# templates

name: Automated n8n Workflow Backup System with Google Drive and Archiving
author_name: Theo Marcadet
author_username: theomarcadet
categories: []
created_at: 2025-04-15T09:32:46.476Z
updated_at: 2025-04-15T09:32:46.476Z
url: https://n8n.io/workflows/3559
scraped_at: 2025-12-24 13:02:46
description:
  Click here to access this Workflow for free.

  Automatically save and archive your workflows


  This workflow provides a fully automated system for backing up your n8n workflows to Google Drive. It runs on a schedule (which you can customize) and saves each workflow as a .json file, named after the workflow itself. All active workflows are backed up by default and stored in a specified Google Drive folder of your choice.

  A special feature of this workflow is its ability to archive and clean up old workflows. If you tag a workflow with ARCHIVE (case-sensitive), the workflow will be backed up into a designated Archive subfolder inside your main Google Drive folder — and then automatically deleted from your n8n instance. This keeps your workspace clean and reduces clutter while ensuring everything is safely stored.

  Requirements

  A Google Drive account connected to n8n (via OAuth2 or preferred method)
  An n8n instance with access to your workflows
  A folder in Google Drive for storing backups
    Inside this folder, create a subfolder named Archive
  Folder IDs for both the main and archive folders* (Find it in folder's URL e.g. https://drive.google.com/drive/u/0/folders/1F7Exr2xkZQFvmHmjdFD43K0jfd*)

  Setup

  In your Google Drive, create a main folder for storing backups.
  Inside it, create a subfolder called Archive.
  Retrieve the folder IDs for both folders
  In n8n, connect your Google Drive credentials.
  Open the workflow and insert the folder IDs in the appropriate nodes.
  Optionally, edit the schedule trigger to set your preferred backup frequency.

  Usage

  Once configured, the workflow will:
  Run automatically on the defined schedule
  Back up all existing workflows into your Drive
  For workflows tagged with ARCHIVE:
    Save them into the Archive subfolder
    Delete them from your n8n instance

  The workflow ensures your automation logic is safely versioned, with optional cleanup for outdated or unused flows.

  Customization

  You can modify the schedule to run daily, weekly, or at any custom interval using the built-in Cron node.

  You can also change the folder structure or naming conventions in the Google Drive nodes to match your personal or team preferences.

  Workflows are archived only if they are explicitly tagged with ARCHIVE — this tag is case-sensitive.

  If you need any help, or have any question, feel free to come discuss about it on Telegram 

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleDrive","n8n-nodes-base.n8n","n8n-nodes-base.stickyNote","n8n-nodes-base.convertToFile","n8n-nodes-base.convertToFile","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.n8n","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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


name: Extract Trends, Auto-Generate Social Content with AI, Reddit, Google & Post
author_name: Immanuel
author_username: spectrabit
categories: []
created_at: 2025-04-15T10:37:51.062Z
updated_at: 2025-04-15T10:37:51.062Z
url: https://n8n.io/workflows/3560
scraped_at: 2025-12-24 13:02:41
description:
  Extract Trends and Auto-Generate Social Media Content with OpenAI, Reddit, and Google Trends: Approve and Post to Instagram, TikTok, and More

  Description
  What Problem Does This Solve? 🛠️
  This workflow automates trend extraction and social media content creation for businesses and marketers. It eliminates manual trend research and content generation by fetching trends, scoring them with AI, and posting tailored content to multiple platforms. Target audience: Social media managers, digital marketers, and businesses aiming to streamline content strategies.

  What Does It Do? 🌟
  Fetches trending topics from Reddit, X and Google Trends
  Scores trends for relevance using OpenAI.
  Generates content for Twitter/X, LinkedIn, Instagram and Facebook
  Posts to supported platforms 
  Stores results in Google Sheets for tracking

  Key Features 📋
  Real-time trend fetching from Reddit and Google Trends.
  AI-driven trend scoring and content generation (OpenAI).
  Automated posting to Twitter/X, LinkedIn, Instagram, and Facebook.
  Persistent storage in Google Sheets.
  Setup Instructions

  Prerequisites ⚙️
  n8n Instance**: Self-hosted or cloud n8n instance.
  API Credentials**:
    Reddit API: Client ID and secret from Reddit.
    SerpApi (Google Trends): API key from SerpApi, stored in n8n credentials 
    OpenAI API: API key with GPT model access.
    Twitter/X API: OAuth 1.0a credentials with write permissions.
    LinkedIn API: OAuth 2.0 credentials with w_organization_social scope.
    Instagram/Facebook API: Meta Developer app with posting permissions.
    Google Sheets API: Credentials from Google Cloud Console.

  Installation Steps 📦
  Import the Workflow:
     Copy the workflow JSON from the "Template Code" section below.
     Import it into n8n via "Import from File" or "Import from URL".
  Configure Credentials:
     Add API credentials in n8n’s Credentials section for Reddit, SerpApi, OpenAI, Twitter/X, LinkedIn, Instagram/Facebook, and Google Sheets.
     Assign credentials to respective nodes. For example:
       In the Fetch Google Trends node (HTTP Request), use n8n credentials for SerpApi instead of hardcoding the API key.
       Example: Set the API key in n8n credentials as SerpApiKey and reference it in the node’s query parameter: api_key={{ $credentials.SerpApiKey }}.
  Set Up Google Sheets with the following columns (exact column names are case-sensitive)
   -Timestamp | Trend | Score | BrandVoice | AudienceMood |

  Customize Nodes:
  OpenAI Nodes** (Trend Relevance Scoring, Generate Social Media Content): Update the model (e.g., gpt-4o) and prompt as needed.
  HTTP Request Nodes** (Post to Twitter/X, Post to LinkedIn, etc.): Verify URLs, authentication, and payloads.
  Brand Voice/Audience Mood**: Adjust Prepare Trend Scoring Input for your desired brand_voice (e.g., "casual") and audience_mood (e.g., "curious").
  Test the Workflow:
  Fetch Reddit Trends to Store Selected Trends- to score and store trends.
  Retrieve Latest Trends to end) to generate and post content
  Check Google Sheets for posting statuses
  How It Works

  High-Level Steps 🔍
  Fetch Trends**: Pulls trends from Reddit,X and Google Trends.
  Score Trends**: Uses OpenAI to score trends for relevance.
  Generate Content**: Creates platform-specific social media content.
  Post Content**: Posts to LinkedIn,  Facebook or X

  Detailed descriptions are available in the sticky notes within the workflow screenshot above.

  Node Names and Actions

  Trend Extraction and Scoring
  Daily Trigger Idea: Triggers the workflow daily.
  Set Default Inputs: Sets default brand_voice and inputs.
  Fetch Reddit Trends: Fetches Reddit posts.
  Extract Reddit Trends: Extracts trends from Reddit.
  Fetch Google Trends: Fetches Google Trends via SerpApi.
  Extract Google Trends2: Processes Google Trends data.
  Fetch Twitter Mentions: Fetches Twitter mentions.
  Translate Tweets to English: Translates tweets.
  Fix Tweet Translation Output: Fixes translation format.
  Detect Audience Mood: Detects audience mood.
  Fix Audience Mood Output: Fixes mood output format.
  Analyze News Sentiment: Analyzes news sentiment.
  Combine Data (Merge): Merges all data sources.
  Merge Items into Single Item: Combines data into one item.
  Combine Trends and UGC: Combines trends with UGC.
  Prepare Trend Scoring Input: Prepares data for scoring.
  Trend Relevance Scoring: Scores trends with OpenAI.
  Parse Trend Scores: Parses scoring output.
  Store Selected Trends: Stores trends in Google Sheets.

  Content Generation and Posting
  Retrieve Latest Trends: Retrieves trends from Google Sheets.
  Parse Retrieved Trends: Parses retrieved trends.
  Select Top Trends: Selects the top trend.
  Generate Social Media Content: Generates platform-specific content.
  Parse Social Media Content: Parses generated content.
  Generate Images: Generates images for posts (if applicable).
  -Handle Approvals/Rejection before Posting
  Post to Instagram: Posts to Instagram.
  Post to Facebook: Posts to Facebook.
  Post to LinkedIn: Posts to LinkedIn.

  Customization Tips

  Add Trend Sources** 📡: Include more sources (e.g., Instagram trends) by adding nodes to Combine Data (Merge).
  Change Content Tone** ✍️: Update the Generate Social Media Content prompt for a different tone (e.g., "humorous").
  Adjust Schedule** ⏰: Modify Daily Trigger Idea to run hourly or weekly.
  Automate TikTok/YouTube** 🎥: Add video generation (e.g., FFMPEG) to post TikTok and YouTube Shorts

nodes_used:
  ["n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.merge","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.reddit","n8n-nodes-base.code","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.googleSheets","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.gmail","n8n-nodes-base.googleSheets","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.wait","n8n-nodes-base.wait","n8n-nodes-base.reddit","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.googleSheetsTrigger","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.webhook","n8n-nodes-base.code","n8n-nodes-base.linkedIn","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 5088 characters]


name: Automate Hyper-Personalized Outreach at Scale With Bright Data and LLMs
author_name: Yaron Been
author_username: yaron-nofluff
categories: []
created_at: 2025-04-15T10:47:56.856Z
updated_at: 2025-04-15T10:47:56.856Z
url: https://n8n.io/workflows/3561
scraped_at: 2025-12-24 13:02:45
description:
  LinkedIn Enrichment & Ice Breaker Generator

  For SDRs, growth marketers, and founders looking to scale personalized outreach.  
  This workflow enriches LinkedIn profile data using Bright Data and generates AI-powered ice breakers using Claude (Anthropic).  
  It automates research and messaging to help you connect smarter and faster — without manual effort.

  🧩 How It Works

  This workflow combines Google Sheets, Brigt Data, and Claude (Anthropic) to fully automate your outreach research:

  Trigger  
     Manually trigger the workflow or run it on a schedule (via Manual Trigger or Schedule Trigger).

  Read Input Sheet  
     Fetches rows from a Google Sheet. Each row must contain at least a Linkedin_URL_Person and row_number.

  Prepare Input  
     Formats each row for Bright Data’s API using Set and SplitInBatches nodes.

  Enrich Profile (Bright Data API)  
     Sends LinkedIn URLs to Bright Data’s Dataset API via HTTP Request.
     Waits for snapshot to be ready using polling logic with Wait, If, and Snapshot Progress nodes.
     Once ready, retrieves the enriched profile data including:
       Name
       City
       Current company
       About section
       Recent posts

  Update Sheet with Profile Data  
     Writes the retrieved enrichment data into the corresponding row in Google Sheets (via row_number).

  Generate Ice Breaker (Claude AI)  
     Sends enriched profile content to Claude (Anthropic) using a custom prompt.
     Focuses on recent posts for crafting relevant, respectful, 1–4-line ice breakers.

  Update Sheet with Ice Breaker  
     Writes the generated ice breaker to the Ice Breaker 1 column in the original row.

  ✅ Requirements

  To use this workflow, you must have the following:

  Google Sheets
  A Google account
  A Google Sheet with at least one sheet/tab containing:
    Column: Linkedin_URL_Person
    Column: row_number (used for mapping input and output rows)

  Bright Data
  A Bright Data account with access to the Dataset API
  An active dataset that accepts LinkedIn URLs
  API key with Dataset API access

  Anthropic Claude
  An Anthropic API key (for Claude 3.5 Haiku or other Claude models)

  n8n Environment
  Access to HTTP Request, Set, Wait, SplitInBatches, If, and Google Sheets nodes
  Access to Claude integration (via LangChain nodes: @n8n/n8n-nodes-langchain)
  Credential manager properly configured with:
    Google Sheets OAuth2 credentials
    Bright Data API key
    Anthropic API key

  ⚙️ Setup Instructions

  Step 1: Copy the Google Sheets Template

  &gt; 📄 Click here to make a copy

  Fill the Linkedin_URL_Person column with LinkedIn profile URLs you want to enrich
  Do not modify headers or add filters to the sheet
  Leave other columns (name, city, about, posts, ice breaker) blank — the workflow fills them

  Step 2: Connect Your Accounts in n8n

  Google Sheets: Create a credential under Google Sheets OAuth2 API
  Bright Data: Add your API key as a credential under HTTP Request (Authorization header)
  Anthropic: Create a credential for Anthropic API with your Claude key

  Step 3: Import and Configure the Workflow

  Import the workflow into your n8n instance.
  In each Google Sheets node:
     Select the copied Google Sheet
     Select the correct tab (usually input or Sheet1)
  In the HTTP Request node to Bright Data:
     Paste your Bright Data dataset ID
  In the Claude prompt node:
     Optionally adjust the tone and length of the ice breaker prompt

  Step 4: Run the Workflow

  Test it using the Manual Trigger node
  For daily automation, enable the Schedule Trigger and configure interval settings
  Watch your Google Sheet populate with enriched data and tailored ice breakers

  🧠 Tips & Best Practices

  Bright Data Delay**: Snapshots may take time. The workflow polls the status until complete.
  Retry Protection**: If and Wait nodes avoid infinite loops by checking snapshot status.
  Mapping via row_number**: Critical to ensure data is updated in the right row.
  Prompt Engineering**: You can fine-tune Claude's behavior by editing the text prompt.

  🧾 Output Example

  Once complete, each row in your Google Sheet will contain:

  | Linkedin_URL_Person | Name | City | Company | Recent Post | Ice Breaker |
  |---------------------|------|------|---------|-------------|--------------|
  | linkedin.com/... | Jane Doe | NYC | ACME Corp | “Why AI should replace meetings” | "Loved your post about AI and meetings — finally someone said it!" |

  💬 Support & Feedback

  Questions? Want to tweak the prompt or expand the enrichment?

  📧 Email: Yaron@nofluff.online  
  📺 YouTube: @YaronBeen  
  🔗 LinkedIn: linkedin.com/in/yaronbeen

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatAnthropic","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.splitInBatches"]

workflow_json_compressed:
  [encoded data, 6172 characters]


name: Build an AI Powered Phone Agent 📞🤖 with Retell, Google Calendar and RAG
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-04-15T12:55:32.914Z
updated_at: 2025-04-15T12:55:32.914Z
url: https://n8n.io/workflows/3563
scraped_at: 2025-12-24 13:02:43
description:
  This Workflow simulates an AI-powered phone agent with RetellAI with two main functions:

  📅 Appointment Booking – It can schedule appointments directly into Google Calendar.  
  🧠 RAG-based Information Retrieval – It provides answers using a Retrieval-Augmented Generation (RAG) system. For example, it can respond to questions such as store opening hours, return policies, or product details.

  The guide also explains how to purchase a dedicated phone number (with a +1 prefix) and link it to the AI agent. This setup is cost-effective, as it uses a FREE $10 credit to operate without additional charges in the beginning.

  ✨ Advantages

  🕐 24/7 Availability** – The AI agent can answer calls and assist customers at any time.  
  🤖 Automation** – It reduces the workload on human staff by handling repetitive tasks like appointment scheduling and FAQ responses.  
  🔌 Easy Integration** – Built with n8n, it’s flexible and customizable for various platforms and tools.  
  💸 Low-cost Setup** – Using the free credit, businesses can get started without an upfront investment.

  📦 Use Cases

  🛍 E-commerce** – Answer common product questions or order inquiries.  
  🏬 Retail Stores** – Provide store hours, address info, and return policies.  
  🍽 Restaurants** – Take reservations or share menu information.  
  💼 Service Providers** – Book appointments or consultations.  
  📞 Any Local Business** – Offer phone support without needing a live operator.

