Workflow Templates

This template integrates OpenAI's image generation and editing endpoints via the GPT-Image-1 model to visually create and manipulate images based on prompts. It features base64 conversion, binary handling, and prompt chaining. Perfect for marketing, design, product visuals and creative workflows. 🛠️ Requirements OpenAI account with access to gpt-image-1(probably you need organizations verifications for access to that model) OpenAI API credentials configured in n8n A self-hosted or cloud n8n instance Basic familiarity with the n8n UI (no programming required) 🔧 Step-by-step Instructions Step 1: Manual Trigger Starts the workflow on click. Ideal for testing the generation and edit logic. Step 2: Generate Image The Create image call node sends a prompt to OpenAI and returns a base64 image. Example prompt: A cyberpunk city at night with flying cars and neon lights Step 3: Convert to Binary The base64 image is converted into a usable binary PNG file with the Convert json binary to File node. Step 4: Edit the Image The binary file is passed to OpenAI’s /images/edits endpoint. A new prompt applies changes to the image. Example: Add a glowing robot in the foreground with a neon sword ✅ Supports model: gpt-image-1 ⚠️ Requires binary file (not base64) Step 5: Final Conversion Converts the final edited image from base64 to file so it can be downloaded or used in other nodes. 🎯 Real-World Use Cases 🎨 Artists & Creators: concept art and illustration variations 🛍️ E-commerce: auto-generate product mockups 📰 Marketing: create eye-catching blog or social visuals 💡 Bonus Ideas Add a Telegram or Slack node to generate or edit images via chat Use a Webhook to feed prompts from a form or frontend Add a mask to restrict edits to specific areas (e.g., background only)

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. OAuth Token Generator and Validator This n8n template helps you generate, validate, and store tokens for your customers securely using: n8n** as your backend automation engine Airtable** as your lightweight client and token store 🚀 What It Does Accepts client_id and client_secret via POST webhook. Validates client credentials against Airtable. Generates a long token on success. Stores the generated token in Airtable with metadata. Responds with a JSON containing the token, expiry, and type. Returns clear error messages if validation fails. How It Works Webhook node receives client_id and client_secret. Validator (Code node) checks: Body contains only client_id and client_secret. Rejects missing or extra fields. Airtable search: Looks up the client_id. Rejects if not found. Secret validation (If node): Compares provided client_secret with stored value. Rejects if incorrect. Token generation (Code node): Generates a 128-character secure token. Airtable create: Stores token, client ID, creation date, and type. Webhook response: Returns JSON { access_token, expires_in, token_type } on success. Returns appropriate JSON error messages on failure. Related Workflow You can also use it with the published Bearer Token Validation workflow: 👉 Validate API Requests with Bearer Token Authentication and Airtable to securely validate tokens you generate with this workflow across your protected endpoints. Why Use This Provides OAuth-like flows without a complex backend. Uses n8n + Airtable for client management and token storage. Clean, modular, and ready for your SaaS or internal API automations. Extendable for token expiry, refresh, and rotation handling. Enjoy building secure token-based APIs using n8n + Airtable! 🚀 Built by: Nazmy

Overview Do you often forget to record expenses? 你是不是會常常忘記紀錄花費? Let Spending Tracker Bot help you! 讓 Spending Tracker Bot 來幫你! This AI image/text Spending Tracker LINE Bot Workflow allows you to quickly create a customized spending tracker robot without writing a line of code. At any time, you can speak or send a photo, and the AI will parse it and automatically log the expense to your cloud ledger. 這套 AI 圖片文字記帳 LINE Bot Workflow ，讓你不用寫一行程式碼，就能快速打造一個量身訂製的記帳機器人。無論何時，只需要口述或發送一張照片，AI 就會幫你整理好自動計入雲端帳本 Preparation ① Enable the Google Sheets API in GCP and complete the OAuth setup ② Create the Google Sheet and populate the field names (Feel free to modify based on your own needs) ③ Configure the Webhook URL in the LINE Developers Console ④ OpenAI API Key ① 在 GCP 啟用 Google Sheets API，並完成 OAuth ② 建立並填好 Google Sheet 欄名 (按照自己的需求做更動） ③ 於 LINE Developers 控制台設定 Webhook URL ④ OpenAI API Key Node Configurations Webhook Purpose: The URL is used to receive incoming requests from LINE. Configuration: Paste this URL into the Webhook URL field in your LINE Developers Console. 用途: 要接收 Line 的 URL 設定: 將 URL 放到 Line Webhook URL Switch based on Expense Type & Set/Https Purpose: To distinguish whether the incoming message is text or an image. Configuration: Use a Switch node to route the flow accordingly. 用途: 區分 text 或 image 設定: switch 分流 AI Agent Purpose: To extract and organize the required fields. Configuration: Chat Model & Structured Output Parser. 