Workflow Templates

Who is this for? This workflow is for digital marketers, small business owners, lead generation agencies, and VAs who need a scalable way to find and store local business leads using AI. It’s especially useful for teams that want to enrich leads with real-time news insights and save the structured data to Airtable. What problem is this workflow solving? Manually researching local businesses and staying up to date with relevant news is time-consuming and inefficient. This automation eliminates that burden by using Dumpling AI chat agents to generate leads and context, GPT-4o to summarize, and Airtable to store everything in one place. What this workflow does This AI workflow listens for a manual trigger in n8n and executes the following steps: Extracts local business leads using a Local Business Agent from Dumpling AI. Pulls current news related to the business type or location using a News Agent from Dumpling AI. Uses GPT-4o to combine both responses into a human-readable summary. Extracts structured lead data like name, category, and city. Saves the summary and lead data into Airtable for easy follow-up. Setup 1. Create AI Agents in Dumpling AI Sign in at Dumpling AI Create two separate agents: Local Business Agent: Designed to respond with structured lists of businesses by location and category. News Agent: Designed to fetch relevant recent news and summaries about a specific industry or region. After setting up each agent, copy the Agent Key from Dumpling AI. These keys will be required in the headers of your HTTP Request nodes in n8n. 2. Manual Trigger This workflow begins with a manual trigger inside n8n, Which is the When chat message is recieved. This makes it easy to test and reuse, especially during setup. 3. Get Local Business Data from Dumpling AI The first HTTP Request node sends a prompt like List 5 top real estate companies in Atlanta with full address and services. Include your Local Business Agent Key in the x-agent-key header. The response will return a structured list of business leads. 4. Get News Context from Dumpling AI The second HTTP Request node sends a prompt such as Give me the latest news related to the real estate market in Atlanta. Use your News Agent Key in the header. This fetches a brief set of recent news summaries relevant to the businesses being researched. 5. Use GPT-4o to Merge and Summarize The GPT node combines the list of businesses and news into one coherent summary. You can modify the prompt to output in paragraph format, bullet points, or structured notes. 6. Save Lead to Airtable The Airtable node sends all structured fields into your selected base and table. Be sure to connect your Airtable account and confirm the columns match exactly. How to customize this workflow Replace the prompt inside the HTTP node to focus on different types of businesses or cities. Expand the GPT output to include additional lead info like websites, phone numbers, or emails if the agent includes them. Add a webhook trigger to allow this flow to be run via a chatbot, external app, or button. Link to HubSpot or another CRM to sync the leads automatically. Duplicate the process to run for multiple industries in parallel. Final Notes You must create and configure your Dumpling AI agents first before running this workflow. The Agent Keys from Dumpling AI are required in both HTTP Request nodes. This flow is modular and flexible, ready for deeper CRM integrations. The manual trigger is great for testing, but you can add a Webhook node to automate it. This workflow helps you launch an intelligent lead gen process that combines location-targeted business discovery, AI-generated insights, and structured CRM-friendly output, all powered by Dumpling AI and OpenAI.

Who is this for? This template is designed for anyone who wants to integrate MCP with their AI Agents using Airtable. Whether you're a developer, a data analyst, or an automation enthusiast, if you're looking to leverage the power of MCP and Airtable 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 Airtable MCP service. When integrating MCP with Airtable, manually updating AI Agents after changes to Airtable data 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 Airtable on the MCP Server. In data management, for example, it ensures that record updates or additions in Airtable 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 Airtable within n8n. Specifically, it allows you to build an MCP Server and Client using Airtable nodes in n8n. Any changes made to the Airtable Base/Table on the MCP Server are automatically recognized by the MCP Client in the workflow. This means that you can make changes to your Airtable (such as adding, deleting, or modifying records) on the MCP Server, and the MCP Client in the n8n workflow will immediately detect these changes without any manual intervention. Setup Requirements An active n8n account. Access to Airtable API. A sample base and rows in Airtable that you can use to test. An API key from your preferred LLM to power the AI agent. Step-by-step guide Create a new workflow in n8n: Log in to your n8n account and create a new workflow. Add Airtable nodes: Search for and add the Airtable nodes to your workflow that you wish the MCP client to have access to. Set up the MCP Server and Client: Use the appropriate nodes in n8n to set up the MCP Server and Client. Connect the Airtable nodes to the MCP nodes as required. Activate and test the workflow: Talk to the chat trigger once all credentials have been updated and table data synced and try adding some rows, deleting or finding and updating cells. How to customize this workflow to your needs If you want to customize this workflow, you can: Modify the triggers:** You can change the conditions under which the MCP Client detects changes. For example, you can set it to detect changes only in specific fields or based on certain record values in Airtable. Integrate with other services:** You can add more nodes to the workflow to integrate with other services, such as sending notifications to Slack or triggering further actions based on the detected Airtable changes. Need help? Feel free to contact us at 1 Node. Get instant access to a library of free resources we created.

