Workflow Templates

Who is this template for? Anyone who is drowning in emails Busy parents who has alot of school emails Busy executives with too many emails Case Study I get too many emails from my kid's school about soccer practice, lunch orders and parent events. I use this workflow to read all the emails and tell me what is important and what requires actioning. Read more -> How I used A.I. to read all my emails What this workflow does It uses IMAP to read the emails from your email account (i.e. Gmail). It then passes the email to Openrouter.ai and uses a free A.I. model to read and summarize the email. It then sends the summary as a message to your messenger (i.e. Line). Setup You need to find your email server IMAP credentials. Input your openrouter.ai API credentials or replace the HTTP request node with an A.I. node such as OpenAI. Input your messenger credentials. I use Line but you can change the node to another messenger line Telegram. You need to change the message ID to your ID inside the http request. You can find your user ID inside the https://developers.line.biz/console/. Change the "to": {insert your user ID}. How to adjust it to your needs You can change the A.I. prompt to fit your needs by telling it to mark emails from a certain address as important. You can change the A.I. model from the current meta-llama/llama-3.1-70b-instruct:free to a paid model or other free models. You can change the messenger node to telegram or any other messenger app you like.

Building agentic AI workflows often requires multiple moving parts: memory management, document retrieval, vector similarity, and orchestration. Until now, these pieces had to be custom-wired. But with the new native n8n nodes for MongoDB Atlas, we reduce that overhead dramatically. With just a few clicks: Store and recall long-term memory from MongoDB Query vector embeddings stored in Atlas Vector Search Use these results in your LLM chains and automation logic In this example we present an ingestion and AI Agent flows that focus around Travel Planning. The different interest points that we want the agent to know about can be ingested into the vector store. The AI Agent will use the vector store tool to get relevant context about those points of interest if it needs to. Prerequisites MongoDB Atlas project and Cluster OpenAI Valid API Key for embeddings (can be other provider) Gemini API Key for the LLM (can be other provider) How it works: There are 2 main flows. One is ingesting flow: Gets a document from a webhook and use MongoDB Vector Atlas to embed the document title and description into points_of_interest collection. Embeddings are stored in a field named embedding Embeddings used are OpenAI's but it can be any type of supported embedders. Second flow is an AI Agent node with Chat Memory Stored in MongoDB Atlas and a Vector Search node as a tool: Chat Message Trigger**: Chatting with the AI Agent will trigger the conversation store in the MongoDB Chat Memory node. When data is necessary like a location search or details it will go to the "Vector Search" tool. Vector Search Tool** - uses Atlas Vector Search index created on the points_of_interest collection: // index name : "vector_index" // If you change an embedding provider make sure the numDimensions correspond to the model. { "fields": [ { "type": "vector", "path": "embedding", "numDimensions": 1536, "similarity": "cosine" } ] } Additional Resources MongoDB Atlas Vector Search n8n Atlas Vector Search docs

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Overview This workflow automates the process of transcribing audio files and summarizing them using OpenAI models, with the final output stored neatly in Notion. Whether you're a researcher, content creator, student, or professional, this automation saves time by converting voice recordings into actionable summaries with zero manual effort. Created by: Abdullah Dilshad Contact: iamabdullahdilshad@gmail.com Who It’s For This template is ideal for: Researchers**: Transcribe and summarize interviews, lectures, or research recordings. Content Creators**: Convert podcasts or videos into transcripts and social captions/show notes. Students**: Automatically turn lectures or study group audio into summarized notes. Professionals**: Log meeting notes and summaries directly into your Notion workspace. How It Works This four-step workflow performs the following: Step 1:* *Trigger: New Audio in Google Drive** Automatically triggers when a new audio file (MP3/WAV) is uploaded to a specified Google Drive folder.The file is then downloaded for processing. Step 2: Transcribe Audio with Whisper** The audio file is sent to OpenAI’s Whisper model for high-accuracy transcription. Step 3: Summarize Transcript with GPT-4** The transcript is passed to GPT-4, which generates a clean, concise summary. Step 4: Store Summary in Notion** A new Notion page is created with the generated summary and optional metadata (file name, upload time, etc.). Setup Instructions Step 1: Google Drive Trigger** Connect your Google Drive account. Select the folder you want to monitor. This node detects new file uploads and passes the file for download. Step 2: Download File** Downloads the new audio file for transcription. Step 3: Transcribe Recording (OpenAI Whisper) Connect your OpenAI API Key. Ensure this node receives the binary audio file. It will return the transcription as plain text. Step 3: Transcribe Recording (OpenAI Whisper)** Connect your OpenAI API Key. Ensure this node receives the binary audio file. It will return the transcription as plain text. Step 4: Summarize Transcript (GPT-4 via AI Agent)** Use your OpenAI API Key. Configure a summarization prompt like: "Summarize the following transcript in a clear and concise manner:" Connect the output from Whisper into this GPT-4 prompt. Step 5: Notion Integration** Connect your Notion account. Choose or create a database to store summaries. Map the GPT output (summary) to a "Text" or "Rich Text" property. Optionally include metadata like filename, file upload date, etc.

