Workflow Templates

How it works Automates the retrieval of Calvin and Hobbes daily comics. Extracts the comic image URL from the website. Translates comic dialogues to English and Korean. Posts the comic and translations to Discord daily. Set up steps Estimated setup time: ~10-15 minutes. Use a Schedule Trigger to automate the workflow at 9 AM daily. Add nodes for parameter setup, HTTP request, data extraction, and integration with Discord. Add detailed notes to each node in the workflow for easy understanding.

Who it's for This workflow is for anyone who wants to build an automated, AI-enhanced reading list. Ideal for: Knowledge workers and researchers who collect and organize articles Students managing study materials Productivity hackers who use Telegram and Notion for personal knowledge management Anyone using the AI-Enhanced Knowledge Base Tracker Notion Template How it works This workflow takes any article link sent to your Telegram bot and automatically: Parses the article into a clean title and body Uses OpenAI to generate a 1–2 sentence highlight and topic tag Saves it into your Notion database Sends a confirmation message with the highlight and Notion link back to Telegram Main steps: Telegram Trigger - Listens for incoming message containing an article link. Fetch Article Title & Content - Calls the article-parser-api deployed on Vercel to fetch and parse the article content into structured JSON (title and content). Generate Highlight + Tag (AI Agent) - Processes the parsed content to generate Highlight and Type tag values. Structured Metadata for Notion - Adjusts the extracted data before saving it to Notion. Save Article to Notion Database - Inserts the article and generated metadata into your Notion knowledge base. Confirm Save via Telegram - Sends a confirmation message and the Notion page link back to the Telegram bot chat after the entry is created. Setup Create and connect your API credentials: Telegram Bot OpenAI API Key Notion Integration Deploy the article parser: Use this repo: article-parser-api Deploy it to Vercel or any serverless environment Link your Notion database: Duplicate the AI‑Enhanced Knowledge Base Tracker Copy the database URL and connect it in the Notion node Test your workflow: Click Execute workflow Send an article link to your Telegram bot Once verified, activate the workflow so it runs automatically Requirements Telegram bot token OpenAI API key Notion integration and shared database A deployed article parser (e.g., article-parser-api) Optional customization Edit the AI Agent prompt to change tone or tagging style Add filtering or additional fields in the Edit Fields node Trigger from other sources (e.g., Slack or Email)

This workflow demonstrates a simple way to run evals on a set of test cases stored in a Google Sheet. The example we are using comes from an info extraction task dataset, where we tested 6 different LLMs on 18 different test cases. You can see our sample data in this spreadsheet here to get started. Once you have this working for our dataset, you can plug in your own test cases matching different LLMs to see how it works with your own data. How it works: It loads test cases from Google Sheets. For each row in our Google Sheet, it grabs the source document, converting it to text. Our "LLM judge" passes the input/output of each LLM to GPT-4.1 to evaluate each test case (Pass/Fail + Reason). It logs the outcome to a Google Sheet. A 0.5s pause between each request gets around OpenAI's API rate limits. Set up steps: Add your credentials for Google Sheets, Google Drive, and OpenRouter. Make a copy of the original data spreadsheet so that you can edit it yourself. You will need to plug your version in the Update Results node to see the spreadsheet update on each run of the loop.

Use case This workflow enables a Telegram bot that can: Accept speech input in one of 55 supported languages Automatically detect the language spoken and translate the speech to another language Responds back with the translated speech output. This allows users to communicate across language barriers by simply speaking to the bot, which will handle the translation seamlessly. How does it work? Translation In the translation step the workflow converts the user's speech input to text and detects the language of the input text. If it's English, it will translate to French. If it's French, it will translate to English. To change the default translation languages, you can update the prompt in the AI node. Output In the output step, we provide the translated text output back to the user and speech output is generated in the translated language. Setup steps Obtain Telegram API Token Start a chat with the BotFather. Enter /newbot and reply with your new bot's display name and username. Copy the bot token and use it in the Telegram node credentials in n8n. Update the Settings node to customize the desired languages Activate the flow Full list of supported languages All supported languages:

This is a demo workflow to showcase how to use Supabase to embed a document, retrieve information from the vector store via chat and update the database. Setup steps: set your credentials for Supabase set your credentials for an AI model of your choice set credentials for any service you want to use to upload documents please follow the guidelines in the workflow itself (Sticky Notes) Feedback & Questions If you have any questions or feedback about this workflow - Feel free to get in touch at ria@n8n.io

