Workflow Templates

Auto-generate & post content using AI This workflow helps you create daily content using just a topic prompt. It writes a tweet, generates an image, and publishes across Twitter, Facebook, and LinkedIn — all on autopilot. How it works Triggers daily at 10 PM to start the flow Uses OpenAI to generate a niche topic title Writes a short-form post (tweet style) with hashtags Generates a Japanese anime-style image for visual context Saves everything in Google Sheets Publishes automatically on Twitter, LinkedIn, and Facebook Set up steps You’ll need OpenAI, Google Sheets, and social media credentials (Twitter, Facebook, LinkedIn) Takes about 10–15 minutes to configure if you already have the credentials ready Make sure your Sheet and API keys are properly linked before activating 📝 Keep detailed notes inside the workflow with sticky notes for easier handoff or collaboration.

This workflow analyzes images submitted via a form using OpenAI Vision, then delivers the analysis result directly to your Telegram chat. ✅ Use case examples: • Users submit screenshots for instant AI interpretation • Automated document or receipt analysis with Telegram delivery • Quick OCR or image classification workflows ⸻ ⚙️ Setup Guide Form Submission Trigger • Connect your form app (e.g. Typeform, Tally, or n8n’s own webhook form) to the On form submission trigger node. • Ensure it sends the image file or URL as input. OpenAI Vision Analysis • In the OpenAI node, select Analyze Image operation. • Provide your OpenAI API key and configure the prompt to instruct the model on what to analyze (e.g. “Describe this receipt in detail”). Set Telegram Chat ID • Use this manual node to input your Telegram Chat ID for delivery. • Alternatively, automate this with a database lookup or user session if building for multiple users. Telegram Delivery Node • Connect your Telegram Bot to n8n using your bot token. • Set up the sendMessage operation, using the analysis result from the previous node as the message text. Testing • Click Execute workflow. • Submit an image via your form and confirm it delivers to your Telegram as expected.

Scrape Latest 20 TechCrunch Articles Who is this for? This workflow is designed for developers, researchers, and data analysts who need to track the latest trending repositories on GitHub. It is useful for anyone who wants to stay updated on popular open-source projects without manually browsing GitHub’s trending page. What problem is this workflow solving? Manually checking GitHub’s trending repositories daily can be time-consuming and inefficient. This workflow automates the extraction of trending repositories, providing structured data including repository name, author, description, programming language, and direct repository links. What this workflow does This workflow scrapes the trending repositories from GitHub’s trending page and extracts essential metadata such as repository names, languages, descriptions, and URLs. It processes the extracted data and structures it into an easy-to-use format. Setup Ensure you have n8n installed and configured. Import this workflow into your n8n instance. Run the workflow manually or schedule it to execute at regular intervals. (Optional) Customize the extracted data or integrate it with other systems. How to customize this workflow to your needs Modify the HTTP request node to target different GitHub trending categories (e.g., specific programming languages). Add further processing steps such as filtering repositories by stars, forks, or specific keywords. Integrate this workflow with Slack, email, or a database to store or notify about trending repositories. Workflow Steps Trigger execution manually using the "When clicking ‘Test workflow’" node. Send an HTTP request to fetch GitHub’s trending page using "Request to Github Trend". Extract the trending repositories box from the HTML response using "Extract Box". Extract all repository data including names, authors, descriptions, and languages using "Extract all repositories". Convert extracted data into a structured list for easier processing using "Turn to a list". Extract detailed repository information using "Extract repository data". Format and set variables to ensure clean and structured data output using "Set Result Variables". Note: Since GitHub’s trending page updates dynamically, ensure you run this workflow periodically to capture the latest trends.

How it works This workflow automatically scrapes real estate listings from Zillow and sends them to a Telegram channel. Key Steps Scheduled Trigger - Runs the workflow at specified intervals to find new listings. AI-Powered Scraping - Uses ScrapeGraphAI to extract property information from Zillow. Data Formatting - Processes and structures the scraped data for Telegram messages. Telegram Integration - Sends formatted listing details to your specified Telegram channel. Set up steps Setup time: 5-10 minutes Configure ScrapeGraphAI credentials - Add your ScrapeGraphAI API key. Set up Telegram connection - Connect your Telegram account and specify the target channel. Customize the Zillow URL - Update the URL to target specific locations or search criteria. Adjust schedule - Modify the trigger timing based on how frequently you want to check for new listings.

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: 1. 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. 2. 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.

