Workflow Templates

This automation runs daily at 8:00 AM to automatically collect and organize business idea insights from IdeaBrowser.com into a structured Google Docs document. The workflow performs the following actions: Data Collection: Fetches the "idea of the day" content from ideabrowser.com/idea-of-the-day using authenticated HTTP requests. Content Processing: Extracts the base idea path and generates links to all related insight pages including value ladder, market analysis, proof signals, execution plans, and community insights. The workflow also cleans the HTML content to extract readable text. Document Creation: Creates a new Google Docs document in a specified folder with a timestamp and idea name in the title format. Content Aggregation: Systematically visits each insight page (main idea page, value ladder, why now, proof signals, market gap, execution plan, value equation, value matrix, ACP, community signals, and keywords) and collects their content. Document Population: Processes the collected content through markdown formatting and appends it to the Google Docs document, creating a comprehensive report of the daily business idea with all its associated insights. Automated Scheduling: Runs automatically every day at 8 AM, ensuring you have fresh business idea analysis delivered to your Google Drive without manual intervention. This automation is perfect for entrepreneurs, business analysts, or anyone who wants to stay updated with curated business ideas and their detailed market analysis in an organized, searchable format.

What it does This is a simplistic demo workflow showing how to extract a license plate number from an image of a car submitted via a form – or in more general terms showcasing how you can: use a form trigger to upload files and feed it into an LLM use a changeable LLM model for image-to-text analysis Set up steps Import the workflow Ensure you have registered and account, purchased some credits and created and API key for OpenRouter.ai Create/adapt the OpenRouter credential with your indivial API key for OpenRouter "Test workflow" and submit an image of a car with license plate to extract its number How to adapt By changing the "prompt" in th "Settings" node you can quickly adapt this exemplatory workflow to other image-to-text use cases, such as: summarization: "summarize what's seen in the image" location finding: "identify the location where the image was taken" text extraction: "extract all text from the image and return it as markdown" Thanks to using OpenRouter, you also can quickly experiment with finding good model choices by simply changing the "model" in the "Settings" node. The following models gave good results for this demo use-case: google/gemini-2.0-flash-001 meta-llama/llama-3.2-90b-vision-instruct openai/gpt-4o The llama-3.2-11b and even claude-3.5-sonnet didn't recognize all characters in all test images. Using a generic LLM-model offers a quick way of prototyping an image-to-text application. For specific use cases in serious and scalable production deployments, consider using an API based service specifically made to that purpose, such as: Google Cloud Vision API Microsoft Azure Computer Vision Azure AI Document Intelligence Amazon Textract

Tags: Supply Chain, Logistics, AI Agents Context Hey! I’m Samir, a Supply Chain Data Scientist from Paris, and the founder of LogiGreen Consulting. We design tools to help companies improve their logistics processes using data analytics, AI, and automation—to reduce costs and minimize environmental impacts. >Let’s use N8N to improve logistics operations! 📬 For business inquiries, you can add me on LinkedIn Who is this template for? This workflow template is designed for logistics or manufacturing operations that receive orders by email. The example above illustrate the challenge we want to tackle using an AI Agent to parse the information and load them in a Google sheet. If you want to understand how I built this workflow, check my detailed tutorial: 🎥 Step-by-Step Tutorial How does it work? The workflow is connected to a Gmail Trigger to open all the emails that include Inbound Order in their subject. The email is parsed by an AI Agent equipped with OpenAI's GPT to collect all the information. The results are pulled in a Google Sheet. These orderlines can then be transferred to warehouse teams to prepare *order receiving. What do I need to get started? You’ll need: Gmail and Google Drive Accounts** with the API credentials to access it via n8n An OpenAI API key (GPT-4o) for the chat model. A Google Sheet with these columns: PO_NUMBER, EXPECTED_DELIVERY DATE, SKU_ID, QUANTITY Next Steps Follow the sticky notes in the workflow to configure each node and start using AI to support your logistic operations. 🚀 Curious how N8N can transform your logistics operations? 📬 Let’s connect on LinkedIn Notes An example of email is included in the template so you can try it with your mailbox. This workflow was built using N8N version 1.82.1 Submitted: March 28, 2025

