# templates

name: AI-Powered Language and Coding Tutor with GPT-4 and Timed Telegram Messages
author_name: YungCEO
author_username: yungceo
categories: []
created_at: 2025-03-28T17:03:17.817Z
updated_at: 2025-03-28T17:03:17.817Z
url: https://n8n.io/workflows/3347
scraped_at: 2025-12-24 13:02:46
description:
  📚 MOTION TUTOR - AI-Powered Language Learning System

  Unlock the Power of Language with Personalized AI Learning!  
  MOTION TUTOR is a revolutionary AI-powered language learning platform that adapts to your progress and guides you from basic vocabulary to complex sentence structures. Whether you're a beginner or looking to refine your skills, MOTION TUTOR provides an engaging and structured learning experience.

  🎯 Key Features:
  ✅ AI-Powered Lessons:  
  Receive interactive, real-life conversation-based lessons generated by cutting-edge AI. MOTION TUTOR dynamically adapts content to match your learning pace.

  ✅ Personalized Progress Tracking:  
  Track your growth effortlessly! The system connects with Airtable to monitor your vocabulary, grammar, and situational language skills, ensuring lessons align with your goals.

  ✅ Memory Retention for Contextual Learning:  
  With built-in memory capabilities, MOTION TUTOR remembers where you left off and keeps the conversation flowing. No need to repeat or restart — pick up right where you left off!

  ✅ Real-Time Telegram Integration:  
  Learn on the go! Engage with MOTION TUTOR directly through Telegram and receive instant feedback and lesson suggestions based on your performance.

  ✅ Structured Learning Path:  
  From mastering essential vocabulary to crafting complex sentences, MOTION TUTOR guides you step-by-step with a well-defined roadmap.  
  Basic Vocabulary & Phrases  
  Basic Grammar Structures  
  Situational Vocabulary  
  Intermediate Grammar Concepts  
  Advanced Vocabulary  
  Complex Sentence Structures  

  ✅ Customized Role-Playing & Practice Scenarios:  
  Practice vocabulary and grammar in context by engaging in situational conversations that simulate real-life interactions.

  💡 How It Works:
  Message on Telegram: Send a message to initiate your learning session.
  AI Processes Input: MOTION TUTOR analyzes your request and customizes the next lesson.
  Context Retention: Continuation from previous sessions to ensure seamless learning.
  Track Progress: Updates and tracks progress in Airtable for personalized learning insights.
  Receive Feedback: AI-generated lessons and tips sent directly through Telegram.

  📡 Why Choose MOTION TUTOR?
  🎉 Engaging & Interactive Learning: Learn in a fun and practical way with interactive scenarios.  
  🚀 AI Adaptation for All Levels: Whether you're learning from scratch or advancing your fluency, MOTION TUTOR customizes content to suit your journey.  
  📝 Progress You Can Measure: Track your achievements and growth with structured Airtable data.  
  🔁 Never Lose Track: Memory-powered learning ensures you continue smoothly where you last paused.  

  🎁 Ideal For:
  Language learners seeking a personalized, guided learning experience.
  Professionals preparing for international business communication.
  Travelers aiming to learn practical vocabulary before their next adventure.
  Students looking to enhance their grammar and conversation skills.

  🚀 Get Started Today!
  Embrace the future of language learning with MOTION TUTOR and gain the confidence to engage in everyday conversations and complex dialogues effortlessly.  

  Ready to Speak with Confidence?  
  💬 Connect with MOTION TUTOR on Telegram and Start Learning Today!

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.airtableTool","n8n-nodes-base.airtableTool","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.telegram","n8n-nodes-base.telegramTrigger","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.airtable","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.airtableTool","n8n-nodes-base.airtableTool","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.airtable","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.airtableTool","n8n-nodes-base.airtableTool","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.airtable","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.telegram","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.airtableTool","n8n-nodes-base.airtableTool","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.airtable","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.airtableTool","n8n-nodes-base.airtableTool","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.airtable","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","n8n-nodes-base.telegramTrigger","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 4296 characters]


name: Automate Blog Content Creation with Notion MCP, DeepSeek AI, and WordPress
author_name: Dr. Firas
author_username: drfiras
categories: []
created_at: 2025-03-29T03:27:21.509Z
updated_at: 2025-03-29T03:27:21.509Z
url: https://n8n.io/workflows/3348
scraped_at: 2025-12-24 13:02:42
description:
  Who Is This For
  This workflow is ideal for content creators, bloggers, marketers, and professionals seeking to automate the creation and publication of SEO-optimized articles. It's particularly beneficial for those utilizing Notion for content management and WordPress for publishing.​

  What Problem Does This Workflow Solve
  Manually creating SEO-friendly articles is time-consuming and requires consistent effort. This workflow streamlines the entire process—from detecting updates in Notion to publishing on WordPress—by leveraging AI for content generation, thereby reducing the time and effort involved.​

  What This Workflow Does
  Monitor Notion Updates: Detects changes in a specified Notion database.​
  AI Content Generation: Utilizes an AI model to produce an SEO-optimized article based on Notion data.​
  Publish to WordPress: Automatically posts the generated article to a WordPress site.​
  Email Notification: Sends an email containing the article's title and URL.​

  Update Notion Database: Updates the corresponding entry in the Notion database with the article details.​

  Setup Guide
  Prerequisites
  WordPress account with API access.​
  API key for the AI model used.​
  Notion integration with the relevant database ID.​
  Credentials for the email service used (e.g., Gmail).​

  Community Node Requirement: This workflow utilizes the n8n-nodes-mcp community node, which is only compatible with self-hosted instances of n8n. For more information on installing and managing community nodes, refer to the n8n documentation.​
  n8n Docs

  Steps
  Import the workflow into your self-hosted n8n instance.​
  Install the required community node (n8n-nodes-mcp).​
  Configure API credentials for WordPress, the AI service, Notion, and the email service.​
  Define necessary variables, such as the notification email address and Notion database IDs.​
  Activate the workflow to automate the process.​

  How to Customize This Workflow
  AI Prompt: Adjust the prompt used for content generation to align with your preferred tone and style.​
  Article Structure: Modify the structure of the generated article by tweaking settings in the content generation node.​
  Notifications: Customize the content and recipients of the emails sent post-publication.​
  Notion Updates: Tailor the fields updated in Notion to suit your specific requirements.

nodes_used:
  ["n8n-nodes-base.notionTrigger","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.gmailTool","n8n-nodes-base.wordpressTool","n8n-nodes-mcp.mcpClientTool","n8n-nodes-mcp.mcpClientTool","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatDeepSeek"]

workflow_json_compressed:
  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


name: Telegram AI Bot-to-Human Handoff for Sales Calls
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2025-03-29T15:23:05.743Z
updated_at: 2025-03-29T15:23:05.743Z
url: https://n8n.io/workflows/3350
scraped_at: 2025-12-24 13:02:42
description:
  This n8n template demonstrates an approach to perform bot-to-human handoff using Human-in-the-loop functionality as a switch.

  In this experiment, we play with the idea of states we want our agent to be in which controls it's interacton with the user.
  First state** - the agent is onboarding the user by collecting their details for a sales inquiry. After which, they are handed-off / transferred to a human to continue the call.
  Second state** - the agent is essentially "deactivated" as further messages to the bot will not reach it. Instead, a canned response is given to the user. The human agent must "reactivate" the bot by completing the human-in-the-loop form and give a summary of their conversation with the user.
  Third state** - the agent is "reactivated" with context of the human-to-user conversation and is set to provide after sales assistance. An tool is made available to the agent to again delegate back to the human agent when requested.

  How it works
  This template uses telegram to handle the interaction between the user and the agent.
  Each user message is checked for a session state to ensure it is guided to the right stage of the conversation. For this, we can use Redis as a simple key-value store.
  When no state is set, the user is directed through an onboarding step to attain their details. Once complete, the agent will "transfer" the user to a human agent - technically, all this involves is an update to the session state and a message to another chat forwarding the user's details.
  During this "human" state, the agent cannot reply to the user and must wait until the human "transfers" the conversation back. The human can do this by replying to "human-in-the-loop" message with a summary of their conversation with the user. This session state now changes to "bot" and the context is implanted in the agent's memory so that the agent can respond to future questions.
  At this stage of the conversation, the agent is now expected to handle and help the user with after-sales questions. The user can at anytime request transfer back to the human agent, repeating the previous steps as necessary.

  How to use
  Plan your user journey! Here is a very basic example of a sales inquiry with at most 3 states. More thought should be developed when many more states are involved.
  You may want to better log and manage session states so no user is left in limbo. Try connecting the user and sessions to your CRM.
  Note, the Onboarding agent and After-Sales agent have separate chat memories. When adding more agents, it is recommend to continue having separate chat memories to help focus between states.

  Requirements
  Telegram for chatbot & interface
  Redis for session store and chat memory
  OpenAI for AI agent

  Customising this workflow
  Not using Telegram? This template works with Whatsapp and other services with equivalent functionality.

nodes_used:
  ["n8n-nodes-base.telegram","n8n-nodes-base.redis","n8n-nodes-base.redis","n8n-nodes-base.redis","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryRedisChat","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.telegramTrigger","n8n-nodes-base.telegram","n8n-nodes-base.switch","@n8n/n8n-nodes-langchain.informationExtractor","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.code","n8n-nodes-base.if","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.redis","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.memoryRedisChat","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.executeWorkflow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.memoryRedisChat","@n8n/n8n-nodes-langchain.memoryManager","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.memoryRedisChat","@n8n/n8n-nodes-langchain.memoryManager","n8n-nodes-base.telegram"]

workflow_json_compressed:
  [encoded data, 9616 characters]


name: Automated Discord Spam Moderation with AI and Human-in-the-Loop
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2025-03-29T15:35:52.637Z
updated_at: 2025-03-29T15:35:52.637Z
url: https://n8n.io/workflows/3351
scraped_at: 2025-12-24 13:02:45
description:
  This n8n template demonstrates how you can automate community moderation using human-in-the-loop functionality for Discord.

  The use-case is for detecting and dealing with spam messages in a predefined and consistent way. Human-in-the-loop allows for a balance between overly aggressive bots and time and effort from the moderation team.

  How it works
  A scheduled trigger is used to scan the most recent messages in a Discord Channel. Messages are tagged via the "Remove Duplicates" node so they don't get processed again in the future.
  Messages are grouped by user to allow for minimising of number of notifications sent.
  An AI text classifier node is then used to detect for spam in each user's message.
  When detected, a notification is sent to a moderation channel using the Send-and-wait mode for Discord. This notification comes with an n8n form and dropdown list of predefined actions to take in dealing with the spam messages. Once sent the workflow waits until a response is received.
  Once a moderator selects an action, the workflow continues and carries out a predefined moderation action.

  How to use
  Depending on how busy your community is and subject to spammers, you may need to increase the scheduled interval.
  Add as many or few moderation actions as required.
  Remember to activate the workflow to  get it started.

  Requirements
  Discord channel for messages to moderate
  OpenAI for text classification

  Customising this template
  It is possible to cover multiple channels. Add as many as your community needs.
  Not using Discord. The template can also work in slack or other services which offer the same bot functionality.

nodes_used:
  ["@n8n/n8n-nodes-langchain.textClassifier","n8n-nodes-base.discord","n8n-nodes-base.removeDuplicates","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.discord","n8n-nodes-base.discord","n8n-nodes-base.code","n8n-nodes-base.splitInBatches","n8n-nodes-base.splitOut","n8n-nodes-base.code","n8n-nodes-base.discord","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.merge","n8n-nodes-base.filter","n8n-nodes-base.if","n8n-nodes-base.code","n8n-nodes-base.discord","n8n-nodes-base.switch","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.executeWorkflow","n8n-nodes-base.stickyNote","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.noOp","n8n-nodes-base.scheduleTrigger"]

workflow_json_compressed:
  [encoded data, 7544 characters]


name: Health Check Websites with Google Sheets & Telegram Alerts
author_name: Hugues Stock
author_username: hpstock
categories: []
created_at: 2025-03-29T15:45:43.053Z
updated_at: 2025-03-29T15:45:43.053Z
url: https://n8n.io/workflows/3352
scraped_at: 2025-12-24 13:02:45
description:
  What does this template do ? 

  This workflow performs scheduled health checks on a list of URLs stored in a Google Sheet. Every X minutes, it retrieves the URLs, sends HTTP requests to check their availability, and notifies a Telegram chat if any request fails.

  You can easily replace Telegram with any other notification service supported by n8n, such as Slack, Discord, Email, or Microsoft Teams.

  Use Case
  Ideal for monitoring internal or external services, ensuring uptime and responsiveness. Integrate with your preferred messaging platform for real-time failure notifications—without setting up complex infrastructure.

  What the Workflow Does
  ⏰ Triggers at regular intervals using Schedule Trigger  
  📄 Reads URLs from a Google Sheet  
  🌐 Sends HTTP requests to check URL health  
  🚨 Sends failure alerts (including error code and URL) to Telegram (or any service of your choice)  

  Apps & Services Used
  Google Sheets  
  Telegram (or alternative notification service)  
  HTTP Request  

  Pre-requisites
  Telegram bot and chat ID (if using Telegram)  
  Connected Google Sheet with a URLS column  (template)

  Customization Tips
  Adjust the schedule interval in the trigger node  
  Replace Telegram with Slack, Email, Discord, or any other notification node  
  Customize alert messages with more context or formatting  
  Add filters, retries, or logging as needed  

nodes_used:
  ["n8n-nodes-base.noOp","n8n-nodes-base.telegram","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.googleSheets","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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


name: Summarize Microsoft 365 Outage Alerts with ChatGPT and Send to Slack
author_name: Luke
author_username: ozskywalker
categories: []
created_at: 2025-03-29T16:18:13.432Z
updated_at: 2025-03-29T16:18:13.432Z
url: https://n8n.io/workflows/3353
scraped_at: 2025-12-24 13:02:45
description:
  Built this for a dedicated Slack outage-notifications channel — works well on both desktop and mobile.

  This is for:

  IT Administrators & small MSPs looking to streamline M365 alerts from one or multiple mailboxes into a single or specific Slack channels
  IT Admins who prefer ChatOps over management-by-email

  What does it do

  Scans for M365 outage alerts emails (every 1 min)
  Checks if it impacts a specific user region (if the alert calls it out, countries have to be manually set)
  Summarizes the incident using OpenAI o4-mini (cheap model - or you can swap for local Ollama)
  Sends a Slack Block to your outage channel with incident link (can be extended)
  Deletes the original alert email after successful delivery

  Credentials

  Outlook:

  Create an Outlook credential (OAuth2.0) to point to the mailbox (regular or shared) where M365 service alerts will be received

  Slack:

  Create a Slack bot credential with access to the slack channel you want updates posted to

  OpenAI:

  Create a OpenAI credential that has access to the GPT-4O-MINI model.
  Recommend you use projects in OpenAI so that you may set a per-project-budget and not impact other projects.

  Review this OpenAI documentation for more info on managing Projects in the API portal. Expect this to consume no more than 1-2 cents per month on average.

  Setup

  Download & import the workflow
  Modify the first Outlook block (Check for 365 Service Alert) to use the Outlook credential
  Modify the OpenAI block's system prompt to call out the countries your users reside in
     ie. "- Assume the organization has users primarily in the U.S. and Australia. If those regions are affected, state: "Your users may have been affected." Otherwise, add: "No impact expected for your user base."" ← swap U.S. & Australia for desired countries
  Modify the Slack block (Post outage to Slack) to specify the channel updates will be posted to

   Sample Slack Output

   Workflow Diagram

nodes_used:
  ["n8n-nodes-base.microsoftOutlookTrigger","n8n-nodes-base.slack","n8n-nodes-base.microsoftOutlook","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.code","n8n-nodes-base.code"]

workflow_json_compressed:
  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


name: Automatically document and backup N8N workflows
author_name: Luke
author_username: ozskywalker
categories: []
created_at: 2025-03-29T17:16:27.848Z
updated_at: 2025-03-29T17:16:27.848Z
url: https://n8n.io/workflows/3354
scraped_at: 2025-12-24 13:02:45
description:
  Automatically backs up your workflows to Github and generates documentation in a Notion database.

