# templates

name: Extract Purchase Orders from Gmail using Gemini AI and Save to Google Sheets
author_name: SayOne Technologies
author_username: sayonetech
categories: []
created_at: 2025-09-15T05:54:18.889Z
updated_at: 2025-09-15T05:54:18.889Z
url: https://n8n.io/workflows/8585
scraped_at: 2025-09-29 22:37:32
metadata_generated_at: 2025-09-30 08:33:37
description:
  🚀 AI-Powered Email to Purchase Order Workflow  

  Automatically scan your inbox for new purchase order requests, extract order details using Gemini AI, and log them into Google Sheets — all without manual effort.  

  ✨ Core Capabilities  
  ⏱ Runs every minute to check unread emails  
  📧 Filters emails by subject  
  🤖 Uses Gemini AI to summarize email content & extract structured order details  
  📅 Formats dates into ISO calendar weeks  
  📊 Adds product data from Google Sheets to complete order info  
  ✅ Appends final purchase order records into a Google Sheet (without replacing previous ones)  

  🛠 Setup Essentials  
  📩 Gmail account for fetching unread emails  
  🔑 Google Gemini (PaLM) API credentials  
  📒 Google Sheet with predefined purchase order headers  

  📖 Activation Guide  
  ⚙️ Configure Gmail & Google Sheets credentials in n8n  
  🎯 Adjust the subject filter to match your email rules  
  🔌 Connect Gemini AI with your API credentials  
  📑 Create a Google Sheet with the required headers  
  ▶️ Activate the workflow and let it run in the background  

  🎨 Customizing the Workflow  
  🔍 Email Filters → Change keywords in the filter node to match your purchase order email subjects  
  🏷 Order Fields → Modify Set and Append to Google Sheet nodes if your schema differs  
  ✍️ AI Instructions → Adjust the AI Agent’s prompt to fit your company’s email style or product details  
  ⏲ Frequency → Update the Cron node if you want to scan emails less often  
  📂 Target Google Sheet → Point to a different sheet or tab depending on your department or customer  

nodes_used:
  ["n8n-nodes-base.gmail","n8n-nodes-base.cron","n8n-nodes-base.if","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.filter","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.googleSheets"]

workflow_json_compressed:
  [encoded data, 5312 characters]

metadata_json:
  {"categories":["automation","integration","data processing","AI/ML"],"complexity":"medium","use_cases":["Automatically capture purchase orders from email","Convert unstructured emails into structured order records","Enrich orders with product data from a catalog","Log orders into Google Sheets for tracking or downstream processing","Generate ISO week-formatted dates for reporting"],"estimated_setup_minutes":20,"required_services":["Gmail (or Google Workspace email)","Google Sheets","Google Gemini (lmChatGoogleGemini / Google Cloud AI)"],"key_features":["Runs on a minute-based cron to check unread emails","Subject-based email filtering","Uses Gemini AI to summarize and extract structured order details","Formats dates into ISO calendar weeks","Enriches order lines with product data from Google Sheets before saving"],"target_audience":["operations/procurement teams","sales/fulfillment teams","small business owners automating order capture workflows`,`automation engineers/developers"]}


name: AI Personal Assistant for Google Tasks
author_name: minh
author_username: minh
categories: []
created_at: 2025-09-15T08:46:04.948Z
updated_at: 2025-09-15T08:46:04.948Z
url: https://n8n.io/workflows/8591
scraped_at: 2025-09-29 22:37:32
metadata_generated_at: 2025-09-30 08:33:37
description:
  Who’s it for
  This template is designed for anyone who wants to use Telegram as a personal AI assistant hub.
  If you often juggle tasks, emails, calendars, and expenses across multiple tools, this workflow consolidates everything into one seamless AI-powered agent.

  What it does
  Jarvis listens to your Telegram messages (text or audio) and processes them with OpenAI.
  Based on your request, it can:

nodes_used:
  ["n8n-nodes-base.stickyNote","@blotato/n8n-nodes-blotato.blotato","@blotato/n8n-nodes-blotato.blotato","@blotato/n8n-nodes-blotato.blotato","n8n-nodes-base.merge","n8n-nodes-base.googleSheets","@blotato/n8n-nodes-blotato.blotato","@blotato/n8n-nodes-blotato.blotato","@blotato/n8n-nodes-blotato.blotato","@blotato/n8n-nodes-blotato.blotato","@blotato/n8n-nodes-blotato.blotato","@blotato/n8n-nodes-blotato.blotato","@blotato/n8n-nodes-blotato.blotato","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.toolThink","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.telegramTrigger","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.telegram","n8n-nodes-base.googleDrive","n8n-nodes-base.googleSheets","n8n-nodes-base.set","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 14084 characters]

metadata_json:
  {"categories":["automation","integration","productivity","AI assistant"],"complexity":"complex","use_cases":["Use Telegram as a single AI-powered personal assistant hub","Convert Telegram text or audio into tasks, calendar entries, or emails","Automate expense or task logging to spreadsheets","Transcribe and process voice notes via OpenAI","Consolidate and route requests across multiple tools (tasks, sheets, email, calendar)"],"estimated_setup_minutes":60,"required_services":["Telegram API (bot)","OpenAI (text + audio/ASR models)","Google Tasks API","Google Sheets","HTTP/Webhooks (for additional integrations like email/calendar or third-party services)"],"key_features":["Listens to Telegram messages (text and audio) and processes them with OpenAI","Creates and manages tasks in Google Tasks","Logs and syncs items to spreadsheets","Structured output parsing and routing","Multi-tool consolidation (tasks, email, calendar, expenses) via webhooks/API calls"],"target_audience":["individuals wanting a personal AI assistant","busy professionals and founders","productivity enthusiasts managing multiple tools and workflows"]}


name: Fireflies Transcripts to Meeting Summaries & Task Extractor to Slack & ClickUp
author_name: Hashir Bin Waseem
author_username: hashirxn8n
categories: []
created_at: 2025-09-15T10:20:02.945Z
updated_at: 2025-09-15T10:20:02.945Z
url: https://n8n.io/workflows/8592
scraped_at: 2025-09-29 22:37:32
metadata_generated_at: 2025-09-30 08:33:37
description:
  AI-powered Meeting Summaries and Action Items to Slack and ClickUp

  How it Works  
  Webhook Trigger: The workflow starts when Fireflies notifies that a transcription has finished.  
  Transcript Retrieval: The transcript is pulled from Fireflies based on the meeting ID.  
  Pre-processing: The transcript is split into sentences and then aggregated into a raw text block.  
  AI Summarization: The aggregated transcript is sent to Google Gemini, which generates a short summary and a structured list of action items.  
  Post-processing: The AI response is cleaned and formatted into JSON. Action items are mapped to titles and descriptions.  
  Distribution:  
     The meeting summary is posted to Slack.  
     Action items are created as tasks in ClickUp.  

  Use Case  
  This workflow is designed for teams that want to reduce the manual effort of writing meeting notes and extracting action items.  

  Automatically generate a clear and concise meeting summary  
  Share the summary instantly with your team on Slack  
  Ensure action items are not lost by automatically creating tasks in ClickUp  
  Ideal for distributed teams, project managers, and product teams managing recurring meetings  

  Requirements  
  n8n instance** set up and running  
  Fireflies.ai account** with API access to meeting transcripts  
  Google Gemini API (via PaLM credentials)** for AI-powered summarization  
  Slack account** with OAuth2 credentials connected in n8n  
  ClickUp account** with OAuth2 credentials connected in n8n

nodes_used:
  ["@firefliesai/n8n-nodes-fireflies.fireflies","n8n-nodes-base.splitOut","n8n-nodes-base.aggregate","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.clickUp","n8n-nodes-base.slack","n8n-nodes-base.webhook","@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.code","n8n-nodes-base.code"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","AI/ML","productivity"],"complexity":"medium","use_cases":["Generate concise meeting summaries from Fireflies transcripts","Extract action items/tasks from meeting transcripts","Create tasks in ClickUp from extracted action items","Send summaries and action items to Slack channels or users","Automate post-meeting follow-up and tracking"],"estimated_setup_minutes":30,"required_services":["Fireflies.ai (transcripts)","Google Gemini (Gemini API / Google Cloud)","Slack","ClickUp","n8n (webhook endpoint)"],"key_features":["Webhook trigger on transcription completion","Retrieve transcript by meeting ID from Fireflies","Pre-process transcript (split/aggregate)","AI-powered summarization and structured action item extraction","Post summary and tasks to Slack channels/users"],"target_audience":["product managers","team leads","operations managers/scrum masters/facilitators — meeting owners/champions? actually keep concise: team leads, operations managers, developers, customer success"]}


name: Build Your First AI Agent with ChatGPT-5
author_name: Dr. Firas
author_username: drfiras
categories: []
created_at: 2025-09-15T12:21:07.592Z
updated_at: 2025-09-15T12:21:07.592Z
url: https://n8n.io/workflows/8597
scraped_at: 2025-09-29 22:37:32
metadata_generated_at: 2025-09-30 08:33:37
description:
  Build Your First AI Agent with ChatGPT-5 

  Who is this for?  
  This workflow is designed for beginners and professionals who want to build their first AI-powered assistant with n8n. It’s perfect for anyone managing online trainings, consultations, or services that require both a knowledge base and appointment scheduling.  

  What problem is this workflow solving?  
  Manually handling client questions, checking your availability, and confirming bookings can be time-consuming and error-prone. This workflow automates the process, ensuring quick, accurate answers and seamless scheduling directly through chat.  

  What this workflow does  
  Answers user questions using your knowledge base stored in Google Sheets.  
  Checks availability in Google Calendar and proposes alternative time slots if needed.  
  Books 1-hour appointments in Paris time only after client confirmation.  
  Sends a professional confirmation email with all appointment details.  

  Setup  
  Import this workflow into your n8n instance.  
  Connect your Google Sheets, Gmail, and Google Calendar credentials.  
  Add your knowledge base into Google Sheets (questions, answers, policies, packs, etc.).  
  Test the workflow using the Connected Chat Trigger node to start conversations with the AI Agent.  

  How to customize this workflow to your needs  
  Update the Google Sheets database with your own training packs, services, or company FAQs.  
  Adjust the email template to reflect your branding and communication style.  
  Modify the appointment duration if you need sessions longer or shorter than 1 hour.  
  Add extra nodes (e.g., CRM integration) to capture leads or sync appointments with external systems.  
  📄 Documentation: Notion Guide

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

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

workflow_json_compressed:
  [encoded data, 6756 characters]

metadata_json:
  {"categories":["automation","AI/ML assistant","scheduling","knowledge management"],"complexity":"medium","use_cases":["Build an AI-powered assistant for customer questions","Automate appointment scheduling and booking confirmations","Integrate a knowledge base with conversational AI for trainings/consultations","Sync availability with Google Calendar and store/lookup data in spreadsheets"],"estimated_setup_minutes":45,"required_services":["OpenAI / ChatGPT-5 (AI API)","Google Calendar","Google Sheets (or spreadsheet service)","Messaging/Notification service (email/Slack/SMS)"],"key_features":["Conversational AI agent using ChatGPT-5","Knowledge-base integration for accurate answers","Automated availability checks against Google Calendar","Booking creation and confirmation workflows","Persistent short-term memory/context window for ongoing conversations"],"target_audience":["Beginners learning to build AI assistants","Consultants, coaches and trainers","Small business owners managing appointments and client Q&A practices"]}


name: Analyze Cryptocurrency Market Data with HTX API, GPT-4o and Telegram
author_name: Don Jayamaha Jr
author_username: don-the-gem-dealer
categories: []
created_at: 2025-09-15T18:15:24.272Z
updated_at: 2025-09-15T18:15:24.272Z
url: https://n8n.io/workflows/8604
scraped_at: 2025-09-29 22:37:32
metadata_generated_at: 2025-09-30 08:33:37
description:
  A fully autonomous, HTX Spot Market AI Agent (Huobi AI Agent) built using GPT-4o and Telegram. This workflow is the primary interface, orchestrating all internal reasoning, trading logic, and output formatting.

  ⚙️ Core Features

  🧠 LLM-Powered Intelligence: Built on GPT-4o with advanced reasoning
  ⏱️ Multi-Timeframe Support: 15m, 1h, 4h, and 1d indicator logic
  🧩 Self-Contained Multi-Agent Workflow: No external subflows required
  🧮 Real-Time HTX Market Data: Live spot price, volume, 24h stats, and order book
  📲 Telegram Bot Integration: Interact via chat or schedule
  🔄 Autonomous Runs: Support for webhook, schedule, or Telegram triggers

  📥 Input Examples

  | User Input      | Agent Action                                  |
  | --------------- | --------------------------------------------- |
  | btc           | Returns 15m + 1h analysis for BTC             |
  | eth 4h        | Returns 4-hour swing data for ETH             |
  | bnbusdt today | Full day snapshot with technicals + 24h stats |

  🖥️ Telegram Output Sample

  📊 BTC/USDT Market Summary

  💰 Price: $62,400  
  📉 24h Stats: High $63,020 | Low $60,780 | Volume: 89,000 BTC

  📈 1h Indicators:
  • RSI: 68.1 → Overbought  
  • MACD: Bearish crossover  
  • BB: Tight squeeze forming  
  • ADX: 26.5 → Strengthening trend

  📉 Support: $60,200  
  📈 Resistance: $63,800

  🛠️ Setup Instructions

  Create your Telegram Bot using @BotFather
  Add Bot Token in n8n Telegram credentials
  Add your GPT-4o or OpenAI-compatible key under HTTP credentials in n8n
  (Optional) Add your HTX API credentials if expanding to authenticated endpoints
  Deploy this main workflow using:

     ✅ Webhook (HTTP Request Trigger)
     ✅ Telegram messages
     ✅ Cron / Scheduled automation

  🎥 Live Demo

  🧠 Internal Architecture

  | Component          | Role                                                     |
  | ------------------ | -------------------------------------------------------- |
  | 🔄 Telegram Trigger | Entry point for external or manual signal                |
  | 🧠 GPT-4o          | Symbol + timeframe extraction + strategy generation      |
  | 📊 Data Collector  | Internal tools fetch price, indicators, order book, etc. |
  | 🧮 Reasoning Layer | Merges everything into a trading signal summary          |
  | 💬 Telegram Output | Sends formatted HTML report via Telegram                 |

  📌 Use Case Examples

  | Scenario                               | Outcome                                                 |
  | -------------------------------------- | ------------------------------------------------------- |
  | Auto-run every 4 hours                 | Sends new HTX signal summary to Telegram                |
  | Human requests “eth 1h”                | Bot replies with real-time 1h chart-based summary       |
  | System-wide trigger from another agent | Invokes webhook and returns response to parent workflow |

  🧾 Licensing & Attribution

  © 2025 Treasurium Capital Limited Company
  Architecture, prompts, and trade report structure are IP-protected.

  No unauthorized rebranding permitted.

  🔗 For support: Don Jayamaha – LinkedIn

nodes_used:
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workflow_json_compressed:
  [encoded data, 11172 characters]

metadata_json:
  {"categories":["automation","integration","data processing","AI/ML","trading"],"complexity":"complex","use_cases":["Autonomous crypto market analysis and signal generation","Real-time trading alerts and notifications via Telegram","Multi-timeframe technical indicator computation","Prototyping LLM-driven trading agents","Monitoring spot market metrics (price, volume, 24h stats)"],"estimated_setup_minutes":60,"required_services":["HTX (Huobi) Spot Market API","OpenAI (GPT-4o) or compatible LLM API","Telegram Bot API","A hosted n8n instance or webhook-accessible runtime","Exchange API keys for trading (if live execution enabled)"],"key_features":["LLM-powered reasoning and trading logic (GPT-4o)","Multi-timeframe indicator logic (15m, 1h, 4h, 1d)","Real-time HTX spot market data ingestion (price, volume, 24h stats)","Self-contained multi-agent workflow (no external subflows)","Telegram notifications and user interaction node(s)"],"target_audience":["Algo traders / quantitative traders","Developers building trading automation","Crypto analysts and researchers using LLMs for market reasoning\",\"n8n power users implementing AI-driven workflows"]}


name: Inventory ABC & Pareto Analysis with Google Sheets for Supply Chain Optimization
author_name: Samir Saci
author_username: samirsaci
categories: []
created_at: 2025-09-15T21:00:03.182Z
updated_at: 2025-09-15T21:00:03.182Z
url: https://n8n.io/workflows/8618
scraped_at: 2025-09-29 22:37:32
metadata_generated_at: 2025-09-30 08:33:37
description:
  Tags*: Supply Chain, Inventory Management, ABC Analysis, Pareto Principle, Demand Variability, Automation, Google Sheets

  Context

  Hi! I’m Samir — a Supply Chain Engineer and Data Scientist based in Paris, and founder of LogiGreen Consulting.

  I help companies optimise inventory and logistics operations by combining data analytics and workflow automation.

  This workflow is part of our inventory optimisation toolkit, allowing businesses to perform ABC classification and Pareto analysis directly from their transactional sales data.

  &gt; Automate inventory segmentation with n8n!

  📬 For business inquiries, feel free to connect with me on LinkedIn

  Who is this template for?

