AI Automation
923 sonuç — tümü kaynağa bağlı n8n referansı
AI-Powered RAG Q&A Chatbot with OpenAI, Google Sheets, Glide & Supabase
Automate AI-Powered RAG System with Contextual Q&A, Google Sheets Integration, and Glide Frontend—Powered by n8n, OpenAI, Supabase, and Google Apps Script. Tools & Services Used Glide (Frontend for user interactions) Google Sheets (Stores questions and answers) Google Apps Script (Forms + media upload handling) OpenAI (Embeddings + GPT-4 to rank and generate answers) Supabase (Optional for image hosting) n8n (Automation logic and backend glue) Workflow Overview This automation performs the following steps: Trigger: Webhook receives a user question from a Glide frontend. Fetch Data: Retrieves Q&A entries (and optionally, image URLs) from a connected Google Sheet. Rank Relevance: OpenAI Embeddings rank the relevance of stored questions to the new input. Top matches are passed to a GPT-4 prompt for answer generation. Generate Answer: GPT-4 creates a contextual answer using the best match. Optional: Includes media URL if attached to the matched answer. Response: Sends the formatted answer back to Glide frontend (text + optional image). Prerequisites Active accounts and API keys for: OpenAI (API key with GPT-4 and embedding access) Google Sheets (linked via Service Account or OAuth2 cre
Adaptive RAG with Google Gemini & Qdrant: Context-Aware Query Answering
Description This workflow automatically classifies user queries and retrieves the most relevant information based on the query type. 🌟 It uses adaptive strategies like; Factual, Analytical, Opinion, and Contextual to deliver more precise and meaningful responses by leveraging n8n's flexibility. Integrated with Qdrant vector store and Google Gemini, it processes each query faster and more effectively. 🚀 How It Works? Query Reception: A user query is triggered (e.g., through a chatbot interface). 💬 Classification: The query is classified into one of four categories: Factual: Queries seeking verifiable information. Analytical: Queries that require in-depth analysis or explanation. Opinion: Queries looking for different perspectives or subjective viewpoints. Contextual: Queries specific to the user or certain contextual conditions. Adaptive Strategy Application: Based on classification, the query is restructured using the relevant strategy for better results. Response Generation**: The most relevant documents and context are used to generate a tailored response. 🎯 Set Up Steps Estimated Time: ⏳ 10-15 minutes Prerequisites: You need an n8n account and a Qdrant vector store connectio
AI-Powered Auto-Generate Exam Questions and Answers from Google Docs with Gemini
This workflow automates the creation of exam questions (both open-ended and multiple-choice) from educational content stored in Google Docs, using AI-powered analysis and vector database retrieval This workflow saves educators hours of manual work while ensuring high-quality, curriculum-aligned assessments. Let me know if you'd like help adapting it for specific subjects! Use Cases Educators**: Rapidly generate quizzes, midterms, or flashcards. E-learning platforms**: Automate question banks for courses. Corporate training**: Create assessments for employee onboarding. Technical Requirements: APIs**: Google Gemini, OpenAI, Qdrant, Google Workspace. n8n Nodes**: LangChain, Google Sheets/Docs, HTTP requests, code blocks. This workflow combines AI efficiency with human-curated quality, making it a powerful tool for modern education and training. Advantages of This Workflow ✅ Fully Automated Exam Generation: From document to fully formatted quiz content with no manual intervention. ✅ Supports Comprehension and Critical Thinking: Questions are designed to go beyond factual recall, including inference and application. ✅ Uses AI and RAG for Accuracy: Ensures that answers are grounded in t
Automated Daily Customer Win-Back Campaign with AI Offers
Proactively retain customers predicted to churn with this automated n8n workflow. Running daily, it identifies high-risk customers from your Google Sheet, uses Google Gemini to generate personalized win-back offers based on their churn score and preferences, sends these offers via Gmail, and logs all actions for tracking. What does this workflow do? This workflow automates the critical process of customer retention by: Running automatically every day** on a schedule you define. Fetching customer data** from a designated Google Sheet containing metrics like predicted churn scores and preferred categories. Filtering* to identify customers with a high churn risk (score > 0.7) who haven't recently received a specific campaign (based on the created_campaign_date field - *you might need to adjust this logic). Using Google Gemini AI to dynamically generate one of three types of win-back offers, personalized based on the customer's specific churn score and preferred product categories: Informational: (Score 0.7-0.8) Highlights new items in preferred categories. Bonus Points: (Score 0.8-0.9) Offers points for purchases in a target category (e.g., Books). Discount Percentage: (Score 0.9-1
