# templates

name: Generate & Enrich LinkedIn Leads with Apollo.io, LinkedIn API, Mail.so & GPT-3.5
author_name: Joseph
author_username: mjomba
categories: []
created_at: 2025-04-29T19:44:52.069Z
updated_at: 2025-04-29T19:44:52.069Z
url: https://n8n.io/workflows/3791
scraped_at: 2025-12-24 13:02:41
description:
  Note: Now includes an Apify alternative for Rapid API (Some users can't create new accounts on Rapid API, so I have added an alternative for you. But immediately you are able to get access to Rapid API, please use that option, it returns more detailed data). *Scroll to bottom for APify setup guide*

  This n8n workflow automates LinkedIn lead generation, enrichment, and activity analysis using Apollo.io, RapidAPI, Google Sheets and Mail.so.

  Perfect for sales teams, founders, B2B marketers, and cold outreach pros who want personalized lead insights to drive better conversion rates.

  ⚙️ How This Workflow Works

  The workflow is broken down into several key steps, each designed to help you build and enrich a valuable list of LinkedIn leads:

  1. 🔑 Lead Discovery (Keyword Search via Apollo)
  Pulls leads using Apollo.io's API based on keywords, industries, or job titles.
  Saves lead name, title, company, and LinkedIn URL to your Google Sheet.
  You can replace the trigger node from the form node to a webhook, whatsapp, telegram, etc, any way for you to send over your query variables over to initiate the workflow.

  2. 🧠 Username Extraction (from LinkedIn URL)
  Extracts the LinkedIn username from profile URLs using a simple script node.
  This is required for further enrichment via RapidAPI.

  3. ✉️ Email Lookup (via Apollo User ID)
  Uses the Apollo User ID to retrieve the lead’s verified work email.
  Ensures high-quality leads with reliable contact info.
  To double check that the email is currently valid, we use the mail.so api and filter out emails that fail deliverability and mx-record check. We don't wanna risk sending emails to no longer existent addresses, right?

  4. 🧾 Profile Summary Enrichment (via RapidAPI)
  Queries the LinkedIn Data API to fetch a lead’s profile summary/bio.
  Gives you a deeper understanding of their background and expertise.

  5. 📰 Recent Activity Collection (Posts & Reposts)
  Retrieves recent posts or reposts from each lead’s profile.
  Great for tailoring outreach with reference to what they’re currently talking about.

  6. 🗂️ Leads Database Update
  All enriched data is written to the same Google Sheet.
  New columns are filled in without overwriting existing data.

  ✅ Smart Retry & Row Status Logic

  Every subworkflow includes a fail-safe mechanism to ensure:

  ✅ Each row has status columns (e.g., done, failed, pending).
  🕒 A scheduled retry workflow resets failed rows to pending after 2 weeks (customizable).
  💬 This gives failed enrichments another chance to be processed later, reducing data loss.

  📋 Google Sheets Setup

  Template 1: Apollo Leads Scraping & Enrichment  

  Template 2: Enriched Leads Database  

  Make a copy to your Drive and use.

  Columns will be filled as each subworkflow runs (email, summary, interests, etc.)

  🔐 Required API Keys

  To use this workflow, you’ll need the following credentials:

  🧩 Apollo.io
  Sign up and get your key here: Apollo.io API Keys
  ⚠️ Important: Toggle the “Master API Key” option to ON when generating your key.  
  This ensures the same key can be used for all Apollo endpoints in this workflow.

  🌐 RapidAPI (LinkedIn Data API)
  Subscribe to the API here: LinkedIn Data API on RapidAPI
  Use the key in the x-rapidapi-key header in the relevant nodes.

  ✉️ Mail.so
  Sign up and get your key here: Mail.so API


  &gt; 💡 For both APIs, set up the credentials in n8n as “Generic Credential” types.  
  This way, you won’t need to reconfigure the headers in each node.

  🛠️ Customization Options

  Modify the Apollo filters (location, industry, seniority) to target your ideal customers.
  Change retry interval in the scheduler (e.g., weekly instead of 2 weeks).
  Connect the database to your email campaign tool like Mailchimp or Instantly.ai.
  Replace the AI nodes with your desired AI agents and customize the system messages further to get desired results.

  🆕 Apify Update Guide

  To use this workflow, you’ll need the following credentials:

  Login to Apify, then open this link; https://console.apify.com/actors/2SyF0bVxmgGr8IVCZ/

  Click on integrations and scroll down to API Solutions and select "Use API endpoints". Scroll to "Run Actor synchronously and get dataset items" and copy the actor endpoint url then paste it in the placeholder inside the http node of Apify alternative flow "apify-actor-endpoint". That's it, you are set to go.


  I am available for custom n8n workflows, if you like my work, please get in touch with me on email at joseph@uppfy.com

nodes_used:
  ["n8n-nodes-base.formTrigger","n8n-nodes-base.splitOut","n8n-nodes-base.googleSheetsTrigger","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.googleSheetsTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheetsTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.googleSheetsTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheetsTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheetsTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.googleSheets","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.merge","n8n-nodes-base.googleSheets","n8n-nodes-base.if"]

workflow_json_compressed:
  [encoded data, 12944 characters]


name: Track SEO Keyword Position in Google SERP (Google Sheets + SerpAPI Integration)
author_name: Joseph
author_username: mjomba
categories: []
created_at: 2025-04-29T19:57:29.645Z
updated_at: 2025-04-29T19:57:29.645Z
url: https://n8n.io/workflows/3792
scraped_at: 2025-12-24 13:02:46
description:
  This n8n workflow automates scheduled checks for your target keywords’ positions in Google’s organic search results using SerpAPI. It logs new positions in Google Sheets, compares them to previous rankings, and sends notifications via WhatsApp and/or email based on the movement.  

  Problem Statement & Target Audience

  Who is this for?  
  This workflow is ideal for:
  SEO specialists monitoring keyword rankings.
  Website owners tracking performance.
  Agencies managing multiple client campaigns.

  It helps reduce manual effort by automatically checking rankings on a schedule and sending notifications only when there’s movement in position.

  Pre-conditions & API Requirements

  APIs & Services Used
  SerpAPI – Fetch Google search results  
     Get API Key: https://serpapi.com/manage-api-key  
     Docs for usage: SerpAPI Search API  
     Note: Use Generic credential type in n8n for this to work with the preset configuration.

  Google Sheets API – To store and update keyword data  
  Email API – To notify users of position changes  
  WhatsApp API (e.g., Twilio, Ultramsg) – To send notifications  

  &gt; Setup help for Google Sheets, Email, and WhatsApp is available via the Docs tab in each respective node in n8n.

  Step-by-Step Workflow Explanation

  🗓 1. Scheduled Trigger (Support Workflow)
  Marks all rows in the sheet as "notchecked" in the checkstatus column.
  This is the user-defined frequency trigger (e.g. weekly, daily).
  Triggers the main workflow.

  🚀 2. Main Workflow Trigger
  Starts when a row with checkstatus = notchecked is found.

  🔍 3. HTTP Node (SerpAPI Call)
  Sends a request to SerpAPI for each keyword.
  Fetches the current Google SERP for that keyword.

  🧠 4. JavaScript Node (Parse Position)
  Loops through the SERP results and finds the position of the target URL.
  Extracts and returns the current rank.

  ✍️ 5. Google Sheets Node (Update Data)
  Writes the new ranking into the spreadsheet.
  Updates the currentposition and sets checkstatus to "checked".

  🔀 6. Switch Node (Compare Rankings)
  Compares previousposition with currentposition:
    If rank has improved → send positive alert.
    If rank has dropped → send negative alert.
    If unchanged → no alert.

  📩 7. Notification Nodes (WhatsApp / Email)
  Sends a message to chosen channels when a change is detected.

  Customization Guide

  ⏱️ Change Frequency
  Adjust the supporting schedule workflow to change how often the tracker runs.

  ✉️ Edit Notification Content
  Customize the WhatsApp and Email message content in their respective nodes.

  🔗 Track Multiple URLs per Keyword (Advanced)
  Extend the JavaScript logic if you need to monitor several pages for the same keyword.

  🌐 Expand Search Engine Targets
  SerpAPI also supports other search engines like Bing and Yahoo. Modify the request params to switch.

  How to Set Up This Workflow

  ✅ Step 1: Use This Google Sheets Template
  To get started quickly, make a copy of the template below:

  👉 Google Sheets Template

  This template includes the required structure:
  keyword
  Website_url
  position
  timestamp
  checkstatus

  &gt; ⚠️ Be sure to make your own copy to edit and connect via the Google Sheets node.

  🔐 Step 2: Configure Your APIs
  SerpAPI:** Get API key from serpapi.com/manage-api-key
    Set credential type to Generic in n8n for compatibility.
  Google Sheets, Email & WhatsApp:* Use the instructions found in each node’s *Docs tab** to connect your accounts.

  📦 Step 3: Import the Workflow
  Both the main keyword tracking workflow and the supporting scheduler workflow are in one file

  ▶️ Step 4: The workflow will start
  Since the check status columns will be 'notChecked' schedule workflow.
  The update sheets node will update each processed row's checkstatus to 'checked' and the workflow will process the next row
  the schedule trigger workflow will update checkstatus = notchecked and triggers the keyword tracking automatically.

  🎉 Done!
  From now on, your keyword positions will be monitored at the frequency you set. You’ll receive WhatsApp and/or email updates only when rankings change. in case you need customized workflows or wanna reach out here is my twitter and email.

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheetsTrigger","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.whatsApp","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.gmail","n8n-nodes-base.whatsApp","n8n-nodes-base.gmail","n8n-nodes-base.whatsApp","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: Optimize Amazon Ads with GPT-4o for Bid, Budget & Keyword Recommendations
author_name: Brian Money
author_username: aspereo
categories: []
created_at: 2025-04-29T20:00:22.269Z
updated_at: 2025-04-29T20:00:22.269Z
url: https://n8n.io/workflows/3793
scraped_at: 2025-12-24 13:02:44
description:
  Overview
  This template is designed for Amazon sellers and advertisers who want to automate their campaign performance analysis and bidding strategy. It solves the common challenge of manually reviewing Sponsored Products reports and guessing how to adjust keywords, placements, and budgets. By combining Amazon Advertising reports with OpenAI's GPT-4o, this workflow delivers real-time, personalized optimization instructions — automatically.

  Features
  📥 Automatically downloads Sponsored Products reports from Google Drive

  🧠 Uses AI to analyze campaign, keyword, placement, targeting, and budget performance

  📊 Supports both .csv and .xlsx report formats

  🔁 Handles multiple ASINs and scales easily across ad accounts

  📧 Sends structured optimization recommendations to your inbox via Gmail

  🗂 Built-in logic to normalize filenames and correctly map reports

  🧹 Includes error handling and formatting cleanup for AI-ready input

  Requirements
  To use this workflow, you’ll need:

  An Amazon Ads account with access to Sponsored Products reports

  A Google Drive folder where Amazon Ads reports are delivered (manually or via Gmail automation)

  A Gmail account (for sending summaries)

  An OpenAI API key with access to GPT-4o

  Optional: a developer account for the Amazon Ads API to fully automate report generation in the future

  Setup Instructions
  📂 Connect your Amazon Ads reports folder in the Google Drive node

  🔐 Add your credentials to the OpenAI and Gmail nodes

  📝 Schedule five reports in the Amazon Ads Console:

  Search Term Report → Detailed

  Targeting Report → Detailed

  Campaign Report → Summary

  Placement Report → Summary

  Budget Report → Summary
  Use “Last 30 Days”, “Daily”, and .xlsx or .csv format

  🔁 (Optional) Automate report ingestion using Gmail + Drive workflows

  🧪 Test with one account, then replicate across additional ad accounts as needed

  ⏱️ Setup time: 15–30 minutes
  📌 All field-specific guidance is included in workflow notes`

nodes_used:
  ["n8n-nodes-base.googleDrive","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.if","n8n-nodes-base.googleDrive","n8n-nodes-base.merge","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.gmail","n8n-nodes-base.extractFromFile","n8n-nodes-base.extractFromFile","n8n-nodes-base.set","n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainLlm"]

workflow_json_compressed:
  [encoded data, 7816 characters]


name: Automate Solar Lead Qualification & Follow-ups with Google Sheets and Gmail
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-04-29T22:42:19.778Z
updated_at: 2025-04-29T22:42:19.778Z
url: https://n8n.io/workflows/3794
scraped_at: 2025-12-24 13:02:46
description:
  Automate Solar Lead Qualification & Follow-ups with Google Sheets and Gmail

  Note: This template is designed for both self-hosted and cloud-based n8n instances. The workflow image above shows the complete automation flow.

  This n8n workflow automates the entire solar lead qualification process - from capturing lead information through a webhook, storing data in Google Sheets, evaluating qualification criteria, and sending personalized email follow-ups based on qualification status.

  Who is this for?

  This workflow is designed for:

  Solar installation companies
  Solar sales teams
  Renewable energy consultants
  Lead generation specialists in the solar industry

  What problem does this workflow solve?

  Managing solar leads efficiently can be challenging. This workflow solves several key pain points:

  Time-consuming manual lead qualification: Automatically evaluates leads against predefined criteria
  Inconsistent follow-up: Ensures every lead receives a timely, personalized response
  Document management: Securely stores and shares utility bill documents
  Lead tracking inefficiency: Centralizes lead data in Google Sheets with qualification status

  What this workflow does

  This workflow creates a complete solar lead management system that:

  Captures lead information through a webhook endpoint
  Securely stores utility bill uploads in Google Drive
  Records all lead data in Google Sheets
  Automatically evaluates leads based on three qualification criteria:
     Homeownership status
     Credit score (must be 650+)
     Absence of trees on roof
  Updates qualification status in the Google Sheet
  Sends personalized email follow-ups based on qualification status:
     Qualified leads receive a congratulatory email with next steps
     Disqualified leads receive helpful information about why they didn't qualify and suggestions for remediation

  Setup

  Prerequisites

  Before setting up this workflow, you'll need:

  A self-hosted n8n instance
  Google account with access to:
     Google Sheets
     Google Drive
     Gmail
  A form on your website that can make POST requests to a webhook

  Step 1: Google Sheets Setup

  Create a new Google Sheet for storing leads
  Add the following columns in the first row (exact naming is important):
     Name
     Address
     Has Trees on Roof
     credit score
     phone
     Zip code
     Email
     Homeowner
     utility bill
     Qualification status
     Disqualification reason

  Step 2: Google Drive Setup

  Sign in to your Google Drive account
  Create a folder named "Solar Lead Utility Bills" (or your preferred name)
  Right-click on the folder and select "Share"
  Set permissions to "Anyone with the link can view"
  Note the folder ID from the URL for configuration (the long string after /folders/ in the URL)

  Step 3: Configure Google Credentials in n8n

  In your n8n instance, go to Settings → Credentials
  Add credentials for:
     Google Sheets: Create new credentials, follow OAuth2 authentication
     Google Drive: Create new credentials, follow OAuth2 authentication
     Gmail: Create new credentials, follow OAuth2 authentication
  Ensure all credentials have the necessary scopes:
     Google Sheets: .../auth/spreadsheets
     Google Drive: .../auth/drive
     Gmail: .../auth/gmail.send

  Step 4: Import and Configure the Workflow

  In n8n, go to Workflows → Import from File
  Upload the workflow JSON file
  Update all Google Sheets nodes with your Google Sheet document ID:
     Open your Google Sheet
     Copy the ID from the URL (long string between /d/ and /edit)
     Update the document ID field in the Google Sheets nodes
  In the "[STEP 2] Upload Utility Bill" node, set the folder destination to your created folder

  Step 5: Configure the Webhook

  Activate the "[STEP 1] Receive Form Submission" webhook node
  Copy the generated webhook URL
  Configure your website form to send data to this URL
  Ensure your form submits the following fields with exact naming:
     firstName
     lastName
     address
     hasTreesOnRoof
     creditScore
     phone
     zipCode
     email
     homeOwnership
     utilityBill (file upload)

  Step 6: Customize Email Templates

  Open the "[STEP 10A] Send Acceptance Email" node
  Customize the email subject and message to match your company's branding
  Open the "[STEP 10B] Send Rejection Email" node
  Customize the rejection email to reflect your company's voice

  Step 7: Activate and Test

  Click "Save" on the workflow
  Toggle the "Active" switch to activate the workflow
  Submit a test lead through your form
  Check that:
     The data appears in your Google Sheet
     The qualification status is updated correctly
     The appropriate email is sent

  How to customize this workflow to your needs

  Adjusting Qualification Criteria

  You can modify the qualification logic in the "[STEP 7] Check Qualification Criteria" node:

  Open the node and click the "Edit Code" button
  Locate the criteria sections (homeowner, credit score, trees on roof)
  Modify the conditions as needed:

  // Example: Change credit score threshold
  if (creditScoreRaw.includes("600 - 649") ||
      creditScoreRaw.includes("650 - 689") ||
      creditScoreRaw.includes("690 - 719") ||
      creditScoreRaw.includes("720+")) {
      creditQualified = true;
  }

  Add additional criteria if needed

  Customizing Email Templates

  Personalize your emails further:

  Open the email nodes
  Use variable references to include more customer data:

  Dear {{ $json.Name }},

  We noticed your utility bill shows an average of {{ $json.monthlyBill }} per month.
  With solar, you could save approximately {{ $json.monthlySavings }}.

  Adding Integration with CRM Systems

  Extend this workflow by connecting it to your CRM:

  Add a Hubspot/Salesforce/etc. node after the "[STEP 8] Update Qualification Status" node
  Configure the node to create or update contacts in your CRM
  Map the lead data fields to your CRM fields

  Troubleshooting

  Common Issues

  Webhook not receiving data
     Verify your form is correctly configured to send POST requests
     Check CORS settings on your website
     Ensure all required fields are being sent

  Google Drive upload failing
     Check Google Drive permissions
     Verify your OAuth scopes include drive.file
     Ensure your Drive has sufficient storage space

  Email not sending
     Verify Gmail credentials
     Check if Gmail API is enabled in your Google Cloud Console
     Look for Send Rate Exceeded errors in execution logs

  Google Sheets Column Format

  If you're having issues with data not appearing correctly:

  Make sure the column names exactly match those in the code
  Check that the Google Sheet permissions allow editing
  Verify the sheet name is correctly referenced in the nodes

  Getting Help

  If you encounter issues with this template, you can:

  Check the n8n documentation on webhooks
  Review Google Sheets integration documentation
  Post in the n8n community forum

  This template was created by David Olusola. If you find it helpful, please consider giving it a star in the n8n template library!

nodes_used:
  ["n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.function","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.if","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.webhook","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.webhook","n8n-nodes-base.stickyNote"]

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name: Auto-Generate Meeting Attendee Research with GPT-4o, Google Calendar, and Gmail
author_name: Adam Janes
author_username: adamjanes
categories: []
created_at: 2025-04-30T03:54:36.015Z
updated_at: 2025-04-30T03:54:36.015Z
url: https://n8n.io/workflows/3796
scraped_at: 2025-12-24 13:02:42
description:
  How it works:

  Whenever a new event is scheduled on your Google Calendar, this workflow generates a Meeting Briefing email, giving an overview of each person on the call and the company they work for.

  It makes use of the web search tool on the OpenAI Responses API to make lookups.

  The workflow triggers when a new event is added to the calendar, loops over each attendee, generating reports on each person and their company, collates the results, and sends the briefing as an email.

  Set up steps:

  Add your credentials for Google Calendar (for viewing events) and Gmail (to send the email)
  Add your OpenAI credentials as a Header Auth on the Company Search and Person Search nodes.
  	Name: Authorization
  	Value: Bearer {{ YOUR_API_KEY }}
  Edit the "Edit Fields" node with the email that you want to send the briefing to, and a short bit of context about yourself.

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.googleCalendarTrigger","n8n-nodes-base.filter","n8n-nodes-base.splitOut","n8n-nodes-base.splitInBatches","n8n-nodes-base.if","n8n-nodes-base.aggregate","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.markdown","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.gmail","n8n-nodes-base.set","n8n-nodes-base.set"]

workflow_json_compressed:
  [encoded data, 4948 characters]


name: Check Tron Wallet USDT Blacklist Status via Telegram
author_name: FORK SOFTWARE TECHNOLOGIES INC.
author_username: fork
categories: []
created_at: 2025-04-30T07:34:25.954Z
updated_at: 2025-04-30T07:34:25.954Z
url: https://n8n.io/workflows/3797
scraped_at: 2025-12-24 13:02:46
description:
  Description
  This n8n workflow template allows users to check if a Tron wallet address is blacklisted on the USDT contract via a Telegram bot. When a user sends the command {walletAddress} through the Telegram bot, the workflow queries the Tronscan API to determine if the provided wallet address is blacklisted. The result is then sent back to the user via the Telegram bot.

  Detailed Description
  Workflow Overview
  This workflow is designed to interact with users through a Telegram bot and check if a given Tron wallet address is blacklisted on the USDT contract. The workflow consists of four main nodes:

  Telegram Trigger Node: Listens for messages from the Telegram bot.
  HTTP Request Node: Sends a GET request to the Tronscan API to check the blacklist status of the provided wallet address.
  Function Node: Processes the API response and formats the message to be sent back to the user.
  Telegram Send Message Node: Sends the formatted message back to the user via the Telegram bot.

