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Summarize YouTube Videos into Structured Content Ideas with AI and Airtable
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
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Find High-Intent Sales Leads by Scraping Glassdoor with Bright Data & GPT
🔍 Scrape Glassdoor with Bright Data Designed for sales teams, recruiters, and marketers aiming to automate job discovery and prospecting. This workflow scrapes Glassdoor job listings using Bright Data and automatically generates targeted pitches using AI, streamlining lead identification and outreach. 🧩 How It Works This automation leverages n8n, Bright Data, Google Sheets, and OpenAI: 1. Trigger Starts with a custom form input (Location, Keyword, Country). 2. Bright Data Job Scrape Triggers a Bright Data dataset snapshot via HTTP Request. Polls snapshot progress using a Wait node, ensuring data readiness. Retrieves full job listings dataset once ready. 3. Google Sheets Integration Writes detailed job data (company, role, location, overview, metrics) into a Google Sheet. Uses a pre-built template for organized data storage. 4. Automated Pitch Generation (AI) Splits listings into actionable parts: company name, title, and description. Sends data to OpenAI (via LangChain) to generate relevant pitches or icebreakers. Saves generated content back into the same sheet for easy access. ✅ Requirements Ensure you have the following: Google Sheets Google account Template Sheet with columns
Document Analysis & Chatbot Creation with Llama Parser, Gemini LLM & Pinecone DB
📄Description This automation workflow enables users to upload files via an N8N form, automatically analyzes the content using Google Gemini agents, and delivers the analyzed results via email along with a chatbot link. The system leverages Llama Cloud API, Google Gemini LLM, Pinecone vector database, and Gmail to provide a seamless, multilingual content analysis experience. ✅ Prerequisites Before setting up this workflow, ensure the following are in place: An active N8N instance. Access to Llama Cloud API. Google Gemini LLM API keys (for Translator & Analyzer agents). A Pinecone account with an active index. A Gmail account with API access configured. Basic knowledge of N8N workflow setup. ⚙️ Setup Instructions Deploy the N8N Form Create a public-facing form using N8N. Configure it to accept: File uploads. User email input. File Preprocessing Store the uploaded files temporarily. Organize and preprocess them as needed. Content Extraction using Llama Cloud API Feed the files into the Llama Cloud API. Extract and parse the content for further processing. Translation (if required) Use a Translator Agent (Google Gemini). Check if the content is in English. If not, translate it. Conten
Gmail MCP Server – Your All‑in‑One AI Email Toolkit
Gmail MCP Server Expose Gmail’s full API as a single SSE “tool server” endpoint for your AI agents. What it does Spins up an MCP Trigger that streams Server‑Sent Events to LangChain/N8N AI Agent nodes. Maps 20+ common Gmail operations (search, send, reply, draft, label & thread management, mark read/unread, delete, etc.) to ai_tool connections, so agents can invoke them with a simple JSON payload. Why you’ll love it Agent‑ready: Plug the SSE URL into any N8N Agent or any other AI tool that uses MCP and start reasoning over email immediately. Extensible: Add more GmailTool operations or swap credentials without touching your agent logic. How to use Import the workflow (n8n ≥ v1.88). Set up a gmailOAuth2 credential and select it on the GmailTool nodes. Open the Gmail MCP Server node, copy the SSE URL, and paste it into your AI agent’s “Tool Server” field.