AI-Powered Product Assistant for E-commerce Transform your online store customer service with an intelligent AI assistant that automatically processes customer inquiries, searches your product database, and provides personalized responses about product availability, pricing, and specifications. Perfect for shoe stores, fashion retailers, and any business with extensive product catalogs - this workflow eliminates manual customer service while increasing response speed and accuracy. How it works Customer sends product inquiry via webhook (Instagram DM, website chat, or messaging app) AI extracts key product details (brand, model, size, color) from natural language text System searches your Google Sheets product database with smart filtering AI generates friendly, personalized response with availability, pricing, and stock information Automatic response sent back to customer with product details or alternatives Screenshots: Customer inquiry: "Do you have Nike Air Max 40 size?" AI response: "Nike Air Max 90, size 40 - in stock 3 pieces, price 120$" Set up steps Prepare your product database - Create Google Sheets with columns: Brand, Model, Size, Color, Price, Quantity Configure AI settings - Connect OpenAI API for natural language processing Set up webhook endpoint - Configure trigger for your messaging platform (Instagram, Telegram, website chat) Test with sample inquiries - Verify AI correctly parses requests and finds products Deploy and monitor - Launch your automated assistant and track performance Time investment: 30-45 minutes setup, works immediately with any product catalog up to 1000+ items.

👤 Who is this for? This workflow is ideal for social media managers, personal brand strategists, ghostwriters, and founders who want to post regularly on LinkedIn without spending hours writing from scratch. It’s also useful for marketing agencies and assistants looking to automate consistent post creation using curated articles as source material. 🧩 What problem does this workflow solve? Manually reading multiple articles, extracting key insights, and writing a clean, professional LinkedIn post is a time-consuming process. This workflow automates everything: from pulling topics, finding related articles, summarizing them using AI, and even generating a matching image to accompany the post. It ensures faster content turnaround, more consistency, and less manual effort. 🔁 What this workflow does This workflow starts manually and retrieves one topic marked as “To do” from a Google Sheet. That topic is used as a search term for Dumpling AI’s search endpoint, which scrapes and returns the top three article contents related to the topic. These articles are sent to a LangChain agent powered by GPT-4o, which analyzes and summarizes the content into a LinkedIn post in a friendly, insightful tone. It also generates an image prompt for the post. After generating the post and image prompt, the data is extracted using a Set node. The prompt is sent to Dumpling AI’s image generation endpoint, which returns an image URL. Finally, the post text, image prompt, image URL, and status update (“created”) are saved back to the original row in Google Sheets. 🛠️ Workflow Breakdown Manual Trigger – Starts the automation. Google Sheets (Get Topic) – Searches for the first row in your content pipeline sheet where the “status” is “To do”. HTTP Request (Dumpling AI Search) – Uses the topic as a search query to pull 3 article contents using Dumpling AI’s API. Set LangChain GPT Model – Defines GPT-4o as the LLM for the LangChain Agent. LangChain Agent (Summarize & Generate) – Summarizes all 3 articles and generates a LinkedIn post and a related image prompt. Set (Extract Data) – Extracts postText and imagePrompt from the LangChain agent output. HTTP Request (Dumpling Image Gen) – Sends imagePrompt to Dumpling AI’s image generation endpoint. Update Google Sheets – Writes the post, image prompt, and image URL back to the sheet and changes the row status to “created”. ⚙️ Setup Instructions Dumpling AI Sign up at Dumpling AI Get your API key and connect it in the HTTP Request nodes (Search and Image endpoints) Use the /search endpoint to retrieve