  This workflow is designed for supply chain analysts, demand planners, or inventory managers who want to:

  Identify their top-performing items (Pareto 80/20 principle)  
  Classify products into ABC categories based on sales contribution  
  Evaluate demand variability (XYZ classification support)  

  Imagine you have a Google Sheet where daily sales transactions are stored:



  The workflow aggregates sales by item, calculates cumulative contribution, and assigns A, B, or C classes. It also computes mean, standard deviation, and coefficient of variation (CV) to highlight demand volatility.

  How does it work?

  This workflow automates the process of ABC & Pareto analysis from raw sales data:

  📊 Google Sheets input provides daily transactional sales  
  🧮 Aggregation & code nodes compute sales, turnover, and cumulative shares  
  🧠 ABC class mapping assigns items into A/B/C buckets  
  📈 Demand variability metrics (XYZ) are calculated  
  📑 Results are appended into dedicated Google Sheets tabs for reporting



  🎥 Watch My Tutorial

  Steps:

  📝 Load daily sales records from Google Sheets  
  🔎 Filter out items with zero sales  
  📊 Aggregate sales by store, item, and day  
  📈 Perform Pareto analysis to calculate cumulative turnover share  
  🧮 Compute demand variability (mean, stdev, CV)  
  🧠 Assign ABC classes based on cumulative share thresholds  
  📥 Append results into ABC XYZ and Pareto output sheets  

  What do I need to get started?

  You’ll need:

  A Google Sheet with sales transactions (date, item, quantity, turnover) that is available here: Test Sheet  
  A Google Sheets account connected in n8n  
  Basic knowledge of inventory analysis (ABC/XYZ)  

  Next Steps

  🗒️ Use the sticky notes in the n8n canvas to:

  Add your Google Sheets credentials  
  Replace the Sheet ID with your own sales dataset  
  Run the workflow and check the output tabs: ABC XYZ, Pareto, and Store Sales  

  This template was built using n8n v1.107.3  
  Submitted: September 15, 2025

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workflow_json_compressed:
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metadata_json:
  {"categories":["Supply Chain","Inventory Management","Analytics","Automation","Data Processing"],"complexity":"medium","use_cases":["ABC (A/B/C) classification of SKUs","Pareto (80/20) analysis to identify high-impact items","Prioritising replenishment and safety stock decisions","Detecting demand variability and segmenting items by risk","Automating regular inventory reports into Google Sheets"],"estimated_setup_minutes":30,"required_services":["Google Sheets (Google Sheets API)"],"key_features":["Automated ABC classification logic implemented via code nodes","Pareto/80-20 analysis data preparation for charts or dashboards","Reads from and writes results to Google Sheets for easy review","Handles demand variability inputs and thresholding via filter/if nodes","Customizable business rules in code nodes (e.g., revenue, volume, lead time) for segmentation and prioritization"],"target_audience":["Supply chain managers","Inventory analysts","Operations managers and planners」「Data scientists/engineers working on inventory optimization"]}


name: Create High Quality UGC Images with Nano Banana (Cheaper & Faster)
author_name: Nasser
author_username: nasser
categories: []
created_at: 2025-09-16T10:54:17.654Z
updated_at: 2025-09-16T10:54:17.654Z
url: https://n8n.io/workflows/8644
scraped_at: 2025-09-29 22:37:32
metadata_generated_at: 2025-09-30 08:33:37
description:
  Who’s it for?

  Content Creators
  E-commerce Stores
  Marketing Team

  Description: Generate unique UGC images for your products. Simply upload a product image into a Google Drive folder, and the workflow will instantly generate 50 unique, high-quality AI UGC images using Nano Banana via Fal.ai. 
  All results are automatically saved back into the same folder, ready to use across social media, e-commerce stores, and marketing campaigns.

  How it works?

  📺 YouTube Video Tutorial: 


  1 - Trigger: Upload a new Product Image (with white background) to a Folder in your Google Drive
  2 - Generate 50 different Image Prompts  for your Product
  3 - Loop over each Prompt Generated
  4 - Generate UGC Content thanks to Fal.ai (Nano Banana)
  5 - Upload UGC Content on the initial Google Drive Folder

  Cost: 0.039$ / image==

  How to set up?

  1. Accounts & APIs
  In the Edit Field "Setup" Node replace all ==[YOUR_API_TOKEN]== with your API Token :
  Fal.ai (gemini-25-flash-image/edit): https://fal.ai/models/fal-ai/gemini-25-flash-image/edit/api

  In Credentials on your n8n Dashboard, connect the following accounts using ==Client ID / Secret==: 
  Google Drive: https://docs.n8n.io/integrations/builtin/credentials/google/

  2. Requirements
  Base Image of your Product preferably have a White Background
  Your Google Drive Folder and every Files it contains should be publicly available

  3. Customizations
  Change the amount of total UGC Generated: In Generate Prompts → Message → "Your task is to generate 50"
  Modify the instructions to generate the UGC Prompts: In Generate Prompts → Message
  Change the amount of Base Image: In Generate Image → Body Parameters → JSON → image_urls
  Change the amount of UGC Generated per prompt: In Generate Image → Body Parameters → JSON → num_images
  Modify the Folder where UGC Generated are stored: In Upload File → Parent Folder

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metadata_json:
  {"categories":["automation","content creation","AI image generation","e-commerce","marketing"],"complexity":"medium","use_cases":["Generate large sets of UGC-style product images","Create social media and marketing visuals","Populate e-commerce product listings with lifestyle shots","Automate image creation workflow from uploaded product photos"],"estimated_setup_minutes":20,"required_services":["Google Drive","Fal.ai (Nano Banana image-generation API)","Mistral Cloud (LLM chat node)","n8n (self-hosted or cloud)","HTTP/Webhook access (for API calls)"],"key_features":["Trigger-on-upload: starts when a product image is added to a Drive folder","Bulk generation: produces ~50 unique UGC images per input","Automatic saving: writes all generated images back to the same Google Drive folder","Batch processing (splitInBatches) to handle multiple outputs reliably","Integration with Nano Banana via Fal.ai for faster/cheaper image generation"],"target_audience":["Content creators","E-commerce store owners/operators","Marketing teams and managers"]}


name: Multi-Agent SEO Optimized Blog Writing System with Hyperlinks for E-Commmerce
author_name: Gloria
author_username: gloria
categories: []
created_at: 2025-09-16T15:25:59.894Z
updated_at: 2025-09-16T15:25:59.894Z
url: https://n8n.io/workflows/8654
scraped_at: 2025-09-29 22:37:32
metadata_generated_at: 2025-09-30 08:33:37
description:
  What if AI didn't just write content—but actually thought about how to write it? This n8n workflow revolutionizes content creation by deploying multiple specialized AI agents that handle every aspect of blog writing—from research to publication-ready posts.

  The breakthrough? AI that creates its own prompts, dynamically adapting to different topics and business needs without constant human intervention. 

  Plus, it doesn’t stop at writing — the system also scans all your website URLs, finds the best internal matches for each topic, and inserts SEO-optimized anchor links automatically.

  🤖 The Self-Thinking Content System

  🧠 Cognitive Content Creation: Unlike basic AI writing tools, this system thinks through the entire content strategy

  🔄 Fully Automated Workflow: Set it up once and produce complete, SEO-optimized blog posts with minimal oversight

  🎯 Industry-Adaptive Intelligence: Automatically tailors content approach based on your specific niche and audience

  📊 Data-Driven Decisions: AI agents analyze search intent and competitive landscape before writing a single word

  ⚡ Modular AI Architecture: Specialized agents handle different stages of content creation for superior quality

  ⚙️ Workflow Breakdown

  1️⃣ Keyword Research & Content Ideation
  Identifies high-value topics with traffic potential
  Analyzes search intent to match user expectations

  Maps content opportunities your competitors are missing

  2️⃣ Dynamic Prompt Generation

  Revolutionary feature: AI creates its own writing instructions

  Customizes prompts based on topic complexity and audience needs


  Eliminates the need for prompt engineering expertise

  3️⃣ Modular AI Writing Process

  Research Agent gathers comprehensive topic information



  Structure Agent creates logical, engaging outlines



  Writing Agent produces in-depth, authoritative content



  Each agent specializes in its role for maximum quality

  4️⃣ Final Refinements


  Editing Agent polishes grammar, tone, and readability


  SEO Agent optimizes for search engines without sacrificing quality


  Fact-checking protocols ensure accuracy and credibility

  5️⃣ Publication-Ready Output



  Delivers complete, formatted blog posts ready for immediate use


  Includes meta descriptions, title suggestions, and header structure


  Automatically organizes content in your preferred format

  6️⃣ Smart Internal Linking

  Collects all URLs from your website automatically

  Identifies the best matching pages for each topic

  Inserts optimized anchor words with hyperlinks for SEO boost

  🎯 Perfect For:

  Content Marketers who need consistent, high-quality output

  Business Owners looking to maintain an authoritative blog without the time investment

  Digital Agencies scaling content creation for multiple clients

  SEO Professionals focusing on quality content that ranks

  Solopreneurs who can't afford to spend days writing blog posts

  📦 What's Included:


  ⚙️ Complete n8n Workflow: Pre-configured with all necessary nodes and connections


  🌐 Internal Linking Function: Automatically matches your content with existing URLs and inserts SEO-friendly anchor words


  🔌 AI Agent Configuration: Ready-to-use AI agent settings for each content creation stage



  📊 Airtable Database Structure: Pre-built tables and fields specifically designed for content writing.


  Requires minimal human editing

  🚀 Getting Started:

  Import the provided workflow into your n8n instance

  Configure your preferred AI provider credentials

  Set your content parameters and business information

  Trigger the workflow and watch as complete blog posts are generated

  Use immediately or make minor adjustments to match your style perfectly

  ⚡ Technical Requirements:

  Access to n8n (self-hosted or cloud)

  OpenAI API account (or alternative AI provider)

  Basic familiarity with workflow automation


  Optional: WordPress, Shopify or similar CMS for direct publishing integration

  This template is based on a video of Wayne Eagle

  💡 You can also connect this workflow with my SEO Keyword Research Automation using DataForSEO and Airtable and my SEO-Based Keyword Categorization & Clustering Strategy Workflow with Airtable, both available on my profile, to build a fully automated, end-to-end SEO content machine.

  Stop spending countless hours writing blog posts or wasting money on mediocre content writers. Upgrade to an AI system that thinks for itself and produces truly exceptional content at scale.

nodes_used:
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metadata_json:
  {"categories":["automation","content-creation","SEO","e-commerce","AI/ML"],"complexity":"complex","use_cases":["Automated SEO-optimized blog post generation","Scalable e-commerce content creation (product/category posts)","Automated research and prompt engineering via multi-agent AI","Generating publish-ready Google Docs with hyperlinks and internal links","Site URL scanning for content/context-aware hyperlinking or link discovery"],"estimated_setup_minutes":90,"required_services":["Anthropic (lmChatAnthropic) or other AI model provider","Google Docs API","Google Drive API","HTTP/Webhooks endpoints and Tool HTTP Request","XML parsing or sitemap access"],"key_features":["Multi-agent AI orchestration that generates and adapts its own prompts","End-to-end workflow from research to publication-ready Google Docs","Automated scanning of website URLs to find context for hyperlinks","Hyperlink and internal-link insertion tailored to e-commerce content","Large node count enabling parallelized tasks, retries and orchestration"],"target_audience":["Content teams and SEO specialists","E-commerce managers and marketers","Growth teams looking to scale content production with automation tools engineers/implementers"]}


name: Automatically Reply to Customer Emails with Airtable, Gmail, and GPT-4.1 Mini
author_name: Baptiste Fort
author_username: baptistefort
categories: []
created_at: 2025-09-17T11:57:27.423Z
updated_at: 2025-09-17T11:57:27.423Z
url: https://n8n.io/workflows/8676
scraped_at: 2025-09-29 22:37:32
metadata_generated_at: 2025-09-30 08:33:37
description:
  Automatically Reply to Customer Emails with Airtable, Gmail, and OpenAI

  Introduction

  This guide walks you step by step through setting up an automated agent that:
  Receives emails sent by your customers.  
  Analyzes the content of the email.  
  Generates an appropriate response using an AI model (OpenAI GPT).  
  Stores all information (received email, AI response, date, customer email) in Airtable.  
  Automatically replies to the customer in the same Gmail thread.  

  Prerequisites

  Before you start, you’ll need:  
  A Gmail account connected to n8n.  
  An Airtable account.  
  An n8n instance (cloud or self-hosted).  
  An OpenAI API key.  

  Prepare the Airtable Base



  No need to build everything from scratch — here’s a ready-to-use base you can copy:  
  👉 Open the Airtable base

  It already contains the following structure:  
  Subject** (text) → email subject.  
  Date** (date/time) → date of reception.  
  Customer Email** (text) → customer’s email address.  
  Message** (long text) → body of the received email.  
  AI Response** (long text) → AI-generated reply.  

  You can reuse it as it is or duplicate it into your Airtable account.  

  1. Set Up Gmail Trigger in n8n



  Alright, now that we have our Airtable base ready, we need to capture customer emails.  
  That’s the job of the Gmail Trigger. Basically, this node lies in wait inside your inbox, and as soon as a new message arrives… bam, your workflow fires up.

  Connect Your Gmail Account
  In n8n, add a Gmail Trigger node.  
  Click Credential to connect with and select your Gmail account.  
  If you haven’t done it yet, click Add new, connect your Google account, and allow access.  
  Pro tip: don’t worry, it won’t read your personal emails to gossip — everything stays inside your workflow.  

  Basic Settings
  Poll Times**: select Every Minute.  
     → This way, n8n checks your inbox every minute.  
  Mode**: Message Received.  
     → You want the flow to trigger whenever a customer writes to you.  
  Event**: Message Received.  
     → Same logic, keep it simple.  
  Simplify**: turn it off (OFF).  
     → Why? Because if you enable "Simplify," you only get a stripped-down version of the email. And you want it all: subject, sender, raw message… the full package.  

  Expected Output
  When you execute the node, you should see:  
  id**: unique identifier of the email.  
  threadId**: conversation identifier (super useful to reply in the same thread).  
  snippet**: a short preview of the email (first lines).  
  From**: your customer’s email address.  
  To**: your email address.  
  Subject**: the subject of the email.  
  payload**: the full body of the email (yep, in base64 — but we’ll handle that later).  

  And that’s it — your Gmail Trigger is set up. In short, the moment a customer writes “Hey, I have an issue with my account,” your workflow kicks in instantly (well, almost — it checks every minute).  

  2. Set Up the AI Agent in n8n



  After configuring your Gmail Trigger (which captures incoming customer emails), you now need a brain to take over, analyze the email, and draft a reply.  
  That’s where the AI Agent node comes in.

  Its Role
  The AI Agent node is used to:  
  Read the email content (via the Gmail Trigger).  
  Understand the context and tone of the customer.  
  Generate a clear, concise, and human-like response.  
  Prepare a personalized reply that will later be sent back via Gmail and stored in Airtable.  

  In short, it’s your 24/7 support colleague, but coded as a bot.  

  How to Configure It
  Source for Prompt (User Message)** → choose Define below.  
  Prompt (User Message)** → describe your business and role as if you were training an intern.  
    Example: “You are an AI support agent for a company that sells solar panels. You respond to technical requests, quotes, and customer questions. Your replies must be short, clear, friendly, and precise.”  
  Chat Model** → connect your AI model (e.g. OpenAI GPT-4.1 Mini).  
  Memory (optional but recommended)* → connect a *Conversation Memory** node.  
    → This allows the AI to retain conversation history and better understand follow-ups.  

  Expected Output
  When you run this node, you should see in the output:  
  A field output containing the automatically generated AI reply.  
  The text should be short, natural, and adapted to the customer’s tone (casual or formal).  

  👉 With the Gmail Trigger you capture emails, and with the AI Agent you get a reply ready to send — as if you had written it yourself.  

  3. Save Emails and Responses in Airtable

  Now that your AI Agent generates replies, you need to store them somewhere to keep a clear record of all interactions.  
  That’s where Airtable comes in.

  Quick Reminder
  You’ve already copied my ready-to-use Airtable base:  
  👉 Access the base

  This base contains a table Email Support Logs with the following columns:  
  Subject**  
  Date**  
  Customer Email**  
  Message**  
  AI Response**  

  How to Connect Airtable in n8n
  Add an Airtable node right after your AI Agent.  
  Under Operation, select Create.  
  In Base → choose BASE AGENT IA EMAIL.  
  In Table → select Email Support Logs.  

  Map the Correct Values
  Then, link the fields as follows:  
  Subject** → {{ $('Email Received').item.json.Subject }}  
  Customer Email** → {{ $('Email Received').item.json.From }}  
  Message** → {{ $('Email Received').item.json.snippet }}  
  AI Response** → {{ $('AI Agent').item.json.output }}  
  Date** → {{ $now }}  

  Expected Output
  For each new email received:  
  Gmail captures the email.  
  Your AI drafts the reply.  
  All details (email, sender, subject, reply) are automatically stored in your Airtable base.  

  👉 You now have a fully automated customer support log.  

  4. Automatically Reply to the Customer in Gmail

  Now that you’re storing each interaction in Airtable, it’s time to send your AI’s reply directly back to the customer.  
  This closes the loop: customer writes → AI replies → everything gets logged in Airtable.

  Add the Gmail Reply Node
  Add a Gmail node right after your AI Agent (or after Airtable if you prefer logging before replying).  
  Under Operation, select Reply.  
  Connect your Gmail account (same credential as your Gmail Trigger).  

