⚡AI-powered YouTube video summarization & analysis
-- Disclaimer: This workflow uses a community node and therefore only works for self-hosted n8n users -- Transform YouTube videos into comprehensive summaries and structured analysis instantly. This n8n workflow automatically extracts, processes, and analyzes video transcripts to deliver clear, organized insights without watching the entire video. Time-Saving Features 🚀 Instant Processing Simply provide a YouTube URL and receive a structured summary within seconds, eliminating the need to watch lengthy videos. Perfect for research, learning, or content analysis. 🤖 AI-Powered Analysis Leverages GPT-4o-mini to analyze video transcripts, organizing key concepts and insights into a clear, hierarchical structure with main topics and essential points. Smart Processing Pipeline 📝 Automated Transcript Extraction Supports public YouTube video Handles multiple URL formats Extracts complete video transcripts automatically 🧠 Intelligent Content Organization Breaks down content into main topics Highlights key concepts and terminology Maintains technical accuracy while improving clarity Structures information logically with markdown formatting Perfect For 📚 Researchers & Students Quick comprehension of educational content and lectures without watching entire videos. 💼 Business Professionals Efficient analysis of industry talks, presentations, and training materials. 🎯 Content Creators Rapid research and competitive analysis of video content in your niche. Technical Implementation 🔄 Workflow Components Webhook endpoint for URL submission YouTube API integration for video details Transcript extraction system GPT-4 powered analysis engine Telegram notification system (optional) Transform your video content consumption with an intelligent system that delivers structured, comprehensive summaries while saving hours of viewing time.
By Joseph LePagePersonal life manager with Telegram, Google services & voice-enabled AI
How it works: This project teaches you to create a personal AI assistant named Jackie that operates through Telegram. Jackie can summarize unread emails, check calendar events, manage Google Tasks, and handle both voice and text interactions. The assistant provides a comprehensive digital life management solution accessible via Telegram messaging. Key Features: Supports hands-free voice interaction Maintains conversation memory Integrates with major Google services Provides personalized assistance for email management, scheduling, and task organization Step-by-step: Telegram Trigger: The workflow starts with a Telegram trigger that listens for incoming message events. The system determines if the incoming message is voice or text input. Voice Processing: If a voice message is received, the workflow retrieves the voice file from Telegram and uses OpenAI's transcription API to convert speech to text. AI Assistant: The processed text (whether original text or transcribed voice) is passed to Jackie, the AI assistant powered by OpenRouter's language model. Tools Integration: Jackie is equipped with several productivity tools: Get Email: Uses Gmail API to fetch unread emails from the inbox with sender, date, subject, and summary information Google Calendar: Retrieves calendar events for specified dates, filtering out irrelevant future events Google Tasks: Both creates new tasks and retrieves existing tasks from Google Tasks lists API Keys Required: Telegram Bot API: Create a bot via @BotFather on Telegram to get your bot token OpenAI API: Required for voice-to-text transcription OpenRouter API: Powers the AI language model responses Google OAuth2: Needed for Gmail, Google Calendar, and Google Tasks integration Response Generation: The AI formulates intelligent responses based on the gathered information, current date context, and conversation history, then sends the response back to the user via Telegram in Markdown format.
