Workflow Templates

This n8n workflow demonstrates how to automate customer interactions and appointment management via WhatsApp Business bot. After submitting a Google Form, the user receives a notification via WhatsApp. These notifications are sent via a template message. In case user sends a message to the bot, the text and user data is stored in Google Sheets. To reply back to the user, fill in the ReplyText column and change the Status to 'Ready'. In a few seconds n8n will fetch the unsent replies and deliver them one by one via WhatsApp Business node. Customize this workflow to fit your specific needs, connect different online services and enhance your customer communication! 🎉 Setup Instructions To get this workflow up and running, you'll need to: 👇 Create a WhatsApp template message on the Meta Business portal. Obtain an Access Token and WhatsApp Business Account ID from the Meta Developers Portal. This is needed for the WhatsApp Business Node to send messages. Set up a WhatsApp Trigger node with App ID and App Secret from the Meta Developers Portal. Right after that copy the WhatsApp Trigger URL and add it as a Callback URL in the Meta Developers Portal. This trigger is needed to receive incoming messages and their status updates. Connect your Google Sheets account for data storage and management. Check out the documentation page. ⚠️ Important Notes WhatsApp allows automatic custom text messages only within 24 hours of the last user message. Outside with time frame only approved template messages can be sent. The workflow uses a Google Sheet to manage form submissions, incoming messages and prepare responses. You can replace these nodes and connect the WhatsApp bot with other systems.

Workflow Overview This workflow automates the process of forwarding e-book files to a Kindle device using a Telegram bot and Outlook email. Setup Steps: Telegram Bot Setup: Create a Telegram bot via BotFather and configure its credentials in the workflow. Outlook Email Configuration: Set up your Outlook email credentials. (Currently, only Outlook is supported, but you can modify the workflow to support other email providers.) Amazon Kindle Email Setup: Find your Kindle device's email address from your Amazon account. This will be the recipient address for the e-books. Allow Email Sending to Kindle: Ensure your Amazon account is configured to allow emails from your Outlook address to send files to your Kindle. Workflow Explanation: The workflow begins with a Telegram bot trigger node that listens for new chat messages. When a new message is received, the workflow checks if the message contains a file attachment. If no file is detected, the bot will send a warning reply to the user in the chat. If a file is found, it will be renamed to ensure it appears correctly on the Kindle device when sent. The workflow then composes an email with the file attached and sends it to the Kindle's receiving address. If the email is sent successfully, the bot will notify the user with a success message in the chat. Only Amazon-supported file types will be accepted by Kindle. If sending fails, you will receive a notification email from Amazon in your Outlook inbox. In case of delivery issues, retry sending the file as network issues may occasionally interfere with the process.

Boost engagement on your Discord server by automatically sharing new YouTube videos along with AI generated summaries of their content. This workflow is ideal for content creators and community managers looking to provide value and spark interest through summarized content, making it easier for community members to decide if a video is of interest to them. Watch this video tutorial to learn more about the template. How it works RSS Feed Trigger**: Monitors your YouTube channel for new uploads using the RSS feed. Video Captions Retrieval**: Fetches video captions using the YouTube API to get detailed content data. AI Summary Generation**: Uses an AI model to generate concise summaries from the video captions, highlighting key points. Discord Notification**: Posts video announcements along with their AI generated summaries to a specified Discord channel using a webhook. Set up steps Configure YouTube RSS Feed: Set up the RSS feed node to detect new video uploads. Add your YouTube channel ID to the URL in the first node: https://www.youtube.com/feeds/videos.xml?channel_id=YOUR_CHANNEL_ID. Connect OpenAI Account: To enable AI summary generation, connect your OpenAI account in n8n. Set Up Discord Webhook: Create a webhook in your Discord server and configure it in the Discord node. Design the Message: Format the Discord message as you like to include the video title, link, and the AI generated summary. Example This template empowers you to maintain a highly engaging Discord community, ensuring members receive not only regular updates but also valuable insights into each video's content without needing to watch immediately.

Who is this for? This subworkflow is ideal for developers and automation builders working with UniPile and n8n to automate message enrichment and LinkedIn lead routing. What problem is this workflow solving? UniPile separates personal and organization accounts into two different API endpoints. This flow handles both intelligently so you're not missing sender context due to API quirks or bad assumptions. What this workflow does This subworkflow is used by: LinkedIn Auto Message Router with Request Detection** LinkedIn AI Response Generator with Slack Approval** It receives a message sender ID and tries to enrich it using UniPile's /people and /organizations endpoints. It returns a clean, consistent profile object regardless of which source was used. Setup Generate a UniPile API token and save it in your n8n credentials Make sure this subworkflow is triggered correctly by your parent flows Test both people and organization lookups to verify responses are normalized How to customize this workflow to your needs Add a secondary enrichment layer using tools like Clearbit or FullContact Customize the fallback logic or error handling Expand the returned data for more AI context or user routing (e.g., job title, region)