  Configure the Reply
  Thread ID** → {{ $('Email Received').item.json.threadId }}  
     → Ensures the reply is sent in the same conversation thread.  
  To** → {{ $('Email Received').item.json.From }}  
     → Customer’s email address.  
  Subject** → Re: {{ $('Email Received').item.json.Subject }}  
     → The "Re:" keeps the continuity of the conversation.  
  Message Body** → {{ $('AI Agent').item.json.output }}  
     → This is the text automatically generated by your AI.  

  Expected Output
  When a customer sends an email:  
  Gmail Trigger captures the message.  
  The AI Agent generates a tailored reply.  
  Airtable logs the full interaction.  
  Gmail automatically sends the response in the same conversation thread.  

  Your customer receives a quick, personalized, and natural reply without you typing a single word.  

  👉 You now have a complete support agent: listen, analyze, log, reply.  

  Want to save hours each week? Visit Agence automatisation 0vni.

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.gmailTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.airtable","n8n-nodes-base.gmail","@n8n/n8n-nodes-langchain.memoryBufferWindow","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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metadata_json:
  {"categories":["automation","integration","AI","customer support"],"complexity":"medium","use_cases":["Automatically reply to customer emails","Generate AI-crafted responses from incoming messages","Log emails and AI responses to Airtable for tracking","Maintain conversation context across replies"],"estimated_setup_minutes":30,"required_services":["Gmail (Google account)","Airtable","OpenAI (GPT-4.1 Mini)","n8n"],"key_features":["Detect incoming Gmail messages and trigger workflow","Analyze email content with an AI model","Generate reply text using GPT-4.1 Mini","Store received emails, AI responses, timestamps, and customer addresses in Airtable","Send replies in the same Gmail thread to preserve context"],"target_audience":["Customer support teams","Developers/automation engineers","Small business owners/SMBs wanting automated replies`,`Product managers or ops teams managing customer communications"]}


name: Categorize SEO Keywords & Create Content Strategy with AI and Airtable
author_name: Gloria
author_username: gloria
categories: []
created_at: 2025-09-18T13:27:50.277Z
updated_at: 2025-09-18T13:27:50.277Z
url: https://n8n.io/workflows/8721
scraped_at: 2025-09-29 22:37:32
metadata_generated_at: 2025-09-30 08:33:37
description:
  Premium n8n Workflow: SMART AI Keyword Categorization & Content Strategy

  This n8n workflow transforms raw keyword data into actionable content intelligence using advanced AI categorization and clustering. It creates a comprehensive content strategy with ready-to-use titles and descriptions for your blog posts.

  🚀 Features

  🧠 AI-Powered Categorization  
  Automatically sorts keywords into strategic buckets — Quick Wins, Authority Builders, Emerging Topics, Intent Signals, and Semantic Topics — for targeted content creation 🎯.  

  🔗 Semantic Clustering  
  Identifies meaningful relationships between keywords to create logically grouped content clusters 🕸️.  

  📑 Content Blueprint Generation  
  Creates compelling titles and descriptions for each keyword and cluster to streamline your content creation process 📝.  

  🌐 Hub & Spoke Strategy Builder  
  Develops a complete site architecture plan with main hub articles and supporting spoke content 🏗️.  

  ⚡ Airtable Integration  
  Organizes all outputs in a structured database for seamless integration with your content workflow.  

  🤖 n8n AI Agents  
  Leverages advanced AI capabilities to analyze keyword intent and potential without manual intervention.  

  👥 This Workflow is Perfect For:

  Content strategists 📊  
  SEO professionals 🔍  
  Content marketing teams 👥  
  Blog managers 💻  
  Digital publishers 📰  
  Website owners (E-Commerce) 🏢  
  Anyone using the SMART AI Keyword Research Workflow 🔄  

  Stop struggling with manual keyword grouping and transform your raw keyword data into a comprehensive content strategy.  
  👉 Get this premium workflow today!

  📝 What's Included?

  ⚙️ n8n Workflow Template  
  Ready-to-use workflow with AI-powered nodes for keyword analysis and categorization.  

  📊 Airtable Database Structure  
  Pre-configured tables for categorized keywords, content ideas, and hub-spoke relationships.  

  🧩 Keyword Categorization System  
  Automated logic to sort keywords by opportunity, competition, and relevance.  

  📋 Content Titles and Descriptions  
  AI prompts to create optimized titles and descriptions for each content piece.  

  🌐 Hub & Spoke Mapper  
  Logic to identify main topics and supporting content opportunities.  

  📚 Documentation  
  Step-by-step instructions for importing keyword data and running the workflow.  

  🏆 Why Choose This Workflow?

  ⏱️ Save Massive Time: Automate what would take days of manual analysis and planning.  
  📈 Improve Content ROI: Create content that targets the right keywords in the right way.  
  🔄 Seamless Integration: Works perfectly with the AI Keyword Research and Blog Writing workflows available on my profile.  
  🏗️ Build Site Authority: Create topically relevant content clusters that boost domain expertise.  
  🎯 Strategic Focus: Stop guessing which keywords to target and how to organize your content.  

  🛠️ How It Works

  1️⃣ Import the provided n8n workflow into your n8n instance 📥.  
  2️⃣ Connect to your Airtable base containing keyword research data ⚙️.  
  3️⃣ Configure the AI agents with your preferred content parameters 🤖.  
  4️⃣ Run the workflow to automatically categorize, cluster, and create content briefs 🔄.  
  5️⃣ Use the generated content strategy to guide your blogging or feed directly into the Multi-Agent Blog Writing System available on my profile 📝.  


  Additional detailed instructions are provided in the workflow.  

  🏁 What You Need to Get Started

  🔹 Access to n8n (self-hosted or cloud) ☁️  
  🔹 An Airtable account with keyword data (ideally from the AI Keyword Research Workflow on my profile) 📊  
  🔹 OpenAI API credentials for the AI categorization and title generation 🔑  
  🔹 Basic understanding of content strategy and n8n workflows 🧠  

  💡 You can also connect this workflow with my SEO Keyword Research Automation using DataForSEO and Airtable and my Multi-Agent SEO Optimized Blog Writing System with Hyperlinks for E-Commerce, both available on my profile, to build a fully automated, end-to-end SEO content machine.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","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.filter","n8n-nodes-base.set","n8n-nodes-base.airtable","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.airtable","n8n-nodes-base.set","n8n-nodes-base.splitOut","n8n-nodes-base.set","n8n-nodes-base.splitOut","n8n-nodes-base.splitOut","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.splitOut","n8n-nodes-base.aggregate","n8n-nodes-base.set","n8n-nodes-base.splitInBatches","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.airtable","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi"]

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metadata_json:
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name: Automated Daily Stock Market Report with Bright Data, GPT-4.1, Airtable/Gmail
author_name: Baptiste Fort
author_username: baptistefort
categories: []
created_at: 2025-09-18T14:02:05.882Z
updated_at: 2025-09-18T14:02:05.882Z
url: https://n8n.io/workflows/8724
scraped_at: 2025-09-29 22:37:32
metadata_generated_at: 2025-09-30 08:33:37
description:
  📘 Workflow Documentation – Stock Market Daily Digest

  👋 Introduction
  Wake up to a clean, analyst-style stock digest in your inbox—top gainers/losers, a readable performance table, 3–5 insights, and upcoming events—no spreadsheets, no manual scraping, no copy-paste.  
  This article explains, step by step, how to build a robust, daily, end-to-end automation that collects market data (Bright Data), waits until scraping is done, aggregates results, asks an AI model (OpenAI) to draft a styled HTML email, logs everything to Airtable, and finally sends the report via Gmail.

  You’ll find a friendly but technical tour of every single node, so you can rebuild or adapt the same pipeline with confidence.

  🎯 Who is this workflow for?
  Investors & traders** who want a quick, readable daily summary.  
  Finance/Product teams** building data-driven alerts/digests.  
  Consultants & agencies** sending recurring client updates.  
  Automation builders** prototyping finance ops quickly.

  🧰 Tools you’ll need
  Bright Data** — dataset triggers & snapshots for reliable web data.  
  OpenAI (GPT)** — to generate a professional HTML digest.  
  Airtable** — store daily rows for history, filters, dashboards.  

  Example Airtable Table: Daily Stocks

  | Ticker | Company                  | Price   | Change % | Sentiment | Date                |
  |--------|--------------------------|---------|----------|-----------|---------------------|
  | AAPL   | Apple Inc.               | 225.80  | +1.4%    | 🟢 Positive | 2025-09-18 09:00    |
  | MSFT   | Microsoft Corporation    | 415.20  | -0.7%    | 🔴 Negative | 2025-09-18 09:00    |
  | NVDA   | NVIDIA Corporation       | 124.55  | +2.1%    | 🟢 Positive | 2025-09-18 09:00    |
  | TSLA   | Tesla Inc.               | 260.00  | -3.0%    | 🔴 Negative | 2025-09-18 09:00    |
  | META   | Meta Platforms Inc.      | 310.45  | +0.5%    | 🟡 Neutral  | 2025-09-18 09:00    |



  Gmail** — deliver the final HTML email to stakeholders.  
  n8n** — the automation engine that orchestrates every step.

  &gt; Keep API keys in n8n Credentials (never hard-code secrets).

  🗺️ Architecture at a glance
  Schedule fires daily  
  Seed list of tickers  
  Split into one item per stock  
  Prepare keyword for scraping  
  Launch Bright Data job  
  Poll progress with a wait-loop  
  Fetch snapshot data  
  Aggregate for the AI  
  Generate HTML summary (GPT)  
  Save rows to Airtable  
  Send email via Gmail

  ⚙️ Step-by-step — Every node explained

  ⏰ Daily Run Trigger (Schedule Trigger)

  Purpose
  Start the automation at a precise time each day so nobody needs to push a button.

  Parameters (English)
  Trigger Type**: Time Interval or Cron  
  Every X**: 1 Day (or your preferred cadence)  
  Timezone**: UTC (or your own)  
  Start Time**: optional (e.g., 09:00)

  📝 Set Stock List (Set Node – SAMPLE DATA)

  Purpose
  Define the universe of stocks to monitor. This acts as the seed data for scraping.

  Parameters
  Values to Set**: Fixed JSON (array of objects)  
  Keep Only Set**: true  
  Fields per item**: ticker, name, market_cap (you may add sector, isin, etc.)

  🔀 Split Stocks (Split Out)

  Purpose
  Turn the array into individual items so each ticker is processed independently (scraping, polling, results).

  Parameters
  Operation**: Split Out Items  
  Field to Split**: the array defined in the previous Set node

  🏷 Prepare Stock Keyword (Set Node)

  Purpose
  Create a keyword field (typically equal to ticker) for Bright Data discovery.

  Parameters
  Values to Set**: Add Field  
  Field Name**: keyword  
  Value**: use an expression referencing the current item’s ticker (e.g., ` {{ $json.ticker }} `)

  🕸 Bright Data Scraper (HTTP Request)

  Purpose
  Trigger the Bright Data dataset to start collecting information for the keyword. Returns a snapshot_id to poll later.

  Parameters
  Method**: POST  
  Endpoint**: https://api.brightdata.com/datasets/v1/trigger  
  Authentication**: Authorization: Bearer &lt;token&gt; (header)  
  Body Fields**:  
    dataset_id: your Bright Data dataset ID  
    discover_by: usually keyword  
    keyword: the value prepared above

  &gt; Add a retry/backoff policy on 429/5xx in node options.

  🔄 Check Scraper Progress (HTTP Request)

  Purpose
  Poll Bright Data to see whether the snapshot is running or ready.

  Parameters
  Method**: GET  
  Endpoint**: https://api.brightdata.com/datasets/v1/snapshots/{snapshot_id}  
  Authentication**: Authorization: Bearer &lt;token&gt;  
  Expected Output**: a status field (running, ready)

  ⏳ Wait for Data (Wait Node)

  Purpose
  Pause between progress checks to avoid rate limits and give Bright Data time to finish.

  Parameters
  Mode**: Wait a fixed amount of time  
  Time**: e.g., 30 seconds (tune to your dataset size)

  🔀 Scraper Status Switch (Switch Node)

  Purpose
  Route logic based on the polled status.

  Parameters
  Value to Check**: status  
  Rules**:  
    Equals running → go to Wait for Data (then re-check)  
    Equals ready → proceed to Fetch Scraper Results

  &gt; Loop pattern: Check → Wait → Check, until ready.

  📥 Fetch Scraper Results (HTTP Request)

  Purpose
  Download the completed snapshot data once Bright Data marks it ready.

  Parameters
  Method**: GET  
  Endpoint**: https://api.brightdata.com/datasets/v1/snapshots/{snapshot_id}/data  
  Authentication**: Authorization: Bearer &lt;token&gt;  
  Query**: format=json  
  Output**: array of rows per ticker (price, change %, any fields your dataset yields)

  &gt; Normalize fields with a Set/Code node if needed.

  📊 Aggregate Stock Data (Aggregate Node)

  Purpose
  Combine all individual items into one consolidated object so the AI can analyze the entire market snapshot.

  Parameters
  Mode**: Aggregate (merge to a single item)  
  Fields to Include**: ticker, name, price, change, sentiment (plus any extra fields captured)  
  Output**: one JSON item containing an array/map of the day’s stocks

  🤖 Generate Daily Summary (AI Node – OpenAI)

  Purpose
  Ask the model to convert raw data into a styled HTML email: headline, top movers, table, insights, and (optional) upcoming events.

  Parameters
  Model**: gpt-4.1 
  Input**: the aggregated JSON from the previous node  
  Prompt guidelines**:  
    Output HTML only with inline styles (email-safe)  
    Include a table (Ticker, Company, % Change with ↑/↓ & color, Market Cap, Sentiment icon)  
    Highlight top 2 gainers & 2 losers with short reasoning if present  
    Provide 3–5 insights (sector rotation, volatility, outliers)  
    Add upcoming events when available (earnings, launches, macro)  
    Footer: “Generated automatically by your AI-powered stock monitor”  
  Output field**: confirm the exact property that contains the HTML (e.g., output, message, text)

  🗂 Save to Airtable (Airtable – Create Record)

  Purpose
  Log each item (or the roll-up) to Airtable for history, filtering, and dashboards.

  Parameters
  Operation**: Create Record  
  Base ID**: from your Airtable URL  
  Table**: e.g., Daily Stocks  
  Field Mapping**:  
    Ticker ← ` {{ $json.ticker }} `  
    Company ← ` {{ $json.name }} `  
    Price ← ` {{ $json.price }} `  
    Change % ← ` {{ $json.change }} `  
    Sentiment ← ` {{ $json.sentiment }} `  
    Date ← ` {{ $now.toISO() }} `

  &gt; Use a Single-Select for Sentiment (🟢 / 🟡 / 🔴) to build clean Airtable views.

  📧 Send Report via Gmail (Gmail Node)

  Purpose
  Deliver the AI-generated HTML digest to your recipients.

  Parameters
  Operation**: Send Email  
  Send To**: one or more recipients (e.g., investor@domain.com)  
  Subject**: Daily Stock Market Digest – {{ $now.format("yyyy-MM-dd") }}  
  Message (HTML)**: reference the AI node’s HTML property (e.g., ` {{ $('Generate Daily Summary').first().json.output }} `)  
  Options: set **Append Attribution to false (keep the email clean)

  &gt; Test in Gmail, Outlook, and mobile to validate inline CSS.

  🧪 Error handling & reliability tips
  Backoff on Bright Data* — If scraping many tickers, increase *Wait** or batch requests.  
  Guard against empty results** — If a snapshot returns 0 rows, branch to a fallback email (“No data today”).  
  AI guardrails** — Enforce “HTML-only” and skip missing sections gracefully.  
  Airtable normalization** — Strip %, cast numbers to float before insert.  
  Observability* — Add a final Slack/Email *On Fail** node with run ID and error message.

  🧩 Customization ideas
  Sector deep-dives**: add sector fields and a second AI paragraph on sector rotation.  
  CSV attachment**: generate & attach a CSV for power users.  
  Multiple lists**: run parallel branches for Tech, Healthcare, or regions.  
  Other asset classes**: Crypto, ETFs, Indices, FX.  
  Audience targeting**: different “To” lists and slightly different prompts per audience.

  ✅ Why this workflow is powerful
  Hands-off** — the report simply shows up every day.  
  Analyst-grade** — clean HTML, top movers, tidy table, actionable insights.  
  Auditable** — rows archived in Airtable for history and dashboards.  
  Composable** — swap scrapers, LLMs, storage, or email service.  
  Scalable** — start with 10 tickers, grow to many lists using the same loop.