  Nodes Configuration

  1.Telegram Trigger Node

  Event: Message
  Update Types: Message
  Command: /sorgu
  Description: This node listens for the {walletAddress} command followed by a wallet address from the user.

  2.HTTP Request Node

  Method: GET
  URL: https://apilist.tronscanapi.com/api/stableCoin/blackList?blackAddress={{ $json.message.text }}
  Response Format: JSON
  Description: This node sends a GET request to the Tronscan API using the wallet address provided by the user.

  3.Code Node
   Check Api Response:
  let message;

  if (response.total && response.total &gt; 0) {
    message = 🚨🛑 This Wallet is Blacklisted! 🛑🚨: ${response.data[0].blackAddress};
  } else {
    message = ✅💚 This Wallet is NOT Blacklisted! 💚✅.;
  }

  return [
    {
      json: {
        text: message,
      },
    },
  ];
  Description:** This node processes the API response to determine if the wallet address is blacklisted and formats the message to be sent back to the user.

  4.Telegram Send Message Node

  Resource: Message
  Operation: Send
  Chat ID: ={{$json["chat_id"]}}
  Text: ={{$json["text"]}}
  Description: This node sends the formatted message back to the user via the Telegram bot.

  How to Use
  Set Up Telegram Bot: Create a Telegram bot and obtain the API token. Configure the bot to listen for the {walletAddress} command.

  Import Workflow: Import this workflow into your n8n instance.

  Configure Credentials: Add your Telegram API credentials to the Telegram Trigger and Telegram Send Message nodes.

  Run Workflow: Start the workflow. Users can now send the {walletAddress} command to the Telegram bot to check if a Tron wallet address is blacklisted.

  Example Usage
  User Telegram Command: {TR7NHqjeKQxGTCi8q8ZY4pL8otSzgjLj6t}
  API Request: https://apilist.tronscanapi.com/api/stableCoin/blackList?blackAddress=TR7NHqjeKQxGTCi8q8ZY4pL8otSzgjLj6t
  API Response:
    "total": 1,
    "data": [
      {
        "blackAddress": "TR7NHqjeKQxGTCi8q8ZY4pL8otSzgjLj6t",
        "tokenName": "USDT",
        "num": "367583344429",
        "time": 1593184959,
        "transHash": "af4bc4d793f82ca5ba500cf13cf93ca3e7a56fccc2aabf8b09e55fc756500ea8",
        "contractAddress": "TR7NHqjeKQxGTCi8q8ZY4pL8otSzgjLj6t"
      }
    ]
  }

  Bot Response: 🚨🛑 This Wallet is Blacklisted! 🛑🚨: TR7NHqjeKQxGTCi8q8ZY4pL8otSzgjLj6t


  &gt; This workflow provides a simple and efficient way to check the blacklist status of Tron wallet addresses via a Telegram bot, making it easy for users to stay informed about the status of their wallets.

nodes_used:
  ["n8n-nodes-base.telegramTrigger","n8n-nodes-base.telegram","n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.stickyNote"]

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name: Create a Session-Based Telegram Chatbot with GPT-4o-mini and Google Sheets
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-04-30T08:31:26.629Z
updated_at: 2025-04-30T08:31:26.629Z
url: https://n8n.io/workflows/3798
scraped_at: 2025-12-24 13:02:41
description:
  How It Works  
  This workflow creates an AI-powered Telegram chatbot with session management, allowing users to:  
  Start new conversations** (/new).  
  Check current sessions** (/current).  
  Resume past sessions** (/resume).  
  Get summaries** (/summary).  
  Ask questions** (/question).  

  Key Components:  
  Session Management**:  
    Uses Google Sheets to track active/expired sessions (storing SESSION IDs and STATE).  
    /new creates a session; /resume reactivates past ones.  
  AI Processing**:  
    OpenAI GPT-4 generates responses with contextual memory (via Simple Memory node).  
    Summarization: Condenses past conversations when requested.  
  Data Logging**:  
    All interactions (prompts/responses) are saved to Google Sheets for audit and retrieval.  
  User Interaction**:  
    Telegram commands trigger specific actions (e.g., /question [query] fetches answers from session history).  

  Main Advantages

  1. Multi-session Handling  
  Each user can create, manage, and switch between multiple sessions independently, perfect for organizing different conversations without confusion.

  2. Persistent Memory  
  Conversations are stored in Google Sheets, ensuring that chat history and session states are preserved even if the server or n8n instance restarts.

  3. Commands for Full Control  
  With intuitive commands like /new, /current, /resume, /summary, and /question, users can manage sessions easily without needing a web interface.

  4. Smart Summarization and Q&A  
  Thanks to OpenAI models, the workflow can summarize entire conversations or answer specific questions about past discussions, saving time and improving the chatbot’s usability.

  5. Easy Setup and Scalability  
  By using Google Sheets instead of a database, the workflow is easy to clone, modify, and deploy — ideal for quick prototyping or lightweight production uses.

  6. Modular and Extensible  
  Each logical block (new session, get current session, resume, summarize, ask question) is modular, making it easy to extend the workflow with additional features like analytics, personalized greetings, or integrations with CRM systems.

  Setup Steps  

  Prerequisites:  
  Telegram Bot Token**: Create via BotFather.  
  Google Sheets**:  
    Duplicate this template.  
    Two sheets: Session (active/inactive sessions) and Database (chat logs).  
  OpenAI API Key**: For GPT-4 responses.  

  Configuration:  
  Telegram Integration:  
     Add your bot token to the Telegram Trigger and Telegram Send nodes.  

  Google Sheets Setup:  
     Authenticate the Google Sheets nodes with OAuth.  
     Ensure sheet names (Session, Database) and column mappings match the template.  

  OpenAI & Memory:  
     Add your API key to the OpenAI Chat Model nodes.  
     Adjust contextWindowLength in the Simple Memory node for conversation history length.  

  Testing:  
     Use Telegram commands to test:  
       /new: Starts a session.  
       /question [query]: Tests AI responses.  
       /summary: Checks summarization.  

  Deployment:  
     Activate the workflow; the bot will respond to Telegram messages in real-time.  

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

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.telegramTrigger","n8n-nodes-base.switch","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainSummarization","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.set","n8n-nodes-base.googleSheets","n8n-nodes-base.if","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.code","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.telegram","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent"]

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name: Interactive Knowledge Base Chat with Supabase RAG using AI 📚💬
author_name: Immanuel
author_username: spectrabit
categories: []
created_at: 2025-04-30T09:12:57.114Z
updated_at: 2025-04-30T09:12:57.114Z
url: https://n8n.io/workflows/3799
scraped_at: 2025-12-24 13:02:42
description:
  Google Drive File Ingestion to Supabase for Knowledge Base 📂💾

  Overview 🌟

  This n8n workflow automates the process of ingesting files from Google Drive into a Supabase database, preparing them for a knowledge base system. It supports text-based files (PDF, DOCX, TXT, etc.) and tabular data (XLSX, CSV, Google Sheets), extracting content, generating embeddings, and storing data in structured tables. This is a foundational workflow for building a company knowledge base that can be queried via a chat interface (e.g., using a RAG workflow). 🚀

  Problem Solved 🎯
  Manually managing a knowledge base with files from Google Drive is time-consuming and error-prone. This workflow solves that by:
  Automatically ingesting files from Google Drive as they are created or updated.
  Extracting content** from various file types (text and tabular).
  Generating embeddings for text-based files to enable vector search.
  Storing data in Supabase for efficient retrieval.
  Handling duplicates and errors to ensure data consistency.

  Target Audience:
  Knowledge Managers**: Build a centralized knowledge base from company files.
  Data Teams**: Automate the ingestion of spreadsheets and documents.
  Developers**: Integrate with other workflows (e.g., RAG for querying the knowledge base).

  Workflow Description 🔍

  This workflow listens for new or updated files in Google Drive, processes them based on their type, and stores the extracted data in Supabase tables for later retrieval. Here’s how it works:

  File Detection: Triggers when a file is created or updated in Google Drive.
  File Processing: Loops through each file, extracts metadata, and validates the file type.
  Duplicate Check: Ensures the file hasn’t been processed before.
  Content Extraction:
     Text-based Files: Downloads the file, extracts text, splits it into chunks, generates embeddings, and stores the chunks in Supabase.
     Tabular Files: Extracts data from spreadsheets and stores it as rows in Supabase.
  Metadata Storage: Stores file metadata and basic info in Supabase tables.
  Error Handling: Logs errors for unsupported formats or duplicates.

  Nodes Breakdown 🛠️

  1. Detect New File 🔔
  Type**: Google Drive Trigger
  Purpose**: Triggers the workflow when a new file is created in Google Drive.
  Configuration**:
    Credential: Google Drive OAuth2
    Event: File Created
  Customization**:
    Specify a folder to monitor specific directories.

  2. Detect Updated File 🔔
  Type**: Google Drive Trigger
  Purpose**: Triggers the workflow when a file is updated in Google Drive.
  Configuration**:
    Credential: Google Drive OAuth2
    Event: File Updated
  Customization**:
    Currently disconnected; reconnect if updates need to be processed.

  3. Process Each File 🔄
  Type**: Loop Over Items
  Purpose**: Processes each file individually from the Google Drive trigger.
  Configuration**:
    Input: {{ $json.files }}
  Customization**:
    Adjust the batch size if processing multiple files at once.

  4. Extract File Metadata 🆔
  Type**: Set
  Purpose**: Extracts metadata like file_id, file_name, mime_type, and web_view_link.
  Configuration**:
    Fields:
      file_id: {{ $json.id }}
      file_name: {{ $json.name }}
      mime_type: {{ $json.mimeType }}
      web_view_link: {{ $json.webViewLink }}
  Customization**:
    Add more metadata fields if needed (e.g., size, createdTime).

  5. Check File Type ✅
  Type**: IF
  Purpose**: Validates the file type by checking the MIME type.
  Configuration**:
    Condition: mime_type contains supported types (e.g., application/pdf, application/vnd.openxmlformats-officedocument.spreadsheetml.sheet).
  Customization**:
    Add more supported MIME types as needed.

  6. Find Duplicates 🔍
  Type**: Supabase
  Purpose**: Checks if the file has already been processed by querying knowledge_base.
  Configuration**:
    Operation: Select
    Table: knowledge_base
    Filter: file_id = {{ $node['Extract File Metadata'].json.file_id }}
  Customization**:
    Add additional duplicate checks (e.g., by file name).

  7. Handle Duplicates 🔄
  Type**: IF
  Purpose**: Routes the workflow based on whether a duplicate is found.
  Configuration**:
    Condition: {{ $node['Find Duplicates'].json.length &gt; 0 }}
  Customization**:
    Add notifications for duplicates if desired.

  8. Remove Old Text Data 🗑️
  Type**: Supabase
  Purpose**: Deletes old text data from documents if the file is a duplicate.
  Configuration**:
    Operation: Delete
    Table: documents
    Filter: metadata-&gt;&gt;'file_id' = {{ $node['Extract File Metadata'].json.file_id }}
  Customization**:
    Add logging before deletion.

  9. Remove Old Data 🗑️
  Type**: Supabase
  Purpose**: Deletes old tabular data from document_rows if the file is a duplicate.
  Configuration**:
    Operation: Delete
    Table: document_rows
    Filter: dataset_id = {{ $node['Extract File Metadata'].json.file_id }}
  Customization**:
    Add logging before deletion.

  10. Route by File Type 🔀
  Type**: Switch
  Purpose**: Routes the workflow based on the file’s MIME type (text-based or tabular).
  Configuration**:
    Rules: Based on mime_type (e.g., application/pdf for text, application/vnd.openxmlformats-officedocument.spreadsheetml.sheet for tabular).
  Customization**:
    Add more routes for additional file types.

  11. Download File Content 📥
  Type**: Google Drive
  Purpose**: Downloads the file content for text-based files.
  Configuration**:
    Credential: Google Drive OAuth2
    File ID: {{ $node['Extract File Metadata'].json.file_id }}
  Customization**:
    Add error handling for download failures.

  12. Extract PDF Text 📜
  Type**: Extract from File (PDF)
  Purpose**: Extracts text from PDF files.
  Configuration**:
    File Content: {{ $node['Download File Content'].binary.data }}
  Customization**:
    Adjust extraction settings for better accuracy.

  13. Extract DOCX Text 📜
  Type**: Extract from File (DOCX)
  Purpose**: Extracts text from DOCX files.
  Configuration**:
    File Content: {{ $node['Download File Content'].binary.data }}
  Customization**:
    Add support for other text formats (e.g., TXT, RTF).

  14. Extract XLSX Data 📊
  Type**: Extract from File (XLSX)
  Purpose**: Extracts tabular data from XLSX files.
  Configuration**:
    File ID: {{ $node['Extract File Metadata'].json.file_id }}
  Customization**:
    Add support for CSV or Google Sheets.

  15. Split Text into Chunks ✂️
  Type**: Text Splitter
  Purpose**: Splits extracted text into manageable chunks for embedding.
  Configuration**:
    Chunk Size: 1000
    Chunk Overlap: 200
  Customization**:
    Adjust chunk size and overlap based on document length.

  16. Generate Text Embeddings 🌐
  Type**: OpenAI
  Purpose**: Generates embeddings for text chunks using OpenAI.
  Configuration**:
    Credential: OpenAI API key
    Operation: Embedding
    Model: text-embedding-ada-002
  Customization**:
    Switch to a different embedding model if needed.

  17. Store Text in Supabase 💾
  Type**: Supabase Vector Store
  Purpose**: Stores text chunks and embeddings in the documents table.
  Configuration**:
    Credential: Supabase credentials
    Operation: Insert Documents
    Table Name: documents
  Customization**:
    Add metadata fields to store additional context.

  18. Store Tabular Data 💾
  Type**: Supabase
  Purpose**: Stores tabular data in the document_rows table.
  Configuration**:
    Operation: Insert
    Table: document_rows
    Columns: dataset_id, row_data
  Customization**:
    Add validation for tabular data structure.

  19. Store File Metadata 📋
  Type**: Supabase
  Purpose**: Stores file metadata in the document_metadata table.
  Configuration**:
    Operation: Insert
    Table: document_metadata
    Columns: file_id, file_name, file_type, file_url
  Customization**:
    Add more metadata fields as needed.

  20. Record in Knowledge Base 📚
  Type**: Supabase
  Purpose**: Stores basic file info in the knowledge_base table.
  Configuration**:
    Operation: Insert
    Table: knowledge_base
    Columns: file_id, file_name, file_type, file_url, upload_date
  Customization**:
    Add indexes for faster lookups.

  21. Log File Errors ⚠️
  Type**: Supabase
  Purpose**: Logs errors for unsupported file types.
  Configuration**:
    Operation: Insert
    Table: error_log
    Columns: error_type, error_message
  Customization**:
    Add notifications for errors.

  22. Log Duplicate Errors ⚠️
  Type**: Supabase
  Purpose**: Logs errors for duplicate files.
  Configuration**:
    Operation: Insert
    Table: error_log
    Columns: error_type, error_message
  Customization**:
    Add notifications for duplicates.

  Interactive Knowledge Base Chat with Supabase RAG using GPT-4o-mini 📚💬

  Introduction 🌟

  This n8n workflow creates an interactive chat interface that allows users to query a company knowledge base using Retrieval-Augmented Generation (RAG). It retrieves relevant information from text documents and tabular data stored in Supabase, then generates natural language responses using OpenAI’s GPT-4o-mini model. Designed for teams managing internal knowledge, this workflow enables users to ask questions like “What’s the remote work policy?” or “Show me the latest budget data” and receive accurate, context-aware responses in a conversational format. 🚀

  Problem Statement 🎯

  Managing a company knowledge base can be a daunting task—employees often struggle to find specific information buried in documents or spreadsheets, leading to wasted time and inefficiencies. Traditional search methods may not understand natural language queries or provide contextually relevant results. This workflow solves these issues by:
  Offering a chat-based interface for natural language queries, making it easy for users to ask questions in their own words.
  Leveraging RAG to retrieve relevant text and tabular data from Supabase, ensuring responses are accurate and context-aware.
  Supporting diverse file types, including text-based files (e.g., PDFs, DOCX) and tabular data (e.g., XLSX, CSV), for comprehensive knowledge access.
  Maintaining conversation history to provide context during interactions, improving the user experience.

  Target Audience 👥

  This workflow is ideal for:
  HR Teams**: Quickly access company policies, employee handbooks, or benefits documents.
  Finance Teams**: Retrieve budget data, expense reports, or financial summaries from spreadsheets.
  Knowledge Managers**: Build a centralized assistant for internal documentation, streamlining information access.
  Developers**: Extend the workflow with additional tools or integrations for custom use cases.

  Workflow Description 🔍

  This workflow consists of a chat interface powered by n8n’s Chat Trigger node, an AI Agent node for RAG, and several tools to retrieve data from Supabase. Here’s how it works step-by-step:

  User Initiates a Chat: The user interacts with a chat interface, sending queries like “Summarize our remote work policy” or “Show budget data for Q1 2025.”
  Query Processing with RAG: The AI Agent processes the query using RAG, retrieving relevant data from Supabase tables and generating a response with OpenAI’s GPT-4o-mini model.
  Data Retrieval and Response Generation: The workflow uses multiple tools to fetch data:
     Retrieves text chunks from the documents table using vector search.
     Fetches tabular data from the document_rows table based on file IDs.
     Extracts full document text or lists available files as needed.
     Generates a natural language response combining the retrieved data.
  Conversation History Management: Stores the conversation history in Supabase to maintain context for follow-up questions.
  Response Delivery: Formats and sends the response back to the chat interface for the user to view.

  Nodes Breakdown 🛠️

  1. Start Chat Interface 💬
  Type**: Chat Trigger
  Purpose**: Provides the interactive chat interface for users to input queries and receive responses.
  Configuration**:
    Chat Title: Company Knowledge Base Assistant
    Chat Subtitle: Ask me anything about company documents!
    Welcome Message: Hello! I’m your Company Knowledge Base Assistant. How can I help you today?
    Suggestions: What is the company policy on remote work?, Show me the latest budget data., List all policy documents.
    Output Chat Session ID: true
    Output User Message: true
  Customization**:
    Update the title and welcome message to align with your company branding (e.g., HR Knowledge Assistant).
    Add more suggestions relevant to your use case (e.g., What are the company benefits?).

  2. Process Query with RAG 🧠
  Type**: AI Agent
  Purpose**: Orchestrates the RAG process by retrieving relevant data using tools and generating responses with OpenAI’s GPT-4o-mini.
  Configuration**:
    Credential: OpenAI API key
    Model: gpt-4o-mini
    System Prompt: You are a helpful assistant for a company knowledge base. Use the provided tools to retrieve relevant information from documents and tabular data. If the query involves tabular data, format it clearly in your response. If no relevant data is found, respond with "I couldn’t find any relevant information. Can you provide more details?"
    Input Field: {{ $node['Start Chat Interface'].json.message }}
  Customization**:
    Switch to a different model (e.g., gpt-3.5-turbo) to adjust cost or performance.
    Modify the system prompt to change the tone (e.g., more formal for HR use cases).

  3. Retrieve Text Chunks 📄
  Type**: Supabase Vector Store (Tool)
  Purpose**: Retrieves relevant text chunks from the documents table using vector search.
  Configuration**:
    Credential: Supabase credentials
    Operation Mode: Retrieve Documents (As Tool for AI Agent)
    Table Name: documents
    Embedding Field: embedding
    Content Field: content_text
    Metadata Field: metadata
    Embedding Model: OpenAI text-embedding-ada-002
    Top K: 10
  Customization**:
    Adjust Top K to retrieve more or fewer results (e.g., 5 for faster responses).
    Ensure the match_documents function (see prerequisites) is defined in Supabase.

  4. Fetch Tabular Data 📊
  Type**: Supabase (Tool, Execute Query)
  Purpose**: Retrieves tabular data from the document_rows table based on a file ID.
  Configuration**:
    Credential: Supabase credentials
    Operation: Execute Query
    Query: SELECT row_data FROM document_rows WHERE dataset_id = $1 LIMIT 10
    Tool Description: Run a SQL query - use this to query from the document_rows table once you know the file ID you are querying. dataset_id is the file_id and you are always using the row_data for filtering, which is a jsonb field that has all the keys from the file schema given in the document_metadata table.
  Customization**:
    Modify the query to filter specific columns or add conditions (e.g., WHERE dataset_id = $1 AND row_data-&gt;&gt;'year' = '2025').
    Increase the LIMIT for larger datasets.

  5. Extract Full Document Text 📜
  Type**: Supabase (Tool, Execute Query)
  Purpose**: Fetches the full text of a document by concatenating all text chunks for a given file_id.
  Configuration**:
    Credential: Supabase credentials
    Operation: Execute Query
    Query: SELECT string_agg(content_text, ' ') as document_text FROM documents WHERE metadata-&gt;&gt;'file_id' = $1 GROUP BY metadata-&gt;&gt;'file_id'
    Tool Description: Given file id fetch the text from the documents
  Customization**:
    Add filters to the query if needed (e.g., limit to specific metadata fields).