用途: 整理出需要的欄位 設定: Chat Model & Structured Output Parser Create a deduplication field Purpose: To prevent duplicate entries by creating a unique "for_deduplication" field. Configuration: Join multiple field names using hyphens (-) as separators. 用途: 確保不會重複寫入，先創建一個"去重使用"欄位 設定: 用 - 連接多個欄位 Aggregrate & Merge_all Purpose: To prevent duplicate entries in the data table. Configuration: Read the Google sheet, extract the existing "for_deduplication" column into a dedupeList, and compare it against the newly generated "for_deduplication" value from the previous step. 用途: 防止重複寫入資料表 設定：讀取雲端表，將原本的"去重使用欄位"整理成dedupeList，與前一步整理好的"去重使用"欄位做比對 Response Switch Purpose: To route data and send appropriate replies based on the content. Configuration: Use the replyToken to respond after branching logic. Depending on the result, either write to the data table or return a message: ✅ Expense recorded successfully: <for_deduplication> Irrelevant details or images will not be logged. ⚠️ This entry has already been logged and will not be duplicated. 用途: 資料分流，回應訊息 設定：使用 replyToken ，資料分流後，寫入資料表或回應訊息 ✅ 記帳成功 : <去重使用欄位> 不相關明細或圖片，不會計入 ⚠️ 此筆資料已記錄過，不會重複記帳 Step by step teaching notes 【Auto Expense Tracker from LINE Messages with GPT-4 and Google Sheets】 【AI 圖片文字記帳 Line Bot，自動記帳寫入 Google Sheet】

This automated n8n workflow detects and manages fraudulent booking transactions through comprehensive AI-powered analysis and multi-layered security checks. The system processes incoming travel booking data, performs IP geolocation verification, enriches transaction details with AI insights, calculates dynamic risk scores, and executes automated responses based on threat levels. All transactions are logged and appropriate notifications are sent to relevant stakeholders. Good to Know The workflow combines multiple detection methods, including IP geolocation, AI analysis, and risk scoring algorithms Google Gemini Chat Model provides advanced natural language processing for transaction analysis Risk levels are dynamically calculated and categorized as CRITICAL, HIGH, or standard risk Automated blocking and flagging system protects against fraudulent transactions in real-time All transaction data is logged to Google Sheets for audit trails and pattern analysis The system respects API rate limits and includes proper error handling mechanisms How It Works 1. Initial Data Ingestion & Extraction Monitors and captures incoming booking transaction data from various sources Extracts key booking details including user information, payment data, booking location, and transaction metadata Performs initial data validation and formatting for downstream processing 2. IP Geolocation and AI Analysis IP Geolocation Check**: Validates booking IP addresses by checking geolocation details and comparing against expected user locations AI Agent Integration**: Utilizes Google Gemini Chat Model to analyze booking patterns, user behavior, and transaction anomalies Enhanced Data Processing**: Enriches transaction data with geographical context and AI-driven risk indicators 3. Risk Calculation and Decision Logic Enhanced Risk Calculator**: Combines AI-generated risk scores with geolocation-based factors, payment method analysis, and historical patterns Critical Risk Check**: Flags transactions with risk levels marked as CRITICAL for immediate action High Risk Check**: Identifies HIGH risk transactions requiring additional verification steps Dynamic Scoring**: Adjusts risk calculations based on real-time threat intelligence and pattern recognition 4. Action & Notification Block User Account**: Automatically blocks user accounts for CRITICAL risk transactions to prevent immediate fraud Flag for Review**: Marks HIGH risk transactions for manual review by fraud prevention teams Send Notifications**: Dispatches real-time alerts via email and messaging systems to security teams Automated Responses**: Sends appropriate messages to users based on transaction status and risk level 5. Logging & Response Log to Google Sheets**: Records all transaction details, risk scores, and actions taken for comprehensive audit trails Flag for Review**: Maintains detailed logs of flagged transactions for pattern analysis and machine learning improvements Response Tracking**: Monitors and logs all automated responses and manual interventions How to Use Import the workflow into your n8n instance Configure Google Gemini Chat Model API credentials for AI analysis Set up IP geolocation service API access for location verification Configure Google Sheets integration for transaction logging Establish Gmail/email credentials for notification delivery Define risk thresholds and scoring parameters based on your fraud tolerance levels Test the workflow with sample booking data to verify all components function correctly Monitor initial deployments closely to fine-tune risk scoring algorithms Establish manual review processes for flagged transactions Set up regular monitoring and maintenance schedules for optimal performance Requirements Google Gemini Chat Model API access IP Geolocation service API credentials Google Sheets API integration Gmail API or SMTP email service for