WScrape Indeed Job Listings for Hiring Signals Using Bright Data and LLMs
Scrape Indeed Job Listings for Hiring Signals Using Bright Data and LLMs How the flow runs Fill the form with job position you're hunting for. Bright data's scraper will scrape Indeed based on your requirments. Workflow waits for the snapshot. Data returns as JSON. Jobs append to Google Sheets. Each row goes to an LLM to analyze if you're a good fit for the job (based on your prompts). The LLMswrites YES or NO next to each job opportunity, helping you find job posts that are relevant to you. What you need Google Sheets with our template. Bright Data dataset and API key. OpenAI key for GPT‑4o mini (or any other LLM). n8n with required nodes. Form fields To Fill Job Location** – city or region. Keyword** – role or skills. Country** – two‑letter code. Setup steps Copy the sheet template link. Import the JSON workflow. Add your credentials in nodes. Test the form manually. Add a schedule if desired. Bright Data filter example [ { "country": "US", "domain": "indeed.com", "keyword_search": "Growth Marketer", "location": "Miami", "date_posted": "Last 24 hours" } ] Tips -Choose Last 24 hours often. -Increase wait time for big snapshots. -Narrow keywords to save credits. **Need help? **Emai
LINE Chatbot with Google Sheets Memory and Gemini AI
Main Use Case This workflow enables automated, AI-assisted replies to users messaging a LINE Official Account, while storing and referencing chat history from Google Sheets to maintain context. Ideal for businesses or support teams that want to provide smart, personalized customer interactions using AI with memory. How It Works (Step-by-Step) Connect to LINE Official Account's API A Webhook listens for incoming messages from users on LINE. When a message is received, it triggers the workflow. Prepare the Data An Edit Fields module structures incoming data (e.g. extracts user ID, message content). This ensures data is clean and usable downstream. Retrieve Chat History The user’s previous conversations are fetched from a Google Sheet. This ensures the AI has memory and can continue conversations contextually. Prepare Prompt The retrieved chat history is combined with the new message to form a complete prompt for the AI. Example format: “User previously said X. Now they said Y. How should we respond?” AI Agent: Google Gemini The formatted prompt is passed to an AI Agent (Google Gemini Chat Model). The AI generates a response based on the message + history. Tools used: Chat ModeMemory,
Log meal nutrients from Telegram to Google Sheets using an AI agent
Who is this for? This workflow is ideal for individuals focused on nutrition tracking, meal planning, or diet optimization—whether you’re a health-conscious individual, fitness coach, or developer working on a healthtech app. It also fits well for anyone who wants to capture their meal data via voice or text, without manually entering everything into a spreadsheet. What problem is this workflow solving? Manually logging meals and breaking down their nutritional content is time-consuming and often skipped. This workflow automates that process using Telegram for input, OpenAI for natural language understanding, and Google Sheets for structured tracking. It enables users to record meals by typing or sending voice messages, which are transcribed, analyzed for nutrients, and automatically stored for tracking and review. What this workflow does This n8n automation lets users send either a text or voice message to a Telegram bot describing their meal. The workflow then: Receives the Telegram message Checks if it’s a voice message • If yes: Downloads the audio file and transcribes it using OpenAI • If no: Uses the text input directly Sends the meal description to OpenAI to extract a struct
Daily AI News Translation & Summary with GPT-4 and Telegram Delivery
📝 What this workflow does Every morning at 8 a.m., this workflow fetches the latest AI-related articles from both GNews and NewsAPI. It merges up to 40 new articles daily, selects the 15 most relevant ones on AI technology and applications, and uses GPT-4.1 to generate concise summaries in accurate Traditional Chinese (while preserving essential English technical terms). Each summary also includes the article link for easy referral. The compiled digest is then posted to your designated Telegram account or group. 👥 Who is this for? AI enthusiasts, professionals, and anyone interested in artificial intelligence news Individuals and teams wanting a concise daily digest of AI developments in Traditional Chinese Telegram users who prefer automated information delivery 🎯 What problem does this workflow solve? With the rapid evolution of AI technology, it can be overwhelming to keep up with new developments. This workflow addresses information overload by automatically collecting, summarizing, and translating the most important AI news each morning — all delivered conveniently to your chosen Telegram channel or group. ⚙️ Setup 🔑 Add NewsAPI and GNews API Keys Register for accounts on
