and get your API key Add your ImgBB API key to the HTTP Request node (key parameter) Optional: Configure OpenAI credentials for image generation Connect your image input source How to Adjust it to Your Needs Skip OpenAI nodes if using your own image files Adjust image optimization parameters Customize image hosting settings Modify output format for your needs More templates and n8n workflows >>> @simonscrapes

This workflow creates an end-to-end Instagram content pipeline that automatically discovers trending content from competitor channels, extracts valuable insights, and generates new high-quality scripts for your own content creation. The system helped scale an Instagram channel from 0 to 10,000 followers in just 15 days through intelligent content repurposing. Benefits: Complete Content Automation - Monitors competitor Instagram accounts, downloads new reels, and processes them without manual intervention AI-Powered Script Generation - Uses ChatGPT and Perplexity to analyze content, identify tools/technologies, and rewrite scripts with fresh angles Smart Duplicate Prevention - Automatically tracks processed content in a database to avoid redundant work Multi-Platform Intelligence - Combines Instagram scraping, AI transcription, web research, and content generation in one seamless flow Scalable Content Strategy - Process content from multiple niches and creators to fuel unlimited content ideas Revenue-Focused Approach - Specifically designed to identify monetizable tools and technologies for business-focused content How It Works: Instagram Content Discovery: Uses Apify's Instagram scraper to monitor specified creator accounts for new reels Automatically downloads video content and metadata from target accounts Filters content based on engagement metrics and relevance Intelligent Processing Pipeline: Transcribes video content using OpenAI Whisper for accurate text extraction Filters content using AI to identify tools, technologies, and automation opportunities Cross-references against existing database to prevent duplicate processing Enhanced Research & Analysis: Searches Perplexity AI for additional insights about discovered tools Generates step-by-step usage guides and implementation instructions Identifies unique angles and opportunities for content improvement Script Generation & Optimization: Creates new, original scripts optimized for your specific audience Maintains consistent brand voice while adding fresh perspectives Includes strategic call-to-action elements for audience engagement Required Google Sheets Database Setup: Before running this workflow, create a Google Sheets database with these exact column headers: Essential Columns: id - Unique Instagram post identifier (primary key for duplicate detection) timestamp - When the reel was posted caption - Original reel caption text hashtags - Hashtags used in the post videoUrl - Direct link to download the video file username - Account that posted the reel scrapedTranscript - Original transcript from video (added by workflow) newTranscript - AI-generated script for your content (added by workflow) Additional Tracking Columns: shortCode - Instagram's internal post code url - Public Instagram post URL commentsCount - Number of comments firstComment - Top comment on the post likesCount - Number of likes videoViewCount - View count metrics videoDuration - Length of video in seconds Setup Instructions: Create a new Google Sheet with these column headers in the first row Name the sheet "Reels" Connect your Google Sheets OAuth credentials in n8n Update the document ID in the workflow nodes The merge logic relies on the id column to prevent duplicate processing, so this structure is essential for the workflow to function correctly. Business Use Cases: Content Creators - Scale content production by 10x while maintaining quality and originality Marketing Agencies - Offer content research and ideation as a premium service Course Creators - Identify trending tools and technologies for educational content Revenue Potential: This exact system can be sold as a service for $3,000-$5,000 to growing channels or agencies. The automation saves 10+ hours weekly of manual research and content planning. Difficulty Level: Intermediate Estimated Build Time: 1-2 hours Monthly Operating Cost: ~$30 (API usage) Watch the Complete Build Process Want to see exactly how this system was built from scratch? Nick Saraev walks through the entire development process in this comprehensive tutorial, including all the debugging, dead ends, and problem-solving that goes into building real automation systems. 🎥 Watch: "The N8N Instagram Parasite System (10K Followers In 15 Days)" This 1.5-hour deep-dive shows the actual build process - not a polished demo, but real system development with all the thinking and iteration included. Set Up Steps: Configure Apify Integration: Sign up for Apify account and obtain API key Replace the bearer token in the "Run Actor Synchronously" node Customize the username array with your target Instagram accounts Set Up AI Services: Add OpenAI API credentials for transcription and content generation Configure Perplexity API for enhanced research capabilities Set up appropriate rate limiting for cost control Database Configuration: Create Google Sheets database with provided column structure Connect Google Sheets OAuth credentials Configure the merge logic for duplicate detection Content Filtering Setup: Customize the AI prompts for your specific niche and requirements Adjust the filtering criteria for tool/technology detection Set up the script generation template to match your brand voice Automation Schedule: Configure the schedule trigger for daily content monitoring Set optimal timing based on your content creation workflow Test the complete flow with a small number of accounts first Advanced Customization: Add additional content sources beyond Instagram Integrate with your existing content management systems Scale up monitoring to dozens of competitor accounts More AI Automation Systems:* For more advanced automation tutorials and business systems, check out My YouTube Channel where I share proven automation strategies that generate real revenue.