notifications n8n instance with appropriate node modules installed Customizing This Workflow Risk Scoring Parameters**: Adjust risk calculation algorithms and thresholds based on your specific fraud patterns and business requirements AI Model Configuration**: Fine-tune Google Gemini prompts and analysis parameters for improved accuracy in your use case Notification Channels**: Add or modify notification methods including Slack, SMS, or webhook integrations Data Sources**: Extend input methods to accommodate additional booking platforms or payment processors Logging Destinations**: Configure alternative or additional logging systems such as databases or external SIEM platforms Geographic Rules**: Customize geolocation validation rules based on your service areas and customer base Automated Actions**: Modify or expand automated response actions based on your fraud prevention policies Review Workflows**: Integrate with existing fraud review systems or ticketing platforms for seamless manual review processes

This guide walks you through setting up an AI-driven workflow to automate flight and hotel reservation processes using a conversational travel booking system. The workflow accepts booking requests, processes them via APIs, and sends confirmations, enabling a seamless travel booking experience. What’s the Goal? Automatically accept and process booking requests for flights and hotels via HTTP POST. Use AI to understand natural language requests and route them to appropriate data processors. Search for flights and hotels using external APIs and process booking confirmations. Send confirmation emails and return structured booking data to users. Enable an automated system for efficient travel reservations. By the end, you’ll have a self-running system that handles travel bookings effortlessly. Why Does It Matter? Manual booking processes are time-consuming and prone to errors. This workflow offers: Zero Human Error**: AI ensures accurate request parsing and booking processing. Time-Saving Automation**: Automates the entire booking lifecycle, boosting efficiency. Seamless Confirmation**: Sends automated emails and responses without manual intervention. Enhanced User Experience**: Provides a conversational interface for bookings. Think of it as your reliable travel booking assistant that keeps the process smooth and efficient. How It Works Here’s the step-by-step flow of the automation: Step 1: Trigger the Workflow Webhook Trigger**: Accepts incoming booking requests via HTTP POST, initiating the workflow. Step 2: Parse the Request AI Request Parser**: Uses AI to understand natural language booking requests (e.g., flight or hotel) and extracts relevant details. Step 3: Route Booking Type Booking Type Router**: Determines whether the request is for a flight or hotel and routes it to the respective data processor. Step 4: Process Flight Data Flight Data Processor**: Handles flight-specific data and prepares it for the search API. Step 5: Search Flight API Flight Search API**: Searches for available flights based on parameters (e.g., https://api.aviationstack.com) and returns results. Step 6: Process Hotel Data Hotel Data Processor**: Handles hotel-specific data and prepares it for the search API. Step 7: Search Hotel API Hotel Search API**: Searches for available hotels based on parameters (e.g., https://api.booking.com) and returns results. Step 8: Process Flight Booking Flight Booking Processor**: Processes flight bookings and generates confirmation details. Step 9: Process Hotel Booking Hotel Booking Processor**: Processes hotel bookings and generates confirmation details. Step 10: Generate Confirmation Message Confirmation Message Generator**: Creates structured confirmation messages for the user. Step 11: Send Confirmation Email Send Confirmation Email**: Sends booking confirmation via email to the user. Step 12: Send Response Send Response**: Returns structured booking data to the user, completing the workflow. How to Use the Workflow? Importing the workflow in n8n is a straightforward process. Follow these steps to import the Conversational Travel Booker workflow: Download the Workflow: Obtain the workflow file (e.g., JSON export from n8n). Open n8n: Log in to your n8n instance. Import Workflow: Navigate to the workflows section, click "Import," and upload the workflow file. Configure Nodes: Adjust settings (e.g., API keys, webhook URLs) as needed. Execute Workflow: Test and activate the workflow to start processing bookings. Requirements n8n account and instance setup. Access to flight and hotel search APIs (e.g., Aviationstack, Booking.com). Email service integration for sending confirmations. Webhook URL for receiving booking requests. Customizing this Workflow Modify the AI Request Parser to handle additional languages or booking types. Update API endpoints in Flight Search API and Hotel Search API nodes to match your preferred providers. Adjust the Send Confirmation Email node to include custom email templates or additional recipients. Schedule the Webhook Trigger to align with your business hours or demand peaks.