  How It Works  
  This Workflow simulates an AI-powered phone agent with two primary functions:  

  Appointment Booking  
     The workflow captures call events (e.g., call_ended or call_analyzed) and extracts key details (transcript, caller info, duration, etc.).  
     Using OpenAI, it summarizes the conversation and parses structured data (e.g., names, contact info, dates).  
     For scheduling, it converts user-provided dates into Google Calendar-compatible formats and creates events automatically.  

  RAG-Based Information Retrieval  
     When a query is received (e.g., store hours, product details), the workflow retrieves relevant information from a Qdrant vector store.  
     An AI agent processes the query using the retrieved data and responds via a webhook, ensuring accurate, context-aware answers.   

  Set Up Steps  
  Prepare Qdrant Vector Store  
     Create/refresh a Qdrant collection (via HTTP requests).  
     Upload and vectorize documents (e.g., from Google Drive) using OpenAI embeddings.  

  Configure RetellAI Agent  
     Sign up for RetellAI, create an agent, and set the webhook URLs (n8n_call for call events, n8n_rag_function for RAG queries).  
     Purchase a Twilio phone number and link it to the agent.  

  n8n Workflow Setup  
     Connect OpenAI, Qdrant, Google Calendar, and Telegram nodes with credentials.  
     Customize prompts for summarization, date parsing, and RAG responses.  
     Test the workflow to ensure data flows from call events → processing → actions (e.g., calendar bookings, Telegram alerts).  

  Deploy  
     Trigger the workflow via RetellAI webhooks during calls.  
     Monitor outputs (e.g., call summaries in Telegram, calendar events).  

  Note: Replace placeholders (e.g., QDRANTURL, COLLECTION, CHAT_ID) with actual values. 

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

nodes_used:
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workflow_json_compressed:
  [encoded data, 10184 characters]


name: Create Daily Israeli Economic Newsletter using RSS and GPT-4o
author_name: Elay Guez
author_username: elay96
categories: []
created_at: 2025-04-15T17:51:10.645Z
updated_at: 2025-04-15T17:51:10.645Z
url: https://n8n.io/workflows/3564
scraped_at: 2025-12-24 13:02:44
description:
  Daily Economic News Brief for Israel (Hebrew, RTL, GPT-4o)

  Overview
  Stay ahead of the curve with this AI-powered workflow that delivers a daily economic summary tailored for professionals tracking the Israeli economy.
  At 8:00 PM Israel Time, this workflow:

  Retrieves the latest articles from Calcalist and Mako via RSS
  Filters duplicates and irrelevant stories
  Uses OpenAI’s GPT-4o to identify the 5 most important stories of the day
  Summarizes each article in concise, readable Hebrew
  Generates a fully styled, responsive HTML email (with proper RTL layout)
  Sends it to your inbox using your preferred SMTP email provider

  Perfect for economists, analysts, investors, or policymakers who want an actionable and personalized news digest -- no distractions, no fluff.

  Setup Instructions
  Estimated setup time:
  10 minutes

  Required credentials:

  OpenAI API Key
  SMTP credentials (for email delivery)

  Steps:
  Import this template into your n8n instance.
  Add your OpenAI API Key under credentials.
  Configure the SMTP Email node with:
  	Host (e.g. smtp.gmail.com)
  	Port (465 or 587)
  	Username (your email)
  	Password (app-specific password or login)
  Set your target email address in the last node.
  (Optional) Customize the GPT prompt to adjust tone or audience (e.g. general public, policy makers).
  Activate the workflow and receive daily updates straight to your inbox.

  Customization Tips
  Change the RSS sources to pull from other Hebrew or international news websites
  Modify the summarization prompt to fit different sectors (e.g. tech, health, politics)
  Add integrations like Notion, Airtable, or Telegram for logging or distribution
  Apply your branding to the HTML output (logos, footer, colors)

  Why Use This?
  This is more than a news digest. It’s an intelligent economic assistant that filters noise, highlights what matters, and keeps you informed-automatically.

  You can set it up in 10 minutes and benefit every single day.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.emailSend","n8n-nodes-base.function","n8n-nodes-base.merge","n8n-nodes-base.html","n8n-nodes-base.aggregate","n8n-nodes-base.set","n8n-nodes-base.html","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.code","n8n-nodes-base.sort","n8n-nodes-base.filter","n8n-nodes-base.merge","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.rssFeedRead","n8n-nodes-base.rssFeedRead","n8n-nodes-base.scheduleTrigger"]

workflow_json_compressed:
  [encoded data, 6744 characters]


name: Automate PDF Image Extraction & Analysis with GPT-4o and Google Drive
author_name: merfy
author_username: merfylee
categories: []
created_at: 2025-04-16T02:02:34.526Z
updated_at: 2025-04-16T02:02:34.526Z
url: https://n8n.io/workflows/3567
scraped_at: 2025-12-24 13:02:43
description:
  Use Case

  Manually extracting images from PDF files for analysis is often slow and inefficient. Many users resort to taking screenshots of each page, uploading them to an AI tool like OpenAI for image analysis, and then manually copying the insights into a document. This manual process is time-consuming and prone to errors.

  This workflow streamlines the entire process by automatically extracting images from a PDF, analyzing them using the GPT-4o model, and saving the results in seconds—eliminating the need for manual effort.

  What This Workflow Does

  Extracts all images from the uploaded PDF file automatically
  The workflow scans each page of the PDF and identifies embedded images without manual intervention.

  Uses the GPT-4o model to analyze each extracted image

  Each image is processed through GPT-4o to generate descriptive insights, summaries, or context-specific analysis depending on the use case.

  Saves the analysis results to a .txt file, including image URLs
  The final output is a plain text file containing both the image URLs (e.g., hosted on cloud storage) and the corresponding GPT-4o analysis, ready for further use or sharing.

  Setup

  1.Set up your credentials when you first open the workflow. You’ll need accounts for OpenAI, Convert API, and Google Drive.

  2.Convert API does not rate-limit your API, sometimes you may receive 503 service unavailable error.

  Nevertheless, it doesn’t mean that you cannot convert your file. It simply means that you should retry the conversion in a few seconds.

  3.Upload a PDF with images to Google Drive.

  4.Remove unnecessary parts and retrieve image-related information.

  5.Integrate image and image analysis information together.

  6.Analyze each image using the OPENAI GPT-4o model.

  7.Retrieve all image analysis content and image URL

  8.Integrate multiple image URLs and analysis content

  9.Output content to a .txt file.

  Template was created in n8n v1.83.2

  How to Customize

  Replace the manual trigger with a Google Drive trigger or other automation triggers

  Change the image analysis model (e.g., switch or fine-tune GPT-4o)

  Send the results to other platforms (e.g., Slack, Telegram, LINE, etc.) instead of saving to a .txt file

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

workflow_json_compressed:
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name: Deploy Workflows from Google Drive to n8n Instance
author_name: Jason Guest
author_username: jpguest
categories: []
created_at: 2025-04-16T09:04:20.466Z
updated_at: 2025-04-16T09:04:20.466Z
url: https://n8n.io/workflows/3568
scraped_at: 2025-12-24 13:02:47
description:
  Automatically deploy n8n workflows by simply dropping JSON files into a Google Drive folder—this template watches for new exports, cleans and imports them into your n8n instance, applies a tag, and then archives the processed files.

  Who is this template for?  
  This workflow template is designed for n8n power users, and automation specialists who need a simple, reliable way to bulk‑deploy or version‑control n8n workflows via Google Drive. It’s perfect if you:  
  Manage multiple n8n instances (staging, production, etc.)  
  Want an easy “drop‑in” approach to publish new or updated workflows  
  Prefer storing/exporting JSON in Drive rather than editing in the UI  

  Use case
  Manually importing .json exports into n8n is slow and error‑prone. With this template you can:  
  Keep your workflows in a shared Drive folder (version control friendly)  
  Automatically sanitize each file so only supported settings go through  
  Tag deployed workflows consistently for easy filtering  
  Move processed files to a “Deployed” folder for clear change tracking  

  How it works
  Watch “ToDeploy” folder in Google Drive for new .json files  
  Download & parse each file into a JSON object  
  Clean payload: strip out everything except the allowed executionOrder (and timezone if you choose)  
  POST the cleaned workflow to your n8n instance via /api/v1/workflows  
  PUT a predefined tag onto the newly created workflow  
  Move file to your “Deployed” folder when import succeeds, or capture the workflow name & error if it fails  

  Setup instructions

  1. In Google Drive create a ToDeploy folder and a Deployed folder
  Update "Google Drive Trigger -ToDeploy folder" to your ToDeploy folder
  Update "Move JSON file to Deployed folder" to you Deployed folder


  2. Create a n8n API key:
  +Go to Settings &gt; n8n API
  +Select Create an API key
  +Copy API Key

  3. In "Get Existing Workflow Tags" node: 
  Create n8n API Authentication
  Authentication: Predefined Credential Type
  Credential Type: n8n API

  Create new credential:
  +Paste in API key
  +Baseurl: https://SUBDOMAIN.YOURDOMAINNAME.com/api/v1/

  4. Add n8n API authentication to: 
  "Create n8n Workflow" node
  "Set Workflow Tag" node


  5. Add your N8N instance URL to the N8N_Instance_URL variable in "Set n8n URL variable" node.

  6. Run "1. Get Workflow Tags" flow and copy the ID of your chosen tag.

  7. In "Set n8n API URL & Tag ID variables" node:
  Add the Workflow Tag ID to the N8N_Instance_Tag variable
  Add your N8N instance URL to the N8N_Instance_URL variable


  8. Set workflow to Active

  How to adjust it to your needs 
   Use different tags: run Get Existing Workflow Tags on start‑up to refresh available tags, or hard‑code multiple tags in the Set Workflow Tag node.  
  Add notifications**: connect the error branch to Slack or Email nodes so you get alerted if an import fails.  
  Swap Drive for another storage**: replace Google Drive nodes with Dropbox, S3, or GitHub triggers if you prefer a different source for your JSON files.

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

workflow_json_compressed:
  [encoded data, 4156 characters]


name: Build an MCP Server with Google Calendar
author_name: GuanNan
author_username: sun-guannan
categories: []
created_at: 2025-04-16T09:13:50.769Z
updated_at: 2025-04-16T09:13:50.769Z
url: https://n8n.io/workflows/3569
scraped_at: 2025-12-24 13:02:43
description:
  Who is this for?
  This template is designed for anyone who wants to integrate MCP with their AI Agents. Whether you're a developer, a data analyst, or an automation enthusiast, if you're looking to leverage the power of MCP and Google Calendar in your n8n workflows, this template is for you.

  What problem is this workflow solving?
  This template caters to MCP beginners seeking a hands - on example and developers looking to integrate Google Calendar MCP service. When integrating MCP with Google Calendar, manually updating AI Agents after changes to Google Calendar tools on the MCP Server is time - consuming and error - prone. 

  This template automates the process, enabling the AI Agent to instantly recognize changes made to Google Calendar on the MCP Server. In project management, for example, it ensures that task schedule updates in Google Calendar are automatically detected by the AI Agent. With detailed steps, it simplifies the integration process for all users. 

  What this workflow does
  This workflow focuses on integrating MCP with Google Calendar within n8n. Specifically, it allows you to build an MCP Server and Client using Google Calendar nodes in n8n. Any changes made to the Google Calendar tools on the MCP Server are automatically recognized by the MCP Client in the workflow. This means that you can make changes to your Google Calendar (such as adding, deleting, or modifying events) on the MCP Server, and the MCP Client in the n8n workflow will immediately detect these changes without any manual intervention.

  Setup
  Requirements
  An active n8n account.
  Access to Google Calendar API. You need to  enable the Google Calendar API, and create the necessary credentials (OAuth 2.0 client ID).
  Basic knowledge of n8n workflows and MCP concepts.

  Step - by - step guide
  Create a new workflow in n8n: Log in to your n8n account and create a new workflow.
  Add Google Calendar nodes: Search for and add the Google Calendar nodes to your workflow. Configure the nodes with your Google Calendar API credentials.
  Set up the MCP Server and Client: Use the appropriate nodes in n8n to set up the MCP Server and Client. Connect the Google Calendar nodes to the MCP nodes as required.
  Test the workflow: Make some changes to your Google Calendar on the MCP Server and check if the MCP Client in the n8n workflow can detect these changes.

  How to customize this workflow to your needs
  If you want to customize this workflow, you can:
  Modify the triggers**: You can change the conditions under which the MCP Client detects changes. For example, you can set it to detect only specific types of events in Google Calendar.
  Integrate with other services**: You can add more nodes to the workflow to integrate with other services, such as sending notifications to Slack or saving data to a database when a change is detected.

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

workflow_json_compressed:
  [encoded data, 5708 characters]


name: Get Scaleway Server Info with Dynamic Filtering
author_name: Pablo
author_username: pablobarrier
categories: []
created_at: 2025-04-16T11:20:04.714Z
updated_at: 2025-04-16T11:20:04.714Z
url: https://n8n.io/workflows/3571
scraped_at: 2025-12-24 13:02:47
description:
  Get Scaleway Server Info with Dynamic Filtering

  Description

  This workflow is designed for developers, system administrators, and DevOps engineers who need to retrieve and filter Scaleway server information quickly and efficiently. It gathers data from Scaleway instances and baremetal servers across multiple zones and is ideal for:
  Quickly identifying servers by tags, names, public IPs, or zones.
  Automating server status checks in production, staging, or test environments.
  Integrating Scaleway data into broader monitoring or inventory systems.

  High-Level Steps

  Webhook Trigger:**
    Receives an HTTP POST request (with basic authentication) containing the search criteria (search_by and search).

  Server Data Collection:**
    Fetches server data from Scaleway’s API endpoints for both instances and baremetal servers across defined zones.

  Data Processing:**
    Aggregates and normalizes the fetched data using a Code node with helper functions.

  Dynamic Filtering:**
    Routes data to dedicated filtering routines (by tags, name, public_ip, or zone) based on the input criteria.

  Response:**
    Returns the filtered data (or an error message) via a webhook response.

  Set Up Steps

  Insert Your Scaleway Token:
     In the “Edit Fields” node, replace the placeholder Your personal Scaleway X Auth Token with your Scaleway API token.

  Configure Zones:
     Review or update the zone lists (ZONE_INSTANCE and ZONE_BAREMETAL) to suit your environment.

  Send a Request:
     Make a POST request to the workflow’s webhook endpoint with a JSON payload, for example:
        {
       "search_by": "tags",
       "search": "Apiv1"
     }
     
  View the Results:
     The workflow returns a JSON array of servers matching your criteria, including details like name, tags, public IP, type, state, zone, and user.

nodes_used:
  ["n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.switch","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.webhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.splitInBatches","n8n-nodes-base.respondToWebhook","n8n-nodes-base.httpRequest","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.if","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 8312 characters]


name: Create a RAG System with Paul Essays, Milvus, and OpenAI for Cited Answers
author_name: Cheney Zhang
author_username: zc277584121
categories: []
created_at: 2025-04-16T12:54:47.941Z
updated_at: 2025-04-16T12:54:47.941Z
url: https://n8n.io/workflows/3573
scraped_at: 2025-12-24 13:02:46
description:
  Create a RAG System with Paul Essays, Milvus, and OpenAI for Cited Answers

  This workflow automates the process of creating a document-based AI retrieval system using Milvus, an open-source vector database. It consists of two main steps:

  Data collection/processing
  Retrieval/response generation

  The system scrapes Paul Graham essays, processes them, and loads them into a Milvus vector store. When users ask questions, it retrieves relevant information and generates responses with citations.

  Step 1: Data Collection and Processing

  Set up a Milvus server using the official guide
  Create a collection named "my_collection"
  Execute the workflow to scrape Paul Graham essays:
     Fetch essay lists
     Extract names
     Split content into manageable items
     Limit results (if needed)
     Fetch texts
     Extract content
     Load everything into Milvus Vector Store

  This step uses OpenAI embeddings for vectorization.

