by Samir Saci
Tags*: Sustainability, Business Travel, Carbon Emissions, Flight Tracking, Carbon Interface API Context Hi! I’m Samir — a Supply Chain Engineer and Data Scientist based in Paris, and founder of LogiGreen Consulting. I help companies monitor and reduce their environmental footprint by combining AI automation, carbon estimation APIs, and workflow automation. This workflow is part of our sustainability reporting initiative, allowing businesses to track the CO₂ emissions of employee flights. > Automate carbon tracking for your business travel with AI-powered workflows in n8n! 📬 For business inquiries, feel free to connect with me on LinkedIn Who is this template for? This workflow is designed for travel managers, sustainability teams, or finance teams who need to measure and report on emissions from business travel. Let’s imagine your company receives a flight confirmation email: The AI Agent reads the email and extracts structured data, such as flight dates, airport codes, and number of passengers. Then, the Carbon Interface API is called to estimate CO₂ emissions, which are stored in a Google Sheet for sustainability reporting. How does it work? This workflow automates the end-to-end process of tracking flight emissions from email to CO₂ estimation: 📨 Gmail Trigger captures booking confirmations 🧠 AI Agent extracts structured data (airports, dates, flight numbers) ✈️ Each flight leg is processed individually 🌍 Carbon Interface API returns distance and carbon emissions 📄 A second Google Sheet node appends the emission data for reporting Steps: 💌 Trigger on new flight confirmation email 🧠 Extract structured trip data using AI Agent (flights, airports, dates) 📑 Store flight metadata in Google Sheets 🧭 For each leg, call the Carbon Interface API 📥 Append distance, CO₂ in kg, and timestamp to the flight row What do I need to get started? You’ll need: A Gmail account receiving SAP Concur or travel confirmation emails A Google Sheet to record trip metadata and CO₂ emissions A free Carbon Interface API key Access to OpenAI for parsing the email via AI Agent A few sample flight confirmation emails to test Next Steps 🗒️ Use the sticky notes in the n8n canvas to: Add your Gmail and Carbon Interface credentials Send a sample booking email to your inbox Verify that emissions and distances are correctly added to your sheet This template was built using n8n v1.93.0 Submitted: June 7, 2025
by WeblineIndia
This workflow automatically fetches newly uploaded files from a specific folder in Google Drive, shares them via email with specified recipients, and logs the file details (name, ID, created time, modified time) into Airtable for easy tracking. It streamlines the process of file sharing and management while keeping track of important metadata in a central place. Step-by-Step Instructions Google Drive Node (Fetch New File) Action: This node fetches newly uploaded files from the specific folder you’ve mentioned in your Google Drive. Configuration: Set the folder ID in the Google Drive node where the files are uploaded. Use the “New File in Folder” trigger to automatically detect new files added to the folder. Send Email Node (Share File via Email) Action: After detecting the new file, this node shares the file via email with the recipient you specify. Configuration: Set the recipient's email address. Include the file URL from the Google Drive node in the email body, allowing easy access to the file. Add the file name as part of the email subject or body to notify the recipient about the new file. Airtable Node (Store File Metadata) Action: This node stores the file’s metadata, such as name, ID, creation time, modification time, and the email address to which it was sent, in your Airtable database. Configuration: Set up Airtable with a table. Map the output from the Google Drive node to store the file metadata, and use the email address from the email node for tracking. About WeblineIndia WeblineIndia specializes in delivering innovative and custom AI solutions to simplify and automate business processes. If you need any help, please reach out to us.
by Matteo
This n8n workflow automates the handling of incoming emails. It detects and filters out spam, searches a knowledge base (FAQ) stored in a Pinecone vector database, and sends a reply using Gmail — all powered by an AI model (GPT-4o mini). How It Works Receiving Emails The Gmail Trigger node checks a Gmail inbox every hour. When a new email arrives, it starts the workflow. Fetching Full Email Content The get_message node retrieves all the details of the message: sender, subject, text, message ID, etc. Spam Filtering The Spam checker node uses GPT-4o mini to classify the email as either "spam" or "no spam". It detects not only classic spam but also automated messages (e.g. from Google or Microsoft). If marked as "spam", the workflow ends and nothing is processed. Conditional Filter The If node checks the spam result. Only "no spam" emails proceed to the AI Agent. AI-Based Reply The AI Agent node generates a response based on: The email content A system prompt defining the assistant’s behavior (polite, professional, under the name “Total AI Solutions”) Information retrieved from the Pinecone Vector Store, which contains FAQs The AI is instructed to always check the vector store before replying. The AI prepares both the subject and the body of the reply. Sending the Reply The Gmail node sends the reply to the original sender. It uses the original email's ID to keep the thread intact. Language Model The OpenAI Chat Model node provides GPT-4o mini as the language engine for generating responses. Memory Support The Simple Memory node maintains short-term context, helpful in multi-turn conversations. Knowledge Base (FAQ) The Pinecone Vector Store node connects to a Pinecone index (faqmattabott) containing vectorized FAQ content. Vectors are created using the Embeddings OpenAI node.