CGitLab Merge Request Review & Risk Analysis with Claude/GPT AI
Trigger The workflow runs when a GitLab Merge Request (MR) is created or updated. Extract & Analyze It retrieves the code diff and sends it to Claude AI or GPT-4o for risk assessment and issue detection. Generate Report AI produces a structured summary with: Risk levels Identified issues Recommendations Test cases Notify Developers The report is: Emailed to developers and QA teams Posted as a comment on the GitLab MR Setup Guide Connect GitLab Add GitLab API credentials Select repositories to track Configure AI Analysis Enter Anthropic (Claude) or OpenAI (GPT-4o) API key Set Up Notifications Add Gmail credentials Update the email distribution list Test & Automate Create a test MR to verify analysis and email delivery Key Benefits Automated Code Review** – AI-driven risk assessment and recommendations Security & Compliance** – Identifies vulnerabilities before code is merged Integration with GitLab CI/CD** – Works within existing DevOps workflows Improved Collaboration** – Keeps developers and QA teams informed Developed by Quantana, an AI-powered automation and software development company.
Access Control for AI Agents (RBAC) using Airtable and Telegram
Purpose This workflow allows granular control over the access to tools connected to AI Agents (including Multi-Agent setups) using Role Based Access Control. Demo & Explanation How it works User permissions are managed in Airtable where every restricted AI tool is listed by name and connected via roles to users Requests to the Main Agent can be sent through a Telegram message (can be replaced by Whatsapp, IMAP or similar) On every request the Telegram username is used to query a list of all allowed tools which are linked in Airtable A LangChain Code node is used to compare that list against the connected tools Every tool which is not permitted to be used is being replaced by a tool, which has a status response, telling the Agent to return a message to the user, that he is not authorized to use the tool Otherwise allowed tools are passed through to the Agent, as if they were connected directly to the Agent The parameters can also be passed to a sub-agent called as a sub-workflow where permissions can be checked the same way Every response is sent back to the same Telegram conversation Setup Clone the workflow and select the belonging credentials. You'll need an OpenAI and Airtable A
Personalized AI Tech Newsletter Using RSS, OpenAI and Gmail
Combine Tech News in a Personalized Weekly Newsletter This n8n template automates the collection, storage, and summarization of technology news from top sites, turning it into a concise, personalized weekly newsletter. If you like staying informed but want to reduce daily distractions, this workflow is perfect for you. It leverages RSS feeds, vector databases, and LLMs to read and curate tech content on your behalf—so you only receive what truly matters. How it works A daily scheduled trigger fetches articles from multiple popular tech RSS feeds like Wired, TechCrunch, and The Verge. Fetched articles are: Normalized to extract titles, summaries, and publish dates. Converted to vector embeddings via OpenAI and stored in memory for fast semantic querying. A weekly scheduled trigger activates the AI summarization flow: The AI is provided with your interests (e.g., AI, games, gadgets) and the desired number of items (e.g., 15). It queries the vector store to retrieve relevant articles and summarizes the most newsworthy stories. The summary is converted into a clean, email-friendly format and sent to your inbox. How to use Connect your OpenAI and Gmail accounts to n8n. Customize the lis
Summarise MS Teams Channel Activity for Weekly Reports with AI
This n8n template lets you summarize individual team member activity on MS Teams for the past week and generates a report. For remote teams, chat is a crucial communication tool to ensure work gets done but with so many conversations happening at once and in multiple threads, ideas, information and decisions usually live in the moment and get lost just as quickly - and all together forgotten by the weekend! Using this template, this doesn't have to be the case. Have AI crawl through last week's activity, summarize all messages and replies and generate a casual and snappy report to bring the team back into focus for the current week. A project manager's dream! How it works A scheduled trigger is set to run every Monday at 6am to gather all team channel messages within the last week. Messages are grouped by user. AI analyses the raw messages and replies to pull out interesting observations and highlights. This is referred to as the individual reports. All individual reports are then combined and summarized together into what becomes the team weekly report. This allows understanding of group and similar activities. Finally, the team weekly report is posted back to the channel. The tim
Summarise Slack Channel Activity for Weekly Reports with AI