  6. List Available Files 📋
  Type**: Supabase (Tool, Select)
  Purpose**: Lists all files in the knowledge base from the document_metadata table.
  Configuration**:
    Credential: Supabase credentials
    Operation: Select
    Schema: public
    Table: document_metadata
    Tool Description: Use this tool to fetch all documents including the table schema if the file is csv, excel or xlsx
  Customization**:
    Add filters to list specific file types (e.g., WHERE file_type = 'application/pdf').
    Modify the columns selected to include additional metadata (e.g., file_size).

  7. Manage Chat History 💾
  Type**: Postgres Chat Memory (Tool)
  Purpose**: Stores and retrieves conversation history to maintain context.
  Configuration**:
    Credential: Supabase credentials (Postgres-compatible)
    Table Name: n8n_chat_history
    Session ID Field: session_id
    Session ID Value: {{ $node['Start Chat Interface'].json.sessionId }}
    Message Field: message
    Sender Field: sender
    Timestamp Field: timestamp
    Context Window Length: 5
  Customization**:
    Increase the context window length for longer conversations (e.g., 10 messages).
    Add indexes on session_id and timestamp in Supabase for better performance.

  8. Format and Send Response 📤
  Type**: Set
  Purpose**: Formats the AI Agent’s response and sends it back to the chat interface.
  Configuration**:
    Fields:
      response: {{ $node['Process Query with RAG'].json.output }}
  Customization**:
    Add additional formatting to the response if needed (e.g., prepend with a timestamp or apply markdown formatting).

  Setup Instructions 🛠️

  Prerequisites 📋
  n8n Setup:
     Ensure you’re using n8n version 1.0 or higher.
     Enable the AI features in n8n settings.
  Supabase:
     Create a Supabase project and set up the following tables:
       documents: id (uuid), content_text (text), embedding (vector(1536)), metadata (jsonb)
       document_rows: id (uuid), dataset_id (varchar), row_data (jsonb)
       document_metadata: file_id (varchar), file_name (varchar), file_type (varchar), file_url (text)
       knowledge_base: id (serial), file_id (varchar), file_name (varchar), file_type (varchar), file_url (text), upload_date (timestamp)
       n8n_chat_history: id (serial), session_id (varchar), message (text), sender (varchar), timestamp (timestamp)
     Add the match_documents function to Supabase to enable vector search:
            CREATE OR REPLACE FUNCTION match_documents (
         query_embedding vector(1536),
         match_count int DEFAULT 5,
         filter jsonb DEFAULT '{}'
       ) RETURNS TABLE (
         id uuid,
         content_text text,
         metadata jsonb,
         similarity float
       ) LANGUAGE plpgsql AS $$  
       BEGIN
         RETURN QUERY
         SELECT
           documents.id,
           documents.content_text,
           documents.metadata,
           1 - (documents.embedding &lt;=&gt; query_embedding) as similarity
         FROM documents
         WHERE documents.metadata @&gt; filter
         ORDER BY similarity DESC
         LIMIT match_count;
       END;
         $$;

nodes_used:
  ["n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.splitInBatches","n8n-nodes-base.set","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.extractFromFile","n8n-nodes-base.extractFromFile","n8n-nodes-base.extractFromFile","n8n-nodes-base.aggregate","n8n-nodes-base.summarize","n8n-nodes-base.extractFromFile","n8n-nodes-base.supabase","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.supabase","@n8n/n8n-nodes-langchain.vectorStoreSupabase","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","n8n-nodes-base.set","n8n-nodes-base.googleDrive","n8n-nodes-base.set","n8n-nodes-base.postgres","n8n-nodes-base.switch","n8n-nodes-base.stickyNote","n8n-nodes-base.extractFromFile","n8n-nodes-base.gmail","n8n-nodes-base.gmail","n8n-nodes-base.supabase","n8n-nodes-base.extractFromFile","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryPostgresChat","n8n-nodes-base.postgresTool","n8n-nodes-base.postgresTool","@n8n/n8n-nodes-langchain.textSplitterCharacterTextSplitter","n8n-nodes-base.postgres","n8n-nodes-base.supabase","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.vectorStoreSupabase","@n8n/n8n-nodes-langchain.embeddingsOpenAi","n8n-nodes-base.postgresTool"]

workflow_json_compressed:
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name: Update Airtable CRM with telli Call Event Data and Appointment Status
author_name: Paulo Ramirez
author_username: paulocerez
categories: []
created_at: 2025-04-30T13:23:38.696Z
updated_at: 2025-04-30T13:23:38.696Z
url: https://n8n.io/workflows/3802
scraped_at: 2025-12-24 13:02:46
description:
  Receive realtime call-event data from telli

  Purpose and Problem Solved

  This template automates the process of receiving and acting upon real-time call event data from telli, an AI-powered voice agent platform. It solves the challenge of manually updating CRM records and initiating follow-up actions based on call outcomes. By leveraging webhooks and n8n's powerful workflow capabilities, this template enables businesses to instantly update their Airtable CRM and trigger appropriate follow-up actions, enhancing efficiency and responsiveness in customer interactions.

  Prerequisites

  An active telli account with API access and webhook capabilities
  An Airtable base set up as your CRM
  n8n instance (cloud or self-hosted)

  Airtable Specifications

  Create an Airtable base with the following table and fields:

  Table: Contacts
  Fields:
    Name (Single line text)
    Phone (Phone number)
    Email (Email)
    Appointment_Booked (Checkbox)
    Interest (Single select: High, Medium, Low)
    Last_Call_Date (Date)
    Notes (Long text)

  Step-by-Step Setup Instructions

  Webhook Configuration in telli:
     Log into your telli dashboard
     Navigate to the webhook settings
     Set the endpoint URL to your n8n Webhook node URL
     Select the "call_ended" event to trigger the webhook

  n8n Workflow Setup:
     Create a new workflow in n8n
     Add a Webhook node as the trigger
     Configure the Webhook node to receive POST requests

  Parse Webhook Data:
     Add a Set node to extract relevant information from the webhook payload
     Map fields such as call_outcome, appointment_booked, and interest

  Decision Logic:
     Add a Switch node to create different paths based on the call outcome
     Create branches for scenarios like "Appointment Booked", "Interested", and "Not Interested"

  Airtable Integration:
     Add Airtable nodes for each outcome to update the Contacts table
     Configure the nodes to update fields like Appointment_Booked, Interest, and Last_Call_Date

  Follow-up Actions:
     For "Interested" but not booked outcomes, add an Email node to trigger a follow-up email campaign
     For "Appointment Booked", add a node to create a calendar event or task

  Testing and Activation:
     Use the n8n testing feature to simulate webhook calls and verify each path
     Once satisfied, activate the workflow

  Example Workflow

  Webhook receives a "call_ended" event from telli
  Set node extracts call_outcome: appointment_booked = true, interest = true
  Switch node directs to the "Appointment Booked" path
  Airtable node updates the contact record:
     Set Appointment_Booked to true
     Set Interest to "High"
     Update Last_Call_Date
  Calendar node creates an appointment for the booked slot

  Example Payload

  Below is an example of the payload you might receive from telli when a call ends:
  {
    "event": "call_ended",
    "call": {
      "call_id": "b4a05730-2abc-4eb0-8066-2e4d23b53ba9",
      "attempt": 1,
      "from_number": "+17755719467",
      "to_number": "+16506794960",
      "external_contact_id": "external-123",
      "contact_id": "6bd1e7e0-6d00-4c0b-ad5b-daa72457a27d",
      "agent_id": "d8931604-92ad-45cf-9071-d9cd2afbad0c",
      "triggered_at": 1731956924302,
      "started_at": 1731956932264,
      "booked_slot_for": "2025-02-24T15:30:00Z",
      "ended_at": 1731957002078,
      "call_length_min": 2,
      "call_status": "COMPLETED",
      "transcript": "Agent: Hello...",
      "transcriptObject": [
        {
          "role": "agent",
          "content": "Hello..."
        }
      ],
      "call_analysis": {
        "summary": {
          "value": true,
          "details": "A call between an agent and a customer talking about buying an ice cream machine"
        },
        "appointment": {
          "value": true,
          "details": "2025-02-18T15:30:00Z"
        },
        "interest": {
          "value": true,
          "details": "The customer is interested in buying an ice cream machine"
        }
      }
    }
  }

  In this example, you can see that the call resulted in a booked appointment and showed customer interest. Your n8n workflow would process this data, updating the Airtable CRM and triggering any necessary follow-up actions.

  By implementing this template, businesses can automate their post-call processes, ensuring timely follow-ups and accurate CRM updates. This real-time integration between telli's AI voice agents and your Airtable CRM streamlines operations, improves customer engagement, and increases the efficiency of your sales and support teams.

nodes_used:
  ["n8n-nodes-base.webhook","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 5224 characters]


name: Connect Airtable Contacts to telli for Automated AI Voice Call Scheduling
author_name: Paulo Ramirez
author_username: paulocerez
categories: []
created_at: 2025-04-30T13:28:59.728Z
updated_at: 2025-04-30T13:28:59.728Z
url: https://n8n.io/workflows/3803
scraped_at: 2025-12-24 13:02:46
description:
  Upload your CRM contacts to telli and schedule AI voice-agent calls

  Introduction to telli and AI Voice-Agent Calls

  telli is an innovative platform that provides AI-powered voice agents capable of making calls and performing tasks tailored to specific customer use cases. These AI voice-agents can handle a wide range of communication tasks, from appointment scheduling to customer support, with remarkable efficiency and natural conversation flow.

  This template is designed for businesses and organizations looking to automate their outbound calling processes using telli's AI voice-agents in conjunction with Airtable as their CRM. It solves the problem of manual call scheduling and data transfer between your CRM and calling system, saving time and reducing human error.

  Prerequisites

  telli account
  Airtable base with contact information
  n8n instance

  Step-by-Step Setup Guide

  n8n Setup:
     Create a new workflow in n8n.
     Add the Airtable node to connect to your CRM table.

  telli API Configuration:
     Log in to your telli dashboard.
     Locate and copy your API key under telli - Settings - API/Webhooks.

  Workflow Configuration:
     Add two telli nodes to your n8n workflow.
     Configure the first telli node to use the "Add a contact" operation.
     Set up the second telli node to use the "Schedule a call" operation.

  Data Mapping:
     Map the relevant fields from your Airtable node to the telli API requests.

  Testing and Activation:
     Run a test execution of your workflow.
     Once satisfied with the results, activate the workflow.

  API Endpoint Details

  Add Contact

  URL**: https://api.telli.com/v1/add-contact
  Method**: POST
  Headers**:
    Authorization: YOUR-API-KEY
    Content-Type: application/json
  Payload**:
  {
    "external_contact_id": "string",
    "salutation": "string",
    "first_name": "string",
    "last_name": "string",
    "phone_number": "string",
    "email": "jsmith@example.com",
    "contact_details": {},
    "timezone": "string"
  }

  Schedule Call Endpoint

  URL**: https://api.telli.com/v1/schedule-call
  Method**: POST
  Headers**:
    Authorization: YOUR-API-KEY
    Content-Type: application/json
  Payload**:
  {
    "contact_id": TELLI-CONTACT-ID,
    "agent_id": "string",
    "max_retry_days": 123,
    "call_details": {
      "message": "Hello, this is your friendly reminder!",
      "questions": [
        {
          "fieldName": "email",
          "neededInformation": "email of the customer",
          "exampleQuestion": "What is your email address?",
          "responseFormat": "email string"
        }
      ]
    },
    "override_from_number": "string"
  }

  Use Cases

  This template is versatile and can be applied to various scenarios, including:

  Lead Qualification*: Automatically schedule calls to new leads entered in your CRM.

  Appointment Reminders*: Set up calls to remind clients of upcoming appointments.

  Customer Feedback*: Schedule follow-up calls after product deliveries or service completions.

  Uploading Multiple Contacts

  For bulk operations, you can use the Loop node in your n8n workflow to process multiple contacts sequentially.

  By leveraging this template, you can seamlessly integrate your Airtable CRM with telli's powerful AI voice-agents, automating your outbound calling process and enhancing your customer communication strategy.

nodes_used:
  ["n8n-nodes-base.airtableTrigger","n8n-nodes-telli.telli","n8n-nodes-telli.telli","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: Automated PR Code Reviews with GitHub, GPT-4, and Google Sheets Best Practices
author_name: Jihene
author_username: jiheneme
categories: []
created_at: 2025-04-30T14:07:04.489Z
updated_at: 2025-04-30T14:07:04.489Z
url: https://n8n.io/workflows/3804
scraped_at: 2025-12-24 13:02:42
description:
  AI-Agent Code Review for GitHub Pull Requests 

  Description:

  This n8n workflow automates the process of reviewing code changes in GitHub pull requests using an OpenAI-powered agent. 

  It connects your GitHub repo, extracts modified files, analyzes diffs, and uses an AI agent to generate a code review based on your internal code best practices (fed from a Google Sheet).

  It ends by posting the review as a comment on the PR and tagging it with a visual label like ✅ Reviewed by AI.

  🔧 What It Does
  Triggered on PR creation

  Extracts code diffs from the PR

  Formats and feeds them into an OpenAI prompt

  Enriches the prompt using a Google Sheet of Swift best practices

  Posts an AI-generated review as a comment on the PR

  Applies a PR label to visually mark reviewed PRs

  ✅ Prerequisites
  Before deploying this workflow, ensure you have the following:

  n8n Instance (Self-hosted or Cloud)
  GitHub Repository with PR activity
  OpenAI API Key** for GPT-4o, GPT-4-turbo, or GPT-3.5
  GitHub OAuth App** (or PAT) connected to n8n to post comments and access PR diffs
  (Optional) Google Sheets API credentials if using the code best practices lookup node.
  ⚙️ Setup Instructions

  1. Import the Workflow in n8n, click on Workflows → Import from file or JSON
  Paste or upload the JSON code of this template

  2. Configure Triggers and Connections

  🔁 GitHub Trigger

  Node**: PR Trigger
  Repository**: Select the GitHub repo(s) to monitor
  Events**: Set to pull_request
  Auth**: Use GitHub OAuth2 credentials

  📥 HTTP Request

  Node: Get file's Diffs from PR

  No authentication needed; it uses dynamic path from trigger

  🧠 OpenAI Model
  Node**: OpenAI Chat Model

  Model**: Select gpt-4o, gpt-4-turbo, or gpt-3.5-turbo

  Credential**: Provide your OpenAI API Key

  🧑‍💻 Code Review Agent
   Node : Code Review Agent
  Connected to OpenAI and optionally to tools like Google Sheets

  💬 GitHub Comment Poster

  Uses GitHub API to post review comments back on PR
  Node: GitHub Robot
  Credential: Use the agent Github account (OAuth or PAT)
  Repo : Pick your owen Github Repository

  🏷️ PR Labeler (optional)
  Adds label ReviewedByAI after successful comment

  Node: Add Label to PR
  Label : you ca customize the label text of your owen tag.

  📊 Google Sheet Best Practices config (optional)
  Connects to a Google Sheet for coding guideline lookups, we can replace Google sheet by another tool or data base

  First prepare your best practices list with the clear description and the code bad/good examples
  Add al the best practices in your Google Sheet
  Configure* the Code *Best Practices node** in the template :
  Credential : Use your Google Sheet account by OAuth2
  URL : Add your Google Sheet document URL
  Sheet : Add the name of the best practices sheet

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.githubTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.github","n8n-nodes-base.github","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.stickyNote","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"]

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name: Connect Retell Voice Agents to Custom Functions
author_name: Agent Studio
author_username: agentstudio
categories: []
created_at: 2025-04-30T14:18:50.227Z
updated_at: 2025-04-30T14:18:50.227Z
url: https://n8n.io/workflows/3805
scraped_at: 2025-12-24 13:02:45
description:
  Overview
  This workflow allows you to trigger custom logic in n8n directly from Retell's Voice Agent using Custom Functions.
  It captures a POST webhook from Retell every time a Voice Agent reaches a Custom Function node.
  You can plug in any logic—call an external API, book a meeting, update a CRM, or even return a dynamic response back to the agent.

  Who is it for
  For builders using Retell who want to extend Voice Agent functionality with real-time custom workflows or AI-generated responses.

  Prerequisites
  Have a Retell AI Account
  A Retell agent with a Custom Function node in its conversation flow (see template below)
  Set your n8n webhook URL in the Custom Function configuration (see "How to use it" below)
  (Optional) Familiarity with Retell's Custom Function docs
  Start a conversation with the agent (text or voice)

  Retell Agent Example
  To get you started, we've prepared a Retell Agent ready to be imported, that includes the call to this template.
  Import the agent to your Retell workspace (top-right button on your agent's page)
  You will need to modify the function URL in order to call your own instance.
  This template is a simple hotel agent that calls the custom function to confirm a booking, passing basic formatted data.

  How it works
  Retell sends a webhook to n8n whenever a Custom Function is triggered during a call (or test chat).
  The webhook includes:
      Full call context (transcript, call ID, etc.)
      Parameters defined in the Retell function node
  You can process this data and return a response string back to the Voice Agent in real-time.

  How to use it
  Copy the webhook URL (e.g. https://your-instance.app.n8n.cloud/webhook/hotel-retell-template)
  Modify the Retell Custom Function webhook URL (see template description for screenshots)
      Edit the function
      
      Modify the URL
      
  Modify the logic in the Set node or replace it with your own custom flow
  Deploy and test: Retell will hit your n8n workflow during the conversation

  Extension Ideas
  Call a third-party API to fetch data (e.g. hotel availability, CRM records)
  Use an LLM node to generate dynamic responses
  Trigger a parallel automation (Slack message, calendar invite, etc.)

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

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

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name: Track Amazon Product Prices with ScrapeOps API & Google Sheets Alerts
author_name: ScrapeOps
author_username: scrapeops-main
categories: []
created_at: 2025-04-30T15:25:45.450Z
updated_at: 2025-04-30T15:25:45.450Z
url: https://n8n.io/workflows/3806
scraped_at: 2025-12-24 13:02:45
description:
  Amazon Product Price Tracker

  This workflow automatically monitors Amazon product prices, tracks price changes, and sends alerts when significant price fluctuations occur. Built with ScrapeOps' structured data API, it provides a reliable, maintenance-free solution for price tracking without worrying about anti-bot measures or complex selectors.

  What This Workflow Does

  Monitors multiple Amazon products simultaneously using their ASINs
  Calculates both absolute and percentage price changes
  Sends customizable email alerts when prices cross defined thresholds
  Maintains a historical record of all price data for trend analysis
  Updates a Google Sheets with the latest price information

  Prerequisites

  A ScrapeOps API key (register at https://scrapeops.io)
  Google account for Google Sheets integration
  SMTP email configuration for alerts

  Setup Instructions

  Spreadsheet Setup
     Make a copy of the template spreadsheet: https://docs.google.com/spreadsheets/d/1hRv-TBXrpN6rkIU65WorttNHt-IPWas_An0sF4Of39U
     Add your Amazon product ASINs in the "Products to Monitor" sheet
     Set your desired alert thresholds for price increases/decreases

  Workflow Configuration
     Add your ScrapeOps API key to the "Setup" node
     Update the spreadsheet URL in the "Setup" node with YOUR copy
     Configure your email settings for notifications
     Adjust the schedule frequency as needed (default: hourly)

  How It Works

  The workflow reads product ASINs from your Google Sheet, fetches current pricing data via ScrapeOps' Amazon Product API, calculates price changes, updates your spreadsheet, and sends alerts when price movements exceed your defined thresholds.

  Unlike traditional web scrapers that break when websites change, this solution uses ScrapeOps' reliable API that handles all the complexity of Amazon data extraction, ensuring consistent results without maintenance.

  Additional Notes

  This workflow is ideal for deal hunters, price comparison services, and e-commerce analytics
  The alerting system can be extended to additional channels like Slack or Telegram
  ScrapeOps handles all anti-bot measures, proxy management, and parsing complexities

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

workflow_json_compressed:
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name: Hostinger Daily VPS Snapshot and server metrics
author_name: Leandro Melo
author_username: lsmelo
categories: []
created_at: 2025-04-30T15:51:44.149Z
updated_at: 2025-04-30T15:51:44.149Z
url: https://n8n.io/workflows/3807
scraped_at: 2025-12-24 13:02:46
description:
  Keep your Hostinger VPS servers secure with automated backups! This n8n (self-hosted) workflow for is designed to create daily snapshots and send server metrics effortlessly, ensuring you always have an up-to-date recovery copy.

  Key Features:
  ✅ Automated Snapshots: Daily execution with zero manual intervention.
  ✅ Smart Replacement: Hostinger allows only 1 snapshot per VPS—the workflow automatically replaces the previous one.
  ✅ Notifications: Alerts via WhatsApp (Evolution API) or other configurable channels for execution confirmation.

