Workflow Templates

How it works • Transcribes a WhatsApp voice or text message from a prospect using Whisper or GPT • Extracts key information (name, need, context, urgency) via AI • Matches the most relevant service pack by comparing the prospect’s need with Airtable data • Dynamically fills a branded template via APITEMPLATE (HTML or PDF) • Generates a clean, personalized business proposal — including dynamic links (payment, calendar, etc.) • Sends the final PDF back instantly via WhatsApp or email Set up steps • ⏱ Estimated setup time: 45–60 minutes • ✅ You’ll need:  ◦ WhatsApp Business Cloud API access (with webhook configured)  ◦ OpenAI API key (Whisper + GPT)  ◦ Airtable (to store service packs and client input)  ◦ APITEMPLATE account (template with placeholders like {{nom}}, {{prix}}, {{lien_reservation}}, etc.)  ◦ n8n instance (cloud or self-hosted) • 📦 Create your service packs in Airtable with associated links (Stripe, Calendly…) • 🔗 The proposal auto-includes these links dynamically inside the PDF • 🚀 Workflow orchestrates the end-to-end process: from WhatsApp input to PDF delivery

How it works: Webhook URL that responds to Requests with an AI generated Image based on the prompt provided in the URL. Setup Steps: Ideate your prompt URL Encode The Prompt (as shown in the Template) Authenticate with your OpenAI Credentials Put together the Webhook URL with your prompt and enter into a webbrowser In this way you can expose a public url to users, employee's etc. without exposing your OpenAI API Key to them. Click here to find a blog post with additional information.

How it works This template is a complete, hands-on tutorial that lets you build and interact with your very first AI Agent. Think of an AI Agent as a standard AI chatbot with superpowers. The agent doesn't just talk; it can use tools to perform actions and find information in real-time. This workflow is designed to show you exactly how that works. The Chat Interface (Chat Trigger): This is your window to the agent. It's a fully styled, public-facing chat window where you can have a conversation. The Brain (AI Agent Node): This is the core of the operation. It takes your message, understands your intent, and intelligently decides which "superpower" (or tool) it needs to use to answer your request. The agent's personality and instructions are defined in its extensive system prompt. The Tools (Tool Nodes): These are the agent's superpowers. We've included a variety of useful and fun tools to showcase its capabilities: Get a random joke. Search Wikipedia for a summary of any topic. Calculate a future date. Generate a secure password. Calculate a monthly loan payment. Fetch the latest articles from the n8n blog. The Memory (Memory Node): This gives the agent a short-term memory, allowing it to remember the last few messages in your conversation for better context. When you send a message, the agent's brain analyzes it, picks the right tool for the job, executes it, and then formulates a helpful response based on the tool's output. Set up steps Setup time: ~3 minutes This template is nearly ready to go out of the box. You just need to provide the AI's "brain." Configure Credentials: This workflow requires an API key for an AI model. Make sure you have credentials set up in your n8n instance for either Google AI (Gemini) or OpenAI. Choose Your AI Brain (LLM): By default, the workflow uses the Google Gemini node. If you have Google AI credentials, you're all set! If you prefer to use OpenAI, simply disable the Gemini node and enable the OpenAI node. You only need one active LLM node. Make sure it is connected to the Agent parent node. Explore the Tools: Take a moment to look at the different tool nodes connected to the Your First AI Agent node. This is where the agent gets its abilities! You can add, remove, or modify these to create your own custom agent. Activate and Test! Activate the workflow. Open the public URL for the Example Chat Window node (you can copy it from the node's panel). Start chatting! Try asking it things like: "Tell me a joke." "What is n8n?" "Generate a 16-character password for me." "What are the latest posts on the n8n blog?" "What is the monthly payment for a $300,000 loan at 5% interest over 30 years?"