Who is this for? This workflow is designed for teams using Slack for communication and ServiceNow for incident management. It simplifies incident lookup by enabling team members to fetch incident details directly within Slack via a Slash Command. What problem is this workflow solving? Manually switching between Slack and ServiceNow to retrieve incident details can be time-consuming and disrupt workflow efficiency. This workflow bridges the two platforms, providing instant access to critical incident information in Slack, saving time, and improving response efficiency. What this workflow does? The workflow listens for a Slash Command in Slack that includes an incident ID, extracts the ID from the incoming payload, queries ServiceNow for the corresponding incident details, and sends a formatted response back to Slack. Depending on the query result, it can: Display incident details (e.g., ID, description, severity, and priority). Notify the user if no matching incident is found. Alert the user if there’s an issue connecting to ServiceNow. Setup Slack Setup: Create a Slash Command in Slack with the appropriate endpoint URL. Configure the command to send a POST request to the webhook endpoint of this workflow. For details on how to setup the Slack app using Slash commands and n8n, check out this video. ServiceNow Setup: Create or use an existing account with the necessary permissions to access incident data. Configure the ServiceNow node with your ServiceNow credentials. n8n Workflow Activation: Deploy and activate the workflow in your n8n instance. Ensure all nodes are properly configured and connected. How to customize this workflow to your needs Modify Incident Query Parameters:** Adjust the query logic in the Search For Incident in ServiceNow node to include additional filters or data points based on your organization’s needs. Slack Response Customization:** Customize the Slack response template to display additional incident details or to match your team’s tone and style. Error Handling:** Enhance the error handling nodes to include more detailed logs or send alerts to a dedicated Slack channel.

Auto-generate & post content using AI This workflow helps you create daily content using just a topic prompt. It writes a tweet, generates an image, and publishes across Twitter, Facebook, and LinkedIn — all on autopilot. How it works Triggers daily at 10 PM to start the flow Uses OpenAI to generate a niche topic title Writes a short-form post (tweet style) with hashtags Generates a Japanese anime-style image for visual context Saves everything in Google Sheets Publishes automatically on Twitter, LinkedIn, and Facebook Set up steps You’ll need OpenAI, Google Sheets, and social media credentials (Twitter, Facebook, LinkedIn) Takes about 10–15 minutes to configure if you already have the credentials ready Make sure your Sheet and API keys are properly linked before activating 📝 Keep detailed notes inside the workflow with sticky notes for easier handoff or collaboration.

Who this is for? Extract Amazon Best Seller Electronic Info is an automated workflow that extracts best seller data from Amazon's Electronics section using Bright Data Web Unlocker, transform it into structured JSON using Google Gemini's LLM, and forwards a fully structured JSON response to a specified webhook for downstream use. This workflow is tailored for: eCommerce Analysts** Who need to monitor Amazon best-seller trends in the Electronics category and track changes in real-time or on a schedule. Product Intelligence Teams** Who want structured insights on competitor offerings, including rankings, prices, ratings, and promotions. AI-powered Chatbot Developers** Who are building assistants capable of answering product-related queries with fresh, structured data from Amazon. Growth Hackers & Marketers** Looking to automate competitive research and surface trending product data to inform pricing strategies. Data Aggregators and Price Trackers** Who need reliable and smart scraping of Amazon data enriched with AI-driven parsing. What problem is this workflow solving? Keeping up with Amazon's best sellers in Electronics is a time-consuming, error-prone task when done manually.This workflow automates the process, ensuring:​ Automating Data Extraction from Amazon Best Sellers using Bright Data, ensuring reliable access to real-time, structured data. Enhancing Raw Data with Google Gemini, turning product lists into structured JSON using the Google Gemini LLM. Sending Results to a Webhook, enabling seamless integration into dashboards, databases, or chatbots. What this workflow does The workflow performs the following steps:​ Extracts Amazon Best Seller Electronics page info using Bright Data's Web Unlocker API. Processes the unstructured content using Google Gemini's Flash Exp model to extract structured product data. Sends the structured information to a webhook endpoint. Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). Update the Amazon URL with the Bright Data zone by navigating to the Amazon URL with the Bright Data Zone node. Update the Webhook HTTP Request node with the Webhook endpoint of your choice. How to customize this workflow to your needs This workflow is built to be flexible - whether you're a market researcher, e-commerce entrepreneur, or data analyst. Here's how you can adapt it to fit your specific use case: Change the Amazon Category** Update the Amazon URL with the topic of your interest such as Computers & Accessories, Home Audio, etc. Customize the Gemini Prompt** Update the Gemini prompt to get different styles of output — comparison tables, summaries, feature highlights, etc. Send Output to Other Destinations** Replace the Webhook URL to forward output to: Google Sheets Airtable Slack or Discord Custom API endpoints