🧠 FloWatch 👁️ Analyze and Diagnose n8n Workflow Errors via OpenAI and Email
🧠 Analyze and Diagnose n8n Workflow Errors Automatically via OpenAI and Email > ⚠️ This template is available on ☁️ Cloud & 🖥️ self-hosted n8n instances with the OpenAI node enabled. 👤 Who is this for? This workflow is designed for n8n developers, automation engineers, and DevOps teams who want to automatically capture and analyze workflow errors, and receive professional HTML-styled diagnostics directly in their inbox. 💥 What problem does this solve? Manually troubleshooting failed workflows in n8n can be time-consuming. This template streamlines error detection by: Capturing workflow failures using the Error Trigger node Diagnosing root causes with the help of OpenAI Sending a fully-formatted, human-readable HTML error report via email Including practical resolutions and next-step suggestions This helps you or your team resolve issues faster and avoid repeated manual debugging. ⚙️ What this workflow does ⚡ Triggers on any n8n workflow error 📦 Extracts relevant error metadata including node, execution ID, and timestamps 🧠 Sends error content to OpenAI for analysis and recommendations 💌 Generates an HTML email report with inline styles and clear formatting 📥 Emails the r
WExtract Business Leads from Google Maps with Dumpling AI to Google Sheets
Who is this for? This workflow is built for marketers, sales teams, agencies, virtual assistants, and anyone who regularly researches or contacts local businesses. It's ideal for building lead lists, tracking competitors, or creating location-specific outreach campaigns. What problem is this workflow solving? Instead of manually searching Google Maps and copying business info into spreadsheets, this automation pulls structured business data (e.g. restaurants, gyms, service providers) and logs it directly into Google Sheets. It saves hours of work and ensures cleaner, more usable data. What this workflow does The workflow takes a Google Maps search query (like "best restaurants in New York") and sends it to Dumpling AI. It returns a list of places including their name, address, website, phone number, rating, and more. Each result is split into a row and automatically added to a Google Sheet. Setup Dumpling AI Sign up at Dumpling AI Generate your API key In the HTTP Request node, select Header Auth and paste your key in the Authorization field Google Sheets Create a sheet with tab name Leads Add the following column headers to row 1: Name, Address, Phone number, Website, Rating, Pric
Automate Web Interactions with Claude 3.5 Haiku and Airtop Browser Agent
About this AI Agent This workflow is designed to automate web interactions by simulating a human user, using a combination of the Agent node and AI tools powered by Airtop. How does this workflow works? Form Submission Trigger: The workflow starts with a form submission trigger node named "On form submission". This node collects user instructions for the web AI agent, including a prompt and an optional Airtop profile name for sites requiring authentication. AI Agent: The core of the workflow is the "AI Agent" node, which uses a smart web agent to manage a remote web browser. It is designed to fulfill user requests by interacting with the browser through various tools. Browser Session Management Start Browser: The "Start browser" node initiates a new browser session and window. It is essential for obtaining the sessionId and windowId required for subsequent operations. Session and Window Management: The workflow includes nodes for creating and managing browser sessions and windows, such as "Session" and "Window". Web Interaction Tools: Load URL: This node loads a specified URL into the browser window. Query: The "Query" node allows the agent to ask questions and extract information
WTrack Daily Product Hunt Launches with Website Verification in Google Sheets
This workflow helps you stay updated with daily launches on Product Hunt. It automatically fetches product details (name, tagline, description, and website), checks if the website redirects to another URL, and logs the final information into a Google Sheet. Perfect for indie hackers, product managers, content curators, and anyone tracking daily launches. How It Works Schedule Trigger – Runs the workflow daily. Set Date – Captures today’s date in ISO format for filtering Product Hunt posts. HTTP Request (Product Hunt API) – Retrieves Product Hunt posts for the day using GraphQL. Extract Product Info (Code Node) – Parses the response to pull key details: Name Tagline Description Website URL HTTP Request (URL Check) – Follows each website URL to detect if it redirects. Merge Data – Combines product info with the final destination URL. Google Sheets Node – Appends all processed product info to your sheet. Pre-conditions A valid Product Hunt API token A Google account with access to Google Sheets A Google Sheet already created with the correct columns (see below) Connected Google Sheets and HTTP credentials in n8n Google Sheets Setup Your spreadsheet should include the following columns