This workflow automates web scraping of Amazon search result pages by retrieving raw HTML, cleaning it to retain only the relevant product elements, and then using an LLM to extract structured product data (name, description, rating, reviews, and price), before saving the results back to Google Sheets. It integrates Google Sheets to supply and collect URLs, BrightData to fetch page HTML, a custom n8n Function node to sanitize the HTML, LangChain (OpenRouter GPT-4) to parse product details, and Google Sheets again to store the output. URL to scape . Result Who Needs Amazon Search Result Scraping? This scraping workflow is ideal for teams and businesses that need to monitor Amazon product listings at scale: E-commerce Analysts** – Track competitor pricing, ratings, and inventory trends. Market Researchers** – Collect data on product popularity and reviews for market analysis. Data Teams** – Automate ingestion of product metadata into BI pipelines or data lakes. Affiliate Marketers** – Keep affiliate catalogs up to date with latest product details and prices. If you need reliable, structured data from Amazon search results delivered directly into your spreadsheets, this workflow saves you hours of manual copy-and-paste. Why Use This Workflow? End-to-End Automation** – From URL list to clean JSON output in Sheets. Robust HTML Cleaning** – Strips scripts, styles, unwanted tags, and noise. Accurate Structured Parsing** – Leverages GPT-4 via LangChain for reliable extraction. Scalable & Repeatable** – Processes thousands of URLs in batches. Step-by-Step: How This Workflow Scrapes Amazon Get URLs from Google Sheets – Reads a list of search result URLs. Loop Over Items – Iterates through each URL in controlled batches. Fetch Raw HTML – Uses BrightData’s Web Unlocker proxy to retrieve the page. Clean HTML – A Function node removes doctype, scripts, styles, head, comments, classes, and non-whitelisted tags, collapsing extra whitespace. Extract with LLM – Passes cleaned HTML into LangChain → GPT-4 to output JSON for each product: name, description, rating, reviews, price Save Results – Appends the JSON fields as columns back into a “results” sheet in Google Sheets. Customization: Tailor to Your Needs Adaptable Sites** – This workflow can be adapted to any e-commerce or other website, for example Walmart or eBay. Whitelist Tags** – Modify the allowedTags array in the Code node to keep additional HTML elements. Schema Changes** – Update the Structured Output Parser schema to include more fields (e.g., availability, SKU). Alternate Data Sink** – Instead of Sheets, route output to a database, CSV file, or webhook. 🔑 Prerequisites Google Sheets Credentials** – OAuth credentials configured in n8n. BrightData API token** – Stored in n8n credentials as BRIGHTDATA_TOKEN. OpenRouter API Key** – Configured for the LangChain node to call GPT-4. n8n Instance** – Self-hosted or cloud with sufficient quota for HTTP requests and LLM calls. 🚀 Installation & Setup Configure Credentials** In n8n, set up Google Sheets OAuth under “Credentials.” Add BrightData token as a new HTTP Request credential. Create an OpenRouter API key credential for the LangChain node. Import the Workflow** Copy the JSON workflow into n8n’s “Import” dialog. Map your Google Sheet IDs and GIDs to the {{WEB_SHEET_ID}}, {{TRACK_SHEET_GID}}, and {{RESULTS_SHEET_GID}} placeholders. Ensure the BRIGHTDATA_TOKEN credential is selected on the HTTP Request node. Test & Run** Add a few Amazon search URLs to your “track” sheet. Execute the workflow and verify product data appears in your “results” sheet. Tweak batch size or parser schema as needed. ⚠ Important API Rate Limits** – Monitor your BrightData and OpenRouter usage to avoid throttling. Amazon’s Terms** – Ensure your scraping complies with Amazon’s policies and legal requirements. Summary This workflow delivers a fully automated, scalable solution to extract structured product data from Amazon search pages directly into Google Sheets—streamlining your competitive analysis and data collection. 🚀 Phil | Inforeole