Objective In industry and production sometimes machine data is available in databases. That might be sensor data like temperature or pressure or just binary information. In this sample flow reads machine data and sends an alert to your SIGNL4 team when the machine is down. When the machine is up again the alert in SIGNL4 will get closed automatically. Setup We simulate the machine data using a Notion table. When we un-check the Up box we simulate a machine-down event. In certain intervals n8n checks the database for down items. If such an item has been found an alert is send using SIGNL4 and the item in Notion is updates (in order not to read it again). Status updates from SIGNL4 (acknowledgement, close, annotation, escalation, etc.) are received via webhook and we update the Notion item accordingly. This is how the alert looks like in the SIGNL4 app. The flow can be easily adapted to other database monitoring scenarios.

Overview This workflow automates the process of sending personalized cold email sequences to your prospects. It fetches un-emailed leads from your Google Sheet, validates their email addresses, and then dispatches tailored emails according to a predefined schedule. It updates your CRM (Google Sheet) with the status of each sent email, ensuring your outreach efforts are tracked and efficient. Use Case This workflow is perfect for: Sales Teams**: Automate the delivery of multi-stage cold email campaigns to a large volume of leads. Business Development**: Nurture prospects over time with a structured email sequence. Recruiters**: Send out introductory emails to potential candidates for open positions. Marketers**: Distribute personalized outreach for events, content, or product launches. Anyone doing cold outreach**: Ensure consistent follow-up and track email performance without manual effort. How It Works Scheduled Trigger: The workflow is set to run automatically at a defined interval (e.g., every 6 hours, as currently configured by the "Set Timer" node). This ensures regular outreach without manual intervention. Fetch Unsent Emails: The "Get Emails" node queries your Google Sheet to identify prospects who haven't yet received the current email in the sequence (i.e., "Email Sent " is "No"). Control Volume: A "Limit" node can be used to control the number of emails sent in each batch, preventing you from sending too many emails at once and potentially hitting sending limits. Loop Through Prospects: The "Loop Over Items" node processes each selected prospect individually. Email Validation (Conditional Send): An "If" node checks if the prospect's "Email Address" is valid and exists. This prevents sending emails to invalid addresses, improving deliverability. Send Email: "Send Email" Node: For valid email addresses, this node dispatches the personalized email to the prospect. It retrieves the recipient's email, subject, and body from your Google Sheet. "connect" Node: (Note: The provided JSON uses a generic emailSend node named "connect" that links to an SMTP credential. This represents the actual email sending mechanism, whether it's Gmail or a custom SMTP server.) Update CRM: After successfully sending an email, the "Update Records" node updates your Google Sheet. It marks the "Email Sent " column as "Yes" and records the "Sent on" timestamp and a "Message Id" for tracking. Delay Between Sends: A "Wait" node introduces a delay between sending emails to individual prospects. This helps mimic human sending behavior and can improve deliverability. How to Set It Up To set up your Automated Cold Email Sender, follow these steps: Google Sheet Setup: Duplicate the Provided Template: Make a copy of the Google Sheet Template (1TjXelyGPg5G8lbPDI9_XOReTzmU1o52z2R3v8dYaoQM) into your own Google Drive. This sheet should contain columns for "Name", "Email Address ", "Sender Email", "Email Subject", "Email Body", "Email Sent ", "Sent on", and "Message Id". Connect Google Sheets: Ensure your Google Sheets OAuth2 API credentials are set up in n8n and linked to the "Get Emails" and "Update Records" nodes. Update Sheet IDs: In both "Get Emails" and "Update Records" nodes, update the documentId with the ID of your copied template. Email Sending Service Credentials: Gmail: If using Gmail, ensure your Gmail OAuth2 credentials are configured and connected to the "Send Email" node (or the "connect" node, if that's your chosen sender). Other Email Services (SMTP): If you use a different email service, you'll need to set up an SMTP credential in n8n and connect it to the "connect" node. Refer to the "Sticky Note4" for guidance on non-Google email services. Configure Timer: In the "Set Timer" node, adjust the hoursInterval or other time settings to define how frequently you want the email sending process to run (e.g., every 6 hours, once a day, etc.). Control Volume (Optional): In the "Limit" node, you can set the maxItems to control how many emails are processed and sent in each batch. This is useful for managing email sending limits or gradual outreach. Import the Workflow: Import the provided workflow JSON into your n8n instance. Populate Your Sheet: Fill your copied Google Sheet with prospect data, including the email subject and body for each email you wish to send. Ensure the "Email Sent " column is initially "No". Activate and Monitor: Activate the workflow. It will begin fetching and sending emails based on your configured schedule. Monitor your Google Sheet to track the "Email Sent " status. This workflow provides a robust and automated solution for managing your cold email campaigns, saving you time and increasing your outreach efficiency.