  Step 2: Retrieval and Response Generation

  When a chat message is received, the system:

  Sets chunks to send to the model
  Retrieves relevant information from the Milvus Vector Store
  Prepares chunks
  Answers the query based on those chunks
  Composes citations
  Generates a comprehensive response

  This process uses OpenAI embeddings and models to ensure accurate and relevant answers with proper citations.

  For more information on vector databases and similarity search, visit Milvus documentation.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.html","n8n-nodes-base.splitOut","n8n-nodes-base.httpRequest","n8n-nodes-base.limit","n8n-nodes-base.html","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","n8n-nodes-base.set","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.code","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.vectorStoreMilvus","@n8n/n8n-nodes-langchain.vectorStoreMilvus","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.lmChatOpenAi"]

workflow_json_compressed:
  [encoded data, 4580 characters]


name: Create a Paul Graham Essay Q&A System with OpenAI and Milvus Vector Database
author_name: Cheney Zhang
author_username: zc277584121
categories: []
created_at: 2025-04-16T13:00:23.190Z
updated_at: 2025-04-16T13:00:23.190Z
url: https://n8n.io/workflows/3574
scraped_at: 2025-12-24 13:02:46
description:
  Create a Paul Graham Essay Q&A System with OpenAI and Milvus Vector Database
  How It Works

  This workflow creates a question-answering system based on Paul Graham essays. It has two main steps:

  Data Collection & Processing: 
     Scrapes Paul Graham essays
     Extracts text content
     Loads them into a Milvus vector store

  Chat Interaction:
     Provides a question-answering interface using the stored vector embeddings
     Utilizes OpenAI embeddings for semantic search

  Set Up Steps

  Set up a Milvus server following the official guide
  Create a collection named "my_collection"
  Run the workflow to scrape and load Paul Graham essays
  Start chatting with the QA system

  The workflow handles the entire process from fetching essays, extracting content, generating embeddings via OpenAI, storing vectors in Milvus, and providing retrieval for question answering.

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.html","n8n-nodes-base.splitOut","n8n-nodes-base.httpRequest","n8n-nodes-base.limit","n8n-nodes-base.html","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.vectorStoreMilvus","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.vectorStoreMilvus","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainRetrievalQa","@n8n/n8n-nodes-langchain.retrieverVectorStore"]

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name: Paul Graham Essay Search & Chat with Milvus Vector Database
author_name: Cheney Zhang
author_username: zc277584121
categories: []
created_at: 2025-04-16T13:04:56.892Z
updated_at: 2025-04-16T13:04:56.892Z
url: https://n8n.io/workflows/3576
scraped_at: 2025-12-24 13:02:46
description:
  Paul Graham Essay Search & Chat with Milvus Vector Database
  How It Works

  This workflow creates a RAG (Retrieval-Augmented Generation) system using Milvus vector database to search Paul Graham essays:

  Scrape & Load: Fetches Paul Graham essays, extracts text, and stores them as vector embeddings in Milvus
  Chat Interface: Enables semantic search and AI-powered conversations about the essays

  Set Up Steps

  Set up Milvus server following the official installation guide, then create a collection
  Execute the workflow to scrape essays and load them into your Milvus collection
  Chat with the AI agent using the Milvus tool to query and discuss essay content

nodes_used:
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name: Travel Planning Assistant with MongoDB Atlas, Gemini LLM and Vector Search
author_name: Pavel Duchovny
author_username: pash
categories: []
created_at: 2025-04-16T15:14:03.770Z
updated_at: 2025-04-16T15:14:03.770Z
url: https://n8n.io/workflows/3577
scraped_at: 2025-12-24 13:02:44
description:
  Building agentic AI workflows often requires multiple moving parts: memory management, document retrieval, vector similarity, and orchestration. 

  Until now, these pieces had to be custom-wired. 

  But with the new native n8n nodes for MongoDB Atlas, we reduce that overhead dramatically.


  With just a few clicks:

  Store and recall long-term memory from MongoDB

  Query vector embeddings stored in Atlas Vector Search

  Use these results in your LLM chains and automation logic

  In this example we present an ingestion and AI Agent flows that focus around Travel Planning. The different interest points that we want the agent to know about can be ingested into the vector store.

  The AI Agent will use the vector store tool to get relevant context about those points of interest if it needs to. 

  Prerequisites
  MongoDB Atlas project and Cluster
  OpenAI Valid API Key for embeddings (can be other provider)
  Gemini API Key for the LLM (can be other provider)

  How it works:

  There are 2 main flows. 

  One is ingesting flow:
  Gets a document from a webhook and use MongoDB Vector Atlas to embed the document title and description into points_of_interest collection.
  Embeddings are stored in a field named embedding
  Embeddings used are OpenAI's but it can be any type of supported embedders.

  Second flow is an AI Agent node with Chat Memory Stored in MongoDB Atlas and a Vector Search node as a tool:
  Chat Message Trigger**: Chatting with the AI Agent will trigger the conversation store in the MongoDB Chat Memory node.
  When data is necessary like a location search or details it will go to the "Vector Search" tool.
  Vector Search Tool** - uses Atlas Vector Search index created on the points_of_interest collection:

  // index name : "vector_index"
  // If you change an embedding provider make sure the numDimensions correspond to the model.
  {
    "fields": [
      {
        "type": "vector",
        "path": "embedding",
        "numDimensions": 1536,
        "similarity": "cosine"
      }
    ]
  }

  Additional Resources
  MongoDB Atlas Vector Search
  n8n Atlas Vector Search docs

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.memoryMongoDbChat","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.vectorStoreMongoDBAtlas","@n8n/n8n-nodes-langchain.embeddingsOpenAi","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.webhook","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","@n8n/n8n-nodes-langchain.vectorStoreMongoDBAtlas","@n8n/n8n-nodes-langchain.embeddingsOpenAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: Generate Dynamic Line Chart from JSON Data to Upload to Google Drive
author_name: Lucas Correia
author_username: lucascorrei4
categories: []
created_at: 2025-04-16T16:57:21.009Z
updated_at: 2025-04-16T16:57:21.009Z
url: https://n8n.io/workflows/3578
scraped_at: 2025-12-24 13:02:45
description:
  What Does This Flow Do?

  This workflow demonstrates how to dynamically generate a line chart using the QuickChart node based on data provided in a JSON object and then upload the resulting chart image to Google Drive. 

  Use Cases

  You can use it in presentations or requesting for chart generation from a software with HTTP requests.
  Automated report generation (e.g., daily sales charts).
  Visualizing data fetched from APIs or databases.
  Simple monitoring dashboards.
  Adding charts to internal tools or notifications.

  How it Works

  Trigger: The workflow starts manually when you click 'Test workflow'.
  Set Sample Data: A Set node (Edit Fields: Set JSON data to test) defines a sample JSON object named jsonData. This object contains:
      reportTitle: A title (not used in the chart generation in this example, but useful for context).
      labels: An array of strings representing the labels for the chart's X-axis (e.g., ["Q1", "Q2", "Q3", "Q4"]).
      salesData: An array of numbers representing the data points for the chart's Y-axis (e.g., [1250, 1800, 1550, 2100]).
  Generate Chart: The QuickChart node is configured to:
      Create a line chart.
      Dynamically read labels from the jsonData.labels array (Labels Mode: From Array).
      Use the jsonData.salesData array as the input data (Note: This configuration places data in the top-level 'Data' field. For more complex charts with multiple datasets or specific dataset options, configure datasets under 'Dataset Options' instead).
      The node outputs the generated chart image as binary data in a field named data.
  Upload to Google Drive: The Google Drive node (Google Drive: Upload File):
      Takes the binary data (data) from the QuickChart node.
      Uploads the image to your specified Google Drive folder.
      Dynamically names the file based on its extension (e.g., chart.png).

  Setup Steps

  Import: Import this template into your n8n instance.
  Configure Google Drive Credentials:
      Select the Google Drive: Upload File node.
      You MUST configure your own Google Drive credentials. Click on the 'Credentials' dropdown and either select existing credentials or create new ones by following the authentication prompts.
  (Optional) Customize Google Drive Folder: In the Google Drive: Upload File node, you can change the Drive ID and Folder ID to specify exactly where the chart should be uploaded.
  Activate: Activate the workflow if you want it to run automatically based on a different trigger.

  How to Use & Customize

  Change Input Data:** Modify the labels and salesData arrays within the Edit Fields: Set JSON data to test node to use your own data. Ensure the number of labels matches the number of data points.
  Use Real Data Sources:** Replace the Edit Fields: Set JSON data to test node with nodes that fetch data from real sources like:
      HTTP Request (APIs)
      Postgres / MongoDB nodes (Databases)
      Google Sheets node
      Ensure the output data from your source node is formatted similarly (providing labels and salesData arrays). You might need another Set node to structure the data correctly before the QuickChart node.
  Change Chart Type:** In the QuickChart node, modify the Chart Type parameter (e.g., change from line to bar, pie, doughnut, etc.).
  Customize Chart Appearance:** Explore the Chart Options parameter within the QuickChart node to add titles, change colors, modify axes, etc., using QuickChart's standard JSON configuration options.
  Use Datasets (Recommended for Complex Charts):** For multiple lines/bars or more control, configure datasets explicitly in the QuickChart node:
      Remove the expression from the top-level Data field.
      Go to Dataset Options -&gt; Add option -&gt; Add dataset.
      Set the Data field within the dataset using an expression like {{ $json.jsonData.salesData }}.
      You can add multiple datasets this way.
  Change Output Destination:** Replace the Google Drive: Upload File node with other nodes to handle the chart image differently:
      Write Binary File: Save the chart to the local filesystem where n8n is running.
      Slack / Discord / Telegram: Send the chart to messaging platforms.
      Move Binary Data: Convert the image to Base64 to embed in HTML or return via webhook response.

  Nodes Used

  Manual Trigger
  Set
  QuickChart
  Google Drive



  Tags: (Suggestions for tags field)

  QuickChart, Chart, Visualization, Line Chart, Google Drive, Reporting, Automation

nodes_used:
  ["n8n-nodes-base.quickChart","n8n-nodes-base.set","n8n-nodes-base.googleDrive","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger"]

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name: Automated Research Report Generation with AI, Wiki, Search & Gmail/Telegram
author_name: Immanuel
author_username: spectrabit
categories: []
created_at: 2025-04-16T16:59:02.174Z
updated_at: 2025-04-16T16:59:02.174Z
url: https://n8n.io/workflows/3579
scraped_at: 2025-12-24 13:02:43
description:
  Automated Research Report Generation with OpenAI, Wikipedia, Google Search, Gmail/Telegram and PDF Output
  Description

  What Problem Does This Solve? 🛠️
  This workflow automates the process of generating professional research reports for researchers, students, and professionals. It eliminates manual research and report formatting by aggregating data, generating content with AI, and delivering the report as a PDF via Gmail or Telegram. Target audience: Researchers, students, educators, and professionals needing quick, formatted research reports.

  What Does It Do? 🌟
  Aggregates research data from Wikipedia, Google Search, and SerpApi.
  Refines user queries and generates structured content using OpenAI.
  Converts the content into a professional HTML report, then to PDF.
  Sends the PDF report via Gmail or Telegram.

  Key Features 📋
  Real-time data aggregation from multiple sources.
  AI-driven content generation with OpenAI.
  Automated HTML-to-PDF conversion for professional reports.
  Flexible delivery via Gmail or Telegram.
  Error handling for robust execution.

  Setup Instructions

  Prerequisites ⚙️
  n8n Instance**: Self-hosted or cloud n8n instance.
  API Credentials**:
    OpenAI API: API key with GPT model access, stored in n8n credentials.
    SerpApi (Google Search): API key from SerpApi, stored in n8n credentials (do not hardcode in nodes).
    Gmail API: Credentials from Google Cloud Console with Gmail scope.
    Telegram API: Bot token from BotFather on Telegram.

  Installation Steps 📦
  Import the Workflow:
     Copy the workflow JSON from the "Template Code" section below.
     Import it into n8n via "Import from File" or "Import from URL".
  Configure Credentials:
     Add API credentials in n8n’s Credentials section for OpenAI, SerpApi, Gmail, and Telegram.
     Assign credentials to respective nodes. For example:
       In the SerpApi Google Search node, use n8n credentials for SerpApi: api_key={{ $credentials.SerpApiKey }}.
       In the Send Research PDF on Gmail node, use Gmail credentials.
       In the Send PDF to Telegram node, use Telegram bot credentials.
  Set Up Nodes:
     OpenAI Nodes (Research AI Agent, OpenAI Chat Model, OpenAI Chat Middle Memory): Update the model (e.g., gpt-4o) and prompt as needed.
     Input Validation (Input Validation node): Ensure your input query format matches the expected structure (e.g., topic: "AI ethics").
     Delivery Options (Send Research PDF on Gmail, Send PDF to Telegram): Configure recipient email or Telegram chat ID.
  Test the Workflow:
     Run the workflow by clicking the "Test Workflow" node.
     Verify that the research report PDF is generated and sent via Gmail or Telegram.

  How It Works

  High-Level Steps 🔍
  Query Refinement**: Refines the input query for better research.
  Aggregate Data**: Fetches data from Wikipedia, Google Search, and SerpApi.
  Generate Report**: Uses OpenAI to create a structured report.
  Convert to PDF**: Converts the report to HTML, then PDF.
  Deliver Report**: Sends the PDF via Gmail or Telegram.

  Detailed descriptions are available in the sticky notes within the workflow screenshot above.

  Node Names and Actions

  Research and Report Generation
  Test Workflow: Triggers the workflow for testing.
  Input Validation: Validates the input query.
  Query Refiner: Refines the query for better results.
  Research AI Agent: Coordinates research using OpenAI.
  OpenAI Chat Model: Generates content for the report.
  Structured Output Parser: Parses OpenAI output into structured data.
  OpenAI Chat Middle Memory: Retains context during research.
  Wikipedia Google Search: Fetches data from Wikipedia.
  SerpApi Google Search: Fetches data via SerpApi.
  Merge Split Items: Merges data from multiple sources.
  Aggregate: Aggregates all research data.
  Generate PDF HTML: Creates an HTML report.
  Convert HTML to PDF: Converts HTML to PDF.
  Download PDF: Downloads the PDF file.
  Send PDF to Telegram: Sends the PDF via Telegram.
  Send Research PDF on Gmail: Sends the PDF via Gmail.

  Customization Tips

  Expand Data Sources** 📡: Add more sources (e.g., academic databases) by adding nodes to Merge Split Items.
  Change Report Style** ✍️: Update the Generate PDF HTML node to modify the HTML template (e.g., adjust styling or sections).
  Alternative Delivery** 📧: Add nodes to send the PDF via other platforms (e.g., Slack).
  Adjust AI Model** 🧠: Modify the OpenAI Chat Model node to use a different model (e.g., gpt-3.5-turbo).

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.splitOut","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.aggregate","n8n-nodes-base.httpRequest","n8n-nodes-base.gmail","n8n-nodes-base.manualTrigger","n8n-nodes-base.telegram","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.googleDrive","n8n-nodes-base.if"]

workflow_json_compressed:
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name: Scrape LinkedIn Job Listings for Hiring Signals & Prospecting with Bright Data
author_name: Yaron Been
author_username: yaron-nofluff
categories: []
created_at: 2025-04-16T18:34:57.146Z
updated_at: 2025-04-16T18:34:57.146Z
url: https://n8n.io/workflows/3580
scraped_at: 2025-12-24 13:02:42
description:
  LinkedIn Hiring Signal Scraper — Jobs & Prospecting Using Bright Data

  Purpose:
  Discover recent job posts from LinkedIn using Bright Data's Dataset API, clean the results, and log them into Google Sheets — for both job hunting and identifying high-intent B2B leads based on hiring activity.

