# templates

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: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
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, 7516 characters]

metadata_json:
  {"categories":["automation","integration","data processing","AI/ML"],"complexity":"medium","use_cases":["Bulk content generation (product descriptions, summaries, articles)","Large-scale question answering across many inputs","Sentiment analysis or structured data extraction from many text snippets","Running the same prompt across many inputs for research or testing","Batch processing of LLM prompts to improve throughput and reduce latency"],"estimated_setup_minutes":15,"required_services":["Anthropic (Claude) API (access to Batch API and API key)","n8n instance (Cloud or self-hosted)","Optional: n8n Langchain nodes (@n8n/n8n-nodes-langchain) for example branch"],"key_features":["Uses Anthropic Batch API (/v1/messages/batches) to submit many prompts in one request","Polling loop to wait for batch completion and fetch results_url","Parses JSONL (.jsonl) results into separate n8n items","Callable core workflow designed to be executed by other workflows (Execute Workflow node)","Example branch demonstrating payload preparation, including Langchain Chat Memory usage and simple strings, for quick testing/adoption"],"target_audience":["Developers","Data scientists / researchers","Content creators and marketing teams doing bulk generation or analysis using Claude models via n8n"]}


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: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
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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

metadata_json:
  {"categories":["automation","integration","e-commerce","notifications"],"complexity":"medium","use_cases":["Real-time sales notifications for new Stripe checkout purchases","Track multi-item checkouts including order bumps and add-ons","Instant alerts to sales/fulfillment/marketing teams","Quick verification of purchase details without checking Stripe dashboard","Trigger downstream workflows or manual fulfillment based on purchases"],"estimated_setup_minutes":10,"required_services":["Stripe (Checkout / webhooks)","n8n instance (webhook endpoint)","One or more notification channels such as Telegram, WhatsApp, Gmail, Slack, Outlook"],"key_features":["Listens to Stripe checkout completion events (checkout.session.completed)","Fetches full session line items including main products, bumps and extras","Aggregates and formats a complete purchase breakdown","Delivers notifications to multiple channels (Telegram, WhatsApp, Gmail, Slack, etc.)","Configurable notification text/content before sending"],"target_audience":["Creators selling digital or physical products","Marketers tracking conversions and order details","Online sellers and store operators using Stripe Checkout"]}


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: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
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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

metadata_json:
  {"categories":["e-commerce","image processing","AI/ML","content creation","automation"],"complexity":"medium","use_cases":["Generate 360° virtual try-on videos for product listings","Create marketing and influencer content showing garments on models","Rapidly validate garment-on-model presentation without studio shoots","A/B testing of styling and fit for product teams","Customer-facing virtual try-on demos for online stores"],"estimated_setup_minutes":15,"required_services":["PiAPI (Kling API) with X-API-Key","n8n instance (workflow runner)","Public image hosting or URLs for model and clothing images"],"key_features":["Produces realistic 360° rotating try-on videos from model + clothing images","Supports dresses or separates (upper and lower garments) via parameter inputs","Preset parameters node for storing X-API-Key and inputs","Uses HTTP/Webhook + AI/ML nodes to call Kling API and process results","Fast workflow testing via Test Workflow and manual trigger"],"target_audience":["E-commerce product managers","Fashion brands and retailers","Content creators and influencers producing garment videos」「Developers integrating virtual try-on into storefronts"]}


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: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
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, 7876 characters]

metadata_json:
  {"categories":["integration","automation","data processing","productivity","communication"],"complexity":"complex","use_cases":["Save incoming LINE messages as notes in Microsoft Teams","Create tasks in Microsoft To Do from LINE messages","Extract structured contact info from namecards (OCR + GPT)","Upload and organize images to OneDrive","Provide real-time confirmations to LINE users"],"estimated_setup_minutes":45,"required_services":["LINE Developers (webhook/bot)","Microsoft Teams","Microsoft To Do","Microsoft OneDrive","OpenRouter.ai (GPT models)","n8n instance"],"key_features":["Routes messages by type (text, image, namecard)","Creates Microsoft To Do tasks from messages starting with T","Saves plain text messages to a Teams channel","Uses OpenRouter.ai to extract structured namecard data","Uploads and renames images to OneDrive and links them in Teams reply messages to LINE users with confirmations and loading animation"],"target_audience":["Professionals","Teams","Developers building chat automations and integrations','Business owners managing customer interactions"]}


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: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:31
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, 5700 characters]

metadata_json:
  {"categories":["automation","integration","knowledge-management","chatbot","RAG"],"complexity":"complex","use_cases":["Internal knowledge retrieval (policies, procedures, FAQs)","Automating IT support and common requests","HR queries (vacation, benefits, onboarding)","Document search and citation from Google Drive","24/7 team assistance and workflow automation"],"estimated_setup_minutes":120,"required_services":["Slack (bot + channel)","Anthropic Claude 3.7 Sonnet (AI Agent)","Qdrant (vector database)","OpenAI embeddings (or equivalent embeddings provider)","Google Drive (company documents)","n8n platform / HTTP endpoints for collection management"],"key_features":["Slack trigger for mentions and messages","Retrieval-Augmented Generation (RAG) against Qdrant vector store","Document ingestion pipeline: fetch from Google Drive → split → embed → store","Claude 3.7 Sonnet as the response generator","Simple memory buffer (last 10 messages) for conversational continuity​','Top-N retrieval (top 10) and concise synthesized answers with 1–2 sentence answer, supporting details and source citations' , "],"target_audience":["Developers/engineers implementing internal bots","IT/support teams automating common requests","HR and People Ops for policy access and onboarding support」「Knowledge managers and documentation owners"]}


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: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
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:
  H4sIAAAAAAAAE81aYW+bOY7+K+/40x4QDiiJkqhggUV3dnauuN0dYDqYHna2HyiJSow6dtZ+05mg6H8/yC0St32d2m8Ph/vUJpHpRxT1PCTFt4tlXVwu/vLSvd69SHf/vcL8z3/fPltcLG50lMXl28VyvRtlXfR5X2fJu9SSBGzWcxLC2oIXIvFsOArHhsnVUJ0VU7mEbGMk7xtKLD7S4t3FYi03urhcPMuyrpu11uE72Y7DT1o2b3R7P3x/I8vV8Oxu3NzIuNysh5eb7eu22vy2uFiMcrVbXP766mKx3lTt/337Hn/NJXuxCK1JBiIuIE4KmBqzsSGICWbx8NV/1bFc77+227y/7b9b8xr2RiHLTr+9Hsfbn/Tfd7rra243u2XHsrj81RDjBRjCVxeLW9nKjY663S0u3757b+oX3e72K+27iw/gyEgxaB0Yjh4IXQBuXiAYRCtFS2H3CO6FrusHJ/zhh/7PfxwFedX//DE868Jp8MwDPMPGpkgJqnMCFFUgx1YhSjboKKOE8gjvpSzHwdLwn5u77e4ost9k2f1WlzvJK62Ly3F7px8DTYgXFDrO3zRfbzav9wHGrE3JdOdoAKIgwLUS2Bq05JIaKS1O3lplrUgNwXJAoCwBUg4IGakW2xpS1E88/+LvL4Y//PzbcrXcHHf8uP/7FzYYHzZ4ItroJeTaECSGCqQhQcZmgFzLiT2rcQcH8bfN1TBuhh82m6uVDi+uVY9H89V+0X7N7pN4YTotXtwDTGRyWnICo+qBaszA1jA40ySjLdWWduDU683tst0PP2+XV1e6PQpx937d47IDkECPQX0YLFkM5lwr1BIikIpCMqkAa2tJTYoZ8fRgEfTZBmVgUzyQowI5hAIYXUDTyBisj/v64fufh++utbze3I3Di1HGu+O34SiZ8ImXlb59ZBOvQorBg5UoQAYJxGaEEqNrFEuqkR5hfre5uV3p2Fn2A9g/HYW5bJ9QHZ5KdQfwxAaPSgEoUAaKbCA58YBOnPr+yxYf4f1dtq8H2Q3/Wnzgf63DP2S828pqdf+vxakB/cQ1ZMQLPsXD/mELTKIpUwOsrQAhW0i2WbDqODcTA9kDD3//+7iVMg7/pffDL7K60+NxsNPPxOTEy/ctPaBrviVbMoHlGIEwFsgRKxjOMVAzrGIPrt+4LK/vh39sRj0Oa7/mw5LDa7fnBs+fwVuUzXrU9bi4XCye0pUo1tYISbUCGYsgNSpgtAEjJ4qcJ4Ga85HajjR8TranIWVWJ0IITkiAfFLggBZSMdViybkgTSK15yM1oUNlOxNqimKZlKFW14AcZxDTCngtIYVgS1IzCdXNO/65OJlYcrER1CoB5UAgXAlYS7Uxp1zagUi8fE/qx9OJh78/cdOhe9bjpyKhqMlkjsAsDYiNh2QlAaWa0ZUkIecvicQjvTWmyoUFQqgE1GoB8cpQOLvMMdaKadL/NC+o5/o/Ooe1xQyU+q4rK7DxCOolUqi+FZyOEz8jTixegMWZSF1VMt56MOo9EJMBdjGAszW6Yk1DN400nI20yxnNxWldUfapgNQePYYCsHcVNJlIsbVWop/EGc/3qP8qjwYj1jn1kApXoFAQcv+xBXWhem2l8CTSNMujs3FiwpyNFGATElBoApwdQ2xSDeZUnZ3GafBsoPuyKH6utCeymSFx/TY59RmodKTFCiAZj8qaAk3fenO+ltFXnb2lWij1ZMV619OvCFlMALElVFObR2zTSM/Xsj3S2fcpkcYmYiFQCkBiIrAjgaLsyGuurdZppOdLWdzn2TgXagu2KIUAlkwEYpeBjWFAdVg8e1XGaajns36HOpv0i+YQyYP3bIBKyZAKGuCkroZgRPORjOt81u959fww9TZb442F4LwCBe+BDWVIzRhNJpEpRw7/fNaPnaTC3Ktf0CRTnQdvMALFppB9dFBDQJdsdcYdufrn877BfcI99/gNNtGkBOxcBrLVQEqmdj5t7Cm04uI0VP4/l6hcrFPjoarNQNl44FYykGJGKjbFMi365nyN6jeK5vq05IQhUwARZiAXHbDBAFSca1l8Qz3QqGfL7djT06MY5XHBE5msMfakotV+e0Cn2bpYYgEfYi9aKQNXdBAdYzDkY7LT6Yk9X0yN29etZnYR08U0hgxBTS9ijANWZuCGTYPliG369O35cmq+Tk+bmCyhJWBBBIq2gURnwEdjcraxuSLTUM/XU7NPUmYzFQaW4IoHyU6B0FfIwQjYpBQkJlNwmv3t+YK6j4C5x+9CLTlXAVt6ua2mgnjxUIzHJMaTxiPHf76cmj37Gzu3NdCqJib0wD4JEFoLLDGB2uY9snOtTeeo9nxJNft24PzUz5ampSUwqVMVmgCcMYJrWkJEtuSn6d+er6lmL/9xrldTjtY0Y6GIj0BUGwhTBkkeMWdP4UgPy84Q1R4Bc0O1CQaxLUCy2EsUZMg+K4SgMZpAWtMRn54vqXugYXZGhbmoCkGRXkwV34A9Ijj0sZieXNkjSM9X1D1SY+Y2BkvMIbpYoEYJQLFkYHUWsFCg4NVEcwD12e3t6n74YbXJshr+styVzd16HL7b1Ccamp/1WZM92mhdb0bdPV//tb8wXjZZ7fSJ5mtqZJXQArFHIHEEOWcEz77Z0IJwKdPtt/NV1to9aDfXzS0Xsj4hFHYOyPWntEYMzBSKNixZp6nLna+y1n1VOVAs+9QaAXF/yIlWQGJCCOKbwRiD99MFljtfZa39qgasoCm9awENrQKV5CElbpBCcWo526I6DfV8ld1HwFygNVUfc+6lda+vc7W9U9x73K1yzLZkeyRUz1fZPdDZiYuruRiuCkGphwB6yJoCNCeE2ThhP91+d+drrA37VsBsmkWHXrsgOGsskIkWuOeDUcn1UYgU7HQ+6M4XWRvpay5VisakkCNU7mmW6d2AFCuIc6laic1YNw31fJG1YZ9mzZYEi7W4UC1E6lUgk0ISq8A2MbNkqu3IrTpfZ93+1c3PzghdUBNLg9YCA9XeDELcP8NIayZ3v05DPV9o99E6ux1QYkyKtT+8mJ5mM3AgBe4iRWJCqtMZAc3Qqg50NgEEb3zB1gCTSHepB0kpQVYMJnFpaA+6QfsJlN3mRofrU2ZQDhuWPDlGwNWFisWBSuefKBHEFAPqmrFcS2GrXxwjOCjFRWx2YS+8oQFF1xNbZHCNjYaANUeZ2k6V+3N2497nB5+P0PwvbyeiY5O5APreUu4DB1mCgdZJ2zUOCQ/E+W+bze3w4xvdDs9HvXniLfx2tRyfr/8sY7nWL7zh74dU+jTC6UMq3mlLrvYczVSgXgdxYwZHXfvEJVMPiuGJYY7jOdDH0xxPwHa9nTfFiU+Nd6CzzjiTwIv05zBNICIMLVuDUk0s9qA4/kml3vcxoFFe6zBeL3f7H651WOvv47DSN7r60/BSBxmH5+v15k0fqRtkq0O5Vt0u11dD22yH+83dN998M/xZd+OwacPqrrweZF2Hq63KOOzuStHdTve27zd3g6xWwx/d8IuuNC/HoW03N4/Wj/ptvfnx9gsO8/QQ0ScO7SDGpigVqkMDlAoBo+uvu5iytBZbC5MkN0M3ov2axLFEcsG5BlI9AdWCfdAogFOHlCRbDdNIac48RJgD9dXFQsq4fKMPhdBOx3G5vtp/VH/Xcte/4Mdt1e3icvHGdBtv3n9+Tz2kIbiUOtX1ubVkEThwAPQ5YJVqNPWUo2zWay3d1N7wwTjm5dvFjSz7Fn59u5/xPLEA3H/oYrFcV/19cYnvXr16d/Eps07ZfmLk71OTFw8f+n996SY98SkpH7ji1cWjN56mwUnLn473TTn5qQmpLxzce4WfMvrk/N001M/OesrukfnWSYsTGztqciphOQ7z4/nfKaNPjn9OGp4aBZyyfPJM3mffchBKUyPWk6imznEK1ZODjEeC/nMPPX08Hydgp8I1R87nSB40afYJjjtKW0dmxCftf+lMDwnh1bt37/4HwIf3ihcwAAA=

metadata_json:
  {"categories":["automation","e-commerce","marketing","notifications","data-logging"],"complexity":"medium","use_cases":["Recover abandoned Shopify carts","Automated email recovery sequences","Optional SMS/WhatsApp reminders","Track recovery attempts in Google Sheets","Apply fallback discounts to encourage conversions"],"estimated_setup_minutes":30,"required_services":["n8n instance","Shopify (Checkout Create webhook)","Gmail","Google Sheets","Twilio (SMS/WhatsApp)"],"key_features":["Detects created but uncompleted Shopify checkouts","Configurable wait/check logic (default 1 hour)","Pulls cart contents and applies fallback discount","Sends recovery emails via Gmail","Optional 24‑hour SMS/WhatsApp reminder via Twilio','Logs each interaction to Google Sheets for tracking','Visual sticky-note guidance inside the workflow for easier customization'"],"target_audience":["Solo store owners","eCommerce marketers","Automation beginners/no-code users」「Small businesses using Shopify"]}


