Workflow Templates

n8n Template: Store Chat Data in Supabase PostgreSQL for WhatsApp/Slack Integration This n8n template captures chat data (like user ID, name, or address) and saves it to a Supabase PostgreSQL database. It’s built for testing now but designed to work with WhatsApp, Slack, or similar platforms later, where chat inputs aren’t predefined. Guide with images can be found on: https://github.com/JimPresting/Supabase-n8n-Self-Hosted-Integration/ Step 1: Configure Firewall Rules in Your VPC Network To let your n8n instance talk to Supabase, add a firewall rule in your VPC network settings (e.g., Google Cloud, AWS, etc.). Go to VPC Network settings. Add a new firewall rule: Name: allow-postgres-outbound Direction: Egress (outbound traffic) Destination Filter: IPv4 ranges Destination IPv4 Ranges: 0.0.0.0/0 (allows all; restrict to Supabase IPs for security) Source Filter: Pick IPv4 ranges and add the n8n VM’s IP range, or Pick None if any VM can connect Protocols and Ports: Protocol: TCP Port: 5432 (default PostgreSQL port) Save the rule. Step 2: Get the Supabase Connection String Log into your Supabase Dashboard. Go to your project, click the Connect button in the header. Copy the PostgreSQL connection string: postgresql://postgres.fheraruzdahjd:[YOUR-PASSWORD]@aws-0-eu-central-1.pooler.supabase.com:6543/postgres Replace [YOUR-PASSWORD] with your Supabase account password (no brackets) and replace the string before that with your actual unique identifier. Note the port (6543 or 5432)—use what’s in the string. Step 3: Set Up the n8n Workflow This workflow takes chat data, maps it to variables, and stores it in Supabase. It’s built to handle messy chat inputs from platforms like WhatsApp or Slack in production. Workflow Steps Trigger Node: "When clicking 'Test workflow'" (manual trigger). For now, it’s manual. In production, this will be a WhatsApp or Slack message trigger, which won’t have a fixed input format. Set Node: "Set sample input variables (manual)". This node sets variables like id, name, address to mimic chat data. Why? Chat platforms send unstructured data (e.g., a message with a user’s name or address). We map it to variables so we can store it properly. The id will be something unique like a phone number, account ID, or account number. Sample Agent Node: Uses a model (e.g., GeminiFlash2.0 but doesn't matter). This is a placeholder to process data (e.g., clean or validate it) before saving. You can skip or customize it. Supabase PostgreSQL Node: "Supabase PostgreSQL Database". Connects to Supabase using the connection string from Step 2. Saves the variables (id, name, address) to a table. Why store extra fields? The id (like a phone number or account ID) is the key. Extra fields like name or address let us keep all user info in one place for later use (e.g., analytics or replies). Output Node: "Update additional values e.g., name, address". Confirms the data is saved. In production, this could send a reply to the chat platform. Why This Design? Handles Unstructured Chat Data**: WhatsApp or Slack messages don’t have a fixed format. The "Set" node lets us map any incoming data (e.g., id, name) to our database fields. Scales for Production**: Using id as a key (phone number, account ID, etc.) with extra fields like name makes this workflow flexible for many use cases, like user profiles or support logs. Future-Ready**: It’s built to swap the manual trigger for a real chat platform trigger without breaking. Step 4: Configure the Supabase PostgreSQL Node In the n8n workflow, set up the Supabase PostgreSQL node: Host: aws-0-eu-central-1.pooler.supabase.com (from the connection string) Port: 6543 (or what’s in the connection string) Database: postgres User: postgres.fhspudlibstmpgwqmumo (from the connection string) Password: Your Supabase password SSL: Enable (Supabase usually requires it) Set the node to Insert or Update: Map id to a unique column in your Supabase table (e.g., phone number, account ID). Map fields like name, address to their columns. Test the workflow to confirm data saves correctly. Security Tips Limit Firewall Rules**: Don’t use 0.0.0.0/0. Find Supabase’s IP ranges in their docs and use those. Hide Passwords**: Store your Supabase password in n8n’s environment variables. Use SSL**: Enable SSL in the n8n node for secure data transfer.

When you fill out the form with business challenges and requirements GPT-4 analyzes the input and generates a customized proposal using your template System automatically creates a Google Slides presentation with personalized content Professional proposal email is sent directly to the prospect with the presentation link Set up steps Estimated time: 15-20 minutes Connect your OpenAI API key for GPT-4 access Link your Google account for Slides and Gmail integration Create your proposal template in Google Slides with placeholder variables Customize the AI prompt and email template with your branding Test with sample data and activate the workflow

🎉 Do you want to master AI automation, so you can save time and build cool stuff? I’ve created a welcoming Skool community for non-technical yet resourceful learners. 👉🏻 Join the AI Atelier 👈🏻 Accepting payments via credit card online is a crucial component for the majority of businesses. Stripe provides a robust suite of tools for processing payments, yet many people still find it challenging to create a simple payment page and distribute it to their customers. 📋 Blog post 📺 Youtube Video This n8n workflow aims to offer the simplest and most direct method for generating a Stripe payment link. Features Quick Stripe Payment Link Creation:** Simply enter a title and select a price to create a Stripe payment link in seconds. Set Up Steps Connect your Stripe credentials. Fill the config node (currency). This n8n workflow template is crafted to significantly reduce the creation time of a Stripe Payment link. Created by the n8ninja.

This workflow allows you to collect tweets, store them in MongoDB, analyse their sentiment, insert them into a Postgres database, and post positive tweets in a Slack channel. Cron node: Schedule the workflow to run every day Twitter node: Collect tweets MongoDB node: Insert the collected tweets in MongoDB Google Cloud Natural Language node: Analyse the sentiment of the collected tweets Set node: Extract the sentiment score and magnitude Postgres node: Insert the tweets and their sentiment score and magnitude in a Posgres database IF node: Filter tweets with positive and negative sentiment scores Slack node: Post tweets with a positive sentiment score in a Slack channel NoOp node: Ignore tweets with a negative sentiment score

AI-Powered Workflow for Auto-Responding to Positive Cold Email Replies This workflow is designed for agencies, freelancers, and sales teams who want to turn positive cold email replies into booked meetings automatically—without hiring VAs or spending hours on manual responses. ❓ The Problem Most teams waste time replying manually or pay for virtual assistants, leading to delays and missed opportunities. This template eliminates that bottleneck. ✅ What the Workflow Does Detects positive replies from Instantly.ai campaigns Uses AI to analyze intent and craft natural, human-like responses Adds personalization to keep replies authentic Includes Calendly links, product docs, or FAQs based on the lead’s intent Sends responses instantly—so you never miss a hot lead again No robotic AI text. Just smooth, human-style emails that get booked calls faster. 👥 Who is This For? Agencies** running Instantly.ai or similar outbound tools Founders** handling their own cold email outreach Sales teams** looking to automate follow-up and booking Anyone who gets 5–20 positive replies a week and wants to 2x–4x conversions ✅ Requirements n8n** (Cloud or self-hosted) Instantly.ai account** with API access OpenAI API key** (stored securely in n8n credentials) (Optional) Calendly or booking link, Notion or Google Docs for resources ⚙️ How to Set Up Import the workflow into n8n Add your Instantly.ai API credentials and OpenAI key using n8n’s credential manager Customize the AI prompt for your tone, CTA, and offer Insert your Calendly or booking link in the response template Test with one positive reply to confirm filtering and response quality Activate the workflow to auto-reply in real time 🔧 How to Customize Adjust the filtering logic for different keywords or intent signals Add branching for multiple booking links (e.g., based on region or service type) Push responses to a CRM for tracking Include extra resources like case studies or pricing docs

This workflow automatically connects to a Cloudflare R2 bucket (via S3-compatible API), filters out files older than 14 days, deletes them, and then sends a Telegram notification for each deletion. It runs on a daily schedule. 🕘 Schedule Trigger Executes the workflow once a day at a specified hour (e.g., 9 AM). 📦 S3 Node – List Files Retrieves all objects from a specific folder (prefix) in a Cloudflare R2 bucket using the S3 API. 🔎 Code Node – Filter Files Older Than 2 Weeks Filters the retrieved files by comparing their LastModified timestamps to the current date. Files older than 14 days (2 weeks) are selected for deletion. 🗑️ S3 Node – Delete File Deletes each filtered file from the R2 bucket. 📨 Telegram Node – Notify Deletion Sends a Telegram message with the name of the deleted file to a specified chat ID. The message includes:

This sub-workflow uses two custom Hugging Face regression models from Open Paws to evaluate and predict the real-world performance and advocacy alignment of text content. It’s designed to support animal advocacy organizations in optimizing their messaging across platforms like social media, email campaigns, and more. 🛠️ What It Does Sends input text to two deployed Hugging Face endpoints: Predicted Performance Model – Estimates real-world content success (e.g., engagement, shares, opens) based on patterns from real online data. Advocate Preference Model – Predicts how well the content will resonate with animal advocates (emotional impact, relevance, rationality, etc.) Outputs structured scores for both models Can be integrated into larger workflows for automated content review, filtering, or revision 📊 About the Models Text Performance Prediction Model** Trained on real-world data from 30+ animal advocacy organizations, this model predicts actual online performance of content—including social media, email marketing, and other outreach channels. Advocate Preference Prediction Model** Trained on ratings from animal advocates to evaluate how well a piece of text aligns with advocacy goals and values. Model Repositories: open-paws/text_performance_prediction_longform open-paws/animal_advocate_preference_prediction_longform > 📌 You must deploy each model as an inference endpoint on Hugging Face. Click "Deploy" on each model’s repo, then add the endpoint URL and your Hugging Face access token using n8n credentials. 📦 Use Cases Advocacy content review before publishing Automated scoring of outreach messages Filtering or flagging content with low predicted impact A/B testing support for message optimization

This workflow builds a valid RSS feed (which is an XML feed under the hood) for ARD Audiothek podcasts. This allows you to subscribe to such podcasts using your favourite podcatcher without using the ARD Audiothek app. The example builds a feed for Kalk & Welk, but the workflow can be easily adjusted by providing another podcast URL on the Get overview page HTTP Request node. To subscribe to the feed, active your n8n workflow and then use the Production URL from the intitial Feed Webhook node in your podcatcher. I've tested the resulting feed using Pocket Casts... ...and Miniflux: When using Miniflux, you can add your feed via this page to your account. Make sure you select Private when doing so to avoid sharing your n8n instance with the world. The resulting feed passes the W3C Feed Validation Service: The workflow can also be used as a foundation to free other podcasts from propriertary big media platforms, though not all of them will be as simple to deal with as the ARD Audiothek.

n8n does not currently offer a way to retrieve emails from arbritrary providers via a regular node. Unless you're using Gmail or Outlook, you can only use the email trigger to start a workflow when a new email arrives. This currently limits the possible use cases you can cover in your n8n workflows, as you cannot (for example) get an idea of how many unread messages there are in an inbox, or search for specific messages when an event occurs. But fear not, there's a new sheriff in town! The JMAP standard allows you to interact with your mailboxes, calendars and contacts through single HTTP requests whenever needed. This n8n workflow demonstrates how to retrieve the total number of unread messages from a JMAP server and also retrieve details for the first 3 messages. It can easily be adapted to search for messages other than unread, or to return details for more than the first 3 messages. Screenshots FAQ Which n8n version do I need? The workflow was built using n8n 1.20 and should work here out of the box. HTTP requests are also supported on older n8n versions, so the workflow can be backported as an alternative. Which credentials do I need? The JMAP standard does not limit the available authentication options. Fastmail (the sponsor of the standard) supports Bearer authentication as well as OAuth2. In n8n you can implement the Fastmail Bearer authentication by creating Header Auth credentials with a name of Authorization and a value of Bearer $apiToken (replacing $apiToken with your actual API token from Fastmail). For other services you'd need to check the respective API documentation for more details on the support authentication methods. What even is JMAP? It's an official Internet Engineering Task Force (IETF) standard, sponsored by Fastmail, that will hopefully replace the legacy standards CalDAV, CardDAV, and IMAP soon. The full specs are available here. How can I use JMAP? If you're a Fastmail customer or if you're hosting your own Stalwart mail server you can use JMAP today. If your email provider doesn't yet support JMAP, you might want to contact them and let them know you're interested in this functionality.

Travel Agent that Auto Response on Mail In this guide, we’ll break down how to set up an AI-powered auto-reply system that works while you sleep. Ready to 10X your efficiency? Let’s dive in! What’s the Goal? AI-driven auto-responses for Email. Instant replies to FAQs, order confirmations, and support queries. 24/7 availability - no more “We’ll get back to you soon” delays. Seamless integration with existing business tools. By the end, you’ll have a self-running communication assistant that never takes a coffee break. Why Does It Matter? Why automate replies? Because time = money and manual typing is so 2010. Here’s why this workflow is a game changer: Zero Human Error: AI doesn’t get tired or make typos. Lightning-Fast Replies: Customers get instant answers, improving satisfaction. 24/7 Availability: No more “Out of Office” replies. Focus on High-Value Work: Free your team from mundane tasks. Think of it as hiring a super efficient virtual assistant - minus the salary. How It Works Here’s the step by step magic behind the automation Step 1: Trigger the Workflow Detect new messages from WhatsApp, Email, or Slack. Use n8n’s webhook or API integration to capture incoming queries. Step 2: Process the Message with AI Send the message to an AI model (like OpenAI GPT-4 or Gemini). Generate a context-aware, human-like response. Step 3: Send the Automated Reply Route the AI-generated response back to the original platform. Ensure personalization (e.g., “Hi [Name], thanks for reaching out!”). Step 4: Log & Optimize Store interactions in a database (Airtable, Google Sheets). Continuously improve AI responses based on past data. How to use the workflow? Importing a workflow in n8n is a straightforward process that allows you to use pre-built or shared workflows to save time. Below is a step-by-step guide to importing a workflow in n8n, based on the official documentation and community resources. Steps to Import a Workflow in n8n 1. Obtain the Workflow JSON Source the Workflow:** Workflows are typically shared as JSON files or code snippets. You might receive them from: The n8n community (e.g., n8n.io workflows page). A colleague or tutorial (e.g., a .json file or copied JSON code). Exported from another n8n instance (see export instructions below if needed). Format:** Ensure you have the workflow in JSON format, either as a file (e.g., workflow.json) or as text copied to your clipboard. 2. Access the n8n Workflow Editor Log in to n8n:** Open your n8n instance (via n8n Cloud or your self-hosted instance). Navigate to the Workflows tab in the n8n dashboard. Open a New Workflow:** Click Add Workflow to create a blank workflow, or open an existing workflow if you want to merge the imported workflow. 3. Import the Workflow Option 1: Import via JSON Code (Clipboard): In the n8n editor, click the three dots (⋯) in the top-right corner to open the menu. Select Import from Clipboard. Paste the JSON code of the workflow into the provided text box. Click Import to load the workflow into the editor. Option 2: Import via JSON File: In the n8n editor, 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.

What this workflow does? This workflow primarily uses the GPT-4o API from PiAPI and automatically creates front/side/top views of 3D models from commands. Who is this for？ 3D Designers： Quickly generate standardized orthographic views for design review E-commerce Operators： Create multi-angle product display images 3D Modeling Beginners： Instantly produce basic reference views Step-by-step Instruction Fill in X-API-Key of your PiAPI account and the image prompt based on your inspiration. Click Test workflow. Get the image url in the final node. OutPut

This workflow allows users to generate beautifully stylized 3D-rendered food emoji icons based on a simple text prompt. It combines user input, structured visual design generation, and image rendering using OpenAI’s GPT models. ✨ What It Does Collects user input via a form: e.g. "green apple" Generates a structured JSON specification describing the emoji’s form, lighting, texture, and color scheme Uses AI to render an image based on that spec—styled like a high-quality emoji icon with a transparent background 🧠 Use Case This template is ideal for: Designers or creators needing icon ideas or drafts for food items Developers building emoji packs or digital stickers Inspiration for AI-assisted product illustration or branding 💡 Why It's Useful Instead of prompting a model directly with vague terms, this flow creates a structured visual spec tailored to food items. The final emoji-style icon is polished, modern, and downloadable. ✅ Requirements To get started with this workflow, follow these steps: 🔑 Configure Credentials: Set up your API credentials for OpenAI and Google Drive 💳 Add OpoenAI Credit: Make sure to add credit to your OpenAI account, verify your organization (required for generating images) 📊 Connect Google Drive: Authenticate your Google Drive account ⚙️ (Optional) Customize Prompts: Adjust the prompts within the workflow to better suit your specific needs Note: Each image generation will cost you about $0.17