  Quick Setup:
  Prerequisites:

  Install the Community Node n8n-nodes-hostinger-api and n8n-nodes-evolution-api in your n8n instance.

  Generate a Hostinger API Key in their dashboard: hpanel.hostinger.com/profile/api.

  Workflow Configuration:
  Add the Hostinger API credential in the first node and reuse it across the workflow.

  Customize the schedule (e.g., daily at 2 AM) and notification method (Evolution API for WhatsApp, email, etc.).

  Important Note:
  Hostinger overwrites the previous snapshot with each new execution, keeping only the latest version.

  VPS Metrics avaliables (send in messages):

  🔹Status: snapshot status
  🔹Date: snapshot date time
  🔹Server: server name
  🔹IP: external server IP

  ⚙️ Métrics: 
  🔹 Number of vCPUs 
  🔹 Ram usage / avaliable 
  🔹 Hard Disk usage / avaliable
  🔹 Operational Sys and version
  🔹 Uptime time (days, hours)

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.splitOut","n8n-nodes-base.stickyNote","n8n-nodes-base.splitInBatches","n8n-nodes-base.wait","n8n-nodes-base.scheduleTrigger","n8n-nodes-hostinger-api.hostingerApi","n8n-nodes-base.filter","n8n-nodes-hostinger-api.hostingerApi","n8n-nodes-evolution-api.evolutionApi","n8n-nodes-hostinger-api.hostingerApi","n8n-nodes-base.noOp","n8n-nodes-evolution-api.evolutionApi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote"]

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  [encoded data, 4780 characters]


name: YouTube to WhatsApp Sales Automation with WordPress, FluentCRM and Whinta
author_name: GYANENDRA DWIVEDI
author_username: gyandwivedi
categories: []
created_at: 2025-04-30T17:23:09.567Z
updated_at: 2025-04-30T17:23:09.567Z
url: https://n8n.io/workflows/3808
scraped_at: 2025-12-24 13:02:46
description:
  🚀 WhatsApp Automation Template  
  Designed & Developed by Infridet Solutions Private Limited  

  🔧 Objective:  
  Automate your lead nurturing and sales process from YouTube/Instagram → Landing Page → CRM → Email → WhatsApp → Sales → Deal Closure using tools like:  

  🌐 WordPress (Landing Page + Fluent Forms)  
  🧾 Google Sheets (Backup Log)  
  📩 FluentCRM (Lead Tagging + Email Sequences)  
  💬 Whinta.com (WhatsApp Messaging API)  
  ⚙️ N8N (Workflow Automation Engine)  

  🧩 System Flow Overview:

  Lead Source: YouTube or Instagram CTA  
  Landing Page: Built on WordPress with a story-driven design  
  Form Capture: Fluent Forms with dynamic input fields  
  Data Sync:
     Backup to Google Sheets  
     Push lead to FluentCRM and tag as New Lead  
  Email Sequence:
     Warm-up emails (1 to 5)  
     Introduce offer or service  
  WhatsApp Outreach:
     Send personalized message via Whinta  
     Triggered 1 hour after form fill or last email  
  Sales Follow-Up:
     Sales team handles replies manually  
     CRM tag updated to Customer upon closing  

  📁 Folder Structure (Optional Git/Zip File):
  📦 WhatsApp-Automation-Infridet/
  │
  ├── whatsapp-automation-n8n.json      # N8N Flowchart Import File
  ├── email-templates.docx              # Warm-up Email Scripts
  ├── whinta-api-integration.pdf        # API Documentation
  ├── crm-tagging-notes.txt             # CRM Tag Setup Details
  └── readme.md                         # This Instruction File

  🛠️ Required Integrations & Setup

  ✅ Fluent Forms (WordPress)  
  Embed form with Name, Email, Phone  
  Enable webhook to N8N: /lead-capture  

  ✅ Google Sheets  
  Use n8n-nodes-base.googleSheets node  
  Capture name, email, phone, source, timestamp  

  ✅ FluentCRM  
  REST API enabled  
  Push contact and assign tag New Lead  
  Setup Email Automation via tag trigger  

  ✅ SMTP Email (Optional)  
  Use Gmail SMTP or Brevo  
  Trigger email on form submission  

  ✅ Whinta.com (WhatsApp API)  
  Send POST request  
  Payload includes phone, message, sender_id  
  Customize message with personalization  

  💬 Sample WhatsApp Message:
  Hey {{name}}, Gyan here from Account Craft 👋  
  I saw your form submission – would you like help in starting your YouTube journey this week?  
  Let me know. I'm just one text away. ✅  

  📧 Sample Email (Warmup Day 1):
  &gt; Subject: Welcome to Account Craft 🚀  
  &gt; Body:  
  &gt; Hi {{name}},  
  &gt;  
  &gt; I’m Gyan from Account Craft. Thanks for joining us!  
  &gt; Here’s what’s coming next: exclusive videos, personalized tips, and real support to get your YouTube channel earning.  
  &gt;  
  &gt; Let’s go!  
  &gt; – Gyan

  🔁 CRM Tag Updates:
  | Action            | Tag Assigned     |
  |-------------------|------------------|
  | On form fill      | New Lead       |
  | After WhatsApp    | Engaged        |
  | After sale closed | Customer       |

  📌 Final Output:
  Once completed, the system will:
  Log all leads into a database  
  Automatically send emails and WhatsApp messages  
  Notify your sales team  
  Update lead status without manual entry  

  &gt; Automation Template Designed & Deployed by  
  &gt; Infridet Solutions Private Limited  
  &gt; Smart Integrations. Seamless Business.  
  &gt; 🌐 www.infridetsolutions.com | 📞 +91-8853354829  

nodes_used:
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name: Generate SEO Content Audit Reports with DataForSEO and Google Search Console
author_name: Custom Workflows AI
author_username: customworkflowsai
categories: []
created_at: 2025-04-30T19:12:11.896Z
updated_at: 2025-04-30T19:12:11.896Z
url: https://n8n.io/workflows/3809
scraped_at: 2025-12-24 13:02:43
description:
  Introduction
  The Content SEO Audit Workflow is a powerful automated solution that generates comprehensive SEO audit reports for websites. 

  By combining the crawling capabilities of DataForSEO with the search performance metrics from Google Search Console, this workflow delivers actionable insights into content quality, technical SEO issues, and performance optimization opportunities. 

  The workflow crawls up to 1,000 pages of a website, analyzes various SEO factors including metadata, content quality, internal linking, and search performance, and then generates a professional, branded HTML report that can be shared directly with clients. 

  The entire process is automated, transforming what would typically be hours of manual analysis into a streamlined workflow that produces consistent, thorough results. 

  This workflow bridges the gap between technical SEO auditing and practical, client-ready deliverables, making it an invaluable tool for SEO professionals and digital marketing agencies.

  Who is this for?
  This workflow is designed for SEO consultants, digital marketing agencies, and content strategists who need to perform comprehensive content audits for clients or their own websites. It's particularly valuable for professionals who:

  Regularly conduct SEO audits as part of their service offerings
  Need to provide branded, professional reports to clients
  Want to automate the time-consuming process of content analysis
  Require data-driven insights to inform content strategy decisions

  Users should have basic familiarity with SEO concepts and metrics, as well as a basic understanding of how to set up API credentials in n8n. 

  While no coding knowledge is required to run the workflow, users should be comfortable with configuring workflow parameters and following setup instructions.

  What problem is this workflow solving?
  Content audits are essential for SEO strategy but are traditionally labor-intensive and time-consuming. This workflow addresses several key challenges:

  Manual Data Collection: Gathering data from multiple sources (crawlers, Google Search Console, etc.) typically requires hours of work. This workflow automates the entire data collection process.

  Inconsistent Analysis: Manual audits can suffer from inconsistency in methodology. This workflow applies the same comprehensive analysis criteria to every page, ensuring thorough and consistent results.

  Report Generation: Creating professional, client-ready reports often requires additional design work after the analysis is complete. This workflow generates a fully branded HTML report automatically.

  Data Integration: Correlating technical SEO issues with actual search performance metrics is difficult when working with separate tools. This workflow seamlessly integrates crawl data with Google Search Console metrics.

  Scale Limitations: Manual audits become increasingly difficult with larger websites. This workflow can efficiently process up to 1,000 pages without additional effort.

  What this workflow does
  Overview
  The Content SEO Audit Workflow crawls a specified website, analyzes its content for various SEO issues, retrieves performance data from Google Search Console, and generates a comprehensive HTML report. 

  The workflow identifies issues in five key categories: status issues (404 errors, redirects), content quality (thin content, readability), metadata SEO (title/description issues), internal linking (orphan pages, excessive click depth), and performance (underperforming content). 

  The final report includes executive summaries, detailed issue breakdowns, and actionable recommendations, all branded with your company's colors and logo.

  Process
  Initial Configuration: The workflow begins by setting parameters including the target domain, crawl limits, company information, and branding colors.

  Website Crawling: The workflow creates a crawl task in DataForSEO and periodically checks its status until completion.

  Data Collection: Once crawling is complete, the workflow:
     Retrieves the raw audit data from DataForSEO
     Extracts all URLs with status code 200 (successful pages)
     Queries Google Search Console API for each URL to get clicks and impressions data
     Identifies 404 and 301 pages and retrieves their source links

  Data Analysis: The workflow analyzes the collected data to identify issues including:
     Technical issues: 404 errors, redirects, canonicalization problems
     Content issues: thin content, outdated content, readability problems
     SEO metadata issues: missing/duplicate titles and descriptions, H1 problems
     Internal linking issues: orphan pages, excessive click depth, low internal links
     Performance issues: underperforming pages based on GSC data

  Report Generation: Finally, the workflow:
     Calculates a health score based on the severity and quantity of issues
     Generates prioritized recommendations
     Creates a comprehensive HTML report with interactive tables and visualizations
     Customizes the report with your company's branding
     Provides the report as a downloadable HTML file

  Setup
  To set up this workflow, follow these steps:

  Import the workflow: Download the JSON file and import it into your n8n instance.

  Configure DataForSEO credentials:
     Create a DataForSEO account at https://app.dataforseo.com/api-access (they offer a free $1 credit for testing)
     Add a new "Basic Auth" credential in n8n following the HTTP Request Authentication guide
     Assign this credential to the "Create Task", "Check Task Status", "Get Raw Audit Data", and "Get Source URLs Data" nodes

  Configure Google Search Console credentials:
     Add a new "Google OAuth2 API" credential following the Google OAuth guide
     Ensure your Google account has access to the Google Search Console property you want to analyze
     Assign this credential to the "Query GSC API" node

  Update the "Set Fields" node with:
     dfs_domain: The website domain you want to audit
     dfs_max_crawl_pages: Maximum number of pages to crawl (default: 1000)
     dfs_enable_javascript: Whether to enable JavaScript rendering (default: false)
     company_name: Your company name for the report branding
     company_website: Your company website URL
     company_logo_url: URL to your company logo
     brand_primary_color: Your primary brand color (hex code)
     brand_secondary_color: Your secondary brand color (hex code)
     gsc_property_type: Set to "domain" or "url" depending on your Google Search Console property type

  Run the workflow: Click "Start" and wait for it to complete (approximately 20 minutes for 500 pages).

  Download the report: Once complete, download the HTML file from the "Download Report" node.

  How to customize this workflow to your needs
  This workflow can be adapted in several ways to better suit your specific requirements:

  Adjust crawl parameters: Modify the "Set Fields" node to change:
     The maximum number of pages to crawl (dfs_max_crawl_pages). This workflow supports up to 1000 pages.
     Whether to enable JavaScript rendering for JavaScript-heavy sites (dfs_enable_javascript)

  Customize issue detection thresholds: In the "Build Report Structure" code node, you can modify:
     Word count thresholds for thin content detection (currently 1500 words)
     Click depth thresholds (currently flags pages deeper than 4 clicks)
     Title and description length parameters (currently 40-60 chars for titles, 70-155 for descriptions)
     Readability score thresholds (currently flags Flesch-Kincaid scores below 55)

  Modify the report design: In the "Generate HTML Report" code node, you can:
     Adjust the HTML/CSS to change the report layout and styling
     Add or remove sections from the report
     Change the recommendations logic
     Modify the health score calculation algorithm

  Add additional data sources: You could extend the workflow by:
     Adding Pagespeed Insights data for performance metrics
     Incorporating backlink data from other APIs
     Adding keyword ranking data from rank tracking APIs

  Implement automated delivery: Add nodes after the "Download Report" to:
     Send the report directly to clients via email
     Upload it to cloud storage
     Create a PDF version of the report

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name: Create and Send Multi-Item PayPal Invoices via Email
author_name: Andrew
author_username: licoricewhip
categories: []
created_at: 2025-05-01T02:23:34.149Z
updated_at: 2025-05-01T02:23:34.149Z
url: https://n8n.io/workflows/3811
scraped_at: 2025-12-24 13:02:46
description:
  Who Is This For?  
  Small businesses, consultants, agencies… anyone who bills with PayPal.

  What Problem Does It Solve?  
  Automates itemized invoicing and delivery via PayPal’s Invoicing API with support for an “Add more items?” loop.

  What This Workflow Does  
  Collects your PayPal credentials & invoice data via a form  
  Dynamically generates a timestamp + random 4 digit number for the invoice_number  
  Calls PayPal to create and then send the invoice  
  Supports endless line-items via a Switch + Merge loop.

  Setup  
  Grab a PayPal REST App’s Client ID & Secret.  
  Toggle “Live or Test” to point at production vs sandbox.  
  PayPal Credentials  
     If you select Test, this workflow points at PayPal’s sandbox (api-m.sandbox.paypal.com) and requires your sandbox Client ID & Secret.  
     If you select Live, it points at PayPal’s production endpoint (api-m.paypal.com) and requires your live Client ID & Secret.  
  (Optional) Customize your logo, currency, default notes.

  How to Customize  
  – Change the currency dropdown  
  – Tweak the invoice-number format in the “Set Invoice Number and Date” code node  
  – Swap out or style the n8n Form nodes for your own UX

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name: Standardize US Phone Numbers with Multiple Format Options and Validation
author_name: Sunny Thaper
author_username: sunnythaper
categories: []
created_at: 2025-05-01T04:03:14.963Z
updated_at: 2025-05-01T04:03:14.963Z
url: https://n8n.io/workflows/3812
scraped_at: 2025-12-24 13:02:46
description:
  Workflow Overview:

  This n8n workflow template takes a US phone number as input, validates it, and returns it in multiple standard formats, including handling extensions. It's designed to streamline the process of standardizing phone number data within your automations.

  How it Works:

  Input: Accepts a phone number string as input. This number can be in various common formats (e.g., (555) 123-4567, 555.123.4567, +15551234567, 5551234567x890).

  Formatting Removal: Strips all non-numeric characters to isolate the core number and any potential extension.

  Validation:

  Country Code Check:** Verifies if the number starts with the US country code (+1 or 1) or assumes US if no country code is present and the length is correct.

  Length Check:** Ensures the main number component consists of exactly 10 digits after stripping formatting and the country code.

  Output Generation (if valid): If the number passes validation, the workflow outputs the phone number in several standardized formats:

  Number Only:** 5551234567

  E.164 Standard:** +15551234567

  National Standard:** (555) 123-4567

  Full National Standard:** 1 (555) 123-4567

  International Standard:** 00-1-555-123-4567

  Extension Handling: If an extension is detected in the input, it is separated and provided as:

  Extension (Number):** 890

  Extension (String):** "890"

  Use Cases:

  Cleaning and standardizing phone number data in CRM systems.

  Formatting numbers before sending SMS messages via APIs.

  Validating user input from forms.

  Ensuring consistent phone number representation across different applications.

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  ["n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.switch","n8n-nodes-base.set","n8n-nodes-base.set"]

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name: Generate AI Songs + Music Videos Using Suno API, Flux, Runway and Creatomate
author_name: Joseph
author_username: mjomba
categories: []
created_at: 2025-05-01T07:13:34.261Z
updated_at: 2025-05-01T07:13:34.261Z
url: https://n8n.io/workflows/3814
scraped_at: 2025-12-24 13:02:44
description:
  This automation is designed to help you generate AI-powered music tracks, cover art, and fully rendered music videos — all triggered from a simple Telegram chat and managed via Google Sheets.

  Sample Audio Generated: Youtube Link

  ⚠️ Note: This is only a summary. The full setup guide (with detailed steps, API setup, code snippets, and integrations) is included in a PDF on the Gumroad download page. Follow it to configure everything seamlessly.

  ⚙️ What This Workflow Does

  Lets users interact with an AI agent on Telegram to submit song ideas.
  Automatically logs ideas in Google Sheets.
  Generates lyrics, music, cover images, and looping video backgrounds using AI APIs.
  Merges audio + video into a polished final clip.
  Uploads everything to Google Drive and sends a final video link back to the Telegram user.

  🔗 Built with 3 Modular Workflows

  Telegram & Sheet Logger  
     Collects ideas from users, stores them in a Google Sheet, and saves their chat ID for later notifications.

  AI Generation Engine  
     Handles lyrics creation, music generation, image prompts, artwork, video background generation, and uploads.

  Final Render + Delivery  
     Merges audio and video, uploads to cloud storage, updates the Sheet, and sends the finished video to Telegram.

  🧩 Integrations Used

  Google Sheets  
  Telegram Bot API  
  Multiple AI APIs (for music, images, video, and compression)  
  Google Apps Script  
  Creatomate for video rendering  
  Cloud storage (via Google Drive)

  🚀 How to Set It Up

  Import the .json file into your n8n instance.
  Copy the included Google Sheets and Creatomate templates.
  Set up the Google Apps Script using the included code.
  Replace all placeholder API keys and folder IDs.
  Test everything end-to-end using your Telegram bot.

  ✅ Tip: No coding experience needed — just follow the step-by-step documentation PDF provided in your Gumroad download. Everything is laid out clearly to get you running fast.

  💰Costs Song generation costs (suno): $5 gets you 1000 credits, 1 song generation costs 12 credits, every api call for one song generates two versions by default. Check the song generation node to see the two versions. You can update the GSheet to append the two versions and send back to you to choose one. Flux Image gen cost is $0.04 per image. Runway cost is $0.16 per 10 seconds of video.

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.wait","n8n-nodes-base.wait","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.wait","n8n-nodes-base.merge","n8n-nodes-base.wait","n8n-nodes-base.telegramTrigger","n8n-nodes-base.switch","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.set","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.googleSheetsTool","@n8n/n8n-nodes-langchain.toolSerpApi","n8n-nodes-base.telegram","n8n-nodes-base.googleSheetsTrigger","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.switch","n8n-nodes-base.httpRequest","n8n-nodes-base.googleDrive","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.googleDrive","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.switch","n8n-nodes-base.httpRequest","n8n-nodes-base.googleDrive","n8n-nodes-base.googleSheets","n8n-nodes-base.telegram","n8n-nodes-base.googleSheetsTrigger","n8n-nodes-base.googleSheets","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.googleDrive","n8n-nodes-base.googleSheets","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 5252 characters]


name: Upload & Rename Videos to Google Drive via Apps Script from URL
author_name: Joseph
author_username: mjomba
nodes_used: ["n8n-nodes-base.manualTrigger","n8n-nodes-base.googleDrive","n8n-nodes-base.httpRequest"]
categories: []
created_at: 2025-05-01T07:57:29.410Z
updated_at: 2025-05-01T07:57:29.410Z
url: https://n8n.io/workflows/3815
scraped_at: 2025-12-24 13:02:46
description:
  📄 Google Script Workflow: Upload File from URL to Google Drive (via n8n)

  🔧 Purpose:
  This lightweight Google Apps Script acts as a server endpoint that receives a file URL (from n8n), downloads the file, uploads it to your specified Google Drive folder, and responds with the file’s metadata (like Drive file ID and URL). This is useful for large video/audio files that n8n cannot handle directly via HTTP Download nodes.