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: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:31
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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

metadata_json:
  {"categories":["automation","content creation","video production","AI/ML integration","integration"],"complexity":"complex","use_cases":["Automated generation and publishing of YouTube Shorts","Turning short ideas/notes into animated videos","Batch or single-item content pipeline for social platforms","Creating AI voiceovers and sound effects for short videos","Rapid prototyping of animated social clips"],"estimated_setup_minutes":120,"required_services":["n8n (cloud or self-hosted)","Google Sheets API","Google Drive API","Anthropic Claude or Google Gemini (text prompt generation)","Flux AI (via RapidAPI) (image generation)","RunwayML (image animation/video generation)","ElevenLabs (voiceover and sound FX)","Creatomate (video/audio merging and rendering)","YouTube Data API (video upload/publish)"],"key_features":["Reads one idea at a time from Google Sheets and marks status to avoid duplication","Generates text prompts for images and audio using large language models","Creates high-quality AI images (Flux) and animates them (Runway)","Produces realistic narration and sound effects via ElevenLabs","Merges animated video and audio into a final render with Creatomate and uploads to YouTube automatically"],"target_audience":["Content creators and YouTubers","Social media managers and marketers","Automation engineers and developers building media pipelines"]}


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: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
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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

metadata_json:
  {"categories":["automation","integration","data processing"],"complexity":"simple","use_cases":["Automatically extract YouTube subtitles/transcripts","Clean and format transcripts for readability","Prepare transcripts for summarization or content repurposing","Support language analysis or NLP workflows","Save transcripts to a database or trigger downstream processing"],"estimated_setup_minutes":15,"required_services":["YouTube Transcript API (via RapidAPI)","RapidAPI account and API key","n8n instance"],"key_features":["Retrieve subtitles from YouTube videos via RapidAPI","Clean and normalize raw transcript text (remove timestamps/unwanted characters)","Error handling when transcripts are unavailable","Return structured raw and cleaned transcript outputs","Use environment variables for secure API key handling','Simple form trigger for inputting video URL"],"target_audience":["Content creators","Researchers","Developers integrating video text workflows','Data analysts and NLP practitioners"]}


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: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
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, 4196 characters]

metadata_json:
  {"categories":["automation","compliance","data processing","integration","audit"],"complexity":"medium","use_cases":["Validate CSRD XHTML reports for required tags and namespaces","Automate ESG/CSRD compliance checks and generate error/warning summaries","Extract report metadata (entity name, reporting period) from XHTML","Export results to Google Sheets or send audit summaries via email","Augment reports with AI-generated summaries or insights"],"estimated_setup_minutes":30,"required_services":["n8n","Google Sheets API","OpenAI API (GPT-4o / GPT-40)","Gmail (or SMTP) for email delivery","GitHub (optional) for sample XHTML"],"key_features":["Parses XHTML and checks for XBRL/CSRD tags and namespaces","Custom code node for structural validation and rule customization","Generates structured audit report with errors, warnings, and metadata","Exports results to Google Sheets and/or sends email notifications","Optional AI summarization of report contents via OpenAI agent"],"target_audience":["sustainability teams","ESG consultants","data engineers / developers implementing compliance automation"]}


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: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
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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

metadata_json:
  {"categories":["automation","integration","data processing","knowledge management"],"complexity":"medium","use_cases":["Knowledge ingestion for RAG systems","Prompt management for AI workflows","Populating dynamic form/question sets","Client-specific config or script template loading","Content ingestion for translation/summarization/classification pipelines"],"estimated_setup_minutes":15,"required_services":["Google Drive (OAuth2)","Google Docs (OAuth2)"],"key_features":["Retrieve all Google Docs from a specified Drive folder","Download and extract full document content","Normalize newlines and clean text","Map file names to content (key-value pairs) using Set node","Aggregate entries into a single JSON dictionary with Code node (filename => content)"],"target_audience":["automation engineers","developers","ML/AI practitioners building RAG or prompt workflows\",\"product/ops teams managing content in Google Docs"]}


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: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:31
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, 5316 characters]

metadata_json:
  {"categories":["integration","data retrieval","AI agent","finance/crypto"],"complexity":"complex","use_cases":["Get live crypto prices and volumes","Convert between crypto and fiat","Retrieve token metadata (whitepapers, logos, links)","Analyze global market metrics (total cap, BTC dominance)","Discover and map CoinMarketCap IDs for tokens','Rank top coins by market cap or volume"],"estimated_setup_minutes":15,"required_services":["CoinMarketCap API","OpenAI (GPT-4o-mini) or compatible LLM","n8n automation platform (workflow execution)"],"key_features":["Intelligent tool routing across 6 CoinMarketCap endpoints","Real-time price and volume retrieval","Crypto↔fiat conversion tool","Token metadata and whitepaper lookup","Global metrics (market cap, dominance) reporting','Session buffer memory for context in queries"],"target_audience":["Crypto analysts","Traders","Developers building crypto integrations or bots','Data engineers/analysts','Quant researchers"]}


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: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
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, 5112 characters]

metadata_json:
  {"categories":["integration","data processing","analytics"],"complexity":"medium","use_cases":["Map and lookup exchanges (ID, name, slug)","Fetch exchange metadata and operational details","Analyze exchange token holdings and USD value","Retrieve constituents and weights of the CMC 100 Index","Monitor daily market sentiment using the Fear & Greed Index"],"estimated_setup_minutes":20,"required_services":["CoinMarketCap API","n8n (workflow runtime)","OpenAI (GPT-4o-mini or compatible LLM for AI Brain)"],"key_features":["Exchange Map lookup (IDs, slugs, names)","Exchange Info (launch date, social links, country, status)","Exchange Assets reporting (balances, wallets, USD value)","Live CMC 100 Index constituents and weights","Daily Fear & Greed sentiment score and classification range (Extreme Fear → Extreme Greed) 
  (note: updated daily via API data feed)"],"target_audience":["Developers building crypto integrations","Crypto analysts and researchers","Traders and portfolio analysts wanting exchange holdings and sentiment context"]}


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: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
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, 11176 characters]

metadata_json:
  {"categories":["integration","analytics","data processing","automation"],"complexity":"complex","use_cases":["Rank DEXs by 24h volume and market share","Identify spot pairs with highest liquidity on a given network","Retrieve historical OHLCV time-series for specific DEX pairs","Inspect latest trades and real-time pair stats (price, buy/sell, liquidity)","Filter and monitor pairs by volume, volatility, liquidity or transaction count (alerts/sub-agent)"],"estimated_setup_minutes":20,"required_services":["CoinMarketCap DEXScan API (API Key)","LLM provider supporting gpt-4o-mini (e.g., OpenAI)","n8n workflow runtime with outbound HTTP access"],"key_features":["Tool-based agent with 8 DEXScan endpoints (metadata, networks, listings, pairs, trades, OHLCV)","Real-time and historical OHLCV (1m/hourly/daily)","Session-based memory via sessionId for contextual agent conversations","Can run standalone or be invoked as a sub-agent from a supervisor workflow","Configurable filters for liquidity, volume, volatility, transactions, and rankings"],"target_audience":["crypto analysts and researchers","DeFi traders and market makers","developers integrating DEX analytics into apps/workflows」、「data engineers and BI teams"]}


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: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
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, 8352 characters]

metadata_json:
  {"categories":["automation","integration","data processing","analytics","ai-assistant"],"complexity":"complex","use_cases":["Token intelligence and price lookups","Exchange asset holdings and sentiment analysis","DEX liquidity, pair quotes and OHLCV historical analysis","Cross-market price comparisons (CEX vs DEX)","On-demand crypto queries delivered via Telegram bot"],"estimated_setup_minutes":45,"required_services":["n8n (workflow runtime)","CoinMarketCap API (including CEX and DEX endpoints)","Telegram Bot API (BotFather token)","OpenAI (GPT-4o-mini or compatible LLM)","DEX / on-chain data provider (via the DEXScan sub-agent)"],"key_features":["AI routing agent that selects and executes the correct sub-workflow","Integrates 3 specialized sub-agents (Crypto, Exchange & Community, DEXScan)","Real-time multi-source market data aggregation (CEX, exchanges, DEX)","Structured API request building and response formatting","Telegram delivery of answers and conversational memory (windowed buffer) support for sessions and follow-ups','Built-in error handling guidance and usage notes"],"target_audience":["Crypto traders","Market analysts and researchers","Developers building crypto tools or bots, integrators implementing data pipelines"]}


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: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
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, 26084 characters]

metadata_json:
  {"categories":["automation","communication","scheduling","integration","voice AI"],"complexity":"complex","use_cases":["Automated inbound call handling with AI receptionist","Appointment booking and calendar management","Caller follow-up and confirmation emails","Logging call transcripts and metadata to a database","Slot availability management and conflict resolution"],"estimated_setup_minutes":45,"required_services":["n8n (cloud-hosted instance with public webhook URLs)","Vapi (Twilio) assistant for voice AI","Google Calendar","Airtable","Email provider (Gmail/SMTP) for confirmations"],"key_features":["AI voice receptionist answering inbound calls","Real-time calendar availability checks and slot booking","Create, update and cancel events in Google Calendar","Send email confirmations and follow-ups","Store call summaries/transcripts and metadata in Airtable"],"target_audience":["Small businesses","Agencies","Solo professionals / freelancers who take calls and appointments manually but want automation support tech-savvy operators implementing phone workflows"]}


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: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
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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

metadata_json:
  {"categories":["automation","image-generation","prompt-engineering","design"],"complexity":"medium","use_cases":["Generate AI image prompts matching a target art style","Create multiple design variations for print-on-demand products","Automate prompt engineering for image generators","Extract and expand on text phrases from existing designs"],"estimated_setup_minutes":20,"required_services":["OpenAI (GPT-4o) API key and funded account","n8n instance with local file access (Local File Trigger)","Local filesystem/folders for input and output"],"key_features":["Watch a local folder and trigger workflow when an image is saved","Analyze an image with a GPT-4o agent to extract style, text, and description","Generate five unique image prompts based on analysis using GPT-4o","Extract original phrase and produce nine new text phrases (10 total)","Combine prompts and phrases to produce up to 50 unique variations','Save full output to a local file automatically"],"target_audience":["Print-on-demand sellers","Graphic designers and logo/artwork creators","Content creators using AI image generators','Marketers and small business owners who need scalable design variations"]}


name: Smart Sales Support Chatbot with GPT-4o and Google Sheets
author_name: Babish Shrestha
author_username: bbz
categories: []
created_at: 2025-04-04T09:13:57.734Z
updated_at: 2025-04-04T09:13:57.734Z
url: https://n8n.io/workflows/3433
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:31
description:
  Who is this tempate for?
  This workflow powers a simple yet effective customer and sales support chatbot for your webshop. It's perfect for solopreneurs who want to automate customer interactions without relying on expensive or complex support tools.

  How it works?
  The chatbot listens to user requests—such as checking product availability—and automatically handles the following

  Fetches product information from a Google Sheet
  Answers customer queries
  Places an order
  Updates the stock after a successful purchase

  Everything runs through a single Google Sheet used for both stock tracking and order management.

  Setup Instructions

  Before you begin, connect your Google Sheets credentials by following this guide: This will be used to connect all the tools to Google Sheets
  👉 Setup Google sheets credentials

  Get Stock

  	Open "Get Stock" tool node and select the Google sheet credentials you created. 
  	Choose the correct google sheet document and sheet name and you are done.
  Place order
  	
  	Go to your "Place Order" tool node and select the Google sheet credentials you have created. 
  	Choose the correct google sheet document and sheet name.

  Update Stock
  	- Open your "Update Stock" tool node and select the Google sheet credentials you have created. 
  	Choose the correct google sheet document and sheet name.
  	In "Mapping Column Mode" section select map each column manually.
  	In "Column to match on" select the column with a unique identifier (e.g., Product ID) to match stock items.
  	In values to update section, add only the column(s) that need to be updated—usually the stock count.
  AI Agent node 
  	Adjust the prompt according to your use case and customize what you need. 