By Derek CheungAnalyze any website with OpenAI and get on-page SEO audit
Instantly Find & Fix What’s Holding Your Page Back You’ve put in the work. Your content is strong. Your design is polished. But… ❌ Your page isn’t ranking where it should. ❌ Your competitors are outranking you—even with weaker content. ❌ You have no idea what’s wrong—or how to fix it. The truth? SEO isn’t just about keywords. Your technical setup, content structure, and on-page elements must work together seamlessly. And if anything is off? Google won’t rank your page. Who Is This For? SaaS Founders & Startups – Get higher rankings & organic traffic that converts. Marketing Teams & Agencies – Audit & optimize pages in seconds. E-commerce & Content Sites – Improve rankings for product pages, blogs, and landing pages. How It Works Paste your URL Get an instant audit + recommendations list Implement changes & watch your rankings jump The workflow scrapes the url you input, gets the htlm source code of the landing page, and sends it to OpenAI AI Agent. The Agent makes a deep analysis, audit the Technical + Content SEO of the page, and provides 10 Recommendations to improve your SEO. Setup Guide You will need OpenAI Credentials with an API Key to run the workflow. The workflow is using the OpenAI-o1 model to deliver the best results. It costs between $0.20/0.30 per run. You can adjust the prompt to your wish in the AI Agent parameters. Once the audit has been completed, it will send an email (don't forget to add your email address here) Below is an example of what you can expect
By Not Another MarketerCv resume PDF parsing with multimodal vision AI
This n8n workflow demonstrates how we can use Multimodal LLMs to parse and extract from PDF documents in n8n. In this particular scenario, we're passing a candidate's CV/resume to an AI which filters out unqualified applications. However, this sneaky candidate has added in hidden prompt to bypass our bot! Whatever will we do? No fret, using AI Vision is one approach to solve this problem... read on! How it works Our candidate's CV/Resume is a PDF downloaded via Google Drive for this demonstration. The PDF is then converted into an image PNG using a tool called Stirling PDF. Since the hidden prompt has a white font color, it is is invisible in the converted image. The image is then forwarded to a Basic LLM node to process using our multimodal model - in this example, we'll use Google's Gemini 1.5 Pro. In the Basic LLM node, we'll need to set a User Message with the type of Binary. This allows us to directly send the image file in our request. The LLM is now immune to the hidden prompt and its response is has expected. The example CV/Resume with hidden prompt can be found here: https://drive.google.com/file/d/1MORAdeev6cMcTJBV2EYALAwll8gCDRav/view?usp=sharing Requirements Google Gemini API Key. Alternatively, GPT4 will also work for this use-case. Stirling PDF or another service which can convert PDFs into images. Note for data privacy, this example uses a public API and it is recommended that you self-host and use a private instance of Stirling PDF instead. Customising the workflow Swap out the manual trigger for another trigger such as a webhook to integrate into your existing services. This example demonstrates a validation use-case ie. "does the candidate look qualified?". You can try additionally extracting data points instead such as years of experiences, previous companies etc.
By JimleukBuild a PDF-based RAG system with OpenAI, Pinecone and Cohere reranking
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This workflow provides a complete, ready-to-use template for a Retrieval-Augmented Generation (RAG) system. It allows you to build a powerful AI chatbot that can answer questions based on the content of PDF documents you provide, using a modern and powerful stack for optimal performance. Good to know Costs: This workflow uses paid services (OpenAI, Pinecone, Cohere). Costs will be incurred based on your usage. Please review the pricing pages for each service to understand the potential expenses. Video Tutorial (Bahasa Indonesia): For a step-by-step guide on how this workflow functions, you can watch the accompanying video tutorial here: N8N Tutorial: Membangun Chatbot RAG dengan Pinecone, OpenAI, & Cohere How it works This workflow operates in two distinct stages: Data Ingestion & Indexing: It begins when a .pdf file is uploaded via the n8n Form Trigger. The Default Data Loader node processes the PDF, and the Recursive Character Text Splitter breaks down the content into smaller, manageable chunks. The Embeddings OpenAI node converts these text chunks into vector embeddings (numerical representations). Finally, the Pinecone Vector Store node takes these embeddings and stores (upserts) them into your specified Pinecone index, creating a searchable knowledge base. Conversational AI Agent: A user sends a message through the Chat Trigger. The AI Agent receives the message and uses its VectorDB tool to search the Pinecone index for relevant information. The Reranker Cohere node refines these search results, ensuring only the most relevant context is selected. The user's original question and the refined context are sent to the OpenAI Chat Model (gpt-4.1), which generates a helpful, context-aware