Get started with Google Sheets in n8n
A hands-on starter workflow that teaches beginners how to: Pull rows from a Google Sheet Append a new record that mimics a form submission Generate AI-powered text with GPT-4o based on a “Topic” column Write the AI output back into the correct row using an update operation Along the way you’ll learn the three essential Google Sheets operations in n8n (read → append → update), see how to pass sheet data into an OpenAI node, and document each step with sticky-note instructions—perfect for anyone taking their first steps in no-code automation. 0️⃣ Prerequisites Google Sheets Open Google Cloud Console → create / select a project. Enable Google Sheets API under APIs & Services. Create an OAuth Desktop credential and connect it in n8n. Share the spreadsheet with the Google account linked to the credential. OpenAI Create a secret key at <https://platform.openai.com/account/api-keys>. In n8n → Credentials → New → choose OpenAI API and paste the key. Sample sheet to copy (make your own copy and use its link) <https://docs.google.com/spreadsheets/d/15i9WIYpqc5lNd5T4VyM0RRptFPdi9doCbEEDn8QglN4/edit?usp=sharing> --- 1️⃣ Trigger Manual Trigger – lets you run on demand while learning. (Swap for a Schedule or Webhook once you automate.) --- 2️⃣ Read existing rows Node: Get Rows from Google Sheets Reads every row from Sheet1 of your copied file. --- 3️⃣ Generate a demo row Node: Generate 1 Row of Data (Set node) Pretends a form was submitted: Name, Email, Topic, Submitted = "Yes" --- 4️⃣ Append the new row Node: Append Data to Google Operation append → writes to the first empty line. --- 5️⃣ Create a description with GPT-4o OpenAI Chat Model – uses your OpenAI credential. Write description (AI Agent) – prompt = the Topic. Structured Output Parser – forces JSON like: { "description": "…" }. --- 6️⃣ Update that same row Node: Update Sheets data Operation update. Matches on column Email to update the correct line. Writes the new Description cell returned by GPT-4o. --- 7️⃣ Why this matters Demonstrates the three core Google Sheets operations: read → append → update. Shows how to enrich sheet data with an AI step and push the result right back. Sticky Notes provide inline docs so anyone opening the workflow understands the flow instantly. --- 👤 Need help? Robert Breen – Automation Consultant ✉️ robert.j.breen@gmail.com 🔗 <https://www.linkedin.com/in/robert-breen-29429625/>
By Robert BreenChat with your email history using Telegram, Mistral and Pgvector for RAG
Who is this for? Everyone! Did you dream of asking an AI "what hotel did I stay in for holidays last summer?" or "what were my marks last semester like?". Dream no more, as vector similarity searches and this workflow are the foundations to make it possible (as long as the information appears in your e-mails 😅). 100% Local and Open Source! This workflow is designed to use locally-hosted open source. Ollama as LLM provider, nomic-embed-text as the embeddings model, and pgvector as the vector database engine, on top of Postgres. Structured AND Vectorized This workflow combines structured and semantic search on your e-mail. No need for enterprise setups! Leverage the convenience of n8n and open source to get a bleeding edge solution. Setup You will need a PGVector database with embeddings for all your email. Use my other template Gmail to Vector Embeddings with PGVector and Ollama to set it up in a breeze! Make a copy of my Email Assistant: Convert Natural Language to SQL Queries with Phi4-mini and PostgreSQL, you will need it for structured searches. Install this template and modify the Call the SQL composer Workflow step, to point at your copy of the SQL workflow. Adjust the rest of necessary steps: Telegram Trigger, AI Chat model, AI Embeddings... Activate the workflow and chat around!
By Alfonso CorrettiBuild a customer support RAG agent with GPT-5, Telegram & Pinecone
🧠 RAG-Based Customer Support Agent (GPT-5 + Telegram) Description: This workflow builds a powerful Retrieval-Augmented Generation (RAG) Customer Support Agent that interacts with users directly through Telegram using the GPT-5 model. It combines real-time conversational capabilities with context-aware responses by leveraging vector search via Pinecone, making it ideal for automated, intelligent support systems. Watch Video Tutorial Build on Workflows Like These: https://www.youtube.com/@Automatewithmarc 💬 Key Features: Telegram Integration: Listens to customer queries via the Telegram Trigger node and sends back intelligent responses in the same chat. GPT-5 Agent (LangChain): A powerful AI agent node orchestrates the conversation using OpenAI's GPT-5 model. Contextual Memory: A Memory Buffer stores the last 15 interactions per user to provide more personalized and coherent multi-turn conversations. RAG with Pinecone: Integrates with Pinecone to fetch relevant answers from your “Customer FAQ” vector namespace, enabling grounded and accurate responses. Embeddings Generation: Uses OpenAI’s Embeddings node to process and vectorize documents for retrieval. End-to-End AI Pipeline: Connects all components from input to output, providing seamless and intelligent customer support. 🔧 Tech Stack: GPT-5 via OpenAI API Pinecone vector store (namespace: Customer FAQ) Telegram Bot API LangChain agent, memory, and embedding tools n8n self-hosted or cloud instance 📌 Ideal Use Cases: Automated customer support for e-commerce, SaaS, or community support FAQ bots with up-to-date product or policy documents Multilingual support agents (customizable via GPT-5) 🛠️ Setup Instructions: Set up your Telegram bot and insert credentials. Add your OpenAI and Pinecone API keys. Upload or index your support documents into the Customer FAQ namespace on Pinecone. Deploy and test your Telegram bot.