This workflow demonstrates a Retrieval Augmented Generation (RAG) chatbot that lets you chat with the GitHub API Specification (documentation) using natural language. Built with n8n, OpenAI's LLMs and the Pinecone vector database, it provides accurate and context-aware responses to your questions about how to use the GitHub API. You could adapt this to any OpenAPI specification for any public or private API, thus creating a documentation chatbout that anyone in your company can use. How it works: Data Ingestion: The workflow fetches the complete GitHub API OpenAPI 3 specification directly from the GitHub repository. Chunking and Embeddings: It splits the large API spec into smaller, manageable chunks. OpenAI's embedding models then generate vector embeddings for each chunk, capturing their semantic meaning. Vector Database Storage: These embeddings, along with the corresponding text chunks, are stored in a Pinecone vector database. Chat Interface and Query Processing: The workflow provides a simple chat interface. When you ask a question, it generates an embedding for your query using the same OpenAI model. Semantic Search and Retrieval: Pinecone is queried to find the most relevant text chunks from the API spec based on the query embedding. Response Generation: The retrieved chunks and your original question are fed to OpenAI's gpt-4o-mini LLM, which generates a concise, informative, and contextually relevant answer, including code snippets when applicable. Set up steps: Create accounts: You'll need accounts with OpenAI and Pinecone. API keys: Obtain API keys for both services. Configure credentials: In your n8n environment, configure credentials for OpenAI and Pinecone using your API keys. Import the workflow: Import this workflow into your n8n instance. Pinecone Index: Ensure you have a Pinecone index named "n8n-demo" or adjust the workflow accordingly. The workflow is set up to work with this index out of the box. Setup Time: Approximately 15-20 minutes. Why use this workflow? Learn RAG in Action: This is a practical, hands-on example of how to build a RAG-powered chatbot. Adaptable Template: Easily modify this workflow to create chatbots for other APIs or knowledge bases. n8n Made Easy: See how n8n simplifies complex integrations between data sources, vector databases, and LLMs.

AI evaluation in n8n This is a template for n8n's evaluation feature. Evaluation is a technique for getting confidence that your AI workflow performs reliably, by running a test dataset containing different inputs through the workflow. By calculating a metric (score) for each input, you can see where the workflow is performing well and where it isn't. How it works This template shows how to calculate a workflow evaluation metric: whether an output matches an expected output (i.e. has the same meaning). The workflow takes questions about the causes of historical events and compares them with the reference answers in the dataset. We use an evaluation trigger to read in our dataset It is wired up in parallel with the regular chat trigger so that the workflow can be started from either one. More info If we're evaluating (i.e. the execution started from the evaluation trigger), we calculate the correctness metric using AI We pass this information back to n8n as a metric If we're not evaluating we avoid calculating the metric, to reduce cost

How the sequence works: A "Schedule Trigger" node activates the automation at a defined schedule. An "Airtable" node will search for previously posted questions in your question database. Airtable Base Template: here An "Aggregate" node will take all the questions from Airtable and compress them to a single output. ChatGPT, or a model of your choice, will generate a discussion question based on the options in the system prompt. The discussion question will be posted to the subreddit of your choice by the "Reddit" node. You can choose between a text, image, or link post! The recently-posted discussion question will then be uploaded to your Airtable base using the "Airtable" node. This will be used to prevent ChatGPT from creating duplicate questions. Setup Steps The setup process will take about 5 minutes. An Airtable base template is also pre-made for you here: https://airtable.com/app6wzQqegKIJOiOg/shrzy7L9yv8BFRQdY Set the recurrence in the "Schedule" node Create an Airtable account, you can use the link here. Get an Airtable personal access token here. Configure the "Get Previous Discussions" Airtable node. Configure the options in brackets in the "Generate New Discussion" node. Set the desired subreddit to post to and the post type(text, image, or link) in the "Post Discussion" node. Configure the "Create Archived Discussion" node to map to the Airtable base(required) and specific subreddit(optional).