  Weekly run, uses the "internal-infra" tag to look for new or recently modified workflows
  Uses a Notion database page to hold the workflow summary, last updated date, and a link to the workflow
  Uses OpenAI's 4o-mini to generate a summarization of what the workflow does
  Stores a backup of the workflow in GitHub (recommend a private repo)
  Sends notification to Slack channel for new or updated workflows

  Who is this for

  Anyone seeking backup of their most important workflows
  Anyone seeking version control for their most important workflows

  Credentials required

  N8N: You will need an N8N credential created so the workflow can query the N8N instance to find all active workflows with the "internal-infra" tag
  Notion: You will need an Notion credential created
  OpenAI: You will need an OpenAI credential, unless you intend on rewiring this with your AI of choice (ollama, openrouter, etc.)
  GitHub: You will need an GitHub credential
  Slack: You will require an Slack credential, recommend a Bot / access token configuration

  Setup

  Notion

  Create a database with the following columns. Column type is specified in [type].
    Workflow Name [text]
    isActive (dev) [checkbox]
    Error workflow setup [checkbox]
    AI Summary [text]
    Record last update [date/time]
    URL (dev) [text/url]
    Workflow created at [date/time]
    Workflow updated at [date/time]

  Slack

  Create a channel for updates to be posted into

  Github

  Create a private repo for your workflows to be exported into

  N8N

  Download & install the template
  Configure the blocks to use your N8N, Notion, OpenAI & Slack credentials for your own
  Edit the "Set Fields" block and change the URL to that of your N8N instance (cloud or self-hosted)
  Edit the "Add to Notion" action and specify the Database page you wish to update
  Edit the Slack actions to specify the Channel you want slack notifications posted to
  Edit the GitHub actions to specify the Repository Owner & Repository Name


  Sample output in Notion

  Workflow diagram

nodes_used:
  ["n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.notion","n8n-nodes-base.notion","n8n-nodes-base.slack","n8n-nodes-base.slack","n8n-nodes-base.slack","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.github","n8n-nodes-base.github","n8n-nodes-base.slack","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.if","n8n-nodes-base.n8n","n8n-nodes-base.if"]

workflow_json_compressed:
  [encoded data, 5540 characters]


name: Automated Interview Scheduling with GPT-4o and Google Calendar Chat Bot
author_name: Robert Breen
author_username: rbreen
categories: []
created_at: 2025-03-30T03:22:28.201Z
updated_at: 2025-03-30T03:22:28.201Z
url: https://n8n.io/workflows/3363
scraped_at: 2025-12-24 13:02:43
description:
  ✨ Overview

  This workflow allows candidates to schedule interviews through a conversational AI assistant. It integrates with your Google Calendar to check for existing events and generates a list of available 30-minute weekday slots between 9 AM and 5 PM Eastern Time. Once the candidate selects a suitable time and provides their contact information, the AI bot automatically books the meeting on your calendar and confirms the appointment.

  ⚡ Prerequisites

  To use this workflow, you need an OpenAI account with access to the GPT-4o model, a Google account with a calendar that can be accessed through the Google Calendar API, and an active instance of n8n—either self-hosted or via n8n cloud. Within n8n, you must have two credential configurations ready: one for Google Calendar using OAuth2 authentication, and another for your OpenAI API key.

  🔐 API Credentials Setup

  For Google Calendar, go to the Google Cloud Console and create a new project. Enable the Google Calendar API, then create OAuth2 credentials by selecting “Web Application” as the application type. Add http://localhost:5678/rest/oauth2-credential/callback as the redirect URI if using local n8n. After that, go to n8n, navigate to the Credentials section, and create a new Google Calendar OAuth2 credential using your account. For OpenAI, visit platform.openai.com to retrieve your API key. Then go to the n8n Credentials page, create a new credential for OpenAI, paste your key, and name it for reference.

  🔧 How to Make This Workflow Yours

  To customize the workflow for your use, start by replacing all instances of the calendar email rbreen.ynteractive@gmail.com with your own Google Calendar email. This email is referenced in multiple places, including Google Calendar nodes and the ToolWorkflow JSON for the node named "Run Get Availability." Also update any instances where the Google Calendar credential is labeled as Google Calendar account to match your own credential name within n8n. Do the same for the OpenAI credential label, replacing OpenAi account with the name of your own credential.

  Next, go to the node labeled Candidate Chat and copy the webhook URL. This is the public chat interface where candidates will engage with the bot—share this URL with them through email, your website, or anywhere you want to allow access. Optionally, you can also tweak the system message in the Interview Scheduler node to modify the tone, language, or logic used during conversations. If you want to add branding, update the title, subtitle, and inputPlaceholder in the Candidate Chat node, and consider modifying the final confirmation message in Final Response to User to reflect your brand voice. You can also update the business rules such as time zone, working hours, or default duration by editing the logic in the Generate 30 Minute Timeslots code node.

  🧩 Workflow Explanation

  This workflow begins with the Candidate Chat node, which triggers when a user visits the public chat URL. The Interview Scheduler node acts as an AI agent, guiding the user through providing their email, phone number, and preferred interview time. It checks availability using the Run Get Availability tool, which in turn reads your calendar and compares it with generated free time slots from the Generate 30 Minute Timeslots node. The check day names tool helps the AI interpret natural language date expressions like “next Tuesday.”

  The schedule is only populated with 30-minute weekday slots from 9 AM to 5 PM Eastern Time, and no events are scheduled if they overlap with existing ones. When a suitable time is confirmed, the AI formats the result into structured JSON, creates an event on your Google Calendar, and sends a confirmation back to the user with all relevant meeting details.

  🚀 Deployment Steps

  To deploy the interview scheduler, import the provided workflow JSON into your n8n instance. Update the Google Calendar email, OpenAI and Google credential labels, system prompts, and branding as needed. Test the connections to ensure the API credentials are working correctly. Once everything is configured, copy and share the public chat URL from the Candidate Chat node. When candidates engage with the chat, the workflow will walk them through the interview booking process, check your availability, and finalize the booking automatically.

  💡 Additional Tips

  By default, the workflow avoids scheduling interviews on weekends and outside of 9–5 EST. Each interview lasts exactly 30 minutes, and overlapping with existing events is prevented. The assistant does not reveal details about other meetings. You can customize every part of this workflow to fit your use case, including subworkflows like Get Availability and check day names, or even white-label it for client use. This workflow is ready to become your AI-powered interview scheduling assistant.

  🤝 Connect with Me

  Description  
  I’m Robert Breen, founder of Ynteractive — a consulting firm that helps businesses automate operations using n8n, AI agents, and custom workflows. I’ve helped clients build everything from intelligent chatbots to complex sales automations, and I’m always excited to collaborate or support new projects.

  If you found this workflow helpful or want to talk through an idea, I’d love to hear from you.

  Links  
  🌐 Website: https://www.ynteractive.com  
  📺 YouTube: @ynteractivetraining  
  💼 LinkedIn: https://www.linkedin.com/in/robert-breen  
  📬 Email: rbreen@ynteractive.com

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.if","n8n-nodes-base.noOp","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.googleCalendar","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.googleCalendar","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.merge","n8n-nodes-base.if","n8n-nodes-base.code","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 11112 characters]


name: Convert Parquet, Feather, ORC & Avro Files with ParquetReader
author_name: ParquetReader
author_username: parquetreader
categories: []
created_at: 2025-03-30T09:17:15.940Z
updated_at: 2025-03-30T09:17:15.940Z
url: https://n8n.io/workflows/3365
scraped_at: 2025-12-24 13:02:47
description:
  📄 Convert Parquet, Feather, ORC & Avro Files with ParquetReader

  This workflow allows you to upload and inspect Parquet, Feather, ORC, or Avro files via the ParquetReader API. It instantly returns a structured JSON preview of your data — including rows, schema, and metadata — without needing to write any custom code.

  ✅ Perfect For
  Validating schema and structure before syncing or transformation
  Previewing raw columnar files on the fly
  Automating QA, ETL, or CI/CD workflows
  Converting Parquet, Avro, Feather, or ORC to JSON

  ⚙️ Use Cases
  Catch schema mismatches before pipeline runs
  Automate column audits in incoming data files
  Enrich metadata catalogs with real-time schema detection
  Integrate file validation into automated workflows

  🚀 How to Use This Workflow

  📥 Trigger via File Upload

  You can trigger this flow by sending a POST request with a file using curl, Postman, or from another n8n flow.

  🔧 Example (via curl):
  curl -X POST http://localhost:5678/webhook-test/convert \
  -F "file=@converted.parquet"
  &gt; Replace converted.parquet with your local file path. You can also send Avro, ORC or Feather files.

  🔁 Reuse from Other Flows
  You can reuse this flow by calling the webhook from another n8n workflow using an HTTP Request node.  
  Make sure to send the file as form-data with the field name file.

  🔍 What This Flow Does:
  Receives the uploaded file via webhook (file)
  Sends it to https://api.parquetreader.com/parquet as multipart/form-data (field name: file)
  Receives parsed data (rows), schema, and metadata in JSON format

  🧪 Example JSON Response from this flow
  {
    "data": [
      {
        "full_name": "Pamela Cabrera",
        "email": "bobbyharrison@example.net",
        "age": "24",
        "active": "True",
        "latitude": "-36.1577385",
        "longitude": "63.014954",
        "company": "Carter, Shaw and Parks",
        "country": "Honduras"
      }
    ],
    "meta_data": {
      "created_by": "pyarrow",
      "num_columns": 21,
      "num_rows": 10,
      "serialized_size": 7598,
      "format_version": "0.12"
    },
    "schema": [
      { "column_name": "full_name", "column_type": "string" },
      { "column_name": "email", "column_type": "string" },
      { "column_name": "age", "column_type": "int64" },
      { "column_name": "active", "column_type": "bool" },
      { "column_name": "latitude", "column_type": "double" },
      { "column_name": "longitude", "column_type": "double" },
      { "column_name": "company", "column_type": "string" },
      { "column_name": "country", "column_type": "string" }
    ]
  }

  🔐 API Info
  Authentication: None required
  Supported formats: .parquet, .avro, .orc, .feather
  Free usage: No signup needed; API is currently open to the public
  Limits: Usage and file size limits may apply in the future (TBD)

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.webhook","n8n-nodes-base.stickyNote","n8n-nodes-base.code"]

workflow_json_compressed:
  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


name: Webpage Change Detection & Alerts with Google Suite and Hash Tracking
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2025-03-30T11:27:55.561Z
updated_at: 2025-03-30T11:27:55.561Z
url: https://n8n.io/workflows/3366
scraped_at: 2025-12-24 13:02:45
description:
  This n8n template can monitor and detect changes to a webpage's contents and notify you only when a change occurs.

  Great to keep an eye on and track publicly available documents such as  company TOS, government policy or competitor pages.

  How it works
  A scheduled trigger is used so we can run everyday to automate this process.
  A website page is then fetched with the HTTP request node and the contents we want to track are extracted using the HTML node.
  To detect changes, we generate a hash on the contents with the cryptography node and compare it with previously seen hashes using the "remove duplicates" node. If the hash was seen before, the workflow stops here.
  Finally, when new changes are detected a copy of the contents are uploaded to Google Drive and a logged into a Google sheet. A notification email can also be sent if action is required.

  How to use
  Update the URL you want to track in the node named "variables" and ensure the HTML node has updated selectors to get the content you want.
  Ensure the timezone is set correctly when using the Scheduled Trigger node.

  Requirements
  Google Sheets, Drive and Gmail for storing and notifying about changes.
  Webpages should ideally be publicly accessible. If not, you may need to switch the HTTP request node with a webscraping service.

  Customising this workflow
  Not using Google? Easier swap to other Service providers such as Miscrosoft365.
  Need more URLs? Try modifing the variables node to accept multiple URLs though the HTML node will need to be customised.

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.crypto","n8n-nodes-base.gmail","n8n-nodes-base.markdown","n8n-nodes-base.set","n8n-nodes-base.removeDuplicates","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.html","n8n-nodes-base.stickyNote","n8n-nodes-base.googleDrive","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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


name: 🚀 Instagram Reels Automation - Turn YouTube Videos into Viral Instagram Reels ✨
author_name: Matt F.
author_username: lemolex
categories: []
created_at: 2025-03-30T12:26:42.247Z
updated_at: 2025-03-30T12:26:42.247Z
url: https://n8n.io/workflows/3367
scraped_at: 2025-12-24 13:02:45
description:
  Automatically Create and Post Instagram Reels from Podcasts and Other Videos Using AI & Spikes Studio (Simplified)

  Transform podcasts and other videos into engaging, fully optimized Instagram Reels with this all-in-one automation solution—designed for content creators who love repurposing dynamic clips.

  Overview

  This automation transforms YouTube videos into engaging, professional-grade Instagram Reels using AI-powered editing. Perfect for content creators, podcasters, marketers, and brands, the workflow extracts your most captivating moments, enhances them with dynamic captions, transitions, and effects, and auto-schedules them for Instagram—all without manual editing or uploading.

  With just a YouTube link, this template automates the entire lifecycle of short-form content creation—from input to upload—so you can repurpose long-form videos into viral clips in minutes.

  Key Features

  Insights-Driven Highlight Extraction:**  
    Automatically analyzes engagement metrics to select the most resonant moments from podcasts or other creators’ videos, ensuring each clip connects with your audience.

  AI-Powered Editing:**  
    Utilizes professional-grade editing—adding captions, effects, transitions, and more—to produce visually stunning, high-impact reels.

  Auto-Generated Metadata:** 
    Effortlessly creates dynamic titles, descriptions, and hashtags to optimize your clips for immediate upload on Instagram.

  Customizable Scheduling:**  
    Allows you to set upload intervals that fit your content strategy, ensuring a consistent flow of engaging reels on your profile.

  Seamless Instagram Integration:**  
    Directly connects with Instagram’s API, streamlining the posting process so your profile stays active and engaging with minimal effort.

  Customization Options

  🎨 Style & Branding: Modify the Spikes Studio API request to include your custom branding, text overlays, or templates.
  🎥 Clips Length: Previously adjust the length of each clip generated. 
  🕒 Scheduling Delay: Adjust the time interval to control how often posts are made.
  🔁 Multi-Platform Support: Add additional nodes to also post to TikTok, Facebook, or LinkedIn.

  Step-by-Step Setup Instructions

  Prepare Prerequisites:  
     Ensure you have a Spikes Studio account (free at spikes.studio), a YouTube video URL, an Upload-Post account (free at Upload-Post), and a working n8n instance.

  Configure APIs:  
     Spikes Studio: Obtain your API key and connect credentials.
     Upload-Post: Obtain your API key and connect credentials and customize the request.

  Assemble the Workflow:  
     Trigger: Trigger the workflow with a YouTube video URL and desired clip length.  
     Output: Auto-upload the formatted Reels to Instagram on a scheduled basis.

  Designed specifically for content creators repurposing YouTube videos—and requiring only a Spikes Studio account (it’s Free) for full functionality—this simplified automation template streamlines the conversion of long-form content into bite-sized, shareable clips. Elevate your content strategy, maximize audience engagement, and reclaim valuable time with a solution built to drive your channel’s growth.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.formTrigger","n8n-nodes-base.wait","n8n-nodes-base.wait","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.code","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.if"]

workflow_json_compressed:
  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


name: Automate Product Training & Customer Support via WhatsApp, GPT-4 & Google Sheets
author_name: Dr. Firas
author_username: drfiras
categories: []
created_at: 2025-03-30T16:46:52.017Z
updated_at: 2025-03-30T16:46:52.017Z
url: https://n8n.io/workflows/3379
scraped_at: 2025-12-24 13:02:41
description:
  WhatsApp AI Agent: Auto-Train Product Data & Handle Customer Support

  Who Is This For

  This workflow is ideal for eCommerce founders, product managers, customer support teams, and automation builders who rely on WhatsApp to manage product information and interact with clients.