Replace manual task prioritization with intelligent AI reasoning that thinks like a Chief Operating Officer. This workflow automatically fetches your Asana tasks every morning, analyzes them using advanced AI models, and delivers the single most critical task with detailed reasoning - ensuring your team always focuses on what matters most. ✨ What This Workflow Does: 📋 Automated Task Collection**: Fetches all assigned Asana tasks daily at 9 AM 🤖 AI-Powered Analysis**: Uses OpenAI GPT-4 to evaluate urgency, impact, and strategic importance 🎯 Smart Prioritization**: Identifies the #1 most critical task with detailed reasoning 🧠 Contextual Memory**: Leverages vector database for historical context and pattern recognition 💾 Structured Storage**: Saves prioritized tasks to PostgreSQL with full audit trail 🔄 Continuous Learning**: Builds organizational knowledge over time for better decisions 🔧 Key Features: Daily automation** with zero manual intervention Context-aware AI** that learns from past prioritization decisions Strategic reasoning** explaining why each task is prioritized Vector-powered memory** using Pinecone for intelligent context retrieval Clean structured output** with task names, priority levels, and detailed justifications Database integration** for reporting and historical analysis 📋 Prerequisites: Asana account with API access OpenAI API key (GPT-4 recommended) PostgreSQL database Pinecone account (for vector storage and context) 🎯 Perfect For: Operations teams managing multiple competing priorities Startups needing systematic task management Project managers juggling complex workflows Leadership teams requiring strategic focus Any organization wanting AI-driven operational intelligence 💡 How It Works: Morning Automation: Triggers every day at 9 AM Data Collection: Pulls all relevant tasks from Asana AI Analysis: Evaluates each task using COO-level strategic thinking Context Retrieval: Searches vector database for similar past tasks Smart Prioritization: Identifies the single most important task Structured Output: Delivers priority level with detailed reasoning Data Storage: Saves results for reporting and continuous improvement 📦 What You Get: Complete n8n workflow with all AI components configured PostgreSQL database schema for task storage Vector database setup for contextual intelligence Comprehensive documentation and setup guide Sample task data and output examples 💡 Need Help or Want to Learn More? Created by Yaron Been - Automation & AI Specialist 📧 Support: Yaron@nofluff.online 🎥 YouTube Tutorials: https://www.youtube.com/@YaronBeen/videos 💼 LinkedIn: https://www.linkedin.com/in/yaronbeen/ Discover more advanced automation workflows and AI integration tutorials on my channels! 🏷️ Tags: AI, OpenAI, Asana, Task Management, COO, Prioritization, Automation, Vector Database, Operations, GPT-4

This workflow is designed to compare two datasets (Dataset 1 and Dataset 2) based on a common field, "fruit," and provide insights into the differences. Here are the steps: Manual Trigger: The workflow begins when a user clicks "Execute Workflow." Dataset 1: This node generates the first dataset containing information about fruits, such as apple, orange, grape, strawberry, and banana, along with their colors. Dataset 2: This node generates the second dataset, also containing information about fruits, but with some variations in color. For example, it includes a "kiwi" with the color "mostly green." Compare Datasets: The "Compare Datasets" node takes both datasets and compares them based on the "fruit" field. It identifies any differences or matches between the two datasets. In summary, this workflow is used to compare two datasets of fruits and their colors, identify differences, and provide guidance on how to explore the comparison results.

What it is- Very simple connection to your Discord MCP Server and 4o. How to set it up- Just specify your MCP Server's url, select your OpenAI credential, and you're set! How to use it- You can now send a chat message to the production URL from anywhere and the actions will occur on discord! It really is that easy. Note: If you don't yet have a Discord MCP server set up, there is a template called "Discord MCP Server" to get you a jumpstart! Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free? Join the community

This workflow has multiple functionalities. It starts with a manual trigger, "When clicking 'Execute Workflow'", that activates two separate paths. The first path takes a preset string "Tell me a joke" and processes it through a custom Language Learning Model (LLM) chain node. This node interacts with an OpenAI node for query processing. The second path takes another preset string "What year was Einstein born?" and passes it to an "Agent" node. This agent further interacts with a Chat OpenAI node and a custom Wikipedia node to produce the required information. The workflow uses both built-in and custom nodes, and integrates with OpenAI for both paths. It's built for experimenting with language models, specifically in the context of conversational agents and information retrieval. Note that to use this template, you need to be on n8n version 1.19.4 or later.