Google Sheets Company Enrichment with Explorium MCP Template Download the following json file and import it to a new n8n workflow: google\_sheets\_enrichment.json Overview This n8n workflow template enables automatic enrichment of company information in your Google Sheets. When you add a new company or update existing company details (name or website), the workflow automatically fetches additional business intelligence data using Explorium MCP and updates your sheet with: Business ID NAICS industry code Number of employees (range) Annual revenue (range) Key Features Automatic Triggering**: Monitors your Google Sheet for new rows or updates to company name/website fields Smart Processing**: Only processes new or modified rows, not the entire sheet Data Validation**: Ensures both company name and website are present before processing Error Handling**: Processes each row individually to prevent one failure from affecting others Powered by AI**: Uses Claude Sonnet 4 with Explorium MCP for intelligent data enrichment Prerequisites Before setting up this workflow, ensure you have: n8n instance (self-hosted or cloud) Google account with access to Google Sheets Anthropic API key for Claude Explorium MCP API key Installation & Setup Step 1: Import the Workflow Create a new workflow. Download the workflow JSON from above. In your n8n instance, go to Workflows → Add Workflow → Import from File Select the JSON file and click Import Step 2: Create Google Sheet Create a new google sheet (or make a copy of this template) Your Google Sheet must have the following columns (exact names): name - Company name website - Company website URL business_id - Will be populated by the workflow naics - Will be populated by the workflow number_of_employees_range - Will be populated by the workflow yearly_revenue_range - Will be populated by the workflow Step 3: Configure Google Sheets Credentials You'll need to set up two Google credentials: Google Sheets Trigger Credentials: Click on the Google Sheets Trigger node Under Credentials, click Create New If working on n8n Cloud, Click the 'Sign in with Google' button Grant permissions to read and monitor your Google Sheets If working on n8n Instance, Follow the OAuth2 authentication process here Fill the Client ID and Client Secret fields Google Sheets Update Credentials: Click on the Update Company Row node Under Credentials, select the same credentials or create new ones (The same you did above) Ensure permissions include write access to your sheets Step 4: Configure Anthropic Credentials Click on the Anthropic Chat Model node Under Credentials, click Create New Enter your Anthropic API key Save the credentials Step 5: Configure Explorium MCP Credentials Click on the MCP Client node Under Credentials, click Create New (Header Auth) Fill the Name field with api_key Fill the Value field with your Explorium API Key Save the credentials Step 6: Link Your Google Sheet In the Google Sheets Trigger node: Select your Google Sheet from the dropdown Select the worksheet (usually "Sheet1") In the Update Company Row node: Select the same Google Sheet and worksheet Ensure the matching column is set to row_number Step 7: Activate the Workflow Click the Active toggle in the top right to activate the workflow The workflow will now monitor your sheet every minute for changes How It Works Workflow Process Flow Google Sheets Trigger: Polls your sheet every minute for new rows or changes to name/website fields Filter Valid Rows: Validates that both company name and website are present Loop Over Items: Processes each company individually AI Agent: Uses Explorium MCP to: Find the company's business ID Retrieve firmographic data (revenue, employees, NAICS code) Format Output: Structures the data for Google Sheets Update Company Row: Writes the enriched data back to the original row Trigger Behavior First Activation**: May process all existing rows to establish a baseline Ongoing Operation**: Only processes new rows or rows where name/website fields change Polling Frequency**: Checks for changes every minute Usage Adding New Companies Add a new row to your Google Sheet Fill in the name and website columns Within 1 minute, the workflow will automatically: Detect the new row Enrich the company data Update the remaining columns Updating Existing Companies Modify the name or website field of an existing row The workflow will re-process that row with the updated information All enrichment data will be refreshed Monitoring Executions In n8n, go to Executions to see workflow runs Each execution shows: Which rows were processed Success/failure status Detailed logs for troubleshooting Troubleshooting Common Issues All rows are processed instead of just new/updated ones Ensure the workflow is activated, not just run manually Manual test runs will process all rows First activation may process all rows once No data is returned for a company Verify the company name and website are correct Check if the company exists in Explorium's database Some smaller or newer companies may not have data available Workflow isn't triggering Confirm the workflow is activated (Active toggle is ON) Check that changes are made to the name or website columns Verify Google Sheets credentials have proper permissions Authentication errors Re-authenticate Google Sheets credentials Verify Anthropic API key is valid and has credits Check Explorium Bearer token is correct and active Error Handling The workflow processes each row individually, so if one company fails to enrich: Other rows will still be processed The failed row will retain its original data Check the execution logs for specific error details Best Practices Data Quality: Ensure company names and websites are accurate for best results Website Format: Include full URLs (https://example.com) rather than just domain names Batch Processing: The workflow handles multiple updates efficiently, so you can add several companies at once Regular Monitoring: Periodically check execution logs to ensure smooth operation API Limits & Considerations Google Sheets API**: Subject to Google's API quotas Anthropic API**: Each enrichment uses Claude Sonnet 4 tokens Explorium MCP**: Rate limits may apply based on your subscription Support For issues specific to: n8n platform**: Consult n8n documentation or community Google Sheets integration**: Check n8n's Google Sheets node documentation Explorium MCP**: Contact Explorium support for API-related issues Anthropic/Claude**: Refer to Anthropic's documentation for API issues Example Use Cases Sales Prospecting: Automatically enrich lead lists with company size and revenue data Market Research: Build comprehensive databases of companies in specific industries Competitive Analysis: Track and monitor competitor information Investment Research: Gather firmographic data for potential investment targets