  Use Cases:
  Job Seekers** – Spot relevant openings filtered by role, city, and country.  
  Sales & Prospecting** – Use job posts as buying signals.  
    If a company is hiring for a role you support (e.g. marketers, developers, ops) —  
    it's the perfect time to reach out and offer your services.

  Tools Needed:
  n8n Nodes:**
    Form Trigger  
    HTTP Request  
    Wait  
    If  
    Code  
    Google Sheets  
    Sticky Notes (for embedded guidance)
  External Services:**
    Bright Data (Dataset API)  
    Google Sheets

  API Keys & Authentication Required:
  Bright Data API Key**  
    → Add in the HTTP Request headers:  
    Authorization: Bearer YOUR_BRIGHTDATA_API_KEY

  Google Sheets OAuth2**  
    → Connect your account in n8n to allow read/write access to the spreadsheet.

  General Guidelines:
  Use descriptive names for all nodes.  
  Include retry logic in polling to avoid infinite loops.  
  Flatten nested fields (like job_poster and base_salary).  
  Strip out HTML tags from job descriptions for clean output.

  Things to be Aware Of:
  Bright Data snapshots take ~1–3 minutes — use a Wait node and polling.  
  Form filters affect output significantly:  
    🔍 We recommend filtering by "Last 7 days" or "Past 24 hours" for fresher data.  
  Avoid hardcoding values in the form — leave optional filters empty if unsure.

  Post-Processing & Outreach:
  After data lands in Google Sheets, you can use it to:  
    Personalize cold emails based on job titles, locations, and hiring signals.  
    Send thoughtful LinkedIn messages (e.g., "Saw you're hiring a CMO...")  
    Prioritize outreach to companies actively growing in your niche.

  Additional Notes:
  📄 Copy the Google Sheet Template:  
    Click here to make your copy  
    → Rename for each campaign or client.

  Form fields include:  
    Job Location (city or region)  
    Keyword (e.g., CMO, Backend Developer)  
    Country (2-letter code, e.g., US, UK)

  This workflow gives you a competitive edge —  
  📌 For candidates: Be first to apply.  
  📌 For sellers: Be first to pitch.  
  All based on live hiring signals from LinkedIn.

  STEP-BY-STEP WALKTHROUGH

  Step 1: Set up your Google Sheet
  Open this template
  Go to File → Make a copy
  You'll use this copy as the destination for the scraped job posts

  Step 2: Fill out the Input Form in n8n
  The form allows you to define what kind of job posts you want to scrape.

  Fields:
  Job Location** → e.g. New York, Berlin, Remote  
  Keyword** → e.g. CMO, AI Architect, Ecommerce Manager  
  Country Code (2-letter)** → e.g. US, UK, IL

  💡 Pro Tip:  
  For best results, set the filter inside the workflow to:  
  time_range = "Past 24 hours" or "Last 7 days"  
  This keeps results relevant and fresh.

  Step 3: Trigger Bright Data Snapshot
  The workflow sends a request to Bright Data with your input.

  Example API Call Body:
  [
    {
      "location": "New York",
      "keyword": "Marketing Manager",
      "country": "US",
      "time_range": "Past 24 hours",
      "job_type": "Part-time",
      "experience_level": "",
      "remote": "",
      "company": ""
    }
  ]

  Bright Data will start preparing the dataset in the background.

  Step 4: Wait for the Snapshot to Complete
  The workflow includes a Wait Node and Polling Loop that checks every few minutes until the data is ready.

  You don't need to do anything here — it's all automated.

  Step 5: Clean Up the Results
  Once Bright Data responds with the full job post list:

  ✔️ Nested fields like job_poster and base_salary are flattened  
  ✔️ HTML in job descriptions is removed  
  ✔️ Final data is formatted for export

  Step 6: Export to Google Sheets
  The final cleaned list is added to your Google Sheet (first tab).

  Each row = one job post, with columns like:

  job_title, company_name, location, salary_min, apply_link, job_description_plain

  Step 7: Use the Data for Outreach or Research
  Example for Job Seekers:
  You search for:

  Location: Berlin
  Keyword: Product Designer
  Country: DE
  Time range: Past 7 days

  Now you've got a live list of roles — with salary, recruiter info, and apply links.
  → Use it to apply faster than others.

  Example for Prospecting (Sales / SDR):
  You search for:

  Location: London
  Keyword: Growth Marketing
  Country: UK

  And find companies hiring growth marketers.
  → That's your signal to offer help with media buying, SEO, CRO, or your relevant service.

  Use the data to:
  Write personalized cold emails ("Saw you're hiring a Growth Marketer…")
  Start warm LinkedIn outreach
  Build lead lists of companies actively expanding in your niche

  API Credentials Required:
  Bright Data API Key**  
    Used in HTTP headers: Authorization: Bearer YOUR_BRIGHTDATA_API_KEY

  Google Sheets OAuth2**  
    Allows n8n to read/write to your spreadsheet

  Adjustments & Customization Tips:
  Modify the HTTP Request body to add more filters (e.g. job_type, remote, company)
  Increase or reduce polling wait time depending on Bright Data speed
  Add scoring logic to prioritize listings based on title or location

  Final Notes:
  📄 Google Sheet Template:  
    Make your copy here

  ⚙️ Bright Data Dataset API:  
    Visit BrightData.com

  📬 Personalization works best when you act quickly.  
    Use the freshest data to reach out with context — not generic pitches.

  This workflow turns LinkedIn job posts into sales insights and job leads.
  All in one click. Fully automated. Ready for your next move.

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

workflow_json_compressed:
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name: Domain to Email Extraction using Apollo API
author_name: Hueston
author_username: hueston
categories: []
created_at: 2025-04-16T19:03:08.744Z
updated_at: 2025-04-16T19:03:08.744Z
url: https://n8n.io/workflows/3581
scraped_at: 2025-12-24 13:02:45
description:
  Who is this for?
  Sales professionals looking to build lead lists from target company domains
  Business development teams conducting outreach campaigns
  Marketers building contact databases for account-based marketing
  Recruiters searching for potential candidates at specific companies
  Anyone needing to transform a list of company domains into actionable contact information

  What problem is this workflow solving?
  Finding business email addresses for outreach is a time-consuming process. The Apollo API doesn't provide a direct way to extract email contacts from domains in a single call. This workflow bridges that gap by:

  Automating the two-step process required by Apollo's API
  Processing multiple domains in batches without manual intervention
  Extracting, enriching, and storing contact information in a structured format
  Eliminating hours of manual data entry and API interaction

  What this workflow does
  This workflow creates an automated pipeline between Google Sheets and Apollo's API to:

  Pull a list of target domains from a Google Sheet
  Submit each domain to Apollo's search API to find associated people
  Loop through each person found and enrich their profile data
  Extract key information: name, title, email address, and LinkedIn URL
  Write the enriched contact information back to a results sheet
  Process the next domain automatically until all are complete

  Setup

  Prerequisites:
  An n8n instance (cloud or self-hosted)
  Apollo.io account with API access
  Google account with access to Google Sheets

  Google Sheets Setup:
  Create a new Google Sheet with two tabs:
     Tab 1: "Target Domains" with a column named "Domain To Enrich"
     Tab 2: "Results" with columns: Company, First Name, Last Name, Title, Email, LinkedIn

  n8n Setup:
  Import the workflow JSON into your n8n instance
  Set up Google Sheets credentials in n8n
  Update the Google Sheets document ID in both Google Sheets nodes
  Add your Apollo API key to both HTTP Request nodes
  Review and adjust API rate limits if needed

  Testing:
  Add a few test domains to your "Target Domains" sheet
  Run the workflow manually to verify it's working correctly
  Check the "Results" sheet to confirm data is being properly populated

  How to customize this workflow to your needs

  Adding More Contact Fields:
  Modify the "Clean Up" node to extract additional fields from the Apollo API response
  Add corresponding columns to your "Results" sheet
  Update the "Results To Results Sheet" node mapping to include the new fields

  Filtering Results:
  Add a Filter node after "Clean Up" to include only contacts with specific roles
  Create conditions based on title, seniority, or other fields returned by Apollo

  Automating Workflow Execution:
  Replace the manual trigger with a Schedule Trigger to run daily/weekly
  Add a Filter node to process only domains with "Not Processed" status
  Update the status field in Google Sheets after processing

  Additional Notes
  This workflow respects Apollo's API rate limits by processing one contact at a time
  The Apollo API may not return contact information for all domains or all employees
  Consider legal and privacy implications when collecting and storing contact information

  Made with ❤️ by Hueston

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

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name: High-Level Service Page SEO Blueprint Report Generator
author_name: Custom Workflows AI
author_username: customworkflowsai
categories: []
created_at: 2025-04-17T10:36:18.449Z
updated_at: 2025-04-17T10:36:18.449Z
url: https://n8n.io/workflows/3583
scraped_at: 2025-12-24 13:02:42
description:
  Introduction
  The "High-Level Service Page SEO Blueprint Report" workflow is a powerful, AI-driven solution designed to generate comprehensive SEO content strategies for service-based businesses.

  By analyzing competitor websites and user intent, this workflow creates a detailed blueprint that outlines the optimal structure, content, and conversion elements for a service page. The workflow leverages the JINA Reader API to extract content from competitor websites and uses Google Gemini AI to perform deep analysis across multiple dimensions: competitor content structure, user intent, strategic opportunities, and conversion optimization.

  The final output is a professionally formatted Markdown document that provides actionable guidance for creating a high-performing service page that satisfies both user needs and search engine requirements. This workflow eliminates the time-consuming process of manually analyzing competitors and developing content strategies, providing a data-driven foundation for service page creation that would typically require hours of expert analysis.

  Who is this for?
  This workflow is designed for digital marketers, SEO specialists, content strategists, and web developers who need to create or optimize service pages for businesses. It's particularly valuable for marketing agencies and freelancers who regularly develop content strategies for clients across various industries. 

  Users should have a basic understanding of SEO concepts, content marketing, and website structure. While technical SEO knowledge is beneficial, the workflow is designed to provide comprehensive guidance even for those with intermediate-level expertise. 

  The ideal user is someone who wants to streamline their content planning process and ensure their service pages are built on data-driven insights rather than guesswork.

  What problem is this workflow solving?
  Creating effective service pages that rank well in search engines while converting visitors is a complex challenge that typically requires extensive competitive research, content planning, and conversion optimization expertise. This workflow addresses several key pain points:

  Time-consuming competitor analysis: Manually analyzing multiple competitor websites to identify content patterns, heading structures, and meta tag strategies can take hours.

  Difficulty identifying content gaps: Determining what topics competitors are missing that could provide a competitive advantage requires deep analysis and industry knowledge.

  Balancing SEO and conversion elements: Creating content that satisfies both search engines and user needs while driving conversions is a delicate balance that many struggle to achieve.

  Lack of structured approach: Many content creators work without a comprehensive blueprint, leading to inconsistent results and missed opportunities.

  Difficulty translating analysis into actionable recommendations: Even when analysis is performed, turning those insights into a concrete content plan can be challenging.

  This workflow automates these processes, providing a structured, data-driven approach to service page creation that saves hours of research and planning time.

  What this workflow does
  Overview
  The workflow takes a list of competitor URLs and a target keyword as input, then performs a multi-stage analysis to generate a comprehensive service page blueprint. It extracts and analyzes competitor content, evaluates user intent, identifies strategic opportunities, and creates detailed recommendations for page structure, content, and conversion elements. 

  The final output is a professionally formatted Markdown document that serves as a complete roadmap for creating an effective service page.

  Process
  Data Collection: The workflow begins with a form that collects essential information: competitor URLs, target keyword, services offered, brand name, and whether the page is a homepage.

  Competitor Content Extraction: The workflow processes each competitor URL, using the JINA Reader API to extract the HTML content from each site.

  Content Structure Analysis: For each competitor site, the workflow extracts and analyzes heading structures, meta tags, schema markup, and recurring phrases (n-grams).

  Competitor Analysis Report: The AI synthesizes the competitive data to identify patterns in meta titles/descriptions, common outline sections, key heading concepts, and structural elements.

  User Intent Analysis: The workflow analyzes the target keyword to determine primary and secondary user intents, user personas, and their position in the buyer's journey.

  Gap Analysis: The AI identifies content overlaps ("table stakes"), content gaps (opportunities), SEO keyword priorities, and potential UX/conversion advantages.

  Page Outline Generation: Based on the previous analyses, the workflow creates an optimal page structure with H1, H2s, H3s, and potentially H4s, with justifications for each section.

  UX & Conversion Recommendations: The workflow adds detailed recommendations for calls-to-action, trust signals, copywriting tone, visual elements, and risk reversal strategies.

  Final Blueprint Creation: All analyses and recommendations are compiled into a comprehensive, well-structured Markdown document that serves as a complete service page blueprint.

  Setup
  Download or import the "High-Level Service Page SEO Blueprint Report" workflow JSON file into your n8n instance.

  Create a JINA Reader API key by visiting https://jina.ai/api-dashboard/key-manager. You can claim a free API key that allows up to 1 million tokens.

  Set up Google Gemini (PaLM) credentials by following the guide at https://docs.n8n.io/integrations/builtin/credentials/googleai/#using-geminipalm-api-key.

  Update the "Edit Fields" node with:
     Your JINA Reader API Key
     Adjust the "Waiting Time" to 20 seconds if using the free Google Gemini API tier (which limits to 5 requests per minute)
     Optionally change the Gemini model if needed

  Activate the workflow and start the form trigger.

  Complete the form with:
     Competitors (up to 5 direct competitor URLs)
     Target Keyword (the query related to your service)
     Services Offered (details of your complete service offerings)
     Brand Name (your company name)
     Whether the page is a homepage

  After processing, download the generated .txt file, which contains the blueprint in Markdown format.

  How to customize this workflow to your needs
  Adjust AI parameters: Modify the temperature settings in the Google Gemini Chat Model nodes to control creativity vs. precision in the AI outputs.

  Customize extraction logic: Edit the "Extract HTML Elements" code node to focus on specific HTML elements that are most relevant to your industry or content type.

  Modify analysis prompts: Customize the prompts in the various analysis nodes to focus on specific aspects of SEO or content strategy that are most important for your use case.

  Add industry-specific guidance: Enhance the prompts with industry-specific instructions or examples to make the output more relevant to particular sectors.

  Integrate with content management systems: Extend the workflow to automatically send the blueprint to content management systems, project management tools, or document storage platforms.

  Add competitor scoring: Implement a scoring system to evaluate and rank competitors based on specific criteria relevant to your strategy.

  Expand the analysis: Add additional analysis nodes to evaluate other aspects of competitor websites, such as page speed, mobile-friendliness, or backlink profiles.

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name: Create AI-Powered YouTube Shorts with OpenAI, ElevenLabs, 0CodeKit!
author_name: dada
author_username: lin
categories: []
created_at: 2025-04-17T11:00:05.102Z
updated_at: 2025-04-17T11:00:05.102Z
url: https://n8n.io/workflows/3584
scraped_at: 2025-12-24 13:02:46
description:
  YouTube Shorts Automation with AI-Powered Video Generation

  Overview

  This workflow automates the creation of engaging YouTube Shorts by leveraging AI tools to generate scripts, audio, images, and videos, then compiles them into a final video ready for upload. It uses OpenAI for script and image prompt generation, ElevenLabs for text-to-speech, Replicate for image and video generation, Cloudinary for audio storage, and Creatomate for final video rendering. The workflow segments the script into 6-second clips, creates corresponding visuals, and combines them with audio, adding smooth transitions for a polished result.