Define Your ICP from Customer LinkedIn Profiles Use Case This automation helps marketing and sales teams define their Ideal Customer Profile (ICP) using real LinkedIn profiles of current high-fit customers. By enriching and analyzing profile data, it generates a clear ICP definition and scoring methodology for future targeting. What This Automation Does This automation analyzes LinkedIn profiles of your existing customers and produces: A structured ICP definition A scoring model to evaluate future prospects A Google Boolean search string to find similar prospects Input: LinkedIn profile URLs of existing high-fit customers (e.g., https://www.linkedin.com/in/amirashkenazi/) Output: A Google Doc containing the ICP analysis and scoring methodology How It Works Trigger: Waits for a chat message containing one or more LinkedIn profile URLs. AI Agent: Parses and processes the URLs. Airtop Data Enrichment: Uses Airtop to extract structured information from each LinkedIn profile (e.g., job title, company, experience, skills). Memory: Maintains state between inputs for consistent analysis. LLM Analysis: Uses Claude 3.7 Sonnet to synthesize enriched data into a meaningful ICP. Google Docs: Automatically creates a new doc with a timestamped title and appends the ICP definition. Setup Requirements Airtop Profile connected to LinkedIn, Insert the profile name in the Airtop Tool Airtop API credentials. Get it free here If you choose to activate saving the profiles in Google Docs you will need OAuth2 credentials (or just copy the ICP definition from the chat) Next Steps Use the ICP for Scoring**: Feed new LinkedIn profiles through the same Airtop enrichment and use the scoring function to evaluate fit. Automate Target Discovery**: Plug the Boolean search output into LinkedIn, Google, or People Data Labs for ICP-matching lead generation. Refine Continuously**: Repeat the workflow as your customer base grows or segments evolve. Read more about how to Define ICP from Customer Examples

✉️ Telegram Email Agent with GPT + Gmail Category: Messaging / AI Agent Level: Beginner-Friendly Tags: Telegram, Email Automation, AI Agent, Gmail, GPT Model Watch Step-by-step video guide here: https://www.youtube.com/watch?v=nyI40s9QOuw&t=420s&pp=0gcJCb4JAYcqIYzv 🤖 What This Workflow Does This workflow turns your Telegram bot into a personal email assistant powered by AI. With just a message on Telegram, users can: Send an email via Gmail Automatically generate the email content using OpenAI Models. Get confirmation or responses directly in Telegram It's like ChatGPT meets Gmail, inside your Telegram chat. 🔧 How It Works Telegram Trigger – Listens for incoming messages from your bot. AI Agent – Processes the input using OpenAI Model and converts it into structured email content (To, Subject, Body). Memory Node – Stores short-term context per user (via chat ID), so the agent can hold simple conversations. Gmail Node – Sends the generated email using your Gmail account. Telegram Node – Replies to the user confirming the output or status. 🧠 Why This is Useful Ever wanted to send an email while on the go, without typing the whole thing out in Gmail? This is a fast, intuitive, and AI-powered way to: Dictate or draft emails from anywhere Create an AI-powered virtual assistant via Telegram Integrate n8n's Langchain Agent with real-world productivity use cases 🪜 Setup Instructions Connect your Telegram bot via BotFather and add the credentials in n8n. Set up your OpenAI API key (GPT-4o-mini recommended). Add your Gmail OAuth credentials. Activate the workflow and start messaging your bot!