Create a recommendation tool without hallucinations based on RAG with the Qdrant Vector database. This example is based on movie recommendations on the IMDB-top1000 dataset. You can provide your wishes and your "big no's" to the chatbot, for example: "A movie about wizards but not Harry Potter", and get top-3 recommendations. How it works a video with the full design process Upload IMDB-1000 dataset to Qdrant Vector Store, embedding movie descriptions with OpenAI; Set up an AI agent with a chat. This agent will call a workflow tool to get movie recommendations based on a request written in the chat; Create a workflow which calls Qdrant's Recommendation API to retrieve top-3 recommendations of movies based on your positive and negative examples. Set Up Steps You'll need to create a free tier Qdrant Cluster (Qdrant can also be used locally; it's open-sourced) and set up API credentials You'll OpenAI credentials You'll need GitHub credentials & to upload the IMDB Kaggle dataset to your GitHub.

Who’s it for 👥 This template is perfect for content creators, marketers, and researchers managing WeChat public account articles! 🚀 It’s ideal for n8n newcomers or anyone wanting to save time on manual content analysis, especially if you use Google Sheets for tracking. 📊 Whether you’re into AI, 欧阳良宜, or automation, this is for you! 😄 How it works / What it does 🔧 This workflow automates the retrieval, filtering, classification, and summarization of WeChat articles. 🌐 It reads RSS feed links from a Google Sheet, filters articles from the last 10 days ⏳, cleans HTML content 🧹, classifies them as relevant or not 🎯, generates insightful Chinese summaries with AI 🤖, and saves results to Google Sheets and Notion. 📝 Outputs are Slack-formatted for team collaboration! 💬 How to set up 🛠️ Prepare Google Sheets: Use your own documentId (replace the example) and set up sheets "Save Initial Links" (gid=198451233) and "Save Processed Data" (gid=1936091950). 📋 Configure Credentials: Add Google Sheets and OpenAI API credentials—avoid hardcoding keys! 🔐 Set RSS Feed: Update the rss_feed_url in the "RSS Read" node with your WeChat RSS feed. 🌐 Customize AI: Tweak "Relevance Classification" and "Basic LLM Chain" prompts for your topics (e.g., 欧阳良宜, AI). 🎨 Notion (Optional): Swap the databaseId (e.g., 22e79d55-2675-8055-a143-d55302c3c1b1) with your own. 📚 Run Workflow: Trigger manually via the "When clicking ‘Execute workflow’" node. 🚀 Requirements ✅ n8n account with Google Sheets and OpenAI integrations. Access to a WeChat public account RSS feed. Basic JSON and node config knowledge. How to customize the workflow 🎛️ Topic Adjustment: Update categories in "Relevance Classification" for new topics (e.g., "technology", "education"). 🌱 Summary Length: Modify the LLM prompt in "Basic LLM Chain" to adjust length or style. ✂️ Output Destination: Add Slack or Email nodes for more outputs. 📩 Date Filter: Change the "IF (Filter by Date)" condition (e.g., 7 days instead of 10). ⏰ Scalability: Use a "Schedule Trigger" node for automation. ⏳