answer. The Simple Memory node maintains conversation history, allowing for natural, multi-turn dialogues. How to use Using this workflow is a two-step process: Populate the Knowledge Base: First, you need to add documents. Trigger the workflow by using the Form Trigger and uploading a PDF file. Wait for the execution to complete. You can do this for multiple documents. Start Chatting: Once your data has been ingested, open the Chat Trigger's interface and start asking questions related to the content of your uploaded documents. The Form Trigger is just an example. Feel free to replace it with other triggers, such as a node that watches a Google Drive or Dropbox folder for new files. Requirements To run this workflow, you will need active accounts and API keys for the following services. OpenAI Account & API Key: Function: Powers text embedding and the final chat generation. Required for the Embeddings OpenAI and OpenAI Chat Model nodes. Pinecone Account & API Key: Function: Used to store and retrieve your vector knowledge base. Required for the Pinecone Vector Store and VectorDB nodes. You also need to provide your Pinecone Environment. Cohere Account & API Key: Function: Improves the accuracy of your chatbot by re-ranking search results for relevance. Required for the Reranker Cohere node. Customising this workflow This template is a great starting point. Here are a few ways you can customize it: Change the AI Personality: Edit the System Message in the AI Agent node to change the bot's behavior, tone, or instructions. Use Different Models: You can easily swap the OpenAI model for another one (e.g., gpt-3.5-turbo for lower costs) in the OpenAI Chat Model node. Adjust Retrieval: In the VectorDB tool node, you can modify the Top K parameter to retrieve more or fewer document chunks to use as context. Automate Ingestion: Replace the manual Form Trigger with an automated one, like a node that triggers whenever a new file is added to a specific cloud storage folder.
By Aji PrakosoScrape Google Maps by area & generate outreach messages for lead generation
This n8n workflow automates lead extraction from Google Maps, enriches data with AI, and stores results for cold outreach. It uses the Bright Data community node and Bright Data MCP for scraping and AI message generation. How it works Form Submission User provides Google Maps starting location, keyword and country. Bright Data Scraping Bright Data community node triggers a Maps scraping job, monitors progress, and downloads results. AI Message Generation Uses Bright Data MCP with LLMs to create a personalized cold call script and talking points for each lead. Database Storage Enriched leads and scripts are upserted to Supabase. How to use Set up all the credentials, create your Postgres table and submit the form. The rest happens automatically. Requirements LLM account (OpenAI, Gemini…) for API usage. Bright Data account for API and MCP usage. Supabase account (or other Postgres database) to store information.
By SolomonExtract internal links from a webpage
[](https://xqus.relezy.com/extract-internal-links-from-webpage) Who Is This For? Web developers SEO specialists Digital marketers What Problem Is This Workflow Solving? Automates the extraction of internal links from a webpage Eliminates the manual and error-prone process of collecting links Facilitates analysis of website structure and optimization What This Workflow Does Uses HTTP request node to fetch HTML content from a specified webpage Parses the HTML to identify and extract internal links Compiles a list of URLs directing to pages within the same domain Setup Configure the Set Base URL node: Set the url field to the URL you want to analyze. How to Customize This Workflow to Your Needs Change the target URL in the Set Base URL node to analyze different webpages. Add nodes to: Filter or categorize the extracted links Export the list to a database or CSV Send links via email or integrate with other tools This workflow can be used as a base for workflows to manage the process of extracting internal links, aiding in website optimization and SEO efforts.
By AudunQuery n8n credentials with AI SQL agent
This n8n workflow is a fun way to query and search over your credentials on your n8n instance. Good to know Your credentials should remain safe as this workflow does not decrypt or use any decrypted data. Example Usage "Which workflows are using Slack and Google Calendar?" "Which workflows have AI in their name but are not using openAI?" How it works Using the n8n API, it fetches all workflow data on the instance. Workflow data contains references to credentials used so this will be extracted. With some necessary reformatting, the workflows and their credentials metadata are stored to a SQLite database. Next, an AI agent is used with a custom SQL tool that reads the SQLite database created in the previous step. The AI agent is instructed to perform SQL queries against our workflow credential table when asked about credentials by the user. Requirements You'll need an n8n API key. Please note that only workflows will be scoped to your API key. Customising the workflow Add extra table fields to the SQLite database to answer even more complex queries such as: workflow status to differentiate between active and inactive workflows.