Main Use Case This workflow enables automated, AI-assisted replies to users messaging a LINE Official Account, while storing and referencing chat history from Google Sheets to maintain context. Ideal for businesses or support teams that want to provide smart, personalized customer interactions using AI with memory. How It Works (Step-by-Step) Connect to LINE Official Account's API A Webhook listens for incoming messages from users on LINE. When a message is received, it triggers the workflow. Prepare the Data An Edit Fields module structures incoming data (e.g. extracts user ID, message content). This ensures data is clean and usable downstream. Retrieve Chat History The user’s previous conversations are fetched from a Google Sheet. This ensures the AI has memory and can continue conversations contextually. Prepare Prompt The retrieved chat history is combined with the new message to form a complete prompt for the AI. Example format: “User previously said X. Now they said Y. How should we respond?” AI Agent: Google Gemini The formatted prompt is passed to an AI Agent (Google Gemini Chat Model). The AI generates a response based on the message + history. Tools used: Chat ModeMemory, ToolOutputParser for accurate replies. Split & Clean History The conversation history is split into smaller chunks for cleaning and storage. This ensures the Google Sheet remains readable and manageable over time. Save Chat History The cleaned new message and AI reply are saved to Google Sheets. This updates the chat history for future context. Send Reply to LINE The AI-generated reply is sent back to the user via a POST HTTP Request to the LINE Messaging API. How to Set Up Prerequisites: LINE Official Account Google Sheet to store chat history Google Gemini API or AI agent with context memory Automation platform (e.g., n8n, as this seems visually similar) Step-by-Step: Create a Webhook on LINE: Set the webhook URL to your automation service. Enable webhook events. Design Your Google Sheet: Create a sheet with columns: User ID, Timestamp, Message, AI Reply. Set Up Modules in Automation Platform: Webhook: receives user messages. Edit Fields: extract user ID and message. Google Sheets Read: fetch message history. Prompt Composer: format prompt using past history + new message. AI Agent: connect to Google Gemini for smart replies. Split & Clean: clean and chunk history if needed. Google Sheets Write: save the updated conversation. HTTP Request: send reply to LINE via Messaging API. Test Your Workflow: Send a message from LINE. Watch the full loop: receive → process → AI → store → reply. Deploy & Monitor: Ensure error handling is in place (e.g., for blank messages or failed API calls). Regularly check your Google Sheets for storage limits. (If limits reached, you can increase the history row.) 📦 Benefits Maintains context in conversations Personalized, AI-driven responses Easy history tracking via Google Sheets Fully automated and scalable

Having a seamless flow of customer data between your online store and your marketing platform is essential. By keeping your systems synchronized, you can ensure that your marketing campaigns are accurately targeted and effective. The integration between Shopify, a leading e-commerce platform, and Mautic, an open-source marketing automation system, is not available out-of-the-box. However, with a n8n workflow you can bridge this gap with. This template will help you: enhance accuracy in marketing lists by ensuring that subscription changes in Shopify are instantly updated in Mautic. improve compliance with data protection laws by respecting users' subscription preferences across platforms achieve integration without the need for additional plugins or software, minimizing complexity and potential points of failure. This template will demonstrate the follwing concepts in n8n: working with Shopify in n8n control flow with the IF node use Webhooks validate Webhooks with the Crypto node use the GraphQL node to call the Shopify Admin API The template consists of two parts: Sync Email Subscriptions from Shopify to Mautic Sync Email Subscriptions from Mautic to Shopify How to get started? Create a custom app in Shopify get the credentials needed to connect n8n to Shopify This is needed for the Shopify Trigger Create Shopify Acces Token API credentials n n8n for the Shopify trigger node Create Header Auth credentials: Use X-Shopify-Access-Token as the name and the Acces-Token from the Shopify App you created as the value. The Header Auth is neccessary for the GraphQL nodes. Enable the Mautic API under Configuration/API Settings, After the settings are saved you will have an additional entry in your settings menu to create API credentials for n8n Create Mautic credentials in n8n Please make sure to read the notes in the template. For a detailed explanation please check the corresponding video: https://youtu.be/x63rrh_yJzI