This n8n template lets you summarize team member activity on Slack for the past week and generates a report. For remote teams, chat is a crucial communication tool to ensure work gets done but with so many conversations happening at once and in multiple threads, ideas, information and decisions usually live in the moment and get lost just as quickly - and all together forgotten by the weekend! Using this template, this doesn't have to be the case. Have AI crawl through last week's activity, summarize all threads and generate a casual and snappy report to bring the team back into focus for the current week. A project manager's dream! How it works A scheduled trigger is set to run every Monday at 6am to gather all team channel messages within the last week. Each message thread are grouped by user and data mined for replies. Combined, an AI analyses the raw messages to pull out interesting observations and highlights. The summarized threads of the user are then combined together and passed to another AI agent to generate a higher level overview of their week. These are referred to as the individual reports. Next, all individual reports are summarized together into a team weekly report
Automated Financial Tracker: Telegram Invoices to Notion with Gemini AI Reports
Automated Financial Tracker: Telegram Invoices to Notion with AI Summaries & Reports Tired of manually logging every expense? Streamline your financial tracking with this powerful n8n workflow! Snap a photo of your invoice in Telegram, and let AI (powered by Google Gemini) automatically extract the details, record them in your Notion database, and even send you a quick summary. Plus, get scheduled weekly reports with charts to visualize your spending. Automate your finances, save time, and gain better insights with this easy-to-use template! Transform your expense tracking from a chore into an automated breeze. Try it out! Overview: This workflow revolutionizes how you track your finances by automating the entire process from invoice capture to reporting. Simply send a photo of an invoice or receipt to a designated Telegram chat, and this workflow will: Extract Data with AI: Utilize Google Gemini's capabilities to perform OCR on the image, understand the content, and extract key details like item name, quantity, price, total, date, and even attempt to categorize the expense. Store in Notion: Automatically log each extracted transaction into a structured Notion database. Instant Fee
WAutomate New Customer Onboarding with HubSpot, Google Calendar, and AI-Powered Gmail
This n8n workflow streamlines the onboarding process for new customers by automating personalized email communication, calendar scheduling, and contact assignment in HubSpot. It is perfect for businesses looking to ensure a smooth and personalized onboarding experience for new clients. 🧑💼 Who is this for? Customer success teams who need to onboard new clients efficiently. Sales teams who want to ensure smooth transitions from prospect to customer. Small businesses that want to automate customer onboarding without complex systems. 🧩 What problem is this workflow solving? This workflow reduces the manual effort involved in onboarding new customers by: Automatically sending personalized welcome emails. Scheduling a welcome meeting using a calendar tool. Assigning the customer to a Customer Success Manager (CSM) in HubSpot. ⚙️ What this workflow does Trigger via Webhook or HubSpot: The workflow can be triggered either by a webhook (direct API call) or a HubSpot trigger (e.g., when a new contact is created). HubSpot Connection: Retrieves the list of HubSpot owners (users with contact access). Identifies the owner of the new contact. Calendar Management: Utilizes a Calendar Agent to
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Extract and Organize Colombian Invoices with Gmail, GPT-4o & Google Workspace
🧾 Personal Invoice Processor This N8N workflow automates the extraction and organization of personal invoices in Colombia received via Gmail. It includes the following key steps: 🔁 Flow Summary Email Trigger Polls Gmail every 30 minutes for emails with .zip attachments (assumed to contain invoices). Expects ZIP file following DIAN standards. ZIP File Handling Extracts all files. Filters only PDF and XML files for processing. Data Extraction & Processing Uses LangChain Agent + OpenAI (GPT-4o-mini) to extract: Tipo de documento (Factura / Nota Crédito) Número de factura Fecha de emisión (YYYY-MM-DD) NIT emisor y receptor (sin dígito de verificación) Razón social del emisor Subtotal, IVA, Total CUFE Resumen de compra (max 20 words, formatted sentence) Validation Ensures Total = Subtotal + IVA using a calculator node. Storage Uploads the original PDF to Google Drive. Renames the file to: YYYY-MM-DD-NUMERO_FACTURA.pdf. Inserts or updates invoice details in Google Sheets using a unique Key (NIT_Emisor + Numero_Factura) to prevent duplication. > ⚙️ Designed for personal use with minimal latency tolerance and high automation reliability.