  Google Sheet Template
  Stock sheet

  |Case ID|Phone Model|Case Name|Case Type|Image URL|Quantity Avaialble|Initital Inventory|Sold|
  |-|-|-|-|-|-|-|-|
  |1023|Iphone 14 pro|Black Leather|Magsafe|https://example.com/url|90|100|10

  Order sheet

  |Case ID|Phone Model|Case Name|Name|Phone Number|Address|
  |-|-|-|-|-|-|
  |1023|Black Leather |Iphone 14 pro|Fernando Torres|9998898888|Paris, France

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.googleSheetsTool","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.googleSheetsTool"]

workflow_json_compressed:
  [encoded data, 4764 characters]

metadata_json:
  {"categories":["automation","integration","customer support","e-commerce","chatbot"],"complexity":"medium","use_cases":["Automate customer and sales support chats","Look up product availability from a sheet","Place orders via chat and record them","Automatically update inventory after purchases","Lightweight order management using a single Google Sheet"],"estimated_setup_minutes":20,"required_services":["Google Sheets","OpenAI (GPT-4o)","n8n"],"key_features":["Fetch product data from Google Sheets","Answer customer product queries with AI","Place orders and write them to a sheet","Update stock counts after purchases","Single-sheet stock + order workflow for simplicity"],"target_audience":["Solopreneurs","Small e-commerce owners","No-code users who want automated support tools without a full helpdesk system"]}


name: Get Qualified Leads in One Click from Apollo to Airtable
author_name: Not Another Marketer
author_username: notanothermarketer
categories: []
created_at: 2025-04-04T11:10:06.804Z
updated_at: 2025-04-04T11:10:06.804Z
url: https://n8n.io/workflows/3435
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:31
description:
  You Don’t Need More Tools. You Just Need the Right Leads.
  Why spend $1,000s on lead gen when your perfect leads are already waiting in Apollo? You’ve already filtered the ideal prospects. You know who they are, where they work, and what they do.

  Now imagine turning that list into enriched, ready-to-contact leads—without paying pricey Apollo's recurring subscription (spoiler: you will pay only 0.60$ per 500 leads).

  From Filter to Outreach-Ready in Seconds
  With the Lead Generation System, you just drop your Apollo search URL.

  The workflow does the rest:
  ✅ Scrapes all matching contacts from your Apollo filter
  ✅ Enriches and organizes the data (names, roles, emails, LinkedIns, companies, etc.)
  ✅ Delivers the final lead list to Airtable—or your CRM of choice

  No more manual exports. 
  No CSV mess. 
  No VA needed.
  Just qualified leads, cleaned and ready to go.

  Perfect For
  Founders doing DIY outbound
  Growth marketers scaling cold email
  Agencies running lead-gen for clients
  Anyone tired of paying too much for messy, outdated lists

  Setup Guide
  I built a step-by-step guide to setup this workflow in 5 to 10 minutes, available here: https://notanothermarketer.gitbook.io/home/templates/lead-generation

  This template is free. Enjoy!

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

workflow_json_compressed:
  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

metadata_json:
  {"categories":["lead generation","integration","data enrichment","automation"],"complexity":"medium","use_cases":["Scrape qualified prospects from Apollo filters","Enrich contact data (emails, LinkedIn, company info)","Automatically push leads into Airtable or CRM","Scale cold outreach without manual CSV exports"],"estimated_setup_minutes":10,"required_services":["Apollo (search URL / account)","Airtable (API key & base)","HTTP access / web scraping capability","Enrichment service or API (used to append emails/LinkedIn)","AI/ML provider (optional — for data parsing/enrichment)"],"key_features":["One-click extraction from an Apollo search URL","Automated enrichment of names, roles, emails, LinkedIn, company data","Direct delivery to Airtable or other CRM","Removes manual exports and CSV handling","Fast setup (5–10 minutes) and low per-lead cost"],"target_audience":["Founders doing DIY outbound","Growth marketers scaling cold email","Agencies running lead-gen for clients"]}


name: Send links from Telegram Channel to Hoarder and Readeck
author_name: Sherlockes
author_username: sherlockes
categories: []
created_at: 2025-04-04T11:19:47.480Z
updated_at: 2025-04-04T11:19:47.480Z
url: https://n8n.io/workflows/3436
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  What this template is made for:
  I have a personal Telegram channel and a bot inside it where I save interesting links that I want to save or read later. The idea is that n8n will take care of reading the new links added to this channel and send them, through the corresponding API, to the Hoarder and Readeck installations.

  How it works
  Since my server where n8n runs is not always on, a "Schedule Trigger" will be responsible for checking every so often if there is any new content in the Telegram channel where I store the links. This request is made through "http request" and the Telegram API.
  Next, a code block is responsible for filtering out everything that is not a hyperlink.
  At this point, the flow splits into two so that parallel and similar processes are performed for Hoarder and Readeck.
  The corresponding API is accessed to get a list of all the links saved in the corresponding service.
  A code block is responsible for filtering the list of hyperlinks previously obtained from Telegram so that only those that are not already saved in the service continue.
  Finally, another "Http Request" node is responsible for using the service API to save the link in the corresponding service.

  Configuration instructions
  The template makes use of the environment variables that I have declared in the n8n "docker-compose.yml" file through an external ".env" file. These are the variables I use:
  Telegram Bot Token Sherlink
  TG_SHERLINK_BOT_TOKEN=XXXXXXXX:XXXXXXXXXXXXXXXX
  Id Telegram Channel Sherlink
  TG_SHERLINK_ID=-XXXXXXXXXXXXX
  Readeck server
  READECK_SERVER=http://readeck.midomain.com
  READECK_API_KEY=xxxxxxxxxxxxx
  Hoarder server
  HOARDER_SERVER=http://hoarder.midomain.com
  HOARDER_API_KEY=xxxxxxxxxxxxxx

  Created in 1.85.4 n8n version

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.splitOut","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.httpRequest"]

workflow_json_compressed:
  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

metadata_json:
  {"categories":["automation","integration","data processing"],"complexity":"medium","use_cases":["Automatically save links posted in a personal Telegram channel to read-later services","Deduplicate links before adding to Hoarder or Readeck","Periodic polling of Telegram channel to collect new links","Sync/backup curated links from Telegram to self-hosted services"],"estimated_setup_minutes":15,"required_services":["Telegram Bot API (bot token and channel ID)","Hoarder API (self-hosted Hoarder instance and API key)","Readeck API (self-hosted Readeck instance and API key)","n8n (hosted where it can run scheduled checks; uses environment variables, typically Docker)"],"key_features":["Schedule-based polling of a Telegram channel via HTTP requests to Telegram API","Extraction and filtering of hyperlinks from channel messages","Parallel workflows to send links to Hoarder and Readeck","Deduplication by querying each service's API before saving","Uses environment variables for credentials and servers (Docker/.env friendly)"],"target_audience":["Users who save links to a personal Telegram channel and want them archived","Self-hosters running Hoarder and Readeck","n8n users with basic scripting/API knowledge (can configure env vars and endpoints)"]}


name: Automatically Create Cinematic Quote Videos with AI and Upload to YouTube
author_name: Jaruphat J.
author_username: jaruphatj
categories: []
created_at: 2025-04-05T01:03:46.035Z
updated_at: 2025-04-05T01:03:46.035Z
url: https://n8n.io/workflows/3438
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  ⚠️ Important Disclaimer:
  This template is only compatible with a self-hosted n8n instance using a community node.
  Who is this for?
  This workflow is ideal for digital content creators, marketers, social media managers, and automation enthusiasts who want to produce fully automated vertical video content featuring inspirational or motivational quotes. Specifically tailored for Thai language, it effectively demonstrates integration of AI-generated imagery, video, ambient sound, and visually appealing quote overlays.

  What problem is this workflow solving?
  Manually creating high-quality, vertically formatted quote videos is often repetitive, time-consuming, and involves multiple tedious steps like selecting suitable visuals, editing audio tracks, and correctly overlaying text. Additionally, manual uploading to platforms like YouTube and maintaining accurate content records are prone to errors and inefficiencies.

  What this workflow does:
  Fetches a quote, author, and scenic background description from a Google Sheet.
  Automatically generates a vertical background image using the Flux AI (txt2img) API.
  Transforms the AI-generated image into a subtly animated cinematic vertical video using the Kling video-generation API.
  Generates an immersive, ambient background sound using ElevenLabs’ sound generation API.
  Dynamically overlays the selected Thai-language quote and author text onto the generated video using FFmpeg, ensuring visually appealing typography (e.g., Kanit font).
  Automatically uploads the final video to YouTube.
  Updates the resulting YouTube video URL back to the Google Sheet, keeping your content records current and well-organized.

  Setup
  Requirements:
  This workflow requires a self-hosted n8n instance, as the execution of FFmpeg commands is not supported on n8n Cloud. Ensure FFmpeg is installed on your self-hosted environment.

  API keys and accounts setup for Flux, Kling, ElevenLabs, Google Sheets, Google Drive, and YouTube.

  Google Sheets Setup:
  Your Google Sheet must include these columns:
  Index**	Unique identifier for each quote
  Quote (Thai)**	Quote text in Thai language (or your chosen language)
  Pen Name (Thai)**	Author or pen name of the quote's creator
  Background (EN)**	Short English description of the scene (e.g., "sunrise over mountains")
  Prompt (EN)**	Detailed English prompt describing the image/video scene (e.g., "peaceful sunrise with misty mountains")
  Background Image**	URL of AI-generated image (updated automatically)
  Background Video**	URL of generated video (updated automatically)
  Music Background**	URL of generated ambient audio (updated automatically)
  Video Status**	YouTube URL (updated automatically after upload)
  A ready-to-use Google Sheets template is provided [here (provide your actual link)].

  To help you get started quickly, you can use this template spreadsheet.
  Next steps:
  Authenticate Google Sheets, Google Drive, YouTube API, Flux AI, Kling API, and ElevenLabs API within n8n.
  Ensure FFmpeg supports fonts compatible with your chosen language (for Thai, "Kanit" font is recommended).
  Prepare your Google Sheets with desired quotes, authors, and image/video prompts.
  How to customize this workflow to your needs:
  Fonts:** Adjust font type, size, color, and positioning within the provided FFmpeg commands in the workflow’s code nodes. Verify that selected fonts properly support your target language.
  Media Customization:** Customize the scene descriptions in your Google Sheet to change image/video backgrounds automatically generated by AI.
  Quote Management:** Easily manage, add, or update quotes and associated details directly via Google Sheets without workflow modifications.
  Audio Ambiance:** Customize or adjust the ambient sound prompt for ElevenLabs within the workflow’s HTTP Request node to match your video's desired mood.

  Benefits of using AI-generated content and localized fonts:
  Leveraging AI-generated visual and audio elements along with localized fonts greatly enhances audience engagement by creating visually appealing, professional-quality content tailored specifically for your target audience. This automated workflow drastically reduces production time and manual effort, enabling rapid, consistent content creation optimized for platforms such as YouTube Shorts, Instagram Reels, and TikTok.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.googleDrive","n8n-nodes-base.readWriteFile","n8n-nodes-base.httpRequest","n8n-nodes-base.readWriteFile","n8n-nodes-base.code","n8n-nodes-base.executeCommand","n8n-nodes-base.httpRequest","n8n-nodes-base.readWriteFile","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 7916 characters]

metadata_json:
  {"categories":["automation","content-creation","video-production","integration","AI-generated media"],"complexity":"complex","use_cases":["Automatically create vertically-formatted cinematic quote videos","Batch/growth content production for YouTube Shorts, Reels, TikTok","Localized (Thai) quote video production with proper fonts","Maintaining a tracked content log in Google Sheets","Rapid prototyping of AI-generated visuals + audio for short-form video"],"estimated_setup_minutes":90,"required_services":["Self-hosted n8n instance (required)","FFmpeg installed on host (with Thai-supporting fonts e.g., Kanit)","Flux AI (txt2img) API","Kling video-generation API","ElevenLabs sound generation API","Google Sheets API","Google Drive API","YouTube Data API","n8n community node(s) used by the workflow"],"key_features":["Fetches quotes and metadata from Google Sheets","Generates vertical background images via Flux (txt2img)","Converts images to subtly animated cinematic vertical videos with Kling","Generates ambient background audio via ElevenLabs","Overlays Thai-language quote and author text onto video using FFmpeg (font support configurable) and ensures appealing typography/positioning (e.g., Kanit)  , uploads final video to YouTube automatically  , and writes resulting URLs back to Google Sheet for record-keeping"],"target_audience":["Digital content creators","Social media managers and marketers","Automation enthusiasts and makers comfortable self-hosting n8n  ,with FFmpeg familiarity  ,developers/ops who can manage API keys and service auth"]}


name: Validate Seatable Webhooks with HMAC SHA256 Authentication
author_name: Vitali
author_username: vquie
categories: []
created_at: 2025-04-05T09:40:14.968Z
updated_at: 2025-04-05T09:40:14.968Z
url: https://n8n.io/workflows/3439
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  📌 Validate Seatable Webhooks with HMAC SHA256 Authentication

  This mini workflow is designed to securely validate incoming Seatable webhooks using HMAC SHA256 signature verification.

  🔐 What it does:
  Listens for incoming Seatable webhook requests.
  Calculates a SHA256 HMAC hash of the raw request body using your shared secret.
  Compares the computed hash with the x-seatable-signature header (after removing the sha256= prefix).
  If the hashes match: responds with 200 OK and forwards the request to subsequent nodes.
  If the hashes don’t match: responds with 403 Forbidden.

  ⚠️ Important Notes:
  This workflow is provided as a template and is not intended to work standalone. Please duplicate it and integrate it with your custom logic at the "Add nodes for processing" node.

  Configuration steps:
  Set your secret key in the “Calculate sha256” crypto node (replace the placeholder).
  Adjust the webhook path to suit your environment (or set it to "manual" for testing).
  Connect your actual logic after the verification step.

nodes_used:
  ["n8n-nodes-base.respondToWebhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.crypto","n8n-nodes-base.webhook","n8n-nodes-base.noOp","n8n-nodes-base.if","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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

metadata_json:
  {"categories":["integration","security","webhook processing","automation"],"complexity":"simple","use_cases":["Securely validate Seatable webhook payloads","Protect downstream workflows from tampered webhooks","Gate webhook-triggered automations by signature verification","Integrate Seatable events into internal systems after verification"],"estimated_setup_minutes":10,"required_services":["Seatable (webhook sender)","n8n (workflow host)","Any service that can send HTTPS webhooks with an HMAC SHA256 signature header"],"key_features":["Calculates SHA256 HMAC of raw request body using a shared secret","Compares computed hash to x-seatable-signature header (removes sha256= prefix)","Returns 200 OK on match and forwards to downstream nodes","Returns 403 Forbidden on mismatch","Configurable secret in the crypto node and adjustable webhook path for testing/production"],"target_audience":["Developers","DevOps / SRE","Security-conscious integrators/engineers handling webhooks」「No-code/low-code automation builders who need secure webhook endpoints"]}


name: Track LLM Token Costs per Customer Using the Langchain Code Node
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2025-04-05T10:50:28.430Z
updated_at: 2025-04-05T10:50:28.430Z
url: https://n8n.io/workflows/3440
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  Note: This template only works for self-hosted n8n.