by Gopal Debnath
💡 How It Works: ⏰ Triggers daily at 6:00 AM 📊 Fetches one random question from your Google Sheet 🧠 Formats question, options, correct answer, and explanation 📤 Sends it to: 📧 Email 💬 Telegram (via Bot) 📱 WhatsApp/SMS (via Twilio) 🔧 What You Need to Configure: YOUR_GOOGLE_SHEET_ID → your sheet with columns: question, optionA, optionB, optionC, optionD, correctAnswer, explanation Email credentials (SMTP) Telegram Bot Token & Chat ID Twilio phone numbers and credentials
by Harshil Agrawal
This workflow allows you to release a new version via a Telegram bot command. This workflow can be used in your Continous Delivery pipeline. Telegram Trigger node: This node will trigger the workflow when a message is sent to the bot. If you want to trigger the workflow via a different messaging platform or a service, replace the Telegram Trigger node with the Trigger node of that service. IF node The IF node checks for the incoming command. If the command is not deploy, the IF node will return false, otherwise true. Set node: This node extracts the value of the version from the Telegram message and sets the value. This value is used later in the workflow. GitHub node: This node creates a new version release. It uses the version from the Set node to create the tag. NoOp node: Adding this node is optional.
by Lorena
This workflow detects toxic language (such as profanity, insults, threats) in messages sent via Telegram. This blog tutorial explains how to configure the workflow nodes step-by-step. Telegram Trigger: triggers the workflow when a new message is sent in a Telegram chat. Google Perspective: analyzes the text of the message and returns a probability value between 0 and 1 of how likely it is that the content is toxic. IF: filters messages with a toxic probability value above 0.7. Telegram: sends a message in the chat with the text "I don't tolerate toxic language" if the probability value is above 0.7. NoOp: takes no action if the probability value is below 0.7.
by Friedemann Schuetz
What this workflow does This workflow retrieves Google Analytics data from the last 7 days and the same period in the previous year. The data is then prepared by AI as a table, analyzed and provided with a small summary. The summary is then sent by email to a desired address and, shortened and summarized again, sent to a Telegram account. This workflow has the following sequence: time trigger (e.g. every Monday at 7 a.m.) retrieval of Google Analytics data from the last 7 days assignment and summary of the data retrieval of Google Analytics data from the last 7 days of the previous year allocation and summary of the data preparation in tabular form and brief analysis by AI. sending the report as an email preparation in short form by AI for Telegram (optional) sending as Telegram message. Requirements The following accesses are required for the workflow: Google Analytics (via Google Analytics API): Documentation AI API access (e.g. via OpenAI, Anthropic, Google or Ollama) SMTP access data (for sending the mail) Telegram access data (optional for sending as Telegram message): Documentation Feel free to contact me via LinkedIn, if you have any questions!