Automate Video Creation with Luma AI Dream Machine and Airtable (Part 2) Description This is the second part of the Luma AI Dream Machine automation. It captures the webhook response from Luma AI after video generation is complete, processes the data, and automatically updates Airtable with the video and thumbnail URLs. This completes the end-to-end automation for video creation and tracking. 👉 Airtable Base Template 👉 Tutorial Video Setup 1. Luma AI Setup Ensure you’ve created an account with Luma AI and generated an API key. Confirm that the API key has permission to manage video requests. 2. Airtable Setup Make sure your Airtable base includes the following fields (set up in Part 1): Use the Airtable Base Template linked above to simplify setup. Generation ID** – To match incoming webhook data. Status** – Workflow status (e.g., "Done"). Video URL** – Stores the generated video URL. Thumbnail URL** – Stores the thumbnail URL. 3. n8n Setup Ensure that the n8n workflow from Part 1 is set up and configured. Import this workflow and connect it to the webhook callback from Luma AI. How It Works 1. Webhook Trigger The Webhook node listens for a POST response from Luma AI once video generation is finished. The response includes: Video URL – Direct link to the video. Thumbnail URL – Link to the video thumbnail. Generation ID – Used to match the record in Airtable. 2. Process Webhook Data The Set node extracts the video data from the webhook response. The If node checks if the video URL is valid before proceeding. 3. Store in Airtable The Airtable node updates the record with: Video URL – Direct link to the video. Thumbnail URL – Link to the video thumbnail. Status – Marked as "Done." Uses the Generation ID to match and update the correct record. Why This Workflow is Useful ✅ Automates the completion step for video creation ✅ Ensures accurate record-keeping by matching generation IDs ✅ Simplifies the process of managing and organizing video content ✅ Reduces manual effort by automating the update process Next Steps Future Enhancements** – Adding more complex post-processing, video trimming, and multi-platform publishing.

🤖 AI Customer Support Agent with Google Docs Knowledge (Telegram + OpenAI) This no-code workflow turns your Telegram bot into an intelligent, always-on AI support agent that references your business documentation in Google Docs to respond to customer queries—instantly and accurately. Watch full step-by-step video tutorial of the build here: https://youtu.be/Mlv7CjGO7wI 🔧 How it works: Telegram Trigger – Captures incoming messages from users on your Telegram bot Langchain AI Agent (OpenAI GPT) – Interprets the message and uses RAG (retrieval-augmented generation) techniques to craft an answer Google Docs Tool – Connects to and retrieves context from your specified Google Doc (e.g. FAQ, SOPs, policies) Memory Buffer – Keeps track of recent chat history for more human-like conversations Telegram Reply Node – Sends the AI-generated response back to the user 💡 Use Cases: E-commerce customer service SaaS product onboarding Internal helpdesk bot for teams WhatsApp-style support for digital businesses 🧠 What makes this powerful: Supports complex questions by referencing a live Google Doc knowledge base Works in plain conversational language (no buttons or forms needed) Runs 24/7 with zero code Easily extendable to Slack, WhatsApp, or email support 🛠️ Tools used: Telegram Node (trigger + send) Langchain Agent with OpenAI GPT Google Docs Tool Memory Buffer Sticky Notes for easy understanding

Who’s it for 🎯 This workflow is ideal for outreach specialists, fundraisers, campaigners, and professionals who want to build authentic connections by researching prospects deeply and strategically. It helps users understand prospects’ backgrounds, interests, and mutual connections to craft effective outreach. How it works / What it does ⚙️ Using the Multi-tool Research Agent subworkflow, it analyzes both the prospector’s and prospect’s profiles, social media, and online presence. The workflow verifies identities, uncovers key connection points, and generates a comprehensive HTML report with actionable insights, conversation starters, and suggested engagement tactics. How to set up 🛠️ Import this workflow and the Multi-tool Research Agent subworkflow. Configure required API credentials. Provide inputs: prospector and prospect names, social media URLs, and outreach goal. Test the workflow to ensure accurate research and report generation. Requirements 📋 n8n instance with internet access Valid API keys Multi-tool Research Agent subworkflow installed and linked Optional email node for sending reports directly How to customize 🔧 Update input parameters to suit your outreach use case. Modify research prompts in the subworkflow for tone or focus. Customize the HTML report design for branding or format preferences. Attach an email node to send reports automatically or route output as needed. Use this workflow to power personalized, strategic outreach with data-driven insights.