article content Use the /generate-image endpoint to create the image Google Sheets Create a spreadsheet with columns: topic, status, postText, imagePrompt, imageURL Add sample topics and set their status to To do LangChain (GPT-4o) Connect your OpenAI credentials to n8n Make sure GPT-4o is available in your OpenAI account Use the LangChain node to process multi-input summarization and generate a social media caption Customize the Prompt (Optional) Adjust the Set node to tweak the input format sent to the LangChain agent Add constraints like tone, hashtags, or emojis to fit your brand style 🧠 How to Customize This Workflow Change the content source (RSS feed, Notion DB, etc.) instead of Google Sheets Add a scheduler node to run this automatically every morning or weekly Use Airtable instead of Google Sheets for more control and filtering Send the final post to LinkedIn using the Buffer or LinkedIn API Add a Telegram or Slack notification when new content is ready for approval

✨ What It Does Mello is a Claude-powered Slack assistant that helps you stay on top of unread messages across all your channels. It: Summarizes conversations contextually using Claude AI. Generates reply suggestions and sends them as private (ephemeral) Slack messages. Lets you respond instantly with one-click AI-suggested replies. Perfect for busy teams, founders, and anyone looking to reduce Slack noise and save hours each week. 🔧 Setup Instructions Create a Slack App Go to Slack API → Your Apps Click Create New App and set it up for your workspace Under OAuth & Permissions, add: Bot Token Scopes: commands, chat:write, channels:history, users:read User Token Scopes: channels:history, chat:write Enable Interactivity, and point the Request URL to your n8n webhook (e.g. /slash-summarize) Add Claude API Get an API key from Claude (Anthropic) In n8n, set up the Claude API credential (or switch to OpenAI) Import This Workflow Go to your n8n instance, click Import, and paste this template Update any placeholders (Slack app, Claude key, webhook URLs) Follow the inline sticky notes for guidance Test It Type /summarize in any Slack channel Mello will fetch unread messages, summarize them, and show reply buttons in a private message ⏱ Setup time: ~10 minutes 🛠 Workflow Highlights Slash command trigger (/summarize) Slack API integration to fetch messages Claude AI for contextual summaries Reply suggestions with smart buttons Private Slack delivery (ephemeral messages) Designed to be easily extended (e.g. add support for OpenAI, custom storage) 🔒 Note This is a lite preview of the full Mello workflow. ✅ The full version includes: Slack reply buttons with thread context Full OAuth flow with token storage MongoDB integration Custom Claude/OpenAI configuration Hosted version with onboarding, branding & support 💡 Want access to the complete version? 📩 Email nina@baloon.dev

Overview This release introduces a Voice-Enabled Tour Recommendation System that leverages n8n, ElevenLabs Voice Agent, OpenAI GPT-4o, and Pinecone Vector DB to deliver personalized travel itineraries based on spoken input. Users speak their preferences to the ElevenLabs voice agent, which then triggers an n8n workflow that returns a tailored tour plan. Features Voice interaction with AI-powered travel agent via ElevenLabs Uses ChatGPT-4o for contextual understanding and generation Dynamic query handling with vector-based search using Pinecone Fast response generation using n8n webhook Modular agent memory and role design for scalable enhancement Pre-requisites n8n account with workflow creation access ElevenLabs account with agent and webhook setup OpenAI API key (GPT-4o access) Pinecone account for vector database A list of vectorized tour packages using this n8n embedder (https://creators.n8n.io/workflows/5085) Setup Instructions Step 1: Configure the Voice Agent Webhook in ElevenLabs Use POST method Webhook URL: https://... Breakdown voice input into: Destination Type of tour Number of days Number of passengers Step 2: Set Up the AI Agent Prompt in ElevenLabs Use a conversational style with summaries, clarifying questions, and affirmations. Example Prompt: “You use a natural