This workflow allows you to add positive feedback messages to a table in Notion. Prerequisites Create a Typeform that contains Long Text filed question type to accepts feedback from users. Get your Typeform credentials by following the steps mentioned in the documentation. Follow the steps mentioned in the documentation to create credentials for Google Cloud Natural Language. Create a page on Notion similar to this page. Create credentials for the Notion node by following the steps in the documentation. Follow the steps mentioned in the documentation to create credentials for Slack. Follow the steps mentioned in the documentation to create credentials for Trello. Typeform Trigger node: Whenever a user submits a response to the Typeform, the Typeform Trigger node will trigger the workflow. The node returns the response that the user has submitted in the form. Google Cloud Natural Language node: This node analyses the sentiment of the response the user has provided and gives a score. IF node: The IF node uses the score provided by the Google Cloud Natural Language node and checks if the score is positive (larger than 0). If the score is positive we get the result as True, otherwise False. Notion node: This node gets connected to the true branch of the IF node. It adds the positive feedback shared by the user in a table in Notion. Slack node: This node will share the positive feedback along with the score and username to a channel in Slack. Trello node: If the score is negative, the Trello node is executed. This node will create a card on Trello with the feedback from the user.

If you have made some investments in cryptocurrency, this workflow will allow you to create an Airtable base that will update the value of your portfolio every hour. You can then track how well your investments are doing. You can check out my Airtable base to see how it works or even copy my base so that you can customize this workflow for yourself. To implement this workflow, you will need to update the Airtable nodes with your own credentials and make sure that they are pointing to your Airtable

📩 Gmail → GPT → Supabase | Task Extractor This n8n workflow automates the extraction of actionable tasks from unread Gmail messages using OpenAI's GPT API, stores the resulting task metadata in Supabase, and avoids re-processing previously handled emails. ✅ What It Does Triggers on a schedule to check for unread emails in your Gmail inbox. Loops through each email individually using SplitInBatches. Checks Supabase to see if the email has already been processed. If it's a new email: Formats the email content into a structured GPT prompt Calls ChatGPT-4o to extract structured task data Inserts the result into your emails table in Supabase 🧰 Prerequisites Before using this workflow, you must have: An active n8n Cloud or self-hosted instance A connected Gmail account with OAuth credentials in n8n A Supabase project with an emails table and: ALTER TABLE emails ADD CONSTRAINT unique_email_id UNIQUE (email_id); An OpenAI API key with access to GPT-4o or GPT-3.5-turbo 🔐 Required Credentials | Name | Type | Description | |-----------------|------------|-----------------------------------| | Gmail OAuth | Gmail | To pull unread messages | | OpenAI API Key | OpenAI | To generate task summaries | | Supabase API | HTTP | For inserting rows via REST API | 🔁 Environment Variables or Replacements Supabase_TaskManagement_URI → e.g., https://your-project.supabase.co Supabase_TaskManagement_ANON_KEY → Your Supabase anon key These are used in the HTTP request to Supabase. ⏰ Scheduling / Trigger Triggered using a Schedule node Default: every X minutes (adjust to your preference) Uses a Gmail API filter: unread emails with label = INBOX 🧠 Intended Use Case > Designed for productivity-minded professionals who want to extract, summarize, and store actionable tasks from incoming email — without processing the same email twice or wasting GPT API credits. This is part of a larger system integrating GPT, calendar scheduling, and optional task platforms (like ClickUp). 📦 Output (Stored in Supabase) Each processed email includes: email_id subject sender received_at body (email snippet) gpt_summary (structured task) requires_deep_work (from GPT logic) deleted (initially false)