AI-Powered WhatsApp Chatbot 🤖📲 for Text, Voice, Images & PDFs with memory 🧠
This workflow is a highly advanced multimodal AI assistant designed to operate through WhatsApp. It can understand and respond to text, images, voice messages, and PDF documents by combining OpenAI models with smart logic to adapt to the content received. 🎯 Core Features 📥 1. Automatic Message Type Detection Using the Input type node, the bot detects whether the user has sent: Text Voice messages Images Files (PDF) Other unsupported content 💬 2. Smart Text Message Handling Text messages are processed by an OpenAI GPT-4o-mini agent with a customized system prompt. Replies are concise, accurate, and formatted for mobile readability. 🖼️ 3. Image Analysis & Description Images are downloaded, converted to base64, and analyzed by an image-aware AI model. The output is a rich, structured description, designed for visually impaired users or visual content interpretation. 🎙️ 4. Voice Message Transcription & Reply Audio messages are downloaded and transcribed using OpenAI Whisper. The transcribed text is analyzed and answered by the AI. Optionally, the AI reply can be converted back to voice using OpenAI's text-to-speech, and sent as an audio message. 📄 5. PDF Document Extraction & Sum
High-Level Service Page SEO Blueprint Report Generator
Introduction The "High-Level Service Page SEO Blueprint Report" workflow is a powerful, AI-driven solution designed to generate comprehensive SEO content strategies for service-based businesses. By analyzing competitor websites and user intent, this workflow creates a detailed blueprint that outlines the optimal structure, content, and conversion elements for a service page. The workflow leverages the JINA Reader API to extract content from competitor websites and uses Google Gemini AI to perform deep analysis across multiple dimensions: competitor content structure, user intent, strategic opportunities, and conversion optimization. The final output is a professionally formatted Markdown document that provides actionable guidance for creating a high-performing service page that satisfies both user needs and search engine requirements. This workflow eliminates the time-consuming process of manually analyzing competitors and developing content strategies, providing a data-driven foundation for service page creation that would typically require hours of expert analysis. Who is this for? This workflow is designed for digital marketers, SEO specialists, content strategists, and web deve
WScrape LinkedIn Job Listings for Hiring Signals & Prospecting with Bright Data
LinkedIn Hiring Signal Scraper — Jobs & Prospecting Using Bright Data Purpose: Discover recent job posts from LinkedIn using Bright Data's Dataset API, clean the results, and log them into Google Sheets — for both job hunting and identifying high-intent B2B leads based on hiring activity. Use Cases: Job Seekers** – Spot relevant openings filtered by role, city, and country. Sales & Prospecting** – Use job posts as buying signals. If a company is hiring for a role you support (e.g. marketers, developers, ops) — it's the perfect time to reach out and offer your services. Tools Needed: n8n Nodes:** Form Trigger HTTP Request Wait If Code Google Sheets Sticky Notes (for embedded guidance) External Services:** Bright Data (Dataset API) Google Sheets API Keys & Authentication Required: Bright Data API Key** → Add in the HTTP Request headers: Authorization: Bearer YOUR_BRIGHTDATA_API_KEY Google Sheets OAuth2** → Connect your account in n8n to allow read/write access to the spreadsheet. General Guidelines: Use descriptive names for all nodes. Include retry logic in polling to avoid infinite loops. Flatten nested fields (like job_poster and base_salary). Strip out HTML tags from job descript
Automated Research Report Generation with AI, Wiki, Search & Gmail/Telegram