Remember when you were doing some large research and wanted to quickly bookmark a page and save it, only to find premium options? Worry not; n8n got you covered. You can now create a simple bookmarking app straight to your browser using simple scrips on your browser called bookmarklets. A bookmarklet is a bookmark stored in a web browser that contains JavaScript commands that add new features to the browser. To create one, we need to add a short script to the bookmark tab of our browser like below A simple hack is to open a new tab and click on the star that appears on the right side Now that we have our bookmark, it's time for the fun part. Right-click on the bookmark we just created and select the edit option. This will allow you to set the name you want for your bookmark and the destination URL. The URL used here will be the script that shall "capture" the page we want to bookmark. The code below has been used and tested to work for this example javascript:(() => { var currentUrl = window.location.href; var webhookUrl = 'https://$yourN8nInstanceUrl/webhook/1rxsxc04b027-39d2-491a-a9c6-194289fe400c'; var xhr = new XMLHttpRequest(); xhr.open('POST', webhookUrl, true); xhr.setRequestHeader('Content-Type', 'application/json'); var data = JSON.stringify({ url: currentUrl }); xhr.send(data); })(); Your Bookmark should look like something like this Now that we have this setup, we are now going to n8n to receive the data sent by this script. Create a new webhook node that receives the POST request as in the workflow and replace $yourN8nInstanceUrl with your actual n8n instance. This workflow can then be configured to send this data to a notion database. Make sure the notion database has all the required permissions before executing the workflow. Otherwise the URLs will not be saved

Talk to Your Apps: Building a Personal Assistant MCP Server with Google Gemini Wouldn't it be cool to just tell your computer or phone to "schedule a meeting with Sarah next Tuesday at 3 PM" or "find John Doe's email address" and have it actually do it? That's the dream of a personal assistant! With n8n and the power of MCP and AI models like Google Gemini, you can actually build something pretty close to that. We've put together a workflow that shows you how you can use a natural language chat interface to interact with your other apps, like your CRM, email, and calendar. What You Need to Get Started Before you dive in, you'll need a few things: n8n:** An n8n instance (either cloud or self-hosted) to build and run your workflow. Google Gemini Access:** Access to the Google Gemini model via an API key. Credentials for Your Apps:** API keys or login details for the specific CRM, Email, and Calendar services you want to connect (like Google Sheets for CRM, Gmail, Google Calendar, etc., depending on your chosen nodes). A Chat Interface:** A way to send messages to n8n to trigger the workflow (e.g., via a chat app node or webhook). How it Works (In Simple Terms) Imagine this workflow is like a helpful assistant who sits between you and your computer. Step 1: You Talk, the AI Agent Listens It all starts when you send a message through your connected chat interface. Think of this as you speaking directly to your assistant. Step 2: The Assistant's Brain (Google Gemini) Your message goes straight to the assistant's "brain." In this case, the brain is powered by a smart AI model like Google Gemini. In our template we are using the latest Gemini 2.5 Pro. But this is totally up to you. Experiment and track which model fits the kind of tasks you will pass to the agent. Its job is to understand exactly what you're asking for. Are you asking to create something? Are you asking to find information? Are you asking to update something? The brain also uses a "memory" so it can remember what you've talked about recently, making the conversation feel more natural. We are using the default context window, which is the past 5 interactions. Step 3: The Assistant Decides What Tool to Use Once the brain understands your request, the assistant figures out the best way to help you. It looks at the request and thinks, "Okay, to do this, I need to use one of my tools." Step 4: The Assistant's Toolbox (MCP & Your Apps) Here's where the "MCP" part comes in. Think of "MCP" (Model Context Protocol) as the assistant's special toolbox. Inside this toolbox are connections to all the different apps and services you use – your CRM for contacts, your email service, and your calendar. The MCP system acts like a manager for these tools, making them