# templates

name: Generate Marketing Ad Banners with LINE, Gemini, and Nano Banana Pro
author_name: Masaki Go
author_username: pippi
categories: []
created_at: 2025-11-25T03:59:11.780Z
updated_at: 2025-11-25T03:59:11.780Z
url: https://n8n.io/workflows/11205
scraped_at: 2025-12-24 13:02:41
description:
  About This Template
  This workflow creates high-quality, text-rich advertising banners from simple LINE messages. 

  It combines Google Gemini (for marketing-focused prompt engineering) and Nano Banana Pro (accessed via Kie.ai API) to generate images with superior text rendering capabilities. It also handles the asynchronous API polling required for high-quality image generation.

  How It Works
  Input: Users send a banner concept via LINE (e.g., "Coffee brand, morning vibe").
  Prompt Engineering: Gemini optimizes the request into a detailed prompt, specifying lighting, composition, and Japanese catch-copy placement.
  Async Generation: The workflow submits a job to Nano Banana Pro (Kie API) and intelligently waits/polls until the image is ready.
  Hosting: The final image is downloaded and uploaded to a public AWS S3 bucket.
  Delivery: The image is pushed back to the user on LINE.

  Who It’s For
  Marketing teams creating A/B test assets.
  Japanese market advertisers needing accurate text rendering.
  Developers looking for an example of Async API Polling patterns in n8n.

  Requirements
  n8n** (Cloud or Self-hosted).
  Kie.ai API Key** (for Nano Banana Pro model).
  Google Gemini API Key**.
  AWS S3 Bucket** (Public access enabled).
  LINE Official Account** (Messaging API).

  Setup Steps
  Credentials: Configure the "Header Auth" credential for the Kie.ai nodes (Header: Authorization, Value: Bearer YOUR_API_KEY).
  AWS: Ensure your S3 bucket allows public read access so LINE can display the image.
  Webhook: Add the production webhook URL to your LINE Developers console.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.webhook","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.googleGemini","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.wait","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.awsS3","n8n-nodes-base.httpRequest","n8n-nodes-base.code"]

workflow_json_compressed:
  [encoded data, 5692 characters]


name: Generate Hollywood-Style Video Ads from Images with GPT-5 Mini and Fal.ai Sora-2
author_name: Sandeep Patharkar | www.FastTrackAiMastery.com
author_username: sandy4v
categories: []
created_at: 2025-11-27T00:09:30.986Z
updated_at: 2025-11-27T00:09:30.986Z
url: https://n8n.io/workflows/11268
scraped_at: 2025-12-24 13:02:44
description:
  🎬 Hollywood-Style Ads in Seconds (Fal.ai Sora-2 Version)

  Turn a single product image + short text description into a cinematic 8-second ad using Fal.ai’s Sora-2 Image-to-Video model. Perfect for ad agencies, marketing teams, and UGC creators who want to produce high-quality video ads instantly without editors or camera crews.

  🧩 Problem It Solves
  Producing ad videos usually requires writers, editors, equipment, and several review cycles.  
  This workflow replaces that process with a fully automated pipeline that generates studio-quality ads on demand.

  🏢 Real Use Cases
  1. Ad Agencies  
  Deliver Hollywood-style ads to clients instantly from a simple image upload.

  2. UGC Creators  
  Create multiple ad variations in minutes instead of spending hours filming.

  3. SMB Marketing  
  Generate product promo videos for websites, social media, and email campaigns.

  ⚙️ How It Works  
  Your frontend sends image + text to the workflow webhook.  
  Image is resized to 1280×720.  
  GPT-5 Mini writes a cinematic Sora-2 compatible prompt.  
  Fal.ai Sora-2 generates a realistic 8-second lifestyle ad.  
  Workflow polls status and retrieves the final video.  
  Sends the video URL back to your frontend.  
  Optional: Sends a Telegram notification.  

  🧪 Requirements  
  Fal.ai API Key  
  Cloudinary Account (optional, for image storage)  
  n8n (latest recommended version)  
  Frontend (Lovable / AI Studio / Bubble / React)

  🎨 Template Metadata  
  Template Author: Sandeep Patharkar  
  Category: Content Generation / Video Ads  
  Difficulty: Intermediate  
  Setup Time: 10–12 minutes  

  📬 Connect With Me  
  LinkedIn → www.linkedin.com/in/sandeeppatharkar  
  YouTube → https://www.youtube.com/@fasttrackaimastery  
  Website → https://fasttrackaimastery.com  
  Skool Community → https://www.skool.com/aic-plus

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.merge","n8n-nodes-base.editImage","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.webhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-cloudinary.cloudinary","n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","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.stickyNote"]

workflow_json_compressed:
  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


name: Build Enterprise RAG System with Google Gemini File Search & Retell AI Voice
author_name: Sandeep Patharkar | www.FastTrackAiMastery.com
author_username: sandy4v
categories: []
created_at: 2025-11-27T01:06:29.689Z
updated_at: 2025-11-27T01:06:29.689Z
url: https://n8n.io/workflows/11269
scraped_at: 2025-12-24 13:02:43
description:
  🧠 Enterprise RAG System with Google Gemini File Search + Retell AI Voice Agent

  Build a complete enterprise-grade RAG pipeline using Google Gemini’s brand-new File Search API, combined with a powerful Retell AI voice agent (JARVIS) as the conversational front end.  
  This workflow is designed for AI automation agencies, SMBs, enterprise teams, and internal AI copilots.