Automated Research Report Generation with OpenAI, Wikipedia, Google Search, Gmail/Telegram and PDF Output Description What Problem Does This Solve? 🛠️ This workflow automates the process of generating professional research reports for researchers, students, and professionals. It eliminates manual research and report formatting by aggregating data, generating content with AI, and delivering the report as a PDF via Gmail or Telegram. Target audience: Researchers, students, educators, and professionals needing quick, formatted research reports. What Does It Do? 🌟 Aggregates research data from Wikipedia, Google Search, and SerpApi. Refines user queries and generates structured content using OpenAI. Converts the content into a professional HTML report, then to PDF. Sends the PDF report via Gmail or Telegram. Key Features 📋 Real-time data aggregation from multiple sources. AI-driven content generation with OpenAI. Automated HTML-to-PDF conversion for professional reports. Flexible delivery via Gmail or Telegram. Error handling for robust execution. Setup Instructions Prerequisites ⚙️ n8n Instance**: Self-hosted or cloud n8n instance. API Credentials**: OpenAI API: API key with GPT mod
Generate Dynamic Line Chart from JSON Data to Upload to Google Drive
What Does This Flow Do? This workflow demonstrates how to dynamically generate a line chart using the QuickChart node based on data provided in a JSON object and then upload the resulting chart image to Google Drive. Use Cases You can use it in presentations or requesting for chart generation from a software with HTTP requests. Automated report generation (e.g., daily sales charts). Visualizing data fetched from APIs or databases. Simple monitoring dashboards. Adding charts to internal tools or notifications. How it Works Trigger: The workflow starts manually when you click 'Test workflow'. Set Sample Data: A Set node (Edit Fields: Set JSON data to test) defines a sample JSON object named jsonData. This object contains: reportTitle: A title (not used in the chart generation in this example, but useful for context). labels: An array of strings representing the labels for the chart's X-axis (e.g., ["Q1", "Q2", "Q3", "Q4"]). salesData: An array of numbers representing the data points for the chart's Y-axis (e.g., [1250, 1800, 1550, 2100]). Generate Chart: The QuickChart node is configured to: Create a line chart. Dynamically read labels from the jsonData.labels array (Labels Mode: Fro
Travel Planning Assistant with MongoDB Atlas, Gemini LLM and Vector Search
Building agentic AI workflows often requires multiple moving parts: memory management, document retrieval, vector similarity, and orchestration. Until now, these pieces had to be custom-wired. But with the new native n8n nodes for MongoDB Atlas, we reduce that overhead dramatically. With just a few clicks: Store and recall long-term memory from MongoDB Query vector embeddings stored in Atlas Vector Search Use these results in your LLM chains and automation logic In this example we present an ingestion and AI Agent flows that focus around Travel Planning. The different interest points that we want the agent to know about can be ingested into the vector store. The AI Agent will use the vector store tool to get relevant context about those points of interest if it needs to. Prerequisites MongoDB Atlas project and Cluster OpenAI Valid API Key for embeddings (can be other provider) Gemini API Key for the LLM (can be other provider) How it works: There are 2 main flows. One is ingesting flow: Gets a document from a webhook and use MongoDB Vector Atlas to embed the document title and description into points_of_interest collection. Embeddings are stored in a field named embedding Embedding
Paul Graham Essay Search & Chat with Milvus Vector Database
Paul Graham Essay Search & Chat with Milvus Vector Database How It Works This workflow creates a RAG (Retrieval-Augmented Generation) system using Milvus vector database to search Paul Graham essays: Scrape & Load: Fetches Paul Graham essays, extracts text, and stores them as vector embeddings in Milvus Chat Interface: Enables semantic search and AI-powered conversations about the essays Set Up Steps Set up Milvus server following the official installation guide, then create a collection Execute the workflow to scrape essays and load them into your Milvus collection Chat with the AI agent using the Milvus tool to query and discuss essay content
WCreate a Paul Graham Essay Q&A System with OpenAI and Milvus Vector Database
Create a Paul Graham Essay Q&A System with OpenAI and Milvus Vector Database How It Works This workflow creates a question-answering system based on Paul Graham essays. It has two main steps: Data Collection & Processing: Scrapes Paul Graham essays Extracts text content Loads them into a Milvus vector store Chat Interaction: Provides a question-answering interface using the stored vector embeddings Utilizes OpenAI embeddings for semantic search Set Up Steps Set up a Milvus server following the official guide Create a collection named "my_collection" Run the workflow to scrape and load Paul Graham essays Start chatting with the QA system The workflow handles the entire process from fetching essays, extracting content, generating embeddings via OpenAI, storing vectors in Milvus, and providing retrieval for question answering.