  🚀 Setup Steps:

  1. Create a New Script Project

  Go to https://script.google.com
  Click “New Project”
  Rename the project to something like: DriveUploader

  2. Paste the Script Code

  Replace the default Code.gs content with the following (your custom script):

  function doPost(e) {
    const SECRET_KEY = 'your-strong-secret-here'; // Set your secret key here

    try {
      const data = JSON.parse(e.postData.contents);

      // 🔒 Check for correct secret key
      if (!data.secret || data.secret !== SECRET_KEY) {
        return ContentService.createTextOutput("Unauthorized")
          .setMimeType(ContentService.MimeType.TEXT);
      }

      const videoUrl = data.videoUrl;
      const folderId = 'YOUR_FOLDER_ID_HERE'; // Replace with your target folder ID
      const folder = DriveApp.getFolderById(folderId);

      const response = UrlFetchApp.fetch(videoUrl);
      const blob = response.getBlob();
      const file = folder.createFile(blob);
      file.setName('uploaded_video.mp4'); // You can customize the name

      return ContentService.createTextOutput(file.getUrl())
        .setMimeType(ContentService.MimeType.TEXT);
    } catch (err) {
      return ContentService.createTextOutput("Error: " + err.message)
        .setMimeType(ContentService.MimeType.TEXT);
    }
  }

  3. Generate & Set Up Secret Key
  To allow authorized post requests to your script only, we need to generate a secret key from aany reliable key generator. 
  You can head over to acte, click generate and copy the "Encryption key 256". 
  Paste it in the 'your-strong-secret-here' placeholder in your script then click save

    const SECRET_KEY = 'your-strong-secret-here'; // Set your secret key here;
  4. Replace Folder ID in Code

  Open the target Drive folder in your browser  
  The folder ID is the part of the URL after /folders/  
    Example:  
      https://drive.google.com/drive/u/0/folders/1Xabc12345678defGHIJklmn
    Paste that ID in the script:  
      var folderId = "1Xabc12345678defGHIJklmn";
  5. Set Up Deployment as Web App

  Click “Deploy” &gt; “Manage Deployments” &gt; “New Deployment”
  Under Select type, choose Web app
  Description: Upload from URL to Drive
  Execute as: Me
  Who has access: Anyone
  Click Deploy
  Authorize the script when prompted
  Copy the Web App URL

  📤 How to Use in n8n

  1. HTTP Request Node

  Method: POST
  URL: (your web app URL)
  Secret Key: (Secret Key set in script)
  Body Content Type: JSON
  Paste code:
  {
    "videoUrl": "https://example.com/path/to/your.mp4",
    "secret": "your-strong-secret-here"
  }
    videoUrl: The file download URL
    secret: The generated and set up secret key 

  2. Rename Node

  A simple drive update node to rename the file using the file drive url returned from the script.

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name: Multi-Platform Social Media Publisher with Blotato, GPT-4 Mini & Airtable
author_name: Max aka Mosheh
author_username: aiwithapex
categories: []
created_at: 2025-05-01T09:16:01.301Z
updated_at: 2025-05-01T09:16:01.301Z
url: https://n8n.io/workflows/3816
scraped_at: 2025-12-24 13:02:44
description:
  How it works  
  • Automates multi-platform social media posting (Instagram, YouTube, TikTok, etc.) using AI-generated content  
  • Integrates Airtable, n8n, and Blotato for full content scheduling and publishing  
  • Supports both image and video uploads with dynamic text and account routing

  Set up steps  
  • Takes ~15–30 minutes to set up depending on how many platforms you connect  
  • Requires Airtable personal access token and Blotato API key  
  • Uses sticky notes throughout the workflow to explain config, tokens, and troubleshooting clearly

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.airtable","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.formTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 11744 characters]


name: Find & Verify Business Emails Automatically with OpenRouter, Serper & Prospeo
author_name: Naveen Choudhary
author_username: n8nstein
categories: []
created_at: 2025-05-01T09:50:42.047Z
updated_at: 2025-05-01T09:50:42.047Z
url: https://n8n.io/workflows/3817
scraped_at: 2025-12-24 13:02:45
description:
  Who is this template for?

  Growth teams, SDRs, recruiters, or anyone who routinely hunts for hard‑to‑find business emails and would rather spend time reaching out than guessing formats.

  What problem does this workflow solve?

  Manually piecing together email patterns, cross‑checking them in a verifier, and updating a tracking sheet is slow and error‑prone. This template automates the entire loop—research, guess, verify, and log—so you hit Start and watch rows fill up with ready‑to‑send addresses.

  What this workflow does

  Pull fresh leads – Grabs only the rows in your Google Sheet where Status = FALSE.
  Find the company pattern – Queries Serper.dev for snippets and feeds them to Gemini Flash (via OpenRouter) to spot the dominant email format.
  Build the address – Constructs a likely email for every first/last name.
  Verify in real time – Pings Prospeo by default (API) or lets you bulk‑clean in Sparkle.io.
  Write it back – Updates the sheet with pattern, email, confidence, verification status, and flips Status to TRUE.
  Loop until done – Runs batch‑by‑batch so you never hit API limits.

  🆓 Work free‑tier magic (up to \~2,500 contacts/month)

  | Service        | Free allowance                | How this template uses it                                                            |
  | -------------- | ----------------------------- | ------------------------------------------------------------------------------------ |
  | Serper.dev | 2,500 searches/mo             | Scrapes three public email snippets per domain to learn the pattern                  |
  | Sparkle.io | 10,000 bulk verifications/day | Manual upload‑download option—perfect to clean your first 2.5k emails at zero cost |
  | Prospeo    | 75 API calls/mo             | Built‑in if you prefer fully automated verification                                |

  Quick Sparkle workflow:

  Let the template generate emails.
  Export the “Email” column to CSV → upload to Sparkle.io.
  Download the results and paste the "verification\_status" back into the sheet (or add a small n8n import sub‑flow).

  Setup (5 minutes)

  Copy the Google Sheet linked in the sticky note and paste its ID into the Get Rows and Update Rows nodes.
  Add credentials for Google Sheets, Serper (X‑API‑KEY), OpenRouter, and optionally Prospeo.
  Hit Execute Workflow—that’s it.

  How to customise

  Prefer Sparkle for volume:** Skip the Prospeo node, export emails in one click, bulk‑verify in Sparkle, and re‑import results.
  Swap the search source:* Replace the *Get Email Pattern HTTP node with Bing, Brave, etc.
  Extend enrichment:* Add phone look‑ups or LinkedIn scrapers before the *Update Rows node.
  Auto‑run:** Replace the Manual Trigger with a Cron node so the sheet cleans itself every morning.

  Additional resources

  | Tool                                                                                                                              | Purpose                                   | Link                                                     |
  | --------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------- | -------------------------------------------------------- |
  | Prospeo – API‑ready email verificationSpecial offer: 20 % free credits for the first 3 months on any plan using this link! | Real‑time, single‑call mailbox validation | prospeo.io |
  | Sparkle.io – high‑volume bulk verifier (manual upload)                                                                        | Free daily quota of 10 000 verifications  | app.sparkle.io/sign‑up |
  | OpenRouter – API gateway for Gemini Flash & other LLMs                                                                        | One key unlocks multiple frontier models  | openrouter.ai                  |
  | Serper.dev – Google Search API                                                                                                | 2 500 searches/month on the free tier     | serper.dev                        |

  Add the relevant keys or signup details from these links, drop them into the matching n8n credentials, and you’re all set to enrich your first 2 500 contacts at zero cost. Happy building!

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenRouter","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.httpRequest","n8n-nodes-base.splitInBatches","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 5136 characters]


name: Sync New Shopify Customers to Odoo Contacts
author_name: Evozard
author_username: evozardconsulting
categories: []
created_at: 2025-05-01T10:40:04.019Z
updated_at: 2025-05-01T10:40:04.019Z
url: https://n8n.io/workflows/3818
scraped_at: 2025-12-24 13:02:46
description:
  This workflow functions by integrating Shopify customers into Odoo customers.

  Trigger: Shopify – New Customer Created
     The workflow starts when a new customer is added in Shopify.

  Action: Odoo – Search Contact by Email
     It checks in Odoo to see if a contact already exists with the same email address as the Shopify customer.

  Condition: Email Match Check
      If a contact with the same email is found, the workflow ends (no duplicate contact is created).
      If no match is found, the workflow proceeds to the next step.

  Action: Odoo – Create New Contact
     A new contact is created in Odoo using the customer's:
      Full name
      Email address
      Phone number
      Full Address (whichever is available)

nodes_used:
  ["n8n-nodes-base.filter","n8n-nodes-base.odoo","n8n-nodes-base.shopifyTrigger","n8n-nodes-base.odoo","n8n-nodes-base.code"]

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name: Sync New Shopify Products to Odoo Products
author_name: Evozard
author_username: evozardconsulting
categories: []
created_at: 2025-05-01T10:58:17.969Z
updated_at: 2025-05-01T10:58:17.969Z
url: https://n8n.io/workflows/3819
scraped_at: 2025-12-24 13:02:46
description:
  Seamlessly sync newly created Shopify products into Odoo with this smart, no-code n8n workflow. Automatically checks for duplicates and creates new products only when needed – fully compatible with standard Odoo setup.

  🚀 Key Features:
  Trigger-based Automation: Starts instantly when a new product is created in Shopify

  Duplicate Check: Searches for an existing product in Odoo using Shopify’s product SKU as the Internal Reference (Default Code)

  Smart Condition Logic:

  If a matching product is found, the workflow stops – preventing duplicates

  If not found, a new product is created in Odoo

  Auto-Creation of Odoo Product using Shopify data:

  Product Name

  Description

  Price

  Internal Reference (SKU)

  Works with both Odoo Community and Enterprise editions

  Fully customizable and extendable in n8n

  No coding, no custom modules required

  🛠 Requirements:
  n8n (self-hosted or cloud)

  Shopify API access

  Odoo instance with standard API access

  💡 A perfect solution for businesses that regularly add new products to Shopify and want them mirrored in Odoo – automatically and without manual effort.

  +++++++++++++++++++++++++++++++++++++++++
  Workflow functions like the integration of a Shopify product with an Odoo product.
      
  Trigger: The workflow starts when a new product is created in Shopify.

  Check Existing Products: It searches in Odoo to check if any product already exists with the same Internal Reference or Default Code (taken from the Shopify product data).

  Condition:
      If a matching product is found: The workflow stops (no duplicates are created).
      If no matching product is found: It proceeds to create a new product in Odoo.

  Create Product in Odoo: A new product is created in Odoo using the following fields from Shopify:
      Product Name
      Description
      Price
      Internal Reference (or Default Code)

nodes_used:
  ["n8n-nodes-base.odoo","n8n-nodes-base.filter","n8n-nodes-base.odoo","n8n-nodes-base.shopifyTrigger","n8n-nodes-base.code"]

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name: Dynamically switch between LLMs for AI Agents using LangChain Code
author_name: Mario
author_username: octionic
categories: []
created_at: 2025-05-01T12:28:53.112Z
updated_at: 2025-05-01T12:28:53.112Z
url: https://n8n.io/workflows/3820
scraped_at: 2025-12-24 13:02:43
description:
  Dynamically switch between LLMs for AI Agents using LangChain Code

  Purpose

  This example workflow demonstrates a way to connect multiple LLMs to a single AI Agent/LangChain Node and programmatically use one – or in this case loop through them.

  What it does

  This AI workflow takes in customer complaints and generates a response that is being validated before returned. If the answer was not satisfactory, the response will be generated again with a more capable model.

  How it works

  A LangChain Code Node allows multiple LLMs to be connected to a single Basic LLM Chain. On every call only one LLM is actually being connected to the Basic LLM Chain, which is determined by the index defined in a previous Node.
  The AI output is later validated by a Sentiment Analysis Node
  If the result was not satisfactory, it loops back to the beginning and executes the same query with the next available LLM
  The loop ends either when the result passed the requirements or when all LLMs have been used before.

  Setup
  Clone the workflow and select the belonging credentials. You'll need an OpenAI Account, alternatively you can swap the LLM nodes with ones from a different provider like Anthropic after the import.

  How to use

  Beware that the order of the used LLMs is determined by the order they have been added to the workflow, not by the position on the canvas.

  After cloning this workflow into your environment, open the chat and send this example message:

  &gt; I really love waiting two weeks just to get a keyboard that doesn’t even work. Great job. Any chance I could actually use the thing I paid for sometime this month?

  Most likely you will see that the first validation fails, causing it to loop back to the generation node and try again with the next available LLM.

  Since AI responses are unpredictable, the results and number of tries will differ for each run.

  Disclaimer
  Please note, that this workflow can only run on self-hosted n8n instances, since it requires the LangChain Code Node.

nodes_used:
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name: Auto-Post Breaking News Content Using Perplexity AI to X (Twitter)
author_name: Aitor | 1Node
author_username: aitoralonso
categories: []
created_at: 2025-05-01T16:38:32.582Z
updated_at: 2025-05-01T16:38:32.582Z
url: https://n8n.io/workflows/3822
scraped_at: 2025-12-24 13:02:43
description:
  Stay ahead of the curve and keep your followers informed—automatically.  
  This n8n workflow uses Perplexity AI to generate insightful answers to scheduled queries, then auto-posts the responses directly to X (Twitter).

  ⚙️ What this workflow does

  Scheduled Trigger – Runs at set times (daily, hourly, etc.).
  searchQuery – Define what kind of trending or relevant insight you want (e.g. “latest AI trends”).
  set API Key – Securely insert your Perplexity API key.
  Perplexity API Call – Fetches a short, insightful response to your query.
  Post to X – Automatically publishes the result as a tweet.

  🧩 Requirements

  An n8n account (self-hosted or cloud)
  A Perplexity API key
  A connected X (Twitter) account via n8n’s credentials

  ✅ Setup Steps

  Add this workflow into your n8n account.
  Edit the searchQuery node with a topic (e.g. “What’s new in ecommerce automation?”).
  Paste your Perplexity API key into the set API key node.
  Connect your X (Twitter) account in the final node.
  Adjust the schedule timing to suit your content frequency.

  💡 Ideas to Improve

  💬 Add a formatting step to shorten or hashtag the response.
  📊 Pull multiple trending questions and auto-schedule posts.
  🔁 Loop responses to queue a full week of content.
  🌐 Translate content before posting to reach a global audience.

  🆘 Need help?

  Feel free to contact us at 1 Node.
  Get instant access to a library of free resources we created.

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.twitter"]

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name: Auto-Generate MVP Startup Ideas from Reddit with AI & Excel Storage
author_name: Jesse White
author_username: creativeaitools
categories: []
created_at: 2025-05-01T18:16:15.089Z
updated_at: 2025-05-01T18:16:15.089Z
url: https://n8n.io/workflows/3824
scraped_at: 2025-12-24 13:02:45
description:
  🧠 Reddit MVP Generator – Auto-Generate Startup Ideas from Real User Pain Points
  The Reddit MVP Generator is a fully automated business idea mining system built in n8n. It scans trending posts and user comments on Reddit to identify real-world pain points and uses an AI-powered chain to generate detailed, structured MVP business ideas—including industry, business type, revenue potential, and startup cost estimates.

  🚀 Key Features
  Live Reddit Scraper: Select from a curated list of entrepreneur and business subreddits (e.g., r/smallbusiness, r/entrepreneur, r/marketing) to fetch top trending posts.

  Comment Analysis: Pulls Reddit comments and flattens threads to extract community pain points.

  AI Business Generator: Uses OpenRouter-compatible LLMs (like GPT-4o-mini or Gemini Flash) to analyze post content and comments. Outputs are structured JSON objects with MVP ideas.

  Zero Duplicate Analysis: Automatically deduplicates previously analyzed Reddit posts using a dynamic blocklist.

  Excel Integration: Saves each MVP idea directly into your Microsoft Excel 365 sheet, organized by MVP, Industry, Pain Point, Startup Costs, and Revenue potential.

  Easy Subreddit Selection: User-friendly form trigger lets you choose your target subreddit without editing the workflow.

  Offline-Friendly: All results are stored in Excel for later review or distribution—ideal for content creators, market researchers, or startup founders.

  🛠️ Requirements
  Free n8n account (self-hosted or cloud)

  Reddit developer credentials

  OpenRouter API key (supports Qwen, GPT-4o-mini, Gemini)

  Microsoft Excel 365 with Azure OAuth2 app setup

  📦 What You Get
  Complete n8n workflow file

  Setup notes inside the workflow (via Sticky Notes)

  Instructions for Excel table formatting and OAuth2 setup

  Output schema designed for downstream automation or AI agents

nodes_used:
  ["n8n-nodes-base.reddit","n8n-nodes-base.reddit","@n8n/n8n-nodes-langchain.lmChatOpenRouter","@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.formTrigger","n8n-nodes-base.microsoftExcel","n8n-nodes-base.aggregate","n8n-nodes-base.aggregate","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.merge","n8n-nodes-base.code","n8n-nodes-base.removeDuplicates","n8n-nodes-base.splitOut","n8n-nodes-base.microsoftExcel","n8n-nodes-base.merge","n8n-nodes-base.code","n8n-nodes-base.splitInBatches","n8n-nodes-base.merge","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.splitOut","n8n-nodes-base.microsoftExcel"]

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name: 🧹 Archive (delete) duplicate items from a Notion database
author_name: Lucía Maio Brioso
author_username: luciamb
categories: []
created_at: 2025-05-01T19:04:14.005Z
updated_at: 2025-05-01T19:04:14.005Z
url: https://n8n.io/workflows/3825
scraped_at: 2025-12-24 13:02:46
description:
  🧑‍💼 Who is this for?

  If you’re using Notion to manage a database (like saving links, tasks, notes, or anything really), and it’s starting to get messy with duplicate entries, this workflow is for you.

  It’s especially useful if you want to keep things tidy without doing any manual cleanup.

  🧠 What problem is this workflow solving?

  Notion doesn’t have a built-in way to find or remove duplicates, so you either clean them up manually 😩 or just let them pile up.

  This workflow automatically finds entries that share the same property (like a URL or title) and archives the extra copies, keeping just one.

  ⚙️ What this workflow does

  Pulls all pages from a Notion database.
  Identifies duplicates based on a property you choose.
  Archives the duplicate pages (which is like soft-deleting them).
  Keeps one version of each duplicate group.

  It includes two optional triggers:

  Run it every day ⏰
  Or trigger it automatically when a new page is added to the database ⚡

  🛠️ Setup

  Connect your Notion account in n8n.
  Select your database in the Notion nodes.
  In the “Format items properly” node, replace "SET YOUR PROPERTY HERE" with a reference to the property you want to use for detecting duplicates. I recommend using the n8n property drag-and-drop feature.
  Enable whichever trigger you prefer — or both.

  And that’s it. It runs on its own after that.

  🧩 How to customize this workflow to your needs

  Use a different property for detecting duplicates by updating the Set node.
  Want to tag duplicates instead of archiving them? Just replace the last Notion node with an update operation.
  Adjust the schedule to run it hourly, weekly, or whenever suits your setup.

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.notion","n8n-nodes-base.aggregate","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.notion","n8n-nodes-base.notionTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: AI Chat Agent: Dumpling AI + GPT-4o to Auto-Save Local Business Data to Airtable
author_name: Yang
author_username: yang
categories: []
created_at: 2025-05-01T19:21:12.203Z
updated_at: 2025-05-01T19:21:12.203Z
url: https://n8n.io/workflows/3826
scraped_at: 2025-12-24 13:02:45
description:
  Who is this for?
  This workflow is for digital marketers, small business owners, lead generation agencies, and VAs who need a scalable way to find and store local business leads using AI. It’s especially useful for teams that want to enrich leads with real-time news insights and save the structured data to Airtable.

  What problem is this workflow solving?
  Manually researching local businesses and staying up to date with relevant news is time-consuming and inefficient. This automation eliminates that burden by using Dumpling AI chat agents to generate leads and context, GPT-4o to summarize, and Airtable to store everything in one place.

  What this workflow does
  This AI workflow listens for a manual trigger in n8n and executes the following steps:
  Extracts local business leads using a Local Business Agent from Dumpling AI.
  Pulls current news related to the business type or location using a News Agent from Dumpling AI.
  Uses GPT-4o to combine both responses into a human-readable summary.
  Extracts structured lead data like name, category, and city.
  Saves the summary and lead data into Airtable for easy follow-up.

  Setup

  1. Create AI Agents in Dumpling AI
  Sign in at Dumpling AI
  Create two separate agents:
    Local Business Agent: Designed to respond with structured lists of businesses by location and category.
    News Agent: Designed to fetch relevant recent news and summaries about a specific industry or region.
  After setting up each agent, copy the Agent Key from Dumpling AI. These keys will be required in the headers of your HTTP Request nodes in n8n.

  2. Manual Trigger
  This workflow begins with a manual trigger inside n8n, Which is the When chat message is recieved.
  This makes it easy to test and reuse, especially during setup.

  3. Get Local Business Data from Dumpling AI
  The first HTTP Request node sends a prompt like List 5 top real estate companies in Atlanta with full address and services.
  Include your Local Business Agent Key in the x-agent-key header.
  The response will return a structured list of business leads.

  4. Get News Context from Dumpling AI
  The second HTTP Request node sends a prompt such as Give me the latest news related to the real estate market in Atlanta.
  Use your News Agent Key in the header.
  This fetches a brief set of recent news summaries relevant to the businesses being researched.

  5. Use GPT-4o to Merge and Summarize
  The GPT node combines the list of businesses and news into one coherent summary.
  You can modify the prompt to output in paragraph format, bullet points, or structured notes.

  6. Save Lead to Airtable
  The Airtable node sends all structured fields into your selected base and table.
  Be sure to connect your Airtable account and confirm the columns match exactly.

  How to customize this workflow
  Replace the prompt inside the HTTP node to focus on different types of businesses or cities.
  Expand the GPT output to include additional lead info like websites, phone numbers, or emails if the agent includes them.
  Add a webhook trigger to allow this flow to be run via a chatbot, external app, or button.
  Link to HubSpot or another CRM to sync the leads automatically.
  Duplicate the process to run for multiple industries in parallel.

  Final Notes
  You must create and configure your Dumpling AI agents first before running this workflow.
  The Agent Keys from Dumpling AI are required in both HTTP Request nodes.
  This flow is modular and flexible, ready for deeper CRM integrations.
  The manual trigger is great for testing, but you can add a Webhook node to automate it.