  📌 Who Is This For?
  Enterprise teams building internal search copilots  
  AI automation agencies delivering RAG products to clients  
  SMBs wanting automated knowledge lookup  
  Anyone needing a production-ready, zero-Pinecone RAG workflow

  🚧 Problem This Solves
  Traditional RAG requires:
  Vector DB setup  
  Embedding jobs  
  Chunking pipelines  
  Custom search APIs  

  Gemini File Search eliminates all of this — you simply create a store and upload files.  
  Indexing, chunking, embeddings = fully automated.

  This workflow turns that into a plug-and-play enterprise template.

  🧩 What This Workflow Does (High-Level)
  1️⃣ Create a Gemini File Search Store  
  Calls fileSearchStores API  
  Creates a persistent embedding store  
  Automatically saved to Google Sheets for future retrieval

  2️⃣ Auto-Upload Documents from Google Drive  
  When a new file is added:
  Download → Start resumable upload → Upload actual bytes  
  Gemini auto-indexes the document for retrieval

  3️⃣ Chat-Based Retrieval (Chat Trigger)  
  User question → Gemini File Search → Short, precise answer returned.

  4️⃣ Voice Search (Retell AI Agent)  
  Your Gemini RAG can now be searched by voice.

  🎙️ Retell AI (JARVIS) Voice Agent – Integration Steps

  🔧 Step 1 — Paste This Prompt Into Retell AI
  You are JARVIS, an advanced AI assistant designed to help user with their daily tasks.
  Always call the user “Sir”.

  You remember the user's name and important details to improve the experience.

  Whenever the user asks for information that requires external lookup:

  Make a short, witty remark related to their request.

  Immediately call the n8n tool — do NOT repeat the question back.

  Be concise, professional, and efficient.

  n8n tool call:
  Use this tool for all knowledge-based or RAG lookups.
  It sends the user’s query to the n8n workflow.

  JSON Schema:
  {
  "type": "object",
  "properties": {
  "query": {
  "type": "string",
  "description": "The user’s full request for JARVIS to process."
  }
  },
  "required": ["query"]
  }

  🔧 Step 2 — Add This URL to Retell (YOUR WEBHOOK)
  Paste the webhook URL from your Respond to Webhook node:

  https://YOUR-N8N-URL/webhook/Gemini
   ← replace with your actual webhook ID


  This is the endpoint Retell calls every time the user speaks.

  🔧 Step 3 — End-to-End Flow
  User speaks to JARVIS  
  Retell sends query → n8n  
  n8n forwards query to Gemini using File Search  
  Gemini returns answer  
  Retell speaks the response out loud  

  You now have a voice-powered enterprise RAG agent.

  📦 Requirements
  Google Gemini File Search API access  
  Google Drive folder for document uploads  
  Retell AI agent  
  n8n instance  
  (Optional) Google Sheets for storing store IDs  

  📝 Estimated Setup Time
  ⏱️ 25–30 minutes (end-to-end)

  👨‍💻 Template Author
  Sandeep Patharkar  
  Founder – FastTrackAI  
  AI Automation Architect | Enterprise Workflow Designer  

  🔗 Website: https://fasttrackaimastery.com  
  🔗 LinkedIn: https://www.linkedin.com/in/sandeeppatharkar/  
  🔗 Skool Community: https://www.skool.com/aic-plus  
  🔗 YouTube: https://www.youtube.com/@FastTrackAIMastery  

  🏁 Summary
  This template gives you a full enterprise RAG infrastructure:
  Automatic document indexing  
  Gemini File Search retrieval  
  Chat + Voice interfaces  
  Zero-vector-database setup  
  Seamless Retell AI integration  
  Fully production-ready  

  Perfect for creating internal AI copilots, employee knowledge assistants, client-facing search apps, and enterprise RAG systems.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.googleDrive","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.httpRequestTool","n8n-nodes-base.set","n8n-nodes-base.webhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","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:
  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


name: Financial Document Extraction from Gmail to Google Sheets
author_name: Gtaras
author_username: tarasidis
categories: []
created_at: 2025-11-27T12:41:35.176Z
updated_at: 2025-11-27T12:41:35.176Z
url: https://n8n.io/workflows/11290
scraped_at: 2025-12-24 13:02:41
description:
  Overview
  Manual financial reconciliation is tedious and prone to error. This workflow functions as an AI Financial Controller, automatically monitoring your inbox for invoices, receipts, and bills, extracting the data using OCR, and syncing it to Google Sheets for approval.

