Workflow Templates

🧠 Writes original, thought-provoking blog posts using AI 🕓 Runs every 12 hours automatically ✍️ Publishes directly to Ghost blog with title, tags, and SEO meta 🔧 Features Scheduled every 12 hours OpenAI generates a multi-part blog post with metadata Markdown-compatible output (no HTML) Automatically published to Ghost CMS using authenticated API (🔐 no hardcoded keys) Fully modular and general-purpose — edit prompt for any blog theme! ⚙️ Nodes Overview Step Node Type Purpose 1️⃣ Schedule Trigger Runs every 12 hours 2️⃣ OpenAI Generates blog post + meta info 3️⃣ Code Extracts content, title, meta, and tags 4️⃣ Code Formats content as Ghost mobiledoc payload 5️⃣ HTTP Request Publishes post to Ghost via Admin API 📝 OpenAI Prompt (Generalized) Write a high-quality blog post on a creative or thought-provoking topic. The tone should be engaging and immersive. Length: 2–4 paragraphs. Then add a brief paragraph offering an alternative perspective or logical counterpoint. Finally, generate: Blog post title Meta description 5 tags 🔐 Notes ✅ No hardcoded API keys 🛠️ Ghost Admin API credentials must be set using the Credential Manager 📌 Prompt and Ghost URL are both easily customizable

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.

This workflow enables users to submit food images to a Telegram bot, which uses OpenAI’s GPT-4 Vision to identify the item and estimate its caloric value. The results are stored in Google Sheets and sent back to the user. What it does: Triggers on a photo sent via Telegram. Acknowledges the user with a sticky note message. Downloads the image file securely using Telegram's API. Sends the image to GPT-4 Vision with a prompt: “Describe this food and estimate its calories.” Logs the GPT response to a Google Sheet (with timestamp). Replies to the user with the result (e.g., food name and estimated calories). Use cases: Personal food tracking Nutrition logging via chat Meal journaling for fitness or health Requirements: Telegram Bot Token (via credentials) OpenAI GPT-4 Vision access Google Sheets credential with access to the target sheet Notes: You can extend this template to calculate daily totals, categorize meals (breakfast/lunch/dinner), or even integrate with calorie goals. The sticky note node confirms receipt to enhance UX. Ideal for wellness apps, chat-based food journals, or AI-powered health bots.

This guide walks you through building an intelligent AI Agent in n8n that routes tasks to the appropriate sub-agent using the new @n8n/n8n-nodes-langchain agent framework. You’ll create a Manager Agent that evaluates user input and delegates it to either an Email Agent or a Data Agent—each with its own role, memory, and OpenAI model. This is perfect for use cases where you want a single entry point but intelligent branching behind the scenes. 🔧 Step 1: Set Up the Manager Agent Start by dragging in an Agent node and name it something like ManagerAgent. This agent will act as the “brain” of your system, analyzing the user's input and determining whether it should be handled by the email-writing sub-agent or the data-summary sub-agent. Open the node’s settings and paste the following into the System Message: You are an AI Manager that delegates tasks to specialized agents. Your job is to analyze the user's message and decide whether it requires: An EmailAgent for writing outreach, follow-up, or templated emails, or A DataAgent for tasks involving data summaries, metrics, or analysis. Send the instructions to the sub agents. This instruction gives the Manager Agent clarity on what roles exist and what types of tasks belong to each one. 🧠 Step 2: Add Memory to the Manager Agent Drag in a Memory (BufferWindow) node and label it Manager Memory. Connect it to the ai_memory input of the Manager Agent. This ensures the agent can remember recent inputs and outputs from the user and agents during the conversation. No extra configuration is needed in this memory node—just connect it to the agent. 🔌 Step 3: Connect a Language Model to the Manager Agent Next, add a Language Model node and choose OpenAI Chat Model. Select a model like gpt-4o-mini or gpt-4, depending on what you have access to. Under Credentials, connect your OpenAI API key. If you haven’t created this credential yet: Click "OpenAI API" under Credentials. Choose "Create New". Paste your OpenAI API key (found at https://platform.openai.com/account/api-keys). Save it and return to the workflow. Once the model is set, connect it to the ai_languageModel input of the Manager Agent. ✉️ Step 4: Create the Email Agent Tool Now you’ll create a specialized sub-agent that only writes emails. Add an Agent Tool node and call it EmailAgent. In the tool’s settings, describe its job clearly. For example: Writes professional, friendly, or action-oriented emails based on instructions. Then scroll down to the System Message section and enter the following: You are a professional Email Writing Assistant. You write polished, effective emails for tasks such as outreach, follow-ups, and client communication. Follow the instruction provided exactly and return only the email content. Use a warm, business-appropriate tone. For the text input field, use the expression: {{ $fromAI('Prompt__User_Message_', ``, 'string') }} This allows the Email Agent to receive exactly what the Manager Agent wants it to handle. Add another Memory node and link it to this tool to help it maintain short-term context. Then add a second Language Model node, configured just like the first one (you can even clone it), and connect it to the EmailAgent. Finally, connect this entire EmailAgent setup back to the ManagerAgent by attaching it to its ai_tool input. 📊 Step 5: Create the Data Agent Tool Repeat the same steps, but this time for data summaries and analysis. Add another Agent Tool node and name it DataAgent. In the Tool Description, write something like: Responds to instructions requiring metrics, summaries, or data analysis explanations. For its input text field, you can use: {{json.query}} If desired, provide a system message that gives the agent more detailed instruction on how to behave: You are a helpful Data Analyst. Summarize trends, explain metrics, and break down data clearly based on user instructions. As with the EmailAgent, you’ll also need: A dedicated Memory node A dedicated Language Model node A connection to the ai_tool input of the Manager Agent Now the Manager Agent has two tools it can delegate to: one for communication and one for insights. 🧪 Step 6: Test Your AI Agent System Deploy the workflow and start testing by sending prompts like: > “Write a cold outreach email to a software company.” The ManagerAgent should route that to the EmailAgent. Then try: > “Summarize how our lead volume changed last month.” The DataAgent should receive that task. If routing isn’t working as expected, double-check your system messages and input bindings in each agent tool. ✅ You’re Done! You now have a modular, multi-agent AI system powered by n8n. The Manager Agent delegates intelligently, each sub-agent is optimized for its role, and all of them benefit from context memory. For more advanced setups, you can chain tools, add additional memory types, or use retrieval (RAG) tools for external document support.

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

This workflow allows users to generate beautifully stylized 3D-rendered food emoji icons based on a simple text prompt. It combines user input, structured visual design generation, and image rendering using OpenAI’s GPT models. ✨ What It Does Collects user input via a form: e.g. "green apple" Generates a structured JSON specification describing the emoji’s form, lighting, texture, and color scheme Uses AI to render an image based on that spec—styled like a high-quality emoji icon with a transparent background 🧠 Use Case This template is ideal for: Designers or creators needing icon ideas or drafts for food items Developers building emoji packs or digital stickers Inspiration for AI-assisted product illustration or branding 💡 Why It's Useful Instead of prompting a model directly with vague terms, this flow creates a structured visual spec tailored to food items. The final emoji-style icon is polished, modern, and downloadable. ✅ Requirements To get started with this workflow, follow these steps: 🔑 Configure Credentials: Set up your API credentials for OpenAI and Google Drive 💳 Add OpoenAI Credit: Make sure to add credit to your OpenAI account, verify your organization (required for generating images) 📊 Connect Google Drive: Authenticate your Google Drive account ⚙️ (Optional) Customize Prompts: Adjust the prompts within the workflow to better suit your specific needs Note: Each image generation will cost you about $0.17

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