  For advanced no-code & AI projects, see 0vni – Agence automatisation. 

nodes_used:
  ["@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.set","n8n-nodes-base.splitOut","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.switch","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.aggregate","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.gmailTool","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.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","data processing","integration","web scraping","reporting"],"complexity":"complex","use_cases":["Daily stock market digest delivered by email","Automated watchlist and top gainers/losers reporting","Generating AI-written insights and commentary","Aggregating scraped market data into Airtable","Scheduling recurring investor or internal briefings"],"estimated_setup_minutes":90,"required_services":["Bright Data (web scraping proxy/service)","OpenAI (GPT-4.1 or compatible model)","Airtable (or other database/storage)","Gmail (or SMTP/email service)","n8n (workflow runner)"],"key_features":["Scheduled daily scraping of market data","Wait/monitor pattern to ensure scraping completion","Aggregation and tabular performance calculations","AI-generated styled HTML digest with 3–5 insights","Storage/update of results in Airtable and email delivery via Gmail/SMTP"],"target_audience":["developers/automation engineers","data analysts","financial analysts/traders"]}


name: Manage WooCommerce Store with Natural Language using GPT-4.1 and MCP Server
author_name: Rohit Dabra
author_username: rohitdabra
categories: []
created_at: 2025-09-20T06:30:22.933Z
updated_at: 2025-09-20T06:30:22.933Z
url: https://n8n.io/workflows/8769
scraped_at: 2025-09-29 22:37:32
metadata_generated_at: 2025-09-30 08:33:37
description:
  WooCommerce AI Agent — n8n Workflow (Overview)

  Description: Turn your WooCommerce store into a conversational AI assistant — create products, place orders, run reports and manage coupons using natural language via n8n + an MCP Server.

  Key features
  Natural-language commands mapped to WooCommerce actions (products, orders, reports, coupons).  
  Structured JSON outputs + lightweight mapping to avoid schema errors.  
  Calls routed through your MCP Server for secure, auditable tool execution.  
  Minimal user prompts — agent auto-fetches context and asks only when necessary.  
  Extensible: add new tools or customize prompts/mappings easily.

  Demo of the workflow:

  Youtube Video

  🚀 Setup Guide: WooCommerce + AI Agent Workflow in n8n

  1. Prerequisites
  Running n8n instance  
  WooCommerce store with REST API keys  
  OpenAI API key  
  MCP server (production URL)  

  2. Import Workflow
  Open n8n dashboard  
  Go to Workflows → Import  
  Upload/paste the workflow JSON  
  Save as WooCommerce AI Agent  

  3. Configure Credentials

  OpenAI
  Create new credential → OpenAI API  
  Add your API key → Save & test  

  WooCommerce
  Create new credential → WooCommerce API  
  Enter Base URL, Consumer Key & Secret → Save & test  

  MCP Client
  In MCP Client node, set Server URL to your MCP server production URL  
  Add authentication if required  

  4. Test Workflow
  Open workflow in editor  
  Run a sample request (e.g., create a test product)  
  Verify product appears in WooCommerce  

  5. Activate Workflow
  Once tested, click Activate in n8n  
  Workflow is now live 🎉  

  6. Troubleshooting
  Schema errors** → Ensure fields match WooCommerce node requirements  
  Connection issues** → Re-check credentials and MCP URL  

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.mcpClientTool","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.wooCommerceTool","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["e-commerce","automation","integration","AI/ML"],"complexity":"complex","use_cases":["Create and manage WooCommerce products via natural language","Place and manage orders using conversational commands","Generate sales/revenue reports and analytics on demand","Create and manage coupons and promotions with natural language","Provide a conversational AI assistant for store operations"],"estimated_setup_minutes":60,"required_services":["WooCommerce (store + REST API)","MCP Server (for secure tool execution/auditing)","GPT-4.1 (or compatible LLM) / OpenAI API","n8n (workflow automation platform)"],"key_features":["Natural-language commands mapped to WooCommerce actions (products, orders, reports, coupons)","Structured JSON outputs and lightweight schema mapping to reduce errors","Requests routed through MCP Server for secure, auditable execution","Conversation memory (memoryBufferWindow + chat) for contextual interactions","Wide node usage across 28 nodes to handle tool orchestration and logic"],"target_audience":["E-commerce store owners / managers","Developers and automation engineers","Customer support teams wanting conversational tooling for store ops"]}


name: 🤖 Automate Lead Qualification & Multi-Channel Follow-up with AI (BANT)
author_name: Lucas Peyrin
author_username: lucaspeyrin
categories: []
created_at: 2025-09-20T09:54:57.584Z
updated_at: 2025-09-20T09:54:57.584Z
url: https://n8n.io/workflows/8773
scraped_at: 2025-09-29 22:37:32
metadata_generated_at: 2025-09-30 08:33:37
description:
  How it works

  This workflow is your automated sales assistant, designed to intelligently qualify incoming leads and route them to the most appropriate follow-up channel. It uses the powerful BANT (Budget, Authority, Need, Timing) framework, powered by Google Gemini AI, to score leads as 'hot', 'mid', or 'cold', ensuring your sales team focuses on the most promising opportunities.

  Here's a step-by-step breakdown:

  Lead Capture: A public Form Trigger collects essential lead information, including their name, email, what they want to build, their budget, desired start time, and job role. These questions are specifically designed to gather BANT data.
  AI Lead Scoring: The collected data is sent to Google Gemini. A detailed prompt instructs the AI to act as a Lead Scoring Expert, evaluating each BANT component individually and then assigning an overall 'hot', 'mid', or 'cold' score based on predefined criteria.
  Intelligent Routing: A Switch node acts as the central router. Based on the AI's 'hot', 'mid', or 'cold' score, the workflow directs the lead down one of three distinct follow-up paths.
  Hot Leads (Calendar Booking): For highly qualified 'hot' leads, the workflow immediately redirects them to your calendar booking link, making it easy for them to schedule a direct conversation.
  Mid Leads (WhatsApp Engagement): For 'mid' priority leads, Google Gemini generates a personalized, pre-filled WhatsApp message summarizing their inquiry. The lead is then redirected to a WhatsApp chat with your sales team, allowing for quick, informal engagement.
  Cold Leads (Nurturing Email): For 'cold' leads who might be in an early research phase, Google Gemini crafts a helpful, non-salesy follow-up email. This email provides valuable resources (like templates or community links) and is sent via Gmail, keeping them engaged without pressure.

  Set up steps

  Setup time: ~10-15 minutes

  This workflow requires connecting your Google AI and Gmail accounts, and customizing several nodes to fit your sales process.

  Get Your Google AI API Key:
      Visit Google AI Studio at aistudio.google.com/app/apikey.
      Click "Create API key in new project" and copy the key.
      In the workflow, select the Score Lead node. Click the Credential dropdown and select + Create New Credential.
      Paste your key into the API Key field and Save.
      Repeat this for the Write Placeholder WA Message and Write Follow up Email nodes, selecting the credential you just created.

  Connect Your Gmail Account:
      Select the Send Follow up Email with Gmail node.
      Click the Credential dropdown and select + Create New Credential to connect your Google account. Follow the prompts to grant n8n access.

  Customize Lead Scoring Criteria:
      Go to the Score Lead node.
      In the Text parameter, carefully review and adapt the BANT Criteria Mapping and Scoring Logic to align with your specific sales process and ideal customer profile. This is crucial for accurate lead qualification.

  Configure Follow-up Channels:
      Hot Leads: Select the Calendar Booking Link node. Update the redirectUrl parameter with your personal or team's calendar booking link (e.g., Calendly, Chili Piper).
      Mid Leads: Select the Phone Number node. Set the whatsapp_phone value to your company's WhatsApp phone number (e.g., +15551234567). You can also customize the pre-filled WhatsApp message by adjusting the prompt in the Write Placeholder WA Message node.
      Cold Leads: Select the Redirect to Website node. Update the redirectUrl parameter to your company's main website or a relevant resource page (e.g., a free templates page, a blog post). You can also customize the email content and resources shared by adjusting the prompt in the Write Follow up Email node.

  Activate and Test:
      Activate the workflow using the toggle at the top right.
      Go to the Lead Contact Form node and click the "Open Form URL" button.
      Submit several test applications with different answers (e.g., one "hot" lead, one "mid", one "cold") to ensure the AI scores them correctly and they are routed to the appropriate follow-up action.

  Start qualifying and engaging your leads more effectively!

nodes_used:
  ["n8n-nodes-base.switch","@n8n/n8n-nodes-langchain.googleGemini","n8n-nodes-base.form","n8n-nodes-base.form","@n8n/n8n-nodes-langchain.googleGemini","n8n-nodes-base.form","@n8n/n8n-nodes-langchain.googleGemini","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.gmail","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.formTrigger"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","sales & CRM","lead management","AI"],"complexity":"medium","use_cases":["Automated lead qualification using BANT (Budget, Authority, Need, Timing)","Prioritizing leads into hot / mid / cold segments","Routing leads to appropriate follow-up channels (email, SMS, chat, CRM)","Triggering multi-channel follow-up sequences for qualified leads","Collecting lead data via a public form and enriching/processing it with AI"],"estimated_setup_minutes":30,"required_services":["Google Gemini (Gemini API) for AI scoring","Public Form or n8n Form Trigger to capture leads","One or more communication providers for follow-up (email/SMS/Slack/phone) — e.g., SMTP/SendGrid, Twilio, Slack","Optional CRM integration (Salesforce, HubSpot, etc.) depending on routing"],"key_features":["BANT-based AI lead scoring (hot / mid / cold)","Public form trigger to ingest leads","Multi-channel routing and automated follow-up","Configurable thresholds and routing logic (switch/set nodes)","Uses Google Gemini for natural-language understanding and scoring"],"target_audience":["Sales teams / SDRs","Revenue operations / RevOps","Marketers running lead capture campaigns"]}


name: 🤖 Self Improving Email AI Support with Human-in-the-Loop
author_name: Lucas Peyrin
author_username: lucaspeyrin
categories: []
created_at: 2025-09-20T15:06:17.309Z
updated_at: 2025-09-20T15:06:17.309Z
url: https://n8n.io/workflows/8779
scraped_at: 2025-09-29 22:37:32
metadata_generated_at: 2025-09-30 08:33:37
description:
  How it works

  This workflow creates a sophisticated, self-improving customer support system that automatically handles incoming emails. It's designed to answer common questions using an AI-powered knowledge base and, crucially, to learn from human experts when new or complex questions arise, continuously expanding its capabilities.

  Think of it like having an AI assistant with a smart memory and a human mentor. Here's the step-by-step process:

  New Email Received: The workflow is triggered whenever a new email arrives in your designated support inbox (via Gmail).
  Classify Request: An AI model (Google Gemini 2.5 Flash Lite) first classifies the incoming email to ensure it's a genuine support request, filtering out irrelevant messages.
  Retrieve Knowledge Base: The workflow fetches all existing Question and Answer pairs from your dedicated Google Sheet knowledge base.
  AI Answer Attempt: A powerful AI model (Google Gemini 2.5 Pro) analyzes the customer's email against the entire knowledge base. It attempts to find a highly relevant answer and drafts a complete HTML email response if successful.
  Decision Point: An IF node checks if the AI found a confident answer.
      If Answer Found: The AI-generated HTML response is immediately sent back to the customer via Gmail.
      If No Answer Found (Human-in-the-Loop):
          Escalate to Human: The customer's summarized question and original email are forwarded to a human expert (you or your team) via Gmail, requesting their assistance.
          Human Reply & AI Learning: The workflow waits for the human expert's reply. Once received, another AI model (Google Gemini 2.5 Flash) processes both the original customer question and the expert's reply to distill them into a new, generic, and reusable Question/Answer pair.
          Update Knowledge Base: This newly created Q&A pair is then automatically added as a new row to your Google Sheet knowledge base, ensuring the system can answer similar questions automatically in the future.

  Set up steps

  Setup time: ~10-15 minutes

  This workflow requires connecting your Gmail and Google Sheets accounts, and obtaining a Google AI API key. Follow these steps carefully:

  Connect Your Gmail Account:
      Select the On New Email Received node.
      Click the Credential dropdown and select + Create New Credential to connect your Gmail account. Grant the necessary permissions.
      Repeat this for the Send AI Answer and Ask Human for Help nodes, selecting the credential you just created.

  Connect Your Google Sheets Account:
      Select the Get Knowledge Base node.
      Click the Credential dropdown and select + Create New Credential to connect your Google account. Grant the necessary permissions.
      Repeat this for the Add to Knowledge Base node, selecting the credential you just created.

  Set up Your Google Sheet Knowledge Base:
      Create a new Google Sheet in your Google Drive.
      Rename the first sheet (tab) to QA Database.
      In the first row of QA Database, add two column headers: Question (in cell A1) and Answer (in cell B1).
      Go back to the Get Knowledge Base node in n8n. In the Document ID field, select your newly created Google Sheet. Do the same for the Add to Knowledge Base node.

  Get Your Google AI API Key (for Gemini Models):
      Visit Google AI Studio at aistudio.google.com/app/apikey.
      Click "Create API key in new project" and copy the key.
      In the workflow, go to the Google Gemini 2.5 Pro node, click the Credential dropdown, and select + Create New Credential.
      Paste your key into the API Key field and Save.
      Repeat this for the Google Gemini 2.5 Flash Lite and Google Gemini 2.5 Flash nodes, selecting the credential you just created.

  Configure Human Expert Email:
      Select the Ask Human for Help node.
      In the Send To field, replace the placeholder email address with the actual email address of your human expert (e.g., your own email or a team support email).

  Activate the Workflow:
      Once all credentials and configurations are set, activate the workflow using the toggle switch at the top right of your n8n canvas.

  Start Learning!
  Send a test email to the Gmail account connected to the On New Email Received node. Observe how the AI responds, or how it escalates to your expert email and then learns from the reply. Check your Google Sheet to see new Q&A pairs being added!

nodes_used:
  ["n8n-nodes-base.noOp","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.if","@n8n/n8n-nodes-langchain.textClassifier","n8n-nodes-base.set","n8n-nodes-base.aggregate","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.gmailTrigger","n8n-nodes-base.gmail","n8n-nodes-base.gmail","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@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.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","customer support","ai-assisted"],"complexity":"complex","use_cases":["Automated email triage and replies","Human-in-the-loop escalation for complex queries","Continuous expansion of a support knowledge base","Generating training data and improving AI answers over time","Logging and auditing of support interactions"],"estimated_setup_minutes":90,"required_services":["Email provider (Gmail/IMAP/SMTP)","Google Gemini (or another LLM API)","Google Sheets (or other spreadsheet/DB for knowledge base and logs)","n8n account with relevant nodes enabled","Optional: Slack/Teams or email for human reviewer notifications"],"key_features":["Email trigger and ingestion","AI-generated responses from LLM","Confidence scoring/text classification to decide escalation","Human review/approval workflow for uncertain cases","Automatic updates to the knowledge base (spreadsheet) from human corrections/answers"],"target_audience":["Customer support teams","Customer success managers","Operations/IT teams automating workflows with n8n」「Developers integrating LLMs into support processes"]}


name: 🛠️ Re-Access Binary Data from Any Previous Node
author_name: Lucas Peyrin
author_username: lucaspeyrin
categories: []
created_at: 2025-09-20T15:28:27.172Z
updated_at: 2025-09-20T15:28:27.172Z
url: https://n8n.io/workflows/8780
scraped_at: 2025-09-29 22:37:32
metadata_generated_at: 2025-09-30 08:33:37
description:
  How it works

  Ever had binary data (like images, PDFs, or files) disappear in your n8n workflow after an intermediate node processed it? This workflow provides a powerful solution by demonstrating how to re-access and re-attach binary data from any previous node, even if it was dropped along the way. Think of it like having a reliable backup copy of your file always available, no matter what happens to the original as it moves through your workflow.

  Here's how this template works step-by-step:

  Initial Binary Fetch: The workflow starts by fetching a binary image (the n8n logo) from a URL using an HTTP Request node. This is our original binary data.
  Simulated Data Loss: A Set node then processes this data. Crucially, by default, Set nodes (and many others) do not pass binary data to subsequent nodes. This step intentionally simulates a common scenario where your binary data might seem to "disappear" from the workflow's output.
  Re-Access and Re-Attach: The core of the solution is a Code node. It uses a specific n8n expression ($(nodeName).item) to reach back to the original node that produced the binary data (Get n8n Logo (Binary)). It then retrieves that binary data and uses this.helpers.prepareBinaryData() to correctly re-attach it to the current item, making it available for all subsequent nodes.

  Set up steps

  Setup time: 0 minutes!

  This is a self-contained tutorial workflow, so no external accounts or credentials are required.

  Simply click the "Execute Workflow" button to run it.
  Observe the output of the Re-Access Binary Data from Previous Node to see the binary data successfully re-attached.
  Important for Customization: If you adapt this technique to your own workflows, remember to update the previousNodeName variable within the Re-Access Binary Data from Previous Node (Code node) to match the exact name of the node that originally produced the binary data you wish to retrieve.

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

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metadata_json:
  {"categories":["automation","integration","data processing","file handling"],"complexity":"medium","use_cases":["Preserve and reattach uploaded files (images, PDFs, documents) after intermediate processing","Recover binary data dropped by downstream nodes","Add reliable backup copies of binaries for retries and error handling","Enable debugging or inspection of binary payloads at any point in the workflow","Serve previously uploaded files via webhook or re-send as email/attachment"],"estimated_setup_minutes":10,"required_services":["n8n (self-hosted or cloud) — no external APIs required","Public HTTP endpoint / webhook (optional, if using the Webhooks node)"],"key_features":["Capture and store binary data from any earlier node","Re-attach binary files to items later in the workflow even if they were dropped","Works with images, PDFs and arbitrary file types","Demonstrates using Code and Set nodes alongside Webhooks/manual triggers","Provides a reliable backup pattern for file handling and retry logic"],"target_audience":["integration developers","automation engineers","IT/ops teams managing file workflows"]}


name: Transform Product Photos into Studio-Quality Visuals with Nano Banana & Telegram
author_name: Sulieman Said
author_username: sulieman-said
categories: []
created_at: 2025-09-22T22:53:36.260Z
updated_at: 2025-09-22T22:53:36.260Z
url: https://n8n.io/workflows/8843
scraped_at: 2025-10-27 22:15:59
description:
  Turn a single product photo into scroll-stopping, studio-quality visuals in minutes.  
  Perfect for Shopify / WooCommerce / Amazon / Etsy sellers, agencies, and UGC creators who want consistent, on-brand images without costly photoshoots or endless editing.