  Unlike simple scrapers, this workflow uses a "Guardrail" AI agent to filter out non-financial emails (like newsletters) before they are processed, ensuring only actual transactions are recorded.

  Who is it for?
  Finance Teams:** To automate the collection of vendor invoices.
  Freelancers:** To track expenses and receipts for tax season.
  Operations Managers:** To monitor budget spend and categorize costs automatically.

  How it works
  Ingest: The workflow watches a specific Gmail label (e.g., "INBOX") for new emails.
  Guardrail: A Gemini-powered AI agent analyzes the email text to determine if it is a valid financial transaction. If not, the workflow stops.
  Extraction (OCR):
      If an attachment exists: An AI Agent (GPT-4o) extracts data from the PDF.
      If no attachment: An AI Agent extracts data directly from the email body.
  Validation: Code nodes check for missing fields or invalid amounts.
  Business Logic: The system automatically assigns General Ledger (GL) categories (e.g., "Uber" -&gt; "Travel") and sets approval statuses based on amount thresholds.
  Sync: Validated data is logged to Google Sheets, and a confirmation email is sent. Errors are logged to a separate error sheet.

  How to set up
  Google Sheets: Copy this Google Sheet template to your drive. It contains the necessary tabs (Invoices, Error Logs, Success Metrics).
  Configure Workflow:
      Open the node named "Configuration: User Settings".
      Paste your Google Sheet ID (found in the URL of your new sheet).
      Enter the Admin Email address where you want to receive error notifications.
  Credentials:
      Connect your Gmail account.
      Connect your Google Sheets account.
      Connect your OpenAI (for OCR) and Google Gemini/PaLM (for Guardrails) accounts.

  Requirements
  n8n version 1.0 or higher.
  Gmail account.
  OpenAI API Key.
  Google Gemini (PaLM) API Key.

nodes_used:
  ["n8n-nodes-base.gmailTrigger","n8n-nodes-base.gmail","@n8n/n8n-nodes-langchain.guardrails","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.filter","n8n-nodes-base.if","n8n-nodes-base.extractFromFile","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.code","n8n-nodes-base.googleSheets","n8n-nodes-base.gmail","n8n-nodes-base.googleSheets","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 8748 characters]


name: kaizenCrypto - Smarter Crypto Decisions with Multi-Timeframe AI Analysis
author_name: Paul Abraham
author_username: hellopaul
categories: []
created_at: 2025-11-30T08:59:24.469Z
updated_at: 2025-11-30T08:59:24.469Z
url: https://n8n.io/workflows/11366
scraped_at: 2025-12-24 13:02:41
description:
  This n8n template automates crypto market analysis by combining multi-timeframe candlestick data with real-time news sentiment to generate actionable AI-backed Buy / Sell / Hold signals for any cryptocurrency. Built around the Kaizen principle of continuous improvement, it delivers sharper, more reliable insights with every run.

  Use cases
  Automatically analyze crypto market trends using 15m, 1h, and 1d candlestick data.
  Aggregate global crypto news sentiment to reinforce price-action confidence.
  Generate AI-powered Buy / Sell / Hold recommendations for traders.
  Build your own Telegram-based crypto trading assistant or analytics bot.
  Create backends for crypto dashboards, portfolio advisors, or auto-alert systems.
  Expand trading strategies using AI reasoning instead of manual analysis.

  Good to know
  This workflow merges both technical analysis (candlesticks) and fundamental sentiment (news), then passes them through an AI model (Gemini) to produce clean, easy-to-understand trading signals.
  It works seamlessly on n8n Cloud and self-hosted setups, with configuration taking about 5 minutes.

  Requirements
  n8n Cloud or self-hosted instance
  Crypto price data API key 
  Crypto news API key
  AI model API key (Google Gemini)
  Telegram Bot Token (via @BotFather)

  Customising this workflow
  Replace Telegram with Slack, Discord, WhatsApp Cloud API, or Notion for alerts.
  Switch AI models (Gemini, OpenAI, Claude) to change tone or reasoning style.
  Modify candlestick intervals (15m, 1h, 1d) or add more timeframes.
  Attach TradingView webhooks for live alerts or automated triggers.
  Add portfolio tracking, price-level alerts, or risk scoring for advanced users.
  Expand the template to support forex, stocks, indices, or commodities with minimal edits.

nodes_used:
  ["n8n-nodes-base.telegramTrigger","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.merge","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.merge","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.code","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.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.googleGemini","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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


name: Forex & Gold Trading Signal Handler for MT5 using Webhooks (telegram/discord)
author_name: Cj Elijah Garay
author_username: elijahbuilds-ai
categories: []
created_at: 2025-12-02T19:35:29.788Z
updated_at: 2025-12-02T19:35:29.788Z
url: https://n8n.io/workflows/11439
scraped_at: 2025-12-24 13:02:41
description:
  MT5 Trading Signal Handler
  Metatrader5 and N8N Integration for Forex and Gold Trading via Webhooks for discord and telegram

  This n8n template demonstrates a workflow that bridges AI workflows which can be used to communicate directly to a trading platform/broker like metatrader 5

  Use cases are many:

  Try automating TradingView alerts into live MT5 trades, building a custom trading bot that sends trade execution signals to MT5 via webhooks, or creating a centralized signal management system for multiple trading strategies these features are just the beggining!