  It’s perfect for businesses that want to automate product data entry and support responses directly from WhatsApp messages using GPT-4 and Google Sheets.

  What Problem Does This Workflow Solve

  Manual Product Data Entry**: Collecting and organizing product data from links is tedious and error-prone.  
  Slow Customer Response Times**: Responding to client questions manually leads to delays and inconsistent support.  
  No Logging System for Issues**: Without automation, support issues often go undocumented, making it harder to learn and improve.

  What This Workflow Does

  Step 1 – Incoming Message Detection
  Listens for incoming messages via WhatsApp.
  If the message starts with train:, it routes to the product training process.
  Otherwise, it routes to the customer support assistant.

  Step 2 – Product Data Training
  Extracts URL** from the message using a regex script.
  Fetches HTML content** from the URL.
  Cleans HTML data** to extract readable product description.
  Saves raw data** (URL + description) into Google Sheets.
  Uses GPT-4** to enhance product data:  
    → Name, price (one-time or subscription), topic, and FAQs.  
  Updates the product row** in Google Sheets with structured information.

  Step 3 – Customer Support Flow
  Analyzes user messages with GPT-4 to understand the request or issue.
  Looks up relevant product info in Google Sheets.
  Detects potential problems (e.g. payment, login, delivery).
  Suggests an appropriate solution.
  Logs the problem, solution, and category to the Customer Issues sheet.
  Sends a response back to the client via WhatsApp.

  Step 4 – Client Response
  Sends the AI-generated response to the client via WhatsApp.
  Keeps the communication fast, clear, and professional.

  Setup Guide

  Prerequisites

  WhatsApp Business API access**
  OpenAI API Key**
  Google Account** with Google Sheets access
  A hosted instance of n8n (Cloud or self-hosted)

  Setup Steps

  Import the Workflow into your n8n instance.
  Connect your credentials for WhatsApp, OpenAI, and Google Sheets.
  Customize Google Sheet IDs and names as needed.
  Test by sending a train: message or a regular customer message to WhatsApp.
  Activate the workflow to make it live.

  How to Customize This Workflow

  Edit AI prompts** to reflect your product type, language style, or tone.
  Change the trigger keyword** (e.g. from train: to add: or anything else).
  Add integrations** like Notion, Airtable, or CRM tools.
  Expand the Sheets structure** with more product fields (e.g. stock status, image link).
  Add notifications** to Slack or email after product updates or issue logging.

  📄 Documentation: Notion Guide

  Need help customizing?
  Contact me for consulting and support : Linkedin / Youtube

nodes_used:
  ["n8n-nodes-base.whatsApp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.whatsAppTrigger","n8n-nodes-base.switch","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.googleSheetsTool","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.googleSheetsTool"]

workflow_json_compressed:
  [encoded data, 7744 characters]


name: Transcribe YouTube Videos with AI Enhancement via Chat Interface
author_name: ivn
author_username: jnr
categories: []
created_at: 2025-03-30T22:40:55.999Z
updated_at: 2025-03-30T22:40:55.999Z
url: https://n8n.io/workflows/3381
scraped_at: 2025-12-24 13:02:44
description:
  About:

  This workflow automates the transcription of YouTube videos by processing a video URL provided via a chat message. Designed for users who need quick access to video content in text form, this workflow ensures a seamless experience for transcribing videos on demand, regardless of the topic.

  Who is this for?

  This workflow is designed for individuals who need quick and accurate transcriptions of YouTube videos without watching them in full. It is particularly useful for:

  Students who need text-based notes from educational videos.

  Researchers looking to extract information from lectures or discussions.

  Professionals who prefer reading over watching videos.

  Casual users who want an efficient way to summarize video content.

  What problem is this workflow solving?

  Manually transcribing YouTube videos is time-consuming and prone to errors. Watching long videos just to extract key information is inefficient. This workflow automates transcription, allowing users to quickly convert video content into text. Use cases include:

  Summarizing lectures or webinars.

  Extracting insights from interviews and discussions.

  Creating searchable text from video content.

  Generating reference material without watching entire videos.

  What This Workflow Does?

  This workflow automates the transcription of YouTube videos by:

  Accepting Input: User provide a YouTube video URL through a chat message.

  Processing the Video: It utilizes an external transcription service to retrieve the full transcript of the YouTube video from the provided URL.

  Enhancing Output: An AI model (OpenAI) refines the transcription for accuracy and readability.

  Delivering Results: The final text transcript is returned to the user via the chat interface.

  Setup:

  Install n8n: Ensure you have n8n installed and running.

  Import the Workflow: Copy the JSON workflow file into your n8n instance.

  Configure API Keys:

  	Set up your Supadata (Supadata) API key for transcription.

  	Configure the OpenAI (OpenAI) API key for additional processing.

  Run the Workflow: Provide a YouTube video URL and receive a transcription in response.

  How to customize this workflow to your needs:

  The workflow is flexible and can be tailored to suit specific requirements. Here are some customization ideas:

  Language Support:** Adjust the transcription language in both the HTTP Request and OpenAI nodes to support transcriptions in different languages (e.g., French, German).

  Integrate with Other Services:** Store transcriptions in a database, send them via email, or connect with a document management system.

  Notification:** Add a notification node (e.g., email or Slack) to alert you when the transcription is complete, especially for long videos.

  Quality Check:** Integrate an additional AI step to summarize or highlight key points in the transcript for quicker insights.

  This workflow is designed to be scalable, efficient, and adaptable to various transcription needs.

  Limitations

  Video Length Limitation:** Very long videos may not have a complete transcription due to constraints in processing capacity or service limitations.

  Transcription Dependency:** The accuracy of the transcription relies entirely on the presence of video captions or subtitles. If a video lacks these, no transcription will be generated.

  Access Restrictions:** Private or restricted YouTube videos may not be accessible for transcription due to permission limitations.

  Processing Time:** The time required to process a video can vary significantly, especially for longer videos, depending on the transcription service and server resources.

  Regional Restrictions:** Some YouTube videos may have geographic or regional access limitations, which could prevent the workflow from retrieving the content for transcription.

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.openAi"]

workflow_json_compressed:
  [encoded data, 4852 characters]


name: Populate Retell Dynamic Variables with Google Sheets Data for Call Handling
author_name: Agent Studio
author_username: agentstudio
categories: []
created_at: 2025-03-31T14:40:28.707Z
updated_at: 2025-03-31T14:40:28.707Z
url: https://n8n.io/workflows/3385
scraped_at: 2025-12-24 13:02:46
description:
  Overview
  This workflow provides Retell agent builders with a simple way to populate dynamic variables using n8n.
  The workflow fetches user information from a Google Sheet based on the phone number and sends it back to Retell.
  It is based on Retell's Inbound Webhook Call.
  Retell is a service that lets you create Voice Agents that handle voice calls simply, based on a prompt or using a conversational flow builder.

  Who is it for
  For builders of Retell's Voice Agents who want to make their agents more personalized.

  Prerequisites
  Have a Retell AI Account
  Create a Retell agent
  Purchase a phone number and associate it with your agent
  Create a Google Sheets - for example, make a copy of this one.
  Your Google Sheet must have at least one column with the phone number. The remaining columns will be used to populate your Retell agent’s dynamic variables.
  All fields are returned as strings to Retell (variables are replaced as text)

  How it works
  The webhook call is received from Retell. We filter the call using their whitelisted IP address.
  It extracts data from the webhook call and uses it to retrieve the user from Google Sheets.
  It formats the data in the response to match Retell's expected format.
  Retell uses this data to replace dynamic variables in the prompts.

  How to use it
  See the description for screenshots!
  Set the webhook name (keep it as POST).
  Copy the Webhook URL (e.g., https://your-instance.app.n8n.cloud/webhook/retell-dynamic-variables) and paste it into Retell's interface. Navigate to "Phone Numbers", click on the phone number, and enable "Add an inbound webhook".


  In your prompt (e.g., "welcome message"), use the variable with this syntax: {{variable_name}} (see Retell's documentation).


  These variables will be dynamically replaced by the data in your Google Sheet.

  Notes
  In Google Sheets, the phone number must start with '+.
  Phone numbers must be formatted like the example: with the +, extension, and no spaces.
  You can use any database—just replace Google Sheets with your own, making sure to keep the phone number formatting consistent.

  👉 Reach out to us if you're interested in analysing your Retell Agent conversations.

nodes_used:
  ["n8n-nodes-base.webhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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


name: Automate Docker Container Updates with Telegram Approval System
author_name: Jaber Zare
author_username: j4b3r
categories: []
created_at: 2025-03-31T15:35:34.305Z
updated_at: 2025-03-31T15:35:34.305Z
url: https://n8n.io/workflows/3386
scraped_at: 2025-12-24 13:02:44
description:
  Who is this for?
  This workflow is for DevOps engineers, system administrators, and Docker users who want to automate the process of checking for updates, verifying them, and applying updates to their Docker-based deployment in a controlled manner.
  For example, this workflow is used to update the n8n Docker image.

  What does this workflow do?
  Fetches the latest Docker image version from GitHub.
  Compares it with the currently running version on the server.
  Sends a Telegram message requesting confirmation if an update is available.
  If confirmed, pulls the latest n8n Docker image, updates the container, and restarts it.
  Sends a Telegram message confirming the update is completed.
  Schedules automatic checks, Uses a cron trigger in n8n to check for updates at regular intervals.

  Setup
  Ensure n8n is installed and running in a Docker container.
  Create a Telegram bot using BotFather.
  Set up Telegram credentials. 
  Set up SSH credentials (ensure the SSH user has sudo access).
  Obtain the bot token and chat ID.
  Set the Default variable node
     telegram-id (You can find it by messaging @get_id_bot).
     n8n-container-name (Specify the name of the n8n container.)
     working-directory (The directory where your docker-compose.yml is).
   You can use a manual trigger or a schedule trigger to update (for n8n, I use Cron every 3 days to check for a new version).

  How to customize this workflow
  Change the update mechanism: Modify the Docker commands if using a different container runtime or orchestration tool.
  Modify the confirmation flow: Add extra validation steps, such as checking for breaking changes before updating.
  Use a different notification method: Replace Telegram with Slack, email, or another notification service.

nodes_used:
  ["n8n-nodes-base.ssh","n8n-nodes-base.ssh","n8n-nodes-base.ssh","n8n-nodes-base.manualTrigger","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.ssh","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.merge","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.telegram"]

workflow_json_compressed:
  [encoded data, 4880 characters]


name: Transcribe audio files with Google Gemini and Telegram
author_name: Solomon
author_username: solomon
categories: []
created_at: 2025-03-31T17:32:21.750Z
updated_at: 2025-03-31T17:32:21.750Z
url: https://n8n.io/workflows/3388
scraped_at: 2025-12-24 13:02:44
description:
  Transcribe audio messages from Telegram using Google Gemini for free.

  Send an audio file to your Telegram bot and get a full transcription using Gemini’s free model.

  Who is this template for?

  Anyone who needs quick and accurate transcriptions of audio messages.

  Perfect for:

  Creators and podcasters
  Coaches or educators
  People who receive a lot of audio on Telegram

  You can also adapt this workflow to transcribe audio from other sources, like WhatsApp, Google Drive, or direct uploads.

  How it works

  You send an audio file to your Telegram bot
  n8n downloads the file and converts it to text using Google Gemini
  The transcript is sent back to you on Telegram

  How to set up

  You’ll just need:

  A Telegram bot token
  A Google Gemini API key (free tier supported)

  The rest works out by itself.

  Check out my other templates

  👉 https://n8n.io/creators/solomon/

nodes_used:
  ["n8n-nodes-base.switch","n8n-nodes-base.telegram","n8n-nodes-base.telegramTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.merge","n8n-nodes-base.httpRequest","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.readWriteFile","n8n-nodes-base.httpRequest","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.webhook","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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


name: Stripe Payment Order Sync – Auto Retrieve Customer & Product Purchased
author_name: Matt F.
author_username: lemolex
nodes_used: ["n8n-nodes-base.stripeTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.set"]
categories: []
created_at: 2025-03-31T20:51:21.991Z
updated_at: 2025-03-31T20:51:21.991Z
url: https://n8n.io/workflows/3391
scraped_at: 2025-12-24 13:02:45
description:
  Overview

  This automation template is designed to streamline your payment processing by automatically triggering upon a successful Stripe payment. The workflow retrieves the complete payment session and filters the information to display only the customer name, customer email, and the purchased product details. This template is perfect for quickly integrating Stripe transactions into your inventory management, CRM, or notification systems.

  Step-by-Step Setup Instructions

  Stripe Account Configuration:
     Ensure you have an active Stripe account.
     Connect your Stripe Credentials.
    
  Retrieve Product and Customer Data:
     Utilize Stripe’s API within the automation to fetch the purchased product details.
     Retrieve customer information such as: email and full name.

  Integration and Response:
     Map the retrieved data to your desired format.
     Trigger subsequent nodes or actions such as sending a confirmation email, updating a CRM system, or logging the transaction.

  Pre-Conditions and Requirements

  Stripe Account:** A valid Stripe account with access to API keys and webhook configurations.
  API Keys:** Ensure you have your Stripe secret and publishable keys ready.

  Customization Guidance

  Data Mapping:** Customize the filtering node to match your specific data schema or to include additional data fields if needed.
  Additional Actions:** Integrate further nodes to handle post-payment actions like sending SMS notifications, updating order statuses, or generating invoices.

  Enjoy seamless integration and enhanced order management with this automation template!

workflow_json_compressed:
  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


name: Create and Join Call Sessions with Plivo and UltraVox AI Voice Assistant
author_name: Yohita
author_username: yohita
categories: []
created_at: 2025-04-01T04:12:57.053Z
updated_at: 2025-04-01T04:12:57.053Z
url: https://n8n.io/workflows/3392
scraped_at: 2025-12-24 13:02:42
description:
  This workflow template creates an audio stream session on UltraVox compatible with Plivo and sends it to Plivo.

  How It Works : 
  Plivo initiates a call and requests the Answer URL.

  The workflow responds with Plivo XML to join the session.

  Note:
  Ensure you update the UltraVox API Key in the credentials.
  Update System Prompt based on your requirements.

   Check Youtube Video

nodes_used:
  ["n8n-nodes-base.webhook","n8n-nodes-base.httpRequest","n8n-nodes-base.respondToWebhook","n8n-nodes-base.set","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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


name: Google Calendar 📅 Reminder System with GPT-4o and Telegram
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-04-01T07:27:50.382Z
updated_at: 2025-04-01T07:27:50.382Z
url: https://n8n.io/workflows/3393
scraped_at: 2025-12-24 13:02:44
description:
  How many times have you missed a meeting or forgotten an appointment because a calendar reminder got lost in the noise? Traditional notifications are often dry, easy to ignore, or scattered across different apps—leaving you scrambling at the last minute.

  This smart Google Calendar workflow fixes that by sending you a clear, friendly reminder exactly 1 hour before your event starts—delivered through Telegram as if a personal assistant were looking out for you. Powered by AI, it transforms cold calendar alerts into warm, conversational nudges you won't ignore.

  Why This Works Better:  
  ✅ No More Overlooked Alerts – Consolidates reminders into one clear, accessible place (Telegram), so you never miss them.  
  ✅ Friendly & Engaging – AI transforms robotic calendar entries into natural, human-like reminders that are harder to ignore.  
  ✅ Works Everywhere – Whether you're on your phone, laptop, or tablet, you’ll get the same clear notification, no matter the platform.  