Create a RAG System with Paul Essays, Milvus, and OpenAI for Cited Answers
Create a RAG System with Paul Essays, Milvus, and OpenAI for Cited Answers This workflow automates the process of creating a document-based AI retrieval system using Milvus, an open-source vector database. It consists of two main steps: Data collection/processing Retrieval/response generation The system scrapes Paul Graham essays, processes them, and loads them into a Milvus vector store. When users ask questions, it retrieves relevant information and generates responses with citations. Step 1: Data Collection and Processing Set up a Milvus server using the official guide Create a collection named "my_collection" Execute the workflow to scrape Paul Graham essays: Fetch essay lists Extract names Split content into manageable items Limit results (if needed) Fetch texts Extract content Load everything into Milvus Vector Store This step uses OpenAI embeddings for vectorization. Step 2: Retrieval and Response Generation When a chat message is received, the system: Sets chunks to send to the model Retrieves relevant information from the Milvus Vector Store Prepares chunks Answers the query based on those chunks Composes citations Generates a comprehensive response This process uses OpenA
Get Scaleway Server Info with Dynamic Filtering
Get Scaleway Server Info with Dynamic Filtering Description This workflow is designed for developers, system administrators, and DevOps engineers who need to retrieve and filter Scaleway server information quickly and efficiently. It gathers data from Scaleway instances and baremetal servers across multiple zones and is ideal for: Quickly identifying servers by tags, names, public IPs, or zones. Automating server status checks in production, staging, or test environments. Integrating Scaleway data into broader monitoring or inventory systems. High-Level Steps Webhook Trigger:** Receives an HTTP POST request (with basic authentication) containing the search criteria (search_by and search). Server Data Collection:** Fetches server data from Scaleway’s API endpoints for both instances and baremetal servers across defined zones. Data Processing:** Aggregates and normalizes the fetched data using a Code node with helper functions. Dynamic Filtering:** Routes data to dedicated filtering routines (by tags, name, public_ip, or zone) based on the input criteria. Response:** Returns the filtered data (or an error message) via a webhook response. Set Up Steps Insert Your Scaleway Token: In th
SBuild an MCP Server with Google Calendar
Who is this for? This template is designed for anyone who wants to integrate MCP with their AI Agents. Whether you're a developer, a data analyst, or an automation enthusiast, if you're looking to leverage the power of MCP and Google Calendar in your n8n workflows, this template is for you. What problem is this workflow solving? This template caters to MCP beginners seeking a hands - on example and developers looking to integrate Google Calendar MCP service. When integrating MCP with Google Calendar, manually updating AI Agents after changes to Google Calendar tools on the MCP Server is time - consuming and error - prone. This template automates the process, enabling the AI Agent to instantly recognize changes made to Google Calendar on the MCP Server. In project management, for example, it ensures that task schedule updates in Google Calendar are automatically detected by the AI Agent. With detailed steps, it simplifies the integration process for all users. What this workflow does This workflow focuses on integrating MCP with Google Calendar within n8n. Specifically, it allows you to build an MCP Server and Client using Google Calendar nodes in n8n. Any changes made to the Google
WDeploy Workflows from Google Drive to n8n Instance
Automatically deploy n8n workflows by simply dropping JSON files into a Google Drive folder—this template watches for new exports, cleans and imports them into your n8n instance, applies a tag, and then archives the processed files. Who is this template for? This workflow template is designed for n8n power users, and automation specialists who need a simple, reliable way to bulk‑deploy or version‑control n8n workflows via Google Drive. It’s perfect if you: Manage multiple n8n instances (staging, production, etc.) Want an easy “drop‑in” approach to publish new or updated workflows Prefer storing/exporting JSON in Drive rather than editing in the UI Use case Manually importing .json exports into n8n is slow and error‑prone. With this template you can: Keep your workflows in a shared Drive folder (version control friendly) Automatically sanitize each file so only supported settings go through Tag deployed workflows consistently for easy filtering Move processed files to a “Deployed” folder for clear change tracking How it works Watch “ToDeploy” folder in Google Drive for new .json files Download & parse each file into a JSON object Clean payload: strip out everything except the allowe