by Automate With Marc
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 🧠 AI News Update Every 24 Hours (with Perplexity + GPT Formatter) Description: This workflow automatically delivers a clean, AI-curated summary of the latest AI news headlines from the past 24 hours directly to your inbox — every morning at 9 AM. For step-by-step video tutorial for this build, watch here: https://youtu.be/O-DLvaMVLso 🧰 How It Works: 🕘 Schedule Trigger Runs daily at 9 AM to start the workflow. 🔎 Perplexity AI Agent Searches for AI-related headlines published in the last 24 hours, including: Headline 1-sentence summary Source Full URL 🧠 GPT Formatter AI Agent Uses an OpenAI language model (GPT-4.1-mini) to reformat raw news data into a clean, readable email update. 🧷 Memory Buffer (Optional) Gives the formatter context and continuity if you want to personalize formatting further over time. 📧 Gmail Node Sends the formatted AI news digest to your inbox (or your team’s) daily. 📦 Tools & APIs Required: ✅ Perplexity AI API ✅ OpenAI API ✅ Gmail Account (OAuth2 credentials) 🔄 Use Cases: Daily AI trend monitoring for individuals or teams Automating internal market intelligence Research triggers for blog or content creation Email digests for newsletters or Slack updates 🛠️ Customizable Ideas: Swap Gmail for Slack, Telegram, Discord, etc. Modify the topic (e.g., Climate Tech, Crypto News) Add sentiment analysis or AI-generated commentary Send summary to Google Docs or Notion instead
by jason
This is the workflow that I presented at the April 9, 2021 n8n Meetup. This workflow uses Baserow.io to store registration information collected using n8n as both the web server and the data processor. To get this workflow working properly, you will either need to run it on n8n.cloud or using the on premise version with people having the ability to connect to n8n externally. You will need an account on Baserow.io to store your subscriptions with a table with the following fields: GUID First Name Last Name Email Confirmed
by Eska
Deadlock Match Stats Bot is an automated workflow for n8n designed to send detailed player statistics from the most recent Deadlock match directly to Telegram. When the user sends the /match command to the Telegram bot, the workflow performs the following steps: Loads the HTML content of the player's profile page from deadlocktracker.gg using a preconfigured Steam ID. Extracts the most recent match ID using a regular expression from the embedded JavaScript data. Loads the HTML page for the specified match. Parses the match page using cheerio to extract relevant data for each player, including their nickname, selected hero, and current rank. Formats the collected information into a single message and sends it to the Telegram chat that issued the command.
by DevCode Journey
Who is this for? This n8n workflow is designed for investors, financial analysts, automated trading system developers, and finance enthusiasts who require daily, comprehensive, data-driven insights into specific stock symbols. It's perfect for users who need to automate the complex process of combining technical indicators, news sentiment, professional analyst ratings, and social media buzz into a single, actionable recommendation. This system provides a 24/7 automated "analyst" for portfolio monitoring. What this Workflow Does This n8n workflow executes a daily, multi-faceted analysis of a target stock. It starts by gathering all relevant data (price history, news, ratings, social posts) and processes it through specialized Code nodes to calculate technical indicators (SMA, RSI), determine price predictions (Linear Regression), and perform sentiment analysis on news and social media. Finally, it uses a weighted model to synthesize all data into a single, comprehensive Buy/Sell/Hold recommendation and delivers a detailed report via Telegram. Key Features Daily Scheduling**: Automatically triggers analysis every day at a specified time (e.g., 9:00 AM). Multi-Factor Analysis: Combines **four key domains for a holistic view: Technical, Prediction, News Sentiment, Analyst Ratings, and Social Sentiment. Technical Indicator Calculation: Calculates **SMA (20, 50, 200), RSI (14-day), and identifies Support/Resistance levels. Price Prediction: Uses **Simple Linear Regression to forecast a 7-day price trend and generate an initial recommendation. Sentiment Analysis: Custom Code nodes perform **keyword-based sentiment analysis on news articles and social media posts. Composite Recommendation: A weighted model combines all analysis scores (35% Technical, 25% News, 25% Analyst, 15% Social) to generate a **final recommendation, confidence score, and summary. Automated Alerting: Delivers a fully formatted, easily readable **Markdown report via Telegram. Requirements API Configuration Node**: A preliminary node (implied by the expression references) containing: Target stockSymbols (e.g., TSLA, AAPL). telegramChatId for receiving the report. API Keys for data sources (e.g., a Financial Data API for price/news/ratings, a Social Media API). Telegram Credentials**: For the Telegram node to send the final message. Financial Data Source Workflow**: Requires preceding nodes (not fully visible) to fetch: Historical price data (required for SMA/RSI/Regression). Recent news headlines and summaries. Recent analyst ratings. Social media data (e.g., from Twitter/StockTwits). n8n Instance**: Self-hosted or cloud-based n8n installation. How to Use Step-by-Step Setup 1. Configure Scheduling Open the "Daily Stock Check" node. Set the interval rule to the precise hour you want the report to run (e.g., 9:00 AM). 