How It Works This workflow automates the analysis of security alerts from Sophos Central, turning raw events into actionable intelligence. It uses the official Sophos SIEM integration tool to fetch data, enriches it with VirusTotal, and leverages Google Gemini to provide a real-time threat summary and mitigation plan via Telegram. Prerequisite (Important): This workflow is triggered by a webhook that receives data from an external Python script. You must first set up the Sophos-Central-SIEM-Integration script from the official Sophos GitHub. This script will fetch data and forward it to your n8n webhook URL. Tool Source Code: Sophos/Sophos-Central-SIEM-Integration The n8n Workflow Steps Webhook: Receives enriched event and alert data from the external Python script. IF (Filter): Immediately filters the incoming data to ensure only events with a high or critical severity are processed, reducing noise from low-priority alerts. Code (Prepare Indicator): Intelligently inspects the Sophos event data to extract the primary threat indicator. It prioritizes indicators in the following order: File Hash (SHA256), URL/Domain, or Source IP. HTTP Request (VirusTotal): The extracted indicator is sent to the VirusTotal API to get a detailed reputation report, including how many security vendors flagged it as malicious. Code (Prompt for Gemini): The raw JSON output from VirusTotal is processed into a clean, human-readable summary and a detailed list of flagging vendors. AI Agent (Google Gemini): All collected data—the original Sophos log, the full alert details, and the formatted VirusTotal reputation—is compiled into a detailed prompt for Gemini. The AI acts as a virtual SOC analyst to: Create a concise incident summary. Determine the risk level. Provide a list of concrete, actionable mitigation steps. Telegram: The complete analysis and mitigation plan from Gemini is formatted into a clean, easy-to-read message and sent to your specified Telegram chat. Setup Instructions Configure the external Python script to forward events to this workflow's Production URL. In n8n, create Credentials for Google Gemini, VirusTotal, and Telegram. Assign the newly created credentials to the corresponding nodes in the workflow.

How it works Trigger: Listens for an incoming chat message Copy Assistant: Feeds the message (plus memory) into an OpenAI Chat Model and exposes two “tools” Cold Email Writer Tool Sales Letter Tool• Tool execution: Depending on the user’s intent, the appropriate tool generates the copy • Save output: Writes the generated email or sales letter into your target document via the Update a document node Set up steps • Configure your OpenAI Chat Model credentials in n8n (no hard-coded keys!) • Add and authenticate the Simple Memory credential (to keep context across messages) • Create Google Docs (or MS Word) credentials for the Update a document node • Ensure your Chat trigger is pointing at your incoming-message endpoint • Mandatory: Drop sticky-note annotations on each tool node explaining where to enter API keys and how to tweak prompts Once everything’s wired up, send a test chat message like “Write me a cold email for a fintech startup” and watch the workflow spin up a polished draft in your document. How to use Import the workflow JSON into n8n. Configure your Chat trigger (webhook or form) to receive incoming messages. Send a chat prompt like: “Write me a cold email for a B2B SaaS offering.” The “Copy Assistant” custom GPT picks the right tool (Cold Email or Sales Letter). Generated copy is written directly into your linked Google Doc or Word document. Requirements OpenAI API Key (with Chat Completions & Custom GPTs enabled) Custom Assistant created in your ChatGPT dashboard (Assistant ID pasted into the Chat Model node) n8n instance (Cloud or self-hosted) with credentials set up for: Simple Memory (to persist context) Google Docs or Microsoft Word (for document output) Customising this workflow Tweak system and user prompts inside the Copy Assistant node to fit your brand voice. Swap in Slack, Teams or email nodes instead of a document writer to deliver copy where you need it. Add or remove tools (e.g., “Follow-up Email Writer”) by duplicating the existing tool pattern. Use sticky-note annotations on every node to explain where to enter API keys, Assistant IDs, or prompt tweaks.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This workflow provides a complete, ready-to-use template for a Retrieval-Augmented Generation (RAG) system. It allows you to build a powerful AI chatbot that can answer questions based on the content of PDF documents you provide, using a modern and powerful stack for optimal performance. Good to know Costs:** This workflow uses paid services (OpenAI, Pinecone, Cohere). Costs will be incurred based on your usage. Please review the pricing pages for each service to understand the potential expenses. Video Tutorial (Bahasa Indonesia):** For a step-by-step guide on how this workflow functions, you can watch the accompanying video tutorial here: N8N Tutorial: Membangun Chatbot RAG dengan Pinecone, OpenAI, & Cohere How it works This workflow operates in two distinct stages: 1. Data Ingestion & Indexing: It begins when a .pdf file is uploaded via the n8n Form Trigger. The Default Data Loader node processes the PDF, and the Recursive Character Text Splitter breaks down the content into smaller, manageable chunks. The Embeddings OpenAI node converts these text chunks into vector embeddings (numerical representations). Finally, the Pinecone Vector Store node takes these embeddings and stores (upserts) them into your specified Pinecone index, creating a searchable knowledge base. 2. Conversational AI Agent: A user sends a message through the Chat Trigger. The AI Agent receives the message and uses its VectorDB tool to search the Pinecone index for relevant information. The Reranker Cohere node refines these search results, ensuring only the most relevant context is selected. The user's original question and the refined context are sent to the OpenAI Chat Model (gpt-4.1), which generates a helpful, context-aware answer. The Simple Memory node maintains conversation history, allowing for natural, multi-turn dialogues. How to use Using this workflow is a two-step process: Populate the Knowledge Base: First, you need to add documents. Trigger the workflow by using the Form Trigger and uploading a PDF file. Wait for the execution to complete. You can do this for multiple documents. Start Chatting: Once your data has been ingested, open the Chat Trigger's interface and start asking questions related to the content of your uploaded documents. The Form Trigger is just an example. Feel free to replace it with other triggers, such as a node that watches a Google Drive or Dropbox folder for new files. Requirements To run this workflow, you will need active accounts and API keys for the following services. OpenAI Account & API Key:** Function: Powers text embedding and the final chat generation. Required for the Embeddings OpenAI and OpenAI Chat Model nodes. Pinecone Account & API Key:** Function: Used to store and retrieve your vector knowledge base. Required for the Pinecone Vector Store and VectorDB nodes. You also need to provide your Pinecone Environment. Cohere Account & API Key:** Function: Improves the accuracy of your chatbot by re-ranking search results for relevance. Required for the Reranker Cohere node. Customising this workflow This template is a great starting point. Here are a few ways you can customize it: Change the AI Personality:* Edit the *System Message** in the AI Agent node to change the bot's behavior, tone, or instructions. Use Different Models:** You can easily swap the OpenAI model for another one (e.g., gpt-3.5-turbo for lower costs) in the OpenAI Chat Model node. Adjust Retrieval:** In the VectorDB tool node, you can modify the Top K parameter to retrieve more or fewer document chunks to use as context. Automate Ingestion:** Replace the manual Form Trigger with an automated one, like a node that triggers whenever a new file is added to a specific cloud storage folder.