By Automate With MarcMonitor CISA critical vulnerability alerts with RSS feed & Slack notifications
--- How It Works: The 5-Node Monitoring Flow This concise workflow efficiently captures, filters, and delivers crucial cybersecurity-related mentions. Monitor: Cybersecurity Keywords (X/Twitter Trigger) This is the entry point of your workflow. It actively searches X (formerly Twitter) for tweets containing the specific keywords you define. Function: Continuously polls X for tweets that match your specified queries (e.g., your company name, "Log4j," "CVE-2024-XXXX," "ransomware"). Process: As soon as a matching tweet is found, it triggers the workflow to begin processing that information. Format Notification (Code Node) This node prepares the raw tweet data, transforming it into a clean, actionable message for your alerts. Function: Extracts key details from the raw tweet and structures them into a clear, concise message. Process: It pulls out the tweet's text, the user's handle (@screen_name), and the direct URL to the tweet. These pieces are then combined into a user-friendly notificationMessage. You can also include basic filtering logic here if needed. Valid Mention? (If Node) This node acts as a quick filter to help reduce noise and prevent irrelevant alerts from reaching your team. Function: Serves as a simple conditional check to validate the mention's relevance. Process: It evaluates the notificationMessage against specific criteria (e.g., ensuring it doesn't contain common spam words like "bot"). If the mention passes this basic validation, the workflow continues. Otherwise, it quietly ends for that particular tweet. Send Notification (Slack Node) This is the delivery mechanism for your alerts, ensuring your team receives instant, visible notifications. Function: Delivers the formatted alert message directly to your designated communication channel. Process: The notificationMessage is sent straight to your specified Slack channel (e.g., cyber-alerts or security-ops). End Workflow (No-Op Node) This node simply marks the successful completion of the workflow's execution path. Function: Indicates the end of the workflow's process for a given trigger. --- How to Set Up Implementing this simple cybersecurity monitor in your n8n instance is quick and straightforward. Prepare Your Credentials Before building the workflow, ensure all necessary accounts are set up and their respective credentials are ready for n8n. X (Twitter) API: You'll need an X (Twitter) developer account to create an application and obtain your Consumer Key/Secret and Access Token/Secret. Use these to set up your Twitter credential in n8n. Slack API: Set up your Slack credential in n8n. You'll also need the Channel ID of the Slack channel where you want your security alerts to be posted (e.g., security-alerts or it-ops). Import the Workflow JSON Get the workflow structure into your n8n instance. Import: In your n8n instance, go to the "Workflows" section. Click the "New" or "+" icon, then select "Import from JSON." Paste the provided JSON code (from the previous response) into the import dialog and import the workflow. Configure the Nodes Customize the imported workflow to fit your specific monitoring needs. Monitor: Cybersecurity Keywords (X/Twitter): Click on this node. Select your newly created Twitter Credential. CRITICAL: Modify the "Query" parameter to include your specific brand names, relevant CVEs, or general cybersecurity terms. For example: "YourCompany" OR "CVE-2024-1234" OR "phishing alert". Use OR to combine multiple terms. Send Notification (Slack): Click on this node. Select your Slack Credential. Replace "YOURSLACKCHANNEL_ID" with the actual Channel ID you noted earlier for your security alerts. (Optional: You can adjust the "Valid Mention?" node's condition if you find specific patterns of false positives in your search results that you want to filter out.)* Test and Activate Verify that your workflow is working correctly before setting it live. Manual Test: Click the "Test Workflow" button (usually in the top right corner of the n8n editor). This will execute the workflow once. Verify Output: Check your specified Slack channel to confirm that any detected mentions are sent as notifications in the correct format. If no matching tweets are found, you won't see a notification, which is expected. Activate: Once you're satisfied with the test results, toggle the "Active" switch (usually in the top right corner of the n8n editor) to ON. Your workflow will then automatically monitor X (Twitter) at the specified polling interval. ---
By MarthIntelligent real-time financial fraud detection and risk scoring engine
How It Works Automates fraud risk detection for financial transactions by analyzing real-time webhook events through AI-powered scoring. Target audience: fintech companies, payment processors, and banking teams preventing fraud losses. Problem solved: manual fraud checks are reactive and slow; automated detection catches suspicious transactions instantly. Workflow receives transactions via webhook, configures processing parameters, runs OpenAI GPT-4 fraud analysis, calculates risk scores, branches on risk level, holds high-risk transactions, alerts fraud teams, logs incidents, and documents evidence for compliance investigations. Setup Steps Configure webhook endpoint for transaction ingestion. Set OpenAI API key and fraud detection prompts. Connect Google Sheets for incident logging. Enable email alerts to fraud team distribution list. Map risk thresholds (high/low). Prerequisites OpenAI API key, webhook-capable transaction source, Gmail for alerts, Google Sheets access, incident tracking database. Use Cases Payment processors detecting card fraud, fintech platforms catching account takeovers Customization Adjust risk thresholds and scoring logic. Add phone/SMS alerts for urgency. Benefits Detects fraud within seconds, reduces financial losses by up to 90%
By Cheng Siong Chin