speech style and periodically summarize... Your goal is to help callers create a personalized tour plan.” Step 3: Select LLM LLM: GPT-4o Mini Memory window: Up to 5 contexts Step 4: Integrate Tools Use Custom Tool: n8n ID: tool_xxxxxx Tool Description: “Generates travel plan once the details are collected” Step 5: Build n8n Workflow Trigger: Webhook (POST) Process user input: Tour Recommendation AI Agent Use OpenAI Chat Model (GPT-4o) for reasoning Query Pinecone Vector Store using Tour Builder Q&A node Respond with structured Itinerary Plan via webhook response How to use: Execute the n8n workflow (the webhook waits for the voice trigger from elevenlabs) Start the Elevenlabs Voice Agent Request for a tour plan to any destination giving the details of your tour preferences. Wait for the Voice Agent to respond back with tour package suggestions after fetching the tour details from the n8n workflow. Close the conversation. | Area | Improvement | | ------------------ | ----------------------------------------------------- | | 🔉 Voice UX | Natural-sounding travel agent using ElevenLabs | | 💡 Personalization | ChatGPT-4o adapts based on travel style & preferences | | 📚 Knowledge Base | Pinecone-powered vector retrieval of real tour data | | 🔁 Reusability | Modular workflow with reusable embedding tools | | ⚙️ System Design | Separation of memory, logic, and data layers | Who is this for? Travel Agencies & DMCs Offer ultra-personalized packages based on customer queries. Let AI do the matching. Tour Package Aggregators Auto-curate and send matching packages from your catalog — no manual searching needed. Content & Marketing Teams Craft customized tour recommendations for email campaigns and newsletters. Tech-enabled Travel Startups Embed this intelligence in your workflows, CRMs, or chatbots to delight customers.

Who is this for? This workflow is perfect for operations teams, accountants, e-commerce businesses, or finance managers who regularly process digital invoices and need to automate data extraction and record-keeping. What problem is this workflow solving? Manually reading invoice PDFs, extracting relevant data, and entering it into spreadsheets is time-consuming and error-prone. This workflow automates that process—watching a Google Drive folder, extracting structured invoice data using Dumpling AI, and saving the results into Google Sheets. What this workflow does Watches a specific Google Drive folder for new invoices. Downloads the uploaded invoice file. Converts the file into a Base64 format. Sends the file to Dumpling AI’s extract-document endpoint with a detailed parsing prompt. Parses Dumpling AI’s JSON response using a Code node. Splits the items array into individual rows using the Split Out node. Appends each invoice item to a preformatted Google Sheet along with the full header metadata (order number, PO, addresses, etc.). Setup Google Drive Setup Create or select a folder in Google Drive and place the folder ID in the trigger node. Make sure your n8n Google Drive credentials are authorized for access. Google Sheets Create a Google Sheet with the following headers: Order number, Document Date, Po_number, Sold to name, Sold to address, Ship to name, Ship to address, Model, Description, Quantity, Unity price, Total price Paste the Sheet ID and sheet name (Sheet1) into the Google Sheets node. Dumpling AI Sign up at Dumpling AI Go to your account settings and generate your API key. Paste this key into the HTTP header of the Dumpling AI request node. The endpoint used is: https://app.dumplingai.com/api/v1/extract-document Prompt (already included) This prompt extracts: order number, document date, PO number, shipping/billing details, and detailed line items (model, quantity, unit price, total). How to customize this workflow to your needs Adjust the Google Sheet fields to fit your invoice structure. Modify the Dumpling AI prompt if your invoices have additional or different data points. Add filtering logic if you want to handle different invoice types differently. Replace Google Sheets with Airtable or a database if preferred. Use a different trigger like an email attachment if invoices come via email.