  Why is this workflow useful?

  Creating YouTube Shorts manually is time-consuming, requiring scriptwriting, voiceovers, and video editing. This workflow automates the entire process, enabling creators to produce high-quality, engaging Shorts in minutes. It’s ideal for content creators, marketers, or businesses looking to maintain a consistent social media presence with minimal effort.

  Who is this workflow for?

  Content Creators**: YouTubers or TikTok creators aiming to produce viral Shorts quickly.
  Digital Marketers**: Businesses promoting products or services through short-form video content.
  Hobbyists**: Individuals experimenting with AI-driven video production without advanced editing skills.

  What will you need to use this workflow?

  n8n Account**: Free or paid, hosted on n8n Cloud or self-hosted.
  OpenAI API Key**: For generating scripts and image prompts (GPT-4o-mini model).
  ElevenLabs API Key**: For text-to-speech conversion.
  Replicate API Key**: For generating images and videos.
  Cloudinary Account**: For storing audio files.
  Creatomate API Key**: For rendering the final video.
  Basic JSON Knowledge**: To understand and tweak the workflow output if needed.

  How does this workflow work?

  Trigger: The workflow starts manually via the "Test Workflow" button.
  Script Generation: OpenAI (GPT-4o-mini) generates a script with an intro (40-70 chars), base (280-350 chars), and CTA (55 chars), plus a title and description with hashtags.
  Text-to-Speech: ElevenLabs converts the script into an MP3 audio file with timestamps.
  Audio Upload: The audio is uploaded to Cloudinary for storage.
  Script Segmentation: A Python script segments the audio into 6-second clips, creating transcription chunks.
  Image Prompt Generation: OpenAI generates a visual prompt for each segment in the style of CNSTLL, ensuring simple, animatable visuals.
  Image Generation: Replicate creates a 9:16 image for each segment based on the prompt.
  Video Generation: Replicate’s Minimax model generates 5-second video clips from the images.
  Video Compilation: Creatomate combines the video clips with the audio, adding fade transitions and a final black shape for a smooth close.
  Final Output: The rendered video is retrieved from Creatomate, ready for YouTube Shorts.

  Setup instructions

  Import the Workflow: Copy the provided JSON into n8n and import it as a new workflow.
  Add Credentials:
     Configure OpenAI API credentials for the "Ideator" and "Image Prompter" nodes.
     Set up ElevenLabs API key in the "Script Generator" node.
     Add Replicate API key for the "Request Image," "Request Video," and "Get Video" nodes.
     Configure Cloudinary credentials for the "Upload to Cloudinary" node.
     Set Creatomate API key for the "Editor" and "Get Final Video" nodes.
  Test the Workflow:
     Click "Test Workflow" to run it manually.
     Replace &lt;user-query&gt; in the "Ideator" node with your video topic (e.g., "Fitness tips for beginners").
  Verify Outputs: Check each node’s output, ensuring audio, images, and videos are generated correctly.
  Customize (Optional):
     Adjust segment duration in the "HTTP Request" node’s Python code (default: 6 seconds).
     Modify fade duration or video resolution in the "Create Editor JSON" node.
  Save and Activate: Save the workflow and activate it for regular use.

  Tips for getting the most out of this workflow

  Optimize Prompts**: Use specific, concise queries in the "Ideator" node for better script quality (e.g., "Minimalist home decor ideas").
  Check API Limits**: Ensure your API accounts (OpenAI, ElevenLabs, Replicate, Creatomate) have sufficient credits.
  Experiment with Styles**: Tweak the CNSTLL style in the "Image Prompter" node for unique visuals, but keep prompts simple to avoid AI generation errors.
  Monitor Rendering Time**: Video rendering in Creatomate may take up to 70 seconds; adjust the "Rendering" node’s wait time if needed.
  Add Hashtags**: Enhance the generated description with trending hashtags like #YouTubeShorts, #AI, or niche-specific tags.

  Troubleshooting

  Error in Script Generation**: Verify your OpenAI API key and ensure the query is clear.
  Audio Issues**: Check ElevenLabs API key and confirm the script length fits within API limits.
  Image/Video Failures**: Ensure Replicate API key is valid and prompts are simple (avoid complex scenes or humans).
  Rendering Errors**: Confirm Creatomate API key and check JSON structure in the "Create Editor JSON" node.
  Contact Support**: Reach out to n8n Community Forum or respective API providers for persistent issues.

  Built with

  n8n Nodes**: Manual Trigger, OpenAI, HTTP Request, Set, Split Out, Aggregate, Merge, Wait.
  External Services**: OpenAI (GPT-4o-mini), ElevenLabs (Multilingual v2), Replicate (Flux-Cinestill, Minimax Video-01), Cloudinary, Creatomate.
  Custom Code**: Python for audio segmentation and JSON rendering.

  Example output

  For a query like "Morning routine hacks":

  Script**:
    Intro: "Struggling to start your day right?"
    Base: "Try these 3 morning hacks: 5-min meditation to clear your mind, a quick stretch routine to boost energy, and a cold shower to stay alert. Set your day up for success!"
    CTA: "Save this and try it tomorrow!"
  Title**: "3 Morning Hacks You NEED to Try!"
  Description**: "Kickstart your day with these easy hacks! 🧘‍♀️🚿 #MorningRoutine #Productivity #YouTubeShorts"
  Video**: A 15-second Short with three 5-second clips (meditation scene, stretching POV, cold shower close-up), synced with audio and fade transitions.

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name: 🤖 AI Restaurant Assistant for WhatsApp, Instagram & Messenger
author_name: Amanda Benks
author_username: amanda
categories: []
created_at: 2025-04-17T11:46:33.900Z
updated_at: 2025-04-17T11:46:33.900Z
url: https://n8n.io/workflows/3585
scraped_at: 2025-12-24 13:02:42
description:
  Hi, I’m Amanda! 💌

  This workflow was created with so much love, care, and attention… especially for you, who runs a restaurant, a cozy little burger place, or a delivery business full of heart. 🥰

  I know how busy your days can be, so I made this sweet AI assistant to help you take care of your customers on WhatsApp, Instagram, Messenger (or Evolution API). It sends your beautiful menu, checks ZIP codes, creates payment links, and even notifies the kitchen when the order is ready. All gentle, all automatic, all with love. 💛

  💡 What this workflow does

  Replies to customers via WhatsApp API, Instagram Direct, Messenger, and Evolution API  
  Checks ZIP codes to see if delivery is available using Google Maps  
  Sends your menu as images, because food should look as good as it tastes 🍕  
  Collects item selections and offers lovely upsells like drinks or extras  
  Creates payment links with the Asaas API  
  Confirms when the payment is complete and sends the order to the kitchen  
  Stores all messages and session data safely in Supabase  
  Uses OpenAI GPT-4o to talk naturally and kindly with your customers

  ⚙️ How to set it up (I’ll guide you with care 🧸)

  Connect your webhook from WhatsApp, Instagram, Messenger, or Evolution API  
  Create a Supabase table called n8n_workflow_followup  
     You can use this ready-made template here:  
       👉 Supabase Sheet Template  
  Add your API keys (OpenAI, Supabase, Google Maps, and Asaas) securely in n8n  
  Customize the AI prompt with your brand’s voice and sweet style 💫  
  Set your delivery radius (default is 10km, but you can change it!)  
  Upload your menu images (from Google Drive, your website, or any link)  
  That’s it! Your assistant is now ready to serve with kindness and automation 💕

  🍯 Works with:

  ✅ n8n Cloud and Self-Hosted n8n  
  🔐 All API credentials are safely stored using n8n’s secure credential manager

  Want something customized just for you?
  Chat with me, I’d love to help 💻💛 Chat via WhatsApp (+55 17 99155-7874)
  .
  .
  .
  Tradução em Português:
  Oi, eu sou a Amanda! 💌
  Esse workflow foi feito com muito carinho, dedicação e cuidado... pensando especialmente em você, que tem um restaurante, lanchonete ou delivery cheio de amor pelo que faz. 🥰

  Eu sei como o dia a dia pode ser corrido, e foi por isso que eu criei esse atendente com IA: pra te ajudar a responder clientes no WhatsApp, Instagram, Messenger (ou Evolution API), enviar cardápio com imagens lindas, calcular entregas, gerar links de pagamento e até avisar a cozinha. Tudo com jeitinho, sem complicação, e com muito coração. 💛

  💡 O que esse fluxo faz

  Atende clientes pelo WhatsApp API, Instagram Direct, Messenger e Evolution API  
  Valida CEP e calcula se o cliente está dentro da área de entrega (usando Google Maps)  
  Envia cardápio com imagens, porque comer começa pelos olhos 🍕  
  Coleta os pedidos e também oferece bebidas e adicionais  
  Gera link de pagamento automaticamente com a API do Asaas  
  Confirma o pagamento e avisa a cozinha quando estiver tudo certo  
  Armazena mensagens, horários e histórico no Supabase  
  Usa o GPT-4o da OpenAI pra conversar de forma educada e natural com seus clientes

  ⚙️ Como configurar (com meu passo a passo cheio de cuidado 🧸)

  Conecte seu webhook do WhatsApp, Instagram, Messenger ou Evolution API  
  Crie uma tabela no Supabase chamada n8n_workflow_followup  
     Você pode usar esse modelo aqui:  
       👉 Planilha modelo Supabase  
  Adicione suas chaves de API do OpenAI, Google Maps, Supabase e Asaas no gerenciador do n8n  
  Personalize o prompt da IA com o nome do seu restaurante, estilo de fala e sua magia 💫  
  Defina a distância máxima de entrega (padrão: 10km)  
  Coloque seus próprios links de imagens do cardápio (pode ser do Drive, site ou CDN)  
  Prontinho! Agora o seu restaurante tem um atendente inteligente, gentil e muito eficiente 💕

  🍯 Funciona com:

  ✅ n8n Cloud e n8n auto-hospedado  
  🔐 E suas credenciais ficam guardadinhas com segurança no próprio n8n, tá bom?

  Quer algo feito especialmente pra você?
  Fala comigo com todo carinho 💻💛 Falar no WhatsApp (+55 17 99155-7874)

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name: AI-Powered WhatsApp Chatbot 🤖📲 for Text, Voice, Images & PDFs with memory 🧠
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-04-17T14:22:12.162Z
updated_at: 2025-04-17T14:22:12.162Z
url: https://n8n.io/workflows/3586
scraped_at: 2025-12-24 13:02:41
description:
  This workflow is a highly advanced multimodal AI assistant designed to operate through WhatsApp. It can understand and respond to text, images, voice messages, and PDF documents by combining OpenAI models with smart logic to adapt to the content received.

  🎯 Core Features

  📥 1. Automatic Message Type Detection
  Using the Input type node, the bot detects whether the user has sent:
  Text
  Voice messages
  Images
  Files (PDF)
  Other unsupported content

  💬 2. Smart Text Message Handling
  Text messages are processed by an OpenAI GPT-4o-mini agent with a customized system prompt.
  Replies are concise, accurate, and formatted for mobile readability.

  🖼️ 3. Image Analysis & Description
  Images are downloaded, converted to base64, and analyzed by an image-aware AI model.
  The output is a rich, structured description, designed for visually impaired users or visual content interpretation.

  🎙️ 4. Voice Message Transcription & Reply
  Audio messages are downloaded and transcribed using OpenAI Whisper.
  The transcribed text is analyzed and answered by the AI.
  Optionally, the AI reply can be converted back to voice using OpenAI's text-to-speech, and sent as an audio message.

  📄 5. PDF Document Extraction & Summary
  Only PDFs are allowed (filtered via MIME type).
  The document’s content is extracted and combined with the user's message.
  The AI then provides a relevant summary or answer.

  🧠 6. Contextual Memory
  Each user has a personalized session ID with a memory window of 10 interactions.
  This ensures a more natural and contextual conversation flow.

  How It Works  

  Thisworkflow is designed to handle incoming WhatsApp messages and process different types of inputs (text, audio, images, and PDF documents) using AI-powered analysis. Here’s how it functions:  

  Trigger: The workflow starts with the **WhatsApp Trigger node, which listens for incoming messages (text, audio, images, or documents).  
  Input Routing: The **Input type (Switch node) checks the message type and routes it to the appropriate processing branch:  
    Text: Directly forwards the message to the AI agent for response generation.  
    Audio: Downloads the audio file, transcribes it using OpenAI, and sends the transcription to the AI agent.  
    Image: Downloads the image, analyzes it with OpenAI’s GPT-4 model, and generates a detailed description.  
    PDF Document: Downloads the file, extracts text, and processes it with the AI agent.  
    Unsupported Formats: Sends an error message if the input is not supported.  
  AI Processing: The **AI Agent1 node, powered by OpenAI, processes the input (text, transcribed audio, image description, or PDF content) and generates a response.  
  Response Handling**:  
    For audio inputs, the AI’s response is converted back into speech (using OpenAI’s TTS) and sent as a voice message.  
    For other inputs, the response is sent as a text message via WhatsApp.  
  Memory: The **Simple Memory node maintains conversation context for follow-up interactions.  

  Setup Steps  
  To deploy this workflow in n8n, follow these steps:  

  Configure WhatsApp API Credentials:  
     Set up WhatsApp Business API credentials (Meta Developer Account).  
     Add the credentials in the WhatsApp Trigger, Get Image/Audio/File URL, and Send Message nodes.  

  Set Up OpenAI Integration:  
     Provide an OpenAI API key in the Analyze Image, Transcribe Audio, Generate Audio Response, and AI Agent1 nodes.  

   Adjust Input Handling (Optional):  
     Modify the Switch node ("Input type") to handle additional message types if needed.  
     Update the "Only PDF File" IF node to support other document formats.  

  Test & Deploy:  
     Activate the workflow and test with different message types (text, audio, image, PDF).  
     Ensure responses are correctly generated and sent back via WhatsApp.  

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

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name: Track Daily Product Hunt Launches with Website Verification in Google Sheets
author_name: Ifeoluwa Ajetomobi
author_username: vickylove
categories: []
created_at: 2025-04-17T14:43:40.711Z
updated_at: 2025-04-17T14:43:40.711Z
url: https://n8n.io/workflows/3588
scraped_at: 2025-12-24 13:02:46
description:
  This workflow helps you stay updated with daily launches on Product Hunt. It automatically fetches product details (name, tagline, description, and website), checks if the website redirects to another URL, and logs the final information into a Google Sheet.  

  Perfect for indie hackers, product managers, content curators, and anyone tracking daily launches.

  How It Works  

  Schedule Trigger – Runs the workflow daily.
  Set Date – Captures today’s date in ISO format for filtering Product Hunt posts.
  HTTP Request (Product Hunt API) – Retrieves Product Hunt posts for the day using GraphQL.
  Extract Product Info (Code Node) – Parses the response to pull key details:  
     Name  
     Tagline  
     Description  
     Website URL  
  HTTP Request (URL Check) – Follows each website URL to detect if it redirects.
  Merge Data – Combines product info with the final destination URL.
  Google Sheets Node – Appends all processed product info to your sheet.

  Pre-conditions  

  A valid Product Hunt API token
  A Google account with access to Google Sheets
  A Google Sheet already created with the correct columns (see below)
  Connected Google Sheets and HTTP credentials in n8n

  Google Sheets Setup  

  Your spreadsheet should include the following columns (in order):  
  Name  
  Tagline  
  Description  
  Original URL  
  Final URL (after redirect)  

  Ensure your Google Sheets node uses the correct Spreadsheet ID and Sheet Name.