This n8n template provides a powerful AI-powered chatbot that acts as your personal Spotify DJ. Simply tell the chatbot what kind of music you're in the mood for, and it will intelligently create a custom playlist, give it a fitting name, and populate it with relevant tracks directly in your Spotify account. The workflow is built to be flexible, allowing you to easily change the underlying AI model to your preferred provider, making it a versatile starting point for any AI-driven project. Who is this for? Music lovers:** Instantly create playlists for any activity, mood, or genre without interrupting your flow. Developers & AI enthusiasts:** A perfect starting point to understand how to build a functional AI Agent that uses tools to interact with external services. Automation experts:** See a practical example of how to chain AI actions and sub-workflows for more complex, stateful automations. What problem does this solve? Manually creating a good playlist is time-consuming. You have to think of a name, search for individual songs, and add them one by one. This workflow solves that by: Automating playlist creation:** Turns a simple natural language request (e.g., "I need a playlist for my morning run") into a fully-formed Spotify playlist. Reducing manual effort:** Eliminates the tedious task of searching for and adding multiple tracks. Providing player control:** Allows you to manage your Spotify player (play, pause, next) directly from the chat interface. Centralizing music management:** Acts as a single point of control for both creating playlists and managing playback. How it works Trigger & input: The workflow starts when you send a message in the Chat Trigger interface. AI agent & tool-use: An AI Agent, powered by a Large Language Model (LLM), interprets your message. It has access to a set of "tools" that allow it to interact with Spotify. Playlist creation sub-workflow: If you ask for a new playlist, the Agent calls a sub-workflow using the Create new playlist tool. This sub-workflow uses another AI call to brainstorm a creative playlist name and a list of suitable songs based on your request. Spotify actions: The sub-workflow then connects to Spotify to: Create a new, empty playlist with the generated name. Search for each song from the AI's list to get its official Spotify Track ID. Add each track to the new playlist. Player control: If your request is to control the music (e.g., "pause the music"), the Agent uses the appropriate tool (Pause player, Resume player, etc.) to directly control your active Spotify player. Setup Accounts & API keys: You will need active accounts and credentials for: Your AI provider (e.g., OpenAI, Groq, local LLMs via Ollama): To power the AI Agent and the playlist generation. Spotify: To create playlists and control the player. You'll need to register an application in the Spotify Developer Dashboard to get your credentials. Configure credentials: Add your AI provider's API key to the Chat Model nodes. The template uses OpenAI by default, but you can easily swap this out for any compatible Langchain model node. Add your Spotify OAuth2 credentials to all Spotify and Spotify Tool nodes. Activate workflow: Once all credentials are set and the workflow is saved, click the "Active" toggle. You can now start interacting with your Spotify AI Agent via the chat panel! Taking it further This template is a great foundation. Here are a few ideas to expand its capabilities: Become the party DJ:** Make the Chat Trigger's webhook public. You can then generate a QR code that links to the chat URL. Party guests can scan the code and request songs directly from their phones, which the agent can add to a collaborative playlist or the queue. Expand the agent's skills:** The Spotify Tool node has more actions available. Add a new tool for Add to Queue so you can ask the agent to queue up a specific song without creating a whole new playlist. Integrate with other platforms:** Swap the Chat Trigger for a Telegram or Discord trigger to build a Spotify bot for your community. You could also connect it to a Webhook to take requests from a custom web form.

Description 📄 Turn your Slack workspace into a smart AI-powered HelpDesk using this workflow. This automation listens to Slack messages and uses an AI assistant (powered by OpenAI or any other LLM) to respond to employee questions about HR, IT, or internal policies by referencing your internal documentation (such as the Policy Handbook). If the answer isn't available, it can optionally email the relevant department (HR or IT) and ask them to update the handbook. It remembers recent messages per user, cleans up intermediate responses to keep Slack threads tidy, and ensures your team gets consistent and helpful answers—without manually searching docs or escalating simple questions. Perfect for growing teams who want to streamline internal support using n8n, Slack, and AI. How it works 🛠️ This workflow turns n8n into a Slack-based HelpDesk assistant powered by AI. It listens to Slack messages using the Events API, detects whether a real user is asking a question, and responds using OpenAI (or another LLM of your choice). Here's how it works step-by-step: Webhook Trigger: The workflow starts when a message is posted in Slack via the Events API. It filters out any messages from bots to avoid loops. Identify the User: It fetches the full Slack profile of the user who posted the message and stores their name. Send Receipt Message: An initial message is sent to the user saying, “I’m on it!”, confirming their request is being processed. AI Response Handling: The message is processed using the OpenAI Chat model (GPT-4o by default). Before responding, it checks if the query matches any HR or IT policy from the Policy Handbook. If the question can’t be answered based on internal data, it can optionally alert the HR or IT department via Gmail (after user confirmation). Memory Retention: It keeps track of the last 5 interactions per user using Simple Memory, so it remembers previous context in a Slack conversation. Cleanup and Final Reply: It deletes the initial receipt message and sends a final, clean response to the user. How to use 🚀 Clone the Workflow: Download or import the JSON workflow into your n8n instance. Connect Your Credentials: Slack API (for messaging) Google Sheets API (for department contact info) Google Docs API (for the Policy Handbook) Gmail API (optional, for notifying departments) OpenAI or another AI model Slack Setup: Set up a Slack App and enable the Events API. Subscribe to message events and point them to the Webhook URL generated by the workflow. Customize Responses: Edit the initial and final Slack message nodes if you want to personalize the wording. Swap out the LLM (ChatGPT) with your preferred model in the AI Agent node. Adjust AI Behavior: Tune the prompt logic in the “AI Agent” node if you want the AI to behave differently or access different data sources. Expand Memory or Integrations: Use external databases to store longer histories. Integrate with tools like Asana, Notion, or CRM platforms for further automation. Requirements 📋 n8n (self-hosted or cloud) Slack Developer Account & App OpenAI (or any LLM provider) Google Sheets with department contact details Google Docs containing the policy Handbook Gmail account (optional, for email alerts) Knowledge of Slack Events API setup