An AI-powered email marketing automation workflow that generates personalized marketing emails using data from Google Sheets and delivers them directly to clients. This workflow combines the power of AI content generation with spreadsheet-based campaign management for seamless email marketing automation. What's the Goal? Automatically pull marketing offer details from Google Sheets (Sheet 1) Fetch client information from Google Sheets (Sheet 2) Use AI to generate compelling, personalized marketing content Format emails with professional structure and personalization Send targeted marketing emails directly to clients Enable scalable email marketing campaigns with minimal manual effort By the end, you'll have a fully automated email marketing system that creates and sends personalized campaigns based on your spreadsheet data. Why Does It Matter? Manual email marketing is labor-intensive and lacks personalization at scale. Here's why this workflow is a game changer: Zero Manual Drafting**: AI generates unique content for each recipient Data-Driven Personalization**: Leverages spreadsheet data for targeted messaging Scalable Campaigns**: Handle hundreds of clients with a single workflow execution Consistent Quality**: AI ensures professional, engaging content every time Time Efficiency**: Transform hours of work into minutes of automation Cost-Effective**: Reduce marketing team workload while increasing output Think of it as your intelligent marketing assistant that creates personalized campaigns at enterprise scale. How It Works Here's the step-by-step process behind the automation: Step 1: Track Offer Updates Node**: Track Offer Sheet Updates (Sheet 1) Function**: Monitor Google Sheets for new marketing offers or updates Trigger**: Automatically activates when new data is added to Sheet 1 Step 2: Generate Marketing Content Node**: Generate Marketing Content with AI Function**: Process offer details through AI model (Llama 3.2) Process**: Creates compelling marketing copy based on offer parameters Step 3: Fetch Client Information Node**: Fetch Client List (Sheet 2) Function**: Retrieve client names and email addresses from Sheet 2 Data**: Pulls client_name and client_email for personalization Step 4: Content Personalization Node**: Format Personalized Email Function**: Combine AI-generated content with client-specific data Output**: Creates personalized email for each recipient Step 5: Email Delivery Node**: Send Marketing Email to Client Function**: Deliver personalized emails directly to client inboxes Method**: Uses Gmail integration for professional delivery Google Sheets Structure Sheet 1: Marketing Offer Details | Column | Description | Example | |--------|-------------|---------| | title | Campaign/offer name | "Summer Sale 2024" | | discount | Discount percentage or amount | "25% OFF" | | validity | Offer expiration date | "Valid until July 31st" | | products_included | Items covered by offer | "All summer collection" | | original_price | Pre-discount pricing | "$199.99" | | discounted_price | Final pricing | "$149.99" | | cta | Call-to-action text | "Shop Now" | | bonus | Additional incentives | "Free shipping included" | Sheet 2: Client Information | Column | Description | Example | |--------|-------------|---------| | client_name | Customer's full name | "John Smith" | | client_email | Customer's email address | "john.smith@email.com" | How to Use the Workflow Prerequisites Google Sheets Setup: Create two sheets with the required column structure n8n Account: Access to n8n workflow platform Gmail API: Gmail account with API access configured AI Model Access: Llama 3.2 API credentials Importing the Workflow in n8n Step 1: Obtain the Workflow JSON Download the workflow file or copy the JSON code Ensure you have the complete workflow configuration Step 2: Access n8n Workflow Editor Log in to your n8n instance (Cloud or self-hosted) Navigate to the Workflows section Click "Add Workflow" to create a new workflow Step 3: Import the Workflow Option A: Import from Clipboard Click the three dots (⋯) in the top-right corner Select "Import from Clipboard" Paste the JSON code into the text box Click "Import" to load the workflow Option B: Import from File Click the three dots (⋯) in the top-right corner Select "Import from File" Choose the .json file from your computer Click "Open" to import the workflow Configuration Setup Google Sheets Integration Authenticate Google Sheets: Connect your Google account