n8n Workflow: AI-Powered Stock Chart Analysis Bot for Telegram This is a powerful n8n automation workflow that integrates a Telegram bot with OpenAI's multimodal large language model (GPT-4 Vision) to provide users with real-time stock chart analysis. Workflow Breakdown Receive Image:** The workflow is initiated by a Telegram Trigger. It activates whenever a user sends an image (e.g., a stock's candlestick chart) to a designated Telegram chat, automatically downloading the file. Image Pre-processing:** To optimize the AI's performance and efficiency, the Edit Image node resizes the incoming image to a standard 512x512 pixel format. AI Vision Analysis:** The processed image is then passed to a LangChain Chain, which utilizes the OpenAI GPT-4 Vision model. A sophisticated system prompt instructs the AI to act as a professional stock analyst. Intelligent Interpretation:** The AI analyzes the image to identify the stock's name, price trend (uptrend, downtrend, or sideways), key support/resistance levels, and volume changes. It then generates a comprehensive analysis report combining technical indicators and market sentiment. Structured Output:** To ensure reliability and consistency, the AI's output is parsed into a specific JSON format. This structure includes a search_word (for the industry/sector) and the main content (the analysis text). Send Response:** Finally, the workflow extracts the content field from the JSON output and uses the Telegram node to send this professional analysis back to the user as a text message in the same chat. Key Features User-Friendly:** Users simply send an image to get an analysis, requiring no complex commands. Instant & Efficient:** The entire analysis and response process is fully automated and completed within moments. Professional-Grade Analysis:** Leverages the advanced image recognition and reasoning capabilities of GPT-4 Vision to deliver insights comparable to those of a human analyst. Reliable & Consistent:** The use of structured output ensures that the format of the response is always consistent and easy to read or process further.

🏖️ AI-Based Tour Itineraries via Email Using OpenAI & Pinecone Vector Search Overview This workflow automates the process of handling new tour package requests received via email, analyzes the request, and provides personalized tour package recommendations using AI and a vector database. It’s designed to streamline customer interactions and deliver quick, relevant responses. Precondition Create a Embedded Tour Package Database (refer to the link below): Pinecone Database setup Register and create API Keys for OpenAI, Pinecone Database. Copy Mail Credentials to access Email Inbox from n8n node This workflow automates the process of extracting tour information from PDF files stored in a Google Drive folder, processes and vectorizes the extracted data, and stores it in a Pinecone vector database for efficient querying. This is especially useful for building AI-powered search or recommendation systems for travel packages. 🛠️ Tools & Nodes Used Email Trigger (IMAP): Monitors the inbox for new tour package requests. Text Classifier: Categorizes incoming emails (e.g., New Request, Follow-up, Other). Code Node: Extracts and structures relevant data from the email (subject, sender, content, etc.). Tour Recommendation AI Agent: An AI agent that interprets the request and formulates a prompt for package recommendations. OpenAI & OpenRouter Chat Models: Used for natural language understanding and generating responses. Simple Memory: Maintains context for ongoing conversations. Pinecone Vector Store: Stores and retrieves tour packages using semantic search. Embeddings (OpenAI): Converts text data into vector embeddings for similarity search. Answer Questions with a Vector Store: Retrieves the most relevant packages from Pinecone. Send Email: Sends the AI-generated recommendations back to the customer. 🔄 Process & Flow Email Reception: The workflow starts with the Email Trigger (IMAP) node, which listens for new emails in the inbox. Classification: The Text Classifier node determines if the email is a new tour package request. Data Extraction: The Code node parses the email, extracting key details like sender, subject, and content. AI Agent Processing: The Tour Recommendation AI Agent receives the structured request and crafts a prompt for package recommendations. Vector Search: The agent queries the Pinecone Vector Store, which holds previously created tour packages, using OpenAI embeddings for semantic matching. Recommendation Generation: The AI agent selects the top 3 most relevant packages and generates a friendly, personalized response. Response Delivery: The Send Email node sends the recommendations back to the customer. 🚀 Recommendations & Improvements for Next Version Error Handling: Add error handling nodes to manage failed email parsing or AI response issues. Logging & Analytics: Integrate logging to track requests, recommendations, and customer responses for continuous improvement. Personalization: Enhance the AI agent to consider customer history or preferences for even more tailored recommendations. Multi-language Support: Add language detection and translation for international customers. Feedback Loop: Include a mechanism for customers to rate recommendations, feeding this data back into the system for improved future suggestions. Attachment Handling: Enable the workflow to process attachments (e.g., customer itineraries or preferences). Scalability: Consider batching or queueing requests if email volume increases. 💡 Conclusion This workflow demonstrates how n8n, combined with AI and vector databases, can automate and personalize customer service in the travel industry. With a few enhancements, it can become even more robust and customer-centric!