Who is this for? This template is designed for internal support teams, product specialists, and knowledge managers in technology companies who want to automate ingestion of product documentation and enable AI-driven, retrieval-augmented question answering. What problem is this workflow solving? Support agents often spend too much time manually searching through lengthy documentation, leading to inconsistent or delayed answers. This solution automates importing, chunking, and indexing product manuals, then uses retrieval-augmented generation (RAG) to answer user queries accurately and quickly with AI. What these workflows do Workflow 1: Document Ingestion & Indexing Manually triggered to import product documentation from Google Docs. Automatically splits large documents into chunks for efficient searching. Generates vector embeddings for each chunk using OpenAI embeddings. Inserts the embedded chunks and metadata into a MongoDB Atlas vector store, enabling fast semantic search. Workflow 2: AI-Powered Query & Response Listens for incoming user questions (can be extended to webhook). Converts questions to vector embeddings and performs similarity search on MongoDB vector store. Uses OpenAI’s GPT-4o-mini model with retrieval-augmented generation to produce direct, context-aware answers. Maintains short-term conversation context using a memory buffer node. Setup Setting up vector embeddings Authenticate Google Docs and connect your Google Docs URL containing the product documentation you want to index. Authenticate MongoDB Atlas and connect the collection where you want to store the vector embeddings. Create a search index on this collection to support vector similarity queries. Ensure the index name matches the one configured in n8n (data_index). See the example MongoDB search index template below for reference. Setting up chat Configure the AI system prompt in the “Knowledge Base Agent” node to reflect your company’s tone, answering style, and any business rules. Update the workflow description and instructions to help users understand the chat’s purpose and capabilities. Connect the MongoDB collection used for vector search in the chat workflow and update the vector search index if needed to match your setup. Make sure Both MongoDB nodes (in ingestion and chat workflows) are connected to the same collection, with: An embedding field storing vector data, Relevant metadata fields (e.g., document ID, source), and The same vector index name configured (e.g., data_index). Search Index Example: { "mappings": { "dynamic": false, "fields": { "_id": { "type": "string" }, "text": { "type": "string" }, "embedding": { "type": "knnVector", "dimensions": 1536, "similarity": "cosine" }, "source": { "type": "string" }, "doc_id": { "type": "string" } } } }

Company Website Chatbot Agent Overview This workflow implements a modular Website AI Chatbot Assistant capable of handling multiple types of customer interactions autonomously. Instead of relying on a single large agent to handle all logic and tools, this system routes user queries to specialized sub-agents—each dedicated to a specific function. By using a manager-style orchestration layer, this approach prevents overloading a single AI model with excessive context, leading to cleaner routing, faster execution, and easier scaling as your automation needs grow. How It Works 1. Chat Trigger The flow is initiated when a chat message is received via the website widget. 2. Manager Agent (Ultimate Website AI Assistant) The central LLM-based agent is responsible for parsing the message and deciding which specialized sub-agent to route it to. It uses an OpenAI GPT model for natural language understanding and a lightweight memory system to preserve recent context. 3. Sub-Agent Routing calendarAgent: Handles availability checks and books meetings on connected calendars. RAGAgent: Searches company documentation or FAQs to provide accurate responses from your internal knowledge base. ticketAgent: Forwards requests to human support by generating and sending support tickets to a designated email. Setup Instructions Embed the Chatbot Use a custom HTML widget or script to embed the chatbot interface on your website. Connect the frontend to the webhook that triggers the When chat message received node. Configure Your OpenAI Key Insert your API key in the OpenAI Chat Model node. Adjust the model parameters for temperature, max tokens, etc., based on how formal or creative you want the bot to be. Customize Sub-Agents calendarAgent: Connect to your Google or Outlook calendar. RAGAgent: Link to a vector store or document database via API or native integration. ticketAgent: Set the destination email and format for ticket generation (e.g. via SendGrid or SMTP). Deploy in Production Host on n8n Cloud or your self-hosted instance. Monitor usage through the Executions tab and refine prompts based on user behavior. Benefits Modular system with dedicated logic per function Reduces token bloat by offloading complexity to sub-agents Easy to scale by adding more tools (e.g. CRM, analytics) Fast and responsive user experience for customers on your site Cleaner code structure and easier debugging

