by n8n Team
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.
by Tom
This workflow builds a valid RSS feed (which is an XML feed under the hood) for ARD Audiothek podcasts. This allows you to subscribe to such podcasts using your favourite podcatcher without using the ARD Audiothek app. The example builds a feed for Kalk & Welk, but the workflow can be easily adjusted by providing another podcast URL on the Get overview page HTTP Request node. To subscribe to the feed, active your n8n workflow and then use the Production URL from the intitial Feed Webhook node in your podcatcher. I've tested the resulting feed using Pocket Casts... ...and Miniflux: When using Miniflux, you can add your feed via this page to your account. Make sure you select Private when doing so to avoid sharing your n8n instance with the world. The resulting feed passes the W3C Feed Validation Service: The workflow can also be used as a foundation to free other podcasts from propriertary big media platforms, though not all of them will be as simple to deal with as the ARD Audiothek.
by David Olusola
When you fill out the form with business challenges and requirements GPT-4 analyzes the input and generates a customized proposal using your template System automatically creates a Google Slides presentation with personalized content Professional proposal email is sent directly to the prospect with the presentation link Set up steps Estimated time: 15-20 minutes Connect your OpenAI API key for GPT-4 access Link your Google account for Slides and Gmail integration Create your proposal template in Google Slides with placeholder variables Customize the AI prompt and email template with your branding Test with sample data and activate the workflow
by Antonio Cheong
Run Apache Airflow DAG and Retrieve XCom Value What this workflow does This workflow integrates the Apache Airflow API DAGRun and XCom. It enables n8n to trigger Airflow DAGs and retrieve the execution results. Preparation: Update Airflow API Link Prefix Navigate to the airflow-api node. Update the prefix of the Airflow API link in the format: http(s)://ip:port. Example: https://airflow.example.com Configure Authentication Go to the Airflow: dag_run node. Update the Basic Auth credentials with your Airflow username and password. Repeat this step for Airflow: dag_run - state and Airflow: dag_run - get result nodes. Security Note: Using Basic Authentication requires storing credentials in plaintext. If possible, consider using API Keys or Tokens for enhanced security. An example is setting Airflow's API Authentication to basic\_auth. Choose other authentication methods if needed. Ensure the user account has the following permissions: can create on DAG Runs, can read on DAG Runs, can read on XComs, can edit on DAGs, and can read on DAGs. How to Use: To execute this workflow, use the Execute Sub-workflow node with the following input parameters: dag\_id**: The DAG ID (name) in Airflow that you want to trigger. task\_id**: The Task ID (name) from which you want to retrieve the XCom return\_value. conf**: Input data for the Airflow DAG run. wait**: Delay (in seconds) between each Airflow: dag_run - state check. wait\_time**: The maximum time (in seconds) to wait for Airflow: dag_run - state before returning an error. Output: The workflow returns the XCom result from Airflow: dag_run - get result. The XCom return_value is stored in the value field.
by Hugues Stock
What does this template do ? This workflow performs scheduled health checks on a list of URLs stored in a Google Sheet. Every X minutes, it retrieves the URLs, sends HTTP requests to check their availability, and notifies a Telegram chat if any request fails. You can easily replace Telegram with any other notification service supported by n8n, such as Slack, Discord, Email, or Microsoft Teams. Use Case Ideal for monitoring internal or external services, ensuring uptime and responsiveness. Integrate with your preferred messaging platform for real-time failure notifications—without setting up complex infrastructure. What the Workflow Does ⏰ Triggers at regular intervals using Schedule Trigger 📄 Reads URLs from a Google Sheet 🌐 Sends HTTP requests to check URL health 🚨 Sends failure alerts (including error code and URL) to Telegram (or any service of your choice) Apps & Services Used Google Sheets Telegram (or alternative notification service) HTTP Request Pre-requisites Telegram bot and chat ID (if using Telegram) Connected Google Sheet with a URLS column (template) Customization Tips Adjust the schedule interval in the trigger node Replace Telegram with Slack, Email, Discord, or any other notification node Customize alert messages with more context or formatting Add filters, retries, or logging as needed
by Samir Saci