Automated system for monitoring and analyzing competitor activities, funding rounds, and market movements using CrunchBase data. 🚀 What It Does Tracks competitor funding rounds Monitors leadership changes Analyzes investment patterns Identifies new market entries Tracks product launches 🎯 Perfect For Startup founders Business strategists Market analysts Investment professionals Corporate development ⚙️ Key Benefits ✅ Competitive intelligence ✅ Early warning system ✅ Market trend analysis ✅ Strategic insights ✅ Time-saving automation 🔧 What You Need CrunchBase API access n8n instance Google Sheets (for data storage) Notification preferences 📊 Tracking Metrics Funding amounts and rounds Investor networks Hiring trends Market expansion Product updates 🛠️ Setup & Support Quick Setup Start tracking in 20 minutes with our step-by-step guide 📺 Watch Tutorial 💼 Get Expert Support 📧 Direct Help Gain a competitive edge with automated tracking and analysis of your competitors' activities and strategies.

AI-Powered Daily Gmail Digest Summary using LangChain & OpenRouter This n8n template helps you automatically summarize your daily Gmail messages using OpenRouter's GPT model via LangChain. It generates a structured email digest highlighting key information, tasks, issues, and action items — all delivered to your inbox every morning. Who’s it for Busy professionals who want a quick overview of their daily emails Founders or managers needing to track team or client communication Anyone looking to automate inbox triage and reduce time spent on emails How it works / What it does This n8n workflow runs every morning at 7 AM, automatically: Fetches emails from the last 24 hours Collects important fields: sender, subject, and snippets Feeds them into an AI-powered agent (OpenRouter + LangChain) The AI: Extracts key topics, tasks, deadlines, and issues Formats the info clearly with a bullet-point summary Sends the final summarized report to your inbox How to set up Clone or import the workflow into your n8n instance Replace <Your Email ID> in the Code node with your actual Gmail address (or remove if not needed) Ensure your Gmail and OpenRouter credentials are set up in n8n Update the recipient email in the Send Summary node if you want it sent to a fixed address Activate the workflow once tested How to customize the workflow Change Summary Style:** Edit the system message in the LangChain Agent to match your tone (e.g. casual, business, detailed) Adjust Digest Time:** Change the Schedule Trigger to any preferred hour Customize Recipients:** Change or add recipients dynamically or statically in the Gmail send node Filter Email Type:** Modify the Gmail query in the Code node to include filters like from:, is:unread, subject:project

This n8n template offers a simple yet capable chatbot assistant who can answer course enquiries over SMS. Given the right access to data, AI Agents are capable of planning and performing relatively complex research tasks to get their answers. In this example, the agent must first understand the database schema, retrieve lists of values before generating it's own query to search over the database. Checkout the example database here - https://airtable.com/appO5xvP1aUBYKyJ7/shr8jSFDaghubDOrw How it works A Twilio trigger gives us the ability to receive SMS input into our workflow via webhook. The message is then directed to our AI agent who is instructed to assist the user and use the course database as reference. The database is an Airtable base. The agent autonomously figures out which tool it needs to use and generates it's own "filter_by_formula" query to search over the available courses. On successful search results, the Agent can then use this information to answer the user's query. The Agent's output is logged in a second sheet of the Airtable base. We can use this later for analysis and lead gen. Finally, the response is sent back to the user through SMS using Twilio. How to use Ensure your Twilio number is set to forward messages to this workflow's webhook URL. Configure and update the course database as required. If you're not interested in courses, you can swap this out for inventory, deliveries or any other data relevant to your business. Ask questions like: "Can you help me find suitable courses to fill my Wednesday mornings?" "Which courses are being instructed by profession Lee?" "I'm interested in creative arts. What courses are available which could be relevant to me?" Requirements Twilio for SMS receiving and sending OpenAI for LLM and Agent Airtable for Course Database Customising this workflow Add additional tools and expand the range of queries the agent is able to answer or assist with. Not using Airtable? This technique also works with SQL databases like PostgreSQL.