  How It Works
  Scheduled Trigger: The workflow starts with a Schedule Trigger node that runs every minute to check for upcoming events.
  Google Calendar Check: The "Get upcoming event" node queries Google Calendar for events starting within the next hour (between timeMin and timeMax).
  Duplicate Prevention: The "Already sent?" node ensures reminders are not sent multiple times for the same event by filtering out duplicates.
  AI-Powered Reminder: The "Secretary Agent" node, powered by GPT-4, crafts a friendly and professional reminder message. It includes event details like name, description, location, start/end time, and creator, formatted in a conversational tone.
  Telegram Notification: The final "Send reminder" node delivers the reminder via Telegram, ensuring the user receives it in a clear and accessible format.
  Set Up Steps
  Configure Schedule Trigger: Set the interval (e.g., every minute) to check for events.
  Connect Google Calendar: Link your Google Calendar account and specify the calendar to monitor.
  Set Up AI Agent: Customize the "Secretary Agent" with the provided system message to ensure reminders are warm, professional, and detailed.
  Link Telegram: Add your Telegram credentials and specify the CHAT_ID where reminders will be sent.
  Activate Workflow: Ensure the workflow is active and set to the correct timezone (e.g., Europe/Rome).
  Why It’s Useful
  Never Miss an Event**: Traditional calendar reminders can be easy to overlook, especially when scattered across platforms. This workflow consolidates reminders into a single, accessible channel (Telegram).
  Clear and Friendly**: The AI agent transforms generic calendar alerts into personalized, conversational reminders, making them harder to ignore.
  Cross-Platform Accessibility**: By delivering reminders via Telegram, users receive them in a consistent format, regardless of the device or platform they’re using. No more missed events due to unclear notifications!
  Need help customizing?  
  Contact me for consulting and support or add me on Linkedin.

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.googleCalendar","n8n-nodes-base.removeDuplicates","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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


name: Airtable Batch Update / Insert rows (send faster + save API call requests)
author_name: Simon Mayerhofer
author_username: s3n
categories: []
created_at: 2025-04-01T08:53:00.311Z
updated_at: 2025-04-01T08:53:00.311Z
url: https://n8n.io/workflows/3394
scraped_at: 2025-12-24 13:02:47
description:
  This workflow allows you to batch update/insert Airtable rows in groups of 10, significantly reducing the number of API calls and increasing performance.

  🚀 How It Works

  Copy the 3 Nodes  
     Copy the three nodes inside the red note box into your workflow.

  Set Your Fields  
     In the Set Fields node, define the fields you want to update.  
     ➤ Only include fields that match column names in your Airtable table.  
     ➤ Make sure the field names are spelled exactly as they appear in Airtable.
     ➤ Make sure the field type are correctly set. So numbers columns in Airtable need numbers type set as the field.

  Configure the Airtable Batch Node  
     Enter your Airtable Base ID
       The part with app... in the URL:
  airtable\.com / app8pqOLeka1Cglwg / tblnXZOdy8VtkAAJD/...
     Enter your Airtable Table ID
       The part with tbl... in the URL:
  airtable\.com / app8pqOLeka1Cglwg / tblXXZOdy8VtkAAJD /...
     Set Matching Fields (fieldsToMergeOn)  
     Provide a string array that tells Airtable how to match existing rows. Examples:
       Match by one field (e.g. TaskID):  
       {{["TaskID"]}}
       Match by multiple fields (e.g. firstname and lastname):  
       {{["firstname", "lastname"]}}

     Choose the Mode (mode field)  
     Available options:
       upsert: Update if a record exists, otherwise insert a new one
       insert: Always insert as new records
       update: Only update existing records (you must provide a field named id)

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.debugHelper","n8n-nodes-base.manualTrigger","n8n-nodes-base.splitOut","n8n-nodes-base.splitInBatches","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.switch","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.wait","n8n-nodes-base.merge","n8n-nodes-base.if","n8n-nodes-base.merge","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.merge","n8n-nodes-base.aggregate","n8n-nodes-base.set","n8n-nodes-base.aggregate","n8n-nodes-base.set","n8n-nodes-base.aggregate","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.executeWorkflow","n8n-nodes-base.set","n8n-nodes-base.aggregate","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.wait","n8n-nodes-base.wait","n8n-nodes-base.wait","n8n-nodes-base.code"]

workflow_json_compressed:
  [encoded data, 6080 characters]


name: Auto-assign Support Tickets with JIRA, Supabase and AI
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2025-04-01T09:04:10.393Z
updated_at: 2025-04-01T09:04:10.393Z
url: https://n8n.io/workflows/3395
scraped_at: 2025-12-24 13:02:45
description:
  This n8n template builds a simple automation to ensure no JIRA issues go unassigned for more than a week to prevent them falling through the cracks. It uses AI to perform searching tasks against a Supabase Vector Store.
  This can be one way to help reduce the amount of manual work in managing the issue backlog for busy teams with little effort.

  How it works
  This template contains 2 separate flows which run continuously via schedule triggers.
  The first populates our Supabase vector store with resolved issues within the last day. This helps keep our vector store up-to-date and relevant for the purpose of finding similar issues.
  It does this by pulling the latest resolved issues from JIRA and populating the Supabase vectorstore with carefully chosen metadata. This will come in handy later.
  The second flow watches for stale, unassigned issues for the purpose of aut-assigning to a relevant team member.
  It does this by comparing the stale issue against our vector store of resolved issues with the goal of identifying which team member would have best context regarding the issue.
  In a busy team, this may net a few team members as possible candidates to assign. Therefore, we can introduce additional logic to count each team member's assigned, in-progress issues. This is intended to not overload our busiest members.
  The team member with the least assigned issues is pressumed to have the most capacity and therefore is assigned. A comennt is left in the issue to notify the team member that they've been auto-assigned due to age of issue.

  How to use
  Modify the project and interval parameters to match those of your use-case and team members.
  Add additional criteria before assigning to a team member eg. department, as required.

  Requirements
  OpenAI for LLM
  JIRA for Issue Management
  Supabase for Vector Store

  Customising this workflow
  Not using JIRA or Supabase? The beauty of these AI templates are these components are entirely interchangeable with competing services. Try Linear and Qdrant instead!
  Auto-assigning logic is simplified in this template. Expand criteria as required for your team and organisation. eg. Might be a good idea to pull in annual leave information from HR system to prevent assigning to someone who is on currently on holiday!

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","@n8n/n8n-nodes-langchain.vectorStoreSupabase","@n8n/n8n-nodes-langchain.vectorStoreSupabase","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.jira","n8n-nodes-base.splitInBatches","n8n-nodes-base.jira","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.removeDuplicates","n8n-nodes-base.set","n8n-nodes-base.jira","n8n-nodes-base.splitInBatches","n8n-nodes-base.noOp","n8n-nodes-base.jira","n8n-nodes-base.stickyNote","n8n-nodes-base.jira","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.splitOut","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.summarize","n8n-nodes-base.set","n8n-nodes-base.sort","n8n-nodes-base.if","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 8268 characters]


name: Parse Incoming Invoices From Outlook using AI Document Understanding
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2025-04-01T09:17:11.659Z
updated_at: 2025-04-01T09:17:11.659Z
url: https://n8n.io/workflows/3396
scraped_at: 2025-12-24 13:02:42
description:
  This n8n template monitors an Outlook mailbox for invoices, automatically parses/extracts data from them and then uploads the output to an Excel Workbook.

  One of my top workflow requests, this template can save many hours of manual labour for you or your finance/accounts team.

  How it works
  A scheduled trigger is set to fetch recent Outlook messages to the Accounts receivable mailbox.
  Each message is analysed to determine whether or not it from a supplier and is issuing/contains an invoice.
  For each valid message, the attachments are downloaded and non-invoice documents are filtered out via AI Vision classification.
  Invoices are then processed through a AI vision model again to extract the details.
  The extracted data can then be used for reconciliation or otherwise. For this demonstration, we'll just append the row to an Excel sheet for now.

  How to use
  Ensure your Microsoft365 credential points to the correct mailbox. If a shared folder is used, toggle "shared folder" option to "on" and for the principal ID, use the email address.
  If you receive lots of other types of messages such as replies and forwards, you may want to implement additional checks to prevent processing invoices twice. The "remove duplicates" node can help with this.

  Requirements
  Outlook for Mailbox
  Google Gemini for Document Understanding and Invoice Extraction
  Excel for Data Storage

  Customising this workflow
  Note the assumption for this template is that all invoices will come as a PDF attachment. In real life, this is rarely the case! Adding in document conversion to cover all invoice formats.
  Human feedback is also an important factor in AI workflows. Try tagging emails as a way to notify team members that the invoice was processed.

nodes_used:
  ["n8n-nodes-base.code","n8n-nodes-base.microsoftOutlook","n8n-nodes-base.set","n8n-nodes-base.splitInBatches","n8n-nodes-base.noOp","@n8n/n8n-nodes-langchain.textClassifier","n8n-nodes-base.extractFromFile","n8n-nodes-base.markdown","n8n-nodes-base.set","n8n-nodes-base.wait","n8n-nodes-base.filter","n8n-nodes-base.if","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.microsoftOutlook","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.stickyNote","n8n-nodes-base.microsoftExcel","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 7584 characters]


name: Personalise Outreach Emails using Customer data and AI
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2025-04-01T10:47:11.390Z
updated_at: 2025-04-01T10:47:11.390Z
url: https://n8n.io/workflows/3397
scraped_at: 2025-12-24 13:02:44
description:
  This n8n template uses existing emails from customers as context to customise and "finetune" outreach emails to them using AI.

  By now, it should be common knowledge that we can leverage AI to generate unique emails but in a way, they can remain generic as the AI lacks the customer context to be truly personalised. One way to solve this is by pulling in a source of customer data - and what better way then by using existing email correspondence.

  How it works
  Customers to target are pulled from Hubspot and each customer is then run in a loop. We're using a loop as the retrieved emails for each customer become separate items and a loop helps with item reference.
  We connect to our Gmail account to pull all emails recieved from the customer.
  The contents of the email will be suitable to build a short persona of the customer. We use the Information Extractor to get our AI model to pull out the key attributes of this persona such as decision making style and communication preferences.
  With this persona, we can now pass this to our AI model to generate a personalised outreach email specifically for our customer.
  Finally, a draft email is created for human review before sending. If you would rather send the email straight away, this is also possible.

  How to use
  Define the topic of the outreach email in the "variables" node. This directs the AI on what outreach email to generate.
  Ensure the emails are pulled from the right account. If emails may contain sensitive data, adjust the filters and text parsing to ensure these are not leaked to the AI (which might then leak into the generated email).

  Requirements
  Hubspot for Contacts List
  OpenAI for LLM
  Gmail for Existing Emails and Sending Emails

  Customising this workflow
  Not using Hubspot? Any CRM would work just as well or even a simple text csv!
  If you have customer past deals or engagements in your CRM, consider using this as additional context for the AI to use.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.gmail","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.stickyNote","n8n-nodes-base.gmail","n8n-nodes-base.hubspot","n8n-nodes-base.splitInBatches","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 7140 characters]


name: Send structured logs to BetterStack from any workflow using HTTP Request
author_name: Audun
author_username: xqus
categories: []
created_at: 2025-04-01T18:56:59.369Z
updated_at: 2025-04-01T18:56:59.369Z
url: https://n8n.io/workflows/3400
scraped_at: 2025-12-24 13:02:46
description:
  Send structured logs to BetterStack from any workflow using HTTP Request

  Who is this for?

  This workflow is perfect for automation builders, developers, and DevOps teams using n8n who want to send structured log messages to BetterStack Logs. Whether you're monitoring mission-critical workflows or simply want centralized visibility into process execution, this reusable log template makes integration easy.

  What problem is this workflow solving?

  Logging failures or events across multiple workflows typically requires duplicated logic. This workflow solves that by acting as a shared log sender, letting you forward consistent log entries from any other workflow using the Execute Workflow node.

  What this workflow does

  Accepts level (e.g., "info", "warn", "error") and message fields via Execute Workflow Trigger
  Sends the structured log to your BetterStack ingestion endpoint via HTTP Request
  Uses HTTP Header Auth for secure delivery
  Includes a manual trigger for testing and a sample call to demonstrate usage
  Comes with clear sticky notes to help you get started

  Setup

  Copy your BetterStack Logs ingestion URL.
  Create a Header Auth credential in n8n with your Authorization: Bearer YOUR_API_KEY.
  Replace the URL in the HTTP Request node with your BetterStack endpoint.
  Optionally modify the test data or log levels for custom scenarios.
  Use Execute Workflow in any of your workflows to send logs here.

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.manualTrigger","n8n-nodes-base.executeWorkflow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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


name: Automatically Create Facebook Ads from Google Sheets
author_name: Artur
author_username: arlusm1
categories: []
created_at: 2025-04-02T08:50:00.915Z
updated_at: 2025-04-02T08:50:00.915Z
url: https://n8n.io/workflows/3406
scraped_at: 2025-12-24 13:02:44
description:
  Who is this for?

  This template is designed for Marketing Managers, Performance Marketers, and Ad Ops professionals who want to automate Facebook ad creation using structured data in Google Sheets. It’s ideal for teams running multiple creatives or testing ad variations without having to manually use Meta Ads Manager.

  &gt; ⚠️ Important Note:  
  This is not a plug-and-play workflow. It requires:
  A configured Facebook Business account
  A valid Facebook App, Page, and Ad Account
  Access tokens** and the correct Facebook Graph API credentials
  A basic understanding of the Meta API and JSON to tweak ad set parameters like demographics, optimization goals, or sales objectives

  Additionally, launching Facebook ads will incur real advertising costs, so this template is best suited for users willing to make a time investment to set things up properly and test responsibly. Expect to spend time customizing targeting and budget strategies based on your campaign needs.

  What problem does this solve?

  Manually uploading creatives, setting up ad sets, and creating ads in Meta’s Ad Manager is time-consuming, repetitive, and error-prone—especially at scale. This workflow eliminates the manual work by pulling data from Google Sheets and using it to automatically:

  Generate Facebook Ad Sets
  Upload creative images
  Build and launch Ad Creatives and Ads
  Update your source spreadsheet with generated Ad IDs

  What this workflow does

  Using a trigger from a Google Sheets row update, this workflow:

  Reads ad parameters (like message, render URL, and campaign info) from a Google Sheet  
  Generates ad set configuration dynamically using variables in an “Edit Fields” node  
  Creates a Facebook Ad Set via the Graph API  
  Fetches the ad image from a render URL  
  Uploads the image to Facebook Ads Library  
  Creates the Ad Creative using the uploaded image and dynamic text  
  Launches the Ad using the generated Ad Set and Creative  
  Updates the same Google Sheet with the generated Ad ID and status

  All configuration fields like campaign_id, act_id, pixel_id, age ranges, interest targeting, and call-to-action links are defined up front in a single Edit Fields node, making the template easy to maintain or extend.

  Google Sheet Structure
  | Hooks | Render URL | Generate Ad | Ad ID |
  |-------|------------|-------------|-------|
  | Static ad text (e.g., “Visit us at...”) | Link to the creative asset (image) | Status: generate, generated, or error | Populated by the workflow with the created Facebook Ad ID |

  Hooks**: This is the primary ad copy. It will be used as the main text for the static ad.
  Render URL**: Direct link to the media asset (image or video) for the ad.
  Generate Ad**: Dropdown or text value that controls workflow execution:
    generate — workflow will attempt to create the ad
    generated — already processed
    error — error occurred during generation
  Ad ID**: The Meta Ad ID will be written here once the ad is successfully created.

  Setup

  Copy this Google Sheet template and populate it with your data
  Create a Facebook App and retrieve the access credentials for the Facebook Graph API
  In n8n:
     Connect your Google Sheets and Facebook Graph API accounts
     Update the Edit Fields node with your actual ad account ID, page ID, campaign ID, pixel ID, and destination link
     Deploy the workflow

  This workflow runs every time the generate ad column in your sheet is updated.

  How to customize this workflow to your needs

  Modify the Edit Fields node to adjust ad set parameters like targeting, budget strategy, CTA type, and more
  Expand interest-based targeting using more interest objects in the array
  Add extra Google Sheet columns and map them to Facebook ad fields (e.g. different messages, URLs, creative assets)
  Add logic to pause or duplicate ads based on performance

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.googleSheetsTrigger","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.googleSheets","n8n-nodes-base.set"]

workflow_json_compressed:
  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


name: Convert Reddit threads into short vertical videos with AI
author_name: Artur
author_username: arlusm1
categories: []
created_at: 2025-04-02T09:29:33.331Z
updated_at: 2025-04-02T09:29:33.331Z
url: https://n8n.io/workflows/3407
scraped_at: 2025-12-24 13:02:45
description:
  Convert Reddit threads into short vertical videos with AI

  Who is this for?