By JimleukTransform data in Google Sheets
This workflow appends, lookup, updates, and reads data from a Google Sheet spreadsheet. Set node: The Set node is used to generate data that we want to add to Google Sheets. Depending on your use-case you might have data coming from a different source. For example, you might be fetching data from a WebHook call. Add the node that will fetch the data that you want to add to the Google Sheet. Use can then use the Set node to set the data that you want to add to the Google Sheets. Google Sheets node: This node will add the data from the Set node in a new row to the Google Sheet. You will have to enter the Spreadsheet ID and the Range to specify which sheet you want to add the data to. Google Sheets1 node: This node looks for a specific value in the Google Sheet and returns all the rows that contain the value. In this example, we are looking for the value Berlin in our Google Sheet. If you want to look for a different value, enter that value in the Lookup Value field, and specify the column in the Lookup Column field. Set1 node: The Set node sets the value of the rent by $100 for the houses in Berlin. We pass this new data to the next nodes in the workflow. Google Sheets2 node: This node will update the rent for the houses in Berlin with the new rent set in the previous node. We are mapping the rows with their ID. Depending on your use-case, you might want to map the values with a different column. To set this enter the column name in the Key field. Google Sheets3 node: This node returns the information from the Google Sheet. You can specify the columns that should get returned in the Range field. Currently, the node fetches the data for columns A to D. To fetch the data only for columns A to C set the range to A:C. This workflow can be broken down into different workflows each with its own use case. For example, we can have a workflow that appends new data to a Google Sheet, and another workflow that lookups for a certain value and returns that value. You can learn to build this workflow on the documentation page of the Google Sheets node.
By Harshil AgrawalIntelligent web query and semantic re-ranking flow using Brave and Google Gemini
Workflow Description This workflow is a powerful, fully automated web query and semantic reranking system that allows users to perform precise, detailed searches, intelligently rank search results and provide high-quality, structured output. Built with AI-powered components, the workflow leverages semantic query generation, result re-ranking, and real-time reporting to deliver actionable insights. It is particularly well-suited for real-time data retrieval, market research, and any domain requiring automated yet customizable search result processing. --- How It Works Webhook Integration for Input: The workflow begins with a Webhook Node that captures the user's search query as input, enabling seamless integration with other systems. Step 1: Semantic Query Generation (Powered by "Semantic Search - Query Maker"): Using AI (Google Gemini), the initial query is refined and transformed into a context-aware, expert-level search query. The process ensures that the search engine retrieves the most relevant and precise results. Step 2: Web Search Execution: A free Brave Search API processes the refined query to fetch search results, ensuring speed and cost efficiency. Step 3: Semantic Re-Ranking of Results (Powered by "Semantic Search - Result Re-Ranker"): The workflow reranks the search results based on relevance to the original question, prioritizing the most relevant URLs dynamically. Results are passed through AI-powered intelligent reranking to ensure the final output reflects optimal relevance and quality. Step 4: Structured Output Generation: Results are converted into a well-structured, organized JSON format, ranking the top 10 search results with their titles, links, and descriptions. Missing ranks (if fewer than 10 results) are handled gracefully with placeholders, ensuring consistency. Step 5: Real-Time Reporting: The reranked search results are sent back to the user or integrated system via the Webhook Node in a JSON-formatted response. Reports are highly structured and ready for downstream processing or consumption. --- Key Features AI-Powered Query Refinement: Transforms basic queries into detailed, expert-level search terms for optimal results. Dual-Stage Semantic Search: Combines query generation and result reranking for precise, high-relevance outputs. Top 10 Result Reranking: Dynamically ranks and organizes the top 10 results based on semantic relevance to the query. Customizable Integration: Fully modifiable for alternative APIs or integrations, such as other search engines or custom ranking logic. JSON-Formatted Structured Results: Outputs reranked results in a standardized format, ideal for integration into systems requiring machine-readable data. Webhook-Based Flexibility: Works