Who is this for? This workflow is perfect for: Companies that manage invoices through Google Drive Business owners who want to minimize manual data entry and maximize accuracy Accounting teams and finance departments seeking to automate invoice processing What problem is this workflow solving? Processing invoices manually is time-consuming, error-prone, and inconsistent. This workflow solves those issues by: Automating invoice processing** from detection to data extraction to storage Improving accuracy** by using AI to extract key invoice data fields reliably Reducing human workload** while maintaining compliance and consistency What this workflow does This workflow creates a fully automated invoice processing system by: Monitoring a Google Drive folder for new PDF invoices in real time Downloading the PDF files and extracting their content using OCR technology Using AI (OpenAI) to parse and extract key invoice fields such as invoice number, date, total amount, vendor name, itemized details, tax, and category Validating the extracted data to ensure compliance with a structured JSON schema Storing structured data in Google Sheets for easy access, review, and reporting Key Features: AI-powered extraction handles both text-based and scanned PDF invoices Provides a structured, searchable invoice database in Google Sheets Configured to run as frequently as the user needs, ensuring timely processing. Setup Copy the Google Sheet template here: 👉 PDF Invoice Parser – Google Sheet Template Connect your Google Drive account to the Drive Trigger and File Download nodes Add your OpenAI API key in the AI Parser node Link the Google Sheet in the final storage node Drop a test invoice PDF into the monitored Drive folder Required Credentials: OpenAI API Key** Google Drive Credentials** Google Sheets Credentials** How to customize this workflow to your needs Modify the polling interval** (default: every minute) for higher/lower frequency. Integrate with your accounting software** by adding nodes (e.g., QuickBooks, Xero). Use alternative LLM** such as Gemini, Claude.

👤 Who is this for? This workflow is ideal for social media managers, personal brand strategists, ghostwriters, and founders who want to post regularly on LinkedIn without spending hours writing from scratch. It’s also useful for marketing agencies and assistants looking to automate consistent post creation using curated articles as source material. 🧩 What problem does this workflow solve? Manually reading multiple articles, extracting key insights, and writing a clean, professional LinkedIn post is a time-consuming process. This workflow automates everything: from pulling topics, finding related articles, summarizing them using AI, and even generating a matching image to accompany the post. It ensures faster content turnaround, more consistency, and less manual effort. 🔁 What this workflow does This workflow starts manually and retrieves one topic marked as “To do” from a Google Sheet. That topic is used as a search term for Dumpling AI’s search endpoint, which scrapes and returns the top three article contents related to the topic. These articles are sent to a LangChain agent powered by GPT-4o, which analyzes and summarizes the content into a LinkedIn post in a friendly, insightful tone. It also generates an image prompt for the post. After generating the post and image prompt, the data is extracted using a Set node. The prompt is sent to Dumpling AI’s image generation endpoint, which returns an image URL. Finally, the post text, image prompt, image URL, and status update (“created”) are saved back to the original row in Google Sheets. 