  This workflow helps you launch an intelligent lead gen process that combines location-targeted business discovery, AI-generated insights, and structured CRM-friendly output, all powered by Dumpling AI and OpenAI.

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

workflow_json_compressed:
  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


name: Generate Business Leads with OpenStreetMap Data and Save to Google Sheets
author_name: Akram Kadri
author_username: akramkadri
categories: []
created_at: 2025-05-01T23:25:05.944Z
updated_at: 2025-05-01T23:25:05.944Z
url: https://n8n.io/workflows/3827
scraped_at: 2025-12-24 13:02:46
description:
  Who is this template for?

  This n8n lead generation workflow template is designed for business owners, marketers, sales professionals, and entrepreneurs who need to generate targeted business leads without paying for expensive APIs or third-party services. The template leverages the free Overpass API (powered by OpenStreetMap data) to gather comprehensive business information for any industry in almost any region worldwide.

  Perfect for:
  Small businesses looking to expand their customer base
  Real estate agents seeking potential clients
  Marketing agencies needing targeted lead lists
  Entrepreneurs researching potential markets
  Sales teams building prospect databases

  You can watch the video tutorial here: https://youtu.be/6WVfAIXdwsE

  How it works

  This n8n lead generation template provides a powerful solution using n8n core nodes and the free OpenStreetMap Overpass API, allowing you to generate thousands of business leads with comprehensive contact information:

  Data Collection Process

  1. Define Your Queries
  Create a list of search queries combining keywords (like "real estate", "dentist", etc.) with target locations
  Use both inner areas (specific cities) and outer areas (broader regions) to precisely target your search

  2. Iterative Query Execution
  The workflow processes each query in sequence, preventing API timeout issues
  Each query triggers a sub-workflow that handles the data extraction process

  3. Data Extraction via OpenStreetMap's Overpass API
  Searches for businesses matching your keywords in name, description, and relevant OpenStreetMap tags
  Collects comprehensive business data including names, addresses, and contact details from the OpenStreetMap database
  Searches using both regular expressions and exact tag matching for optimal n8n lead generation results

  4. Website Scraping for Additional Information
  For businesses with websites but missing email addresses, the workflow visits their homepage
  Extracts email addresses using regular expressions for more complete contact information
  Filters out irrelevant emails using configurable patterns

  5. Data Cleaning and Organization
  Removes duplicate contact information and standardizes data format
  Filters results based on your specific requirements for contact details
  Saves all extracted data to Google Sheets for easy access and management

  Setup

  1. Configure Your Queries:
  Open the "Run Workflow" node and input your list of queries
  Format queries with outer area, inner area, and keyword (e.g., "California,Los Angeles,real estate")

  2. Optimize Tag Targeting:
  After initial runs, identify the specific tags used by your target industry
  Update the HTTP Request node with these specific tags to improve result quality
  Common tags include "office", "shop", and "amenity" with industry-specific values

  3. Configure Data Filters:
  Customize the filter node to keep only results meeting your specific requirements
  Adjust the email filtering regex to remove irrelevant email addresses

  4. Set up Google Sheets Integration:
  Configure the Google Sheets node to save results to your preferred spreadsheet
  Authorize the connection to your Google account

  5. Run the Workflow:
  Start the workflow and watch as your Google Sheet fills with comprehensive business leads
  The template automatically handles pagination and rate limiting to prevent API timeouts

  The resulting leads include comprehensive contact information such as:
  Business names
  Complete addresses (street, city, postal code)
  Phone numbers
  Email addresses (both from API and website scraping)
  Website URLs
  Social media accounts
  Opening hours
  And more!

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.wait","n8n-nodes-base.noOp","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.splitInBatches","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.executeWorkflow","n8n-nodes-base.splitInBatches","n8n-nodes-base.filter","n8n-nodes-base.merge","n8n-nodes-base.merge","n8n-nodes-base.httpRequest","n8n-nodes-base.merge","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.googleSheets","n8n-nodes-base.splitOut","n8n-nodes-base.httpRequest","n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: Generate YouTube Video Summaries with SearchAPI Transcripts and LLM
author_name: Pedro Santos
author_username: dapedu
categories: []
created_at: 2025-05-02T04:03:32.827Z
updated_at: 2025-05-02T04:03:32.827Z
url: https://n8n.io/workflows/3828
scraped_at: 2025-12-24 13:02:46
description:
  🎥 Summarize YouTube Videos using SearchApi & LLM

  Who is this for?

  This workflow is ideal for content creators, students, digital marketers, educators, and researchers who want to quickly summarize YouTube videos.

  What problem does this workflow solve?

  Manually extracting important information from lengthy YouTube videos can be tedious and prone to errors. This workflow streamlines the process by automatically fetching video transcripts using SearchApi.io and producing concise, informative summaries through a summarization chain powered by any LLM provider. This allows users to quickly access crucial information without the need for manual transcription or detailed viewing.

  What this workflow does

  Fetches the complete transcript of a YouTube video using SearchApi.
  Combines the retrieved transcript into a single, continuous text.
  Utilizes a Summarization Chain with an LLM (e.g., OpenRouter models) to create a concise summary of the video content.

  Setup

  Install the SearchApi community node:
     Open Settings → Community Nodes inside your self‑hosted n8n instance.
     Fill npm Package Name with @searchapi/n8n-nodes-searchapi.
     Accept the risk prompt, and hit Install.
     It should now appear as a node when you search for it.

  API Configuration:
     Set up your SearchApi.io credentials in n8n.
     Add your preferred LLM provider credentials (e.g., OpenRouter API).

  Input Requirements:
     Provide the YouTube video ID (e.g., wBuULAoJxok).

  Connect LLM Integration:
     Configure the summarization chain with your chosen model and parameters for text splitting.

  How to customize this workflow to meet your needs

  Adjust the summarization model or modify text-splitter parameters to accommodate different lengths and complexities of video transcripts.
  Integrate additional nodes to export summaries directly into your preferred tools, such as Google Drive, Slack, or email.
  Customize prompt templates in the summarization chain to obtain various summary styles (bullet points, paragraphs, etc.).
  Modify the trigger to suit your workflow.

  Example Usage

  Input: YouTube video ID (wBuULAoJxok).
  Output: A concise, actionable summary that highlights key ideas, recommendations, and insights from the video.

nodes_used:
  ["n8n-nodes-base.manualTrigger","@n8n/n8n-nodes-langchain.chainSummarization","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","n8n-nodes-base.splitOut","n8n-nodes-base.summarize","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@searchapi/n8n-nodes-searchapi.searchApi","@n8n/n8n-nodes-langchain.lmChatOpenAi"]

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name: AI Agent Web Search using SearchAPI & LLM
author_name: Pedro Santos
author_username: dapedu
categories: []
created_at: 2025-05-02T04:16:38.453Z
updated_at: 2025-05-02T04:16:38.453Z
url: https://n8n.io/workflows/3829
scraped_at: 2025-12-24 13:02:44
description:
  🤖 AI Agent Web Search using SearchApi & LLM

  Who is this for?

  This workflow is ideal for anyone conducting online research, including students, researchers, content creators, and professionals looking for accurate, up-to-date, and verifiable information. It also serves as an excellent foundation for building more sophisticated AI-driven applications.

  What problem does this workflow solve? / Use case

  This workflow automates web searches by enabling an AI agent to efficiently retrieve and summarize external, verifiable information, ensuring accuracy through source citations.

  What this workflow does

  Connects an AI agent node to SearchApi.io as an integrated search tool.
  Empowers the AI agent to perform real-time web searches using various SearchApi engines (e.g., Google, Bing).
  Allows the AI agent to dynamically determine search parameters based on user interaction, delivering contextually relevant results.
  Ensures responses include clearly cited sources for validation and further exploration.

  Setup

  Install the SearchApi community node:
     Open Settings → Community Nodes inside your self‑hosted n8n instance.
     Fill npm Package Name with @searchapi/n8n-nodes-searchapi.
     Accept the risk prompt, and hit Install.
     It should now appear as a node when you search for it.

  API Configuration:
     Set up your SearchApi.io credentials in n8n.
     Add your preferred LLM provider credentials (e.g., OpenRouter API).

  Input Requirements:
     Provide the YouTube video ID (e.g., wBuULAoJxok).

  Connect LLM Integration:
     Configure the summarization chain with your chosen model and parameters for text splitting.

  How to customize this workflow to your needs

  Integrate additional nodes to structure or store search results (e.g., saving to databases, Notion, Google Sheets).
  Extend chatbot capabilities to integrate with messaging platforms (Slack, Discord) or email notifications.
  Adjust search parameters and filters within the AI agent node to tailor information retrieval.

  Example Usage

  Input**: User asks, "What are the latest developments in AI regulation?"
  Output**: AI retrieves, summarizes, and cites recent, authoritative articles and news sources from the web.

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

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name: Discover & Enrich Decision-Makers with Apollo and Human Verification
author_name: Uche Madu
author_username: uchemadu
categories: []
created_at: 2025-05-02T10:16:09.559Z
updated_at: 2025-05-02T10:16:09.559Z
url: https://n8n.io/workflows/3830
scraped_at: 2025-12-24 13:02:45
description:
  🧩 What This Workflow Does
  This workflow automates the process of identifying and enriching decision-maker contacts from a list of companies. By integrating with Apollo's APIs and Google Sheets, it streamlines lead generation, ensures data accuracy through human verification, and maintains an organized leads database.

  📚 Use Case
  Ideal for sales and marketing teams aiming to:
  Automate the discovery of key decision-makers (e.g., CEOs, CTOs).
  Enrich contact information with LinkedIn profiles, emails, and phone numbers.
  Maintain an up-to-date leads database with minimal manual intervention.
  Receive weekly summaries of newly verified leads.

  🧪 Setup
  1. Google Sheets Preparation:

  Use the following pre-configured Google Sheet: Company Decision Maker Discovery Sheet.
  This spreadsheet includes the necessary tabs and columns: Companies, Contacts, and Contacts (Verified).
  It also contains a custom onEdit Apps Script function that automatically updates the Status column to Pending whenever the Domain field is modified.
  To review or modify the script, navigate to Extensions &gt; Apps Script within the Google Sheet.

  2. Credentials Setup:

  Configure the following credentials in your n8n instance:
  	Google Sheets: To read from and write to the spreadsheet.
  	Slack: To send verification prompts and weekly reports.
  	Apollo: To access the Organization Search, Organization Enrichment, People Search, and Bulk People Enrichment APIs.
  	LLM Service (e.g., OpenAI): To generate company summaries and determine departments based on job titles.

  3. Workflow Configuration:

  Import the workflow into your n8n instance.
  Update the nodes to reference the correct Google Sheet and Slack channel.
  Ensure that the Apollo and LLM nodes have the appropriate API keys and configurations.

  4. Testing the Workflow:

  Add a new company entry in the Companies tab of the Google Sheet.
  Verify that the workflow triggers automatically, processes the data, and updates the Contacts and Contacts (Verified) tabs accordingly.
  Check Slack for any verification prompts and confirm that weekly reports are sent as scheduled.

nodes_used:
  ["n8n-nodes-base.googleSheetsTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.googleSheets","n8n-nodes-base.splitInBatches","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.slack","n8n-nodes-base.googleSheets","n8n-nodes-base.filter","n8n-nodes-base.if","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.splitOut","n8n-nodes-base.splitOut","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.slack","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.merge"]

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name: Message Buffer System with Redis for Efficient Processing
author_name: Edisson Garcia
author_username: eagarciah707
categories: []
created_at: 2025-05-02T13:42:05.798Z
updated_at: 2025-05-02T13:42:05.798Z
url: https://n8n.io/workflows/3832
scraped_at: 2025-12-24 13:02:46
description:
  🚀 Message-Batching Buffer Workflow (n8n)

  This workflow implements a lightweight message-batching buffer using Redis for temporary storage and a JavaScript consolidation function to merge messages. It collects incoming user messages per session, waits for a configurable inactivity window or batch size threshold, consolidates buffered messages via custom code, then clears the buffer and returns the combined response—all without external LLM calls.

  🔑 Key Features

  Redis-backed buffer** queues incoming messages per context_id.
  Centralized Config Parameters** node to adjust thresholds and timeouts in one place.
  Dynamic wait time** based on message length (configurable minWords, waitLong, waitShort).
  Batch trigger** fires on inactivity timeout or when buffer_count ≥ batchThreshold.
  Zero-cost consolidation** via built-in JavaScript Function (consolidate buffer)—no GPT-4 or external API required.

  ⚙️ Setup Instructions

  Extract Session & Message

     Trigger: When chat message received (webhook) or When clicking ‘Test workflow’ (manual).
     Map inputs: set variables context_id and message into a Set node named Mock input data (for testing) or a proper mapping node in production.

  Config Parameters

     Add a Set node Config Parameters with:

            minWords: 3         # Word threshold
       waitLong: 10        # Timeout (s) for long messages
       waitShort: 20       # Timeout (s) for short messages
       batchThreshold: 3   # Messages to trigger batch early
          All downstream nodes reference these JSON values dynamically.

  Determine Wait Time

     Node: get wait seconds (Code)
     JS code:

            const msg = $json.message || '';
       const wordCount = msg.split(/\s+/).filter(w =&gt; w).length;
       const { minWords, waitLong, waitShort } = items[0].json;
       const waitSeconds = wordCount &lt; minWords ? waitShort : waitLong;
       return [{ json: { context_id: $json.context_id, message: msg, waitSeconds } }];
       
  Buffer Message in Redis

     Buffer messages: LPUSH buffer_in:{{$json.context_id}} with payload {text, timestamp}.
     Set buffer\_count increment: INCR buffer_count:{{$json.context_id}} with TTL {{$json.waitSeconds + 60}}.
     Set last\_seen: record last_seen:{{$json.context_id}} timestamp with same TTL.

  Check & Set Waiting Flag

     Get waiting\_reply: if null, Set waiting\_reply to true with TTL {{$json.waitSeconds}}; else exit.

  Wait for Inactivity

     WaitSeconds (webhook): pauses for {{$json.waitSeconds}} seconds before batch evaluation.

  Check Batch Trigger

     Get last\_seen and Get buffer\_count.
     IF (now - last_seen) ≥ waitSeconds * 1000 OR buffer_count ≥ batchThreshold, proceed; else use Wait node to retry.

  Consolidate Buffer

     consolidate buffer (Code):

            const j = items[0].json;
       const raw = Array.isArray(j.buffer) ? j.buffer : [];
       const buffer = raw.map(x =&gt; {
         try { return typeof x === 'string' ? JSON.parse(x) : x;
         } catch { return null; }
       }).filter(Boolean);
       buffer.sort((a, b) =&gt; new Date(a.timestamp) - new Date(b.timestamp));
       const texts = buffer.map(e =&gt; e.text?.trim()).filter(Boolean);
       const unique = [...new Set(texts)];
       const message = unique.join(' ');
       return [{ json: { context_id: j.context_id, message } }];
       
  Cleanup & Respond

     Delete Redis keys: buffer_in, buffer_count, waiting_reply, last_seen (for the context_id).
     Return consolidated message to the user via your chat integration.

  🛠 Customization Guidance

  Adjust thresholds* by editing the *Config Parameters** node.
  Change concatenation** (e.g., line breaks) by modifying the join separator in the consolidation code.
  Add filters** (e.g., ignore empty or system messages) inside the consolidation Function.
  Monitor performance**: for very high volume, consider sharding Redis keys by date or user segments.

  © 2025 Innovatex • Automation & AI Solutions • innovatexiot.carrd.co • LinkedIn

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.noOp","n8n-nodes-base.code","n8n-nodes-base.redis","n8n-nodes-base.redis","n8n-nodes-base.set","n8n-nodes-base.if","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.redis","n8n-nodes-base.redis","n8n-nodes-base.redis","n8n-nodes-base.redis","n8n-nodes-base.wait","n8n-nodes-base.redis","n8n-nodes-base.redis","n8n-nodes-base.redis","n8n-nodes-base.redis","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.redis","n8n-nodes-base.noOp","n8n-nodes-base.wait","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.set","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.code"]

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name: Retrieve NASA Space Weather & Asteroid Data with GPT-4o-mini and Telegram
author_name: Ghufran Ridhawi
author_username: ghufran-ridhawi
categories: []
created_at: 2025-05-02T14:29:24.567Z
updated_at: 2025-05-02T14:29:24.567Z
url: https://n8n.io/workflows/3834
scraped_at: 2025-12-24 13:02:46
description:
  Who is this for?

  This workflow is intended for researchers, educators, developers, media publications, scientists, engineers, science teachers, researchers & scientists in the fields of astronomy, astrophysics, and space weather, science journalists & content creators, educators & education platform managers, data analysts & data engineers in the space research sector, museum managers or public science institutions and professions that use space data who want and need to automate workflows involving NASA space data, facilitate access to NASA science data without manual coding, increase the efficiency of research/education teams until they can be combined with other tools. In reality, data if processed and work on data one by one is very time-consuming and tiring, especially if we have to compile it first and send it. This is also a form of devotion to the community at n8n and the n8n company, as well as devotion to scientists, teachers, developers, education and media publications so that the reality is no longer tiring and is able to answer the problems of existing reality.

  How it works?

  Easy explanation:
  1) The First Nodes Telegram Trigger is used to receive data that is delivered to the next Nodes. It is like inputting information or instructions to the AI ​​Agent and the AI ​​Agent will process it.

  2) After that, the AI ​​Agent will accept to work together with the AI ​​Model, in this case I chose OpenAI because of its capabilities.
  3) After that, the AI ​​Agent will carry out its task, namely taking data from each NASA Node that I have set up.
  4) Then, after the NASA Nodes send and are received and then processed by the AI ​​Agent, the AI ​​Agent will send it to the last node, namely Telegram, which receives and sends the results of the AI ​​Agent's task.
  5) You can immediately see the data in a mature manner, because the data has been processed. If you want to process it again, just direct this AI System to process it again.
  6) And all tasks and responsibilities are completed

  It is very practical and easy, plus its automated nature. I'm sure you can understand it wisely.

  Set up instructions

  Complete what is in the nodes as stated in the notes column. 

  1) First you have to connect Telegram "Credential Account" to the nodes.
  2) Second, Next I set up using "Define below" it's like customization, because the output that is issued later will be in accordance with the existing trigger. Here I have set it up so just use it.
  3) Then, the AI ​​Model that fits the purpose and has the capability is the OpenAI Model. Here you need to create or connect a "Credential Account" and just choose the model. Here I have set up with a wise and capable choice.
  4) Next, for each NASA Nodes, you must create or connect a "Credential Account" (Using only one Credential Account). Here, just follow the instructions of the module that has been given n8n, it makes it very easy.
  5) Finally, for the Telegram node action, you only need to connect the "Credential Account" that you have created on the first Telegram node.

  Everything has been set up wisely, so you just have to follow the notes or setup instructions given, or you can add other things according to your specific purpose and congratulations you are ready to use it.

  Requirements

  As a reminder:

  It must be set in each node, such as what your goals are, also according to the conditions of your goals.
  There must be (If not, make sure it is registered) in each "Credential Account" by following the guide on how to do it n8n the guide is very complete
  Don't forget to save, and make sure the workflow is active.

  How to customize this workflow to your needs


  You can directly add nodes to store them again in the 2nd data layer. Prepare a knowledge base for your purposes in additional nodes, so that the accuracy is also high when performing tasks and answering them, and in accordance with the objectives of the task and responsibilities.

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

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name: Analyze Google Sheets Data with OpenAI-powered Data Agent
author_name: ozkary
author_username: ozkary
categories: []
created_at: 2025-05-02T18:52:11.348Z
updated_at: 2025-05-02T18:52:11.348Z
url: https://n8n.io/workflows/3835
scraped_at: 2025-12-24 13:02:45
description:
  Welcome to Ozki Your Data Analyst Agent V1.

  The Ozki Data Analyst Agent is designed to analyze data from Google Sheets. To use it, you'll need to provide the URL of your Google Sheet file. The agent will then process the data and provide you with analysis results, including key performance indicators (KPIs).

  Configuration:

  Configure your credentials on the OpenAI model or select the n8n free OpenAI credits.
  Set up your agent memory. Use Simple Memory as default.
  Set your credentials to Google Sheets. Log in with the Google Sheet tool.

  Instructions:

  Start with a "Hi" to get the instructions.
  Ozki needs your Google Sheet URL, which you can get from the address bar of your browser when you have the file open.
  Follow the conversation with Ozki for your data analysis results.

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

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name: Google Autocomplete Keyword Scraper
author_name: Ludovic Bablon
author_username: ludoeducatif
categories: []
created_at: 2025-05-02T19:04:08.683Z
updated_at: 2025-05-02T19:04:08.683Z
url: https://n8n.io/workflows/3836
scraped_at: 2025-12-24 13:02:45
description:
  Who is this template for?

  This workflow template is built for SEO specialists and digital marketers looking to uncover keyword opportunities effortlessly.

  It uses Google's autocomplete magic to help you spot what's trending.

  How it works

  Just give it a keyword.