  This workflow is ideal for:
  Content creators* and *video editors** automating short-form content production
  Reddit storytellers** converting text posts into engaging TikTok, YouTube Shorts, or Reels
  Social media managers** repurposing community discussions into visual narratives

  What problem is this solving?

  Manually converting Reddit posts into vertical video content is time-consuming:
  You have to read, summarize, write a script
  Generate TTS
  Find stock footage
  Edit everything in a timeline

  This workflow automates the full pipeline. It converts any Reddit thread into a polished video with:
  TTS narration
  Subtitle overlays
  B-roll from Pexels
  Automatic rendering via Shotstack

  What this workflow does

  This workflow:
  Extracts Reddit post and comments via Reddit API
  Summarizes the thread into structured clips using OpenAI
  Generates search queries for each clip for stock footage
  Queries Pexels API for relevant vertical videos
  Generates TTS audio for each clip using OpenAI Whisper
  Creates subtitles matching the audio
  Uploads footage/audio to Shotstack
  Renders a full vertical video (720x1280) with synced TTS, subtitles, and b-roll
  Returns a final video URL

  Setup

  Create accounts and API keys for:
    Reddit Developer App
    OpenAI
    Pexels
    Shotstack
  Add credentials in n8n:
    Reddit (HTTP Basic Auth)
    OpenAI (API Key)
    Shotstack (HTTP Header Auth)
    Pexels (HTTP Header Auth)
  Trigger via webhook or manual node. The input must include:
      {
      "voice": "nova",
      "ttsSpeed": 1,
      "videoLength": 60,
      "redditLink": "https://www.reddit.com/r/example/comments/example_id/example_title"
    }
  How to customize this workflow

  Tweak OpenAI prompts** to change tone or clip granularity
  Change stock source** by swapping Pexels for another API
  Adjust TTS voices** or languages by modifying the voice field
  Modify video styling** (fonts, colors, fit modes) in the timeline construction code node
  Control duration** by editing the character length formula in the Limit comments length node

  Additional Notes

  All stock videos are selected to match clip themes using generalized keywords to avoid API misses
  Includes wait nodes to ensure Shotstack's async upload/render processes complete before proceeding
  Annotated with sticky notes explaining major sections like TTS, Reddit input, and media timeline
  Avoids community nodes to ensure cloud compatibility

  Template Category
  AI, Marketing, Building Blocks, Other (Content Creation)

nodes_used:
  ["n8n-nodes-base.itemLists","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.merge","n8n-nodes-base.webhook","n8n-nodes-base.splitOut","n8n-nodes-base.code","n8n-nodes-base.splitOut","n8n-nodes-base.code","n8n-nodes-base.merge","n8n-nodes-base.splitOut","n8n-nodes-base.merge","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.merge","n8n-nodes-base.if","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.renameKeys","n8n-nodes-base.merge","n8n-nodes-base.merge","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.wait","n8n-nodes-base.manualTrigger","n8n-nodes-base.code","n8n-nodes-base.merge","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.set"]

workflow_json_compressed:
  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


name: AI YouTube Playlist & Video Analyst Chatbot
author_name: dmr
author_username: dmr
categories: []
created_at: 2025-04-02T13:25:05.835Z
updated_at: 2025-04-02T13:25:05.835Z
url: https://n8n.io/workflows/3408
scraped_at: 2025-12-24 13:02:45
description:
  AI YouTube Playlist & Video Analyst Chatbot

  This n8n workflow transforms entire YouTube playlists or single videos into interactive knowledge bases you can chat with. Ask questions and get summaries without needing to watch hours of content.

  🌟 How it Works

  🔗 Provide a Link: Start by giving the workflow a URL for a YouTube playlist or a single video.
  📄 Content Retrieval: The workflow automatically fetches the video details and transcripts for the provided link. For playlists, it can process multiple videos at once (you might be asked how many).
  🧠 AI Processing: Google's Gemini AI reads through the transcripts, understands the content, and creates summaries.
  💾 Storage & Context: The processed information and summaries are stored in a vector database (Qdrant), making them ready for conversation. Context is managed using Redis, remembering the current video/playlist you're discussing.
  💬 Chat & Ask: Now, you can ask the AI agent questions about the playlist or video content! Because context is maintained, you can ask follow-up questions (like "expand on point X") without needing to provide the URL again.

  🛠️ Requirements

  Community Node:** This workflow uses the youtubeTranscripter community node to fetch video transcripts. You'll need to install it in your n8n environment.
      Installation: npm install n8n-nodes-youtube-transcription-dmr
      Important: Community nodes require a self-hosted n8n instance.
  Redis:** A Redis instance is required for managing conversation context and status between interactions.
  Credentials:** You will need API credentials configured in your n8n instance for:
      Google Gemini (AI Models)
      Qdrant (Vector Store)
      Redis (Context Store)

  🤖 AI Agent Capabilities

  Engage with the AI agent to explore the video content. Since the agent remembers the context of your conversation, you can ask detailed follow-up questions naturally:

  Get a quick summary of a single video or an entire playlist.
  Ask for key takeaways or main topics discussed.
  Query for specific information mentioned in the videos.
  Ask the agent to elaborate on a specific point previously mentioned.
  Understand complex subjects without watching the full duration.

  🚀 Use Cases

  📊 Content Analysis:** Quickly understand the themes and key points across a playlist or long video.
  📚 Research & Learning:** Extract insights from educational series or tutorials efficiently.
  ✍️ Content Creation:** Easily repurpose video transcript information into blog posts, notes, or social media content.
  ⏱️ Save Time:** Get the essence of video content when you're short on time.
  ♿ Accessibility:** Offers a text-based way to interact with and understand video content.

  ✨ Sample Prompts

  Please analyze this playlist and tell me the main topics covered: [YouTube Playlist URL]
  Summarize the first 5 videos in this playlist: [YouTube Playlist URL]
  (Follow-up) Tell me more about the main point in video 3.
  What are the key points discussed in this video? [YouTube Video URL]
  (Follow-up) Expand on the second key point you mentioned.
  Does the video at [YouTube Video URL] mention [specific topic]?

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.splitOut","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.summarize","n8n-nodes-base.splitOut","n8n-nodes-base.limit","@n8n/n8n-nodes-langchain.vectorStoreQdrant","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","@n8n/n8n-nodes-langchain.embeddingsGoogleGemini","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.embeddingsGoogleGemini","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.splitOut","n8n-nodes-base.merge","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.summarize","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-youtube-transcription-dmr.youtubeTranscripter","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.redis","n8n-nodes-base.redis","n8n-nodes-base.switch","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.switch","n8n-nodes-base.code","n8n-nodes-base.redis","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.httpRequest","n8n-nodes-youtube-transcription-dmr.youtubeTranscripter","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.splitOut","n8n-nodes-base.summarize","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.embeddingsGoogleGemini","@n8n/n8n-nodes-langchain.vectorStoreQdrant","@n8n/n8n-nodes-langchain.toolVectorStore","@n8n/n8n-nodes-langchain.vectorStoreQdrant","@n8n/n8n-nodes-langchain.toolVectorStore","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.vectorStoreQdrant","@n8n/n8n-nodes-langchain.embeddingsGoogleGemini","n8n-nodes-base.if","n8n-nodes-base.summarize","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.redis","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 20580 characters]


name: Batch Process Prompts with Anthropic Claude API
author_name: Greg Evseev
author_username: greg
categories: []
created_at: 2025-04-02T19:20:04.824Z
updated_at: 2025-04-02T19:20:04.824Z
url: https://n8n.io/workflows/3409
scraped_at: 2025-12-24 13:02:46
description:
  This workflow template provides a robust solution for efficiently sending multiple prompts to Anthropic's Claude models in a single batch request and retrieving the results. It leverages the Anthropic Batch API endpoint (/v1/messages/batches) for optimized processing and outputs each result as a separate item.

  Core Functionality & Example Usage Included

  This template includes:
  The Core Batch Processing Workflow: Designed to be called by another n8n workflow.
  An Example Usage Workflow: A separate branch demonstrating how to prepare data and trigger the core workflow, including examples using simple strings and n8n's Langchain Chat Memory nodes.

  Who is this for?

  This template is designed for:

  Developers, data scientists, and researchers** who need to process large volumes of text prompts using Claude models via n8n.
  Content creators** looking to generate multiple pieces of content (e.g., summaries, Q&As, creative text) based on different inputs simultaneously.
  n8n users** who want to automate interactions with the Anthropic API beyond single requests, improve efficiency, and integrate batch processing into larger automation sequences.
  Anyone needing to perform bulk text generation or analysis tasks with Claude programmatically.

  What problem does this workflow solve?

  Sending prompts to language models one by one can be slow and inefficient, especially when dealing with hundreds or thousands of requests. This workflow addresses that by:

  Batching:** Grouping multiple prompts into a single API call to Anthropic's dedicated batch endpoint (/v1/messages/batches).
  Efficiency:** Significantly reducing the time required compared to sequential processing.
  Scalability:** Handling large numbers of prompts (up to API limits) systematically.
  Automation:** Providing a ready-to-use, callable n8n structure for batch interactions with Claude.
  Structured Output:** Parsing the results and outputting each individual prompt's result as a separate n8n item.

  Use Cases:

  Bulk content generation (e.g., product descriptions, summaries).
  Large-scale question answering based on different contexts.
  Sentiment analysis or data extraction across multiple text snippets.
  Running the same prompt against many different inputs for research or testing.

  What the Core Workflow does

  (Triggered by the 'When Executed by Another Workflow' node)

  Receive Input: The workflow starts when called by another workflow (e.g., using the 'Execute Workflow' node). It expects input data containing:
      anthropic-version (string, e.g., "2023-06-01")
      requests (JSON array, where each object represents a single prompt request conforming to the Anthropic Batch API schema).
  Submit Batch Job: Sends the formatted requests data via POST to the Anthropic API /v1/messages/batches endpoint to create a new batch job. Requires Anthropic credentials.
  Wait & Poll: Enters a loop:
      Checks if the processing_status of the batch job is ended.
      If not ended, it waits for a set interval (10 seconds by default in the 'Batch Status Poll Interval' node).
      It then checks the batch job status again via GET to /v1/messages/batches/{batch_id}. Requires Anthropic credentials.
      This loop continues until the status is ended.
  Retrieve Results: Once the batch job is complete, it fetches the results file by making a GET request to the results_url provided in the batch status response. Requires Anthropic credentials.
  Parse Results: The results are typically returned in JSON Lines (.jsonl) format. The 'Parse response' Code node splits the response text by newlines and parses each line into a separate JSON object, storing them in an array field (e.g., parsed).
  Split Output: The 'Split Out Parsed Results' node takes the array of parsed results and outputs each result object as an individual item from the workflow.

  Prerequisites

  An active n8n instance (Cloud or self-hosted).
  An Anthropic API account with access granted to Claude models and the Batch API.
  Your Anthropic API Key.
  Basic understanding of n8n concepts (nodes, workflows, credentials, expressions, 'Execute Workflow' node).
  Familiarity with JSON data structures for providing input prompts and understanding the output.
  Understanding of the Anthropic Batch API request/response structure.
  (For Example Usage Branch) Familiarity with n8n's Langchain nodes (@n8n/n8n-nodes-langchain) if you plan to adapt that part.

  Setup

  Import Template: Add this template to your n8n instance.
  Configure Credentials:
      Navigate to the 'Credentials' section in your n8n instance.
      Click 'Add Credential'.
      Search for 'Anthropic' and select the Anthropic API credential type.
      Enter your Anthropic API Key and save the credential (e.g., name it "Anthropic account").
  Assign Credentials: Open the workflow and locate the three HTTP Request nodes in the core workflow:
      Submit batch
      Check batch status
      Get results
      In each of these nodes, select the Anthropic credential you just configured from the 'Credential for Anthropic API' dropdown.
  Review Input Format: Understand the required input structure for the When Executed by Another Workflow trigger node. The primary inputs are anthropic-version (string) and requests (array). Refer to the Sticky Notes in the template and the Anthropic Batch API documentation for the exact schema required within the requests array.
  Activate Workflow: Save and activate the core workflow so it can be called by other workflows.

  ➡️ Quick Start & Input/Output Examples: Look for the Sticky Notes within the workflow canvas! They provide crucial information, including examples of the required input JSON structure and the expected output format.

  How to customize this workflow

  Input Source:* The core workflow is designed to be called. You will build *another workflow that prepares the anthropic-version and requests array and then uses the 'Execute Workflow' node to trigger this template. The included example branch shows how to prepare this data.
  Model Selection & Parameters:* Model (claude-3-opus-20240229, etc.), max_tokens, temperature, and other parameters are defined *within each object inside the requests array you pass to the workflow trigger. You configure these in the workflow calling this template.
  Polling Interval:** Modify the 'Wait' node ('Batch Status Poll Interval') duration if you need faster or slower status checks (default is 10 seconds). Be mindful of potential rate limits.
  Parsing Logic:** If Anthropic changes the result format or you have specific needs, modify the Javascript code within the 'Parse response' Code node.
  Error Handling:** Enhance the workflow with more specific error handling for API failures (e.g., using 'Error Trigger' or checking HTTP status codes) or batch processing issues (batch.status === 'failed').
  Output Processing:* In the workflow that *calls this template, add nodes after the 'Execute Workflow' node to process the individual result items returned (e.g., save to a database, spreadsheet, send notifications).

  Example Usage Branch (Manual Trigger)

  This template also contains a separate branch starting with the Run example Manual Trigger node.

  Purpose:** This branch demonstrates how to construct the necessary anthropic-version and requests array payload.
  Methods Shown:** It includes steps for:
      Creating a request object from a simple query string.
      Creating a request object using data from n8n's Langchain Chat Memory nodes (@n8n/n8n-nodes-langchain).
  Execution:** It merges these examples, constructs the final payload, and then uses the Execute Workflow node to call the main batch processing logic described above. It finishes by filtering the results for demonstration.
  Note:** This branch is for demonstration and testing. You would typically build your own data preparation logic in a separate workflow. The use of Langchain nodes is optional for the core batch functionality.

  Notes

  API Limits:** According to the Anthropic API documentation, batches can contain up to 100,000 requests and be up to 256 MB in total size. Ensure your n8n instance has sufficient resources for large batches.
  API Costs:** Using the Anthropic API, including the Batch API, incurs costs based on token usage. Monitor your usage via the Anthropic dashboard.
  Completion Time:** Batch processing time depends on the number and complexity of prompts and current API load. The polling mechanism accounts for this variability.
  Versioning:** Always include the anthropic-version header in your requests, as shown in the workflow and examples. Refer to Anthropic API versioning documentation.

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.wait","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.aggregate","n8n-nodes-base.set","n8n-nodes-base.executeWorkflow","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.memoryManager","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.memoryManager","n8n-nodes-base.executionData","n8n-nodes-base.executionData","n8n-nodes-base.splitOut","n8n-nodes-base.filter","n8n-nodes-base.filter","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.merge","n8n-nodes-base.set","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.memoryManager"]

workflow_json_compressed:
  [encoded data, 7540 characters]


name: Get notified on Gmail, Telegram and Slack on new Stripe purchase
author_name: Solomon
author_username: solomon
categories: []
created_at: 2025-04-03T00:14:38.539Z
updated_at: 2025-04-03T00:14:38.539Z
url: https://n8n.io/workflows/3410
scraped_at: 2025-12-24 13:02:46
description:
  Get notified when a customer completes a Stripe checkout — including all products they purchased in a single checkout session.



  Using the Stripe API can be tricky, especially when you're selling multiple products, order bumps, or recommended add-ons in one checkout. This workflow solves that for you.