🛠️ Workflow Breakdown Manual Trigger – Starts the automation. Google Sheets (Get Topic) – Searches for the first row in your content pipeline sheet where the “status” is “To do”. HTTP Request (Dumpling AI Search) – Uses the topic as a search query to pull 3 article contents using Dumpling AI’s API. Set LangChain GPT Model – Defines GPT-4o as the LLM for the LangChain Agent. LangChain Agent (Summarize & Generate) – Summarizes all 3 articles and generates a LinkedIn post and a related image prompt. Set (Extract Data) – Extracts postText and imagePrompt from the LangChain agent output. HTTP Request (Dumpling Image Gen) – Sends imagePrompt to Dumpling AI’s image generation endpoint. Update Google Sheets – Writes the post, image prompt, and image URL back to the sheet and changes the row status to “created”. ⚙️ Setup Instructions Dumpling AI Sign up at Dumpling AI Get your API key and connect it in the HTTP Request nodes (Search and Image endpoints) Use the /search endpoint to retrieve article content Use the /generate-image endpoint to create the image Google Sheets Create a spreadsheet with columns: topic, status, postText, imagePrompt, imageURL Add sample topics and set their status to To do LangChain (GPT-4o) Connect your OpenAI credentials to n8n Make sure GPT-4o is available in your OpenAI account Use the LangChain node to process multi-input summarization and generate a social media caption Customize the Prompt (Optional) Adjust the Set node to tweak the input format sent to the LangChain agent Add constraints like tone, hashtags, or emojis to fit your brand style 🧠 How to Customize This Workflow Change the content source (RSS feed, Notion DB, etc.) instead of Google Sheets Add a scheduler node to run this automatically every morning or weekly Use Airtable instead of Google Sheets for more control and filtering Send the final post to LinkedIn using the Buffer or LinkedIn API Add a Telegram or Slack notification when new content is ready for approval

Who is this for? This template is for any website owner, digital agency, or compliance officer operating within the European Union. It's designed for users who need to comply with the upcoming European Accessibility Act (EAA) but may not have deep technical or legal expertise. Disclaimer This workflow uses an npm package called "cheerio" to work with the specified URLs HTML code. Installing packages is only possible in self hosting. What problem is this workflow solving? / Use Case Starting June 28, 2025, the European Accessibility Act (EAA) mandates that most websites offering products or services in the EU must be accessible and publish a formal Accessibility Statement. Manually creating this legal document is complex, requiring both a technical site analysis and knowledge of specific legal requirements. This workflow automates the generation of a compliant first draft, saving significant time and effort. What this workflow does After you input your details (like website URL and API key) in a central configuration node, this workflow automatically: Scans your live website for accessibility issues using the powerful WAVE API. Processes the scan results to identify the main problem areas. Instructs a Google Gemini AI agent with a specialized legal prompt based on the European Accessibility Act. Generates a formal Accessibility Statement in your desired language. Saves the statement as an .html file and sends it to you as an email attachment. Setup This workflow is designed for a quick setup: Configure All Variables: Click the 'CHANGE THESE: dependencies' node. This is your central control panel. Fill in all the values, including your WAVE API Key, the URL to analyze, company details, and desired output language. Set Up Credentials: You will need to connect your Google accounts for the workflow to run. Gemini: Click the 'gemini 2.5 pro' node, click the gear icon (⚙️) next to the "Credential" field, and connect your Google Gemini API credentials. Gmail: Click the 'Send report by email' node and connect your Gmail account to allow sending the final report. Activate & Execute: Make sure the workflow is active in the top-right corner, then click 'Execute Workflow' to run your first analysis. How to customize this workflow to your needs This template is a great starting point for any EU country. Here's how to adapt it: Localize for Your Country (Important!):* The generated statement contains a placeholder for the "Enforcement Procedure". You *must* edit the prompt in the *'Accessibility Statement Generator'** node to replace this placeholder with the name and link to your specific country's official enforcement body. Change the AI:** Swap the Google Gemini node for any other AI model, like OpenAI or Anthropic Claude, by replacing the node and connecting it to the agent. Change the Trigger:* Replace the *'When clicking ‘Execute workflow’'** node with a Form Trigger or Webhook Trigger to run this workflow based on external inputs, for example, to offer this analysis as a service to your clients.