  Good to know

  Always test on demo accounts first** - start with 1-2% risk before considering any live deployment
  This workflow uses unauthenticated webhook endpoints by default - you must add authentication (API keys, OAuth) before any production use
  Signals are stored in n8n's in-memory static data - they will be lost if n8n restarts unless you implement persistent storage
  MT5 WebRequest whitelist required** - you must add all webhook URLs to MT5's allowed URLs list (Tools → Options → Expert Advisors)
  The workflow requires a custom MT5 Expert Advisor (EA) that polls the GET endpoint every 5 seconds to fetch pending signals
  This is designed for local machine or private VPS deployment only - not suitable for public cloud without significant security hardening
  No automatic signal expiration** - old signals will accumulate without manual cleanup or additional workflow logic
  Compatible with standard MT5 brokers that support automated trading and WebRequest functionality

  How it works

  Signal Reception**: The "Receive Signal (POST)" webhook accepts trading signals containing asset, entry price, stop loss, take profit, and risk percentage, then passes them to the "Store Signal" Code node
  Signal Storage**: The Code node creates a unique signal ID with timestamp, stores it in n8n's workflow static data with "pending" status, and returns a success response with the signal ID
  Queue Polling**: The "Get Pending Signals (GET)" webhook is polled every 5 seconds by your MT5 Expert Advisor to check for new signals
  Signal Retrieval**: The "Fetch Pending Signals" Code node filters all stored signals to return only those with "pending" status as a JSON array
  Market Orders**: The "Market Order (POST)" webhook accepts instant-execution orders with direction (BUY/SELL), risk percentage, and stop loss in pips, calculating SL/TP prices automatically
  Limit Orders**: The "Limit Order (POST)" webhook handles pending orders with specific entry prices, converting pip-based SL/TP into actual price levels for both standard and JPY pairs
  Trade Confirmation**: After the MT5 EA executes a trade, it sends the signal ID to the "Confirm Signal Processed (POST)" webhook, which marks the signal as "processed" and removes it from the pending queue
  Emergency Cleanup**: The "Clear all signals (POST)" webhook provides a reset function to remove all signals from the queue, useful for testing or when signals get stuck

  How to use

  Import this workflow into your n8n instance and activate it by toggling the switch in the top-right corner
  Copy all webhook URLs from each Webhook node (click the node to view the production URL)
  Configure your MT5 Expert Advisor with the correct webhook URLs (SIGNAL_URL for GET requests, CONFIRM_URL for POST confirmations)
  Add all webhook URLs to MT5's WebRequest whitelist (Tools → Options → Expert Advisors → "Allow WebRequest for listed URL")
  Restart MT5 after adding URLs to the whitelist to ensure they're loaded properly
  Test the workflow by sending a test signal using curl or Postman: curl -X POST http://127.0.0.1:5678/webhook/mt5-signal -H "Content-Type: application/json" -d "{\"asset\": \"EURUSD\", \"risk\": 2, \"entry\": 1.0850, \"sl\": 1.0830, \"tp\": 1.0900}"
  Verify the signal appears when checking the GET endpoint: curl http://127.0.0.1:5678/webhook/mt5-signals
  Attach the MT5 EA to any chart (the symbol doesn't matter - it monitors all symbols) and enable Auto Trading in MT5
  Monitor the MT5 Experts log to confirm the EA is polling every 5 seconds and executing trades correctly

  Requirements

  n8n instance** running locally or on a private VPS (version 1.x recommended)
  MetaTrader 5 terminal** installed with a demo or live account logged in
  Custom MT5 Expert Advisor** compiled and loaded (MQL5 code that polls the webhook and executes trades)
  Auto Trading enabled** in MT5 (Tools → Options → Expert Advisors → "Allow Algorithmic Trading")
  WebRequest permissions** configured in MT5 for all webhook URLs
  Basic understanding of REST APIs, webhooks, and trading concepts (orders, SL/TP, position sizing)

  Customising this workflow

  Add authentication middleware to all webhook endpoints using HTTP Authentication or custom API keys for security
  Implement persistent storage by replacing static data with a database node (PostgreSQL, MySQL) or external storage service
  Create an automatic signal cleanup workflow that runs on a schedule to delete processed signals older than 24 hours
  Add notification nodes (Telegram, Discord, Email) to alert you when trades are executed or errors occur
  Implement position size validation in the Code nodes to cap maximum lot sizes or risk percentages
  Create an asset whitelist by adding validation logic to only accept signals for approved trading instruments
  Add duplicate signal detection to prevent the same signal from being processed multiple times
  Build a signal analytics dashboard by logging all signals to a database and visualizing execution metrics
  Try a popular use-case such as connecting this to a TradingView webhook to automate Pine Script strategy alerts directly into MT5 trades!
  Add tags for certain setups and or magic numbers for easier journaling later on

  Purchasing this N8N workflow comes with the AI trading assistant workflow too so it is sold together and vice versa as well along with the MQL code for the ExpertAdvisor listener all for the price of 120 usd total

  Questions?