How it works Automatic Detection: Instantly identifies abandoned carts via webhook from your e-commerce store. Progressive Sequence: Automatically sends 3 recovery emails over 7 days with increasing incentives. Dynamic Personalization: Inserts abandoned products, customer name, and unique promo codes. Performance Tracking: Analyzes conversion rates and recovered revenue. Set up steps Configure the webhook: Connect your e-commerce platform (Shopify, WooCommerce, Magento) to trigger the workflow when a cart is abandoned. Email service: Set up your email sending service (Gmail, SendGrid, Mailgun) with proper credentials. Customization: Adapt email templates with your brand guidelines, logo, and tone of voice. Promo codes: Integrate your discount code system (10%, 15%, 20%). Analytics tracking: Connect a Google Sheet to track recovery performance. Testing: Validate the workflow with test data before activation. Key Features 🎯 Smart targeting: Automatically filters qualified carts (minimum value, valid email) ⏰ Optimized timing: Scientifically timed sequence (1h, 24h, 72h) to maximize conversions 💰 Progressive incentives: Increasing discounts (10% → 15% → 20%) to create urgency 📱 Responsive design: Email templates optimized for all devices 🔄 Unique codes: Automatically generates personalized promo codes for each customer 📊 Built-in analytics: Real-time tracking of open rates, clicks, and conversions 🛡️ Error handling: Robust system with notifications in case of technical issues 🎨 Professional templates: Modern email designs with optimized call-to-actions Advanced Features Customer segmentation**: Differentiates between new and returning customers Automatic exclusions**: Avoids sending to customers who already purchased Multi-language**: Supports different languages based on location A/B Testing**: Tests different email versions to optimize performance CRM integration**: Syncs data with your customer management system Metrics Tracked Recovery rate per email in the sequence Real-time recovered revenue Open and click-through rates for each email Promo codes used and their effectiveness Average delay between abandonment and conversion Customization Options Flexible timing**: Adjust sending delays to fit your industry Variable incentives**: Change discount percentages as needed Dynamic content**: Adjust messages based on product types Configurable thresholds**: Set your own qualification criteria Full branding**: Integrate your complete visual identity > This workflow automatically turns abandoned carts into sales opportunities with a scientific and personalized approach, generating measurable ROI for your e-commerce.

This n8n workflow template uses community nodes and is only compatible with the self-hosted version of n8n. Build an AI agent for Notion (with Notion official MCP server) Use case This template empowers Notion power-users to build their own AI assistant, deeply integrated with their workspace. It solves the constant problem of copy-pasting and context-switching between a separate AI chat and Notion by creating a direct, conversational bridge. Now you can interact with an intelligent agent that can create, retrieve, and update your Notion databases and pages on your behalf, turning your workspace into a truly dynamic productivity hub. How it works When you send a message via the chat interface, the workflow passes it to your chosen AI model. The model, connected to the official Notion tool server, analyzes your request to see if it can be fulfilled by one of its available Notion actions. If it matches a tool, the workflow executes the command using the Notion API—like creating a new page or searching a database—and the AI then confirms the action is complete back in the chat. Setup Prerequisite: This template is for self-hosted n8n instances only, as it requires a community node.  Copy this workflow into your self-hosted n8n instance Install the required community node (n8n-nodes-mcp). Add your credentials for your chosen AI Model and the Notion MCP Server. Test the workflow by starting chatting with your new Notion assistant. How to adjust it to your needs You can use the AI model you want and even easily compare different AI models. You can start from this template and then provide other tools to your AI agent to build more powerful workflows.