  Why you’ll love it
  Sell more, faster:** Professional, consistent visuals boost trust and conversions.
  Save time & money:** No studio, no photographer, no retouching marathon.
  Multiple looks instantly:* Get up to *3 variants** per product (great for A/B testing).
  On-brand results:* Subtle *brand color grading** for a polished, consistent look.
  Zero friction delivery:* Images are sent straight to your *Telegram** for instant review.

  Perfect for
  D2C brands, marketplace sellers, and dropshippers  
  Agencies producing UGC for clients  
  Creators who need ad-ready product images today  
  Teams that want reliable results without learning complex tools

  What’s included
  n8n workflow (template)** that automates: form input → prompt creation → image rendering → delivery
  Built-in guidance** (sticky notes) to keep you moving fast
  Prompt logic** tuned for product realism (camera, lighting, materials, subtle brand grading)

  Step-by-Step — How it works

  Open the form  
     Enter:
     Product Name & Product Description (material, finish, color, USP)  
     Product Image (required)  
     Optional: Background Image, or describe it (Background Type + Background Description)  
     Choose Camera Angle, Lighting Style, Aperture (Depth of Field)  
     Set Aspect Ratio, Variants (1–3), and your TG_Chat ID (Telegram target)

  Click “Submit”  
     The workflow automatically:
     Uploads your images  
     Writes photography-accurate prompts (focal length, aperture, lighting, surface/shadows)  
     Renders each variant into clean PNG results

  Receive your images  
     Finished images land in your Telegram chat/group for instant feedback and use

  That’s it. No manual editing, no waiting, no hassle.

  Real problems solved
  Inconsistent brand look?** Get uniform, polished visuals across your store and ads.  
  No time for shoots?** Generate studio/lifestyle shots from one upload.  
  Need options fast?** Produce multiple variants (A/B/C) in a single run.  
  Limited budget?** Cut studio and retouching costs to nearly zero.

  Customize it to your stack
  Swap Telegram for email, Slack, Google Drive, Dropbox, or S3
  Save prompts & URLs to Notion/Airtable for tracking
  Add an approval step before delivery
  Map aspect ratios to fixed pixel sizes for ad platforms

  Requirements
  n8n** (self-hosted or cloud)  
  Access to OpenAI and Kie.ai (API creds)  
  A Telegram bot added to your target chat (for delivery)

  You control which assets and metadata are sent to external APIs. Always ensure you have rights to the images you upload.

  FAQs

  Do I need a background image?  
  No. If you skip it, the workflow builds a realistic scene from your background description.

  How many images do I get?  
  Up to 3 variants per run (you choose 1–3).

  Are the results on-brand?  
  Yes—subtle grading honors your brand colors without over-saturation.

  Can I change the delivery channel?  
  Absolutely. Replace Telegram with your preferred destination (email, storage, chat).

  Use cases
  PDP images, marketplace listings, social ads, story posts, influencer kits, quick mockups for pitch decks and client approvals.

  Ready to level up your product visuals?
  Add to cart, upload your first product image, and get studio-quality results—today.

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.splitOut","n8n-nodes-base.splitInBatches","n8n-nodes-base.merge","n8n-nodes-base.telegram","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.httpRequest","n8n-nodes-base.formTrigger"]

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metadata_json:
  {
    "categories": [
      "automation",
      "image editing",
      "e-commerce",
      "AI/ML",
      "integration"
    ],
    "complexity": "medium",
    "use_cases": [
      "Generate studio-quality product images for Shopify/WooCommerce/Amazon/Etsy listings",
      "Produce multiple visual variants for A/B testing or product galleries",
      "Enable agencies and UGC creators to rapidly batch-process product photos",
      "Automate image editing and delivery via Telegram for quick review/approval"
    ],
    "estimated_setup_minutes": 25,
    "required_services": [
      "NanoBanana (image-editing / AI API)",
      "Telegram Bot API",
      "n8n (self-hosted or cloud) for workflow execution",
      "Image hosting/CDN (e.g., S3, Cloudinary) — recommended",
      "E-commerce platform APIs (Shopify/WooCommerce/Amazon/Etsy) — optional"
    ],
    "key_features": [
      "Turn a single product photo into polished, studio-quality visuals",
      "Generate up to 3 variant looks per product",
      "Telegram-based upload/notification and delivery flow",
      "Webhook & HTTP integration for automated triggers",
      "Batch processing with split/merge nodes for multiple images per run"
    ],
    "target_audience": [
      "E-commerce sellers (Shopify, WooCommerce, Amazon, Etsy)",
      "Digital agencies and image editors",
      "UGC creators and social media marketers wanting consistent visuals on a budget hybrid workflows for developers/automation engineers"
    ]
  }


name: PDF RAG Agent with Telegram Chat & Auto-Ingestion from Google Drive
author_name: Meelioo
author_username: mookielian
categories: []
created_at: 2025-09-23T10:13:36.936Z
updated_at: 2025-09-23T10:13:36.936Z
url: https://n8n.io/workflows/8860
scraped_at: 2025-10-27 22:15:59
description:
  How it works

  This workflow creates an intelligent document assistant called "Mookie" that can answer questions based on your uploaded documents. Here's how it operates:

  Document Ingestion:** The system can automatically load PDF files from Google Drive or accept PDFs uploaded directly through Telegram, then processes and stores them in a PostgreSQL vector database using Mistral embeddings
  Smart Retrieval:** When users ask questions via Telegram or a web chat interface, the AI agent searches through the stored documents to find relevant information using vector similarity matching
  Contextual Responses:** Using GPT-4 and the retrieved document context, Mookie provides accurate answers based solely on the ingested documents, avoiding hallucination by refusing to answer questions not covered in the stored materials
  Memory & Conversation:** The system maintains conversation history for each user, allowing for natural follow-up questions and contextual discussions

  Set up steps

  Estimated setup time: 30-45 minutes
  You'll need to configure several external services and credentials:

  Set up a PostgreSQL database with PGVector extension for document storage
  Create accounts and API keys for Azure OpenAI (GPT-4), Mistral Cloud (embeddings), and Google Drive access
  	Connect your own LLM's if you don't have these credentials.
  Configure a Telegram bot and obtain API credentials for chat functionality
  Update webhook URLs throughout the workflow to match your n8n instance
  Test the document ingestion pipeline with sample PDFs
  Verify the chat interfaces (both Telegram and web) are responding correctly

  &gt;The workflow includes approval mechanisms for PDF ingestion and handles both automated bulk processing from Google Drive and real-time document uploads through Telegram. Read the sticky notes provided in the template code for clear instructions.

nodes_used:
  ["@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.lmChatAzureOpenAi","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.embeddingsMistralCloud","@n8n/n8n-nodes-langchain.vectorStorePGVector","n8n-nodes-base.splitInBatches","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.manualTrigger","n8n-nodes-base.webhook","@n8n/n8n-nodes-langchain.textSplitterTokenSplitter","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.vectorStorePGVector","@n8n/n8n-nodes-langchain.embeddingsMistralCloud","n8n-nodes-base.telegramTrigger","@n8n/n8n-nodes-langchain.lmChatAzureOpenAi","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.vectorStorePGVector","@n8n/n8n-nodes-langchain.embeddingsMistralCloud","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.if","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 7660 characters]

metadata_json:
  {"categories":["automation","integration","data processing","AI/ML","knowledge management"],"complexity":"complex","use_cases":["Document question-answering (RAG)","Automated ingestion of PDF knowledge bases","Team knowledge assistant via chat (Telegram/web)","Customer support/FAQ retrieval from documents","Research or legal document lookup"],"estimated_setup_minutes":90,"required_services":["Google Drive API (for auto-ingest)","Telegram Bot API (for chat and uploads)","PostgreSQL with pgvector extension (vector DB)","Mistral Cloud embeddings (embeddings provider)","Publicly reachable n8n host or webhook tunnel (ngrok / n8n cloud)"],"key_features":["Automatic PDF ingestion from Google Drive","Accept PDFs uploaded via Telegram","Document parsing and embedding with Mistral embeddings","Storage and similarity search in PostgreSQL (pgvector)","Telegram chat bot and optional web chat interface for user queries and responses (RAG)  with context windows (memoryBuffer)  and conversational agent behavior (AI/ML nodes)"],"target_audience":["Developers integrating LLM-powered assistants","Data engineers setting up vector search pipelines","Product teams building document-centric chatbots or helpdesks  for internal/external users"]}


name: Product Review Analysis with BrowserAct & Gemini-Powered Recommendations
author_name: Madame AI Team | Kai
author_username: madame-ai
categories: []
created_at: 2025-09-23T11:08:47.001Z
updated_at: 2025-09-23T11:08:47.001Z
url: https://n8n.io/workflows/8862
scraped_at: 2025-10-27 22:15:59
description:
  Product Review Analysis with BrowserAct & Gemini-Powered Recommendations.

  This n8n template demonstrates how to perform product review sentiment analysis and generate improvement recommendations using an AI Agent.

  This workflow is perfect for e-commerce store owners, product managers, or marketing teams who want to automate the process of collecting feedback and turning it into actionable insights.

  How it works
  The workflow is triggered manually.
  An HTTP Request node initiates a web scraping task with the BrowserAct API to collect product reviews.
  A series of If and Wait nodes are used to check the status of the scraping task. If the task is not yet complete, the workflow pauses and retries until it receives the full dataset.
  An AI Agent node, powered by Google Gemini, then processes the scraped review summaries. It analyzes the sentiment of each review and generates actionable improvement recommendations.
  Finally, the workflow sends these detailed recommendations via a Telegram message and an Email to the relevant stakeholders.

  Requirements
  BrowserAct** API account for web scraping
  BrowserAct* "Product Review Sentiment Analysis*" Template
  Gemini** account for the AI Agent
  Telegram* and *SMTP** credentials for sending messages

  Need Help ?
  How to Find Your BrowseAct API Key & Workflow ID
  How to Connect n8n to Browseract
  How to Use & Customize BrowserAct Templates
  Workflow Guidance and Showcase

  How to INSTANTLY Get Product Improvement Ideas from Amazon Reviews | BrowserAct + n8n + Gemini

nodes_used:
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metadata_json:
  {
    "categories": [
      "automation",
      "data processing",
      "AI/ML",
      "web scraping",
      "notifications"
    ],
    "complexity": "medium",
    "use_cases": [
      "Product review sentiment analysis",
      "Generate AI-powered product improvement recommendations",
      "Automated feedback collection from web review sources",
      "Alert or notify teams about review insights (e.g., via Telegram)",
      "Monitor customer sentiment trends for product management"
    ],
    "estimated_setup_minutes": 30,
    "required_services": [
      "Google Gemini (lmChatGoogleGemini) API / Google Cloud credentials",
      "BrowserAct or other browser automation / scraping endpoint",
      "Target e-commerce or review websites to scrape",
      "Telegram (optional, for notifications)"
    ],
    "key_features": [
      "Web scraping of product reviews (HTTP Request / BrowserAct)",
      "Sentiment analysis and classification via AI/ML nodes",
      "AI agent (Gemini) generates actionable improvement recommendations",
      "Conditional logic to branch workflows based on sentiment",
      "Notifications/communication integration (e.g., Telegram)"
    ],
    "target_audience": [
      "E-commerce store owners",
      "Product managers",
      "Marketing teams and customer success teams"  ]
  }


name: Indeed Job Matching to Telegram with BrowserAct, Gemini & Telegram
author_name: Madame AI Team | Kai
author_username: madame-ai
categories: []
created_at: 2025-09-23T12:22:44.828Z
updated_at: 2025-09-23T12:22:44.828Z
url: https://n8n.io/workflows/8864
scraped_at: 2025-10-27 22:15:59
description:
  Analyze job market data with AI to find matching jobs

  This n8n template helps you stay on top of the job market by matching scraped job offers with your resume using an AI Agent.

  This workflow is perfect for job seekers, recruiters, or market analysts who need to find specific job opportunities without manually sifting through countless listings.

  Steps to Take
  Create BrowserAct Workflow:* Set up the *Job Market Intelligence** template in your BrowserAct account.
  Add BrowserAct Token:* Connect your BrowserAct account credentials to the *HTTP Request** node.
  Update Workflow ID:* Change the workflow_id value in the *HTTP Request** node to match the one from your BrowserAct workflow.
  Connect Gemini:* Add your Google Gemini credentials and update your *resume* inside the prompt in the *AI Agent** node.
  Configure Telegram:* Connect your Telegram account and add your Channel ID to the *Send a text message** node.

  How it works
  The workflow is triggered manually by clicking "Execute workflow," but you can easily set it to run on a schedule.
  It uses an HTTP Request node to start a web scraping task via the BrowserAct API to collect the latest job offers.
  A series of If and Wait nodes monitor the scraping job, ensuring the full data is ready before proceeding.
  An AI Agent node, powered by Google Gemini, processes the job listings and filters them to find the best matches for your resume.
  A Code node then transforms the AI's output into a clean, readable format.
  Finally, the filtered job offers are sent directly to you via Telegram.

  Requirements
  BrowserAct** API account
  BrowserAct* *“Job Market Intelligence”** Template
  Gemini** account
  Telegram** credentials

  Need Help ?
  How to Find Your BrowseAct API Key & Workflow ID
  How to Connect n8n to Browseract
  How to Use & Customize BrowserAct Templates

  Workflow Guidance and Showcase

  Never Manually Search for a Job Again (AI Automation Tutorial)

nodes_used:
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metadata_json:
  {"categories":["automation","integration","data processing","web scraping","AI/ML"],"complexity":"medium","use_cases":["Automatically scrape job listings and match them to a resume","Notify users of relevant job openings via Telegram","Recruitment sourcing and market analysis","Filter and rank job opportunities using an LLM agent"],"estimated_setup_minutes":30,"required_services":["BrowserAct (browser automation)","Google Gemini (lmChatGoogleGemini)","Telegram API/ Bot","Indeed or other job board (scraped via BrowserAct)","n8n (workflow runtime)"],"key_features":["BrowserAct-based scraping of job listings","LLM agent (Gemini) to compare jobs with resume/profile","Structured output parsing and conditional filtering","Telegram notifications for matched jobs","Manual review step and sticky notes for annotations"],"target_audience":["Job seekers","Recruiters and sourcers","Market analysts and labor researchers」「n8n automation builders"]}


name: Automate Google Maps Lead Generation with Perplexity AI and Email Verification
author_name: Antxon Pous
author_username: verificaremails
categories: []
created_at: 2025-09-24T16:54:28.812Z
updated_at: 2025-09-24T16:54:28.812Z
url: https://n8n.io/workflows/8904
scraped_at: 2025-09-29 22:37:32
metadata_generated_at: 2025-09-30 08:33:37
description:
  Overview ✨

  This template finds businesses on Google Maps → writes to Google Sheets → enriches + **verifies email contact so your outreach stays clean and deliverable. It includes Sticky Notes  to explain the flow and setup.

  What it does 🔎➡️📄➡️✅

  Search & save: Fetch places and append rows (Name, Address, Website, Rating, Phone) to your sheet.
  Enrich: Ask an LLM (Perplexity) for Email + a short company Background.
  Verify emails: For Email Verification we use VerificarEmails  to validate every email address before writing back (fewer bounces, less blocking).

  &gt; ℹ️ VerificarEmails node: install the community node @verificaremails/n8n-nodes-verificaremails (Settings → Community Nodes) and use it until it’s approved as a core node. ✨
  Youtube Video

  Requirements 🔐

  Use n8n Credentials for: Google Sheets, Perplexity (Bearer), and VerificarEmails.
  Keep prompts short and temperature: 0 to lower cost.
  Create a Google Sheet with the following headers: UUID, Name, Address, Website, Rating	Email, Valid, Email, Phone, Valid Phone, Summary

  How to run ▶️

  Set your search params (query, locale, pages).
  Run the flow: it saves results → enriches → verifies emails → updates the sheet.
  If the read/update step doesn’t run, ensure the finalization (Done) path and the correct sheet/tab are selected.

  Why verify? ✉️🧹

  Bad emails cause bounces, throttling, and blocks. Verifying first protects sender reputation and improves deliverability.
  Beyond emails, VerificarEmails also offers **phone, names. Check Email Validation API documentation to better understand verification results.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.set","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.splitInBatches","n8n-nodes-base.code","n8n-nodes-base.filter","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.if","@verificaremails/n8n-nodes-verificaremails.verificaremails","n8n-nodes-base.googleSheetsTrigger"]

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metadata_json:
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name: Generate Comprehensive Website Audit Report with GPT-4o-mini & Claude Sonnet 4.5
author_name: Sensonar.com
author_username: sebastian
categories: []
created_at: 2025-09-25T21:33:32.382Z
updated_at: 2025-09-25T21:33:32.382Z
url: https://n8n.io/workflows/8934
scraped_at: 2025-10-27 22:15:59
description:
  🌐 Webpage Audit AI Agent – Automate Your Website Review

  This workflow contains a virtual digital consultant team to professionally analyze Landing Page, generate Audit Report and sent it via Gmail.