in n8n Configure Sheet 1: Set spreadsheet ID and range for marketing offers Configure Sheet 2: Set spreadsheet ID and range for client information AI Model Configuration Set API Credentials: Configure Llama 3.2 API key and endpoint Customize Prompts: Adjust AI prompts for your brand voice and style Set Content Parameters: Define content length, tone, and structure Gmail Integration Gmail API Setup: Enable Gmail API in Google Cloud Console OAuth Configuration: Set up OAuth credentials for email sending Sender Configuration: Configure sender name and email address Content Customization Email Templates: Customize email structure and branding Personalization Fields: Map spreadsheet columns to email variables Brand Guidelines: Set company colors, fonts, and messaging tone Workflow Execution Manual Execution Click "Execute Workflow" in the n8n interface Monitor execution progress through each node Review generated content and delivery status Automated Execution Set up triggers based on sheet updates Configure scheduling for regular campaign runs Enable webhook triggers for real-time processing Best Practices Data Management Keep spreadsheet data clean and formatted consistently Regular validation of email addresses in Sheet 2 Update offer details promptly in Sheet 1 Content Quality Review AI-generated content periodically Adjust prompts based on campaign performance Maintain consistent brand voice across campaigns Deliverability Monitor email bounce rates and engagement metrics Maintain clean email lists with valid addresses Follow email marketing best practices and regulations Performance Optimization Batch process large client lists for efficiency Monitor workflow execution times Implement error handling and retry mechanisms Troubleshooting Common Issues Authentication Errors**: Verify API credentials and permissions Sheet Access**: Ensure proper sharing permissions for Google Sheets Email Delivery**: Check Gmail API quotas and sending limits AI Processing**: Monitor API rate limits and response times Error Handling Implement retry logic for failed operations Set up notification systems for workflow failures Maintain backup data sources for critical campaigns Security Considerations Use environment variables for API keys and credentials Implement proper access controls for sensitive data Regular security audits of connected services Compliance with data protection regulations (GDPR, CAN-SPAM) Conclusion This Smart Email Marketing Generator transforms your marketing campaigns from manual, time-consuming tasks into automated, intelligent processes. By leveraging AI and spreadsheet data, you can create personalized, engaging campaigns that scale with your business needs while maintaining professional quality and consistency. The workflow represents a significant advancement in marketing automation, combining the accessibility of spreadsheet-based data management with the power of AI-driven content generation and automated delivery systems.

Workflow Overview This cutting-edge n8n automation is a sophisticated market research and intelligence gathering tool designed to transform web content discovery into actionable insights. By intelligently combining web crawling, AI-powered filtering, and smart summarization, this workflow: Discovers Relevant Content: Automatically crawls target websites Identifies trending topics Extracts comprehensive article details Intelligent Content Filtering: Applies custom keyword matching Filters for most relevant articles Ensures high-quality information capture AI-Powered Summarization: Generates concise, meaningful summaries Extracts key insights Provides quick, digestible information Seamless Delivery: Sends summaries directly to Slack Enables instant team communication Facilitates rapid information sharing Key Benefits 🤖 Full Automation: Continuous market intelligence 💡 Smart Filtering: Precision content discovery 📊 AI-Powered Insights: Intelligent summarization 🚀 Instant Delivery: Real-time team updates Workflow Architecture 🔹 Stage 1: Content Discovery Scheduled Trigger**: Daily market research FireCrawl Integration**: Web content crawling Comprehensive Site Scanning**: Extracts article metadata Captures full article content Identifies key information sources 🔹 Stage 2: Intelligent Filtering Keyword-Based Matching** Relevance Assessment** Custom Domain Optimization**: AI and technology focus Startup and innovation tracking 🔹 Stage 3: AI Summarization OpenAI GPT