  Setup Instructions  

  Product Hunt API Auth:  
     Replace {{YOUR_PRODUCT_HUNT_API_KEY}} in the HTTP Request headers:
        {
       "Authorization": "Bearer {{YOUR_PRODUCT_HUNT_API_KEY}}"
     }
     
  Google Sheets Node:  
     Connect your Google account.  
     Insert your Spreadsheet ID in the settings.  
     Specify the sheet name (e.g., Daily Launches).  
     Use the “Append” operation and map the 5 data fields accordingly.

  Notes  

  Only fetches the first 10 posts for the day (can be extended).
  Consider adding Slack, Discord, or Email nodes to notify you of new entries.
  Useful for building launch databases, research, or content inspiration.

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

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name: MCP AI Agent Google Calendar - Create, Update & Manage Events
author_name: Amanda Benks
author_username: amanda
categories: []
created_at: 2025-04-17T14:53:38.759Z
updated_at: 2025-04-17T14:53:38.759Z
url: https://n8n.io/workflows/3589
scraped_at: 2025-12-24 13:02:45
description:
  Hi! I’m Amanda :) 💖 
  I created this sweet little workflow with lots of love and care, just for you who wants to manage your Google Calendar in a smart and gentle way 💌

  This AI-powered agent connects with MCP (Multi-Channel Protocol) and understands natural language like “book a meeting tomorrow at 3pm”, “reschedule my call to Monday”, or “what events do I have on Wednesday?” — and it does everything quietly and beautifully in your calendar 🧸

  💡 What this lovely agent does

  🗓️ Creates new events in your Google Calendar (with or without guests)  
  ✏️ Updates existing events with new times or details  
  🧹 Deletes events you no longer need  
  🔍 Retrieves scheduled events by date  
  🤖 Works through GPT-4o or any AI via MCP Agent

  ⚙️ How to set it up (gently and step-by-step)

  Webhook is ready for MCP messages at POST /mcp/calendar  
  Connect your Google Calendar account using OAuth2 inside n8n  
  Link it to your favorite AI tool (like LangChain, Typebot, etc.) that can talk to the MCP agent  
  All details like title, time, date, and guests are parsed automatically from natural language 💫

  ✨ Requirements

  Google Calendar connected to n8n  
  n8n instance (Cloud or Self-hosted — both are supported!)  
  An AI interface that talks to the MCP agent (like LangChain or Typebot)  
  MCP Trigger API set up in your n8n environment

  This agent is perfect for therapists, consultants, coaches, small teams, or anyone who wants to keep their calendar flowing naturally and peacefully with a little help from AI 💆‍♀️🧠

  Want something customized just for you?
  Chat with me 💻💛 Chat via WhatsApp (+55 17 99155-7874)
  .
  .
  Tradução para Português:
  💖 Oi! Eu sou a Amanda :)

  Esse fluxinho aqui foi feito com muito cuidado pra você que quer automatizar sua agenda do Google Calendar de forma inteligente, simples e com muito carinho 💌

  Ele funciona como um agente que conversa com outro sistema de IA (via MCP) e consegue entender pedidos como “agende uma consulta amanhã às 15h”, “remarque a reunião para segunda”, ou “quais eventos tenho na quarta?” — tudo isso feito direto no seu calendário, sem você precisar abrir nada 🧸

  💡 O que ele faz com amor

  🗓️ Cria eventos no seu Google Calendar (com ou sem convidados)  
  ✏️ Atualiza eventos já existentes com novos horários  
  🧹 Exclui eventos que você não precisa mais  
  🔍 Busca seus compromissos com base em datas específicas  
  ❤️ Tudo isso com suporte ao modelo GPT-4o via agente MCP

  ⚙️ Como configurar (bem facinho, prometo!)

  Conecte o webhook: o endpoint do MCP já vem prontinho com o caminho mcp/calendar  
  Conecte sua conta do Google Calendar usando o OAuth2 no n8n  
  Adicione a integração do MCP Trigger com seu sistema de IA (LangChain, Typebot, etc.)  
  Todos os campos como título, data, hora e convidados são extraídos automaticamente via IA 💫

  ✨ Requisitos

  Conta Google Calendar integrada ao n8n  
  Instância n8n (Cloud ou Self-hosted)  
  Integração com uma IA que converse com o MCP Agent (como LangChain)  
  Acesso à API MCP ativado no n8n

  Esse agente é ideal pra psicólogos, consultores, times de atendimento, terapeutas — ou qualquer pessoa fofa que quer deixar sua agenda fluindo sozinha, com a ajuda de um toque de inteligência 💆‍♀️🧠

  Quer algo feito só pra você?
  Fala comigo com carinho 💻💛 Falar no WhatsApp (+55 17 99155-7874)

nodes_used:
  ["@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.googleCalendarTool","n8n-nodes-base.googleCalendarTool","n8n-nodes-base.googleCalendarTool","n8n-nodes-base.googleCalendarTool","n8n-nodes-base.googleCalendarTool"]

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name: ✨ Meta Ads Campaign Report by Period – Auto-Send via WhatsApp & Email
author_name: Amanda Benks
author_username: amanda
categories: []
created_at: 2025-04-17T15:51:47.721Z
updated_at: 2025-04-17T15:51:47.721Z
url: https://n8n.io/workflows/3591
scraped_at: 2025-12-24 13:02:45
description:
  Hi, I’m Amanda 🌷
  I created this workflow with great care to help you simplify your daily reporting routine. If you manage Meta Ads campaigns, you know how time-consuming it can be to open Ads Manager, filter data, and build client reports.

  This automation does all that for you — pulling campaign data by period, calculating key metrics, filtering only what matters, and sending everything via WhatsApp or Email, fully formatted and ready to share.

  What it does

  Collects ad performance data from multiple accounts  
  Filters campaigns and creatives with real results  
  Calculates metrics like CPC, CTR, CPV, conversions, and spend  
  Formats the report clearly and professionally  
  Sends the report via WhatsApp (Evolution API) or Gmail  
  Runs automatically every morning (optional schedule)

  Setup Guide

  Use this Google Sheet template to manage your accounts  
  Connect Google Sheets, Gmail, and Meta Ads to n8n  
  Set your action_type filters (lead, purchase, etc.)  
  Customize your message and delivery time

  Requirements

  Google account connected to n8n  
  Facebook Graph API access  
  WhatsApp via Evolution API (optional)  
  Basic n8n knowledge

  Want it customized for your business?
  Let’s chat → WhatsApp me (+55 17 99155-7874)

nodes_used:
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name: Automate Web Interactions with Claude 3.5 Haiku and Airtop Browser Agent
author_name: Airtop
author_username: cesar-at-airtop
categories: []
created_at: 2025-04-17T19:32:52.649Z
updated_at: 2025-04-17T19:32:52.649Z
url: https://n8n.io/workflows/3592
scraped_at: 2025-12-24 13:02:42
description:
  About this AI Agent

  This workflow is designed to automate web interactions by simulating a human user, using a combination of the Agent node and AI tools powered by Airtop.

  How does this workflow works?

  Form Submission Trigger: The workflow starts with a form submission trigger node named "On form submission". This node collects user instructions for the web AI agent, including a prompt and an optional Airtop profile name for sites requiring authentication.

  AI Agent: The core of the workflow is the "AI Agent" node, which uses a smart web agent to manage a remote web browser. It is designed to fulfill user requests by interacting with the browser through various tools.

  Browser Session Management

  Start Browser: The "Start browser" node initiates a new browser session and window. It is essential for obtaining the sessionId and windowId required for subsequent operations.
  Session and Window Management: The workflow includes nodes for creating and managing browser sessions and windows, such as "Session" and "Window".
  Web Interaction Tools:

  Load URL: This node loads a specified URL into the browser window.
  Query: The "Query" node allows the agent to ask questions and extract information from the current web page.
  Click: This node simulates clicking on elements within the web page.
  Type: The "Type" node types text into specified elements on the page.
  Session Termination: The "End session" node is used to terminate the browser session once the tasks are completed.

  Output Handling

  Structured Output Parser: This node processes the agent's results into a structured format.

  Output: The final results are set and prepared for output.
  Slack Integration: Although currently disabled, there is a "Slack" node intended to send messages to a Slack channel, potentially for notifications or live view URLs.

  Seting up your agent

  Airtop API Credentials:
  Users must have valid Airtop API credentials to interact with the web browser tools. This includes nodes like "Click", "Query", "Load URL", "End session", "Type", "Session", and "Window".
  Slack API Credentials (Optional):

  If users want to enable Slack notifications, they need to configure Slack OAuth2 credentials. The Slack node is currently disabled but can be used to send messages to a Slack channel.
  Anthropic API Credentials:

  The "Claude 3.5 Haiku" node requires Anthropic API credentials. Users need to have access to this API to utilize the language model features.

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.airtopTool","n8n-nodes-base.airtopTool","n8n-nodes-base.airtopTool","n8n-nodes-base.airtopTool","n8n-nodes-base.airtopTool","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.lmChatAnthropic","n8n-nodes-base.formTrigger","n8n-nodes-base.slack","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.set","n8n-nodes-base.airtop","n8n-nodes-base.airtop","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.slack"]

workflow_json_compressed:
  [encoded data, 5580 characters]


name: Extract Business Leads from Google Maps with Dumpling AI to Google Sheets
author_name: Yang
author_username: yang
categories: []
created_at: 2025-04-17T20:51:09.545Z
updated_at: 2025-04-17T20:51:09.545Z
url: https://n8n.io/workflows/3593
scraped_at: 2025-12-24 13:02:42
description:
  Who is this for?

  This workflow is built for marketers, sales teams, agencies, virtual assistants, and anyone who regularly researches or contacts local businesses. It's ideal for building lead lists, tracking competitors, or creating location-specific outreach campaigns.

  What problem is this workflow solving?

  Instead of manually searching Google Maps and copying business info into spreadsheets, this automation pulls structured business data (e.g. restaurants, gyms, service providers) and logs it directly into Google Sheets. It saves hours of work and ensures cleaner, more usable data.

  What this workflow does

  The workflow takes a Google Maps search query (like "best restaurants in New York") and sends it to Dumpling AI. It returns a list of places including their name, address, website, phone number, rating, and more. Each result is split into a row and automatically added to a Google Sheet.

  Setup

  Dumpling AI  
     Sign up at Dumpling AI  
     Generate your API key  
     In the HTTP Request node, select Header Auth and paste your key in the Authorization field

  Google Sheets  
     Create a sheet with tab name Leads  
     Add the following column headers to row 1:  
       Name, Address, Phone number, Website, Rating, Price Level, Type, Booking Link, Position  
     Connect your Google Sheets account and link this sheet in the node

  Customize the Query  
     In the HTTP node, replace the query string (e.g., "best+restaurants+in+New+York") with your own search term

  Run It  
     Use the manual trigger to test  
     Optionally swap in a Schedule or Webhook node to run it automatically

  How to customize this workflow to your needs

  Change the search query to target different cities or business types  
  Use filters to only save leads with a minimum rating or price level  
  Add GPT to summarize listings or qualify leads  
  Swap Google Sheets for Airtable or a CRM system for deeper integration

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

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name: 🧠 FloWatch 👁️ Analyze and Diagnose n8n Workflow Errors via OpenAI and Email
author_name: RealSimple Solutions
author_username: joeperes
categories: []
created_at: 2025-04-17T23:41:56.986Z
updated_at: 2025-04-17T23:41:56.986Z
url: https://n8n.io/workflows/3595
scraped_at: 2025-12-24 13:02:46
description:
  🧠 Analyze and Diagnose n8n Workflow Errors Automatically via OpenAI and Email



  &gt; ⚠️ This template is available on ☁️ Cloud & 🖥️ self-hosted n8n instances with the OpenAI node enabled.

  👤 Who is this for?

  This workflow is designed for n8n developers, automation engineers, and DevOps teams who want to automatically capture and analyze workflow errors, and receive professional HTML-styled diagnostics directly in their inbox.

  💥 What problem does this solve?

  Manually troubleshooting failed workflows in n8n can be time-consuming. This template streamlines error detection by:

  Capturing workflow failures using the Error Trigger node
  Diagnosing root causes with the help of OpenAI
  Sending a fully-formatted, human-readable HTML error report via email
  Including practical resolutions and next-step suggestions

  This helps you or your team resolve issues faster and avoid repeated manual debugging.

  ⚙️ What this workflow does

  ⚡ Triggers on any n8n workflow error
  📦 Extracts relevant error metadata including node, execution ID, and timestamps
  🧠 Sends error content to OpenAI for analysis and recommendations
  💌 Generates an HTML email report with inline styles and clear formatting
  📥 Emails the result to a system administrator or support email

  🛠️ Setup

  Install the OpenAI node in your self-hosted n8n instance.
  Add your OpenAI API Key securely in credentials.
  Configure the SMTP Email node with your email credentials.
  Adjust the Error Trigger to monitor specific workflows or all workflows.
  Set your preferred admin or dev email address in the final node.

  🔧 How to customize this workflow to your needs

  🧩 Use a [Set node] to define your variables, such as:
    Default admin email
    Workflow filter (optional)
  ✍️ Customize the prompt sent to OpenAI if you want deeper or more specific analysis
  🎨 Modify the email HTML styles to match your brand or internal format
  💾 Add additional logging (e.g., to Airtable, Google Sheets, or Notion) for long-term error tracking

  📌 Sticky Note

  Title: Automated Error Reporter with AI-Powered Diagnosis  
  Description: Captures any n8n error, sends it to OpenAI, and emails a beautiful HTML report to the administrator with steps to resolve the issue. Requires OpenAI credentials and SMTP configured.

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

workflow_json_compressed:
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name: Daily AI News Translation & Summary with GPT-4 and Telegram Delivery
author_name: SamirLiu
author_username: samir
categories: []
created_at: 2025-04-18T07:05:41.418Z
updated_at: 2025-04-18T07:05:41.418Z
url: https://n8n.io/workflows/3596
scraped_at: 2025-12-24 13:02:43
description:
  📝 What this workflow does

  Every morning at 8 a.m., this workflow fetches the latest AI-related articles from both GNews and NewsAPI. It merges up to 40 new articles daily, selects the 15 most relevant ones on AI technology and applications, and uses GPT-4.1 to generate concise summaries in accurate Traditional Chinese (while preserving essential English technical terms). Each summary also includes the article link for easy referral. The compiled digest is then posted to your designated Telegram account or group.

  👥 Who is this for?

  AI enthusiasts, professionals, and anyone interested in artificial intelligence news
  Individuals and teams wanting a concise daily digest of AI developments in Traditional Chinese
  Telegram users who prefer automated information delivery

  🎯 What problem does this workflow solve?

  With the rapid evolution of AI technology, it can be overwhelming to keep up with new developments. This workflow addresses information overload by automatically collecting, summarizing, and translating the most important AI news each morning — all delivered conveniently to your chosen Telegram channel or group.

  ⚙️ Setup

  🔑 Add NewsAPI and GNews API Keys
      Register for accounts on NewsAPI.org and GNews to obtain your API keys.
      Input your NewsAPI key directly into the Fetch NewsAPI articles node.
      Input your GNews API key into the Fetch GNews articles node.
  🤖 Set up your Telegram Bot
      Create a Telegram Bot via BotFather and copy the generated Bot Token.
      In n8n, create Telegram Bot credentials using this token.
      In the Send summary to Telegram node, enter the chat ID of your target user, group, or channel to receive the messages.
  🧠 Configure OpenAI Credentials
      In n8n, create a new credential using your OpenAI API key.
      Assign this credential to the GPT-4.1 Model node (or equivalent OpenAI/AI nodes).

  After completing these steps, your workflow is fully configured to fetch, summarize, and deliver daily AI news to your selected Telegram chat automatically.