  This n8n template demonstrates how to use the Langchain code node to track token usage and cost for every LLM call.

  This is useful if your templates handle multiple clients or customers and you need a cheap and easy way to capture how much of your AI credits they are using.

  How it works
  In our mock AI service, we're offering a data conversion API to convert Resume PDFs into JSON documents.
  A form trigger is used to allow for PDF upload and the file is parsed using the Extract from File node.
  An Edit Fields node is used to capture additional variables to send to our log.
  Next, we use the Information Extractor node to organise the Resume data into the given JSON schema.
  The LLM subnode attached to the Information Extractor is a custom one we've built using the Langchain Code node.
  With our custom LLM subnode, we're able to capture the usage metadata using lifecycle hooks.
  We've also attached a Google Sheet tool to our LLM subnode, allowing us to send our usage metadata to a google sheet.
  Finally, we demonstrate how you can aggregate from the google sheet to understand how much AI tokens/costs your clients are liable for.


  Check out the example Client Usage Log - https://docs.google.com/spreadsheets/d/1AR5mrxz2S6PjAKVM0edNG-YVEc6zKL7aUxHxVcffnlw/edit?usp=sharing

  How to use
  SELF-HOSTED N8N ONLY** - the Langchain Code node is only available in the self-hosted version of n8n. It is not available in n8n cloud.
  The LLM subnode can only be attached to non-"AI agent" nodes; Basic LLM node, Information Extractor, Question & Answer Chain, Sentiment Analysis, Summarization Chain and Text Classifier.

  Requirements
  Self-hosted version of n8n 
  OpenAI for LLM
  Google Sheets to store usage metadata

  Customising this template
  Bring the custom LLM subnode into your own templates! In many cases, it can be a drop-in replacement for the regular OpenAI subnode.
  Not using Google Sheets? Try other databases or a HTTP call to pipe into your CRM.

nodes_used:
  ["n8n-nodes-base.googleSheetsTool","n8n-nodes-base.set","n8n-nodes-base.formTrigger","@n8n/n8n-nodes-langchain.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.form","n8n-nodes-base.filter","n8n-nodes-base.googleSheets","n8n-nodes-base.summarize","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.extractFromFile","@n8n/n8n-nodes-langchain.informationExtractor"]

workflow_json_compressed:
  [encoded data, 7752 characters]

metadata_json:
  {"categories":["automation","integration","AI cost-tracking","data processing"],"complexity":"medium","use_cases":["Track LLM token usage and cost per customer","Audit and allocate AI costs across clients","Convert Resume PDFs to structured JSON with LLM assistance","Log and aggregate usage metadata for billing/analytics"],"estimated_setup_minutes":30,"required_services":["n8n (self-hosted)","OpenAI API","Google Sheets"],"key_features":["Custom Langchain Code LLM subnode capturing lifecycle usage metadata","Parse uploaded PDF resumes to JSON (Extract from File + Information Extractor)","Log token/usage metadata to Google Sheets for aggregation","Form trigger for file upload and per-customer attribution","Drop-in replacement for standard OpenAI subnode in supported nodes"],"target_audience":["Developers implementing AI usage tracking","AI engineers integrating LLM usage telemetry","SaaS teams that bill clients for AI consumption (finance/ops)"]}


name: Generate Conversational Twitter/X Threads with GPT-4o AI
author_name: joseph
author_username: josephn8n
categories: []
created_at: 2025-04-05T17:33:45.203Z
updated_at: 2025-04-05T17:33:45.203Z
url: https://n8n.io/workflows/3441
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  🧵 Generate Conversational Twitter/X Threads with GPT-4o AI (n8n Workflow)
  This workflow uses OpenAI (GPT-4o) and Twitter/X to automatically generate and publish engaging, conversational threads in response to a trigger (e.g., from a chatbot or form).

  🚀 What Does It Do?
  Listens for an incoming message (e.g., via webhook or another n8n input).
  Uses GPT-4o to craft a narrative-style Twitter thread in a personal, friendly tone.
  Publishes the first tweet, then automatically posts each following tweet as a reply—building a full thread.

  🛠️ What Do You Need to Configure?
  Before using this template, make sure to set up the following credentials:

  OpenAI
  Add your OpenAI API key in the OpenAI Chat Model node. This is used to generate the thread content.

  Twitter/X
  Add your Twitter OAuth2 credentials to the First Tweet and Thread Reply nodes. This allows the workflow to publish tweets on your behalf.

  ✨ Who Is This For?
  This template is perfect for:
  Content creators who want to share ideas regularly
  Personal brands looking to grow their presence
  Social media managers automating thread creation

  🔧 How to Customize It
  You can easily adjust the tone, structure, or length of the threads by modifying the system prompt in the OpenAI node.

  For example:
  To create threads with humor, change the prompt to “Write in a witty and humorous tone.”
  To tailor it for marketing, prompt it with “Write a persuasive product-focused Twitter thread.”

  You can also integrate this workflow with:
  Telegram bots
  Web forms (e.g., Typeform, Tally)
  CRM tools or newsletter platforms


  📋 Sample Output
  Prompt sent to the workflow:
  “Tips for growing on Twitter in 2025”

  Generated thread:

  ++Tweet 1:++
  Thinking of growing your presence on Twitter/X in 2024? Here's a thread with the most effective strategies that actually work 🧵

  ++Reply 1:++
  Engage, don’t broadcast
  Twitter is a conversation platform. Reply to others, quote-tweet, and start discussions instead of just posting links.

  ++Reply 2:++
  Consistency beats virality
  Tweeting regularly builds trust and visibility. You don't need to go viral — just show up.

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.twitterTool","n8n-nodes-base.twitterTool","@n8n/n8n-nodes-langchain.agent"]

workflow_json_compressed:
  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

metadata_json:
  {"categories":["automation","social media","integration","content creation"],"complexity":"medium","use_cases":["Automatically generate and publish Twitter/X threads from triggers","Scale content creation for personal brands and creators","Automate social media engagement workflows for marketing","Convert chatbot or form responses into threaded posts"],"estimated_setup_minutes":15,"required_services":["OpenAI (GPT-4o API)","Twitter/X (OAuth2 API)","Webhook or form provider (e.g., n8n Webhook, Typeform, Tally)"],"key_features":["Generates narrative-style multi-tweet threads with GPT-4o","Posts initial tweet then publishes replies to build a thread","Customizable system prompt for tone, length, and style","Triggerable from webhooks, chatbots, or form submissions","Supports integrations with Telegram, CRM or newsletter tools via n8n nodes"],"target_audience":["Content creators","Social media managers","Personal brands and influencers」「Developers and automation engineers"]}


name: Fully Automated AI Video Generation & Multi-Platform Publishing
author_name: Juan Carlos Cavero Gracia
author_username: carlosgracia
categories: []
created_at: 2025-04-05T18:28:46.255Z
updated_at: 2025-04-05T18:28:46.255Z
url: https://n8n.io/workflows/3442
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 19:22:11
description:
  Description

  This comprehensive n8n automation template orchestrates a complete end-to-end workflow for generating engaging short-form Point-of-View (POV) style videos using multiple AI services and automatically publishing them across major social media platforms. It takes ideas from a Google Sheet and transforms them into finished videos with captions, voiceovers, and platform-specific descriptions, ready for distribution.

  Who Is This For?

  Content Creators & Agencies:** Mass-produce unique short-form video content for various clients or channels with minimal manual effort.
  Digital Marketers:** Automate video content pipelines to boost online presence and engagement across multiple platforms simultaneously.
  Social Media Managers:** Schedule and distribute consistent video content efficiently without juggling multiple tools and manual uploads.
  Businesses:** Leverage AI to create branded video content for marketing, reducing production time and costs.

  What Problem Does This Workflow Solve?

  Creating and distributing high-quality short-form video content consistently across multiple social networks is incredibly time-consuming and resource-intensive. This workflow tackles these challenges by:

  Automating Idea-to-Video Pipeline:** Generates video concepts, image prompts, scripts, images, video clips, and voiceovers using AI.
  Streamlining Video Assembly:** Automatically combines generated assets into a final video using a template.
  Generating Platform-Optimized Descriptions:** Creates relevant descriptions for posts by transcribing the final video audio.
  Automating Multi-Platform Publishing:** Uploads the final video and description to TikTok, Instagram, YouTube, Facebook, and LinkedIn simultaneously.
  Reducing Manual Workload:** Drastically cuts down the time and effort required for video production and distribution.
  Centralized Tracking:** Updates a Google Sheet with results, costs, and status for easy monitoring.

  How It Works

  Trigger & Input: Runs on a daily schedule (configurable) and fetches new video ideas from a designated Google Sheet.
  AI Content Generation:
      Uses OpenAI to generate video captions and image prompts based on the idea.
      Uses PiAPI (Flux) to generate images from prompts.
      Uses PiAPI (Kling) to generate video clips from the images (Image-to-Video).
      Uses OpenAI to generate a voiceover script based on the captions.
      Uses ElevenLabs to generate voiceover audio from the script and uploads it to Google Drive.
  Video Assembly: Combines the generated video clips, captions, and voiceover audio using a Creatomate template to render the final video.
  Description Generation: Uploads the final video to Google Drive, extracts the audio using OpenAI (Whisper), and generates a social media description using OpenAI (GPT).
  Multi-Platform Distribution: Uses upload-post.com to upload the final video and generated description to TikTok, Instagram, YouTube, Facebook, and LinkedIn.
  Tracking & Notification: Updates the original Google Sheet row with output details (video link, costs, tokens used) and sends a completion notification via Discord.

  Setup

  Accounts & API Keys: Obtain accounts and generate API keys/credentials for:
      n8n
      Google Cloud Platform (for Google Sheets & Google Drive APIs + OAuth Credentials)
      OpenAI
      PiAPI
      ElevenLabs
      Creatomate
      upload-post.com
      Discord (Webhook URL)
  Google Sheet: Make a copy of the provided Google Sheet Template and connect it in the Load Google Sheet node.
  Creatomate Template: Set up a video template in Creatomate (use the provided JSON source code as a base) and note its Template ID.
  Configure Nodes:
      Enter all API Keys/Credentials in the Set API Keys node and other relevant credential sections (Google nodes, upload-post nodes, etc.).
      Configure Google Drive nodes (Folder IDs, Permissions).
      Configure the upload-post.com nodes with your user identifier and necessary platform details (e.g., Facebook Page ID).
      Customize AI prompts within the OpenAI nodes (Generate Video Captions, Generate Image Prompts, Generate Script, Generate Description...) if desired.
      Set the Discord Webhook URL in the Notify me on Discord node.
  Enable Google APIs: Ensure Google Drive API and Google Sheets API are enabled in your Google Cloud Project.

  Requirements

  Accounts:** n8n, Google (Sheets, Drive, Cloud Platform), OpenAI, PiAPI, ElevenLabs, Creatomate, upload-post.com, Discord.
  API Keys & Credentials:** API Keys for OpenAI, PiAPI, ElevenLabs, Creatomate, upload-post.com. Google Cloud OAuth 2.0 Credentials. Discord Webhook URL.
  Templates:** A configured Google Sheet based on the template, a configured Creatomate video template.
  (Potentially) Paid Plans:** Some services (OpenAI, PiAPI, Creatomate, upload-post.com) may require paid plans depending on usage volume after free trials/credits are exhausted.

  Use this template to build a powerful, automated video content factory, scaling your production and distribution efforts across the social media landscape.

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.wait","n8n-nodes-base.wait","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.wait","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.stickyNote","n8n-nodes-base.merge","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.merge","n8n-nodes-base.httpRequest","n8n-nodes-base.discord","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.googleDrive","n8n-nodes-base.httpRequest","n8n-nodes-base.googleDrive","n8n-nodes-base.googleSheets","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.if","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.readBinaryFile","n8n-nodes-base.writeBinaryFile"]

workflow_json_compressed:
  [encoded data, 17316 characters]

metadata_json:
[cell repeats an earlier one in this file: 210 characters]


name: Scrape business leads from Google Maps using OpenAI and Google Sheets
author_name: Dr. Firas
author_username: drfiras
categories: []
created_at: 2025-04-05T19:50:44.805Z
updated_at: 2025-04-05T19:50:44.805Z
url: https://n8n.io/workflows/3443
scraped_at: 2025-09-14 19:10:59
description:
  Google Maps Data Extraction Workflow for Lead Generation

  This workflow is ideal for sales teams, marketers, entrepreneurs, and researchers looking to efficiently gather detailed business information from Google Maps for:

  Lead generation  
  Market analysis  
  Competitive research

  Who Is This Workflow For?

  Sales professionals** aiming to build targeted contact lists  
  Marketers** looking for localized business data  
  Researchers** needing organized, comprehensive business information  

  Problem This Workflow Solves

  Manually gathering business contact details from Google Maps is:

  Tedious  
  Error-prone  
  Time-consuming  

  This workflow automates data extraction to increase efficiency, accuracy, and productivity.

  What This Workflow Does

  Automates extraction of business data (name, address, phone, email, website) from Google Maps  
  Crawls and extracts additional website content  
  Integrates OpenAI to enhance data processing  
  Stores structured results in Google Sheets for easy access and analysis  
  Uses Google Search API to fill in missing information  

  Setup

  Import the provided n8n workflow JSON into your n8n instance.  
  Set your OpenAI and Google Sheets API credentials.  
  Provide your Google Maps Scraper and Website Content Crawler API keys.  
  Ensure SerpAPI is configured to enhance data completeness.  

  Customizing This Workflow to Your Needs

  Adjust scraping parameters:
    Location  
    Business category  
    Country code  
  Customize Google Sheets output format to fit your current data structure  
  Integrate additional AI processing steps or APIs for richer data enrichment  

  Final Notes

  This structured approach ensures:

  Accurate and compliant data extraction** from Google Maps  
  Streamlined lead generation  
  Actionable and well-organized data ready for business use  

  📄 Documentation: Notion Guide

  Demo Video

  🎥 Watch the full tutorial here: YouTube Demo

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolSerpApi","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.aggregate","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.aggregate","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 8060 characters]


name: Redis Locking for Concurrent Task Handling
author_name: Geoffrey Saxena
author_username: geffy
categories: []
created_at: 2025-04-06T08:12:44.136Z
updated_at: 2025-04-06T08:12:44.136Z
url: https://n8n.io/workflows/3444
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  👤 Who is this for?