  If you have questions or need help with this workflow, feel free to reach out:

  elijahmamuri@gmail.com
  elijahfxtrading@gmail.com

  Important Disclaimer: This workflow is provided for educational purposes and demo account testing only. It demonstrates technical concepts for signal routing and automation architecture. Always test thoroughly on demo accounts, implement proper security measures, and understand that automated trading involves substantial risk. No warranties, financial advice, or guarantees are provided. See the full disclaimer in the workflow sticky notes for complete terms.

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

workflow_json_compressed:
  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


name: Create and Send Tech News Digests with RSS, Gemini AI and Gmail
author_name: Paolo Ronco
author_username: paoloronco
categories: []
created_at: 2025-12-03T14:53:00.431Z
updated_at: 2025-12-03T14:53:00.431Z
url: https://n8n.io/workflows/11466
scraped_at: 2025-12-24 13:02:41
description:
  This workflow automates the entire lifecycle of collecting, filtering, summarizing, and delivering the most important daily news in technology, artificial intelligence, cybersecurity, and the digital industry.
  It functions as a fully autonomous editorial engine, combining dozens of RSS feeds, structured data processing, and an LLM (Google Gemini) to transform a large volume of raw articles into a concise, high–value daily briefing delivered straight to your inbox.

  Read: Full setup Guide

  ✅ 1. Scheduled Automation

  The workflow begins with a Schedule Trigger, which runs at predefined intervals.
  Every execution generates a fresh briefing that reflects the most relevant news from the past 24 hours.

  ✅ 2. Massive Multi-Source RSS Collection

  The workflow gathers content from over 25 curated RSS feeds covering:

  🔐 Cybersecurity

  (The Hacker News, Krebs on Security, SANS, CVE feeds, Google Cloud Threat Intelligence, Cisco Talos, etc.)

  🤖 Artificial Intelligence

  (Google Research, MIT News, AI News, OpenAI News)

  💻 Technology & Digital Industry

  (Il Sole 24 Ore, Cybersecurity360, Graham Cluley, and more)

  ⚙️ Nvidia Ecosystem

  (Nvidia Newsroom, Nvidia Developer Blog, Nvidia Blog)

  Each RSS feed is handled by a dedicated node, which ensures:

  source isolation
  easier debugging
  no single point of failure

  The feeds are grouped using category-specific Merge nodes (Cyber1/2/3, AI, Nvidia), enabling modular scalability.

  ✅ 3. Unified Feed Aggregation

  All category merges feed into the Merge_All node, creating a single combined dataset of articles from every source.

  ✅ 4. Intelligent Filtering (last 24 hours only)

  The Filter node removes:

  articles older than 24 hours (based on isoDate)
  invalid items
  duplicated or redundant entries

  This keeps the briefing strictly relevant to the current day.

  ✅ 5. Chronological Sorting

  The Sort – Articles by Date node orders all remaining items in descending date order.
  More recent or time-sensitive news is therefore prioritized.

  ✅ 6. Data Normalization (JavaScript Code)

  A dedicated Code node transforms all incoming items into one clean JSON object:

  {
    "articles": [
      {
        "title": "...",
        "content": "...",
        "link": "...",
        "isoDate": "..."
      }
    ]
  }

  This standardized structure becomes the input for the LLM summarization stage.

  ✅ 7. AI Editorial Processing – Google Gemini

  The node LLM – News Summarizer is the workflow’s editorial brain.

  A complex prompt instructs Gemini to behave like the editor-in-chief of a major tech newspaper, enforcing strict rules:

  Selection rules:

  choose only 8–10 truly important stories
  ignore low-value content (minor product releases, clickbait, rumors…)

  Relevance criteria:

  AI research & foundation models
  Big Tech developments
  cybersecurity incidents
  regulation and digital policy
  semiconductors, cloud, and infrastructure
  digital rights, governance, sovereignty

  Deduplication:

  If multiple feeds report the same story, only one version is kept.

  Output format:

  Gemini must output a valid JSON object containing:

  subject: the email subject line
  html: a fully structured HTML body grouped into categories

  Each news item ends with a clickable HTML source link, NEVER plaintext URLs.

  This step condenses dozens of articles into a polished, editorial-grade briefing.