Integration** Contextual Understanding** Concise Insight Generation**: 3-point summary format Captures essential information 🔹 Stage 4: Team Notification Slack Integration** Instant Information Sharing** Formatted Insight Delivery** Potential Use Cases Market Research Teams**: Trend tracking Innovation Departments**: Technology monitoring Startup Ecosystems**: Competitive intelligence Product Management**: Industry insights Strategic Planning**: Rapid information gathering Setup Requirements FireCrawl API Web crawling credentials Configured crawling parameters OpenAI API GPT model access Summarization configuration API key management Slack Workspace Channel for insights delivery Appropriate app permissions Webhook configuration n8n Installation Cloud or self-hosted instance Workflow configuration API credential management Future Enhancement Suggestions 🤖 Multi-source crawling 📊 Advanced sentiment analysis 🔔 Customizable alert mechanisms 🌐 Expanded topic tracking 🧠 Machine learning refinement Technical Considerations Implement robust error handling Use exponential backoff for API calls Maintain flexible crawling strategies Ensure compliance with website terms of service Ethical Guidelines Respect content creator rights Use data for legitimate research Maintain transparent information gathering Provide proper attribution Workflow Visualization [Daily Trigger] ⬇️ [Web Crawling] ⬇️ [Content Filtering] ⬇️ [AI Summarization] ⬇️ [Slack Delivery] Connect With Me Ready to revolutionize your market research? 📧 Email: Yaron@nofluff.online 🎥 YouTube: @YaronBeen 💼 LinkedIn: Yaron Been Transform your information gathering with intelligent, automated workflows! #AIResearch #MarketIntelligence #AutomatedInsights #TechTrends #WebCrawling #AIMarketing #InnovationTracking #BusinessIntelligence #DataAutomation #TechNews

A Discord bot that responds to mentions by sending messages to n8n workflows and returning the responses. Connects Discord conversations with custom automations, APIs, and AI services through n8n. Full guide on: https://github.com/JimPresting/AI-Discord-Bot/blob/main/README.md Discord Bot Summary Overview The Discord bot listens for mentions, forwards questions to an n8n workflow, processes responses, and replies in Discord. This workflow is intended for all Discord users who want to offer AI interactions with their respective channels. What do you need? You need a Discord account as well as a Google Cloud Project Key Features 1. Listens for Mentions The bot monitors Discord channels for messages that mention it. Optional Configuration**: Can be set to respond only in a specific channel. 2. Forwards Questions to n8n When a user mentions the bot and asks a question: The bot extracts the question. Sends the question, along with channel and user information, to an n8n webhook URL. 3. Processes Data in n8n The n8n workflow receives the question and can: Interact with AI services (e.g., generating responses). Access databases or external APIs. Perform custom logic. n8n formats the response and sends it back to the bot. 4. Replies to Discord with n8n's Response The bot receives the response from n8n. It replies to the user's message in the Discord channel with the answer. Long Responses**: Handles responses exceeding Discord's 2000-character limit by chunking them into multiple messages. 5. Error Handling Includes error handling for: Issues with n8n communication. Response formatting problems. Manages cases where: No question is asked. An invalid response is received from n8n. 6. Typing Indicator While waiting for n8n's response, the bot sends a "typing..." indicator to the Discord channel. 7. Status Update For lengthy n8n processes, the bot sends a message to the Discord channel to inform the user that it is still processing their request. Step-by-Step Setup Guide as per Github Instructions Key Takeaways You’ll configure an n8n webhook to receive Discord messages, process them with your workflow, and respond. You’ll set up a Discord application and bot, grant the right permissions/intents, and invite it to your server. You’ll prepare your server environment (Node.js), scaffold the project, and wire up environment variables. You’ll implement message‐chunking, “typing…” indicators, and robust error handling in your bot code. You’ll deploy with PM2 for persistence and know how to test and troubleshoot common issues. 