Automated Facebook Comment Management with GPT-4o and LangChain
🤖 Facebook AI Agent with MCP Server – Built for Smart Engagement and Automation Hi! I’m Amanda 🥰😘 — I build intelligent automations with n8n and Make. This powerful workflow was designed to help teams automatically handle Facebook page interactions with AI. Using Meta Graph API, LangChain, MCP Server, and GPT-4o, it allows your AI agent to search for posts, read captions, fetch comments, and even reply or message followers, all through structured tools. 🔧 What the workflow does Searches for recent media using Facebook Page ID and access token Reads and extracts captions or media URLs Fetches comments and specific replies from each post Replies to comments automatically with GPT-generated responses Sends direct messages to followers who commented Maps user input and session to keep memory context via LangChain Communicates via Server-Sent Events (SSE) using your MCP Server URL 🧰 Nodes & Tech Used LangChain Agent + Chat Model with GPT-4o Memory Buffer for session memory toolHttpRequest to search media, comments, and send replies MCP Trigger and MCP Tool (custom SSE connection) Set node for input and variable assignment Webhook and JSON for Facebook API structure ⚙️ Setup Instruc
Control your discord server with natural language via GPT4o and MCP Client
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
CAI-Powered Restaurant Order Chatbot with GPT-4o for POS Integration
This workflow automates the restaurant POS (Point of Sale) data management process, facilitating seamless order handling, customer tracking, inventory management, and sales reporting. It retrieves order details, processes payment information, updates inventory, and generates real-time sales reports, all integrated into a centralized system that improves restaurant operations. The workflow integrates various systems, including a POS terminal to gather order data, payment gateways to process transactions, inventory management tools to update stock, and reporting tools like Google Sheets or an internal database for generating sales and performance reports. Who Needs Restaurant POS Automation? This POS automation workflow is ideal for restaurant owners, managers, and staff looking to streamline their operations: Restaurant Owners – Automate order processing, track sales, and monitor inventory to ensure smooth operations. Managers – Access real-time sales data and performance reports to make informed decisions. Staff – Reduce manual work, focusing on providing better customer service while the system handles orders and payments. Inventory Teams – Automatically update inventory levels ba
Document Q&A Chatbot with Gemini AI and Supabase Vector Search for Telegram
This template creates a Telegram AI Assistant that answers questions based on your documents, powered by Google Gemini and Supabase. Key features include Intelligent HTML Post-processing for rich formatting in Telegram and Adaptive Message Chunking to handle long text responses. 📹 Watch the Bot in Action ▶️ Click the image above to watch a live demo on YouTube. This video provides a live demonstration of the bot's core features and how it interacts. See a quick walkthrough of its capabilities and user flow. How it works: User uploads a PDF document to a Telegram bot. The workflow processes the PDF, creates embeddings using Google Gemini, and stores these embeddings in a Supabase vector table. Users then ask questions to the bot. The workflow performs a vector search in Supabase to find relevant document chunks based on the user's query. Google Gemini uses the retrieved relevant chunks to generate an intelligent answer. The bot sends the formatted answer back to the user on Telegram, utilizing HTML markup for enhanced presentation. Set up steps: Setup should take approximately 15-20 minutes. Import the workflow into your n8n instance. Configure credentials for Telegram, Google Gemi
Generate Lessons Learned Reports from Jira Epics with AI and Google Docs
Who is this for? Jira users who want to automate the generation of a Lessons Learned or Retrospective report after an Epic is Done. What problem is this workflow solving? / use case Lessons Learned / Retrospective reports are often omitted in Agile teams because they take time to write. With the use of n8n and AI this process can be automated. What is this workflow doing Triggers automatically upon an Epic reaching the "Done" status in Jira. Collects all related tasks and comments associated with the completed Epic. Intelligently filters the gathered data to provide the LLM with the most relevant information. Utilizes an LLM with a structured System Message to generate insightful reports. Delivers the finalized report directly to your specified Google Docs document. Setup Create a Jira API key and follow the Credentials Setup in the Jira trigger node. Create credentials for Google Docs and paste your document ID into the Node. How to customize this workflow to your needs Change the System Message in the AI Agent to fit your needs.