  ✅ 8. HTML Newsletter Assembly (Code Node)

  The Build Final Newsletter HTML node:

  safely parses the JSON from the LLM
  cleans any validates subject and html fields
  embeds the content into a modern, responsive HTML email template

  The output is a single item containing:

  the final email subject
  the final HTML body
    Ready to be sent.

  ✅ 9. Automatic Email Delivery

  The Send Final Digest Email (Gmail node):

  uses the generated subject
  sends the curated HTML newsletter
  delivers it to the configured recipient(s)
  uses a custom sender name (“n8n News”)

  The result is a fully automated Tech & AI Daily Briefing delivered with zero manual effort.

  In Summary: What This Workflow Achieves

  ✔ Collects news from 25+ high-quality RSS sources
  ✔ Normalizes, filters, and sorts all items automatically
  ✔ Uses Google Gemini to select only the stories that truly matter
  ✔ Generates a coherent, readable, professional-looking HTML newsletter
  ✔ Sends the result via email every day

  Perfect for:

  daily executive briefings
  technology and cybersecurity monitoring
  automated newsletter production
  internal knowledge distribution
  competitive intelligence workflows

nodes_used:
  ["n8n-nodes-base.rssFeedRead","n8n-nodes-base.rssFeedRead","n8n-nodes-base.rssFeedRead","n8n-nodes-base.rssFeedRead","n8n-nodes-base.rssFeedRead","n8n-nodes-base.rssFeedRead","n8n-nodes-base.stickyNote","n8n-nodes-base.rssFeedRead","n8n-nodes-base.rssFeedRead","n8n-nodes-base.rssFeedRead","n8n-nodes-base.stickyNote","n8n-nodes-base.filter","n8n-nodes-base.sort","n8n-nodes-base.code","n8n-nodes-base.gmail","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.googleGemini","n8n-nodes-base.rssFeedRead","n8n-nodes-base.rssFeedRead","n8n-nodes-base.rssFeedRead","n8n-nodes-base.stickyNote","n8n-nodes-base.rssFeedRead","n8n-nodes-base.rssFeedRead","n8n-nodes-base.rssFeedRead","n8n-nodes-base.rssFeedRead","n8n-nodes-base.rssFeedRead","n8n-nodes-base.rssFeedRead","n8n-nodes-base.merge","n8n-nodes-base.merge","n8n-nodes-base.merge","n8n-nodes-base.merge","n8n-nodes-base.merge","n8n-nodes-base.merge","n8n-nodes-base.rssFeedRead","n8n-nodes-base.rssFeedRead","n8n-nodes-base.rssFeedRead","n8n-nodes-base.rssFeedRead","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 11940 characters]


name: Automated AI Voice Cloning 🤖🎤 from YouTube videos to ElevenLabs & Google Sheets
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-12-05T13:48:19.398Z
updated_at: 2025-12-05T13:48:19.398Z
url: https://n8n.io/workflows/11532
scraped_at: 2025-12-24 13:02:41
description:
  This workflow automates the process of creating cloned voices in ElevenLabs using audio extracted from YouTube videos. It processes a list of video URLs from Google Sheets, converts them to audio, submits them to ElevenLabs for voice cloning*, and records the generated voice IDs back to the spreadsheet.

  *ONLY FOR STARTER, CREATOR, PRO PLAN



  Important Considerations for Best Results:

  For optimal voice cloning quality with ElevenLabs, carefully select your source YouTube videos:
  Duration**: Choose videos that are sufficiently long (preferably 1-5 minutes of clear speech) to provide enough audio data for accurate voice modeling.
  Audio Quality**: Select videos with high-quality audio, minimal background noise, and clear vocal recording.
  Single Speaker: Use videos featuring only **one primary speaker. Multiple voices in the same audio will confuse the cloning algorithm and produce poor results.
  Consistent Voice**: Ensure the speaker maintains a consistent tone and speaking style throughout the clip for the most faithful reproduction.

  Key Features

  1. ✅ Fully Automated Voice Creation Workflow

  No manual downloading, converting, or uploading is required.
  Just paste the YouTube link and voice name into the sheet—everything else happens automatically.

  2. ✅ Seamless Audio Extraction

  Using RapidAPI ensures:

  High success rate in extracting audio
  Support for virtually any YouTube video
  Consistent output format required by ElevenLabs

  3. ✅  Hands-Off ElevenLabs Voice Creation

  The workflow handles all the steps required by the ElevenLabs API, including:

  Uploading binary audio
  Naming voices
  Capturing and storing the resulting voice ID

  This is much faster than the manual method inside the ElevenLabs dashboard.