  The workflow then queries Google and collects all autocomplete suggestions by appending every letter from A to Z to your keyword.

  Output example with the keyword "n8n" :



  You can sort these keywords and give them to an LLM to produce entity-enriched text.

  Setup instructions

  It works right out of the box. 🛠️

  However, you may want to tweak the output format to better fit your use case.

  Exporting the Keywords

  You can easily add a node to export the keywords in various ways:

  via a webhook
  by email
  as a file (e.g., saved to Google Drive)
  directly to a website

  Adapting the Language

  Autocomplete results depend on the selected language.

  You can change the &hl=en parameter in the Google Autocomplete node.  

  Replace the "en" part with the language code of your choice.

  Examples:  
  &hl=fr → French  
  &hl=es → Spanish  
  &hl=de → German

nodes_used:
  ["n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.splitInBatches","n8n-nodes-base.wait","n8n-nodes-base.code","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote"]

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name: Automated Resume Screening & Ranking with Llama 4 AI and Google Workspace
author_name: Sean Lon
author_username: seanlon
categories: []
created_at: 2025-05-03T05:42:29.439Z
updated_at: 2025-05-03T05:42:29.439Z
url: https://n8n.io/workflows/3838
scraped_at: 2025-12-24 13:02:43
description:
  Target Audience
  You will find this workflow or template perfect if you are in the internal talent acquisition teams, recruitment agencies, HR professionals, and hiring managers seeking to bulk automate the initial screening of CVs and resumes.
   Eg. Automatically get result of candidate who has been  shortlisted/rejected with its rationale and score automatically.

  By eliminating manual evaluation and screening,  you get smart AI-Agent helping you to have standardized efficient, and scalable solution for handling large volumes of applications. 

  With bulk automation, you can focus strategic decision-making rather than tedious screening tasks, ensuring a faster, more accurate, and fair hiring process.  
  Key focus 
  This workflow focusses on having a more organized file-folder management, trackable candidate cv, maintainable job description, autonomous ai-agent.
  Organized Folder-File Structure – CVs are automatically categorized based on their status, ensuring a structured workflow and easy retrieval

  Candidate Tracker – A real-time tracking system records the state of each CV, allowing recruiters to monitor the shortlisted, rejected, or KIV (Keep in View) candidates.

  AI Agent for Decision Automation – The AI autonomously orchestrates screening decisions, replacing manual LLM configurations with dynamic AI-driven evaluations for scalability and accuracy.

  Maintainable Job Description Management – A structured job description file ensures continuous updates, keeping hiring criteria flexible and aligned with recruitment needs.

  Email Notifications – The system automatically sends receipt confirmations upon processing completion, providing timely updates to recruiters.

  Features - Workflow 
  Automated Resume Screening Workflow  

  This workflow leverages Groq Llama4 for intelligent resume analysis, speeding the screening process by generating a matching score, result (shortlisted/rejected/kiv), and key insights/rationale into their suitability for provided job description.  
  Step-by-Step Process:  

  Monitors Google Drive:** Listens and checks for new resume cv in google drive   .  
  Retrieve Resume:** Downloads the CV resumes from google drive .  
  Extract Resume Data:* Extract *text content** from CV resume PDF files  
  Extract Job Description Data:* Extract *text content** from job description

  Analyze with Groq:**  
    Generate a matching score based on job requirements.  [SCORE: 1-10]
    Provide decision into their job suitability.  [SHORTLISTED/REJECTED/KIV]
    Provide actionable insights into their job suitability.  [REASON]

  This ensures a fast, efficient, and accurate screening process, eliminating manual evaluation. 

  Setup Guide
  Step-by-Step Instructions   
  Ensure all credentials are ready and setup (groq, gdrive ,gmail, gsheet, gdoc)
  View official n8n documentation on node setup accordingly.
  See also the notes of setup .
  Folder & File Setup    
  1. Create a google-drive folder like this
   
  View directory example

  2. Create a job description like this
   
  View file example

  3. Configure a tracker like this ( Candidate Name, AI Score,AI Verdict, AI Reason)
   
  View file example
    email conversations report as you like.

  You are ready to go!

   

nodes_used:
  ["n8n-nodes-base.googleDocs","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.googleDrive","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.extractFromFile","n8n-nodes-base.gmailTool","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.googleDriveTool","n8n-nodes-base.googleDriveTool","n8n-nodes-base.googleDriveTool","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatGroq"]

workflow_json_compressed:
  [encoded data, 5668 characters]


name: Analyze Competitor Facebook Ads with AI (GPT-4 & Gemini) & Email Reports
author_name: SamirLiu
author_username: samir
categories: []
created_at: 2025-05-03T07:56:47.137Z
updated_at: 2025-05-03T07:56:47.137Z
url: https://n8n.io/workflows/3839
scraped_at: 2025-12-24 13:02:46
description:
  Facebook Ads Competitor Creative Analysis & Automated Email Report

  📝 What this workflow does

  This workflow automates the process of competitor creative analysis for Facebook Pages. When a user submits their email and a target Facebook Page URL via a web form, the workflow:

  Scrapes the latest image and video Facebook ads from the submitted Page using Apify.
  Deduplicates and selects the top 5 image and top 5 video ads for further analysis.
  Applies advanced AI analysis:
    Uses OpenAI Vision (GPT-4) to analyze image ads.
    Uses Google Gemini to analyze video ads.
  Aggregates all ad insights and generates a structured, visually engaging creative analytics report.
  Sends the report directly to the user’s email with ad previews and detailed creative evaluation.

  👥 Who is this for?

  Marketers** and agencies conducting competitor research on Facebook.
  Brand managers** who want quick, actionable insights into rival ad creative.
  Growth hackers** and performance advertisers analyzing top-performing creative trends.
  Anyone** in need of an automated, AI-based Facebook ad evaluation and reporting tool using n8n.

  🎯 What problem does this workflow solve?

  Manual ad analysis is time-consuming:** Scraping, filtering, and reviewing Facebook competitor ads by hand is labor-intensive.
  Creative insight requires expertise:** Understanding the message, visuals, and targeting of competitor ads needs marketing/creative skill.
  Lack of automated reporting:** Easily and visually summarize findings for yourself or stakeholders—without manual collation or formatting.

  This workflow provides a turnkey, end-to-end solution for competitor creative analysis, combining real ad data with unbiased, AI-driven creative insights in a shareable email format.

  ⚙️ Setup instructions (About 10-15 minutes)

  Before using this AI marketing automation tool, you'll need:
  Add api key to environment variable
  Add required credentials

  🔧 How to Customize This Workflow

  Adapt Image/Video Analysis Goals:** Modify the prompts or parameters sent to OpenAI Vision and Gemini to suit your specific analysis objectives—e.g., evaluate branding consistency, detect call-to-action effectiveness, or extract emotional tone.
  Revise Email Report Design:** Customize the formatting, insights, and visuals in the delivered report to match your organization’s standards or stakeholder preferences.
  Expand Input Fields:** Add more fields to the user-facing form, such as a competitor list, campaign dates, or target audience details, for more granular analysis.

nodes_used:
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name: Automated AI Content Creation & Instagram Publishing from Google Sheets
author_name: Onur
author_username: onurpolat05
categories: []
created_at: 2025-05-03T08:06:31.593Z
updated_at: 2025-05-03T08:06:31.593Z
url: https://n8n.io/workflows/3840
scraped_at: 2025-12-24 13:02:41
description:
  Automated AI Content Creation & Instagram Publishing from Google Sheets

  This n8n workflow automates the creation and publishing of social media content directly to Instagram, using ideas stored in a Google Sheet. It leverages AI (Google Gemini and Replicate Flux) to generate concepts, image prompts, captions, and the final image, turning your content plan into reality with minimal manual intervention.

  Think of this as the execution engine for your content strategy. It assumes you have a separate process (whether manual entry, another workflow, or a different tool) for populating the Google Sheet with initial post ideas (including Topic, Audience, Voice, and Platform). This workflow takes those ideas and handles the rest, from AI generation to final publication.

  What does this workflow do?

  This workflow streamlines the content execution process by:

  Automatically fetching** unprocessed content ideas from a designated Google Sheet based on a schedule.
  Using Google Gemini to generate a platform-specific content concept (specifically for a 'Single Image' format).
  Generating two distinct AI image prompt options based on the concept using Gemini.
  Writing an engaging, platform-tailored caption (including hashtags) using Gemini, based on the first prompt option.
  Generating a visual image using the first prompt option via the Replicate API (using the Flux model).
  Publishing* the generated image and caption directly to a connected *Instagram Business account**.
  Updating the status** in the Google Sheet to mark the idea as completed, preventing reprocessing.

  Who is this for?

  Social Media Managers & Agencies:** Automate the execution of your content calendar stored in Google Sheets.
  Marketing Teams:** Streamline content production from planned ideas and ensure consistent posting schedules.
  Content Creators & Solopreneurs:** Save significant time by automating the generation and publishing process based on your pre-defined ideas.
  Anyone** using Google Sheets to plan social media content and wanting to automate the creative generation and posting steps with AI.

  Benefits

  Full Automation:** From fetching planned ideas to Instagram publishing, automate the entire content execution pipeline.
  AI-Powered Generation:** Leverage Google Gemini for creative concepts, prompts, and captions, and Replicate for image generation based on your initial topic.
  Content Calendar Execution:** Directly turn your Google Sheet plan into published posts.
  Time Savings:** Drastically reduce the manual effort involved in creating visuals and text for each planned post.
  Consistency:** Maintain a regular posting schedule by automatically processing your queue of ideas.
  Platform-Specific Content:** AI prompts are designed to tailor concepts, prompts, and captions for the platform specified in your sheet (e.g., Instagram or LinkedIn).

  How it Works

  Scheduled Trigger: The workflow starts automatically based on the schedule you set (e.g., every hour, daily).
  Fetch Idea: Reads the next row from your Google Sheet where the 'Status' column indicates it's pending (e.g., '0'). It only fetches one idea per run.
  Prepare Inputs: Extracts Topic, Audience, Voice, and Platform from the sheet data.
  AI Concept Generation (Gemini): Creates a single content concept suitable for a 'Single Image' post on the target platform.
  AI Prompt Generation (Gemini): Develops two detailed, distinct image prompt options based on the concept.
  AI Caption Generation (Gemini): Writes a caption tailored to the platform, using the first image prompt and other context.
  Image Generation (Replicate): Sends the first prompt to the Replicate API (Flux model) to generate the image.
  Prepare for Instagram: Formats the generated image URL and caption.
  Publish to Instagram: Uses the Facebook Graph API in three steps:
      Creates a media container by uploading the image URL and caption.
      Waits for Instagram to process the container.
      Publishes the processed container to your feed.
  Update Sheet: Changes the 'Status' in the Google Sheet for the processed row (e.g., to '1') to mark it as complete.

  n8n Nodes Used

  Schedule Trigger
  Google Sheets (Read & Update operations)
  Set (Multiple instances for data preparation)
  Langchain Chain - LLM (Multiple instances for Gemini calls)
  Langchain Chat Model - Google Gemini (Multiple instances)
  Langchain Output Parser - Structured (Multiple instances)
  HTTP Request (for Replicate API call)
  Wait
  Facebook Graph API (Multiple instances for Instagram publishing steps)

  Prerequisites

  Active n8n instance (Cloud or Self-Hosted).
  Google Account** with access to Google Sheets.
  Google Sheets API Credentials (OAuth2):** Configured in n8n.
  A Google Sheet** structured with columns like Topic, Audience, Voice, Platform, Status (or similar). Ensure your 'pending' and 'completed' statuses are defined (e.g., '0' and '1').
  Google Cloud Project** with the Vertex AI API enabled.
  Google Gemini API Credentials:** Configured in n8n (usually via Google Vertex AI credentials).
  Replicate Account** and API Token.
  Replicate API Credentials (Header Auth):** Configured in n8n.
  Facebook Developer Account**.
  Instagram Business Account** connected to a Facebook Page.
  Facebook App** with necessary permissions: instagram_basic, instagram_content_publish, pages_read_engagement, pages_show_list.
  Facebook Graph API Credentials (OAuth2):** Configured in n8n with the required permissions.

  Setup

  Import the workflow JSON into your n8n instance.
  Configure Schedule Trigger: Set the desired frequency (e.g., every 30 minutes, every 4 hours) for checking new ideas in the sheet.
  Configure Google Sheets Nodes:
      Select your Google Sheets OAuth2 credentials for both Google Sheets nodes.
      In 1. Get Next Post Idea..., enter your Spreadsheet ID and Sheet Name. Verify the Status filter matches your 'pending' value (e.g., 0).
      In 7. Update Post Status..., enter the same Spreadsheet ID and Sheet Name. Ensure the Matching Columns (e.g., Topic) and the Status value to update match your 'completed' value (e.g., 1).
  Configure Google Gemini Nodes: Select your configured Google Vertex AI / Gemini credentials in all Google Gemini Chat Model nodes.
  Configure Replicate Node (4. Generate Image...): Select your Replicate Header Auth credentials. The workflow uses black-forest-labs/flux-1.1-pro-ultra by default; you can change this if needed.
  Configure Facebook Graph API Nodes (6a, 6c):
      Select your Facebook Graph API OAuth2 credentials.
      Crucially, update the Instagram Account ID in the Node parameter of both Facebook Graph API nodes (6a and 6c). The template uses a placeholder (17841473009917118); replace this with your actual Instagram Business Account ID.
  Adjust Wait Node (6b): The default wait time might be sufficient, but if you encounter errors during publishing (especially with larger images/videos in the future), you might need to increase the wait duration.
  Activate the workflow.
  Populate your Google Sheet: Ensure you have rows with your content ideas and the correct 'pending' status (e.g., '0'). The workflow will pick them up on its next scheduled run.

  This workflow transforms your Google Sheet content plan into a fully automated AI-powered Instagram publishing engine. Start automating your social media presence today!

   

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name: Extract & Analyze Brand Content with Bright Data and Google Gemini
author_name: Ranjan Dailata
author_username: ranjancse
categories: []
created_at: 2025-05-03T13:17:38.073Z
updated_at: 2025-05-03T13:17:38.073Z
url: https://n8n.io/workflows/3846
scraped_at: 2025-12-24 13:02:46
description:
  Who this is for?

  The Brand Content Extract, Summarization & Sentiment Analysis workflow is designed for professionals and teams who need to monitor, understand, and act on public brand perception at scale. 

  It is ideal for:

  Brand Managers - Looking to track how their brand is portrayed online.

  Marketing Analysts - Seeking insights from competitor and industry content.

  PR & Communications Teams - Evaluating media tone and potential reputation risks.

  Data Scientists & AI Developers - Automating content intelligence pipelines.

  Growth Hackers - Performing large-scale web listening for campaign optimization.

  What problem is this workflow solving?

  Manually tracking and interpreting how your brand is mentioned across blogs, news sites, or product reviews is labor-intensive and unscalable. Traditional scraping tools return raw data but lack insights like summarization, sentiment analysis etc.

  This workflow addresses:

  Scalable extraction of brand-related content using Bright Data's infrastructure.

  Textual data extract for easy decision-making or alerting.

  Automated summarization of verbose or multi-paragraph articles using Gemini.

  Sentiment analysis of how a brand is being portrayed.

  What this workflow does

  Receives input: A brand URL for the data extraction and analysis.

  Uses Bright Data's Web Unlocker to extract content from relevant sites.

  Cleans and preprocesses the scraped content for readability.

  Sends the content to Google Gemini for:

  	Enriched results including:

  		Cleaned content

  		Summary

  		Sentiment Analysis

  		Sends the response to a target system via Webhook notification
  		Perists the response to disk

  Setup

  Sign up at Bright Data.
  Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions.
  In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication).

  The Value field should be set with the 
  Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token.
  A Google Gemini API key (or access through Vertex AI or proxy).
  Update the Set URL and Bright Data Zone for setting the brand content URL and the Bright Data Zone name.
  Update the Webhook HTTP Request node with the Webhook endpoint of your choice.

  How to customize this workflow to your needs

  Update Source** : Update the workflow input to read from Google Sheet or Airbase for dynamically tracking multiple brands or topics.
  AI Prompt Customization** : Tailor Gemini prompts for: 
  	Summary length (brief vs. detailed)

  	Detailed Sentiment with the custom structured data format.

  	Brand-specific tone detection (e.g., trust, excitement, dissatisfaction)

  Output Destinations**: Configure the output node to send the responses to various platforms, such as Slack, CRM systems, or databases.

nodes_used:
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name: Convert PDF to PNG with PDF.co API (Multi-Page Support)
author_name: Ludwig
author_username: ludwig
categories: []
created_at: 2025-05-03T16:37:39.983Z
updated_at: 2025-05-03T16:37:39.983Z
url: https://n8n.io/workflows/3847
scraped_at: 2025-12-24 13:02:46
description:
  Page2Image: Convert PDF to PNG Workflow (Multi-Page Support)

  How It Works
  This workflow automatically converts each page of a multi-page PDF into high-quality PNG images. It uses the PDF.co API in the background to process the PDF and generate individual images for every page. The entire process is streamlined, making it ideal for converting documents quickly and efficiently.

  Set Up Steps
  Create a PDF.co Account: Sign up for a PDF.co account and get your API Key. This will be required for authentication.

  Import the Workflow into n8n: Add the workflow to your n8n instance.

  Enter Your API Key: In the workflow, enter your PDF.co API Key to authenticate the process.

  Test the Workflow: Run the workflow, and check that each page of the sample PDFs has been converted to a PNG image.

  Replace the sample PDFs with your own PDFs and get started automatically converting your PDFs to images!

  Setup should take just a few minutes, assuming you already have your PDF.co API Key.

  Getting Started
  This workflow requires PDF.co’s API for converting PDF pages into PNG images. You’ll need an active PDF.co account and your unique API Key. Once you have that, just plug it into the workflow, and you’re ready to convert multi-page PDFs into PNGs instantly!

  Customizing the Workflow
  The workflow currently uses sample PDF files to demonstrate the conversion process. To make it work for your use case, replace the sample PDF logic with your own logic to retrieve PDFs. Whether the PDFs are uploaded, fetched from a URL, or pulled from a storage service, just plug your retrieval method into the workflow, and it will handle the conversion seamlessly.

  API Reference
  The PDF.co Web API is REST-based, ensuring secure and efficient communication. All requests are transmitted over HTTPS to protect your data.

  To use the API, you simply authenticate with your API Key and make requests to initiate the conversion. For this workflow, your API Key allows seamless communication with PDF.co to convert PDF pages into images.

nodes_used:
  ["n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.merge","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.merge","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.compression","n8n-nodes-base.aggregate","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.html","n8n-nodes-base.splitOut","n8n-nodes-base.limit","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.splitOut","n8n-nodes-base.splitInBatches"]

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name: Build a Document QA System with RAG using Milvus, Cohere, and OpenAI for Google Drive
author_name: Aitor | 1Node
author_username: aitoralonso
categories: []
created_at: 2025-05-03T17:38:37.716Z
updated_at: 2025-05-03T17:38:37.716Z
url: https://n8n.io/workflows/3848
scraped_at: 2025-12-24 13:02:44
description:
  Template Description

  This template creates a powerful Retrieval Augmented Generation (RAG) AI agent workflow in n8n. It monitors a specified Google Drive folder for new PDF files, extracts their content, generates vector embeddings using Cohere, and stores these embeddings in a Milvus vector database. Subsequently, it enables a RAG agent that can retrieve relevant information from the Milvus database based on user queries and generate responses using OpenAI, enhanced by the retrieved context.

  Functionality

  The workflow automates the process of ingesting documents into a vector database for use with a RAG system.

   Watch New Files: Triggers when a new file (specifically targeting PDFs) is added to a designated Google Drive folder.

   Download New: Downloads the newly added file from Google Drive.

   Extract from File: Extracts text content from the downloaded PDF file.

   Default Data Loader / Set Chunks: Processes the extracted text, splitting it into manageable chunks for embedding.

   Embeddings Cohere: Generates vector embeddings for each text chunk using the Cohere API.

   Insert into Milvus: Inserts the generated vector embeddings and associated metadata into a Milvus vector database.

   When chat message received: Adapt the trigger tool to fit your needs. 
   RAG Agent: Orchestrates the RAG process.

   Retrieve from Milvus: Queries the Milvus database with the user's chat query to find the most relevant chunks.

  Memory: Manages conversation history for the RAG agent to optimize cost and response speed.

  OpenAI / Cohere embeddings: Uses ChatGPT 4o for text generation.

  Requirements

  To use this template, you will need:

  An n8n instance (cloud or self-hosted).

  Access to a Google Drive account to monitor a folder.

  A Milvus instance or access to a Milvus cloud service like Zilliz. 

  A Cohere API key for generating embeddings.

  An OpenAI API key for the RAG agent's text generation.

  Usage

  Set up the required credentials in n8n for Google Drive, Milvus, Cohere, and OpenAI.

  Configure the "Watch New Files" node to point to the Google Drive folder you want to monitor for PDFs.

  Ensure your Milvus instance is running and the target cluster is set up correctly.

  Activate the workflow.