Tags: Sustainability, CSRD, Reporting, ESG, Compliance, Automation Context Hey! I'm Samir, a Supply Chain Engineer and Data Scientist from Paris, founder of LogiGreen Consulting We help companies automate sustainability workflows using AI, Data Analytics, and No-Code tools like N8N. > Sustainability Reporting meets Automation with n8n! 📬 For business inquiries, you can add me on Here What is a CSRD XHTML Report? Under the Corporate Sustainability Reporting Directive (CSRD), companies must publish their ESG disclosures in a machine-readable XHTML format, embedding XBRL tags that make the report structured and standardized. These files must follow strict formatting and tagging rules to ensure compliance, traceability, and accessibility for both regulators and analysts. Who is this template for? This workflow is designed for sustainability teams, ESG consultants, or developers who want to automatically check the structure and format of CSRD reports submitted in XHTML. How does it work? This N8N workflow automates the audit process: 📤 Input Node → Uploads or fetches the XHTML file via URL or Webhook. 🧪 Validates Structure → Uses a custom code node to parse HTML and identify required tags (e.g., <ix:nonNumeric>, namespaces). 📋 Outputs a Report → Returns a summary report of errors, warnings, and key metadata (like entity name, reporting period). 📤 Export Option → Save the results in Google Sheets or send via email. Prerequisite A sample XHTML file that you can find in my GitHub Repository Google Sheets API* and *OpenAI API** credentials Next Steps Follow the sticky notes inside each node to adjust parsing rules or extend validation to specific XBRL tags relevant to your sector (e.g., GHG emissions, water usage). *📺 Check my complete tutorial to understand how to use it: * 🎥 Check My Tutorial 🚀 Interested in combining CSRD compliance with automation and analytics? Let’s connect on LinkedIn Notes This workflow includes an example XHTML file to test the validator. You can plug this into your internal systems or even extend it with AI to auto-summarize the sustainability report. This workflow has been created with N8N 1.82.1 Submitted: April 3rd, 2025
by Fahmi Oktafian
This workflow is designed for content creators or AI artists who want to automate posting unique AI-generated images to their Facebook Page multiple times a day. It uses Google Gemini via LangChain to generate imaginative image prompts, and Pollinations AI to generate the images. Posts are published with hashtags and a clean caption. Who Is It For AI artists Facebook page managers Digital marketers looking for automated creative content What It Does Triggers 3x daily at 7:00, 11:00, and 17:00 (local time) Generates random AI image prompts in a retro-futuristic, cinematic, or surreal style using Google Gemini Fetches images from Pollinations AI using custom prompts Posts images automatically to your Facebook Page with hashtags Requirements n8n self-hosted or desktop (workflow uses schedule trigger) Pollinations API (no auth needed) Facebook Page with Facebook Graph API token: Required scopes: pages_manage_posts, pages_read_engagement, pages_show_list Google Gemini API Key (used via LangChain node) How to Customize Change the prompt style in the Basic LLM Chain node (promptType: define) to suit your theme. Adjust Set The Generator Image node if you want: Different image sizes (width / height) Different seed randomness Other Pollinations models (&model=kontext) Add Telegram/Twitter nodes if you want cross-posting Use Set node to allow easy user-level configuration of models, hashtags, times, etc.
by Oriol Seguí
This fun workflow automates the generation and delivery of personalized jokes by email based on the names or objects entered in the form. The process works as follows: On form submission The workflow starts when someone submits a form with the required names or objects to create the joke. You can modify the form fields to make the jokes more creative. Set the output language Manually define the language in which you want to receive the joke. OpenAI Message Model Uses the OpenAI model to generate the joke based on the prompt and in the chosen language. (The response is limited to around 200 tokens.) Gmail: send message The generated joke is automatically sent to the specified email address via Gmail.
by Jonathan
This workflow searches for mentions of a company's name on Twitter and shares the tweets that mention it in a Slack channel. Prerequisites A Slack account and credentials A Twitter account and credentials Nodes Cron node executes the workflow every 10 minutes. Note that if you change the Mode from "Every X" you will need to manually update the Date & Time node to subtract the interval you are using. Set nodes set the required values (name of the Slack channel, name of the Twitter account to search for, the tweet text and URL). Date & Time node subtracts 10 minutes from the workflow execution time. Twitter node gets the latest 50 tweets that mention the specified account. IF node filters tweets posted in the past 10 minutes. Slack node posts tweets in a Slack channel.
by Lorena
This workflow collects images from web search results on a specific query, analyzes the image for labels, formats the text, and adds the information in Google Sheets. HTTP Request node** gets images from the web. AWS Rekognition node** analyzes the images (in this case, it detects text in an image). Set node** sets the values necessary for the data set. Function node** transforms the text detected in the image to lower case. Google Sheets node** adds the image information to a sheet that serves as data set.
by Harshil Agrawal
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.
by Lucas Perret
This workflow will allow you to enrich in real-time a form submission from Webflow using Datagma. Based on the result of this workflow, a specific Calendly link will be shown on the website. If the process outcome is '1', a link for a one-on-one demo will be provided. If the process outcome is '2', a link for a group demo will be shown. Full guide here: Real-time Lead Routing