This template is for self-hosted n8n instances only. This n8n demonstrates how to build a simple FileSystem MCP server. Connecting to this server allows MCP clients and agents to list, read and create directories and files on the local machine or remote server. This MCP example is based off an official MCP reference implementation which can be found here -https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem How it works A MCP server trigger is used and connected to 5 tools: 3 Execute Command tools and 2 custom workflow tools. The 3 Execute Command tools allow for listing, searching and creating directories. The 2 custom workflow tools are for reading and writing files to disk. Special care has been to not allow the MCP agent to execute arbitrary linux commands on the target server. This is achieved by only allowing the agent to provide parameters such as filenames and paths rather than raw commands. How to use This Filesystem MCP server will write to the server which hosts the n8n instance - this can be your local machine or a remove server. If your target filesystem is on neither, then modify the commands to connect to the desired server. Connect your MCP client by following the n8n guidelines here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop Try the following queries in your MCP client: "Please help me list all folders under the project directory." "Help me create a bash script to send a notification to Slack." "Search for the log file on the 22nd April and read its contents. What was the cause of the outage?" Requirements Linux file system for this example template. Feel free to modify if working on Windows. MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download Customising this workflow Implement the moving and renaming of files by adding more custom workflow tools to the MCP server. Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

This workflow analyzes images submitted via a form using OpenAI Vision, then delivers the analysis result directly to your Telegram chat. ✅ Use case examples: • Users submit screenshots for instant AI interpretation • Automated document or receipt analysis with Telegram delivery • Quick OCR or image classification workflows ⸻ ⚙️ Setup Guide Form Submission Trigger • Connect your form app (e.g. Typeform, Tally, or n8n’s own webhook form) to the On form submission trigger node. • Ensure it sends the image file or URL as input. OpenAI Vision Analysis • In the OpenAI node, select Analyze Image operation. • Provide your OpenAI API key and configure the prompt to instruct the model on what to analyze (e.g. “Describe this receipt in detail”). Set Telegram Chat ID • Use this manual node to input your Telegram Chat ID for delivery. • Alternatively, automate this with a database lookup or user session if building for multiple users. Telegram Delivery Node • Connect your Telegram Bot to n8n using your bot token. • Set up the sendMessage operation, using the analysis result from the previous node as the message text. Testing • Click Execute workflow. • Submit an image via your form and confirm it delivers to your Telegram as expected.