Voice-to-Email Response System with Telegram, OpenAI Whisper & Gmail
This workflow gives you the ability to reply to a long email with a voice note, rather than having to type everything out. ChatGPT will format your audio response and create an email draft for you. How it works When a new email arrives in your inbox, the workflow checks if it needs a response, and it it does, it sends a message to you on Telegram via a VoiceEmailer bot. When you reply to that message with an audio message, the second part of this workflow is triggered. It checks if the message is in the right format, transcribes the audio, and creates a draft response that shows up in the same email thread. Set up steps Add your credentials for Gmail and OpenAI Create an Telegram bot following the instructions here. Connect your telegram credentials so the workflow will use your bot. Turn on the workflow, and message the bot from your telegram. Find the Chat ID from the Executions tab of your workflow, and enter it in as a variable.
Collect LinkedIn Profiles with AI Processing using SerpAPI, OpenAI, and NocoDB
What problem does this solve? It fetches LinkedIn profiles for a multitude of purposes based on a keyword and location via Google search and stores them in an Excel file for download and in a NocoDB database. It tries to avoid using costly services and should be n8n beginner friendly. It uses the serpapi.com to avoid being blocked by Google Search and to process the data in an easier way. What does it do? Based on criteria input, it searches LinkedIn profiles It discards unnecessary data and turns the follower count into a real number The output is provided as an Excel table for download and in a NocoDB database How does it do it? Based on criteria input, it uses serpAPI.com to conduct Google search of the respective LinkedI profiles With OpenAI.com the name of the respective company is being added With OpenAI.com the follower number e.g., 300+ is turned into a real number: 300 All unnecessary metadata is being discarded As an output an Excel file is being created The output is stored in a nocodb.com table Step-by-step instruction Import the Workflow: Copy the workflow JSON from the "Template Code" section below. Import it into n8n via "Import from File" or "Import from URL". Set u
WAutomate Lead Qualification with RetellAI Phone Agent, OpenAI GPT & Google Sheet
👉 Build a Phone Agent to qualify outbound leads and schedule inbound calls Who is this for? This workflow is designed for sales teams, call centers, and businesses handling both outbound and inbound lead calls who want to automate their qualification, follow-up, and call documentation process without manual intervention. It’s ideal for teams using Google Sheets, RetellAI, OpenAI, and Gmail as part of their tech stack. Real-World Use Cases 🛍 E-commerce – Instantly handle product FAQs and order status checks, 24/7. 🏬 Retail Stores – Share store hours, directions, and return policies without lifting a finger. 🍽 Restaurants – Take reservations or answer menu questions automatically. 💼 Service Providers – Book appointments or consultations while you focus on your craft. 📞 Any Local Business – Deliver friendly, consistent phone support — no live agent required. What problem is this workflow solving? Managing lead calls at scale can be chaotic—between scheduling outbound qualification calls, handling inbound appointment requests, and making sure every call is documented and followed up. This workflow automates the entire process, reducing human error and saving time by: ✅ Sending re
Comprehensive SEO Keyword Research with OpenAI & DataForSEO Analytics to NocoDB
AI-Powered SEO Keyword Research Workflow with n8n > automates comprehensive keyword research for content creation Table of Contents Introduction Workflow Architecture NocoDB Integration Data Flow Core Components Setup Requirements Possible Improvements Introduction This n8n workflow automates SEO keyword research using AI and data-driven analytics. It combines OpenAI's language models with DataForSEO's analytics to generate comprehensive keyword strategies for content creation. The workflow is triggered by a webhook from NocoDB, processes the input data through multiple stages, and returns a detailed content brief with optimized keywords. Workflow Architecture The workflow follows a structured process: Input Collection: Receives data via webhook from NocoDB Topic Expansion: Generates keywords using AI Keyword Metrics Analysis: Gathers search volume, CPC, and difficulty metrics Competitor Analysis: Analyzes competitor content for ranking keywords Final Strategy Creation: Combines all data to generate a comprehensive keyword strategy Output Storage: Saves results back to NocoDB and sends notifications NocoDB Integration Database Structure The workflow integrates with two tables in