1. n8n: Create & Expose Your Webhook New Workflow Log into your n8n instance. Click Create Workflow (➕), name it e.g. Discord Bot Handler. Webhook Trigger Add a node (➕) → search Webhook. Set: Authentication: None (or your choice) HTTP Method: POST Path: e.g. /discord-bot Click Execute Node to activate. Copy Webhook URL After execution, copy the Production Webhook URL. You’ll paste this into your bot’s .env. Build Your Logic Chain additional nodes (AI, database lookups, etc.) as required. Format the JSON Response Insert a Function node before the end: return { json: { answer: "Your processed reply" } }; Respond to Webhook Add Respond to Webhook as the final node. Point it at your Function node’s output (with the answer field). Activate Toggle Active in the top‐right and Save. 2. Discord Developer Portal: App & Bot New Application Visit the Discord Developer Portal. Click New Application, name it. Go to Bot → Add Bot. Enable Intents & Permissions Under Privileged Gateway Intents, toggle Message Content Intent. Under Bot Permissions, check: Read Messages/View Channels Send Messages Read Message History Grab Your Token In Bot → click Copy (or Reset Token). Store it securely. Invite Link (OAuth2 URL) Go to OAuth2 → URL Generator. Select scopes: bot, applications.commands. Under Bot Permissions, select the same permissions as above. Copy the generated URL, open it in your browser, and invite your bot. 3. Server Prep: Node.js & Project Setup Install Node.js v20.x sudo apt purge nodejs npm sudo apt autoremove curl -fsSL https://deb.nodesource.com/setup_20.x | sudo -E bash - sudo apt install -y nodejs node -v # Expect v20.x.x npm -v # Expect 10.x.x Project Folder mkdir discord-bot cd discord-bot Initialize & Dependencies npm init -y npm install discord.js axios dotenv 4. Bot Code & Configuration Environment Variables Create .env: nano .env Populate: DISCORD_BOT_TOKEN=your_bot_token N8N_WEBHOOK_URL=https://your-n8n-instance.com/webhook/discord-bot Optional: restrict to one channel TARGET_CHANNEL_ID=123456789012345678 Bot Script Create index.js: nano index.js Implement: Import dotenv, discord.js, axios. Set up client with MessageContent intent. On messageCreate: Ignore bots or non‐mentions. (Optional) Filter by channel ID. Extract and validate the user’s question. Send “typing…” every 5 s; after 20 s send a status update if still processing. POST to your n8n webhook with question, channelId, userId, userName. Parse various response shapes to find answer. If answer.length ≤ 2000, message.reply(answer). Else, split into ~1900‑char chunks at sentence/paragraph breaks and send sequentially. On errors, clear intervals, log details, and reply with an error message. Login client.login(process.env.DISCORD_BOT_TOKEN); 5. Deployment: Keep It Alive with PM2 Install PM2 npm install -g pm2 Start & Monitor pm2 start index.js --name discord-bot pm2 status pm2 logs discord-bot Auto‐Start on Boot pm2 startup Follow the printed command (e.g. sudo env PATH=$PATH:/usr/bin pm2 startup systemd -u your_user --hp /home/your_user) pm2 save 6. Test & Troubleshoot Functional Test In your Discord server: @YourBot What’s the weather like? Expect a reply from your n8n workflow. Common Pitfalls No reply → check pm2 logs discord-bot. Intent Errors → verify Message Content Intent in Portal. Webhook failures → ensure workflow is active and URL is correct. Formatting issues → confirm your Function node returns json.answer. Inspect Raw Data Search your logs for Complete response from n8n: to debug payload shapes. `

Use case Workshop We are using this workflow in our workshops to teach how to use Tools a.k.a functions with artificial intelligence. In this specific case, we will use a generic "AI Agent" node to illustrate that it could use other models from different data providers. Enhanced Weather Forecasting In this small example, it's easy to demonstrate how to obtain weather forecast results from the Open-Meteo site to accurately display the upcoming days. This can be used to plan travel decisions, for example. What this workflow does We will make an HTTP request to find out the geographic coordinates of a city. Then, we will make other HTTP requests to discover the weather for the upcoming days. In this workshop, we demonstrate that the AI will be able to determine which tool to call first—it will first call the geolocation tool and then the weather forecast tool. All of this within a single client conversation call. Setup Insert an OpenAI Key and activate the workflow. by Davi Saranszky Mesquita https://www.linkedin.com/in/mesquitadavi/