  🛠️ How to customize this workflow

  🔍 Change the topic:** Update the keywords in the NewsAPI and GNews nodes for other subjects (e.g., "blockchain", "quantum computing").
  ⏰ Adjust delivery time:** Modify the scheduled trigger to your preferred hour.
  ✍️ Tweak summary style or language:** Refine the prompt in the AI summarizer node for different tones or translate into other languages as needed.

  📦 Dependencies

  NewsAPI account
  GNews account
  Telegram Bot
  OpenAI API access (for GPT-4.1) or compatible AI model for Langchain agent

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.merge","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.stickyNote","n8n-nodes-base.telegram"]

workflow_json_compressed:
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name: Log meal nutrients from Telegram to Google Sheets using an AI agent
author_name: PollupAI
author_username: Pollup
categories: []
created_at: 2025-04-18T08:53:18.483Z
updated_at: 2025-04-18T08:53:18.483Z
url: https://n8n.io/workflows/3599
scraped_at: 2025-12-24 13:02:46
description:
  Who is this for?

  This workflow is ideal for individuals focused on nutrition tracking, meal planning, or diet optimization—whether you’re a health-conscious individual, fitness coach, or developer working on a healthtech app. It also fits well for anyone who wants to capture their meal data via voice or text, without manually entering everything into a spreadsheet.

  What problem is this workflow solving?

  Manually logging meals and breaking down their nutritional content is time-consuming and often skipped. This workflow automates that process using Telegram for input, OpenAI for natural language understanding, and Google Sheets for structured tracking. It enables users to record meals by typing or sending voice messages, which are transcribed, analyzed for nutrients, and automatically stored for tracking and review.

  What this workflow does
  This n8n automation lets users send either a text or voice message to a Telegram bot describing their meal. The workflow then:
  Receives the Telegram message
  Checks if it’s a voice message
  	•	If yes: Downloads the audio file and transcribes it using OpenAI
  	•	If no: Uses the text input directly
  Sends the meal description to OpenAI to extract a structured list of ingredients and nutritional details
  Parses and stores the results in Google Sheets
  Responds via Telegram with a personalized confirmation message

  A testing interface also allows you to simulate prompts and view structured outputs for development or debugging.

  Setup
  Create a Telegram bot via BotFather and note the API token.
  Create an empty Google Sheet and store the sheet ID in the environment.
  Set up your OpenAI credentials in the n8n credential manager.
  Customize the “List of Ingredients and Nutrients” node with your prompt if needed.
  (Optional) Use the “Testing” section to simulate messages and refine outputs before going live.

  How to customize this workflow to your needs
  • Enhance prompts in the OpenAI node to improve the structure and accuracy of responses.
  • Add new fields in the Google Sheet and corresponding logic in the parser if you want more detail.
  • Adjust the Telegram response to provide motivational feedback, dietary tips, or summaries.
  • Upgrade to the “Pro” version mentioned in the contact section for USDA database integration and complete nutrient breakdowns.

  This is a lightweight, AI-powered meal logging automation that transforms voice or text into actionable nutrition data—perfect for making healthy eating easier and more data-driven.

  See my other workflows here

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.limit","n8n-nodes-base.telegramTrigger","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.splitOut","n8n-nodes-base.code","n8n-nodes-base.googleSheets","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.telegram","n8n-nodes-base.stickyNote"]

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name: LINE Chatbot with Google Sheets Memory and Gemini AI
author_name: Jakkrapat Ampring
author_username: guitarpmacc
categories: []
created_at: 2025-04-18T09:18:31.925Z
updated_at: 2025-04-18T09:18:31.925Z
url: https://n8n.io/workflows/3600
scraped_at: 2025-12-24 13:02:44
description:
  Main Use Case
  This workflow enables automated, AI-assisted replies to users messaging a LINE Official Account, while storing and referencing chat history from Google Sheets to maintain context. Ideal for businesses or support teams that want to provide smart, personalized customer interactions using AI with memory.

  How It Works (Step-by-Step)
  Connect to LINE Official Account's API
  A Webhook listens for incoming messages from users on LINE.
  When a message is received, it triggers the workflow.

  Prepare the Data
  An Edit Fields module structures incoming data (e.g. extracts user ID, message content).
  This ensures data is clean and usable downstream.

  Retrieve Chat History
  The user’s previous conversations are fetched from a Google Sheet.
  This ensures the AI has memory and can continue conversations contextually.

  Prepare Prompt
  The retrieved chat history is combined with the new message to form a complete prompt for the AI.
  Example format: “User previously said X. Now they said Y. How should we respond?”

  AI Agent: Google Gemini
  The formatted prompt is passed to an AI Agent (Google Gemini Chat Model).
  The AI generates a response based on the message + history.
  Tools used: Chat ModeMemory, ToolOutputParser for accurate replies.

  Split & Clean History
  The conversation history is split into smaller chunks for cleaning and storage.
  This ensures the Google Sheet remains readable and manageable over time.

  Save Chat History
  The cleaned new message and AI reply are saved to Google Sheets.
  This updates the chat history for future context.

  Send Reply to LINE
  The AI-generated reply is sent back to the user via a POST HTTP Request to the LINE Messaging API.

  How to Set Up
  Prerequisites:
  LINE Official Account
  Google Sheet to store chat history
  Google Gemini API or AI agent with context memory
  Automation platform (e.g., n8n, as this seems visually similar)

  Step-by-Step:
  Create a Webhook on LINE:
  Set the webhook URL to your automation service.
  Enable webhook events.

  Design Your Google Sheet:
  Create a sheet with columns: User ID, Timestamp, Message, AI Reply.

  Set Up Modules in Automation Platform:
  Webhook: receives user messages.
  Edit Fields: extract user ID and message.
  Google Sheets Read: fetch message history.
  Prompt Composer: format prompt using past history + new message.
  AI Agent: connect to Google Gemini for smart replies.
  Split & Clean: clean and chunk history if needed.
  Google Sheets Write: save the updated conversation.
  HTTP Request: send reply to LINE via Messaging API.

  Test Your Workflow:
  Send a message from LINE.
  Watch the full loop: receive → process → AI → store → reply.

  Deploy & Monitor:
  Ensure error handling is in place (e.g., for blank messages or failed API calls).
  Regularly check your Google Sheets for storage limits. (If limits reached, you can increase the history row.)

  📦 Benefits
  Maintains context in conversations
  Personalized, AI-driven responses
  Easy history tracking via Google Sheets
  Fully automated and scalable

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

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name: Scrape Indeed Job Listings for Hiring Signals Using Bright Data and LLMs
author_name: Yaron Been
author_username: yaron-nofluff
categories: []
created_at: 2025-04-18T10:30:44.845Z
updated_at: 2025-04-18T10:30:44.845Z
url: https://n8n.io/workflows/3601
scraped_at: 2025-12-24 13:02:44
description:
  Scrape Indeed Job Listings for Hiring Signals Using Bright Data and LLMs

  How the flow runs
  Fill the form with job position you're hunting for.
  Bright data's scraper will scrape Indeed based on your requirments.  
  Workflow waits for the snapshot.  
  Data returns as JSON.  
  Jobs append to Google Sheets.  
  Each row goes to an LLM to analyze if you're a good fit for the job (based on your prompts).  
  The LLMswrites YES or NO next to each job opportunity, helping you find job posts that are relevant to you.

  What you need
  Google Sheets with our template.  
  Bright Data dataset and API key.  
  OpenAI key for GPT‑4o mini (or any other LLM).  
  n8n with required nodes.

  Form fields To Fill
  Job Location** – city or region.  
  Keyword** – role or skills.  
  Country** – two‑letter code.

  Setup steps
  Copy the sheet template link.  
  Import the JSON workflow. 
  Add your credentials in nodes.  
  Test the form manually.  
  Add a schedule if desired.

  Bright Data filter example
  [
    {
      "country": "US",
      "domain": "indeed.com",
      "keyword_search": "Growth Marketer",
      "location": "Miami",
      "date_posted": "Last 24 hours"
    }
  ]

  Tips
  -Choose Last 24 hours often.

  -Increase wait time for big snapshots.

  -Narrow keywords to save credits.


  **Need help?
  **Email me anytime: 
  Yaron@nofluff.online
  YouTube: @YaronBeen
  LinkedIn: https://www.linkedin.com/in/yaronbeen/

  Bright Data Docs: https://docs.brightdata.com/introduction

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.formTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.splitOut","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainLlm"]

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name: Gmail MCP Server – Your All‑in‑One AI Email Toolkit
author_name: Brian Money
author_username: aspereo
categories: []
created_at: 2025-04-18T20:12:47.018Z
updated_at: 2025-04-18T20:12:47.018Z
url: https://n8n.io/workflows/3605
scraped_at: 2025-12-24 13:02:42
description:
  Gmail MCP Server
  Expose Gmail’s full API as a single SSE “tool server” endpoint for your AI agents.

  What it does
  Spins up an MCP Trigger that streams Server‑Sent Events to LangChain/N8N AI Agent nodes. ​

  Maps 20+ common Gmail operations (search, send, reply, draft, label & thread management, mark read/unread, delete, etc.) to ai_tool connections, so agents can invoke them with a simple JSON payload. 

  Why you’ll love it
  Agent‑ready: Plug the SSE URL into any N8N Agent or any other AI tool that uses MCP and start reasoning over email immediately.

  Extensible: Add more GmailTool operations or swap credentials without touching your agent logic.

  How to use
  Import the workflow (n8n ≥ v1.88).

  Set up a gmailOAuth2 credential and select it on the GmailTool nodes.

  Open the Gmail MCP Server node, copy the SSE URL, and paste it into your AI agent’s “Tool Server” field.

nodes_used:
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name: Document Analysis & Chatbot Creation with Llama Parser, Gemini LLM & Pinecone DB
author_name: pavith
author_username: pavith
categories: []
created_at: 2025-04-19T06:23:10.288Z
updated_at: 2025-04-19T06:23:10.288Z
url: https://n8n.io/workflows/3606
scraped_at: 2025-12-24 13:02:44
description:
  📄Description
  This automation workflow enables users to upload files via an N8N form, automatically analyzes the content using Google Gemini agents, and delivers the analyzed results via email along with a chatbot link. The system leverages Llama Cloud API, Google Gemini LLM, Pinecone vector database, and Gmail to provide a seamless, multilingual content analysis experience.

  ✅ Prerequisites
  Before setting up this workflow, ensure the following are in place:

  An active N8N instance.

  Access to Llama Cloud API.

  Google Gemini LLM API keys (for Translator & Analyzer agents).

  A Pinecone account with an active index.

  A Gmail account with API access configured.

  Basic knowledge of N8N workflow setup.

  ⚙️ Setup Instructions
  Deploy the N8N Form

  Create a public-facing form using N8N.

  Configure it to accept:

  File uploads.

  User email input.

  File Preprocessing

  Store the uploaded files temporarily.

  Organize and preprocess them as needed.

  Content Extraction using Llama Cloud API

  Feed the files into the Llama Cloud API.

  Extract and parse the content for further processing.

  Translation (if required)

  Use a Translator Agent (Google Gemini).

  Check if the content is in English. If not, translate it.

  Content Analysis

  Forward the (translated) content to the Analyzer Agent (Google Gemini).

  Perform deep analysis to extract insights.

  Vector Storage in Pinecone

  Store both:

  The parsed and translated content.

  The analyzed content.

  Use Pinecone to store the content as embeddings for chatbot use.

  User Notification via Gmail

  Send the analyzed content and chatbot link to the user’s provided email using Gmail API.

  🧩 Customization Guidance
  To add more languages: Update the translation logic to include additional language support.

  To modify analysis depth: Adjust the prompts sent to the Gemini Analyzer Agent.

  To change the chatbot behavior: Retrain or reconfigure the chatbot to utilize the new Pinecone index contextually.

  🔁 Workflow Summary
  User uploads files and email via N8N form.

  Files are parsed using Llama Cloud API.

  Content is translated (if needed) using Gemini Translator Agent.

  Translated content is analyzed by the Gemini Analyzer Agent.

  Parsed and analyzed data is stored in Pinecone.

  User receives email with analyzed results and a chatbot link.

nodes_used:
  ["n8n-nodes-base.formTrigger","n8n-nodes-base.splitInBatches","n8n-nodes-base.if","n8n-nodes-base.aggregate","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.markdown","n8n-nodes-base.gmail","n8n-nodes-base.code","n8n-nodes-base.convertToFile","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.informationExtractor","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.convertToFile","@n8n/n8n-nodes-langchain.vectorStorePinecone","@n8n/n8n-nodes-langchain.embeddingsMistralCloud","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.chainRetrievalQa","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.retrieverVectorStore","@n8n/n8n-nodes-langchain.vectorStorePinecone","@n8n/n8n-nodes-langchain.embeddingsMistralCloud","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: Find High-Intent Sales Leads by Scraping Glassdoor with Bright Data & GPT
author_name: Yaron Been
author_username: yaron-nofluff
categories: []
created_at: 2025-04-19T06:36:28.418Z
updated_at: 2025-04-19T06:36:28.418Z
url: https://n8n.io/workflows/3607
scraped_at: 2025-12-24 13:02:45
description:
  🔍 Scrape Glassdoor with Bright Data

  Designed for sales teams, recruiters, and marketers aiming to automate job discovery and prospecting.

  This workflow scrapes Glassdoor job listings using Bright Data and automatically generates targeted pitches using AI, streamlining lead identification and outreach.

  🧩 How It Works

  This automation leverages n8n, Bright Data, Google Sheets, and OpenAI:

  1. Trigger
  Starts with a custom form input (Location, Keyword, Country).

  2. Bright Data Job Scrape
  Triggers a Bright Data dataset snapshot via HTTP Request.
  Polls snapshot progress using a Wait node, ensuring data readiness.
  Retrieves full job listings dataset once ready.

  3. Google Sheets Integration
  Writes detailed job data (company, role, location, overview, metrics) into a Google Sheet.
  Uses a pre-built template for organized data storage.

  4. Automated Pitch Generation (AI)
  Splits listings into actionable parts: company name, title, and description.
  Sends data to OpenAI (via LangChain) to generate relevant pitches or icebreakers.
  Saves generated content back into the same sheet for easy access.

  ✅ Requirements

  Ensure you have the following:

  Google Sheets
  Google account  
  Template Sheet with columns for job details and AI-generated pitches

  Bright Data
  Active account with Dataset API access  
  API key and dataset ID

  OpenAI
  Valid OpenAI API key for GPT models

  n8n Environment
  Nodes: HTTP Request, Wait, If, Google Sheets, Split Out, LangChain (OpenAI)
  Credentials:
    Google Sheets OAuth2
    Bright Data API credentials
    OpenAI API key

  ⚙️ Setup Instructions

  Step 1: Prepare Google Sheets  
  Copy the provided Google Sheets template  
  Do not change headers

  Step 2: Import & Configure Workflow in n8n  
  Import the workflow JSON file  
  Set Google Sheets node:
    Link to your copied sheet  
    Confirm correct tab name  

  Step 3: Configure Bright Data  
  Replace &lt;YOUR_BRIGHT_DATA_API_KEY&gt; with your real key  
  Set your dataset ID in all HTTP Request nodes

  Step 4: Configure OpenAI (LangChain)  
  Connect OpenAI API key to the LangChain node  
  Customize prompt to match tone and outreach style

  Step 5: Testing & Scheduling  
  Test via manual form trigger  
  Schedule runs or leave form enabled for on-demand use

  🧠 Tips & Best Practices

  Use specific keywords and locations for better results  
  Adjust polling intervals based on dataset size  
  Refine AI prompts regularly to improve pitch quality  
  Clean unused columns from your sheet to boost performance

  💬 Support & Feedback

  For help or customization:

  📧 Email: Yaron@nofluff.online  
  📺 YouTube: @YaronBeen  
  🔗 LinkedIn: linkedin.com/in/yaronbeen  

  📚 Bright Data Docs: docs.brightdata.com/introduction

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workflow_json_compressed:
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name: Summarize YouTube Videos into Structured Content Ideas with AI and Airtable
author_name: Batu Öztürk
author_username: theozturkbatu
categories: []
created_at: 2025-04-19T14:53:57.801Z
updated_at: 2025-04-19T14:53:57.801Z
url: https://n8n.io/workflows/3609
scraped_at: 2025-12-24 13:02:45
description:
  Extract the main idea and key takeaways from YouTube videos and turn them into Airtable content ideas

  📝 Description
  Automatically turn YouTube videos into clear, structured content ideas stored in Airtable. This workflow pulls new video links from Airtable, extracts transcripts using a RapidAPI service, summarizes them with your favourite LLM, and logs the main idea and key takeaways—keeping your content pipeline fresh with minimal effort.