Automate YouTube Uploads with AI-Generated Metadata from Google Drive
👥 Who Is This For? Content creators, marketing teams, and channel managers who want a simple, hands‑off solution to upload videos and automatically generate optimized metadata from video transcripts. 🛠 What Problem Does This Solve? Manual video uploads with proper metadata creation is time‑consuming and repetitive. This workflow fully automates: Monitoring a specific Google Drive folder for new video uploads Seamless YouTube upload processing Transcript extraction for context understanding AI‑powered generation of titles, descriptions, and tags Metadata application to uploaded videos without manual intervention 🔄 Node‑by‑Node Breakdown | Step | Node Purpose | |------|---------------------------------------------------------------------| | 1 | New Video? (Trigger) – Monitors specified Google Drive folder | | 2 | Download New Video – Retrieves the video file from Google Drive | | 3 | Upload to YouTube – Uploads the video to YouTube with initial settings | | 4 | Get Transcript – Extracts transcript from the uploaded video | | 5 | Adjust Transcript Format – Formats raw transcript for processing | | 6 | Create Description – Generates SEO‑optimized description | | 7 | YT Tags (Message
SBuild a Personal Assistant with Google Gemini, Gmail and Calendar using MCP
Talk to Your Apps: Building a Personal Assistant MCP Server with Google Gemini Wouldn't it be cool to just tell your computer or phone to "schedule a meeting with Sarah next Tuesday at 3 PM" or "find John Doe's email address" and have it actually do it? That's the dream of a personal assistant! With n8n and the power of MCP and AI models like Google Gemini, you can actually build something pretty close to that. We've put together a workflow that shows you how you can use a natural language chat interface to interact with your other apps, like your CRM, email, and calendar. What You Need to Get Started Before you dive in, you'll need a few things: n8n:** An n8n instance (either cloud or self-hosted) to build and run your workflow. Google Gemini Access:** Access to the Google Gemini model via an API key. Credentials for Your Apps:** API keys or login details for the specific CRM, Email, and Calendar services you want to connect (like Google Sheets for CRM, Gmail, Google Calendar, etc., depending on your chosen nodes). A Chat Interface:** A way to send messages to n8n to trigger the workflow (e.g., via a chat app node or webhook). How it Works (In Simple Terms) Imagine this workflow i
WAmazon Product Search Scraper with BrightData, GPT-4, and Google Sheets
This workflow automates web scraping of Amazon search result pages by retrieving raw HTML, cleaning it to retain only the relevant product elements, and then using an LLM to extract structured product data (name, description, rating, reviews, and price), before saving the results back to Google Sheets. It integrates Google Sheets to supply and collect URLs, BrightData to fetch page HTML, a custom n8n Function node to sanitize the HTML, LangChain (OpenRouter GPT-4) to parse product details, and Google Sheets again to store the output. URL to scape . Result Who Needs Amazon Search Result Scraping? This scraping workflow is ideal for teams and businesses that need to monitor Amazon product listings at scale: E-commerce Analysts** – Track competitor pricing, ratings, and inventory trends. Market Researchers** – Collect data on product popularity and reviews for market analysis. Data Teams** – Automate ingestion of product metadata into BI pipelines or data lakes. Affiliate Marketers** – Keep affiliate catalogs up to date with latest product details and prices. If you need reliable, structured data from Amazon search results delivered directly into your spreadsheets, this workflow saves