  How it works

  The template listens for successful Stripe checkout events. When a payment is completed, it retrieves all items from the session, including main products, bumps, and extras — and sends a full breakdown straight to your chosen app.



  You can receive the notification on:

  Telegram**
  WhatsApp**
  Gmail**
  Or any other chat/notification service you prefer (Slack, Outlook etc. )

  Who is this template for?

  Creators, marketers, and online sellers who:

  Use Stripe Checkout
  Want to track what customers are buying in real time
  Prefer to get notifications instantly instead of digging through Stripe logs

  How to set up

  Connect your Stripe account and choose where you want to receive notifications.
  Adjust the notification text for your preference, and you are done!

  Everything else is already configured in the template.

  Check out my other templates

  👉 https://n8n.io/creators/solomon/

nodes_used:
  ["n8n-nodes-base.stripeTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.aggregate","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.whatsApp","n8n-nodes-base.gmail","n8n-nodes-base.filter"]

workflow_json_compressed:
  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


name: Generate 360° Virtual Try-on Videos for Clothing with Kling API (unofficial)
author_name: PiAPI
author_username: piapi
categories: []
created_at: 2025-04-03T06:56:12.469Z
updated_at: 2025-04-03T06:56:12.469Z
url: https://n8n.io/workflows/3411
scraped_at: 2025-12-24 13:02:44
description:
  What's the workflow used for？
  Leverage this Kling API (unofficial) provided by PiAPI workflow to streamline virtual try-on video creation. This tool is designed for e-commerce platforms, fashion brands, content creators and content influencers. By uploading model and clothing images and linking PiAPI account, users can swiftly generate a realistic video of the model sporting the outfit with a 360° turn, offering an immersive viewing experience. 

  Step-by-step Instruction
  For basic settings of virtual try-on, check API doc to get best practice. 
  Fill in your X-API-Key of your PiAPI account in Preset Parameters node.
  Upload the model photo and provide target clothing image urls.
  Click Test Workflow to generate virtual try-on image.
  Get the video output in the final node.

  Param Settings
  If you want to change into a dress, input the model_input URL and the dress_input URL in the parameters.
  If you want to change into separates, input model_input URL, upper_input URL and lower_input URL in Preset Parameters.

  Use Case
  Input images：




  Output Video
   
  The output demonstrates that the model is wearing the clothing from the specified image and showcases a rotating runway-style view.

  This workflow enables you to efficiently test garment-on-model presentation effects while reducing business model validation costs to a certain extent.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.switch","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.wait","n8n-nodes-base.wait","n8n-nodes-base.switch","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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


name: LINE Messages with GPT: Save Notes, Namecard Data and Tasks
author_name: lin@davoy.tech
author_username: linlinmhee
categories: []
created_at: 2025-04-03T07:20:26.138Z
updated_at: 2025-04-03T07:20:26.138Z
url: https://n8n.io/workflows/3413
scraped_at: 2025-12-24 13:02:45
description:
  This workflow template, "Personal Assistant to Note Messages and Extract Namecard Information" is designed to streamline the processing of incoming messages on the LINE messaging platform. It integrates with powerful tools like Microsoft Teams , Microsoft To Do , OneDrive , and OpenRouter.ai to handle tasks such as saving notes, extracting namecard information, and organizing images. Whether you’re managing personal productivity or automating workflows for teams, this template offers a versatile and customizable solution.

  By leveraging this workflow, you can automate repetitive tasks, improve collaboration, and enhance efficiency in handling LINE messages.

  Who Is This Template For?
  This template is ideal for:

  Professionals: Who want to save important messages, extract data from namecards, or organize images automatically.
  Teams: Looking to integrate LINE messages into tools like Microsoft Teams and Microsoft To Do for better collaboration.
  Developers: Seeking to build intelligent workflows that process text, images, and other inputs from LINE.
  Business Owners: Who need to manage customer interactions, follow-ups, and task tracking efficiently.

  What Problem Does This Workflow Solve?
  Managing incoming messages on LINE can be time-consuming, especially when dealing with diverse input types like text, images, and namecards. This workflow solves that problem by:
  Automatically identifying and routing different message types (text, images, namecards) to appropriate actions.
  Extracting structured data from namecards and saving it for follow-up tasks.
  Uploading images to OneDrive and saving text messages to Microsoft Teams or Microsoft To Do for easy access.
  Sending real-time feedback to users via LINE to confirm that their messages have been processed.

  What This Workflow Does
  Receive Messages via LINE Webhook:
  The workflow is triggered whenever a user sends a message (text, image, or other types) to the LINE bot.
  Display Loading Animation:
  A loading animation is displayed to reassure the user that their request is being processed.
  Route Input Types:

  The workflow uses a Switch node to determine the type of input:
  Text Starting with "T": Adds the message as a task in Microsoft To Do.
  Plain Text: Saves the message in Microsoft Teams under a designated channel (e.g., "Notes").
  Images: Identifies whether the image is a namecard, handwritten note, or other content, then processes accordingly.
  Unsupported formats trigger a polite response indicating the limitation.
  Process Namecards:

  *Images *
  If the image is identified as a namecard, the workflow extracts structured data (e.g., name, email, phone number) using OpenRouter.ai and saves it to Microsoft To Do for follow-up tasks.
  Save Images to OneDrive:

  Images are uploaded to OneDrive, renamed based on their unique message ID, and linked in Microsoft Teams for reference.

  Send Feedback via LINE:
  The workflow replies to the user with confirmation messages, such as "[ Task Created ]" or "[ Message Saved ]."

  Setup Guide
  Pre-Requisites
  Access to the LINE Developers Console to configure your webhook and bot.
  Accounts for Microsoft Teams , Microsoft To Do, and OneDrive with API access.
  An OpenRouter.ai account with credentials to access models like GPT-4o.
  Basic knowledge of APIs, webhooks, and JSON formatting.

  Step-by-Step Setup
  1) Configure the LINE Webhook:
  Go to the LINE Developers Console and set up a webhook to receive incoming messages.
  Copy the Webhook URL from the Line Webhook node and paste it into the LINE Console.
  Remove any "test" configurations when moving to production.
  2) Set Up Microsoft Integrations:
  Connect your Microsoft Teams, Microsoft To Do, and OneDrive accounts to the respective nodes in the workflow.
  3) Set Up OpenRouter.ai:
  Create an account on OpenRouter.ai and obtain your API credentials.
  Connect your credentials to the OpenRouter nodes in the workflow.

  Test the Workflow:
  Simulate sending text, images, and namecards to the LINE bot to verify that all actions are processed correctly.

  How to Customize This Workflow to Your Needs
  Add More Actions: Extend the workflow to handle additional input types or integrate with other tools.
  Enhance Image Processing: Use advanced OCR tools to improve text extraction from complex images.
  Customize Feedback Messages: Modify the reply format to include emojis, links, or other formatting options.
  Expand Use Cases: Adapt the workflow for specific industries, such as sales or customer support, by tailoring the actions to relevant tasks.

  Why Use This Template?
  Versatile Automation: Handles multiple input types (text, images, namecards) with ease.
  Seamless Integration: Connects LINE messages to popular productivity tools like Microsoft Teams and To Do.
  Structured Data Extraction: Extracts and organizes data from namecards, saving time and effort.
  Real-Time Feedback: Keeps users informed about the status of their requests with instant notifications.

nodes_used:
  ["n8n-nodes-base.webhook","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.lmChatOpenRouter","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.microsoftTeams","n8n-nodes-base.microsoftToDo","n8n-nodes-base.httpRequest","n8n-nodes-base.if","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.microsoftTeams","@n8n/n8n-nodes-langchain.lmChatOpenRouter","n8n-nodes-base.microsoftOneDrive","n8n-nodes-base.microsoftOneDrive","n8n-nodes-base.httpRequest","n8n-nodes-base.microsoftToDo","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.lmChatOpenRouter","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 7928 characters]


name: Slack AI Chatbot for business team with RAG, Claude 3.7 Sonnet and Google Drive
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-04-03T07:33:30.762Z
updated_at: 2025-04-03T07:33:30.762Z
url: https://n8n.io/workflows/3414
scraped_at: 2025-12-24 13:02:43
description:
  Imagine having an AI chatbot on Slack that seamlessly integrates with your company’s workflow, automating repetitive requests. No more digging through emails or documents to find answers about IT requests, company policies, or vacation days—just ask the bot, and it will instantly provide the right information.  

  With its 24/7 availability, the chatbot ensures that team members get immediate support without waiting for a colleague to be online, making assistance faster and more efficient.  

  Moreover, this AI-powered bot serves as a central hub for internal communication, allowing everyone to quickly access procedures, documents, and company knowledge without searching manually. A simple Slack message is all it takes to get the information you need, enhancing productivity and collaboration across teams.

  How It Works  
  Slack Trigger: The workflow starts when a user mentions the AI bot in a Slack channel. The trigger captures the message and forwards it to the AI Agent.  
  AI Agent Processing:  
     The AI Agent, powered by Anthropic's Claude 3.7 Sonnet model, processes the query.  
     It uses Retrieval-Augmented Generation (RAG) to fetch relevant information from the company’s internal knowledge base stored in Qdrant (a vector database).  
     A Simple Memory buffer retains recent conversation context (last 10 messages) for continuity.  
  Knowledge Retrieval:  
     The RAG tool searches Qdrant’s vector store using OpenAI embeddings to find the most relevant document chunks (top 10 matches).  
  Response Generation:  
     The AI synthesizes the retrieved data into a concise, structured response (1-2 sentences for the answer, 2-3 supporting details, and a source citation).  
     The response is formatted in Slack-friendly markdown (bullet points, blockquotes) and sent back to the user.  

  Set Up Steps  
  Prepare Qdrant Vector Database:  
     Create a Qdrant collection via HTTP request (Create collection node).  
     Optionally, refresh/clear the collection (Refresh collection node) before adding new documents.  
  Load Company Documents:  
     Fetch files from a Google Drive folder (Get folder → Download Files).  
     Process documents: Split text into chunks (Token Splitter) and generate embeddings (Embeddings OpenAI2).  
     Store embeddings in Qdrant (Qdrant Vector Store1).  
  Configure Slack Bot:  
     Create a Slack bot via Slack API with required permissions
     Add the bot to the desired Slack channel and note the channelId for the workflow.  
  Deploy AI Components:  
     Connect the AI Agent to Anthropic’s model, RAG tool, and memory buffer.  
     Ensure OpenAI embeddings are configured for both RAG and document processing.  
  Test & Activate:  
     Use the manual trigger (When clicking ‘Test workflow’) to validate document ingestion.  
     Activate the workflow to enable real-time Slack interactions.  

  Need help customizing?  
  Contact me for consulting and support or add me on Linkedin.

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.vectorStoreQdrant","@n8n/n8n-nodes-langchain.toolCalculator","n8n-nodes-base.slackTrigger","n8n-nodes-base.slack","n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterTokenSplitter","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.vectorStoreQdrant","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.embeddingsOpenAi","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatAnthropic"]

workflow_json_compressed:
  [encoded data, 5696 characters]


name: Abandoned cart recovery for Shopify via Gmail, Google Sheets & Twilio (no-code)
author_name: Velebit from Innovatio
author_username: innovatio
categories: []
created_at: 2025-04-03T10:30:12.179Z
updated_at: 2025-04-03T10:30:12.179Z
url: https://n8n.io/workflows/3415
scraped_at: 2025-12-24 13:02:46
description:
  Who is this for?

  This template is ideal for solo store owners, eCommerce marketers, automation beginners, or anyone using Shopify and Gmail who wants to recover lost revenue without coding.

  What problem does this solve?

  Customers often leave items in their cart without completing the purchase. Manually chasing those sales is inefficient and inconsistent. This workflow automates the recovery process by sending emails (and optionally SMS) based on Shopify checkout activity.

  What this workflow does:

  ✅ Detects when a Shopify checkout is created but not completed,
  ⏳ Waits 1 hour, then checks if the cart was abandoned,
  🧾 Pulls cart contents and applies a fallback discount,
  📧 Sends a recovery email via Gmail,
  🕓 After 24 hours, you can optionally send an SMS or WhatsApp reminder via Twilio,
  📊 Logs each interaction to Google Sheets for tracking.

  Setup:

  Create Shopify, Gmail, Google Sheets, and Twilio credentials in your n8n instance,
  Import the JSON workflow file
  Replace the Shopify trigger with your store's webhook (set to “Checkout Create”),
  Set your wait time (default: 1 hour),
  Link your Gmail account and customize the email message,
  (Optional) Link Twilio to add a multi-channel reminder,
  Update the Google Sheets node with your own Sheet ID and headers.

  🗒 Color-coded workflow notes:

  To make customization easier, this template includes a visual sticky note system inside the n8n canvas.

  You’ll find:

  🟩 Green Notes → Main Steps  
    Clear, essential stages of the automation (e.g. wait, check cart status, send email),

  🟦 Blue Notes → Personalization Tips  
    Small tweaks you can make to match your tone, store brand, or logic (e.g. email copy, discount logic),

  🟨 Yellow Notes → Optional / Advanced  
    Features that you can explore setting up:  
    Twilio SMS, Airtable logging, human approval steps, etc.

  👉 Look around each key node for quick tips, setup instructions, and TO-DO items.

  This system helps you understand and modify the workflow at a glance — whether you're a beginner or scaling a pro setup.

  How to customize this workflow?

  🎯 Swap Shopify for WooCommerce or Stripe by replacing the trigger and cart-check logic,
  💬 Localize and personalize each email message or add multilingual support via AI,
  📈 Upgrade logging from Google Sheets to Airtable for CRM-style features,
  🤝 Add manual approval steps for high-value discounts,
  💡 Want a one-time unique code per user? Let us build that logic for you → velebit@innovatio.design

  Final notes

  This template was designed by Velebit from Innovatio. External links in the workflow (e.g. support or upgrade options) are optional and purely informative, while only leading to the official company webiste with no affiliate or similar links included in the template itself.

  A separate license applies to the paid version on Gumroad, which includes additional modules and commercial use rights.

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.gmail","n8n-nodes-base.wait","n8n-nodes-base.twilio","n8n-nodes-base.googleSheets","n8n-nodes-base.shopifyTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.googleSheets","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.webhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.airtable","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.wait","n8n-nodes-base.wait","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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


name: Generate AI YouTube Shorts with Flux, Runway, Eleven Labs and Creatomate
author_name: Joseph
author_username: mjomba
categories: []
created_at: 2025-04-03T10:42:08.131Z
updated_at: 2025-04-03T10:42:08.131Z
url: https://n8n.io/workflows/3416
scraped_at: 2025-12-24 13:02:45
description:
  Automated Video Creation Workflow Using n8n  

  This workflow automates the creation and publishing of animated videos based on ideas listed in a Google Sheet. It processes one idea at a time, generating text prompts, images, animations, sound effects, and merging them into a final video before uploading it to YouTube.  

  Table of Contents  
  Pre-conditions and Requirements  
  Google Sheets Setup  
  Step-by-Step Workflow Explanation  
  Customization Guide  

  Pre-conditions and Requirements  

  1. API Keys Required  
  To run this workflow, you'll need API access to the following services:  
  Anthropic Claude or Google Gemini** (for text prompt generation)  
  Flux AI** (RapidAPI) (for AI-generated images)  
  RunwayML** (API Documentation) (for AI video animation)  
  ElevenLabs** (for AI-generated voiceovers and sound effects)  
  Creatomate** (Website) (for video/audio merging and rendering)  
  YouTube API** (for video upload and posting)  

  2. n8n Instance Setup  
  Use cloud (n8n.io) or Install and run n8n (Official Guide)  
  Set up credentials for each API in n8n’s settings  

  Google Sheets Setup  

  Before running the workflow, ensure your Google Sheet is structured as follows:  

  | Column Name  | Description  |  
  |------------------|----------------|  
  | title         | Video title (e.g., "Elijah's Fiery Chariot") |  
  | bibleverse    | Corresponding Bible verse reference |  
  | idea         | Brief description of the scene |  
  | style        | Animation style (e.g., cinematic, vibrant, etc.) |  
  | caption      | Suggested caption for social media |  
  | videoStatus  | Status of video creation (To Do, Created) |  
  | publishStatus| Publishing status (Not Processed, Processed) |  

  Step-by-Step Workflow Explanation  

  1. Extract an Idea from Google Sheets  
  The workflow retrieves the first row where videoStatus = "To Do".  
  Marks it as Processing to avoid duplicate processing.  