  4. ✅ Centralized, Reusable Setup

  Once the API keys are added:

  The same workflow can be reused indefinitely
  Users don’t need technical skills
  Updating only requires editing the sheet

  How it works:

  Data Retrieval: The workflow starts by fetching data from a Google Sheets spreadsheet that contains YouTube video URLs in the "YOUTUBE VIDEO" column and desired voice names in the "VOICE NAME" column. It specifically targets rows where the "ELEVENLABS VOICE ID" field is empty, ensuring only unprocessed videos are handled.

  Video Processing Pipeline:
     Video ID Extraction: Each YouTube URL is parsed to extract the unique video identifier using a regular expression.
     Audio Conversion: The video ID is sent to the RapidAPI "YouTube MP3 2025" service, which converts the YouTube video to an audio file (M4A format).
     Audio Download: The resulting audio file is downloaded locally for processing.

  Voice Creation: The downloaded audio file is submitted to ElevenLabs API via a POST request to the /v1/voices/add endpoint. This creates a new voice clone based on the audio sample. The voice name is currently hardcoded as "Teresa Mannino" in the workflow but should be dynamically configured to use the value from the "VOICE NAME" spreadsheet column.

  Data Update: The workflow captures the voice_id returned by ElevenLabs and writes it back to the corresponding row in the Google Sheets spreadsheet in the "ELEVENLABS VOICE ID" column, completing the processing cycle for that video.

  Set up steps:

  Prepare the Data Sheet: Duplicate the provided Google Sheets template. Fill in the "YOUTUBE VIDEO" column with YouTube URLs and the "VOICE NAME" column with your desired names for the cloned voices. Ensure your videos meet the quality criteria mentioned above.

  Configure APIs:
     RapidAPI: Sign up for a free trial API key from the "YouTube MP3 2025" service on RapidAPI. Enter this key into the x-rapidapi-key header field in the "From video to audio" node.
     ElevenLabs: Generate an API key from your ElevenLabs account. Configure the "Create voice" node's HTTP Header Authentication with the name xi-api-key and your ElevenLabs API key as the value.

  Optional Customization: Modify the "Create voice" node to use the dynamic voice name from your spreadsheet instead of the hardcoded "Teresa Mannino" value for more flexible operation.

  Execute: Run the workflow. It will automatically process each qualifying row, create voices in ElevenLabs, and populate the spreadsheet with the new Voice IDs. Monitor the workflow execution to ensure successful processing of each video.
  Need help customizing?  
  Contact me for consulting and support or add me on Linkedin. 

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.splitInBatches","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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


name: Aggregate News Articles from NewsAPI, Mediastack & CurrentsAPI into Database
author_name: Joel Gamble
author_username: joelgamble
categories: []
created_at: 2025-12-07T00:20:50.319Z
updated_at: 2025-12-07T00:20:50.319Z
url: https://n8n.io/workflows/11572
scraped_at: 2025-12-24 13:02:41
description:
  This workflow pulls news articles from NewsAPI, Mediastack, and CurrentsAPI on a scheduled basis.  

  Each provider’s results are normalized into a consistent schema, then written into your database (NocoDB by default).  
  Use case: automated aggregation of categorized news for content pipelines, research agents, or editorial queues.

  What You Must Update Before Running
  1. API Keys
  Replace all placeholder keys:
  call newsapi.org - Top Headlines → update API_KEY in URL  
  call newsapi.org - categories → update API_KEY  
  call mediastack → update "ACCESS_KEY" in JSON  
  call currentsapi → update "API_KEY" param

  2. Database Connection
  Workflow uses NocoDB to store results.

  You must:
  Update the NocoDB API Token credential to your own  
  Ensure your table includes the fields used in the create operations  
    (source_category, title, summary, author, sources, content, images, publisher_date, etc.)

  If you prefer Google Sheets, Airtable, or another DB:
  Replace each NocoDB node with your equivalent “create row” operation  
  The Set nodes already provide all normalized fields you need

  3. Scheduling
  All schedulers are disabled by default.  
  Enable the following so the workflow runs automatically:
  NewsAPI – Top Headlines**
  NewsAPI – Categories**
  Mediastack**
  CurrentsAPI**

  You may change the run times, but all four must be scheduled for the workflow to function as designed.

  What You Can Configure
  1. Categories
  Defined in:
  newsapi.org categories
  mediastack categories

  Edit these arrays to pull only the categories you care about or to match your API plan limits.

  2. Article Limits
  Adjust article_limit in:
  newsapi.org categories
  mediastack categories
  currentsapi config

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

workflow_json_compressed:
  [encoded data, 7260 characters]


name: Automate Data Extraction with Zyte AI (Products, Jobs, Articles & More)
author_name: Zyte
author_username: zyte
categories: []
created_at: 2025-12-09T19:28:29.465Z
updated_at: 2025-12-09T19:28:29.465Z
url: https://n8n.io/workflows/11637
scraped_at: 2025-12-24 13:02:41
description:
  Automated AI Web Scraper

  This workflow uses the Zyte API to automatically detect and extract structured data from E-commerce sites, Articles, Job Boards, and Search Engine Results (SERP) - no custom CSS selectors required.