seamlessly with Webhook inputs for easy deployment in diverse workflows. Cost-Effective API Usage: Pre-integrated with the free Brave Search API, minimizing operational costs while delivering accurate search results. --- Instructions for API Setup Brave Search API: Visit api.search.brave.com to obtain a free-tier API key for web search. AI Integration (Google Gemini): Visit Google AI Studio and generate an API key for semantic query generation and reranking. Webhook Configuration: Set up the input Webhook to capture search queries and the output Webhook to deliver reranked results. --- Why Choose This Workflow? Precision and Relevance: Combines AI-based query generation with advanced reranking for accurate results. Fully Customizable: Easily adapt the workflow to alternative APIs, search engines, or ranking logic. Real-Time Insights: Provides structured, real-time output ready for immediate use. Scalable and Modular: Ideal for businesses, researchers, and data analysts needing a robust, repeatable solution. --- Tags AI Workflow, Semantic Search, Query Refinement, Search Result Reranking, Real-Time Search, Web Search Automation, Google Search, Brave Search, News Search, API Integration, Market Research, Competitive Intelligence, Business Intelligence,Google Gemini, Anthropic Claude, OpenAI, GPT, LLM
By Mind-FrontAI-driven WooCommerce product importer from Google Sheet with Yoast SEO meta
This workflow streamlines your WooCommerce product creation process by integrating directly with Google Sheets. Simply input product details into your spreadsheet, and the workflow takes care of the rest-automatically creating new products on your WooCommerce store with inventory management. But it doesn’t stop there. A dedicated SEO expert chain analyzes each product’s content and generates optimized meta titles and meta descriptions for the plugin Yoast SEO, enhancing visibility and ranking potential on search engines. Key Benefits: 🔄 Automation: No more manual uploads—save time and reduce errors by syncing Google Sheets directly with WooCommerce. ⚡ Speed: Instantly publish multiple products with just one action. 🧠 Built-in SEO Intelligence: Automatically generate SEO-friendly meta titles and descriptions tailored to each product. 📈 Improved Search Visibility: Boost your store's traffic with optimized product listings. 🧩 Customizable: Easily adapt the workflow to your specific needs or integrate with other platforms. --- How It Works This workflow automates the creation of WooCommerce products and generates optimized SEO meta tags (title and description) using AI. Here’s the step-by-step process: Data Retrieval: The workflow starts by fetching product details (title, category, description, price, etc.) from a Google Sheets document. Product Creation: Each product is created in WooCommerce using the retrieved data, including categories, pricing, stock details, and images. AI-Powered SEO Optimization: An AI model (Google Gemini via OpenRouter) analyzes the product details and generates SEO-optimized meta titles (≤60 chars) and meta descriptions (≤160 chars). Meta Tag Assignment: The generated meta tags are saved back to the Google Sheets and applied to the WooCommerce product using Yoast SEO metadata. Completion Tracking: The workflow marks completed entries in Google Sheets and sends a Telegram notification upon finishing all products. --- Set Up Steps Before running the workflow, ensure the following steps are completed: Step 1: Install the Yoast SEO plugin on WordPress and add the provided PHP code to functions.php to enable meta tag API support. Step 2: Enable the WooCommerce REST API in WordPress and configure the Telegram node with a valid CHAT_ID for notifications. Step 3: Prepare a Google Sheet with product data (columns A-I in specific formats) and share its ID in the workflow. Ensure columns B, E, and F are in text format, and column I is numeric. Once set up, the workflow can be triggered manually or scheduled to run automatically, streamlining product creation and SEO optimization. --- Who is it useful for? Ideal for eCommerce managers, digital marketers, or anyone managing large product catalogs-this workflow turns your spreadsheet into a powerful product launcher. --- Need help customizing? Contact me for consulting and support or add me on Linkedin.
By DavideCollect absences from Google Calendars
This workflow checks a Google Calendar at 8am on the first of each month to get anything that has been marked as a Holiday or Illness. It then merges the count for each person and sends an email with the list. To use this workflow you will need to set the credentials to use for the Google Calendar node and Send Email node. You will also need to select the calendar ID and fill out the information in the send email node. This workflow searches for Events that contain "Holiday" or "Illness" in the summary. If you want to change this you can modify it in the Switch node.
By Jonathan