  This workflow is great for n8n users who want to prevent duplicate or overlapping workflow runs. If you're a developer, DevOps engineer, or automation enthusiast managing tasks like database updates, syncing tools, or hitting rate-limited APIs, this one’s for you.

  🧩 What problem does this solve?

  In the real world, automations can get triggered at the same time—whether that’s because of multiple webhook calls, overlapping schedules, or retries. And when two workflows try to do the same thing at once (like updating a record or syncing data), it can cause conflicts, data corruption, or wasted API calls.

  This workflow helps avoid that problem by using Redis as a lock system, so only one instance runs at a time. Think of it like putting up a “🚧 Workflow in Progress” sign while your logic is running.

  ⚙️ What this workflow does

  When the workflow starts, it tries to set a Redis key as a lock with a short expiry.
  If the lock is free:
     Your main business logic runs.
     Once it's done, the lock is cleared.
  If the lock is already taken (i.e., another run is in progress):
     The workflow will wait and retry a few times.
  If a duplicate request shows up while one is already being processed:
     It skips that duplicate to avoid unnecessary work.

  You can customize both the timeout and retry logic to match your needs.

  🛠️ Setup guide

  To use this template:

  You’ll need access to a Redis instance (either self-hosted or managed like Upstash, Redis Cloud, etc).
  Set up your Redis credentials in the n8n Redis node.
  Swap out the webhook node with your actual trigger or logic.
  Adjust the lock timeout to match how long your task typically takes.

  &gt; 💡 Bonus Tip: Use this pattern wherever you need idempotency or want to avoid duplicate processing.

  🧪 Example use case

  Let’s say you have a workflow that syncs ClickUp tickets to Google Sheets. It runs daily at 9 AM and updates tickets, adds notes, and makes sure nothing is missed.

  But what if two runs start at the same time? Or someone triggers a manual sync while the scheduled one is still working?

  By wrapping that whole sync inside this Redis locking template, you can make sure it only runs one at a time, saving your APIs (and your sanity).

nodes_used:
  ["n8n-nodes-base.noOp","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.webhook","n8n-nodes-base.code","n8n-nodes-base.redis","n8n-nodes-base.redis","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.wait","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.redis","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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

metadata_json:
  {"categories":["automation","integration","reliability","devops"],"complexity":"medium","use_cases":["Prevent duplicate or overlapping workflow runs","Ensure idempotent processing for webhooks and scheduled jobs","Protect against race conditions when updating shared resources (databases, sheets, etc.)","Throttle concurrent access to rate-limited APIs","Coordinate distributed workers or tasks using a centralized lock"],"estimated_setup_minutes":15,"required_services":["Redis (self-hosted or managed e.g., Upstash, Redis Cloud)","Any downstream APIs/services your workflow will call (e.g., ClickUp, Google Sheets) — optional"],"key_features":["Distributed locking using Redis keys with expiry","Configurable lock timeout and retry/wait logic","Skips duplicates when a lock is held to avoid wasted work","Simple drop-in pattern to wrap existing webhook/schedule triggers","Works with common n8n nodes (Redis, Set, If, Switch, Code) for flexible logic"],"target_audience":["Developers","DevOps engineers / SREs","Automation engineers / n8n users building integrations"]}


name: Convert n8n tags into folders and move workflows
author_name: Zacharia Kimotho
author_username: imperolq
categories: []
created_at: 2025-04-06T08:40:16.391Z
updated_at: 2025-04-06T08:40:16.391Z
url: https://n8n.io/workflows/3445
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  N8n recently introduced folders and it has been a big improvement on workflow management on top of the tags.

  This means the current workflows need to be moved manually to the folders. 

  The simplest idea to try is to convert the current tags into folders and move all the current workflows within the respective tags into the folders

  This assumes the tag name will be used as the folder name.

  To Note

  For workflows that use more than 1 tag, the workflow will be assigned the last tag that runs as the folder. 

  How does it work
  I took the liberty of simplifying the setup of this workflow that will be needed on your part and also be beginner-friendly 

  Copy and paste this workflow into your n8n canvas. You must have existing workflows and tags before you can run this
  Set your n8n login details on the node set Credentials with the n8n URL, username, and password.
  Setup your n8n API credentials on the n8n node get workflows
  Run the workflow. This opens up a form where you can select the number of tags to move and click on submit
  The workflow responds with the successful number of workflows that were imported 


  Read more about the template

  Built by Zacharia Kimotho - Imperol

nodes_used:
  ["n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.filter","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.removeDuplicates","n8n-nodes-base.splitInBatches","n8n-nodes-base.n8n","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.limit","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.filter","n8n-nodes-base.set","n8n-nodes-base.formTrigger","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.form","n8n-nodes-base.set","n8n-nodes-base.form","n8n-nodes-base.set","n8n-nodes-base.splitOut","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"]

workflow_json_compressed:
  [encoded data, 7088 characters]

metadata_json:
  {"categories":["automation","migration","workflow management"],"complexity":"medium","use_cases":["Convert existing tags into folders in n8n","Bulk-move workflows into folders based on their tags","Cleanup and organize n8n workflows","Migrate tagging system to folder-based structure"],"estimated_setup_minutes":10,"required_services":["n8n instance (cloud or self-hosted) with API access","n8n credentials (username/password)"],"key_features":["Creates folders named after existing tags","Moves workflows into corresponding folders","Handles workflows with multiple tags by assigning the last processed tag as folder","Interactive form to select number of tags to process","Reports count of successfully moved workflows"],"target_audience":["n8n users","Automation engineers","DevOps/sysadmins managing n8n instances (beginner-friendly)"]}


name: Daily Newsletter Service using Excel, Outlook and AI
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2025-04-06T14:53:44.596Z
updated_at: 2025-04-06T14:53:44.596Z
url: https://n8n.io/workflows/3446
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:31
description:
  This n8n template builds a newsletter ("daily digest") delivery service which pulls and summarises the latest n8n.io template in select categories defined by subscribers.

  It's scheduled to run once a day and sends the newsletter directly to subscriber via a nicely formatted email. If you've had trouble keeping up with the latest and greatest templates beign published daily, this workflow can save you a lot of time!

  How it works
  A scheduled trigger pulls a list of subscribers (email and category preferences) from an Excel workbook.
  We work out unique categories amongst all subscribers and only fetch the latest n8n website templates from these categories to save on resources and optimise the number of API calls we make.
  The fetched templates are summarised via AI to produce a short description which is more suitable for our email format.
  For each subscriber, we filter and collect only the templates relevant to their category preferences (as defined in the Excel) and ensure that duplicate templates or those which have been "seen before" are omitted.
  A HTML node is then used to generate the email newsletter. HTML emails are the perfect format since we can add links back to the template.
  Finally, we use the Outlook node to send the email digest to the subscriber.

  How to use
  Populate your Excel sheet with 3 columns: name, email and categories. Categories is a comma-delimited list of categories which match the n8n template website. The available categories are AI, SecOps, Sales, IT Ops, Marketing, Engineering, DevOps, Building Blocks, Design, Finance, HR, Other, Product and Support.
  To subscribe a new user, simply add their email to the Excel sheet with at least one category.
  To unsubscribe a user, remove them from the sheet.
  If you're not interested in paid templates, you may want to filter them out after fetching.

  Requirements
  Microsoft Excel for subscriber list
  Microsoft Outlook for delivering emails
  OpenAI for AI-generated descriptions

  Customising the workflow
  Use AI to summarise the week's trend of templates types and use-cases
  This template can be the basis for other similar newsletters - just pull in a list of things from anywhere!

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.microsoftExcel","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.aggregate","n8n-nodes-base.set","n8n-nodes-base.splitOut","n8n-nodes-base.splitInBatches","n8n-nodes-base.splitOut","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.merge","n8n-nodes-base.httpRequest","n8n-nodes-base.noOp","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.removeDuplicates","n8n-nodes-base.splitOut","n8n-nodes-base.aggregate","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.html","n8n-nodes-base.set","n8n-nodes-base.microsoftOutlook","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.splitInBatches","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 8072 characters]

metadata_json:
  {"categories":["automation","integration","email/newsletter","data processing","content generation"],"complexity":"medium","use_cases":["Daily curated newsletter delivery","Content curation and summarization","Subscriber management via spreadsheet","Personalized email digests by category","Automating template discovery and delivery"],"estimated_setup_minutes":30,"required_services":["Microsoft Excel (workbook for subscribers)","Microsoft Outlook (to send emails)","OpenAI (or compatible LLM for summaries)","n8n website/templates API or web access to fetch templates"],"key_features":["Scheduled daily trigger","Pull subscriber list from Excel","Aggregate unique categories to minimise API calls","Fetch latest n8n templates by category","AI-generated short summaries for each template entry (LLM)"],"target_audience":["marketers","community managers","n8n users/maintainers of template libraries or newsletters (operators)"]}


name: Manage Calendar Events with Slack Using OpenAI-Powered Outlook Assistant
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2025-04-06T18:35:30.751Z
updated_at: 2025-04-06T18:35:30.751Z
url: https://n8n.io/workflows/3449
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  This n8n template demonstrates how easy it is to build an Outlook Calendar Assistant powered by an AI agent equipped with Tools.

  For teams using Outlook Calendar and Slack who need easier calendar management, this workflow can be a great first step to introducing powerful AI tools into your daily activities.

  How it works
  A Slack Trigger node is configured to catch "bot mentions" events in a designated channel.
  The message is parsed using the Edit fields node to extract only the required attributes of the event.
  An AI Agent equipped with Outlook Calendar Tools enables question and answer capability for the organisation's shared calendars and events.
  The AI agent's response is sent back to Slack as a reply to the user's query.

  How to use
  The workflow is triggered via @mention-ing the bot followed by the query. eg. "@bot how many meetings does Paul have to attend to this week?"
  To start listening to real mentions, you must activate the workflow and set it to production mode. You must use the production webhook URL for the event subscription.

  Some sample queries to try
  "What's included in the product team's sprint demo this week?"
  "Who's booked room 7 for this Thursday?"
  "When is Jim & Nik's sales meeting with Microsoft?"

  Requirements
  Slack for Chat and Trigger.
    To get connected to Slack, see the official n8n docs for Slack Credentials.
  Outlook for Agent Tools
    To get connected to Outlook, see the official n8n docs for Outlook Credentials.

  Customising this workflow
  Not using Slack? This template can be modified to work with Teams but requires a little more configuration.
  Agents can have any number of tools but an overloaded agent is prone to confusion! If this happens, try splitting into multiple agents serving separate needs.

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.microsoftOutlookTool","n8n-nodes-base.microsoftOutlookTool","n8n-nodes-base.microsoftOutlookTool","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","n8n-nodes-base.slack","n8n-nodes-base.respondToWebhook","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.webhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 5184 characters]

metadata_json:
  {"categories":["automation","integration","calendar management","AI assistance","team collaboration"],"complexity":"medium","use_cases":["Ask natural-language questions about shared Outlook calendars","Quickly find who booked rooms or attends meetings","Summarize meeting details for a team or individual","Automated calendar Q&A inside Slack channels","Augment team workflows with AI-powered calendar tools"],"estimated_setup_minutes":30,"required_services":["Slack (bot & events API)","Microsoft Outlook / Microsoft 365 (Calendar access via Graph)","OpenAI (lmChat/OpenAI agent)","n8n (workflow runtime)"],"key_features":["Slack trigger for bot mentions to start queries","AI agent equipped with Outlook Calendar tools for Q&A","Parses and extracts event attributes before querying calendars","Replies back in Slack threads with concise answers","Supports shared calendar access and organization-wide queries (e.g., who’s attending)"],"target_audience":["IT/Platform engineers (for setup)","Team leads and managers","Product and operations teams needing quick calendar insights or summaries (non-technical users)"]}


name: Auto-Generate YouTube Chapters with Gemini AI & YouTube Data API v3
author_name: irfan saeed
author_username: irfansaeed
categories: []
created_at: 2025-04-06T18:49:17.804Z
updated_at: 2025-04-06T18:49:17.804Z
url: https://n8n.io/workflows/3450
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  Auto-Generate YouTube Chapters with AI-Powered Transcript Analysis  
  Overview  
  This workflow uses YouTube Data API v3  and Google Gemini 1.5 Flash AI  to automatically generate timestamped chapters for videos by analyzing SRT captions. It enhances viewer navigation, improves SEO , and saves creators time by automating manual tasks.  

  Prerequisites  
  YouTube API Setup  
  Create a Google Cloud Project  
     Go to the Google Cloud Console.  
     Click Select a project &gt; New Project and name it (e.g., "YouTube Chapters Automation") .  

  Enable YouTube Data API v3  
     Navigate to APIs & Services &gt; Library.  
     Search for "YouTube Data API v3" and click Enable .  

  Configure OAuth Consent Screen  
     Go to APIs & Services &gt; OAuth consent screen.  
     Select External (public) or Internal (testing), then add required details (app name, support email) .  

  Generate OAuth 2.0 Credentials  
     Under Credentials, click Create Credentials &gt; OAuth client ID.  
     Choose Web app, then download the JSON key file .  

  Add Credentials to n8n  
  Other Requirements  
  Google Gemini API**: Configure access for the gemini-1.5-flash-8b-exp-0924 model by getting the api key.  
  Workflow Steps  
  Set Video ID  
     Input the target video ID (e.g., r1wqsrW2vmE) using the Set Video ID node.  

  Fetch Video Metadata  
     Use the YouTube API node to retrieve the video’s title, category, and existing description .  

  Download SRT Captions  
     Get Caption ID: Call https://www.googleapis.com/youtube/v3/captions to fetch the caption track ID .  
     Download Transcript: Use the ID to retrieve SRT data via https://www.googleapis.com/youtube/v3/captions/{{ID}}?tfmt=srt .  