2. Configure Stock Symbol and Telegram In the (implied) "API Configuration" node, set the stockSymbols you wish to track. Set the target telegramChatId where the report will be delivered. Ensure your Telegram credentials are set up in n8n. 3. Verify Data Fetching Nodes Ensure the nodes feeding data into "Analyze Stock Trends," "Analyze News Sentiment," "Process Analyst Ratings," and "Analyze Social Sentiment" are correctly configured to fetch the required historical price, news, ratings, and social data. 4. Adjust Analysis Weights (Advanced) If you wish to change the importance of different factors, edit the WEIGHTS object inside the "Generate Comprehensive Recommendation" Code node. Default Weights: Technical (0.35), News (0.25), Analyst (0.25), Social (0.15). 5. Test the Workflow Manually execute the workflow to ensure all Code nodes process the incoming data correctly and the "Send Telegram Alert" successfully delivers the final, formatted message. Workflow Components The workflow is structured into three main phases: Data Processing, Recommendation Synthesis, and Reporting. 1. Data Processing and Indicator Calculation | Node Name | Type | Key Functionality | | :--- | :--- | :--- | | Daily Stock Check | Schedule Trigger | Initiates the entire workflow daily at the set time. | | Analyze Stock Trends | Code | Calculates Technical Indicators: SMA (20, 50, 200), RSI (14-day), Volume Trend, and Support/Resistance levels. | | Predict Future Trends | Code | Performs Simple Linear Regression on historical prices to determine slope and predict the price 7 days ahead. | | Analyze News Sentiment | Code | Performs keyword-based sentiment analysis on news headlines and summaries to categorize overall sentiment (positive/negative/neutral) and assign a score. | | Process Analyst Ratings | Code | Aggregates analyst recommendations (Buy/Hold/Sell) to calculate consensus rating and average price target. | | Analyze Social Sentiment | Code | Performs keyword-based sentiment analysis on social media data to determine community mood and trending hashtags. | 2. Recommendation Synthesis | Node Name | Type | Description | | :--- | :--- | :--- | | Combine All Analysis | Merge | Consolidates the outputs from the four analysis branches (Technical, News, Analyst, Social) into a single data item. | | Generate Comprehensive Recommendation | Code | The core logic. Calculates a weighted composite score (from -100 to 100) based on all four inputs, generating the final STRONG BUY/BUY/HOLD/SELL/STRONG SELL recommendation and a numerical confidence score. | 3. Reporting and Alerting | Node Name | Type | Description | | :--- | :--- | :--- | | Format Telegram Message | Set | Constructs the final detailed report message using Markdown formatting, pulling data from all preceding analysis nodes into a clear, structured report. | | Send Telegram Alert | Telegram | Sends the fully formatted analysis report to the pre-configured Telegram chat ID. | 🙋 For Help & Community 👾 Discord: n8n channel 🌐 Website: devcodejourney.com 🔗 LinkedIn: Connect with Shakil 📱 WhatsApp Channel: Join Now 💬 Direct Chat: Message Now
by Antonio Trento
🤖 Auto-Publish SEO Blog Posts for Jekyll with AI + GitHub + Social Sharing This workflow automates the entire process of publishing SEO-optimized blog posts (e.g., recipes) to a Jekyll site hosted on GitHub. It uses LangChain + OpenAI to write long-form Markdown articles, and commits them directly to your repository. Optional steps include posting to X (Twitter) and LinkedIn. 🔧 Features 📅 Scheduled Execution: Runs daily or manually. 📥 CSV Input: Reads from a local CSV (/data/recipes.csv) with fields like title, description, keywords, and publish date. ✍️ AI Copywriting: Uses a GPT-4 model to generate a professional, structured blog post optimized for SEO in Markdown format. 🧪 Custom Prompting: Includes a detailed, structured prompt tailored for Italian food blogging and SEO rules. 🗂 Markdown Generation: Automatically builds the Jekyll front matter. Generates a clean SEO-friendly slug. Saves to _posts/YYYY-MM-DD-title.md. ✅ Commits to GitHub: Auto-commits new posts using GitHub node. 🧹 Post-Processing: Removes processed lines from the source CSV. 📣 (Optional) Social media sharing: Can post title to X (Twitter) and LinkedIn. 📁 CSV Format Example titolo;prompt_descrizione;keyword_principale;keyword_secondarie;data_pubblicazione Pasta alla Norma;Classic Sicilian eggplant pasta...;pasta alla norma;melanzane, ricotta salata;2025-07-04T08:00:00