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.

This n8n template is one of a 3-part series exploring use-cases for clustering vector embeddings: Survey Insights Customer Insights Community Insights This template demonstrates the Community Insights scenario where HN commments can be quickly grouped by similarity and an AI agent can generate insights on those groupings. With this workflow, Researchers or HN users can quickly breakdown community consensus on a particular topic and identify frequently mentioned positives and negatives. Sample Output: https://docs.google.com/spreadsheets/d/e/2PACX-1vQXaQU9XxsxnUIIeqmmf1PuYRuYtwviVXTv6Mz9Vo6_a4ty-XaJHSeZsptjWXS3wGGDG8Z4u16rvE7l/pubhtml How it works HN comments are imported via the Hacknews API node. Comments are then inserted into a Qdrant collection carefully tagged with the Hackernews API metadata. Comments are then fetched and are put through a clustering algorithm using the Python Code node. The Qdrant points are returned in clustered groups. Each group is looped to fetch the payloads of the points and feed them to the AI agent to summarise and generate insights for. The resulting insights and raw responses are then saved to the Google Spreadsheet for further analysis by the researcher or the HN user. Requirements Works best with lots of comments! Qdrant Vectorstore for storing embeddings. OpenAI account for embeddings and LLM. Customising the Template Adjust clustering parameters which make sense for your data. Adjust sentimentality setting if comments are overwhelmingly negative at times.

This workflow allows users to generate AI videos using Google Veo3, save them to Google Drive, generate optimized YouTube titles with GPT-4o, and automatically upload them to YouTube with Upload-Post. The entire process is triggered from a Google Sheet that acts as the central interface for input and output. IT automates video creation, uploading, and tracking, ensuring seamless integration between Google Sheets, Google Drive, Google Veo3, and YouTube. Benefits of this Workflow 💡 No Code Interface**: Trigger and control the video production pipeline from a simple Google Sheet. ⚙️ Full Automation**: Once set up, the entire video generation and publishing process runs hands-free. 🧠 AI-Powered Creativity**: Generates engaging YouTube titles using GPT-4o. Leverages advanced generative video AI from Google Veo3. 📁 Cloud Storage & Backup**: Stores all generated videos on Google Drive for safekeeping. 📈 YouTube Ready**: Automatically uploads to YouTube with correct metadata, saving time and boosting visibility. 🧪 Scalable**: Designed to process multiple video prompts by looping through new entries in Google Sheets. 🔒 API-First**: Utilizes secure API-based communication for all services. How It Works Trigger: The workflow can be started manually ("When clicking ‘Test workflow’") or scheduled ("Schedule Trigger") to run at regular intervals (e.g., every 5 minutes). Fetch Data: The "Get new video" node retrieves unfilled video requests from a Google Sheet (rows where the "VIDEO" column is empty). Video Creation: The "Set data" node formats the prompt and duration from the Google Sheet. The "Create Video" node sends a request to the Fal.run API (Google Veo3) to generate a video based on the prompt. Status Check: The "Wait 60 sec." node pauses execution for 60 seconds. The "Get status" node checks the video generation status. If the status is "COMPLETED," the workflow proceeds; otherwise, it waits again. Video Processing: The "Get Url Video" node fetches the video URL. The "Generate title" node uses OpenAI (GPT-4.1) to create an SEO-optimized YouTube title. The "Get File Video" node downloads the video file. Upload & Update: The "Upload Video" node saves the video to Google Drive. The "HTTP Request" node uploads the video to YouTube via the Upload-Post API. The "Update Youtube URL" and "Update result" nodes update the Google Sheet with the video URL and YouTube link. Set Up Steps Google Sheet Setup: Create a Google Sheet with columns: PROMPT, DURATION, VIDEO, and YOUTUBE_URL. Share the Sheet link in the "Get new video" node. API Keys: Obtain a Fal.run API key (for Veo3) and set it in the "Create Video" node (Header: Authorization: Key YOURAPIKEY). Get an Upload-Post API key (for YouTube uploads) and configure the "HTTP Request" node (Header: Authorization: Apikey YOUR_API_KEY). YouTube Upload Configuration: Replace YOUR_USERNAME in the "HTTP Request" node with your Upload-Post profile name. Schedule Trigger: Configure the "Schedule Trigger" node to run periodically (e.g., every 5 minutes). Need help customizing? Contact me for consulting and support or add me on Linkedin.

Learn n8n: Interactive Lesson 1 This interactive tutorial teaches you how to build in n8n from scratch, using a live walkthrough with real-time examples. Rather than static documentation, this guided workflow explains key n8n concepts while you execute each step. It is ideal for developers new to n8n but experienced with programming, JSON, and APIs. Requirements An active n8n instance (cloud or self-hosted) Basic programming experience (JavaScript or TypeScript, JSON, and APIs) Web browser with console access (for log inspection) What This Workflow Covers Triggers, Form nodes, and data flow How n8n executes nodes one step at a time How data moves between nodes (variables, context, side effects) Merge, Split, Aggregate, and Loop patterns Code nodes in single vs multiple execution modes Debugging using Logs and console output Step-by-Step Setup Manual Setup Before starting, create your n8n account and optionally enable dark mode. A video link is included with suggested background material. Form-Based Progression The tutorial uses Form Trigger and Form nodes as interactive checkpoints. You will execute the workflow, follow the browser prompts, and observe what happens in the visual editor. Live Code and Flow Examples Key concepts like branching, merging, and data references are shown in action. Sticky notes in the workflow explain what to look for and how things work. Execution Behavior You will see how multiple items affect execution count, and how to control it using options like Execute Once, batching, and aggregation. Debugging with Logs Toward the end, the workflow encourages you to inspect inputs and outputs of each node, and use console.log() inside Code nodes to understand the data being passed around. How to Use This Workflow This workflow is meant to be a long-term reference. If you get stuck building in n8n, return to it. Each section focuses on a core concept such as how data flows, how execution counts behave, or how to merge parallel branches. You can copy and paste working examples from this tutorial directly into your own workflows to solve common problems. This is not just a lesson. It's a toolbox.

SEO Blog Generator with GPT-4o, Perplexity, and Telegram Integration This workflow helps you automatically generate SEO-optimized blog posts using Perplexity.ai, OpenAI GPT-4o, and optionally Telegram for interaction. 🚀 Features 🧠 Topic research via Perplexity sub-workflow ✍️ AI-written blog post generated with GPT-4o 📊 Structured output with metadata: title, slug, meta description 📩 Integration with Telegram to trigger workflows or receive outputs (optional) ⚙️ Requirements ✅ OpenAI API Key (GPT-4o or GPT-3.5) ✅ Perplexity API Key (with access to /chat/completions) ✅ (Optional) Telegram Bot Token and webhook setup 🛠 Setup Instructions Credentials: Add your OpenAI credentials (openAiApi) Add your Perplexity credentials under httpHeaderAuth Optional: Setup Telegram credentials under telegramApi Inputs: Use the Form Trigger or Telegram input node to send a Research Query Subworkflow: Make sure to import and activate the subworkflow Perplexity_Searcher to fetch recent search results Customization: Edit prompt texts inside the Blog Content Generator and Metadata Generator to change writing style or target industry Add or remove output nodes like Google Sheets, Notion, etc. 📦 Output Format The final blog post includes: ✅ Blog content (1500-2000 words) ✅ Metadata: title, slug, and meta description ✅ Extracted summary in JSON ✅ Delivered to Telegram (if connected) Need help? Reach out on the n8n community forum