  Analyze Transcript with Gemini AI  
     Process the SRT file with Google Gemini AI to identify chapters using a prompt like:  
            "Classify this transcript into timestamped chapters (e.g., 00:00 - Introduction)."  
          Validate output with a structured parser (e.g., Structured Captions node) .  

  Update Video Description  
     Append chapters to the description using the YouTube API’s videos.update method .  

  Value Proposition  
  Viewer Experience**: Chapters improve navigation and reduce drop-off rates .  
  SEO Benefits**: Structured descriptions enhance search visibility .  
  Time Savings**: Eliminates manual chapter creation .  

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.extractFromFile","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.youTube","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.set","n8n-nodes-base.youTube","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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

metadata_json:
  {"categories":["automation","content management","AI","video","integration"],"complexity":"medium","use_cases":["Automatically generate timestamped YouTube chapters from SRT captions","Improve video SEO by adding structured chapter timestamps to descriptions","Save creator time by automating manual chapter creation","Improve accessibility and viewer navigation","Ingest transcripts for downstream repurposing or analytics"],"estimated_setup_minutes":45,"required_services":["YouTube Data API v3 (Google Cloud Project + OAuth 2.0 credentials)","Google Gemini API (gemini-1.5-flash-8b model)","n8n (workflow runner)","Internet access to download SRT captions via YouTube captions endpoint"],"key_features":["Fetches video metadata via YouTube Data API","Downloads SRT caption tracks and parses transcript","Uses Google Gemini to analyze transcript and generate timestamped chapters","Validates/structures AI output with a parser node","Updates video description with appended chapters via videos.update API call"],"target_audience":["YouTube creators and influencers","Content marketers and SEO specialists","Social media managers and video editors looking to automate workflows users seeking AI-assisted content automation"]}


name: 🔊 Browser Recording Audio Transcribing and AI Analysis with Deepgram and GPT-4o
author_name: RealSimple Solutions
author_username: joeperes
categories: []
created_at: 2025-04-07T00:41:41.162Z
updated_at: 2025-04-07T00:41:41.162Z
url: https://n8n.io/workflows/3451
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  Overview

  Transcript Evalu8r V2 is a robust browser-based transcript analysis tool powered by Deepgram’s speech-to-text API and built into an n8n workflow template. This release introduces full in-browser audio recording, device selection, and playback functionality, allowing users to capture and analyze conversations without leaving their browser.

  Designed for researchers, support teams, podcasters, legal professionals, and analysts, Transcript Evalu8r simplifies how you extract value from voice data using intuitive tools, AI-powered insights, and seamless automation.

  ✅ What’s New in V2

  |  |  |
  |:--|:--|
  |### 🔴 Record in Browser Users can now record audio directly in the browser with one click. Record meetings, calls, or voice notes instantly—no external software required. | ### 🎧 Select Audio Input Devices Users can choose from connected input devices (e.g., internal mic, USB mic, headset) before recording, ensuring maximum control and audio quality.|

  ▶️ Listen to Recording in Browser  
  Once a recording is complete, users can immediately play back the audio in-browser to review before submitting for transcription—ensuring content is clear and relevant.

  How It Works

  Record or upload an audio file
  **Audio recording is uploaded to Google Drive
  Audio file is sent to Deepgram for transcribing and AI analysis
  Audio File is moved to completed folder
  JSON transcript is saved to Google Drive
  Google Doc of AI analysis, summary, key points, action items, and full transctip is created
  Use the Web UI to Explore:
     Speaker Names
     Transcript
     Key points & tasks
     Sentiment chart
     Topic and intent lists with time stamps
  Download or export transcript and insights
  Add Additional automations or continue workflow (e.g., send to Notion, Slack, Google Sheets, todoist)

  Key Features

  🔹 Transcript Processing & Uploads
  Upload or record audio directly in your browser
  Track progress of file uploads in real-time
  View, select, or download transcripts in Google Docs, JSON, and other formats

  🔹 Interactive Transcript Viewer
  Full transcript display with timestamps, speaker IDs, and auto-scroll
  Clickable word-level navigation for pinpointing dialogue
  Real speaker labeling (not just "Speaker 1")

  🔹 AI-Powered Analysis
  Sentiment visualization** over time, per speaker
  Topic and intent extraction** with confidence scores
  Speaker contribution insights**, interruptions, and common word frequency

  🔹 Insight Summaries Google Doc
  AI-generated summary, key points, and action items
  Speaker-specific task suggestions and idea highlights

  🔹 UI Enhancements
  Collapsible sections for focused analysis
  Light and dark mode support
  Responsive design across desktop, tablet, and mobile

  Use Cases

  ✅ Interview & podcast summarization  
  ✅ Customer support QA monitoring  
  ✅ Legal or compliance transcription logging  
  ✅ Market research and trend analysis  
  ✅ Internal meeting follow-up automation  

  Requirements

  n8n instance** (self-hosted or cloud)
  Deepgram API Key** (for audio transcription)
  Supported browser with mic permissions enabled

  Why Choose Transcript Evalu8r V2?

  🚀 Instantly record and transcribe within your browser  
  🧠 Extract sentiment, insights, and intent automatically  
  📈 Visualize speaker tone and engagement  
  🎧 Choose audio input for cleaner recordings  
  🔗 Connect with your favorite tools using n8n’s integrations  

nodes_used:
  ["n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.googleDocs","n8n-nodes-base.html","n8n-nodes-base.respondToWebhook","n8n-nodes-base.webhook","n8n-nodes-base.webhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.webhook","n8n-nodes-base.webhook","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.respondToWebhook","n8n-nodes-base.gmail","n8n-nodes-base.convertToFile","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDocs","n8n-nodes-base.googleDrive","n8n-nodes-base.merge","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.merge","n8n-nodes-base.googleDrive","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.stickyNote","n8n-nodes-base.googleDrive","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.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.extractFromFile","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.code"]

workflow_json_compressed:
  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

metadata_json:
  {"categories":["automation","transcription","speech-to-text","ai-analysis","integration"],"complexity":"complex","use_cases":["Podcast and interview summarization","Customer support QA and monitoring","Legal/compliance transcription and logging","Market research and trend analysis","Internal meeting follow-up and action-item automation"],"estimated_setup_minutes":60,"required_services":["n8n (self-hosted or cloud)","Deepgram API","Google Drive","Google Docs","OpenAI / GPT-4o (or compatible LLM)","Web browser with mic permissions"],"key_features":["In-browser audio recording with device selection","Upload and track audio files to Google Drive","Deepgram speech-to-text transcription","AI-powered analysis: summaries, sentiment, topics, intents, action items","Interactive transcript viewer with timestamps, speaker labels, word-level navigation, and auto-scroll (Real speaker labeling) ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ )​(trimmed)"],"target_audience":["Researchers","Customer support teams / QA analysts","Podcasters and interviewers / producers / hosts (trimmed) ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ "]}


name: Monitor GitHub Releases with Gemini AI Chinese Translation & Slack Notifications
author_name: tbphp
author_username: tbphp
categories: []
created_at: 2025-04-07T03:07:49.414Z
updated_at: 2025-04-07T03:07:49.414Z
url: https://n8n.io/workflows/3452
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Overview

  This n8n template monitors specified GitHub repositories. When a new release is published, it automatically fetches the information, uses AI (Google Gemini by default) to summarize and translate it into Chinese, and sends a formatted notification to a designated Slack channel.

  Core Features:

  Automated Monitoring**: Checks for updates on a predefined schedule.
  Intelligent Processing**: Uses AI to extract key information and translate.
  Error Handling**: Sends an error notification if fetching RSS for a single repository fails, without affecting others.
  Duplicate Prevention**: Remembers the last processed release ID using Redis to ensure only new content is pushed.

  Prerequisites

  Slack**: Configure your Slack app credentials in n8n.
  Redis**: Have an available Redis service and configure its credentials in n8n.
  AI Provider (Gemini)**: Configure credentials for Google Gemini (or your chosen AI model) in n8n.

  Configuration Instructions

  After importing the template, you need to modify the following key nodes:

  Cron Trigger:
      Adjust the Rule setting to change the update check frequency (default is 0 */10 9-23 * * *, checking every 10 minutes between 9 AM and 11 PM daily).
  GitHub Config (Repository List - Code Node):
      Edit the JavaScript array within this node's code area.
      Modify or add the repositories you want to follow. Each repository object needs a name (custom display name) and github (format: owner/repo).
      Example:
                  {
            "name": "n8n", // Custom display name
            "github": "n8n-io/n8n" // GitHub path
          },
          {
            "name": "LobeChat",
            "github": "lobehub/lobe-chat"
          }
          // ... add more repositories
          Redis and Redis2 (Redis Connection):
      Select your configured Redis credentials in both nodes.
  Gemini (AI Model):
      Select your configured Google Gemini credentials.
      (Optional) Replace with a different supported AI model node and select its credentials.
  Information Extractor (AI Processing & Translation):
      Main Configuration: Review the System Prompt. By default, it asks the AI to extract information and translate it into Chinese. Modify this prompt if you need a different language or summary style.
  Send Message and Send Error (Slack Notifications):
      Select your configured Slack credentials in both Slack nodes.
      Set the target Channel ID for notifications.

  Workflow Overview

  Start: Cron Trigger initiates the workflow on schedule.
  Load Config: GitHub Config provides the list of repositories to monitor.
  Loop: The Loop node iterates through each repository.
  Fetch & Check:
      The RSS node attempts to fetch the repository's releases feed.
      If No Error checks for success:
          Failure: Send Error posts an error to Slack, skips this repository.
          Success: Continues.
  Check for New Release:
      The Redis node retrieves the last recorded Release ID for this repository.
      The If New node compares the latest Release ID with the recorded ID:
          Different IDs (New Release): Proceeds to processing.
          Same ID (Already Processed): Skips this repository.
  Process & Notify (Only for New Releases):
      Information Extractor (with Gemini) extracts, summarizes, and translates the content.
      The Code node formats the information into Slack Block Kit.
      Send Message sends the formatted message to Slack.
      The Redis2 node stores the current Release ID in Redis.
  End: The workflow finishes after processing all repositories.

  Conclusion

  Once configured, this template automates GitHub release monitoring, uses AI to distill key information, and delivers it efficiently to your Slack workspace.

nodes_used:
  ["n8n-nodes-base.limit","n8n-nodes-base.splitInBatches","n8n-nodes-base.set","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.slack","n8n-nodes-base.if","n8n-nodes-base.dateTime","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.slack","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","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.redis","n8n-nodes-base.code","n8n-nodes-base.rssFeedRead","n8n-nodes-base.redis"]

workflow_json_compressed:
  [encoded data, 7288 characters]

metadata_json:
  {"categories":["automation","integration","monitoring","ai","notifications"],"complexity":"medium","use_cases":["Monitor GitHub releases across multiple repos","Automatically summarize release notes and translate to Chinese","Send formatted Slack notifications for new releases","Avoid duplicate notifications using persistent state","Notify on RSS fetch errors per repository"],"estimated_setup_minutes":20,"required_services":["GitHub (releases RSS)","Google Gemini (or other supported AI model)","Redis","Slack","n8n"],"key_features":["Scheduled checks (Cron)","AI-based information extraction and Chinese translation","Per-repository error handling with Slack alerts","Duplicate prevention by storing last processed release ID in Redis","Slack Block Kit formatted messages for rich notifications"],"target_audience":["Developers","Community managers","Product/Release teams','DevOps"]}


name: Automated Weekly Project Cost Reports with MySQL and Outlook HTML Emails
author_name: Amjid Ali
author_username: amjid
categories: []
created_at: 2025-04-07T05:56:27.085Z
updated_at: 2025-04-07T05:56:27.085Z
url: https://n8n.io/workflows/3453
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  Automated Weekly Project Cost Reports with MySQL and Outlook HTML Emails

  🧠 Use Case

  Need to keep your finance or operations team updated on missing project costs?  
  This practical automated report workflow does the job without AI — and saves hours weekly.

  Runs on a weekly schedule
  Queries your MySQL database for projects missing cost data
  Filters by budgeted_project_cost IS NULL
  Generates a clean HTML email report
  Sends it through Microsoft Outlook to relevant teams

  🚀 How It Works

  Schedule Trigger – Runs every Monday at 8 AM
  MySQL Node – Connects and runs SQL to fetch project data missing budgeted_project_cost
  Switch Node – Routes logic based on cost_center (e.g., Retail, Service, Projects)
  Outlook Nodes – Sends formatted HTML emails; each node handles a specific group
  Dynamic Content – Inject values using mustache tags like {{ $json.project_name }}

  🔧 Setup Instructions

  MySQL Setup:
     Ensure the MySQL node is connected using a valid credential set:
       Hostname/IP
       Port (default: 3306)
       Database name
       Username with SELECT permissions
       Password
     Query Example:
            SELECT project_name, cost_center FROM tabProject 
       WHERE status = 'Open' AND project_type = 'External' AND budgeted_project_cost IS NULL;
       
  Outlook Configuration:
     Connect your Microsoft Outlook node using OAuth2 credentials.
     Rename each Outlook node clearly (e.g., Send Email - Retail, Send Email - Service).

  Switch Node:
     Modify cost center values as needed to match your organization (e.g., 'Retail', 'Service', 'Projects').

  HTML Email Formatting:
     Customize the HTML message body using inline styles and mustache syntax.
     Sample:
            Missing Budgeted Cost Report
       
         Project: {{ $json.project_name }}
         Cost Center: {{ $json.cost_center }}
       
       
  Recipients:
     Replace amjid@amjidali.com with the actual email addresses of the concerned teams.