  It features a robust "Two-Phase Architecture" (Crawler + Scraper) that handles pagination loops, error retries, and data aggregation automatically, ensuring you get a clean CSV export even for large sites with thousands of pages.

  If you prefer to use your own parsing logic and just need raw data, it provides a "Manual Mode" for that capability as well.

  Supported Modes
  E-commerce / Product:** Extract prices, images, SKUs, and availability.
  Articles / News / Forums:** Extract headlines, body text, authors, and dates.
  Job Boards / Postings:** Extract salaries, locations, and descriptions.
  SERP (Search Engine Results)**: Extract search rankings, organic results, and snippets.
  General Scraping:** Get raw BrowserHtml, HTTP Response codes, Network API traffic, or Screenshots to parse yourself.

  How it works
  Input:** You enter a URL and choose a goal (e.g., "Scrape all pages") via a user-friendly form.
  Smart Routing:** A logic engine automatically configures the correct extraction model for the target website.
  Two-Phase Extraction:** (Active only for "Scrape all pages") Phase 1 maps out all available URLs (Crawling), and Phase 2 extracts the rich data (Scraping), filtering out errors before saving to CSV.

  Set up steps
  Get your API Key: You need a free Zyte API key to run the AI extraction. Get it here.
  Run: Open the Form view, paste your key, select your target website, and hit Submit.
  Export: The workflow will process the data and output a downloadable CSV file.

  Resources
  Zyte API Documentation
  Get Help (with API errors & extraction logic)

nodes_used:
  ["n8n-nodes-base.formTrigger","n8n-nodes-base.switch","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.merge","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.switch","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.merge","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.if","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.httpRequest","n8n-nodes-base.convertToFile","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.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.convertToFile","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.form","n8n-nodes-base.form"]

workflow_json_compressed:
  [encoded data, 14832 characters]


name: Generate Ideal Customer Profiles & Find Lookalike Companies in Germany
author_name: as311
author_username: implisense
categories: []
created_at: 2025-12-11T15:07:10.467Z
updated_at: 2025-12-11T15:07:10.467Z
url: https://n8n.io/workflows/11709
scraped_at: 2025-12-24 13:02:41
description:
  This workflow generates a data driven  Ideal Customer Profile (ICP) and retrieves lookalike companies in Germany from the official data source (Handelsregister). It starts by ingesting a set of base company IDs, serializes them, and sends a recommendation request to the Implisense API to fetch similar companies. When explanation mode is enabled, the workflow extracts and processes term features to create a structured keyword digest and uses an LLM to generate an ICP narrative. The pipeline outputs both a clean list of lookalike companies, enriched with CRM-ready fields, and a detailed ICP report derived from Implisense feature statistics.

  How it works
  Input → Serialization → Lookalikes → Lists/Report

  Setup steps

  1. Data Source
  ☐ Replace "Mock ICP Companies" with matched companies from the Implisense database
  ☐ Ensure output has: id

  2. Configure Credentials: Set up RapidAPI API credentials
     Get your API key here: https://implisense.com/de/contact
     Insert your API Token in get_lookalikes (Basic auth)

  3. Configure ICP Filters
  ☐ Edit "Build Recommendation Request" node
  ☐ Set locationsFilter (e.g., de-be, de-by, de-nw)
  ☐ Set industriesFilter (NACE codes, e.g., J62 for IT)
  ☐ Set sizesFilter (MICRO, SMALL, MEDIUM, LARGE)

  4. Tune Results
  ☐ Adjust THRESHOLD in "Filter & Normalize Results" (default: 0.5)
  ☐ Adjust MIN_BASE_COMPANIES in "Collect Base Companies" (default: 3)
  ☐ Adjust size parameter in "Configuration" URL (default: 100)

  5. CRM Integration
  ☐ Map fields in "list_of_companies" to match your CRM schema
  ☐ Add CRM upsert node after "list_of_companies"
  ☐ Use implisense-ID or domain as unique identifier

  Additional advice

  Strengthen Base Company Quality
  Use only highly representative base companies located in Germany that strongly match the intended ICP segment. Templates with dozens of mixed or heterogeneous IDs dilute the statistical signal in the /recommend endpoint and reduce relevance.

  Refine Filters Aggressively
  Limit recommendations by state, region, NACE code, or size class. Implisense returns cleaner results when the recommendation space is constrained. Removing unnecessary geography broadens noise.

  Increase the Size Parameter
  Raise the size parameter when building the request to give the ranking model more candidates. This materially improves downstream sorting and selection.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.function","n8n-nodes-base.function","n8n-nodes-base.function","n8n-nodes-base.merge","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.function","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.switch","n8n-nodes-base.splitOut","n8n-nodes-base.function","n8n-nodes-base.function","n8n-nodes-base.function","n8n-nodes-base.function","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.code"]

workflow_json_compressed:
  [encoded data, 7564 characters]