Transform your BeyondPresence video agent conversations into comprehensive insights by automatically analyzing each call with AI and organizing 35+ data points in Google Sheets. This template helps customer success, support, and training teams save 30+ minutes per call on documentation while ensuring no critical action items or insights are missed. How it works Webhook receives** completed call data from BeyondPresence including full transcript Data validation** ensures quality and adds enriched metadata (duration, time calculations) AI analysis** (GPT-4) extracts action items, sentiment, decisions, and recommendations Parse response** handles the AI output and structures it for sheets Auto-append** to Google Sheets with 35+ insights per call organized beautifully Set up steps Copy our Google Sheets template - One click! Get pre-formatted sheet: BeyondPresence Call Analytics Template Connect accounts - Add OpenAI API key and Google Sheets OAuth2 Configure webhook - Copy URL from n8n to BeyondPresence Settings → Webhooks Customize AI prompt (optional) - Adjust analysis focus for your use case Test with a call - Make a test call and watch insights appear! Setup time: 5-10 minutes Requirements: BeyondPresence account, OpenAI API key, Google account

Instantly access real-time decentralized exchange (DEX) insights directly in Telegram! This workflow integrates the DexScreener API with GPT-4o-powered AI and Telegram, allowing users to fetch the latest blockchain token analytics, liquidity pools, and trending tokens effortlessly. Ideal for crypto traders, DeFi analysts, and investors who need actionable market data at their fingertips. How It Works A Telegram bot listens for user queries about tokens or trading pairs. The workflow interacts with the DexScreener API (no API key required) to fetch real-time data, including: Token fundamentals (profiles, images, descriptions, and links) Trending and boosted tokens (hyped projects, potential market movers) Trading pair analytics (liquidity, price action, volumes, volatility) Order and payment activity (transaction insights, investor movements) Liquidity pool depth (market stability, capital flows) Multi-chain pair comparisons (performance tracking across networks) An AI-powered language model (GPT-4o-mini) enhances responses for better insights. The workflow logs session data to improve user interaction tracking. The requested DEX insights are sent back via Telegram in an easy-to-read format. What You Can Do with This Agent This AI-driven Telegram bot enables you to: ✅ Track trending and boosted tokens before they gain mainstream traction. ✅ Monitor real-time liquidity pools to assess token stability. ✅ Analyze active trading pairs across different blockchains. ✅ Identify transaction trends by checking paid orders for tokens. ✅ Compare market activity with detailed trading pair analysis. ✅ Receive instant insights with AI-enhanced responses for deeper understanding. Set Up Steps Create a Telegram Bot Use @BotFather on Telegram to create a bot and obtain an API token. Configure Telegram API Credentials in n8n Add your Telegram bot token under Telegram API credentials. Deploy and Test Send a query (e.g., "SOL/USDC") to your Telegram bot and receive real-time insights instantly! 🚀 Unlock powerful, real-time DEX insights directly in Telegram—no API key required! 📺 Setup Video Tutorial Watch the full setup guide on YouTube:

This n8n workflow leverages a Telegram Message Trigger to activate an intelligent AI Agent capable of processing both text and voice messages. When a user sends a message in text or in voice format, the workflow captures and transcribes it (if necessary), then passes it to the AI Agent for understanding and response generation. To enhance user experience, the bot also displays a typing indicator while processing requests, simulating a natural, human-like interaction. Key Features Multi-Modal Input: Supports both text messages and voice notes from users. Real-Time Interaction: Shows a “typing…” action in Telegram while the AI processes the input. AI Agent Integration: Provides intelligent, context-aware, and conversational responses. Seamless Feedback Loop: Replies are sent directly back to the user within Telegram for smooth interaction. How It Works The workflow triggers whenever a message or voice note is received on Telegram. If the input is a voice note, the workflow transcribes it into text. The text input is sent to the AI Agent for processing. While processing, the bot sends a typing indicator to the user. Once the AI generates a response, the workflow sends it back to the user in Telegram. Setup Instructions Create a Telegram Bot: Use @BotFather to create a bot and obtain your bot token. Configure n8n Credentials: Add Telegram API credentials in n8n with your bot token. Add credentials for any speech-to-text service used for voice transcription (e.g., Open AI Transcribe A Recording). Import the Workflow: Import this workflow into your n8n instance. Update all credential nodes to use your Telegram and transcription service credentials. Set Webhook URLs: Ensure Telegram webhook is set properly for your bot to receive messages. Make sure your n8n instance is publicly accessible for Telegram callbacks. Test the Workflow: Send text messages and voice notes to your Telegram bot and observe the AI responses. Customization Guidance Add new message handlers: Extend the workflow to handle additional message types (images, documents, etc.). Improve transcription: Swap or add speech-to-text services for better accuracy or language support. Enhance AI Agent: Customize prompts and context management to tailor the AI’s personality and responses. AI Model Flexibility: Swap between different AI models (e.g., GPT-4, Claude, or custom LLMs) based on task type, cost, or performance preferences. Tool-Based Control: Add custom tools to the AI Agent such as calendar access, Notion, Google Sheets, web search, database queries, or custom APIs—allowing for dynamic, multi-functional agents Security and Implementation Notes The Telegram node manages message reception and sending but does not directly handle AI processing. Voice transcription requires integration with external APIs; secure those credentials in n8n and monitor usage. To simulate typing, the workflow uses Telegram’s “sendChatAction” API method, providing users with feedback that the bot is processing. Ensure your AI API keys and Telegram tokens are securely stored in n8n credentials and not exposed in workflows or logs. Benefits Handles natural conversational inputs with text or voice. Provides a smooth, engaging user experience via typing indicators. Easy integration of advanced AI conversational agents with Telegram. Flexible for personal assistants, helpdesks, or interactive chatbots.