  📘 Read More

  👉 Why Simple Workflows Work  

  📺 Demo & Tutorial

  🎥 Watch the video walkthrough:  
  https://youtube.com/@syncbricks

  👤 About the Creator

  Amjid Ali  
  🌐 amjidali.com  
  📘 n8n Book  
  🎓 Learn n8n

  &gt; “Start simple, scale smart. Even basic workflows like this can save your team hours!” — Amjid Ali

nodes_used:
  ["n8n-nodes-base.mySql","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.switch","n8n-nodes-base.microsoftOutlook","n8n-nodes-base.microsoftOutlook","n8n-nodes-base.microsoftOutlook"]

workflow_json_compressed:
  H4sIAAAAAAAAE+1Ze2/buhX/Khw3oNtgJhJFPeMUS510d7i3TW+S3qFogoIij2y2MulSVB4LDOw77BvukwyUZcdx3QdWYOsfVpBEEg/P43fO4Y+S7vEUHMfFPVa6cVwL+JvEBWZRnNM055RlPGEVjaSsUiqjUlAJTAKXGU+yFBIWQU7zpMp4EKRSJnFIgyhP8HyAtZHQ4OLtPVZeZZyXaZZUQDJgMWFJmBDOWUzylGWyChkTEccDrPkUcIFf3J3/+gseYHc385c606TTR0rewN707vxjjQd4ZhrllNG4eEuyYEBpcDXAM275FBzYxof1sQV7hwt8fvLLyegCXWqEEBJmOuP6brC8atw7AdqBRUfnSELF29q9W7vdC45OX7+8+OOf/+SlZta8B+GlWu0u9fOz0xe9csfLV4vBS/33n07OTvr7jeOubdAhenI6A/2kv3v08nilywfrx09uHVjN6ycPIqp5x4VT1934G2jWhspWjsGBfPfgUuPQIQoWIpf6r2enr1+hZ282gl+P+1Kfnh2fnG0RehQnOj45Hx1cajzAZuaB9xDP/QVYvkgEhlsQrYNfO9zniwz+BrbpRukemw/6gmCZoEFGKckrCoSFQUaysipJGQVBkuU8Tyl7KIhzMQHZ1oAurBqPwX62Nppe8EFuvUqiZHuZ2LaGRRc4sNe87uq2UlB7T28APjTe4ELlkfvJtBYX2fxqvhlguEdXAUZpnGZJUJIy5YGv+JTwKM9IlHLBaBVyAWsVf36jnJh8Pqzl8Fo0oa/5YHsw3ck1r9u+B03rZq37GXwvHOEBFkZLtUzhWjbx9SpXA1xD5X7zOnCB/RzewDlob/8acOFsC334vFZyWQCNs0o4n3phpqXS3BmLC8y13DC7XBkYjYKElpxAGCaEAaWE8yAgNA+DFDJBmQC8LDKv636JkTelx3ijAD+2vG68/XX3D+/v0R/eN0bvbelvNJ/jAbZqPFnJj3wTjfpFAc+v5gNswafqtANyEb1P9Tqwz34sYCFIYl5mjCRhJQmDRJAsi2KSUxlkJUQQxfkGsH0xVqp2YPeWI3s9poP/MfDPvhX40Y8FfABVJiCKCY2DlLCqYiSTwEkYRllAExrk2WZF//+BPxydnl+g0cnLi5MzNPpW5I9/LOTzMKYQs4wIwXPCWChIycqEZHkQVYzlUtDyh0N+veSPPwv81fwR624yT7TGPGWVVTKuGIHQ1x+DjPAq5KSEiMZ5IpOEwdpeSwlrGlM5dNq62pgPyec3XkvRXvIxH8VJMCAdud5AOTHmQ7eXlAIYzdKI5GHOCZNckLzKJUlzWgY8ysu0SvEmhTVt6bcduMD9Xgp1IL1QTbPIQmnk3choB9oLHR4Of3d8Orp48+oETdy0fnqph/4/qrkeH15i0Je4uwdcPvU7nKHf9iIx4bYBd3iJX188J1kng9DQKVfD094WetbvrxYevDROVUp0BTDcX0h2kxp3158i5J1D96gy2pGKT1V9V6Ajq3g9QA3XDWnAquoAlVx8GFvTakmEqY0t0O8r5n8O0JTbsdIFCg7QjEup9Lg7ny/078GUq5oIox1XGiy6R1N+S26UdJMCJUEwu31QQYPZLeKtM9sNdscBKo2VYInlUrVNgTKvwVyDrWpzU6CJkhL0pn2PZmd8i94gSJ89f36ANu2soqGdkw5uHeG1GusCLfrjYAFbo/4BBQo7P7obN+BbpkClqeWmIz3eG7rX9ST+xtKXqDu2KmmcNXqM7tFmHI9lK2NcF/nKZOhR/tQuW7cbd8fjoP260Wsf7i9raLjfF+rQO7WoL6mukah50/hyfpz+vm63Ci1ytJJA6CtrEyJo2XHH3PFlx/X696W6/rwt7+yapWPg9qvmLoBPB8PSPvW/y5kXE7CAuAXfVj4hT1dqHj+PzOcetE4C9c9HvUCDbpSboGnfxEs9j5p5NXdv3fjPSsv6DgljrPSMAwtNbvLgzZHozDed9w8eOIPamfQzvPDS9GITjipjEReitd245eKD0uO9zcjfmNYip6ZQ3yHu/OqmjEaqQXw2syAUdyA/mXQGY25lM/imJC1Kdy1Nq6Cm75VER7VaBbRu46h1ZtqteugYpgb9+5//QjrTmyaXZ8P9vnKH+/167DnFnIFQMwXaNZ7FvcG/dH95rfaEmeIB9v3krfD6uX/66shgba2/WNCS14k/ocAHAgRWVRDHKQmyhBIWhEDyKolJGuYxT6ksQ5l/gQDD7yDAMNkkQOBZSVMaEFGWCWFpSEmZVxVhJWNRHmcykMHXCbDZMeCOAXcMuGPAHQPuGPAbGDBIKYVSkhCCmDCogHCacgJMlmEeSRBZ8AUGTL+DAaNskwFZAglP0ozInFLCZCVIGYeSZAljaQpJGYSwY8AdA+4YcMeAOwbcMeB3MeBV92ZYg1i9hl58TC7u8ZQrT1Jv77uP01s+unUCA6y0hFtcBPMr/9K1F9o2/0uvTj/RNfjCvPC/nJd+cd7C/c0vp1sDefy9fQsO8/l/AOqbgqIsIAAA

metadata_json:
  {"categories":["automation","reporting","integration","notifications","data processing"],"complexity":"medium","use_cases":["Weekly missing-project-cost reports","Automated finance/ops alerts for incomplete data","Grouping and routing reports by cost center","Sending formatted HTML email summaries to teams"],"estimated_setup_minutes":30,"required_services":["n8n instance","MySQL database (read access)","Microsoft Outlook / Office 365 account (OAuth2)"],"key_features":["Weekly schedule trigger","MySQL query to fetch projects with NULL budgeted_project_cost","Switch node routing by cost_center","Mustache-based HTML email templates","Separate Outlook nodes per recipient group for targeted delivery (Retail, Service, Projects)"],"target_audience":["Finance teams","Operations managers","n8n users / automation builders','IT or data engineers responsible for integrations"]}


name: Auto-Generate and Post Social Media Content to Bluesky using Groq LLM
author_name: Mehdi
author_username: mlstudio
categories: []
created_at: 2025-04-07T09:14:47.000Z
updated_at: 2025-04-07T09:14:47.000Z
url: https://n8n.io/workflows/3455
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  Automatically Generate Content and Post to Bluesky with LLM Workflow

  Unlock the power of automation with the Automatically Generate Content and Post to Bluesky with LLM Workflow, an n8n-powered solution that uses Large Language Model (LLM) APIs to create engaging content and seamlessly posts it to Bluesky. Say goodbye to manual content creation and posting—streamline your social media presence in minutes!

  How It Works
  This workflow harnesses an LLM API (like Openai or your preferred model) to generate tailored content based on your input prompts. Once the content is created, it’s formatted to fit Bluesky’s requirements (e.g., under 300 characters), authenticated via the Bluesky API, and posted automatically. Built-in checks ensure error-free execution, making your content strategy seamless and efficient.

  Set Up Steps (10-15 Minutes)
  Authenticate Bluesky: Add your Bluesky API credentials (username and app password in your Bluesky settings).
  Configure LLM API: Connect your chosen LLM API (e.g., Groq) with your API key.
  Set Prompt: Define a system prompt in the LLM node (e.g., “Write a witty Bluesky post about AI automation”).
  Test Workflow: Run it manually to verify content generation and posting.
  Schedule (Optional): Set a cron node to automate posts (e.g., daily at 9 AM).

  Total setup time: 10-15 minutes, depending on familiarity with n8n.

  Features
  LLM-Powered Content**: Generate unique, high-quality posts using any LLM API.
  Bluesky Automation**: Posts directly to Bluesky with proper formatting and authentication.
  Character Limit Safety**: Ensures posts stay under 300 characters with built-in validation.
  Error Handling**: Stops execution on errors to prevent unwanted posts.
  Customizable Scheduling**: Post instantly or schedule content at your preferred intervals.
  Scalable Design**: Easily adapt for multiple platforms or content types.

  Perfect for Creators Who Want To
  Save time by automating content creation and posting.
  Maintain a consistent Bluesky presence without manual effort.
  Experiment with AI-generated content tailored to their brand.
  Focus on strategy and engagement rather than repetitive tasks.

  Suggested Enhancements
  Multi-Platform Support**: Extend the workflow to post to Twitter, Mastodon, or LinkedIn simultaneously.
  Content Variety**: Add nodes to generate images (e.g., via DALL-E) alongside text for richer posts.
  Analytics Integration**: Track post performance with a Bluesky API feedback loop.
  Dynamic Prompts**: Incorporate trending topics or keywords from the web for timely content.
  Approval Step**: Add a manual review node before posting for extra control.

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.scheduleTrigger","@n8n/n8n-nodes-langchain.lmChatGroq","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.stopAndError","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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

metadata_json:
  {"categories":["automation","integration","content generation","social media management"],"complexity":"medium","use_cases":["Automatically generate and post short-form content to Bluesky","Schedule recurring social posts","Maintain consistent social presence with AI-generated copy","Enforce platform-specific formatting (e.g., <300 characters)","Prototype multi-platform posting workflows"],"estimated_setup_minutes":15,"required_services":["n8n instance","Bluesky API (username + app password)","LLM API (e.g., Groq, OpenAI) / API key"],"key_features":["LLM-powered content generation","Direct posting to Bluesky with authentication","Character-limit validation (<300 chars)","Error handling to prevent unwanted posts","Optional scheduling (cron) for automated posting cycles"],"target_audience":["social media managers","content creators","marketers / growth teams"]}


name: Automated Discord Chatbot for chat Interaction in channel using Gemini 2.0 Flash
author_name: JPres
author_username: stardawnai
categories: []
created_at: 2025-04-07T09:39:02.177Z
updated_at: 2025-04-07T09:39:02.177Z
url: https://n8n.io/workflows/3456
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  A Discord bot that responds to mentions by sending messages to n8n workflows and returning the responses. Connects Discord conversations with custom automations, APIs, and AI services through n8n.

  Full guide on: https://github.com/JimPresting/AI-Discord-Bot/blob/main/README.md 

  Discord Bot Summary

  Overview
  The Discord bot listens for mentions, forwards questions to an n8n workflow, processes responses, and replies in Discord.
  This workflow is intended for all Discord users who want to offer AI interactions with their respective channels.

  What do you need?
  You need a Discord account as well as a Google Cloud Project

  Key Features

  1. Listens for Mentions
  The bot monitors Discord channels for messages that mention it.
  Optional Configuration**: Can be set to respond only in a specific channel.

  2. Forwards Questions to n8n
  When a user mentions the bot and asks a question:
    The bot extracts the question.
    Sends the question, along with channel and user information, to an n8n webhook URL.

  3. Processes Data in n8n
  The n8n workflow receives the question and can:
    Interact with AI services (e.g., generating responses).
    Access databases or external APIs.
    Perform custom logic.
  n8n formats the response and sends it back to the bot.

  4. Replies to Discord with n8n's Response
  The bot receives the response from n8n.
  It replies to the user's message in the Discord channel with the answer.
  Long Responses**: Handles responses exceeding Discord's 2000-character limit by chunking them into multiple messages.

  5. Error Handling
  Includes error handling for:
    Issues with n8n communication.
    Response formatting problems.
  Manages cases where:
    No question is asked.
    An invalid response is received from n8n.

  6. Typing Indicator
  While waiting for n8n's response, the bot sends a "typing..." indicator to the Discord channel.

  7. Status Update
  For lengthy n8n processes, the bot sends a message to the Discord channel to inform the user that it is still processing their request.

  Step-by-Step Setup Guide as per Github Instructions


  Key Takeaways

  You’ll configure an n8n webhook to receive Discord messages, process them with your workflow, and respond.  
  You’ll set up a Discord application and bot, grant the right permissions/intents, and invite it to your server.  
  You’ll prepare your server environment (Node.js), scaffold the project, and wire up environment variables.  
  You’ll implement message‐chunking, “typing…” indicators, and robust error handling in your bot code.  
  You’ll deploy with PM2 for persistence and know how to test and troubleshoot common issues.

  1. n8n: Create & Expose Your Webhook

  New Workflow  
     Log into your n8n instance.  
     Click Create Workflow (➕), name it e.g. Discord Bot Handler.  

  Webhook Trigger  
     Add a node (➕) → search Webhook.  
     Set:  
       Authentication: None (or your choice)  
       HTTP Method: POST  
       Path: e.g. /discord-bot  
     Click Execute Node to activate.

  Copy Webhook URL  
     After execution, copy the Production Webhook URL.  
     You’ll paste this into your bot’s .env.  

  Build Your Logic  
     Chain additional nodes (AI, database lookups, etc.) as required.

  Format the JSON Response  
     Insert a Function node before the end:  
            return {
         json: { answer: "Your processed reply" }
       };
       
  Respond to Webhook  
     Add Respond to Webhook as the final node.  
     Point it at your Function node’s output (with the answer field).

  Activate  
     Toggle Active in the top‐right and Save.

  2. Discord Developer Portal: App & Bot

  New Application  
     Visit the Discord Developer Portal.  
     Click New Application, name it.  
     Go to Bot → Add Bot.

  Enable Intents & Permissions  
     Under Privileged Gateway Intents, toggle Message Content Intent.  
     Under Bot Permissions, check:  
       Read Messages/View Channels  
       Send Messages  
       Read Message History  

  Grab Your Token  
     In Bot → click Copy (or Reset Token).  
     Store it securely.

  Invite Link (OAuth2 URL)  
     Go to OAuth2 → URL Generator.  
     Select scopes: bot, applications.commands.  
     Under Bot Permissions, select the same permissions as above.  
     Copy the generated URL, open it in your browser, and invite your bot.