  Add PDF files to the monitored Google Drive folder. The workflow will automatically process them and insert their embeddings into Milvus.

  Interact with the RAG agent. The agent will use the data in Milvus to provide context-aware answers.

  Benefits

  Automates document ingestion for RAG applications.

  Leverages Milvus for high-performance vector storage and search.

  Uses Cohere for generating high-quality text embeddings.

  Enables building a context-aware AI agent using your own documents.

  Suggested improvements
  Support for More File Types:** Extend the "Watch New Files" node and subsequent extraction steps to handle various document types (e.g., .docx, .txt, .csv, web pages) in addition to PDFs.

  Error Handling and Notifications:** Implement robust error handling for each step of the workflow (e.g., failed downloads, extraction errors, Milvus insertion failures) and add notification mechanisms (e.g., email, Slack) to alert the user.

  Get in touch with us

  Contact us at https://1node.ai 

nodes_used:
  ["@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.embeddingsCohere","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.googleDrive","@n8n/n8n-nodes-langchain.vectorStoreMilvus","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.vectorStoreMilvus","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.embeddingsCohere","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","n8n-nodes-base.extractFromFile","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: Extract Business Leads from Reddit using GPT-4.1-mini Analysis and Google Sheets
author_name: Ari Nakos
author_username: just-aristides
categories: []
created_at: 2025-05-03T18:09:18.082Z
updated_at: 2025-05-03T18:09:18.082Z
url: https://n8n.io/workflows/3849
scraped_at: 2025-12-24 13:02:44
description:
  This n8n workflow automates lead generation by searching Reddit for relevant posts based on keywords, filtering them, using OpenRouter AI to analyze and summarize content, and logging the findings (link, summary, etc.) to Google Sheets.

  Watch the full setup tutorial on how I setup this ETL pipeline using n8n: 
  https://youtu.be/F3-fbU3UmYQ

  Required Authentication:

  To run this workflow, you need to set up credentials in n8n for:

  Reddit: Uses OAuth 2.0. Requires creating an app on Reddit to get a Client ID & Secret. (YT Tutorial for Reddit App Creation: https://youtu.be/zlGXtW4LAK8) 
  OpenRouter: Uses an API Key. Generate this key directly from your OpenRouter account settings. (YT Tutorial : https://youtu.be/Cq5Y3zpEhlc)
  Google Sheets: Uses OAuth 2.0. Requires setup in Google Cloud Console (enable Sheets API, create OAuth Client ID with n8n redirect URI) to get a Client ID & Secret.

  Ensure these credentials are created and selected in the respective n8n nodes (Get Posts, OpenRouter Chat Model nodes, Output The Results).

nodes_used:
  ["n8n-nodes-base.manualTrigger","@n8n/n8n-nodes-langchain.chainSummarization","n8n-nodes-base.merge","n8n-nodes-base.googleSheets","n8n-nodes-base.merge","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.reddit","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenRouter","@n8n/n8n-nodes-langchain.lmChatOpenRouter","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.set","n8n-nodes-base.set"]

workflow_json_compressed:
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name: Automate Etsy Data Mining with Bright Data Scrape & Google Gemini
author_name: Ranjan Dailata
author_username: ranjancse
categories: []
created_at: 2025-05-03T23:15:27.321Z
updated_at: 2025-05-03T23:15:27.321Z
url: https://n8n.io/workflows/3851
scraped_at: 2025-12-24 13:02:44
description:
  Who this is for?
  The Automate Etsy Data Mining with Bright Data Scrape & Google Gemini workflow is designed for eCommerce analysts, product researchers, and AI developers seeking to extract actionable insights from Etsy listings at scale. 

  It is ideal for:

  eCommerce Entrepreneurs** - Researching product demand and competition.

  Market Analysts** - Tracking pricing, reviews, and trends across Etsy categories.

  Product Managers** - Identifying niche opportunities and design inspirations.

  Data Scientists & AI Engineers** - Automating product intelligence pipelines.

  Growth Hackers** - Leveraging Etsy insights to refine product-market fit.

  What problem is this workflow solving?
  Manually browsing Etsy to analyze product listings, pricing, reviews, and seller activity is slow, inconsistent, and unscalable. Scraping Etsy requires unlocking JavaScript-heavy content and structuring noisy data for analysis.

  This workflow solves:

  Automated and scalable scraping of Etsy product listings using Bright Data’s infrastructure.

  A fully paginated data structured Estry production data extraction via the Google Gemini LLM.

  Enables faster decision-making for product research and competitive analysis via the fully automated paginated data extraction.

  What this workflow does

  Receives input: Sets the Esty URL for the data extraction and analysis.

  Uses Bright Data's Web Unlocker to extract content from relevant sites.

  Cleans and preprocesses the scraped content for readability.

  Sends the content to Google Gemini for:

  	Enriched results including:

  		Data persistence over the disk.

  		Sends the response to a target system via Webhook notification.

  Setup

  Sign up at Bright Data.
  Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions.
  In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication).

  The Value field should be set with the 
  Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token.
  A Google Gemini API key (or access through Vertex AI or proxy).
  Update the Set Esty Search Query for setting the brand content URL and the Bright Data Zone name.
  Update the Webhook HTTP Request node with the Webhook endpoint of your choice.

  How to customize this workflow to your needs

  Input Sources** : Replace the static URL with dynamic input from Google Sheets, Webhook, or Airtable to research multiple niches.

  Prompt Customization** : Adjust Gemini prompts to extract specific insights for example:

  	List key features of the product

  	Summarization of the review themes
  Data Output Options** : Update the Webhook notification to save data to:

  	Google Sheets
  	Notion or Airtable
  	SQL/NoSQL
  	Slack/Email

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.informationExtractor","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.stickyNote","n8n-nodes-base.readWriteFile","n8n-nodes-base.function","n8n-nodes-base.splitOut","@n8n/n8n-nodes-langchain.informationExtractor","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: Multi-Level WordPress Blog Generator with PerplexityAI Research & OpenAI Content
author_name: Daniel Ng
author_username: danielng
categories: []
created_at: 2025-05-03T23:29:56.750Z
updated_at: 2025-05-03T23:29:56.750Z
url: https://n8n.io/workflows/3852
scraped_at: 2025-12-24 13:02:46
description:
  WordPress Auto-Blogging Pro v2 \- with DEEPER RESEARCH \- Advanced Content Automation

  (This version is an upgrade from v1: WordPress Auto-Blogging Pro \- with DEEP RESEARCH \- Content Automation Machine v1)

  Take your content automation to unprecedented depths\! This v2 template enhances the acclaimed "WordPress Auto-Blogging Pro \- with DEEP RESEARCH" by introducing Deeper Research, a multi-level research capability designed to generate exceptionally comprehensive, long-form content like detailed articles, reports, and even e-books. 

  If you need more than just standard blog posts and crave truly authoritative, data-driven content generated automatically, this workflow is built for you. It optionally goes beyond simple topic research by breaking down subjects into sub-topics, and then further into sub-sub-topics, conducting real-time online research (powered by the Research Tool Sub-Flow using PerplexityAI) at each stage. This ensures unparalleled depth and coverage, perfect for tackling complex subjects. Combined with enhanced automation features for batch processing, categorization, tagging, linking, and image generation (using OpenAI's latest image model gpt-image-1 accessed via API), this is the ultimate engine for serious content automation on WordPress.

  It operates using a three-flow system: a Trigger Flow acts as a scheduler and dispatcher, a powerful Main Flow executes the complex content generation process, and a dedicated Research Tool Sub-Flow handles interactions with PerplexityAI.

  Visit AI Automation Pro’s website for more powerful n8n templates.

  What problem is this workflow solving? / Use cases

  Standard content automation often struggles with producing truly in-depth, comprehensive content on complex or rapidly evolving topics. This workflow addresses that challenge by:

  Automating Multi-Level Research:** Optionally eliminates the manual effort required to deeply research topics, sub-topics, and sub-sub-topics, ensuring content is thorough and well-supported when needed.  
  Generating Long-Form Content:** Enables the creation of detailed articles, comprehensive reports, or even initial e-book drafts automatically.  
  Scaling Content Production:** Efficiently handles lists of topics from Google Sheets, processing them sequentially for reliable, automated content creation over time.  
  Enhancing SEO Automatically:** Integrates best practices like sitemap-based internal linking, external source linking, automatic categorization, tag generation/management, and Table of Contents generation (with “Deeper Research” articles).  
  Streamlining Complex Tasks:** Combines research (basic or deep), writing, image generation, SEO optimization, publishing, and comprehensive backup (Google Docs, Drive, Sheets) into a single, automated system.

  Ideal use cases include creating cornerstone content, detailed guides, industry reports, niche site authority building, and automating the production of deeply researched articles where maximum depth is required.

  Who is this for?

  This advanced workflow is designed for demanding users who require robust, comprehensive content automation:

  Content Marketers & Strategists:** Needing to produce authoritative, long-form content at scale, with options for varying research depth.  
  SEO Professionals:** Aiming to build topical authority with well-researched, structured, and highly optimized content featuring strong internal/external linking.  
  Niche Site Builders & Affiliates:** Seeking to dominate specific topics with comprehensive guides and articles.  
  Agencies:** Managing large-scale content production for clients requiring in-depth material.  
  Researchers & Analysts:** Looking to automate the drafting of reports or summaries based on online data.  
  Power Users of v1:** Ready to upgrade to the next level of research depth, flexibility, and automation features.

  Unique Features

  This v2 workflow introduces significant upgrades and retains the best features of v1:



  Conditional Deeper Research:** Choose per-topic whether to activate multi-level research (topic \-\&gt; sub-topic \-\&gt; sub-sub-topic) for maximum depth or use standard chapter-level research.  
  Modular Three-Flow Architecture:** Robust system with a Trigger Flow for scheduling, a Main Flow for generation, and a dedicated Research Tool Sub-Flow.  
  Table of Contents Generation:** Automatically creates a ToC for improved readability and SEO on long articles.  
  Latest AI Image Models:** Optimized for high-quality images using OpenAI's latest model (gpt-image-1 via API), with easy node setup to switch to alternatives.  
  Automatic Categorization & Tag Management:** AI assigns posts to the most relevant existing WordPress category and creates/assigns relevant tags.  
  Dual Trigger Options:** Use Google Sheets for batch processing lists of topics or n8n's Native Form for single, ad-hoc topic submission.  
  Batch Topic Processing:** Handles multiple topic inputs efficiently via the Trigger Flow loop, processing them sequentially with status tracking.  
  Advanced External & Internal Linking:** Automatically inserts relevant internal links (from sitemap) and external source links (from research).  
  Comprehensive Backup System:** Saves final content in multiple formats and locations: Markdown to Google Docs, Images to Google Drive, Final URLs and details to Google Sheets.  
  Centralized Control Interface:** Google Sheets acts as the primary interface for bulk input, parameter setting (like Have Deeper Research), and status monitoring.  
  Direct WordPress Integration:** Seamlessly publishes content, metadata, tags, categories, and images.  
  Rate-Limit Aware:** Includes configurable Wait nodes to prevent API errors during intensive processing.  
  Robust Error Catching:** Uses 'If' nodes to validate AI outputs and ensure smooth data flow.

  How this workflow works



  This template utilizes three interconnected n8n flows: a Trigger Flow, a Main Flow, and a Research Tool Sub-Flow, orchestrated primarily via Google Sheets.

  1\. Trigger Flow (Scheduler & Dispatcher):

  Trigger:** Activates via a Schedule (e.g., every 10 minutes) to check for new tasks OR via a direct n8n Form submission.  
  Select Topics:** Reads rows from a specific Google Sheet (e.g., Deeper Research - Create Topics \-\&gt; Create Topic sheet) where the Status column is 'To Do'.  
  Loop & Dispatch:* Processes each 'To Do' topic one by one. For each topic, it executes the *Main Flow**, passing the topic details and parameters.  
  Wait:** Pauses for a configurable duration (default: 1 hour) after each topic's processing is initiated by the Sub-Workflow, helping manage API limits and pacing.

  2\. Main Flow (Content Generation Engine):

  Trigger:** Starts when called by the Trigger Flow.  
  Setup:** Receives topic parameters (Topic, Audience, Style, Word Count, Have Deeper Research flag, etc.) from the Trigger Flow. Updates the topic's Status to 'In Progress' in the Google Sheet.  
  Internal Link Gathering:** Fetches your website's sitemap XML, parses it, and extracts a set number (e.g., up to 50\) of internal URLs for later use.  
  Initial Research & Planning:* Executes the *Research Tool Sub-Flow** to perform initial online research on the main topic. An AI Agent ("Blog Planner") then uses this research to outline the entire article: Title, Subtitle, Introduction, Slug, Meta Description, Tags, Chapter outlines, and prompts for chapter/featured images.  
  Conditional Content Creation (Based on Have Deeper Research flag):**  
    If YES: Executes the Deeper Research path: Loops through planned chapters \-\&gt; Executes Research Tool Sub-Flow for Chapter Research \-\&gt; AI splits chapter into subchapters \-\&gt; Loops through subchapters \-\&gt; Executes Research Tool Sub-Flow for Subchapter Research \-\&gt; AI ("Subchapter Copywriter") writes detailed content for each subchapter \-\&gt; Aggregates subchapters back into the main chapter content.  
    If NO: Executes the Standard Research path: Loops through planned chapters \-\&gt; Executes Research Tool Sub-Flow for Main Chapter Research \-\&gt; AI ("Chapter Copywriter") writes content for each chapter.  
  Image Generation & Upload:** Generates images (using OpenAI's gpt-image-1 or equivalent via HTTP Request) for each main chapter and a featured image. Resizes images and uploads them to both your WordPress Media Library and a specified Google Drive folder.  
  Tag Management:** Checks if the AI-generated tags exist in WordPress. Creates new tags if necessary and retrieves the IDs for all relevant tags.  
  Article Assembly:** Combines Introduction, finalized chapter content (from either research path), Conclusion, CTA, etc., into a complete article structured in Markdown format (using a Code node).  
  HTML Conversion:** Converts the final Markdown content to HTML suitable for WordPress.  
  Backup \- Google Docs:* Saves the final Markdown version of the article as a new document in a specified Google Drive folder. *Note:** saving to Google Docs only works on "My Drive" and will NOT work on a Shared Drive due to Google Drive API restrictions.  
  Publish to WordPress:** Creates a new post on your WordPress site with the Title, HTML content, Slug, assigned Category (auto-detected or default), Tag IDs, and sets the generated Featured Image. Also sets the Meta Description and Post Excerpt.  
  Backup \- Google Sheets:** Saves the final details (Published URL, Markdown, HTML, Tags, Meta info, Slug, Excerpt, etc.) to a dedicated sheet (e.g., Final Blogs) within your main Google Sheet workbook.  
  Final Status Update:** Updates the topic's Status to 'Done' in the original Create Topic sheet.

  3\. Research Tool Sub-Flow (PerplexityAI Interaction):

  Trigger:** Starts when called by the Main Flow.  
  Setup:** Receives parameters like the research query, model (sonar or sonar-deep-research), system message, and max tokens.  
  API Call:** Makes a POST request to the PerplexityAI API with the provided parameters and authentication.  
  Response Handling:** Extracts the content from the API response.  
  Output:** Returns the research content back to the calling Main Flow.

  Requirements

  n8n Instance**.  
  OpenAI API Key:** Or equivalent credentials for a powerful text generation model (default to GPT-4o) and an image generation model (like gpt-image-1, compatible with OpenAI node or direct HTTP Request).  
  Perplexity API Key:** Or equivalent for an online research tool API (used via HTTP Request node in the Research Tool Sub-Flow).  
  WordPress Website:** REST API enabled, with an Application Password granting permissions to create/edit posts, upload media, manage categories, and manage tags. Needs an accessible post-sitemap.xml.  
  Google Account:** For Google Sheets (trigger, control parameters, status tracking, backup), Google Drive (image/text backup), and Google Docs (Markdown backup).

  Setup Step-by-Step

  Import Workflows: Download the three .json files (Trigger Flow, Main Flow, Research Tool Sub-Flow) and import them into your n8n instance. Name them clearly (e.g., "WP Deeper Research \- Trigger Flow", "WP Deeper Research \- Main Flow", "WP Deeper Research \- Research Tool Sub-Flow").  
  Configure Google Sheet:  
     Clone or create a Google Sheet workbook (e.g., Deeper Research - Create Topics).  
     Ensure two sheets within it: Create Topic and Final Blogs.  
     Set up required columns in Create Topic (e.g., Topic, Status, Have Deeper Research, Audience, Style, etc. \- match these exactly to the names used in the workflow nodes).  
     Set up columns in Final Blogs for backup (e.g., Topic Name, Final URL, Markdown, HTML, etc. \- match workflow nodes).  
     Update all Google Sheets nodes in the Trigger Flow and Main Flow to point to the correct Workbook Name, Sheet Name, and ensure column names match.  
  Link Flows:  
     In the Trigger Flow, locate the "Execute Workflow" node (e.g., "Execute Workflow \- Create A Topic"). Edit this node and select the imported Main Flow from the dropdown.  
     In the Main Flow, locate all "Execute Workflow" nodes that perform research. Edit these nodes and select the imported Research Tool Sub-Flow from the dropdown.  
  Configure Credentials: In n8n, add credentials for: OpenAI, PerplexityAI, WordPress (using Application Password), Google (ensure Sheets, Drive, Docs permissions).  
  Connect Workflow Nodes: Open all three workflows. Go through each node requiring authentication (OpenAI, Perplexity HTTP Request, WordPress, Google Sheets, Google Drive, Google Docs) and assign the correct credential. Pay close attention to HTTP Request nodes, ensuring API keys are correctly placed.  
  Configure Triggers: In the Trigger Flow, configure the Schedule Trigger interval (default 10 mins) or the n8n Form Trigger fields as needed. Ensure only one trigger is active unless you intend both.  
  Customize Inputs & Parameters: Review key nodes:  
     Trigger Flow: Adjust Wait time if needed.  
     Main Flow: Verify Website URL (for sitemap), AI prompts (Planner, Copywriters), default Category ID, internal link limit, image sizes.  
     Research Tool Sub-Flow: Review default model (sonar), system message, max tokens if needed.  
  Test Connections: Perform test runs on individual nodes within each workflow (e.g., read Google Sheet, call Perplexity via Research Tool Sub-Flow, connect to WordPress) to verify credentials and configurations.  
  Initial Test Run: Add one test topic to your Create Topic sheet (set Status to 'To Do', specify 'Yes' or 'No' for Have Deeper Research). Manually trigger the Trigger Flow once (or wait for the schedule). Verify the process completes and check outputs (WordPress post, Google Drive folder, Google Doc, Final Blogs sheet, Create Topic status update).  
  Activate Workflows: Toggle the "Active" switch ON for the Trigger Flow in your n8n instance. The Sub-Workflows should remain inactive as they are triggered by other flows.  
  Start Automating: Add content topics to the Create Topic sheet (set Status='To Do'), and the scheduled Trigger Flow will pick them up.

  Tips for Pros

  Control Research Depth:** Use the Have Deeper Research column ('Yes'/'No') in your Google Sheet to control the level of research per topic, optimizing for cost and complexity where needed.  
  AI Model Selection:** Experiment with different models in the AI/Image nodes. An alternative to OpenAI’s image model is Flux.1, which can yield superior images. For research, test Perplexity's sonar-deep-research model (by passing it as a parameter to the Research Tool Sub-Flow if needed) if the standard sonar isn't deep enough.  
  Wait Times:** Adjust the Trigger Flow's "Wait" node duration based on your API plan limits, the typical runtime of the Main Flow, and desired posting frequency.  
  Prompt Engineering:** Refine the prompts within the Main Flow's AI nodes (Blog Planner, Chapter/Subchapter Copywriters, Image Prompts) and the Research Tool Sub-Flow's system message to fine-tune the output style, tone, structure, and image relevance.  
  Sitemap Accessibility:** Ensure your post-sitemap.xml is publicly accessible and correctly formatted for the parsing node to work reliably for internal linking.

  Important Considerations

  API Costs:** Deep research (especially the sub-sub-topic level) and generating multiple high-quality images per post can consume significant API credits. Monitor your costs closely. Use the Have Deeper Research flag wisely.  
  API Rate Limits:** Even with Wait nodes, high volume can hit API rate limits. Be aware of your plan limits (OpenAI, Perplexity) and adjust workflow timing (schedule interval, wait time) accordingly.  
  Sub-Workflow Runtime:** The Main Flow can take a considerable amount of time to run, especially with Deeper Research enabled. Factor this into scheduling and expectations.  
  Tool Dependencies:** The workflow relies heavily on the specific functionalities and APIs of OpenAI, Perplexity, Google Workspace, and WordPress. Changes in these platforms might require workflow adjustments.  
  Initial Setup Complexity:** Due to the advanced features, three-flow structure, and multiple integrations, the initial setup requires careful configuration of credentials, node settings (especially Google Sheets names/columns), and workflow linking. Test thoroughly.  
  For inquiries about this template, please contact AI Automation Pro.

nodes_used:
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  [encoded data, 9880 characters]