  At the center is the Editor-in-Chief AI Agent, who acts as the lead strategist.  
  When you submit a website URL, it is scraped and then sent to to a team of specialist AI Agents (each powered by GPT-4o-mini for speed) then after finishing their sub-porcess deliver outcome to the final Editor (Claude Sonnet for premium quality) which is responsible for generating professional analysis.

  The result: a consulting-style email audit report that combines technical accuracy with business clarity — delivered straight to your inbox.

  🟢 Section 1 – Entry & Leadership

  Nodes:

  🌍 Webhook node → Entry point where the website URL, email and language is entered. 
  📝 Editor-in-Chief Agent → Acts like the consulting team leader. Interprets the request and merges results.  
  💡 Think Tool → Helps structure the analysis, ensuring that findings are presented in a business-friendly way.  
  🧠 CRO/SEO/Tech/Semantic AI Agents → Ensures both efficiency (GPT-4o-mini for scanning large content quickly) and focus on specific task.  

  ✅ Beginner view: Think of this as submitting your website to a digital auditors. They decide what needs to be reviewed from each perspective, delegate to the right experts, and then together - as an AI Agents team - craft the final report.

  📊 Section 2 – Specialist Audit AI Agents

  Each specialist focuses on one critical layer of your website:

  🔧 Technical SEO Agent → Analyzes code structure, page speed, mobile responsiveness, metadata, and accessibility.  
  📝 Content SEO Agent → Reviews keyword strategy, text quality, readability, and semantic richness.  
  🎯 CRO & UX Agent → Checks calls-to-action, user flows, conversion points, and usability.  
  🤖 Semantic Agent → Checks if AI Agents aligns with semantic flows, marketing problems and risks.  
  📈 Analytics Agent → Validates tracking, funnels, and key performance indicators (optional).  

  ✅ Beginner view: This is like hiring a team of experts — SEO, content, UX, and analytics — all working in parallel on your website.

  📋 Section 3 – Flow of Execution

  You submit your website URL.  
  Then it is distributed to the team of AI Agents.  
  Each Agent runs its analysis in parallel.  
  Findings are sent to the Editor-in-Chief.
  The Editor compiles everything into a consulting-style newsletter report.  
  The final report is emailed to you automatically.  

  ✅ Beginner view: It’s like sending your site to a consulting firm, and within minutes you get a polished audit back in your inbox.

  📊 Summary Table

  | Section               | Key Roles                       | Models                     | Purpose                      | Beginner Benefit                          |
  |-----------------------|---------------------------------|----------------------------|------------------------------|-------------------------------------------|
  | 🟢 Entry & Leadership | Editor-in-Chief, Think Tool     | Anthropic Claude Sonnet 4| Organize and refine analysis | Works like a consulting team leader        |
  | 📊 Specialists        | SEO, Content, CRO, Semantic, Analytics    | GPT-4o-mini                | Detailed audits of each area | Each Agent = a specialized consultant      |
  | 📋 Execution Flow     | All connected                   | n8n workflow               | Collaboration and reporting  | Output = clear, professional audit email   |

  🌟 Why This Workflow Rocks

  Full digital consulting team in one workflow**  
  Parallel execution → speed without losing depth**  
  Strategic + tactical insights → both “what” and “how”**  
  Cost-optimized** ($0.40–$0.80 per run) → cheaper than a single human consultant review  
  Action-oriented output** → not just issues, but recommendations and next steps  
  Scalable** → easily extend with more audit dimensions (security, branding, accessibility, etc.)  

  👉 Example Use Case

  “Analyze my e-commerce site for SEO, CRO, technical and semantic issues.”

  Technical SEO Agent → Reports missing alt text, metadata, and slow mobile speed.  
  Content SEO Agent → Flags weak product descriptions, suggests keyword improvements.  
  CRO Agent → Finds unclear CTAs and checkout friction points. 
  Semantic Agent → Finds everything to improve searching by AI Agents.
  Editor-in-Chief Agent → Combines everything into a consulting-style email with prioritized fixes and estimated business impact.  

  ✅ Delivered directly to your inbox in under 5 minutes.

nodes_used:
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metadata_json:
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    "categories": [
      "automation",
      "AI-assisted auditing",
      "web scraping",
      "reporting",
      "integrations"
    ],
    "complexity": "complex",
    "use_cases": [
      "Automated landing page / website audit",
      "SEO and content quality reviews",
      "Conversion rate optimization recommendations",
      "Client-ready audit report generation and delivery",
      "Multi-agent AI analysis orchestration"
    ],
    "estimated_setup_minutes": 45,
    "required_services": [
      "n8n",
      "OpenAI (GPT-4o-mini or equivalent)",
      "Anthropic (Claude Sonnet 4.5 / lmChatAnthropic)",
      "Gmail (or SMTP) for email delivery",
      "HTTP access to target websites (or a headless browser / scraping service for JS-heavy pages)"
    ],
    "key_features": [
      "Webpage scraping and content extraction",
      "Multi-agent AI team (specialist agents) for parallel analysis",
      "Editor-in-Chief consolidation agent to combine specialist outputs",
      "Generates comprehensive audit report (markdown/HTML)",
      "Automated delivery via Gmail/email node or webhook response handling"
    ],
    "target_audience": [
      "Digital marketers",
      "SEO specialists",
      "Web developers and designers doing QA/reviews" 	]
  }


name: AI Call Summary to HubSpot + Follow-Up Task
author_name: Miha
author_username: miha
categories: []
created_at: 2025-09-26T15:43:32.501Z
updated_at: 2025-09-26T15:43:32.501Z
url: https://n8n.io/workflows/8966
scraped_at: 2025-10-27 22:15:59
description:
  This n8n template turns raw call transcripts into clean HubSpot call logs and a single, actionable follow-up task—automatically. Paste a transcript and the contact’s email; the workflow finds the contact, summarizes the conversation in 120–160 words, proposes the next best action, and (optionally) updates missing contact fields.

  Perfect for reps and founders who want accurate CRM hygiene without the manual busywork.

  How it works

  A form trigger collects two inputs:

    Contact email
    Plain-text call transcript
  The workflow looks up the HubSpot contact by email to pull known properties.
  An AI agent reads the transcript (plus known fields) to:

    Extract participants, role, problem/opportunity, requirements, blockers, timeline, and metrics.
    Write a 120–160 word recap a teammate can skim.
    Generate one concrete follow-up task (title + body).
    Suggest updates for missing contact properties (city, country, job title, job function).
  The recap is logged to HubSpot as a completed Call engagement.
  The follow-up is created in HubSpot as a Task with subject and body.
  (Optional) The contact record is updated using AI-suggested values if the transcript clearly mentions them.

  How to use

  Connect HubSpot (OAuth2) on all HubSpot nodes.
  Connect OpenAI on the AI nodes.
  Open Form: Capture Transcript, submit the email + transcript.
  (Optional) In AI: Summarize Call & Draft Task, tweak prompt rules (word count, date normalization).
  (Optional) In Update Contact from Transcript, review the mapped fields before enabling in production.
  Activate the workflow and paste transcripts after each call.

  Requirements

  HubSpot** (OAuth2) for contact search, call logging, and tasks
  OpenAI** for summarization and task drafting

  Notes & customization ideas

  Swap the form for a Google Drive or S3 watcher to ingest saved transcripts.
  Add a speech-to-text step if you store audio recordings.
  Extend Update Contact to include additional fields (timezone, department, seniority).
  Post the summary to Slack or email the AE for quick handoffs.
  Gate updates with a confidence check, or route low-confidence changes for manual approval.

nodes_used:
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metadata_json:
  {"categories":["automation","CRM","AI summarization","sales"],"complexity":"medium","use_cases":["Convert raw call transcripts into concise HubSpot call logs","Create a single actionable follow-up task after each call","Automatically find and update HubSpot contact records by email","Standardize CRM notes and improve data hygiene","Propose the next-best action for sales follow-up"],"estimated_setup_minutes":20,"required_services":["HubSpot","LLM/AI provider (e.g., OpenAI)","n8n instance (to host workflow and form trigger)"],"key_features":["Transcript ingestion via form trigger","Contact lookup by email in HubSpot","AI-generated 120–160 word call summary","AI-suggested next-best action","Create HubSpot call log and single follow-up task automatically」「Optional contact field enrichment/updates」「Structured output parsing for reliable fields"],"target_audience":["Sales reps","Founders/CEOs doing outreach","SDRs/Account managers」「Revenue operations/CRM admins"]}


name: AI Email Reply Based on HubSpot Data + Slack Approval
author_name: Miha
author_username: miha
categories: []
created_at: 2025-09-26T15:48:45.383Z
updated_at: 2025-09-26T15:48:45.383Z
url: https://n8n.io/workflows/8967
scraped_at: 2025-10-27 22:15:59
description:
  This n8n template drafts customer-ready email replies using Google Gemini, enriched with HubSpot context (contact, deals, companies, tickets). Each draft is routed to Slack for one-click approval before it’s sent from Gmail—so you move fast without losing control.

  Ideal for support and sales teams that want speedy, personalized responses while keeping humans in the loop.

  How it works

  Gmail Trigger** watches for new inbound emails.
  Sender filter** excludes internal domains (e.g., n8n.io) to avoid auto-replying to teammates.
  HubSpot contact lookup* finds the sender and fetches associated *deals/companies/tickets** via association + batch read.
  CRM context is normalized** into clean, LLM-friendly fields (no IDs or sensitive noise).
  Gemini (Google AI Studio)** generates a concise, friendly reply using:

    Sender name, subject, and message snippet
    Safe, relevant HubSpot context (e.g., top 1–2 deals or an open ticket)
    Style constraints (≤ \~150 words, single CTA, optional clarifying question)
  Slack approval* posts the draft to a channel; if *approved, n8n **replies via Gmail in the original thread.

  How to use

  Gmail: Connect the same account for the trigger and reply nodes.
  HubSpot: Connect OAuth on the search + HTTP request nodes.
  Gemini: Add your Google AI Studio API key to the Google Gemini Chat Model node.
  Slack: Connect and select the channel for draft approvals.
  (Optional) Filter: Adjust the Allowed Sender filter before going live.
  (Optional) Prompt: Edit “Draft Reply (AI Agent)” tone/length or how much CRM detail to include.
  Activate the workflow. New emails will produce Slack-approved replies automatically.

  Requirements

  Gmail** (trigger + send)
  HubSpot** (OAuth2) for contact + associations
  Slack** for approval step
  Google Gemini** (Google AI Studio API key)

  Notes & customization

  Safety rails:** The prompt avoids exposing IDs/raw JSON and caps CRM details to what’s useful.
  Auto-send mode:** Skip Slack if you want fully automated replies for specific senders/labels.
  Richer context:** Extend the batch read to pull more properties (e.g., next step, renewal date).
  Triage:** Branch on subject/labels to route billing vs. technical requests to different prompts.
  QA queue:* If the model asks a clarifying question, keep it to *one**—the node enforces that.

nodes_used:
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metadata_json:
  {
    "categories": [
      "automation",
      "sales & support",
      "AI-assisted workflows",
      "integrations"
    ],
    "complexity": "medium",
    "use_cases": [
      "Drafting personalized customer-support replies using CRM context",
      "Speeding up sales follow-ups with HubSpot-enriched AI drafts",
      "Human-in-the-loop approval for outgoing customer emails",
      "Maintaining response quality and compliance while automating drafts"
    ],
    "estimated_setup_minutes": 45,
    "required_services": [
      "n8n instance",
      "Gmail (trigger + send) / Google Workspace account",
      "Google Gemini (Gemini API / Google Cloud credentials)",
      "HubSpot API access (contacts, deals, companies, tickets)",
      "Slack workspace (interactive message/approval integration)"
    ],
    "key_features": [
      "Gmail inbound trigger with sender filtering (exclude internal domains)",
      "Enrich AI prompt with HubSpot contact, deal, company, and ticket data",
      "Generate customer-ready drafts via Google Gemini",
      "Route drafts to Slack for one-click human approval",
      "Send approved replies from Gmail automatically (human oversight preserved)"
    ],
    "target_audience": [
      "support teams",
      "sales teams",
      "customer success managers"
    ]
  }


name: Email Outreach Drafter Based on HubSpot Data
author_name: Miha
author_username: miha
categories: []
created_at: 2025-09-26T15:55:13.793Z
updated_at: 2025-09-26T15:55:13.793Z
url: https://n8n.io/workflows/8968
scraped_at: 2025-10-27 22:15:59
description:
  This n8n template turns a small, targeted HubSpot list into tailored outreach. It scans each contact’s recent Gmail conversations, builds a lightweight persona with AI (tone, goals, pain points, decision style), then drafts a concise sales email aligned to your offer—saved to Gmail as a reviewable draft.

  Perfect for SDRs and founders who want personalization at scale without writing from scratch.

  This template was originally created by Jim Le.

  How it works

  Manual trigger** starts a controlled run.
  HubSpot search** pulls a focused list of contacts (e.g., hs_buying_role = DECISION_MAKER).
  Batch loop** processes contacts one by one.
  Gmail fetch** grabs up to 20 recent threads from each contact.
  AI persona extraction** (Information Extractor + Gemini) analyzes messages to capture:

    decision-making style, communication preferences, goals/motivations
    pain points, work style, personality traits, buying behavior, values, market awareness
  Variables node* sets core fields (first name, last name, email) and the *offer** you want to pitch.
  AI email generation* (Gemini) mirrors the contact’s tone and priorities; outputs *subject* + *HTML body**.
  Gmail draft** is created for the contact so a rep can skim, tweak, and send.

  How to use

  Connect HubSpot on the “Get Contacts” node and refine the filter to your segment.
  Connect Gmail on both read and draft nodes (same account recommended).
  Add Gemini key to both Gemini nodes.
  In Variables, update product_to_sell with your offer and confirm the contact field mappings.
  (Optional) Tweak the persona attributes or the email prompt for tone/length/CTA.
  Click Test workflow. Review drafts in Gmail, edit if needed, then send.

  Requirements

  HubSpot** (OAuth2) for contact targeting
  Gmail** (read + draft)
  Google Gemini** (API key) for persona + copy generation

  Notes & customization

  Tighter targeting:** Change the HubSpot filter (e.g., industry, territory, lifecycle stage) to keep the list small and measurable.
  Richer inputs:** Increase Gmail limit or include received/sent filters to capture more context (mind rate limits).
  Brand voice:** Add a short style guide to the email generator’s system prompt (e.g., sentence length, jargon rules, sign-off).
  Offer variants:** Replace product_to_sell per segment, or branch by industry to load different value props.
  Compliance & privacy:** Limit stored outputs to essentials; avoid copying sensitive content from threads verbatim.
  Auto-send option:** Swap the draft step for “send email” if you want hands-off delivery for known segments.

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

workflow_json_compressed:
  [encoded data, 6348 characters]

metadata_json:
  {
    "categories": [
      "automation",
      "CRM integration",
      "email outreach",
      "AI personalization"
    ],
    "complexity": "medium",
    "use_cases": [
      "Personalized outbound email campaigns at scale",
      "SDR outreach and follow-ups",
      "Founders or solo sellers creating tailored outreach quickly",
      "Turning HubSpot contact lists into reviewable Gmail drafts",
      "Extracting contact persona from recent email conversations"
    ],
    "estimated_setup_minutes": 20,
    "required_services": [
      "HubSpot (API access)",
      "Gmail / Google Workspace (API access)",
      "Google Gemini or compatible LLM (lmChatGoogleGemini node)",
      "n8n instance"
    ],
    "key_features": [
      "Imports targeted contact list from HubSpot",
      "Scans recent Gmail conversations per contact",
      "Generates lightweight persona (tone, goals, pain points, decision style) via AI",
      "Drafts concise, offer-aligned sales emails",
      "Saves output as Gmail drafts for human review and editing (non-sent drafts)"
    ],
    "target_audience": [
      "SDRs",
      "Founders / solo sellers",
      "Sales teams and reps seeking personalization at scale" 
    ]
  }


name: HubSpot Contact AI Enrichment
author_name: Miha
author_username: miha
categories: []
created_at: 2025-09-26T15:58:10.491Z
updated_at: 2025-09-26T15:58:10.491Z
url: https://n8n.io/workflows/8969
scraped_at: 2025-10-27 22:15:59
description:
  This n8n template auto-enriches brand-new HubSpot contacts with company details. Each day it finds contacts created in the last 24 hours (skipping free email domains), researches the company from the contact’s email domain, and writes back clean fields—no manual lookup needed.

  Perfect for GTM teams that want better segmentation and faster personalization from day one.

  How it works

  A daily schedule trigger starts the workflow.
  HubSpot: Get recently created/updated contacts** pulls the newest records.
  A filter keeps only contacts:

    created within the last 24 hours
    whose email domain doesn’t contain gmail.com (adjust as needed).
  An AI research agent (Gemini + SerpAPI):

    extracts the company domain from the contact’s email
    searches the web and returns structured JSON:

      company_name, industry, headquarters_city, headquarters_country, employee_count, website, linkedin, description
  HubSpot: Add company info** updates the contact with the enriched fields.