Good to know: This workflow automatically processes product images from Google Drive, generates AI-powered background prompts using multiple AI models (ChatGPT, Claude, or Groq), creates professional background scenes using Pixelcut.ai, and saves enhanced images back to your Google Drive. Perfect for e-commerce businesses and product photography workflows. Who is this for? ➖E-commerce store owners who need professional product backgrounds ➖Product photographers looking to automate background generation ➖Marketing teams creating consistent product imagery ➖Small businesses wanting to enhance their product photos without expensive studio setups ➖Anyone who needs to quickly transform transparent product images into commercial-ready photos What problem is this workflow solving? This workflow solves the challenge of creating professional product photography backgrounds at scale. Instead of manually editing each product image or setting up expensive photo shoots, it automatically generates contextually appropriate backgrounds for your products using AI technology. It eliminates the time-consuming process of background creation while maintaining professional quality and consistency across your product catalog. What this workflow does: ✅Automatically fetches product images from your Google Drive folder ✅Downloads transparent/background-free product images ✅Uses advanced AI models (ChatGPT, Claude, or Groq) to generate intelligent background prompts based on product analysis ✅Creates professional backgrounds using Pixelcut.ai API with AI-generated or custom prompts ✅Saves enhanced product images back to Google Drive with organized naming ✅Processes multiple images in batch automatically How it works: 1️⃣Google Drive node searches for PNG product images in your specified folder 2️⃣Binary download node retrieves the actual image files for processing 3️⃣Optional AI agent analyzes products using your chosen AI model (OpenAI GPT-4, Claude, or Groq) and generates appropriate background prompts 4️⃣Pixelcut.ai API processes images and adds professional backgrounds using AI-generated or manual prompts 5️⃣Enhanced images are automatically saved back to Google Drive with "enhanced-" prefix How to use: Set up Google Drive OAuth2 credentials in n8n Create a Pixelcut.ai account and get your API key Configure your source folder ID in the Google Drive nodes Set up your output folder ID for enhanced images Choose and configure your preferred AI model credentials (OpenAI for ChatGPT, Anthropic for Claude, or Groq) Replace placeholder API keys with your actual credentials Execute the workflow to process your product images Requirements: ✅n8n instance (cloud or self-hosted) ✅Google Drive account with OAuth2 access ✅Pixelcut.ai API account and key ✅Product images in PNG format (transparent backgrounds recommended) ✅AI API credentials for automatic prompt generation (choose from): OpenAI API (for ChatGPT/GPT-4) Anthropic API (for Claude) Groq API (for fast inference) ✅Basic understanding of n8n workflows Customizing this workflow: 🟢Modify the image format filter to support JPG, WEBP, or other formats 🟢Switch between different AI models (ChatGPT, Claude, Groq) for prompt generation 🟢Customize background prompts for different product categories 🟢Add background removal step for products with existing backgrounds 🟢Switch to different AI background services (Deep-Image.ai, Remove.bg, etc.) 🟢Configure different AI model parameters for varied prompt creativity 🟢Add image resizing or quality optimization steps 🟢Create multiple output folders for different product categories 🟢Add error handling and retry mechanisms for failed processes 🟢Implement A/B testing with different AI models for prompt quality comparison