  2. Generate a Prompt for AI Image Creation and Sound Effects/Audio  
  Uses Anthropic Claude or Google Gemini to generate prompts.  

  3. Generate an AI Image with Flux AI  
  Sends the prompt to Flux AI to create a high-quality image.  

  4. Animate the Image Using RunwayML  
  The generated image is sent to RunwayML, which animates the image.  

  5. Generate Sound Effects and Voiceover with ElevenLabs  
  ElevenLabs produces a realistic narration based on the video content.  
  Background sound effects (e.g., storm sounds, fire crackling) are also generated.  

  6. Merge Video and Audio Using Creatomate  
  Creatomate** compiles the animated video with the audio.  

  7. Upload and Publish to YouTube  
  The finalized video is automatically uploaded to YouTube using the YouTube API.  

  8. Update Google Sheet Status  
  Marks videoStatus as Created.  
  Marks publishStatus as Processed.  

  Customization Guide  

  Modifying the Animation Style  
  Update the style column in Google Sheets with custom animation preferences (e.g., cinematic, slow-motion).  
  Modify the prompt generation step in n8n to incorporate different styles.  

  Changing the Video Length  
  Adjust the RunwayML settings to control animation speed and length.  
  Modify the Creatomate rendering step to adjust clip duration.  

  Adding Subtitles  
  Modify the Creatomate step to include AI-generated subtitles from ElevenLabs' text output.  

  Publishing to Other Platforms  
  Add additional steps to post to TikTok, Instagram, or Facebook using their respective APIs.  

  This workflow ensures a fully automated video creation pipeline, reducing manual effort and optimizing content production. 🚀

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.limit","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.googleDrive","n8n-nodes-base.merge","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.googleDrive","n8n-nodes-base.set","n8n-nodes-base.wait","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.lmChatAnthropic","n8n-nodes-base.youTube","n8n-nodes-base.googleSheets","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.agent"]

workflow_json_compressed:
  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


name: Extract and Clean YouTube Video Transcripts with RapidAPI
author_name: Joseph
author_username: mjomba
categories: []
created_at: 2025-04-03T11:10:47.958Z
updated_at: 2025-04-03T11:10:47.958Z
url: https://n8n.io/workflows/3417
scraped_at: 2025-12-24 13:02:44
description:
  Here is your refined template description with detailed step-by-step instructions, markdown formatting, and customization guidance.  

  YouTube Transcript Extraction Workflow  

  This n8n workflow extracts and processes transcripts from YouTube videos using the YouTube Transcript API on RapidAPI. It allows users to retrieve subtitles from YouTube videos, clean them up, and return structured transcript data for further processing.  

  Table of Contents  
  Problem Statement & Target Audience  
  Pre-conditions & API Requirements  
  Step-by-Step Workflow Explanation  
  Customization Guide  
  How to Set Up This Workflow  

  Problem Statement & Target Audience  

  Who is this for?  
  This workflow is ideal for content creators, researchers, and developers who need to:  
  Extract subtitles from YouTube videos automatically.  
  Format and clean** transcript data for readability.  
  Use transcripts for summarization, content repurposing, or language analysis.  

  Pre-conditions & API Requirements  

  API Required  
  YouTube Transcript API** (RapidAPI)  

  n8n Setup Prerequisites  
  A running n8n instance (Installation Guide)  
  A RapidAPI account to access the YouTube Transcript API  
  An API key from RapidAPI to authenticate requests  

  Step-by-Step Workflow Explanation  

  1. Input YouTube Video URL (Trigger)  
  This step provides a simple input form where users enter a YouTube video URL.  

  2. HTTP Request Node (Retrieve Transcript Data)  
  Makes a POST request to the YouTube Transcript API via RapidAPI.  
  Passes the video URL received from the input form.  
  Uses an environment variable to store the API key securely.  

  3. Function Node (Process Transcript)  
  Receives* the API response containing the *raw transcript**.  
  Processes and cleans** the transcript:  
    Removes unwanted characters.  
    Formats text for readability.  
  Handles errors** when no transcript is available.  
  Outputs* both the *raw and cleaned transcript** for further use.  

  4. Set Field Node (Response Formatting)  
  Structures** the processed transcript data into a user-friendly format.  
  Returns** the final transcript data to the client.  

  Customization Guide  

  1. Modify Transcript Cleaning Rules  
  Update the Function Node to apply custom text processing, such as:  
    Removing timestamps.  
    Changing the output format (e.g., JSON, plain text).  

  2. Store Transcripts in a Database  
  Add a Database Node (e.g., MySQL, PostgreSQL, or Firebase) to save transcripts.  

  3. Generate Summaries from Transcripts  
  Integrate AI services (e.g., OpenAI, Google Gemini) to summarize transcripts.  

  4. Convert Transcripts into Speech  
  Use ElevenLabs API to generate an AI-powered voiceover from transcripts.  

  How to Set Up This Workflow  

  Step 1: Import the Workflow into n8n  
  Download or copy the workflow JSON file.  
  Import it into your n8n instance.  

  Step 2: Set Up the API Key  
  Sign up for the YouTube Transcript API.  
  Subscribe to the api.  
  Copy and paste your api key where the "your_api_key" is.

  Step 3: Activate the Workflow  
  Start the workflow in n8n.  
  Enter a YouTube video URL in the input form.  
  The workflow will return a cleaned transcript.  

  This workflow ensures seamless YouTube transcript extraction and processing with minimal manual effort. 🚀

nodes_used:
  ["n8n-nodes-base.formTrigger","n8n-nodes-base.set","n8n-nodes-base.function","n8n-nodes-base.httpRequest"]

workflow_json_compressed:
  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


name: 🌲 AI Agent for Sustainability Report Audit with Gmail and GPT-40
author_name: Samir Saci
author_username: samirsaci
categories: []
created_at: 2025-04-03T13:25:00.897Z
updated_at: 2025-04-03T13:25:00.897Z
url: https://n8n.io/workflows/3420
scraped_at: 2025-12-24 13:02:47
description:
  Tags: Sustainability, CSRD, Reporting, ESG, Compliance, Automation

  Context  
  Hey! I'm Samir, a Supply Chain Engineer and Data Scientist from Paris, founder of  
  LogiGreen Consulting  

  We help companies automate sustainability workflows using AI, Data Analytics, and No-Code tools like N8N.  

  &gt; Sustainability Reporting meets Automation with n8n!

  📬 For business inquiries, you can add me on Here

  What is a CSRD XHTML Report?  
  Under the Corporate Sustainability Reporting Directive (CSRD), companies must publish their ESG disclosures in a machine-readable XHTML format, embedding XBRL tags that make the report structured and standardized.  



  These files must follow strict formatting and tagging rules to ensure compliance, traceability, and accessibility for both regulators and analysts.

  Who is this template for?  
  This workflow is designed for sustainability teams, ESG consultants, or developers who want to automatically check the structure and format of CSRD reports submitted in XHTML.

  How does it work?  
  This N8N workflow automates the audit process:  
  📤 Input Node → Uploads or fetches the XHTML file via URL or Webhook.  
  🧪 Validates Structure → Uses a custom code node to parse HTML and identify required tags (e.g., &lt;ix:nonNumeric&gt;, namespaces).  
  📋 Outputs a Report → Returns a summary report of errors, warnings, and key metadata (like entity name, reporting period).  
  📤 Export Option → Save the results in Google Sheets or send via email.

  Prerequisite  
  A sample XHTML file that you can find in my GitHub Repository
  Google Sheets API* and *OpenAI API** credentials

  Next Steps  
  Follow the sticky notes inside each node to adjust parsing rules or extend validation to specific XBRL tags relevant to your sector (e.g., GHG emissions, water usage).

  *📺 Check my complete tutorial to understand how to use it: *  



  🎥 Check My Tutorial

  🚀 Interested in combining CSRD compliance with automation and analytics? Let’s connect on LinkedIn

  Notes  
  This workflow includes an example XHTML file to test the validator.  
  You can plug this into your internal systems or even extend it with AI to auto-summarize the sustainability report.

  This workflow has been created with N8N 1.82.1  
  Submitted: April 3rd, 2025

nodes_used:
  ["n8n-nodes-base.if","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.gmail","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.extractFromFile","n8n-nodes-base.gmailTrigger","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 4192 characters]


name: Load and Aggregate Files from a Google Drive Folder into a Key-Value Dictionary
author_name: Hugues Stock
author_username: hpstock
categories: []
created_at: 2025-04-03T13:25:13.731Z
updated_at: 2025-04-03T13:25:13.731Z
url: https://n8n.io/workflows/3421
scraped_at: 2025-12-24 13:02:46
description:
  What does this template do?

  This workflow automates the process of retrieving all Google Docs from a specified Google Drive folder, extracting their content, and structuring the data into a unified key-value dictionary, where:
  Key = The file name
  Value = The content of the document (with normalized newlines)

  This is ideal for use cases such as knowledge ingestion, prompt context preparation, or populating a RAG (Retrieval-Augmented Generation) system with reference documents.

  Use Case

  Perfect for teams or individuals who maintain prompts, content, or reference material in Google Docs and want to integrate that data dynamically into automation or AI workflows.

  What the Workflow Does

  ⚡ Triggered via Execute Workflow Trigger from another workflow
  📁 Retrieves all files from a specified Google Drive folder
  📄 Downloads the full content of each Google Docs file
  🧩 Maps each file name to its content using a Set node
  🧠 Aggregates all entries into a single dictionary with Code node
  🧾 Outputs a JSON object like:
  {
    "Doc1": "content of doc 1",
    "Doc2": "content of doc 2"
  }

  Apps & Services Used
  Google Drive
  Google Docs
  n8n Code node

  Pre-requisites
  A connected Google account with access to the target Drive folder
  OAuth2 credentials for both:
  Google Drive
  Google Docs

  Customization Tips
  Change Folder: Update the Google Drive node to select a specific folder rather than root
  Support Other File Types: Replace or complement the Google Docs node to handle other formats (e.g., PDFs or text files)
  Data Cleaning: Modify the Code node to adjust text normalization to your needs

  Additional Use Cases

  🧠 Prompt Management for AI Workflows
  Centralize your GPT prompt templates in Google Docs instead of hardcoding them in n8n
  Easily manage prompt versions and edits directly from Google Drive
  Dynamically load prompts into downstream nodes (like OpenAI or Hugging Face) without redeploying workflows

  📚 Knowledge Base Loader for RAG Systems
  Aggregate internal knowledge, FAQs, SOPs, or policy docs from Google Docs into a structured dictionary
  Use the resulting object as the context source for Retrieval-Augmented Generation models

  🎛️ Dynamic Form Inputs or Question Sets
  Maintain forms, surveys, or interview questions in Google Docs
  Load the content into n8n for form generation, chatbot interaction, or customer intake flows

  📄 Client-Specific Config or Script Templates
  For client onboarding workflows, load client-specific scripts or prompts from individual docs named after each client
  Helps automate client-facing document generation or AI interactions

  📋 Content Ingestion for Translation, Summarization, or Sentiment Analysis
  Batch process internal documentation or user-submitted Google Docs
  Trigger summarization, translation, or classification pipelines using the document contents

  🔄 Document-to-Database Import
  Automatically fetch and normalize Google Docs contents, ready to push into a database (e.g., Notion, Airtable, PostgreSQL)

nodes_used:
  ["n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDocs","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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


name: Get Live Crypto Market Data with AI-Powered CoinMarketCap Agent
author_name: Don Jayamaha Jr
author_username: don-the-gem-dealer
categories: []
created_at: 2025-04-03T13:26:21.792Z
updated_at: 2025-04-03T13:26:21.792Z
url: https://n8n.io/workflows/3422
scraped_at: 2025-12-24 13:02:43
description:
  Access real-time cryptocurrency prices, market rankings, metadata, and global stats—powered by GPT-4o and CoinMarketCap!  
  This modular AI-powered agent is part of a broader CoinMarketCap multi-agent system designed for crypto analysts, traders, and developers. It uses the CoinMarketCap API and intelligently routes queries to the correct tool using AI.

  This agent can be used standalone or triggered by a supervisor AI agent for multi-agent orchestration.

  Supported API Tools (6 Total)  
  This agent intelligently selects from the following tools to answer your crypto-related questions:

  🔍 Tool Summary  
  Crypto Map – Lookup CoinMarketCap IDs and active coins  
  Crypto Info – Get metadata, whitepapers, and social links  
  Crypto Listings – Ranked coins by market cap  
  CoinMarketCap Price – Live prices, volume, and supply  
  Global Metrics – Total market cap, BTC dominance  
  Price Conversion – Convert between crypto and fiat

  What You Can Do with This Agent  
  🔹 Get live prices and volume for tokens (e.g., BTC, ETH, SOL)  
  🔹 Convert crypto → fiat or fiat → crypto instantly  
  🔹 Retrieve whitepapers, logos, and website links for any token  
  🔹 Analyze total market cap, BTC dominance, and circulating supply  
  🔹 Discover new tokens and track their CoinMarketCap IDs  
  🔹 View the top 100 coins ranked by market cap or volume  

  Example Queries  
  ✅ "What is the CoinMarketCap ID for PEPE?"  
  ✅ "Show me the top 10 cryptocurrencies by market cap."  
  ✅ "Convert 5 ETH to USD."  
  ✅ "What’s the 24h volume for ADA?"  
  ✅ "Get the global market cap and BTC dominance."  

  AI Architecture  
  AI Brain**: GPT-4o-mini  
  Memory**: Session buffer with sessionId  
  Agent Type**: Subworkflow AI tool  
  Connected APIs**: 6 CoinMarketCap endpoints  
  Trigger Mode**: Executes when called by a supervisor (via message and sessionId inputs)

  Setup Instructions  
  Get a CoinMarketCap API Key  
     Register here: https://coinmarketcap.com/api/  
  Configure Credentials in n8n  
     Use HTTP Header Auth with your API key for each connected endpoint  
  Connect This Agent to a Supervisor Workflow (Optional)  
     Trigger this agent using Execute Workflow with inputs message and sessionId  
  Test Prompts  
     Try asking: “Convert 1000 DOGE to BTC” or “Top 5 coins in EUR”

  Included Sticky Notes  

  Crypto Agent Guide – Agent overview, node map, and endpoint details  

  Usage Instructions – Step-by-step usage and sample prompts  

  Error Handling & Licensing – Troubleshooting and IP rights

  ✅ Final Notes  
  This agent is part of the CoinMarketCap AI Analyst System, which includes multiple specialized agents for cryptocurrencies, exchanges, community data, and DEX insights. Visit my Creator profile to find the full suite of tools.

  Get smarter about crypto—analyze the market in real time with AI and CoinMarketCap.  

nodes_used:
  ["n8n-nodes-base.executeWorkflowTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 5292 characters]


name: Get Exchange & Sentiment Insights with CoinMarketCap AI Agent
author_name: Don Jayamaha Jr
author_username: don-the-gem-dealer
categories: []
created_at: 2025-04-03T13:33:12.092Z
updated_at: 2025-04-03T13:33:12.092Z
url: https://n8n.io/workflows/3423
scraped_at: 2025-12-24 13:02:44
description:
  Analyze exchange data, market indexes, and community sentiment from CoinMarketCap—powered by AI.  

  This sub-agent provides access to exchange listings, token holdings, metadata, and high-level metrics like the CMC 100 Index and the Fear & Greed Index. It’s designed for use within your larger CoinMarketCap AI Analyst system or as a standalone workflow.

  This agent can be triggered by a supervisor or manually used with message and sessionId inputs.

  Supported Tools (5 Total)

  🔍 Exchange Map  
  Get CoinMarketCap IDs, names, and slugs for exchanges (used as lookup before deeper queries).  