  How to use

  Connect HubSpot on both HubSpot nodes (OAuth2).
  Connect SerpAPI (paste your API key).
  Connect Google Gemini (Google AI Studio API key).
  (Optional) Edit the agent prompt to fetch more/different fields.
  (Optional) Tweak the filter to include/exclude other domains.
  Activate the workflow to run daily.

  Requirements

  HubSpot** (OAuth2) for reading/updating contacts
  SerpAPI** for web search results
  Google Gemini** for company profiling and structured output

  Notes & customization

  Free domains:** Add more exclusions (e.g., yahoo.com, outlook.com) to reduce false positives.
  Confidence gating:** Require website + LinkedIn before writing to HubSpot, or route low-confidence results for manual review.
  Field mapping:** Extend the update step with additional properties (e.g., industry tags, HQ timezone).
  Frequency:** Switch the trigger to hourly for faster enrichment on high-volume inbound.
  Data hygiene:** Normalize employee count ranges and country names to your CRM picklists.

nodes_used:
  ["n8n-nodes-base.hubspot","@n8n/n8n-nodes-langchain.toolSerpApi","n8n-nodes-base.filter","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@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.hubspot","n8n-nodes-base.scheduleTrigger"]

workflow_json_compressed:
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metadata_json:
  {
    "categories": [
      "automation",
      "integration",
      "data enrichment",
      "CRM"
    ],
    "complexity": "medium",
    "use_cases": [
      "Auto-enrich newly created HubSpot contacts",
      "Improve lead segmentation and personalization",
      "Speed up sales & marketing outreach",
      "Maintain CRM data hygiene and consistency",
      "Enrich contacts from email domain research"
    ],
    "estimated_setup_minutes": 20,
    "required_services": [
      "HubSpot (API access)",
      "SerpApi (or other web-search/company lookup API)",
      "Google Gemini (or another LLM provider) / AI service",
      "n8n"
    ],
    "key_features": [
      "Daily scheduled trigger to process new contacts",
      "Skips free email domains",
      "Researches company details from contact email domain",
      "Uses search API + LLM to extract and structure company fields",
      "Writes clean, structured data back to HubSpot fields"
    ],
    "target_audience": [
      "GTM teams",
      "Marketing teams",
      "Sales operations / SDRs / AEs supporting outreach personalizationes (sic) - Wait fix typo"
    ]
  }


name: Weekly HubSpot Lead Report to Slack
author_name: Miha
author_username: miha
categories: []
created_at: 2025-09-26T15:59:48.711Z
updated_at: 2025-09-26T15:59:48.711Z
url: https://n8n.io/workflows/8970
scraped_at: 2025-10-27 22:15:59
description:
  This n8n template posts a quick weekly snapshot to Slack so your team sees last week’s funnel movement at a glance. It counts how many contacts entered the Lead stage in the past week—and optionally reports closed-won deals from the same period.

  Perfect for GTM standups and Monday pipeline reviews.

  How it works

  A weekly schedule (e.g., Mondays 07:00) kicks things off.
  HubSpot → Get all contacts** pulls contacts with hs_v2_date_entered_lead.
  A filter keeps only those whose lead date is within the last 7 days.
  Summarize** counts the resulting leads.
  Slack* posts: “Last week we generated *X** leads.”
  (Optional branch) Deals:

    HubSpot → Get all deals with hs_is_closed_won and hs_closed_won_date.
    Filter to closed-won last week.
    Summarize to report count and (if mapped) total amount.

  How to use

  Connect HubSpot on the contacts and (optional) deals nodes.
  Connect Slack and pick the destination channel.
  In Schedule the report, set your preferred day/time.
  (Optional) For the Deals branch, include the amount property on “Get all deals” and set Summarize → sum over amount.
  Activate the workflow.

  Requirements

  HubSpot** (OAuth2)
  Slack** (OAuth2)

  Notes & customization

  Time windows:** Adjust the filter to different periods (e.g., last 14 or 30 days).
  More stages:** Duplicate the contacts branch for MQL/SQL or custom lifecycle events.
  Richer Slack message:** Add emojis, trend vs. prior week, or a tiny leaderboard by owner.
  Segments:** Split by country/industry using HubSpot lists or additional filters.

nodes_used:
  ["n8n-nodes-base.hubspot","n8n-nodes-base.filter","n8n-nodes-base.summarize","n8n-nodes-base.stickyNote","n8n-nodes-base.slack","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.hubspot","n8n-nodes-base.filter","n8n-nodes-base.summarize","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {
    "categories": [
      "integration",
      "reporting",
      "notifications",
      "sales"
    ],
    "complexity": "simple",
    "use_cases": [
      "Weekly GTM standups",
      "Monday pipeline reviews",
      "Track how many contacts entered Lead stage in the past week",
      "Optionally report closed-won deals from the same period",
      "Quick team visibility into funnel movement"
    ],
    "estimated_setup_minutes": 15,
    "required_services": [
      "HubSpot (Contacts API; optionally Deals API)",
      "Slack (Incoming Webhook or Slack App)",
      "n8n instance"
    ],
    "key_features": [
      "Scheduled weekly trigger (e.g., Monday 07:00)",
      "Fetch HubSpot contacts by hs_v2_date_entered_lead",
      "Filter contacts to the past week",
      "Optional fetch/report of closed-won deals",
      "Summarize counts and format a Slack message snapshot"
    ],
    "target_audience": [
      "Sales managers",
      "GTM teams",
      "Marketing operations / analysts"  ]
  }


name: Chat with Your HubSpot Data
author_name: Miha
author_username: miha
categories: []
created_at: 2025-09-26T16:03:26.575Z
updated_at: 2025-09-26T16:03:26.575Z
url: https://n8n.io/workflows/8971
scraped_at: 2025-10-27 22:15:59
description:
  This n8n template gives you a chat-style assistant that can search your HubSpot CRM on demand. Ask natural-language questions like “show me leads in Germany” or “what deals close next month,” and the agent translates your request into precise HubSpot searches—then answers in plain English.

  Great for founders, AEs, and ops folks who want quick answers without clicking through the CRM.

  How it works

  Chat trigger** starts a session from your n8n chat UI or embed.
  AI Agent (Gemini 2.5 Pro)** interprets the message and:

    Chooses the right HubSpot search (contacts or deals).
    Fills filter property, operator (EQ, NEQ, GT, GTE, LT, LTE, BETWEEN, IN, NOT\_IN), and value(s).
    Requests specific properties (email, name, lifecycle stage, owner, activity timestamps, etc.).
  HubSpot tools** execute live queries:

    Contacts: flexible property filter + free-text query.
    Deals: filters by owner and core deal fields (stage, amount, pipeline, close date).
  Memory buffer** keeps the last turns so you can say “now only show closed won over 10k” and the agent understands context.

  How to use

  Connect credentials

     HubSpot OAuth on both HubSpot Tool nodes.
     Google Gemini API key on the Gemini Chat Model.
  Open the chat (the “When chat message received” node).
  Ask questions like:

     “Find contacts named Hans created after Sept 1.”
     “Deals owned by me in Proposal with amount &gt; 10,000.”
     “Contacts with lead status = New and no email reply in the last 14 days.”
  Refine with follow-ups:

     “Sort by most recently contacted.”
     “Only Germany.”
     “Show top 5 with emails.”

  Requirements

  HubSpot** (OAuth2)
  Google Gemini** (API key)

  Notes & customization

  Property/operator control:** The contact search node lets the agent set both the property (e.g., email, lifecyclestage, hs_lead_status) and the operator (EQ, IN, BETWEEN in epoch ms for dates, etc.).
  Owner filtering for deals:** Uses hs_all_owner_ids; swap or extend to filter by pipeline/stage ranges.
  Guardrails:** Add allowlists for searchable properties or cap result counts to avoid noisy answers.
  Display format:** Have the agent return concise tables (name, email, stage, last activity, CTA).
  Handoffs:** Add Slack/Email actions—e.g., “post this list to #sales” or “export to CSV.”
  Telemetry:** Log queries for later dashboards (common searches, coverage gaps).

  Troubleshooting

  No results?** Loosen operators (use IN lists, broaden dates) or include a free-text query.
  Date filters:** Provide epoch ms for GT/GTE/LT/LTE/BETWEEN on time fields (the agent handles this; keep system time in UTC).
  Too chatty?** Reduce memory window or ask the agent to summarize to bullet points.

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

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metadata_json:
  {
    "categories": [
      "integration",
      "automation",
      "AI/ML",
      "CRM"
    ],
    "complexity": "medium",
    "use_cases": [
      "Ad-hoc natural-language queries against HubSpot CRM",
      "Quick sales & pipeline lookups (leads, deals, close dates)",
      "Operational reporting and CRM triage without opening the UI",
      "Assist AEs/founders with contact and deal discovery"
    ],
    "estimated_setup_minutes": 20,
    "required_services": [
      "HubSpot API (HubSpot account + API key or OAuth)",
      "AI provider (e.g., Google Gemini 2.5 Pro or compatible LLM API)",
      "n8n (chat UI or embed for chat trigger)"
    ],
    "key_features": [
      "Chat-style assistant that accepts natural-language questions",
      "Translates user queries into precise HubSpot searches",
      "Returns answers in plain English",
      "Maintains short-term context via memory buffer window",
      "Uses hubspotTool nodes to query contacts, leads, and deals"
    ],
    "target_audience": [
      "Founders",
      "Account Executives (AEs)",
      "Sales operations / CRM admins"
    ]
  }


name: Manage Shopify Store via Conversational OpenAI Assistant with SmartCommerce
author_name: Rohit Dabra
author_username: rohitdabra
categories: []
created_at: 2025-09-27T15:01:31.229Z
updated_at: 2025-09-27T15:01:31.229Z
url: https://n8n.io/workflows/9014
scraped_at: 2025-10-27 22:15:59
description:
  Shopify MCP AI Agent Workflow for n8n

  Overview

  This n8n workflow showcases a full-featured AI-powered assistant connected to a Shopify store through a custom MCP (Multi-Channel Commerce Platform) Server toolkit. It empowers users to automate comprehensive Shopify store management by leveraging AI to interact conversationally with their data and operations. The workflow can create, fetch, search, update, and delete Shopify products and orders, all triggered via simple chat messages, making day-to-day store operations frictionless and highly efficient.

  Core capabilities include:

  Product and order management (CRUD) via chat commands.

  Smart retrieval: AI proactively fetches details instead of asking repeated questions.

  Contextual memory: AI uses n8n memory to provide context-aware, fluent responses.

  End-to-end automation: Connects Shopify, OpenAI, and n8n’s automation logic for seamless workflows.

  This solution is ideal for Shopify merchants, agencies, and developers aiming to reduce manual overhead and enable conversational, AI-powered commerce automation in their operations.

  🎬 Watch Demo Video on YouTube 

  Step-by-Step Setup Guide

  Follow these steps to import and configure the Shopify MCP AI Agent workflow in n8n:

  1. Import the Workflow File

  Download the workflow file from this Creator Hub listing.

  In your n8n instance, go to Workflows &gt; Import from File and upload the JSON.

  2. Prepare Shopify Access

  Log in to your Shopify admin.

  Create a Custom App or use an existing app and retrieve the Admin API Access Token.

  Storefront access: Ensure your app has relevant permissions for Products, Orders, Discounts, and Store Settings.

  3. Set Up Credentials in n8n

  In n8n, navigate to Credentials and add a new Shopify API credential using your Access Token.

  Name it something memorable (e.g., Shopify Access Token account) to match the credential used in the workflow nodes.

  4. Configure the MCP Server Connection

  Make sure your MCP Server is running and accessible with API endpoints for product/order management.

  Update any relevant connection endpoints in the workflow if you run your MCP Server locally or in a different location.

  5. Connect OpenAI or Other LLM Provider

  Provide your API key for OpenAI GPT or a compatible model.

  Link the credential to the OpenAI Chat Model node (replace with other providers if required).

  6. (Optional) Customize for Your Needs

  Tweak node logic, add new triggers, or extend memory features as required.

  Add, remove, or restrain the AI’s capabilities to fit your operational needs.

  Configure chat triggers for more personalized workflows.

  7. Testing

  Use the “When chat message received” trigger or send http requests to the workflow’s endpoint.

  Example: “Create an order for Jane Doe, 3 Black T-shirts” or “Show today’s fulfilled orders”.

  The workflow and AI Agent will handle context, fetch/store data, and reply accordingly.

  8. Ready to Automate!

  Begin leveraging conversational automation to manage your Shopify store.

  For additional tips, consult the workflow’s internal documentation and n8n’s official guides.

  Additional Notes

  This template includes all core Shopify product and order operations.

  The AI agent auto-resolves context, making routine admin tasks simple and quick.

  Extend or fork the workflow to suit niche scenarios—discounts, analytics, and more.

  Visual thumbnail and schematic are included for easy reference.

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.mcpClientTool","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.shopifyTool","n8n-nodes-base.shopifyTool","n8n-nodes-base.shopifyTool","n8n-nodes-base.shopifyTool","n8n-nodes-base.shopifyTool","n8n-nodes-base.shopifyTool","n8n-nodes-base.shopifyTool","n8n-nodes-base.shopifyTool","n8n-nodes-base.shopifyTool","n8n-nodes-base.shopifyTool","n8n-nodes-base.shopifyTool","n8n-nodes-base.shopifyTool","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {
    "categories": ["e-commerce", "automation", "integration", "AI assistant"],
    "complexity": "complex",
    "use_cases": [
      "Conversational management of Shopify products and orders",
      "Automating CRUD operations on store data via chat",
      "Searching and fetching product/order information on demand",
      "Integrating Shopify with a Multi-Channel Commerce Platform (MCP)",
      "Building an AI-driven e-commerce support or ops assistant"
    ],
    "estimated_setup_minutes": 60,
    "required_services": [
      "Shopify Admin API (store credentials)",
      "MCP (Multi-Channel Commerce Platform) server/toolkit (SmartCommerce)",
      "OpenAI or compatible AI/ML API for chat/assistant",
      "n8n instance (workflow runtime)"
    ],
    "key_features": [
      "Conversational AI interface to manage Shopify store",
      "Full CRUD for products and orders (create, fetch, search, update, delete)",
      "Integration with custom MCP toolkit for multi-channel operations",
      "Memory buffer for conversational context",
      "Modular n8n workflow with multiple Shopify tool nodes"
    ],
    "target_audience": [
      "E-commerce managers and merchants",
      "Developers and integrators building commerce automations",
      "Automation engineers and ops teams managing stores at scale"
    ]
  }


name: AI Sales Assistant with GPT & Claude: Qualify Leads, Book Meetings
author_name: Hassan
author_username: sycorda
categories: []
created_at: 2025-09-27T22:06:33.630Z
updated_at: 2025-09-27T22:06:33.630Z
url: https://n8n.io/workflows/9026
scraped_at: 2025-10-07 11:38:24
metadata_generated_at: 2025-10-07 12:09:46
description:
  Lead Qualification & Meeting Booking System

  This workflow creates a sophisticated AI-powered sales assistant that automatically qualifies website visitors, schedules meetings, and manages the entire lead-to-booking pipeline without human intervention. The system acts as "Hassan," a friendly sales representative who engages prospects through natural conversation and converts interest into booked appointments with remarkable efficiency.

  Benefits:

  Complete Sales Automation - Engages website visitors 24/7, qualifies leads through intelligent conversation, and books meetings automatically without any manual oversight

  AI-Powered Lead Qualification - Uses advanced conversational AI with SPIN Selling and Challenger Sale methodologies to identify high-value prospects and overcome objections naturally

  Smart Calendar Integration - Automatically checks availability, prevents double-bookings, and sends professional confirmation emails with meeting details

  Intelligent Data Management - Tracks all interactions, prevents duplicate outreach, and maintains comprehensive conversation summaries for follow-up optimization

  Scalable Conversion System - Processes unlimited website visitors simultaneously while maintaining personalized, human-like interactions for maximum conversion rates

  Revenue-Focused Approach - Specifically designed to identify prospects interested in AI automation services and guide them toward high-value consulting calls

  How It Works:

  Website Visitor Engagement:
  Captures incoming chat messages from website visitors in real-time
  Initiates conversations using a trained AI persona that feels authentically human
  Gradually qualifies interest in AI automation services through strategic questioning

  Intelligent Lead Processing:
  Collects essential contact information (name, email, company) within first few exchanges
  Cross-references visitor history to provide personalized follow-up experiences
  Updates comprehensive database with conversation summaries and lead scoring

  Advanced Qualification System:
  Uses proven sales methodologies (SPIN, Challenger) to overcome objections naturally
  Identifies pain points, budget indicators, and timeline requirements through conversation
  Accesses company knowledge base to answer specific questions about services and pricing

  Automated Meeting Booking:
  Seamlessly transitions qualified prospects to calendar booking when interest is confirmed
  Checks real-time calendar availability to prevent conflicts and optimize scheduling
  Creates calendar events with proper attendee management and meeting details

  Professional Follow-Up Automation:
  Sends branded confirmation emails with meeting links and company information
  Maintains conversation context across multiple touchpoints for consistency
  Provides detailed handoff information to sales team for optimal meeting preparation

  Required Database Setup:

  Before running this workflow, create a Google Sheets database with these exact column headers:

  Essential Columns:
  Name** - Prospect's full name (collected during qualification)
  Email** - Primary email address (used for matching and updates) 
  Summary** - Detailed conversation summary with key insights
  Date** - Timestamp of interaction for tracking and follow-up

  Setup Instructions:
  Create a new Google Sheet with these column headers in the first row
  Name the sheet "Web Chat Bot Convo Summary"
  Connect your Google Sheets OAuth credentials in n8n
  Update the document ID in the workflow nodes

  The merge logic uses the Email column to prevent duplicate entries and update existing records with new conversation data.

  Business Use Cases:

  Service-Based Businesses - Automatically qualify and book high-value consultation calls without hiring additional sales staff

  Digital Agencies - Scale lead generation for AI automation services while maintaining personalized prospect experiences

  Consultants & Coaches - Convert website traffic into booked discovery calls with intelligent qualification and objection handling

  B2B SaaS Companies - Identify enterprise prospects and schedule product demos through natural conversation flow

  Revenue Potential:

  This system can generate $10,000-$50,000+ monthly by converting website visitors into qualified meetings. A single automated booking for AI automation services typically ranges from $3,000-$15,000 in project value, making the ROI calculation extremely attractive.

  Conversion Metrics:
  Typically converts 15-25% of engaged website visitors into qualified meetings
  Saves 20+ hours weekly of manual lead qualification and follow-up
  Eliminates scheduling back-and-forth and missed appointment opportunities

  Difficulty Level: Advanced
  Estimated Build Time: 3-4 hours  
  Monthly Operating Cost: ~$50-100 (AI API usage + integrations)

  Set Up Steps:

  Configure AI Services:
  Add OpenAI API credentials for conversational AI and Claude Sonnet for specialized tasks
  Set up appropriate rate limiting and cost controls for sustainable operation
  Customize the AI persona and conversation flow for your specific business

  Database Configuration:
  Create Google Sheets database with provided column structure  
  Connect Google Sheets OAuth credentials for seamless data management
  Configure the merge logic for duplicate prevention and record updates

  Calendar Integration Setup:
  Connect Google Calendar OAuth with proper permissions for event creation
  Configure calendar checking logic to prevent double-bookings
  Set up meeting link generation and attendee management

  Email Automation:
  Connect Gmail OAuth for sending confirmation emails
  Customize the branded email template with your company information
  Test email delivery and formatting across different clients

  Conversation Optimization:
  Customize AI prompts for your specific industry and service offerings
  Adjust qualification questions to identify your ideal customer profile  
  Set up objection handling responses that align with your sales methodology

  Advanced Configuration:
  Configure memory management for consistent multi-session conversations
  Set up proper error handling and fallback responses
  Implement conversation logging and analytics for optimization

  Testing & Launch:
  Test the complete flow with sample conversations and edge cases
  Verify calendar integration, email delivery, and data logging
  Deploy with monitoring to track performance and optimize conversion rates

  Advanced Customization:

  Multi-Language Support** - Adapt conversations for international prospects
  Industry-Specific Qualification** - Customize questioning for different market segments  
  Integration Expansion** - Connect to CRM systems, Slack notifications, or other business tools
  Advanced Analytics** - Track conversion funnels, conversation quality, and ROI metrics
  A/B Testing Framework** - Test different conversation approaches and optimize for higher conversion

  This system transforms your website from a passive information source into an active sales machine that works around the clock to generate qualified meetings and drive revenue growth.

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.googleCalendarTool","n8n-nodes-base.gmailTool","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.googleDocsTool","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.googleCalendarTool","@n8n/n8n-nodes-langchain.agentTool","@n8n/n8n-nodes-langchain.lmChatOpenRouter","n8n-nodes-base.code","n8n-nodes-base.googleSheetsTool","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.lmChatOpenRouter","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 10000 characters]

metadata_json:
  {"categories":["automation","sales","integration","ai"],"complexity":"complex","use_cases":["Automated lead qualification via conversational AI","Booking and managing meetings from website leads","Capturing and logging leads to spreadsheets/CRM","Generating follow-up materials and summaries","Maintaining conversational memory across sessions"],"estimated_setup_minutes":60,"required_services":["OpenAI (GPT) or equivalent LLM","Anthropic Claude (or Claude API)","OpenRouter / LLM routing service (lmChatOpenRouter)","Google Workspace (Google Calendar, Google Sheets, Google Docs)","Website chat widget / frontend for visitor interaction","Email/SMS provider for notifications (optional)"],"key_features":["Persona-driven conversational sales agent ('Hassan')","Multi-LLM routing (GPT + Claude) for robust responses","Automated lead qualification and scoring","Real-time calendar booking and conflict handling","Session memory (memoryBufferWindow) to keep context across messages"],"target_audience":["Sales teams and SDRs","Startups and SMBs looking to automate booking","Marketing/ops teams implementing conversational lead capture\",\"No-code/low-code automation builders and developers"]}


name: Automate Shopify Abandoned Cart WhatsApp Reminders with Product Links via Rapiwa
author_name: SpaGreen Creative
author_username: spagreen
categories: []
created_at: 2025-09-28T05:00:04.423Z
updated_at: 2025-09-28T05:00:04.423Z
url: https://n8n.io/workflows/9031
scraped_at: 2025-10-07 11:38:24
metadata_generated_at: 2025-10-07 12:09:46
description:
  Automate Shopify Abandoned Cart WhatsApp Reminders with Product Links via Rapiwa

  Who’s it for
  This n8n workflow automatically identifies customers who have abandoned their carts on your Shopify store, cleans and verifies their WhatsApp numbers, and sends them personalized reminders via the Rapiwa API. It also logs each interaction—whether the number was valid or not—into a connected Google Sheet. Designed for marketers, small business owners, freelancers, and support teams, this solution makes it easy to run bulk WhatsApp campaigns using just a Google Sheet and your own WhatsApp number—no need for the official WhatsApp Business API. It’s a budget-friendly, scalable, and easy-to-manage alternative for anyone looking to automate WhatsApp follow-ups without the tech hassle.

  How it Works / What It Does
  Reads rows from a Google Sheet where the Status column is marked as "pending".
  Cleans each phone number (removes special characters, spaces, etc.).
  Verifies whether the number is a WhatsApp user using the Rapiwa API.
  If valid:
    Sends the message via Rapiwa.
    Updates Status = sent and Verification = verified.
  If invalid:
    Skips sending.
    Updates Status = not sent and Verification = unverified.
  Waits a configurable delay between sends to avoid rate limits.
  Processes rows in small batches and repeats on schedule (default every 5 minutes).

  How to Set Up
  Duplicate the sample Google Sheet format and populate it with contacts and messages.
  Fill required columns such as WhatsApp No, Name, Message, Image URL, and set Status = pending.
  Connect Google Sheets in n8n and grant the required OAuth scopes.
  Create an HTTP Bearer credential in n8n and paste your Rapiwa API key.
  Configure the workflow nodes (Trigger, Google Sheets, Limit, SplitInBatches, Code, HTTP Request, If, Update Google Sheets, Wait).
  Enable the workflow in n8n.

  Requirements
  Google Sheets API credentials (OAuth2) configured in n8n
  Google Sheet matching the template (WhatsApp No, Name, Message, Status, Verification)
  Rapiwa account and Bearer token
  n8n instance with HTTP Request and Google Sheets nodes enabled

  How to Customize the Workflow
  Add or increase delay between messages using the Wait node (e.g., 5–10 seconds).
  Change message content or include media by editing the HTTP Request body.
  Personalize messages using sheet columns (Name, product details, coupon codes).
  Add error handling nodes to retry failed sends or log errors.
  Adjust the Limit node to control how many rows are processed per run.

  Workflow Highlights
  Triggered every 5 minutes using the Schedule Trigger node.
  Filters sheet rows where Status = pending.
  Cleans numbers and verifies WhatsApp existence before sending.
  Sends messages via Rapiwa (unofficial API).
  Updates Google Sheet rows with Status = sent/not sent and Verification = verified/unverified.
  Uses a Wait node to prevent rapid-fire sending.

  Setup in n8n

  1. Connect Google Sheets
  Add a Google Sheets node.
  Authenticate with your Google account.
  Select the document and worksheet.
  Use a filter or query to fetch rows with Status = pending.

  2. Loop Through Rows
  Use SplitInBatches to process rows in small chunks (e.g., 5 rows per batch).
  Add a Code node to clean phone numbers (remove non-digits).
  Add a Wait node to pause between individual sends (recommended 5 seconds).

  3. Send Message via HTTP Node
  Configure an HTTP Request node to POST to:
    https://app.rapiwa.com/api/send-message
  Use Bearer Token authentication with your Rapiwa API key.
  Example JSON body:
  {
    "number": "{{ $json['WhatsApp No'] }}",
    "message": "{{ $json['Message'] }}"
  }
  After the send, update the row in Google Sheets with the result.

  Sample Google Sheet Structure
   A Google Sheet formatted like this ➤ sample

  | name | number | order id | item name | coupon | item link | total price | validity | status |
  | -------------- | ------------- | -------------- | --------------------------------- | ------ | -------------------------------------------------------------------------------- | ------------ | ---------- | -------- |
  | Abdul Mannan | 8801400620056 | 39248398811454 | S25 Ultra 5XXXXX Price Cell Phone | | Product Link | 11500.00 BDT | unverified | not sent |
  | Abdul Mannan | 8801400620055 | 39248402153790 | S25 Ultra 5XXXXX Price Cell Phone | | Product Link | 11500.00 BDT | verified | sent |
  | Abdul Mannan | 8801400620055 | 39248403431742 | S25 Ultra 5XXXXX Price Cell Phone | | Product Link | 11500.00 BDT | verified | sent |

  Tips
  Ensure numbers are in proper format (e.g., 8801XXXXXXXXX — no +, no spaces).
  Use public image URLs if sending media.
  Store Rapiwa API key securely in n8n credentials.
  Test with a small batch before scaling up.
  Mark processed rows as "sent" to avoid duplicates.
  Increase Wait time or reduce batch size if you encounter rate limiting.

  Important Notes
  Avoid Duplicates: The Google Sheet serves as a ledger. Sent messages won’t be repeated in the next cycle.
  Rate Limit Awareness: Use Wait nodes to space out requests and avoid getting banned.
  Add Delay for Safety: If needed, increase wait times or limit message batches.
  Extendable: You can add product images, links, or even abandon cart discounts dynamically.

  Future Enhancements (Ideas)
  Add delay between batches (e.g., 60 messages/hour)
  Auto-update Shopify checkout status (via GraphQL API)
  Use more advanced templates or media (via Rapiwa)
  Add error handling to log failed API calls
  Use Telegram/Slack node to notify when process ends
  Useful Links
  Dashboard:** https://app.rapiwa.com
  Official Website:** https://rapiwa.com
  Documentation:** https://docs.rapiwa.com

  Support
  WhatsApp Support: Chat Now  
  Discord: Join SpaGreen Community  
  Facebook Group: SpaGreen Support  
  Website: https://spagreen.net  
  Developer Portfolio: Codecanyon SpaGreen

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

workflow_json_compressed:
  [encoded data, 8168 characters]

metadata_json:
  {"categories":["automation","e-commerce","messaging","integrations","data processing"],"complexity":"medium","use_cases":["Recover abandoned carts","Send personalized WhatsApp reminders","Clean and verify customer phone numbers","Log outreach and validation results to Google Sheets","Scheduled/batched outreach workflows"],"estimated_setup_minutes":45,"required_services":["Shopify (store or webhooks access)","Rapiwa API (WhatsApp messaging)","Google Sheets (Google Drive account)","Phone-number verification service or validation logic","n8n instance/runtime"],"key_features":["Detect abandoned Shopify carts","Normalize and validate WhatsApp numbers","Personalize message content (AI/ML node)","Send messages via Rapiwa API","Log each interaction and validation result to Google Sheets','Scheduled triggers with batch processing and conditional handling"],"target_audience":["Marketers","Small business owners","E-commerce store owners/managers」「Freelancers and agencies handling store growth」「Customer support teams"]}


name: AI Resume Screening with GPT-4o & Error Handling | Google Sheets & Drive Flow
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-09-28T13:41:49.656Z
updated_at: 2025-09-28T13:41:49.656Z
url: https://n8n.io/workflows/9048
scraped_at: 2025-10-27 22:15:59
description:
  GPT-4o Resume Screener with Error Handling - Google Sheets & Drive Pipeline

  How it works

  Enterprise-grade resume screening automation built for production environments. This workflow combines intelligent AI analysis with comprehensive error handling to ensure reliable processing of candidate applications. Every potential failure point is monitored with automatic recovery and notification systems.

  Core workflow steps:
  Intelligent Email Processing - Monitors Gmail with attachment validation and file type detection
  Robust File Handling - Multi-format support with upload verification and extraction validation
  Quality-Controlled AI Analysis - GPT-4o evaluation with output validation and fallback mechanisms
  Verified Data Extraction - Contact and qualification extraction with data integrity checks
  Dual Logging System - Success tracking in main dashboard, error logging in separate audit trail

  Error Recovery Features:
  Upload failure detection with retry mechanisms
  Text extraction validation with quality thresholds
  AI processing timeout protection and fallback responses
  Data validation before final logging
  Comprehensive error notification and tracking system

  Set up steps

  Total setup time: 25-35 minutes

  Core Credentials Setup (8 minutes)
  Gmail OAuth2 with attachment permissions
  Google Drive API with folder creation rights
  Google Sheets API with read/write access
  OpenAI API key with GPT-4o model access

  Primary Configuration (12 minutes)
  Configure monitoring systems - Set up Gmail trigger with error detection
  Establish file processing pipeline - Create Drive folders for resumes and configure upload validation
  Deploy dual spreadsheet system - Set up main tracking sheet and error logging sheet
  Initialize AI processing - Configure GPT-4o with structured output parsing and timeout settings
  Customize job requirements - Update role specifications and scoring criteria

  Error Handling Setup (10 minutes)
  Configure error notifications - Set administrator email for failure alerts
  Set up error logging spreadsheet - Create audit trail for failed processing attempts
  Customize timeout settings - Adjust processing limits based on expected file sizes
  Test error pathways - Validate notification system with sample failures

  Advanced Customization (5 minutes)
  Modify validation thresholds for resume quality
  Adjust AI prompt for industry-specific requirements
  Configure custom error messages and escalation rules
  Set up automated retry logic for transient failures

  Production-Ready Features:
  Comprehensive logging for compliance and auditing
  Graceful degradation when services are temporarily unavailable
  Detailed error context for troubleshooting
  Scalable architecture for high-volume processing

  Template Features

  Enterprise Error Management
  Multi-layer validation at every processing stage
  Automatic error categorization and routing
  Administrative alerts with detailed context
  Separate error logging for audit compliance
  Timeout protection preventing workflow hangs

  Advanced File Processing
  Upload success verification before processing
  Text extraction quality validation
  Resume content quality thresholds
  Corrupted file detection and handling
  Format conversion error recovery

  Robust AI Integration
  GPT-4o processing with output validation
  Structured response parsing with error checking
  AI timeout protection and fallback responses
  Failed analysis logging with manual review triggers
  Retry logic for transient API failures

  Production Monitoring
  Real-time error notifications via email
  Comprehensive error logging dashboard
  Processing success/failure metrics
  Failed resume tracking for manual review
  Audit trail for compliance requirements

  Data Integrity Controls
  Pre-logging validation of all extracted data
  Missing information detection and flagging
  Contact information verification checks
  Score validation and boundary enforcement
  Duplicate detection and handling

  Designed for HR departments and recruiting agencies that need reliable, scalable resume processing with enterprise-level monitoring and error recovery capabilities.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.gmailTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.googleDrive","n8n-nodes-base.if","n8n-nodes-base.switch","n8n-nodes-base.extractFromFile","n8n-nodes-base.httpRequest","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.extractFromFile","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.if","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.if","n8n-nodes-base.googleSheets","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.merge","n8n-nodes-base.gmail","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleDrive"]

workflow_json_compressed:
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metadata_json:
  {
    "categories": [
      "automation",
      "HR / recruiting",
      "AI/ML",
      "integration",
      "data processing"
    ],
    "complexity": "complex",
    "use_cases": [
      "Automated resume screening and ranking",
      "Applicant data extraction into Google Sheets",
      "Storing and indexing resumes in Google Drive",
      "Production-grade processing with error detection, recovery and alerting",
      "Triggering downstream workflows via webhooks"
    ],
    "estimated_setup_minutes": 90,
    "required_services": [
      "Gmail (or another email provider with IMAP/API access)",
      "Google Drive",
      "Google Sheets",
      "OpenAI (GPT-4o) or compatible AI provider",
      "n8n hosting/environment",
      "Notification service (email/Slack/other) or configured communication node"
    ],
    "key_features": [
      "Monitors email for incoming applications with attachment validation",
      "Extracts resume content from files and feeds to GPT-4o for screening",
      "Writes candidate metadata and scores to Google Sheets",
      "Stores original attachments in Google Drive",
      "Comprehensive error handling nodes with automatic retry/recovery paths and notifications for failures or edge cases (missing attachments, parsing errors, API failures, etc.)"
    ],
    "target_audience": [
      "Recruiters and talent acquisition teams",
      "HR automation engineers",
      "Technical ops / integrations teams deploying hiring workflows in production"
    ]
  }