Set Up ElevenLabs Voice Chat Agent using Graph RAG Knowledge Graphs as Experts This workflow creates an AI voice chatbot agent that has access to several knowledge bases at the same time (used as "experts"). These knowledge bases are provided using the InfraNodus GraphRAG using the knowledge graphs and providing high-quality responses without the need to set up complex RAG vector store workflows. We use ElevenLabs to set up a voice agent that can be embedded to any website or used via their API. The advantages of using GraphRAG instead of the standard vector stores for knowledge are: Easy and quick to set up (no complex data import workflows needed) and to update with new knowledge A knowledge graph has a holistic overview of your knowledge base Better retrieval of relations between the document chunks = higher quality responses Ability to reuse in other n8n workflows How it works This template uses the n8n AI agent node as an orchestrating agent that decides which tool (knowledge graph) to use based on the user's prompt. The user's prompt is received from the ElevenLabs Conversational AI agent via an n8n Webhook, which also takes care of the voice interaction. The response from n8n is then sent to the Webhook, which is polled by the ElevenLabs voice agent. This agent processes the response and provides the final answer. Here's a description step by step: The user submits a question using ElevenLabs voice interface The question is sent via the knowledge_base tool in ElevenLabs to the n8n Webhook with the POST request containing the user's prompt and sessionID for Chat Memory node in n8n. The n8n AI agent node checks a list of tools it has access to. Each tool has a description of the knowledge auto-generated by InfraNodus (we call each tool an "expert"). The n8n AI agent decides which tool should be used to generate a response. It may reformulate user's query to be more suitable for the expert. The query is then sent to the InfraNodus HTTP node endpoint, which will query the graph that corresponds to that expert. Each InfraNodus GraphRAG expert provides a rich response that takes the whole context into account and provides a response from each expert (graph) along with a list of relevant statements retrieved using a combination or RAG and GraphRAG. The n8n AI Agent node integrates the responses received from the experts to produce the final answer. The final answer is sent back to the Webhook endpoint ElevenLabs conversational AI agent picks up the response arriving from the knowledge_base tool via the webhook and then condenses it for conversational format and transforms text into voice. How to use You need an InfraNodus GraphRAG API account and key to use this workflow. Create an InfraNodus account Get the API key at https://infranodus.com/api-access and create a Bearer authorization key for the InfraNodus HTTP nodes. Create a separate knowledge graph for each expert (using PDF / content import options) in InfraNodus For each graph, go to the workflow, paste the name of the graph into the body name field. Keep other settings intact or learn more about them at the InfraNodus access points page. Once you add one or more graphs as experts to your flow, add the LLM key to the OpenAI node and launch the workflow You will also need to set up an ElevenLabs account and to set up a conversational AI agent there. See the Post note in the n8n workflow for a complete step-by-step description or our support article on setting up ElevenLabs AI voice agent Once the voice AI agent is ready, you might want to combine it with a text AI chatbot workflow so your users have a choice between the text and voice interaction. In that case, you may be interested to use our free open-source website popup chat widget popupchat.dev where you can create an embed code to add to your blog or website and allow the user to choose between the text and voice interaction. Requirements An InfraNodus account and API key An OpenAI (or any other LLM) API key An ElevenLabs account FAQ 1. How many "experts" should I aim for? We recommend to aim for the number of experts as the optimal number of people in a team, which is usually 2-7. If you add more experts, your AI orchestrating agent will have troubles choosing the most suitable "expert" tool for the user's query. You can mitigate this by specifying in the AI agent description that it can choose maximum 3-7 experts to provide a response. 2. Why use InfraNodus GraphRAG and not standard vector store for knowledge? First, vector stores are complex to set up and to update. You'd need a separate workflow for that, decide on the vector dimensions, add metadata to your knowledge, etc. With InfraNodus, you have a complete RAG / GraphRAG solution under the hood that is easy to set up and provides high-quality responses that takes the overall structure and the relations between your ideas into account. 3 Why not use ElevenLabs' own knowledge? One of the reasons is that you want your knowledge base to be in one place so you can reuse it in other n8n workflows. Another reason is that you will not have such a good separation between the "experts" when you converse with the agent. So the answers you get will be based on top matches from all the books / articles you upload, while with the InfraNodus GraphRAG setup you can better control which graphs are consulted as experts and have an explicit way to display this data. Customizing this workflow You can use this same workflow with a Telegram bot, so you can interact with it using Telegram. There are many more customizations available on our GitHub repo for n8n workflows. Check out the complete setup guide for this workflow at https://support.noduslabs.com/hc/en-us/articles/20318967066396-How-to-Build-a-Text-Voice-AI-Agent-Chatbot-with-n8n-Elevenlabs-and-InfraNodus Also check out the video tutorial with a demo: