Workflow Templates

📬 N8N Contact Form Workflow: Capture, Notify via Email, and Redirect with Confirmation/Error Handling This n8n workflow facilitates contact form submissions through a customizable form that sends an email notification to support and redirects users based on the submission outcome. It is ideal for embedding a functional "Contact Us" form on websites with automated email notifications. ✨ Features Collects first name, last name, email, company name, and a message Sends formatted email notification to the support team Displays success or error confirmation to the user Customizable UI and form behavior Error fallback handling with user-friendly feedback 🧩 Nodes Overview 1. On form submission (Trigger) Type:** formTrigger Displays the contact form to users and triggers the workflow on submission. 2. Send Email to Support Type:** emailSend Sends an HTML email to a support address with the form details. Uses an SMTP credential for sending. 3. If Email Sent Type:** if Checks if the email was sent successfully using the existence of messageId. 4. Confirmation Form Type:** form Displays a “Thank You” HTML message after a successful submission. 5. Redirect Form Type:** form Redirects the user to a specified URL (e.g., LinkedIn profile). 6. Form (Error) Type:** form Displays an error message if email delivery fails. 7. NoOp Nodes End (Success)* and *End (Error)** to mark flow terminations cleanly. ⚙️ Customization Options Change the form fields, title, or descriptions in the formTrigger node. Update the email body or subject in the emailSend node. Redirect to a different URL by editing the Redirect Form node. Modify success and error UI with HTML content in the Confirmation Form and Form. 🧠 Use Cases Website "Contact Us" form integration Lead generation forms for businesses Customer service inquiry collection Feedback or support ticket system 🚀 How to Use Import this workflow into your n8n instance. Configure SMTP credentials for the emailSend node. Publish the formTrigger endpoint (e.g., /contact-us) publicly or embed in your website. Test submission and confirm email delivery and redirects. 🔐 Notes Ensure SMTP credentials are correctly configured in n8n. Make sure your n8n webhook URLs are reachable from your website or frontend. Made with ❤️ using n8n by Akhil.

This workflow provides automated access to the Openai Clip AI model through the Replicate API. It saves you time by eliminating the need to manually interact with AI models and provides a seamless integration for image generation tasks within your n8n automation workflows. Overview This workflow automatically handles the complete image generation process using the Openai Clip model. It manages API authentication, parameter configuration, request processing, and result retrieval with built-in error handling and retry logic for reliable automation. Model Description: Official CLIP models, generate CLIP (clip-vit-large-patch14) text & image embeddings Key Capabilities High-quality image generation from text prompts** Advanced AI-powered visual content creation** Customizable image parameters and styles** Tools Used n8n**: The automation platform that orchestrates the workflow Replicate API**: Access to the Openai/clip AI model Openai Clip**: The core AI model for image generation Built-in Error Handling**: Automatic retry logic and comprehensive error management How to Install Import the Workflow: Download the .json file and import it into your n8n instance Configure Replicate API: Add your Replicate API token to the 'Set API Token' node Customize Parameters: Adjust the model parameters in the 'Set Image Parameters' node Test the Workflow: Run the workflow with your desired inputs Integrate: Connect this workflow to your existing automation pipelines Use Cases Content Creation**: Generate unique images for blogs, social media, and marketing materials Design Prototyping**: Create visual concepts and mockups for design projects Art & Creativity**: Produce artistic images for personal or commercial use Marketing Materials**: Generate eye-catching visuals for campaigns and advertisements Connect with Me Website**: https://www.nofluff.online YouTube**: https://www.youtube.com/@YaronBeen/videos LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Get Replicate API**: https://replicate.com (Sign up to access powerful AI models) #n8n #automation #ai #replicate #aiautomation #workflow #nocode #imagegeneration #aiart #texttoimage #visualcontent #aiimages #generativeart #openai #machinelearning #artificialintelligence #aitools #automation #digitalart #contentcreation #productivity #innovation

This n8n workflow automatically creates and sends regular performance summaries to parents using data from a Learning Management System (LMS). It pulls student grades and attendance, formats them into easy-to-read reports, and emails them without any manual work. Good to Know Fully Automated**: Generates reports and sends emails using LMS data. Regular Updates**: Sends summaries on a set schedule (e.g., every Monday at 9 AM). Clear Reports**: Includes student grades, attendance, and progress notes. Error Alerts**: Notifies admins via email if something goes wrong. Scalable**: Works for multiple students across different classes. How It Works Report Generation Flow Weekly Trigger: Starts the process every Monday at 9 AM. Fetch LMS Data: Pulls grades, attendance, and progress from the LMS. Process Data: Organizes the data into a clear report format. Generate HTML Report: Creates a readable report with student details. Send Email to Parents: Emails the report to parents’ addresses. Log Report Delivery: Records the sent reports in a log. Example Sheet Columns Student ID**: Unique identifier for each student. Name**: Full name of the student. Grade**: Current academic grade or score. Attendance**: Percentage of classes attended. Progress Notes**: Brief comments on performance. Report Date**: Date the report was generated. How to Use Import Workflow: Add the workflow to n8n using the “Import Workflow” option. Set Up LMS Access: Configure n8n with LMS credentials to fetch data. Configure Email: Add parent email addresses and set up an email service (e.g., Gmail). Activate Workflow: Save and turn on the workflow in n8n. Check Logs: Verify reports are sent and logs are updated. Requirements n8n Instance**: Self-hosted or cloud-based n8n setup. LMS Access**: API or credentials to connect to the LMS. Email Service**: SMTP setup (e.g., Gmail) for sending reports. Admin Oversight**: Someone to monitor and fix any errors. Customizing This Workflow Change Schedule**: Adjust the trigger to send reports weekly or monthly. Add More Data**: Include extra LMS fields like behavior notes. Custom Email**: Change the email template for a personalized touch.

Telegram Voice Message → Automatic Email Imagine: What if you could turn a simple Telegram voice message into a professional email—without typing, copying, pasting, or even opening Gmail? This workflow does it all for you: just record a voice note, and it will transcribe, format, and write a clean HTML email, then send it to the right person—all by itself. Prerequisites Create a Telegram bot** (via BotFather) and get the token. Have an OpenAI account** (API key for Whisper and GPT-4). Set up a Gmail account with OAuth2.** Import the JSON template** into your automation platform. 🧩 Detailed Flow Architecture 1. Telegram Trigger Node: Telegram Trigger This node listens to all Message events received by the specified bot (e.g., “BOT OFFICIEL BAPTISTE”). Whenever a user sends a voice message, the trigger fires automatically. > ⚠️ Only one Telegram trigger per bot is possible (API limitation). Key parameter: Trigger On: Message 2. Wait Node: Wait Used to buffer or smooth out calls to avoid collisions if you receive several voice messages in a row. 3. Retrieve the Audio File Node: Get a file Type:** Telegram (resource: file) Parameter:** fileId = {{$json"message"["file_id"]}} This node fetches the voice file from Telegram received in step 1 4. Automatic Transcription (Whisper) Node: Transcribe a recording Resource:** audio Operation:** transcribe API Key:** Your OpenAI account The audio file is sent to OpenAI Whisper: the output is clean, accurate text ready to be processed. 5. Optional Wait (Wait1) Node: Wait1 Same purpose as step 2: useful if you want to buffer or add a delay to absorb processing time. 6. Structured Email Generation (GPT-4 + Output Parser) Node: AI Agent This is the core of the flow: The transcribed text is sent to GPT-4 (or GPT-4.1-mini here, via OpenAI Chat Model) Prompt used:** You are an assistant specialized in writing professional emails. Based on the text below, you must: {{ $json.text }} Detect if there is a recipient's email address in the text (or something similar like "send to fort.baptiste.pro") If it’s not a complete address, complete it by assuming it ends with @gmail.com. Understand the user's intent (resignation, refusal, application, excuse, request, etc.) Generate a relevant and concise email subject, faithful to the content Write a professional message, structured in HTML: With a polite tone, adapted to the situation Formatted with HTML tags (`, `, etc.) No spelling mistakes in French My first name is jeremy and if the text says he is not happy, specify the wish to resign ⚠️ You must always return your answer in the following strict JSON format, with no extra text: { "email": "adresse@gmail.com", "subject": "Objet de l’email", "body": "Contenu HTML de l’email" } Everything is strictly validated and formatted with the Structured Output Parser node. 7. Automatic Email Sending (Gmail) Node: Send a message To:** {{$json.output.email}} Subject:** {{$json.output.subject}} HTML Body:** {{$json.output.body}} This node takes the JSON structure returned by the AI and sends the email via Gmail, to the right recipient, with the correct subject and full HTML formatting.

Allow your friends to get updates when you do not meet your workout goals so that they can help you get to where you need to be! Monitors your Strava account and sends an email to three accountability partners each day if you have not had enough activity so that they can reach out to you with some encouragement. Make sure that you configure the Accountability Settings to meet your needs along with the Strava and Send Email credentials.

Using Typeform to push task requests to an n8n webhook that then categorizes the request and assigns it in ClickUp accordingly. In order to get his workflow working for yourself, you will need: ClickUp account Typeform account Credentials for these services ClickUp configured with appropriate lists Typeform setup with options that correspond with ClickUp lists. You may modify this workflow to meet your specific needs and configuration. This is a very simple version of this workflow and you can make it as complicated as you wish to meet your requirements.

🧾 Summary This workflow monitors Kubernetes pod CPU usage using Prometheus, and sends real-time Slack alerts when CPU consumption crosses a threshold (e.g., 0.8 cores). It groups pods by application name to reduce noise and improve clarity, making it ideal for observability across multi-pod deployments like Argo CD, Loki, Promtail, applications etc. 👥 Who’s it for Designed for DevOps and SRE teams and platform teams, this workflow is 100% no-code, plug-and-play, and can be easily extended to support memory, disk, or network spikes. It eliminates the need for Alertmanager by routing critical alerts directly into Slack using native n8n nodes. ⚙️ What it does This n8n workflow polls Prometheus every 5 minutes ⏱️, checks if any pod's CPU usage crosses a defined threshold (e.g., 0.8 cores) 🚨, groups them by app 🧩, and sends structured alerts to a Slack channel 💬. 🛠️ How to set up 🔗 Set your Prometheus URL with required metrics (container_cpu_usage_seconds_total, kube_pod_container_resource_limits) 🔐 Add your Slack bot token with chat:write scope 🧩 Import the workflow, customize: Threshold (e.g., 0.8 cores) Slack channel Cron schedule 📋 Requirements A working Prometheus stack with kube-state-metrics Slack bot credentials n8n instance (self-hosted or cloud) 🧑‍💻 How to customize 🧠 Adjust threshold values or query interval 📈 Add memory/disk/network usage metrics 💡 This is a plug-and-play Kubernetes alerting template for real-time observability. 🏷️ Tags: Prometheus, Slack, Kubernetes, Alert, n8n, DevOps, Observability, CPU Spike, Monitoring Prometheus Spike Alert to Slack

This N8N template makes it easy to extract key YouTube video data - including title, view count, like count, comment count, and many more - and save it directly into a connected Google Sheet. Use cases are many: Whether you're YouTubers, content strategists, growth marketers, and automation engineers, this tool gives you fast, structured access to video-level insights in seconds. How It Works The workflow begins when you click Execute Workflow or Test Workflow manually in N8N. It reads the list of video URLs in the connected Google Sheet. Only the URLs marked with the Ready status will be processed. The tool loops through each video and prepares the necessary data for the YouTube API call later. For each available URL, the tool extracts the video ID and sends a request to the YouTube API to fetch key metrics. The response is checked: If successful: the video’s statistics are written back to the corresponding row in the Google Sheet and the row's status is marked as Finished. If unsuccessful: the row's status is updated to Error for later review. How To Use Download the workflow package. Import the workflow package into your N8N interface. Duplicate the YouTube - Get Video Statistics Google Sheet template into your Google Sheets account. Set up Google Cloud Console credentials in the following nodes in N8N, ensuring enabled access and suitable rights to Google Sheets and YouTube services: For Google Sheets access, ensure each node is properly connected to the correct tab in your connected Google Sheet template: Node Google Sheets - Get Video URLs → connected to Tab Video URLs; Node Google Sheets - Update Data → connected to Tab Video URLs; Node Google Sheets - Update Data - Error → connected to Tab Video URLs. For YouTube access, set up a GET method to connect to YouTube API in the following node: Node HTTP - Find Video Data. In your connected Google Sheet, enter the video URLs that you want to crawl and set the rows' status to Ready. Run the workflow by clicking Execute Workflow or Test Workflow in N8N. View the results in your connected Google Sheet: Successful fetches will update the rows' status in Column A in the Video URLs tab to Finished and the video metrics will populate. If the call fails, the rows' status in Column A in the tab will be marked as Error. Requirements Basic setup in Google Cloud Console (OAuth or API Key method enabled) with enabled access to YouTube and Google Sheets. How To Customize By default, the workflow is manually triggered in N8N. However, you can automate the process by adding a Google Sheets trigger that monitors new entries automatically. If you want to fetch additional video fields or analytics (like tags, category ID, etc.), you can expand the HTTP - Find Video Data node to include those. Need Help? Join our community on different platforms for support, inspiration and tips from others. Website: https://www.agentcircle.ai/ Etsy: https://www.etsy.com/shop/AgentCircle Gumroad: http://agentcircle.gumroad.com/ Discord Global: https://discord.gg/d8SkCzKwnP FB Page Global: https://www.facebook.com/agentcircle/ FB Group Global: https://www.facebook.com/groups/aiagentcircle/ X: https://x.com/agent_circle YouTube: https://www.youtube.com/@agentcircle LinkedIn: https://www.linkedin.com/company/agentcircle

Automatically Send Square Summary Report for Yesterday's Sales via Microsoft Outlook What It Does This workflow automatically connects to the Square API and generates a daily sales summary report for all your Square locations. The report matches the figures displayed in Square Dashboard > Reports > Sales Summary. It's designed to run daily and pull the previous day's sales into a CSV file, which is then sent to a manager/finance team for analysis. This workflow builds on my previous template, which allows users to automatically pull data from the Square API into n8n for processing. (See here: https://n8n.io/workflows/6358) Prerequisites To use this workflow, you'll need: A Square API credential (configured as a Header Auth credential) A Microsoft Outlook credential How to Set Up Square Credentials: Go to Credentials > Create New Choose Header Auth Set the Name to Authorization Set the Value to your Square Access Token (e.g., Bearer <your-api-key>) How It Works Trigger: The workflow runs every day at 4:00 AM Fetch Locations: An HTTP request retrieves all Square locations linked to your account Fetch Orders: For each location, an HTTP request pulls completed orders for the specified report_date Filter Empty Locations: Locations with no sales are ignored Aggregate Sales Data: A Code node processes the order data and produces a summary identical to Square’s built-in Sales Summary report Create CSV File: A CSV file is created containing the relevant data Send Email: An email is sent to the chosen third party Example Use Cases Automatically send Square sales data to management to improve the quality of planning and scheduling decisions Automatically send data to an external third party, such as a landlord or agent, who is paid via commission Automatically send data to a bookkeeper for entry into QuickBooks How to Use Configure both HTTP Request nodes to use your Square API credential Set the workflow to Active so it runs automatically Enter the email address of the person you want to send the report to and update the message body If you want to remove the n8n attribution, you can do so in the last node Customization Options Add pagination to handle locations with more than 1,000 orders per day Instead of a daily summary, you can modify this workflow to produce a weekly summary once a week Why It's Useful This workflow saves time, reduces manual report pulling from Square, and enables smarter automation around sales data — whether for operations, finance, or performance monitoring.

Description This workflow is an n8n-based automation that allows users to download TikTok/Reels videos without watermarks simply by sending the video link through a Telegram Bot. It uses a Telegram Trigger to receive the link from the user, then makes an HTTP request to a third-party API (tiktokio.com) to process and retrieve the download link. The workflow filters the results to find the Download without watermark link, downloads the video in MP4 format, and sends it back to the user directly in their Telegram chat. Key features: Supports the best available video quality (bestvideo+bestaudio). Automatically removes watermarks. Instant response directly in Telegram chat. Fully automated — no manual downloads required. How It Works Telegram Trigger The user sends a TikTok or Reels link to the Telegram bot. The workflow captures and stores the link for processing. HTTP Request – MediaDL API The link is sent via POST method to https://mediadl.app/api/download. The API processes the link and returns video file data. Wait Delay The workflow waits a few seconds to ensure the API response is fully ready. Edit Fields Extracts the video file URL from the API response. Additional Wait Delay Adds a short pause to avoid connection errors during the download process. HTTP Request – Proxy Download Downloads the MP4 video file directly from the filtered URL. Send Video via Telegram The downloaded video is sent back to the user in their Telegram chat. How to Set Up Create & Configure a Telegram Bot Open Telegram and search for BotFather. Send /newbot → choose a name & username for your bot. Copy the Bot Token provided — you’ll need it in n8n. Prepare Your n8n Environment Log in to your n8n instance (self-hosted or n8n Cloud). Go to Credentials → create new Telegram API credentials using your Bot Token. Import the Workflow In n8n, click Import and select the PROJECT_DOWNLOAD_TIKTOK_REELS.json file. Configure the Telegram Nodes In the Telegram Trigger and Send Video nodes, connect your Telegram API credentials. Configure the HTTP Request Nodes Ensure the Download2 and HTTP Request nodes have the correct URL and headers (pre-configured for mediadl.app). Make sure the responseFormat is set to file in the final download node. Activate the Workflow Toggle Activate in the top right corner of n8n. Test by sending a TikTok or Reels link to your bot — you should receive the no-watermark video in return.

Description This n8n template demonstrates how to build an AI-powered Market Research Assistant using a multi-agent workflow. It helps you get a 360-degree view of a product idea or research topic by analysing: Customer insights and pain points Market size and macro/micro-economic trends Competitive landscape and alternatives The workflow mirrors how product managers and strategy teams conduct discovery — by breaking down research into parallel workstreams and then synthesizing insights into a single narrative. How it works Planner Agent
The main agent receives your research topic as input and defines: Research objective Key areas of focus (Customer, Market, Competition) Assumptions and constraints Parallel Research Agents
Based on the planner’s output, three specialist agents run in parallel: Customer Insights Agent
Researches public sources such as articles and forums to infer customer behaviour, pain points, and existing tools. Market Scan Agent
Analyses macro-economic and micro-economic trends, estimates TAM/SAM/SOM, and highlights key risks and assumptions. Competitor Insights Agent
Identifies existing competitors and substitutes and summarises how they are positioned in the market. Synthesis Agent
The outputs from all research agents are consolidated and analysed by a synthesis agent, which produces a market discovery memo. Final Output
The discovery memo is generated as a document and sent to your email. How to use Trigger the workflow via the chat message node. Provide your research topic or product idea, along with optional context such as target market. The workflow runs automatically and delivers a structured discovery memo to your inbox. Setup Steps API credentials for: Groq or OpenAI (LLM) Documentero (document generation) A configured Documentero template Gmail OAuth or email credentials for delivery of memo

Who’s it for Teams that start their day in Slack and want a concise, automated summary of yesterday’s emails—ops leads, PMs, founders, and anyone handling busy inboxes without writing code. What it does / How it works Runs every morning at 08:00 (cron 0 0 8 * * ), fetches all emails received *yesterday, and routes the result: if none were found, it posts a polite “no emails” notice; if emails exist, it aggregates them and asks an AI agent to produce a structured digest, then formats and posts to your chosen Slack channel. The flow uses **Gmail → If → Aggregate (Item Lists) → AI Agent (OpenRouter model with structured output) → Code (Slack formatter) → Slack. A set of sticky notes on the canvas explains each step and required inputs. How to set up Connect Gmail (OAuth2) and keep the default date window (yesterday → today at 00:00). Connect Slack (OAuth2) and select your target channel. Add OpenRouter credentials and pick a compact model (e.g., gpt-4o-mini). Keep the provided structured-output schema and formatter code. Adjust the schedule/timezone if needed (the fallback message includes an Asia/Tokyo example). Paste this description into the yellow sticky note at the top of the canvas. Requirements Gmail & Slack accounts with appropriate scopes OpenRouter API key stored in Credentials (no hard-coded keys) n8n Cloud or self-host with LangChain agent nodes enabled How to customize the workflow Narrow Gmail results with label/search filters (e.g., from:, subject:). Change the digest sections or tone in the AI Agent system prompt. Swap the model for cost/quality needs and tweak temperature/max tokens. Localize dates/timezones in the formatter code and Slack messages. Branch the output to email, Google Docs, or Sheets for archival. Security & publishing tips Rename all nodes clearly, do not hardcode API keys, remove real channel IDs/emails before sharing, and group end-user variables in a Set (Fields) node. Keep the sticky notes—they’re mandatory for reviewers.