  🧾 Exchange Info  
  Metadata including launch date, social links, country, and operational status.  

  💰 Exchange Assets  
  Token balances, wallet addresses, and total USD value held by a specific exchange.

  📈 CoinMarketCap 100 Index  
  Constituents and weights of the CMC 100 Index, updated live.

  😱 Fear & Greed Index  
  Market sentiment score updated daily, ranging from Extreme Fear to Extreme Greed.

  What You Can Do with This Agent  
  🔹 Map exchanges to retrieve their ID and slug  
  🔹 Analyze exchange holdings by token and blockchain  
  🔹 Pull metadata for major CEXs like Binance or Coinbase  
  🔹 Compare global sentiment using the Fear & Greed Index  
  🔹 Access index data to understand CMC’s top 100 crypto asset breakdown

  Example Queries You Can Use  
  ✅ "What is the latest Fear and Greed Index reading?"  
  ✅ "Get a list of all exchanges on CoinMarketCap."  
  ✅ "What tokens are held by Binance?"  
  ✅ "Retrieve metadata for Coinbase."  
  ✅ "Show me the top assets in the CMC 100 Index."  

  Agent Architecture  
  AI Brain**: GPT-4o-mini  
  Memory**: Window buffer memory using sessionId  
  Tools**: 5 API-connected nodes  
  Trigger**: External input via message and sessionId  

  Setup Instructions  
  Get a CoinMarketCap API Key  
     Apply here: https://coinmarketcap.com/api/  
  Configure n8n Credentials  
     Use HTTP Header Auth to store your CoinMarketCap API key.  
  Optional: Trigger from a Supervisor  
     Connect to a parent agent using Execute Workflow with message and sessionId inputs.  
  Test Sample Prompts  
     “Get all exchanges”, “Fetch CMC index”, “Show Binance token holdings”

  Sticky Notes Included  

  Exchange & Community Guide – Explains agent purpose and component connections  

  Usage & Examples – Walkthrough for sample use cases  

  Error Handling & Licensing – Includes API error code reference and licensing details

  ✅ Final Notes  
  This agent is part of a broader CoinMarketCap AI Analyst System. Visit my Creator profile to download all available sub-agents and supervisor flows.

  Understand exchange behavior and community sentiment—automated with AI and CoinMarketCap.

nodes_used:
  ["n8n-nodes-base.executeWorkflowTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 5104 characters]


name: Analyze DEX Liquidity, Trades & Spot Pairs with CoinMarketCap AI Agent
author_name: Don Jayamaha Jr
author_username: don-the-gem-dealer
categories: []
created_at: 2025-04-03T13:41:05.768Z
updated_at: 2025-04-03T13:41:05.768Z
url: https://n8n.io/workflows/3424
scraped_at: 2025-12-24 13:02:44
description:
  Gain full visibility into decentralized exchanges using CoinMarketCap’s DEXScan API—powered by AI.  

  This workflow is part of the CoinMarketCap AI Analyst system and delivers real-time and historical insights on spot trading pairs, DEX liquidity, trading activity, and OHLCV data across chains like Ethereum, Polygon, Solana, and more.

  Use this workflow as a sub-agent triggered by a parent supervisor workflow, or run it manually with inputs sessionId and message.

  🔧 Supported Tools (8 Total)

  DEX Metadata → Static info (name, launch date, logo, URLs)  
  DEX Networks List → All supported DEX chains + network metadata  
  DEX Listings Quotes → Ranked list of DEXs with live trading volume, market share  
  DEX Pair Quotes (Latest) → Real-time liquidity, price, and buy/sell stats  
  DEX OHLCV Historical → Time-series data (daily/hourly/1m)  
  DEX OHLCV Latest → Today’s price, volume, open/close for pairs  
  DEX Trades Latest → Up to 100 recent trades for any DEX pair  
  DEX Spot Pairs Latest → Active token pairs across DEXs + filters (volume, liquidity, volatility)

  Agent Architecture  
  AI Model**: gpt-4o-mini  
  Context Memory**: Window buffer using sessionId  
  Trigger Input**: message, sessionId  
  Execution**: Via Execute Workflow or parent AI supervisor  
  Design**: Tool-based LangChain agent with CMC DEXScan endpoints  

  💡 Use Cases  
  🔹 Find top DEXs by 24h volume  
  🔹 Get spot pairs with highest liquidity on a specific network  
  🔹 Track historical OHLCV for Uniswap pairs  
  🔹 View latest trades for SOL/USDC pool  
  🔹 Analyze tax, pooled % and holders for specific pairs  
  🔹 Filter pairs by 24h volume, percent change, liquidity, or number of transactions  

  ✅ Example Queries  
  ✅ "Top 5 DEXs by 24h volume on Ethereum"  
  ✅ "Get historical OHLCV for SOL-USDC on Solana"  
  ✅ "Latest trades for a PancakeSwap pair"  
  ✅ "Show all spot pairs with over $500K in liquidity on Polygon"  
  ✅ "Retrieve metadata for Uniswap and SushiSwap"  

  🛠️ Setup Instructions  
  Get a CoinMarketCap API Key  
     Sign up at: https://coinmarketcap.com/api/  
  Add API Key to Credentials in n8n  
     Use HTTP Header Auth method  
  Trigger from Parent Workflow (Optional)  
     Use Execute Workflow and pass message and sessionId  
  Test Prompt Ideas  
     Try: "Compare liquidity of Uniswap and Curve pairs on Ethereum"

  Sticky Notes Included  

  DEXScan Agent Guide – Workflow architecture + supported tools  

  Usage & API Call Examples – Prompts, test inputs, setup flow  

  Error Codes + Licensing – 400/401/429/500 troubleshooting, IP rights

  ✅ Final Notes  
  This agent is part of the CoinMarketCap AI Analyst System, which includes multiple specialized agents for cryptocurrencies, exchanges, and community data. Visit my Creator profile to find the full suite of tools.

  Master DEX analytics with AI—get powerful liquidity, trading, and pair insights in seconds.

nodes_used:
  ["n8n-nodes-base.executeWorkflowTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 11168 characters]


name: Analyze Crypto Markets with the AI-Powered CoinMarketCap Data Analyst
author_name: Don Jayamaha Jr
author_username: don-the-gem-dealer
categories: []
created_at: 2025-04-03T13:53:03.108Z
updated_at: 2025-04-03T13:53:03.108Z
url: https://n8n.io/workflows/3425
scraped_at: 2025-12-24 13:02:41
description:
  Meet your AI-powered crypto data analyst—fully integrated with CoinMarketCap APIs.  

  This workflow acts as the supervisor agent for a multi-agent architecture built in n8n, connecting three powerful sub-agents to extract real-time insights from centralized and decentralized markets. It’s the ultimate tool for crypto traders, analysts, developers, and researchers who need strategic multi-source intelligence—all through Telegram.

  This workflow requires 3 sub-agent templates to function correctly. See below.

  🔌 Required Sub-Workflows (Install First)

  CoinMarketCap Crypto Agent Tool  
     → Token prices, metadata, conversions, listings

  CoinMarketCap Exchange & Community Agent Tool  
     → Exchange info, token holdings, Fear & Greed index

  CoinMarketCap DEXScan Agent Tool  
     → DEX trading pairs, liquidity, OHLCV data

   Download all from my Creator Profile:  
   https://n8n.io/creators/don-the-gem-dealer/

  What Makes This Workflow Special?

  This is not just another API wrapper—it’s an intelligent routing agent powered by GPT-4o-mini, capable of:

  Understanding complex user queries  
  Choosing the appropriate tool workflow  
  Structuring the API request  
  Executing sub-workflows  
  Formatting the output  
  Returning insights via Telegram

  It connects three domains of market data:
  Cryptocurrencies (CEX)**
  Exchanges & Sentiment**
  DEX trading data**

  🔍 What You Can Do  

  💰 Token Intelligence
  Get token metadata, price, volume, supply
  Compare rankings and conversions

  🏦 Exchange Insights
  View assets held by exchanges
  Track the CMC 100 Index and Fear & Greed Score

  🌐 DEX Market Analysis
  Analyze pair quotes, historical OHLCV, live trades
  Discover the top DEXs by volume across blockchains

  ✅ Example Questions to Ask
  “What’s the market cap of Ethereum today?”  
  “Show liquidity and volume for SOL/USDT on Solana”  
  “Get token holdings for Binance”  
  “Compare BTC price on Uniswap vs Binance”  
  “What’s the Fear & Greed index right now?”

  🛠️ Setup Instructions

  Create Telegram Bot  
     Use @BotFather to get your bot token.

  Get CoinMarketCap API Key  
     Apply here: https://coinmarketcap.com/api/

  Install Sub-Agent Templates  
     Required:
       Crypto Agent Tool  
       Exchange & Community Tool  
       DEXScan Tool

  Configure Credentials in n8n  
     Add both Telegram and CoinMarketCap keys as HTTP Header Auth.

  Deploy & Test  
     Ask your Telegram bot: “Top 10 tokens by 24h volume” or “Convert 5 ETH to USD”

  Workflow Architecture

  AI Brain**: GPT-4o-mini  
  Memory**: Windowed buffer memory via sessionId  
  Tool Agents**:  
     toolWorkflow() → routes requests to the appropriate sub-agent  
     Executes real-time API queries and returns structured output

  Included Sticky Notes  
  System Overview**  
  Error Handling Guide (200, 400, 401, 429, 500)**  
  Step-by-Step Usage Instructions**  
  Prompt Examples + API Docs**  
  Legal & Licensing Notes**

  Your crypto insights—smarter, faster, and all in one Telegram message.

nodes_used:
  ["n8n-nodes-base.telegram","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.telegramTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 8316 characters]


name: Automate Call Scheduling with Voice AI Receptionist using Vapi, Google Calendar & Airtable
author_name: SuperAgent
author_username: customaistudio
categories: []
created_at: 2025-04-03T19:05:21.114Z
updated_at: 2025-04-03T19:05:21.114Z
url: https://n8n.io/workflows/3427
scraped_at: 2025-12-24 13:02:41
description:
  Who is this template for?

  This template is ideal for small businesses, agencies, and solo professionals who want to automate appointment scheduling and caller follow-up through a voice-based AI receptionist. If you’re using tools like Google Calendar, Airtable, and Vapi (Twilio), this setup is for you.

  What problem does this workflow solve?

  Manual call handling, appointment booking, and email coordination can be time-consuming and prone to errors. This workflow solves that by automating the receptionist role: answering calls, checking calendar availability, managing appointments, and storing call summaries—all without human intervention.

  What this workflow does

  This Agent Receptionist manages inbound voice calls and scheduling tasks using Vapi and Google Calendar. It checks availability, books or updates calendar events, sends email confirmations, and logs call details into Airtable. The workflow includes built-in logic for slot management, email triggers, and storing call transcripts. 

  Setup Instructions

  Duplicate Airtable Base: Use this Airtable base templateBASE LINK 

  Import Workflow: Load provided JSON into your n8n instance.

  Credentials: Connect your Google Calendar and Airtable credentials in n8n.

  Activate Workflow: Enable workflow to get live webhook URLs.

  Vapi Configuration:

  Paste provided system prompt into Vapi Assistant. 

  Link the appropriate webhook URLs from n8n (GetSlots, BookSlots, UpdateSlots, CancelSlots, and end-of-call report).



  Disclaimer

  Optimized for cloud-hosted n8n instances. Self-hosted users should verify webhook and credential setups.

nodes_used:
  ["n8n-nodes-base.set","n8n-nodes-base.itemLists","n8n-nodes-base.itemLists","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.respondToWebhook","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.webhook","n8n-nodes-base.googleCalendar","n8n-nodes-base.respondToWebhook","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.googleCalendar","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.respondToWebhook","n8n-nodes-base.webhook","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.googleCalendar","n8n-nodes-base.respondToWebhook","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.airtable","n8n-nodes-base.webhook","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.airtable","n8n-nodes-base.respondToWebhook","n8n-nodes-base.googleCalendar","n8n-nodes-base.airtable","n8n-nodes-base.set","n8n-nodes-base.respondToWebhook","n8n-nodes-base.webhook","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.airtable","n8n-nodes-base.set","n8n-nodes-base.respondToWebhook","n8n-nodes-base.googleCalendar","n8n-nodes-base.airtable","n8n-nodes-base.set","n8n-nodes-base.respondToWebhook","n8n-nodes-base.webhook","n8n-nodes-base.set","n8n-nodes-base.airtable","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 26216 characters]


name: Generate Multiple T-shirt Design Prompts From Images With GPT-4o
author_name: AiAgent
author_username: lifehacks
categories: []
created_at: 2025-04-04T04:35:59.170Z
updated_at: 2025-04-04T04:35:59.170Z
url: https://n8n.io/workflows/3430
scraped_at: 2025-12-24 13:02:46
description:
  Disclaimer
  This Workflow uses a community node.

  What It Does
  Workflow Demonstration

  This powerful workflow eliminates hours of trial and error when trying to craft the perfect prompt for AI-generated images. By simply saving an image of the design you want to model your generated images after into a designated folder on your desktop, the workflow is automatically triggered. Once the image is saved, n8n pulls it into the workflow, where an Analyze Image AI agent powered by GPT-4o analyzes it and generates a detailed text description. This description is then passed to another Prompt/Text Generator AI agent, which uses it as a template to create five entirely new and unique image prompts using the power of GPT-4o. These prompts produce images that share a similar art style with the original, while remaining creatively distinct. In addition, the AI will extract the original phrase and generate nine brand-new phrases that can be incorporated into the image. Finally, all this information is automatically saved into a folder on your computer, ready for you to use whenever you like.
  Who This Is For
  This workflow is ideal for anyone looking to create high-quality AI-generated images with ease. It’s especially valuable for those running a print-on-demand business or working in logo and artwork design. By automating the process of crafting image prompts that match a desired style, it saves you hours of manual effort and guesswork. With this streamlined system, you can focus less on prompt engineering and more on what truly matters—growing your business.
  How It Works
  Once you find an image or design that perfectly matches the style you're aiming for, the rest of the process is seamless. With the workflow running, simply save the image to the designated folder that triggers the automation. This action activates the workflow, and the image is automatically pulled into n8n using the Get Image From File node.
  From there, the Analyze Image AI Agent, powered by GPT-4o, takes over. It examines the image in depth—assessing its overall theme, art style, text phrase, font, aesthetic, and complexity (whether it’s a simple design or a more detailed composition). Based on this analysis, it generates a detailed description of the image along with the original text phrase it contains.
  Once the analysis is complete, a second AI agent, Prompt/Text Generator, receives this information and uses GPT-4o to create five unique image prompts. These prompts are designed to replicate the essence and style of the original image, while still producing completely original results when used with any AI image generator. In addition to the original phrase, the agent also generates nine brand-new text phrases.
  By combining each of the five prompts with the ten total text phrases (original + nine new), the workflow gives you the potential to generate 50 unique image variations.
  Finally, the Save to File node takes the entire output from the Prompt/Text Generator—including all five image prompts and ten text phrases—that was converted to text and automatically saves it into a designated folder on your computer for future use.
  After a completed workflow has been saved to the designated folder you must change the name of the file prior to running the workflow again. The workflow will only save the same file name each time.
  Set Up Steps
  You will need to create a folder on your computer where you would like to save your original image. You will then copy the pathway to this folder into the local file trigger node.
  You will need to obtain an Open AI API key from platform.openai.com/api-keys
  After you obtain this Open AI API key you will need to connect it to the Analyze Image AI Agent and the Open AI Chat Model connected to the Prompt/Text Generator AI Agent.
  You will now need to fund your Open AI account. GPT 4o costs ~$0.01-$0.02 to run the workflow.
  Finally create a folder on your computer you wish to have the summarizations saved to. Copy the pathway to this folder into the Save to File node.

nodes_used:
  ["n8n-nodes-base.localFileTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.code","n8n-nodes-base.readWriteFile","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.convertToFile","n8n-nodes-base.readWriteFile","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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