🧾 Generate Project Summary from meeting transcript Who’s it for 🤝 Project managers looking to automate client meeting summaries Client success teams needing structured deliverables from transcripts Agencies and consultants who want consistent, repeatable documentation How it works / What it does ⚙️ Trigger: Manual or webhook trigger kicks off the workflow. Get meeting transcript: Reads the raw transcript from a specified Google Docs file. Generate summary: Sends transcript + instructions to OpenAI (gpt-4.1-mini) to produce a structured project summary. Convert to HTML: Transforms the LLM-generated Markdown into styled HTML. Prepare request: Wraps HTML and metadata into a multipart request body. Create Google Doc: Uploads the new “Project Summary” document into your Drive folder. How to set up 🛠️ Credentials Google Docs & Drive OAuth2 credentials OpenAI API key (gpt-4.1-mini) Nodes configuration Manual Trigger / webhook node Google Docs “Get meeting transcript” node: set documentURL AI Chat Model node: select gpt-4.1-mini Markdown node: enable tables & emoji Google Drive “CreateGoogleDoc” node: set target folder ID Paste in your IDs Update documentURL to your transcript doc Update google_drive_folder_id in the Set node Execute Click “Execute Workflow” or call via webhook Requirements 📋 n8n Google OAuth2 scopes for Docs & Drive OpenAI account with GPT-4.1-mini access A Google Drive folder to store summaries How to customize ✨ Output format**: Edit the Markdown prompt in the ChainLlm node to adjust headings or tone Timeline section**: Extend LLM prompt template with your own phase table Styling**: Tweak inline CSS in the Code node (Prepare_Request) for fonts or margins Trigger**: Swap Manual Trigger for HTTP/Webhook trigger to integrate with other tools Language model**: Upgrade to a different model by changing model.value in the AI node

How it Works This workflow fetches top news headlines every 10 minutes from NewsAPI, summarizes them using OpenAI's GPT-4o model, and sends a concise email digest to a list of recipients defined in a Google Spreadsheet. It's ideal for anyone who wants to stay updated with the latest news in a short, digestible format. 🎯 Use Case Professionals who want summarized daily news Newsletters or internal communication updates Teams that require contextual summaries of the latest events Setup Instructions 1. Upload the Spreadsheet File name: Emails Column: Email with recipient addresses 2. Configure Google Sheets Nodes Connect your Google account to: Email List Send Email 3. Add API Credentials NewsAPI Key** → for fetching top headlines OpenAI API Key** → for summarizing headlines Gmail Account** → for sending the email digest 4. Activate the Workflow Once active, the workflow runs every 10 minutes via a cron trigger Summarized news is sent to the list of emails in the spreadsheet 🔁 Workflow Logic Trigger: Every 10 minutes via Cron Fetch News: HTTP request to NewsAPI for top headlines Summarize: Headlines are passed to OpenAI's GPT-4o for 5-bullet summary Read Recipients: Google Sheet is used to collect email recipients Send Email: Summary is formatted and sent via Gmail 🧩 Node Descriptions | Node Name | Description | |-----------|-------------| | Cron | Triggers the workflow every 10 minutes. | | HTTP Request - NewsAPI | Fetches top news headlines using NewsAPI. | | Set | Formats or structures raw news data before processing. | | AI Agent | Summarizes the news content using OpenAI into 5 bullet points. | | Email List | Reads recipient email addresses from the 'Emails' Google Spreadsheet. | | Send Email | Sends the email digest to all recipients using Gmail. | 🛠️ Customization Tips Modify the AI prompt for tone, length, or content type Send summaries to Slack, Telegram, or Notion instead of Gmail Adjust cron interval for more/less frequent updates Change email formatting (HTML vs plain text) 📎 Required Files | File Name | Purpose | |-----------|---------| | Emails spreadsheet | Google Sheet containing the list of email recipients | | daily_news.json | Main n8n workflow file to automate daily news digest | 🧪 Testing Tips Add 1–2 test email addresses in your spreadsheet Temporarily change the Cron node to run every minute for testing Check email inbox for delivery and formatting Inspect the execution logs for API errors or formatting issues 🏷 Suggested Tags & Categories #News #OpenAI #Automation #Email #Digest #Marketing