  3. Server Prep: Node.js & Project Setup

  Install Node.js v20.x  
        sudo apt purge nodejs npm
     sudo apt autoremove
     curl -fsSL https://deb.nodesource.com/setup_20.x | sudo -E bash -
     sudo apt install -y nodejs
     node -v    # Expect v20.x.x
     npm -v     # Expect 10.x.x
     
  Project Folder  
        mkdir discord-bot
     cd discord-bot
     
  Initialize & Dependencies  
        npm init -y
     npm install discord.js axios dotenv
  4. Bot Code & Configuration

  Environment Variables  
     Create .env:  
            nano .env
          Populate:  
            DISCORD_BOT_TOKEN=your_bot_token
       N8N_WEBHOOK_URL=https://your-n8n-instance.com/webhook/discord-bot
  Optional: restrict to one channel
  TARGET_CHANNEL_ID=123456789012345678
       
  Bot Script  
     Create index.js:  
            nano index.js
          Implement:  
       Import dotenv, discord.js, axios.  
       Set up client with MessageContent intent.  
       On messageCreate:  
         Ignore bots or non‐mentions.  
         (Optional) Filter by channel ID.  
         Extract and validate the user’s question.  
         Send “typing…” every 5 s; after 20 s send a status update if still processing.  
         POST to your n8n webhook with question, channelId, userId, userName.  
         Parse various response shapes to find answer.  
         If answer.length ≤ 2000, message.reply(answer).  
         Else, split into ~1900‑char chunks at sentence/paragraph breaks and send sequentially.  
         On errors, clear intervals, log details, and reply with an error message.

  Login  
        client.login(process.env.DISCORD_BOT_TOKEN);
  5. Deployment: Keep It Alive with PM2

  Install PM2  
        npm install -g pm2
     
  Start & Monitor  
        pm2 start index.js --name discord-bot
     pm2 status
     pm2 logs discord-bot
     
  Auto‐Start on Boot  
        pm2 startup
  Follow the printed command (e.g. sudo env PATH=$PATH:/usr/bin pm2 startup systemd -u your_user --hp /home/your_user)
     pm2 save
  6. Test & Troubleshoot

  Functional Test  
     In your Discord server:  
            @YourBot What’s the weather like?
          Expect a reply from your n8n workflow.

  Common Pitfalls  
     No reply → check pm2 logs discord-bot.  
     Intent Errors → verify Message Content Intent in Portal.  
     Webhook failures → ensure workflow is active and URL is correct.  
     Formatting issues → confirm your Function node returns json.answer.

  Inspect Raw Data  
     Search your logs for Complete response from n8n: to debug payload shapes.

  `

nodes_used:
  ["n8n-nodes-base.webhook","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.respondToWebhook","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.agent"]

workflow_json_compressed:
  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

metadata_json:
  {"categories":["integration","automation","chatbot","AI integration"],"complexity":"medium","use_cases":["AI-powered Q&A in Discord channels","Connecting Discord messages to custom automations/APIs","Providing contextual assistant or knowledge-base responses","Enriching community workflows with AI-driven replies","Prototyping conversational integrations with Gemini 2.0"],"estimated_setup_minutes":90,"required_services":["Discord (Developer Portal & Bot)","n8n instance (webhook endpoints)","Google Cloud Project (Gemini 2.0 API)"],"key_features":["Listens for mentions and optional channel-restricted responses","Forwards questions to an n8n webhook with user/channel metadata","Integrates with AI (Gemini 2.0) and other n8n nodes/APIs","Typing indicator and periodic status updates while processing","Chunks long replies to respect Discord 2000-character limit and sends sequential messages if needed"],"target_audience":["Developers/engineers building chat integrations","Community managers and server admins","AI hobbyists and makers experimenting with Gemini models"]}


name: Classify Emails & Extract Structured Data from Job Applications with GPT-4o
author_name: Sleak
author_username: sleak
categories: []
created_at: 2025-04-07T10:57:05.716Z
updated_at: 2025-04-07T10:57:05.716Z
url: https://n8n.io/workflows/3457
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  Who is this template for?
  This workflow template is designed for business owners and HR professionals to automatically detect and structure unstructured job applications received through email. Additionally, other email categories can be added, each with it's own workflow.

  How it works
  Every time a new email is received, an OpenAI model classifies it into a predefined category by analyzing the plain text of the email and the extracted content from the attachment.
  If the email is classified as a job application, an OpenAI model uses the email’s plain text and extracted attachment content to populate predefined fields such as age and study.
  A relevant additional step would be to directly push the applicant and their structured job application into a CRM or ATS like Hubspot or Recruitee.

  Set up steps
  Configure your IMAP credentials to connect your email account. Use this n8n documentation page for quickstart guides for common email providers.
  Connect your OpenAI account in the 'Classify email' node.
  	And add or remove any category for classification in this node. Make sure the description is clear and concise.

  	
  Connect your OpenAI account in the 'Extract variables - email & attachment' node.
  	And add or remove any predefined fields that should be populated for job applications in this node. Make sure the description is clear and concise.

  	

nodes_used:
  ["n8n-nodes-base.emailReadImap","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.textClassifier","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.extractFromFile","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.noOp","n8n-nodes-base.noOp","n8n-nodes-base.noOp"]

workflow_json_compressed:
  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

metadata_json:
  {"categories":["automation","integration","email processing","data extraction","HR"],"complexity":"medium","use_cases":["Automatically classify incoming emails into predefined categories","Extract structured applicant information (age, education, etc.) from email body and attachments","Populate CRM/ATS with applicant data","Automate routing or follow-up workflows for different email categories","Preprocess unstructured job applications for downstream processing or analytics"],"estimated_setup_minutes":20,"required_services":["IMAP-compatible email provider (Gmail, Outlook, etc.)","OpenAI (GPT-4o) account","n8n instance","Optional: CRM/ATS (HubSpot, Recruitee, etc.)"],"key_features":["Triggered on new incoming email via IMAP","OpenAI-powered email classification into configurable categories","Extraction of structured variables from email body and attachments","Attachment text extraction (OCR/parse) prior to classification/extraction","Configurable predefined fields for job applications (age, study, etc.) to populate downstream systems or databases"],"target_audience":["Business owners","HR professionals and recruiters","Operations/automation teams implementing email workflows」「Developers/IT who set up integrations and connect CRMs"]}


name: Adaptive RAG Strategy with Query Classification & Retrieval (Gemini & Qdrant)
author_name: dmr
author_username: dmr
categories: []
created_at: 2025-04-07T15:43:47.541Z
updated_at: 2025-04-07T15:43:47.541Z
url: https://n8n.io/workflows/3459
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:32
description:
  This n8n workflow implements a version of the Adaptive Retrieval-Augmented Generation (RAG) framework. It recognizes that the best way to retrieve information often depends on the type of question asked. Instead of a one-size-fits-all approach, this workflow adapts its strategy based on the user's query intent.

  🌟 How it Works

  Receive Query: Takes a user query as input (along with context like a chat session ID and Vector Store collection ID if used as sub-workflow).
  Classify Query: First, the workflow classifies the query into a predefined category. This template uses four examples:
      Factual: For specific facts.
      Analytical: For deeper explanations or comparisons.
      Opinion: For subjective viewpoints.
      Contextual: For questions relying on specific background.
  Select & Adapt Strategy: Based on the classification, it selects a corresponding strategy to prepare for information retrieval. The example strategies aim to:
      Factual: Refine the query for precision.
      Analytical: Break the query into sub-questions for broad coverage.
      Opinion: Identify different viewpoints to look for.
      Contextual: Incorporate implied or user-specific context.
  Retrieve Info: Uses the output of the selected strategy to search the specified knowledge base (Qdrant vector store - change as needed) for relevant documents.
  Generate Response: Constructs a response using the retrieved documents, guided by a prompt tailored to the original query type.

  By adapting the retrieval strategy, this workflow aims to provide more relevant results tailored to the user's intent.

  ⚙️ Usage & Flexibility

  Sub-Workflow:** Designed to be called from other n8n workflows, passing user_query, chat_memory_key, and vector_store_id as inputs.
  Chat Testing:** Can also be triggered directly via the n8n Chat interface for easy testing and interaction.
  Customizable Framework:** The query categories (Factual, Analytical, etc.) and the associated retrieval strategies are examples. You can modify or replace them entirely to fit your specific domain or requirements.

  🛠️ Requirements

  Credentials:** You will need API credentials configured in your n8n instance for:
      Google Gemini (AI Models)
      Qdrant (Vector Store)

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.switch","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.embeddingsGoogleGemini","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.summarize","@n8n/n8n-nodes-langchain.vectorStoreQdrant","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 8404 characters]

metadata_json:
  {"categories":["automation","integration","retrieval-augmented-generation","conversational AI","knowledge management"],"complexity":"complex","use_cases":["Intent-aware document retrieval","Adaptive chatbot responses based on query type","Customer support knowledge base search","Contextual QA over internal docs","Research assistant that breaks down analytical queries into sub-questions for broader coverage"],"estimated_setup_minutes":60,"required_services":["Google Gemini (LLM + embeddings)","Qdrant (vector store)","n8n instance with configured credentials","Optional: external document storage or ingestion pipeline (e.g., S3, Google Drive)"],"key_features":["Query classification into Factual / Analytical / Opinion / Contextual","Adaptive retrieval strategies chosen per query intent","Qdrant vector search for document retrieval","Google Gemini used for classification, embeddings, and response generation","Sub-workflow interface (accepts user_query, chat_memory_key, vector_store_id) for reuse/integration into other workflows"],"target_audience":["Developers","ML/AI Engineers","Data Engineers building knowledge workflows"]}


name: 🎨 AI Design Team - Generate and Review AI Images with Ideogram and OpenAI
author_name: RealSimple Solutions
author_username: joeperes
categories: []
created_at: 2025-04-07T17:05:15.082Z
updated_at: 2025-04-07T17:05:15.082Z
url: https://n8n.io/workflows/3460
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:31
description:
  🎨 AI Graphic Design Team - Generate and Review AI Images with Ideogram and OpenAI

  Description

  Who is this for?
  This workflow is perfect for graphic designers, creative agencies, marketing teams, or freelancers who regularly use AI-generated images in their projects. It's specifically beneficial for teams that want to automate the generation, review, and management of AI-created graphics efficiently.

  What problem does this workflow solve?
  Design teams often face time-consuming manual reviews and inconsistent quality checks for AI-generated images. This workflow addresses these challenges by automating image generation and introducing a systematic, AI-driven vetting process. This ensures only high-quality, relevant images reach your team's assets, saving valuable time and enhancing workflow efficiency.

  What this workflow does
  AI Image Generation:** Integrates Ideogram via HTTP Request to automatically create AI-generated images based on creative briefs.
  Automated Image Review:** Uses OpenAI to automatically evaluate and approve images, ensuring they meet your predefined quality standards.
  Efficient Asset Management:** Automatically creates structured Google Drive folders and compiles key metadata (including creation dates, prompts, and image links) into a CSV file and Google Sheet.
  Immediate Email Notifications:** Delivers a setup confirmation and provides easy access to Google Drive folders and assets via automated email notifications.
  Final Approved Images:** Outputs vetted, ready-to-use images for your creative projects, removing the burden of manual reviews.

  Setup
  Initial Email Configuration
     Update your email details in both the "Setup Gmail" node and the "Gmail" notification node.
     Run the initial setup workflow to automatically create the Google Drive folders "Graphic_Design_Team" and "Image_Generations," and upload your CSV file (n8n-Graphic_Design_Team.csv).

  Review Email & Set Up Google Sheets
     Check your inbox for an automated email containing folder IDs and direct links.
     Create and set up a Google Sheet by importing the provided CSV data from your email.

  Update Workflow Nodes
     Select your newly created Google Sheet in both Google Sheets nodes.
     Update your Creative Brief node with the Google Drive folder IDs provided in the email.

  Run Workflow for AI Image Generation & Review
     Execute the workflow. Your generated images will be automatically vetted, organized, and ready for creative use.

  How to Customize This Workflow
  Tailor Image Generation Prompts:** Adjust prompts and settings in the Ideogram HTTP Request node to better fit your project's creative requirements.
  Set Quality Standards:** Modify the criteria used by the OpenAI node to reflect your specific standards and preferences for image approval.
  Customize Asset Organization:** Adapt Google Drive folder structures, CSV headers, or Google Sheets integrations to match your team's organizational preferences.

  Dependencies & Requirements
  Nodes Used:**
    HTTP Request (Ideogram API integration)
    OpenAI (Image review and quality assessment)
    Gmail (Automated notifications)
    Google Drive (File and asset management)
    Google Sheets (Metadata organization)

  Credentials:**
    Ensure Gmail, Google Drive, Google Sheets, and OpenAI credentials are properly configured in your n8n account. No custom or community nodes are needed.

  Final Outcome
  Upon completion, your workflow efficiently provides vetted, high-quality AI-generated images, organized in Google Drive and accessible via easy-to-use metadata in Google Sheets, drastically reducing manual intervention and accelerating your creative processes.

nodes_used:
  ["n8n-nodes-base.googleSheets","n8n-nodes-base.set","n8n-nodes-base.googleDrive","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.switch","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.googleDrive","n8n-nodes-base.googleSheets","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.gmail","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.manualTrigger","n8n-nodes-base.convertToFile","n8n-nodes-base.set","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.formTrigger","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, 11048 characters]

metadata_json:
  {"categories":["automation","AI/ML","digital asset management","creative workflows","integration"],"complexity":"medium","use_cases":["Automated AI image generation from creative briefs","AI-driven image quality review and approval","Organizing and storing approved assets in Google Drive","Maintaining searchable metadata in CSV/Google Sheets","Notifying teams about new assets via email"],"estimated_setup_minutes":30,"required_services":["Ideogram API (via HTTP Request)","OpenAI API","Gmail account","Google Drive","Google Sheets","n8n platform"],"key_features":["Generate images using Ideogram","Automated image review and approval using OpenAI","Create structured Google Drive folders and upload assets","Compile metadata to CSV and Google Sheets","Automated email notifications with folder links and IDs','Customizable prompts and approval criteria"],"target_audience":["Graphic designers","Creative agencies","Marketing teams and content teams/freelancers"]}