available to the assistant whenever they're needed. Step 5: Using the Right Tool for the Job Based on what you asked for, the assistant picks the correct tool from the toolbox. If you asked to find a contact, it grabs the "Get Contact" node from the CRM section. If you wanted to schedule a meeting, it picks the "Create Event" node from the Calendar section. If you asked to draft an email, it uses the "Draft Email" node. Step 6: The Tool Takes Action Now, the node or set of nodes get to work! It performs the action you requested within the specific app. The CRM tool finds or adds the contact. The Email tool drafts the message. The Calendar tool creates the event. Step 7: Task Completed! And just like that, your request is handled automatically, all because you simply told your assistant what you wanted in plain language. Why This is Awesome This kind of workflow shows the power of combining AI with automation platforms like n8n. You can move beyond clicking buttons and filling out forms, and instead, interact with your digital life using natural conversation. n8n makes it possible to visually build these complex connections between your chat, the AI brain, and all your different apps. Taking it Further (Possible Enhancements) This is just the start! You could enhance this personal assistant by: Connecting more apps and services (task managers, project tools, etc.). Adding capabilities to search the web or internal documents. Implementing more sophisticated memory or context handling. Getting a notification when the AI agent is done completing each task such as in Slack or Microsoft Teams. Allowing the assistant to ask clarifying questions if needed. Building a robust prompt for the AI agent. Ready to Automate Your Workflow? Imagine the dozens of hours your team could save weekly by automating repetitive tasks through a simple, natural language interface. Need help? Feel free to contact us at 1 Node. Get instant access to a library of free resources we created.

This workflow allows users to convert a 2D image into a 3D model by integrating multiple AI and web services. The process begins with a user uploading or providing an image URL, which is then sent to a generative AI model capable of interpreting the content and generating a 3D representation in .glb format. The model is then stored and a download link is returned to the user. Main Steps Trigger Node: Initiates the workflow either via HTTP request, webhook, or manual execution. Image Upload or Input: The image is acquired via direct upload or URL input. API Integration: The image is sent to a 3D generation API (e.g., a service like Kaedim, Luma Labs, or a custom AI model). Model Generation: The external API processes the image and creates a 3D model. File Storage: The resulting 3D model is stored in cloud storage (e.g., S3, Google Drive, or a local server). Response to User: A download link for the 3D model is returned to the user via the same communication channel (HTTP response, email, or chat). Advantages Automation**: Eliminates the need for manual 3D modeling, saving time for artists, developers, and designers. AI-Powered**: Leverages AI to generate realistic and usable 3D models from simple 2D inputs. Scalability**: Can be triggered automatically and scaled up to handle many requests via n8n's automation. Integration-Friendly**: Easily extendable with other services like Discord, Telegram, or marketplaces for 3D assets. No-Code Configuration**: Built with n8n’s visual interface, making it editable without programming knowledge. How It Works Trigger: The workflow can be started manually ("When clicking ‘Test workflow’") or automatically at scheduled intervals ("Schedule Trigger"). Data Retrieval: The "Get new image" node fetches data from a Google Sheet, including the model image, product image, and product ID. 3D Image Creation: The "Create 3D Image" node sends the image data to the Fal.run API (Trellis) to generate a 3D model. Status Check: The workflow periodically checks the request status ("Get status" and "Wait 60 sec.") until the job is marked as "COMPLETED." Result Processing: Once completed, the 3D model URL is retrieved ("Get Url 3D image"), the file is downloaded ("Get File 3D image"), and uploaded to Google Drive ("Upload 3D Image"). Sheet Update: The final 3D model URL is written back to the Google Sheet ("Update result"). Set Up Steps Prepare Google Sheet: Create a Google Sheet with columns: IMAGE MODEL and 3D RESULT (empty). Example sheet: Google Sheet Template. Obtain Fal.run API Key: Sign up at Fal.ai and get an API key. Configure the Authorization header in the "Create 3D Image" node with Key YOURAPIKEY. Configure Workflow Execution: Run manually via the Test workflow button. For automation, set up the Schedule Trigger node (e.g., every 5 minutes). Verify Credentials: Ensure Google Sheets, Google Drive, and Fal.run API credentials are correctly set in n8n. Once configured, the workflow processes new entries in the Google Sheet, generates 3D models, and updates the results automatically. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Use case Slackbots are super powerful. At n8n, we have been using them to get a lot done.. But it can become hard to manage and maintain many different operations that a workflow can do. This is the base workflow we use for our most powerful internal Slackbots. They handle a lot from running e2e tests for Github branch to deleting a user. By splitting the workflow into many subworkflows, we are able to handle each command seperately, making it easier to debug as well as support new usecases. In this template, you can find eveything to setup your own Slackbot (and I made it simple, there's only one node to configure 😉). After that, you need to build your commands directly. This bot can create a new thread on an alerts channel and respond there. Or reply directly to the user. It responds for help request to return a help page. It automatically handles unknown commands. It also supports flags and environment variables. For example /cloudbot-test info mutasem --full-info -e env=prod would give you the following info, when calling subworkflow. How to setup Add Slack command and point it up to the webhook. For example. Add the following to the Set config node alerts_channel with alerts channel to start threads on instance_url with this instance url to make it easy to debug slack_token with slack bot token to validate request slack_secret_signature with slack secret signature to validate request help_docs_url with help url to help users understand the commands Build other workflows to call and add them to commands in Set Config. Each command must be mapped to a workflow id with an Execute Workflow Trigger node Activate workflow 🚀 How to adjust Add your own commands. Depending on your need, you might need to lock down who can call this.

Overall Description & Potential << What Does This Flow Do? >> Overall, this workflow is an intelligent sales outreach automation engine that transforms raw leads from a form or a list into highly personalized, ready-to-send introductory email drafts. The process is: it starts by fetching data, enriches it with in-depth AI research to uncover "pain points," and then uses those research findings to craft an email that is relevant to the solutions you offer. This system solves a key problem in sales: the lack of time to conduct in-depth research on every single lead. By automating the research and drafting stages, the sales team can focus on higher-value activities, like engaging with "warm" prospects and handling negotiations. Using Google Sheets as the main dashboard allows the team to monitor the entire process—from lead entry, research status, and email drafts, all the way to the send link—all within a single, familiar interface. << Potential Future Enhancements >> This workflow has a very strong foundation and can be further developed into an even more sophisticated system: Full Automation (Zero-Touch): Instead of generating a manual-click link, the output from the AI Agent can be directly piped into a Gmail or Microsoft 365 Email node to send emails automatically. A Wait node could be added to create a delay of a few minutes or hours after the draft is created, preventing instant sending. Automated Follow-up Sequences: The workflow can be extended to manage follow-up emails. By using a webhook to track email opens or replies, you could build logic like: "If the intro email is not replied to within 3 days, trigger the AI Agent again to generate follow-up email #1 based on a different template, and then send it." AI-Powered Lead Scoring: After the research stage, the AI could be given the additional task of scoring leads (e.g., 1-10 or High/Medium/Low Priority) based on how well the target company's profile matches your ideal customer profile (ICP). This helps the sales team prioritize the most promising leads. Full CRM Integration: Instead of Google Sheets, the workflow could connect directly to HubSpot, Salesforce, or Pipedrive. It would pull new leads from the CRM, perform the research, draft the email, and log all activities (research results, sent emails) back to the contact's timeline in the CRM automatically. Multi-Channel Outreach: Beyond email, the AI could be instructed to draft personalized LinkedIn Connection Request messages or WhatsApp messages. The workflow could then use the appropriate APIs to send these messages, expanding your outreach beyond just email.