  ⚙️ What It Does
  Scans Airtable for new YouTube video links every 5 minutes.

  Extracts the transcript of the video using a third-party API via RapidAPI.

  Summarizes the content to generate a main idea and takeaways.

  Updates the original Airtable entry with the insights and marks it as completed.

  🛠 Prerequisites
  Before using this template, make sure you have:

  ✅ A RapidAPI account with access to the youtube-video-summarizer-gpt-ai API.

  ✅ A valid RapidAPI key.

  ✅ An OpenAI, Claude or Gemini account connected to n8n.

  ✅ An Airtable account with a base and table ready.

  🧰 Setup Instructions
  Clone this template into your n8n workspace.

  Open the Get YouTube Sources node and configure your Airtable credentials.

  In the Get video transcript node:

  Enter your X-RapidAPI-Key under headers.

  The API endpoint is pre-configured.

  Connect your LLM credentials to the Extract detailed summary node.

  (Optional) Adjust the summarization prompt in the LangChain node to better suit your tone.

  Set your preferred schedule in the Trigger node.

  📋 Airtable Setup
  Create a base (e.g., Content Hub) with a table named Ideas and the following columns:

  | Column Name | Type       | Required | Notes                      |
  |-------------|------------|----------|----------------------------|
  | Type       | Single select | ✅ | Must be set to Youtube Video |
  | Source | URL  | ✅ | The YouTube video URL       |
  | Status      | Checkbox        | ✅ | Leave empty initially; updated after processing   |
  | MainIdea        | Single line text | ✅ | Summary generated by OpenAI
  |
  | Key Takeaways        | Long text | ✅ | List of takeaways extracted from the transcript  


  Activate the workflow—and you're done!

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name: Scrape Trustpilot Reviews Using Bright Data & GPT-4o-mini for Winning Ad Copy
author_name: Yaron Been
author_username: yaron-nofluff
categories: []
created_at: 2025-04-19T15:01:54.627Z
updated_at: 2025-04-19T15:01:54.627Z
url: https://n8n.io/workflows/3610
scraped_at: 2025-12-24 13:02:46
description:
  🔍 Competitor Review Scraper & Ad Copy Generator
  (Trustpilot + Bright Data + GPT-4o-mini)

  📌 Who It's For
  Marketers, business owners, and agencies looking to:

  Analyze competitor pain points

  Generate high-impact Facebook ad copy
   
  Automate manual data processing

  🧩 How It Works
  This n8n-based workflow combines Bright Data, Google Sheets, and OpenAI to scrape, process, and transform Trustpilot reviews into ready-to-use ad copy.

  🔹 Step-by-Step Breakdown
  Trigger (Manual Form Submission)
  Input required:

  Competitor’s Trustpilot URL

  Review timeframe (30d, 3m, 6m, 12m)

  Fetch Reviews

  Calls Bright Data’s Dataset API with URL & timeframe

  Polls until snapshot is ready

  Retrieve & Store

  Extracts all reviews

  Saves them into a structured Google Sheet

  Filter & Aggregate

  Filters to only 1–2 star reviews

  Summarizes common negative feedback

  Generate Ad Copy

  Sends the summary to OpenAI GPT-4o-mini

  Produces 3 variations of ad copy targeting pain points

  Distribute Insights

  Sends ad copy + summary via email to the marketing team

  ✅ Requirements

  -LLM Account 
  -Google Sheets - Copy this sheet:
  https://docs.google.com/spreadsheets/d/1Zi758ds2_aWzvbDYqwuGiQNaurLgs-leS9wjLWWlbUU/edit?gid=0#gid=0

  -Bright Data account

  ⚙️ Setup Instructions
  **Step 1: Google Sheets
  **
  Copy this Google Sheets template

  Do not change column headers

  **Step 2: n8n Credential Setup
  **
  Google Sheets: OAuth2

  Bright Data: Authorization Header

  OpenAI: API Key for GPT-4o-mini

  **Step 3: Import Workflow
  **
  Import the .json file into n8n

  Configure your sheet + dataset ID

  Adjust GPT prompts as needed

  **Step 4: Run the Workflow
  **
  Trigger via form

  Receive ad copy + review insights via email

  🧠 Tips & Best Practices
  Bright Data snapshots may take time — polling is handled

  Focusing on 1–2 star reviews yields the most actionable pain points

  You can customize GPT-4o-mini prompts for tone or vertical

  💬 Support & Feedback
  Need help or customization?

  📧 Email: Yaron@nofluff.online
  📺 YouTube: @YaronBeen
  🔗 LinkedIn: linkedin.com/in/yaronbeen

  📚 Bright Data Docs: docs.brightdata.com/introduction

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name: Telegram giveaways among channel subscribers (Module "Giveaway")
author_name: Andrew
author_username: boanse
categories: []
created_at: 2025-04-19T16:04:43.183Z
updated_at: 2025-04-19T16:04:43.183Z
url: https://n8n.io/workflows/3611
scraped_at: 2025-12-24 13:02:47
description:
  Who is this for?
  This workflow is ideal for businesses or individuals who want to run Telegram-based giveaways that require users to subscribe to certain channels in order to participate.

  What problem is this workflow solving?
  Running a fair and trackable giveaway on Telegram can be difficult without a system that verifies subscriptions and manages participants. This workflow simplifies that process, ensuring only eligible users are included and enabling a smooth drawing process.

  What this workflow does
  The giveaway organizer specifies a list of Telegram channels for participants to must subscribe to.
  The organizer shares a bot link with users.
  Users start the bot, confirming their participation in the giveaway.
  The bot tracks eligible participants.
  The administrator complete the giveaway and select winners.

  Setup
  Create tables in your Postgres database  
     Open the provided SQL script.
     Replace "n8n" with your schema name.
     Execute the script in your Postgres DB.

  Add credentials in n8n  
     Telegram credentials (Bot token, etc.)  
     Postgres database credentials

  How to customize this workflow to your needs
  Modify the SQL script to match your database schema if needed.
  Customize the Telegram bot messages and logic using the n8n Telegram nodes.
  Add additional nodes if you want to notify winners or log data in other services.

nodes_used:
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name: Automate Testing and Collect Responses via Telegram in Postgres (Module "Quiz")
author_name: Andrew
author_username: boanse
categories: []
created_at: 2025-04-19T16:48:32.300Z
updated_at: 2025-04-19T16:48:32.300Z
url: https://n8n.io/workflows/3612
scraped_at: 2025-12-24 13:02:46
description:
  Who is this for?  
  This template is ideal for educators, HR professionals, and anyone looking to automate testing and collect responses through Telegram, while storing results in a Postgres database.

  What problem is this workflow solving?  
  Manually organizing and managing tests can be time-consuming and prone to error. This workflow automates the test distribution, response collection, and scoring process, ensuring a seamless and efficient testing experience.

  What this workflow does  
  Adds test content, including questions and answers, to a Postgres database  
  Sends a Telegram bot link to test participants  
  Automatically delivers the test via the Telegram bot  
  Collects and stores responses  
  Displays the participant's ranking at the end of the test  

  Setup  
  Create tables in Postgres  
     Replace "n8n" in the provided SQL script with the name of your actual schema  
     Run the script in your database to create the required tables  

  Add credentials  
     Set up and connect your Telegram credentials  
     Set up and connect your Postgres credentials  

  How to customize this workflow to your needs  
  Modify the test content directly in the Postgres database  
  Customize the Telegram bot messages to match your tone and branding.
  Adjust scoring logic if needed to reflect different evaluation methods.

nodes_used:
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name: Automate Telegram Channel Posts Using Postgres (Module "Cross-Posting")
author_name: Andrew
author_username: boanse
categories: []
created_at: 2025-04-19T17:41:14.399Z
updated_at: 2025-04-19T17:41:14.399Z
url: https://n8n.io/workflows/3614
scraped_at: 2025-12-24 13:02:46
description:
  Who is this for?
  This template is ideal for users managing Telegram channels who want to automate the process of posting messages, including text and images, directly from an bot.

  What problem is this workflow solving?
  Manually posting content to multiple Telegram channels can be time-consuming and inconsistent. This workflow automates that process, enabling efficient and consistent content delivery to one or more channels.

  What this workflow does
  This workflow connects to a Postgres database to retrieve post content and automatically sends text or image posts to designated Telegram channels where the bot is an admin. It supports scheduling and content customization through the database.

  Setup
  1. Create the necessary tables in your Postgres database
  Open the SQL script provided in the workflow.
  Replace "n8n" with your actual schema name.
  Run the script in your database to set up the required tables.

  2. Add credentials
  Telegram**: Create and connect a bot, ensuring it has admin access to the target channels.
  Postgres**: Connect your Postgres instance using valid credentials.

  How to customize this workflow to your needs
  Modify the SQL script to match your specific table or schema naming conventions.
  Adjust the content format or post frequency directly from your database.

nodes_used:
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workflow_json_compressed:
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name: Verify Telegram Channel Subscriptions with Access Control using Postgres
author_name: Andrew
author_username: boanse
categories: []
created_at: 2025-04-19T18:20:13.113Z
updated_at: 2025-04-19T18:20:13.113Z
url: https://n8n.io/workflows/3615
scraped_at: 2025-12-24 13:02:47
description:
  Who is this for?  
  This workflow is for Telegram bot developers or marketers who want to verify user subscriptions to specific Telegram channels and optionally reward them with downloadable files.

  What problem is this workflow solving?  
  This workflow ensures users have subscribed to required Telegram channels before they are granted access to resources or rewards, such as Google Drive files. It automates the subscription verification process, saving time and reducing manual effort.

  What this workflow does  
  Verifies if a user is subscribed to specific Telegram channels.  
  Allows adding and removing channel IDs for verification.  
  Sends referral links to users for subscription checks.  
  Optionally grants access to Google Drive files upon successful verification (currently inactive)
  Setup  
  Add the bot to your Telegram channels as an admin – this is required for the bot to check subscription status.  
  Add channel IDs via the bot – these are the channels users will need to subscribe to.  
  Send the referral link to users you want to verify.  
  Remove channels anytime from the subscription check list if needed.  
  Create tables in Postgres:  
     Replace "n8n" in the provided SQL script with your DB schema name.  
     Run the script in your Postgres DB.  
  Add credentials:  
     Telegram  
     Postgres  

  How to customize this workflow to your needs  
  Modify the SQL script to suit your existing database structure.  
  Adjust the logic to enable or disable file delivery from Google Drive.  

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  ["n8n-nodes-base.stickyNote","n8n-nodes-base.telegram","n8n-nodes-base.postgres","n8n-nodes-base.aggregate","n8n-nodes-base.if","n8n-nodes-base.telegram","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.postgres","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.telegramTrigger","n8n-nodes-base.postgres","n8n-nodes-base.switch","n8n-nodes-base.switch","n8n-nodes-base.telegram","n8n-nodes-base.postgres","n8n-nodes-base.telegram","n8n-nodes-base.postgres","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.summarize","n8n-nodes-base.telegram","n8n-nodes-base.switch","n8n-nodes-base.postgres","n8n-nodes-base.switch","n8n-nodes-base.telegram","n8n-nodes-base.postgres","n8n-nodes-base.if","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.postgres","n8n-nodes-base.if","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 4296 characters]


name: Automated Lead Generation & Contact Enrichment with Hunter.io and Perplexity AI
author_name: Velebit from Innovatio
author_username: innovatio
categories: []
created_at: 2025-04-19T18:41:48.286Z
updated_at: 2025-04-19T18:41:48.286Z
url: https://n8n.io/workflows/3616
scraped_at: 2025-12-24 13:02:44
description:
  Who is this for?

  This template is ideal for B2B founders, solopreneurs, growth marketers, SDRs, or anyone looking to scale their lead generation and enrichment with no-code tools to low-code tools.

  Whether you're building your first lead pipeline or upgrading a manual spreadsheet, LeadAIgen automates the entire discovery → enrichment → logging process using AI and verified email data.

  What problem does this solve?

  Lead sourcing and enrichment are time-consuming, inconsistent, and often require multiple tools or manual scraping.

  This workflow solves that by chaining AI + contact discovery, validating everything, and storing clean, structured data in Google Sheets — ready for outreach or CRM sync.

  What this workflow does:

  ✅ Accepts an industry/topic prompt via chat trigger or Telegram,  
  🤖 Uses OpenRouter (Perplexity) to find 15 relevant companies with domains,  
  📤 Parses the results, filters duplicates based on your Google Sheet,  
  📩 Enriches each domain with up to 3 real personal emails using Hunter.io,  
  💡 If no emails found → Fallback AI tries to locate general email info,  
  📊 Updates your Google Sheet with all valid enriched leads (status: “Enriched”),  
  🔁 Runs in safe batches of 10 to protect your API limits,  
  🛠️ Includes logic to clean data, validate JSON, and slice leads.

  Setup:

  Create Google Sheets, Hunter.io, and OpenRouter credentials in your n8n instance  
  Import the JSON workflow  
  Set your OpenRouter model (default = Perplexity Sonar Large)  
  Connect your Google Sheet (must contain a header row with Domain, Status, etc.)  
  Paste your Hunter API key into the Hunter.io node  
  (Optional) Add Telegram Bot trigger to chat with your lead generator  

  🗒 Color-coded workflow notes:

  To make customization easier, this template includes Innovatio's signature visual sticky note system inside the n8n canvas.

  You’ll find:

  🟩 Green Notes → Main Steps  
  The core logic blocks — from company generation to AI fallback and Google Sheets update.

  🟦 Blue Notes → Personalization Tips  
  How to adapt filters, prompts, and data mappings for your business needs.

  🟨 Yellow Notes → Optional / Advanced  
  Expand with Telegram chat triggers, Airtable integration, CRM push, or lead scoring logic.

  🟫 Gray Notes → Welcome, Outro & Upgrade Suggestions  
  Clear guidance and CTAs if you want help scaling this further.

  👉 Each node includes comments or setup notes so you can learn the system as you use it.

  How to customize this workflow?

  🎯 Swap Google Sheets for Airtable for relational views and tagging,  
  🤖 Replace Perplexity with GPT-4, Claude, or Mixtral inside OpenRouter,  
  📬 Auto-send enriched leads to Gmail, Slack, or your CRM,  
  🗣️ Connect this to Telegram for on-the-go company generation,  
  🔁 Want dynamic lead scoring or auto-categorization? We can help — velebit@innovatio.design

  Final notes

  This template was designed by Velebit from Innovatio.  
  External links (e.g. documentation or support email) lead only to official sources with no affiliate tracking or paid placements.

  A separate license applies to the paid version on Gumroad, which includes commercial use rights, extended fallback logic, and advanced upgrade tips.

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name: Generate Monthly Financial Reports with Gemini AI, SQL, and Outlook
author_name: Amjid Ali
author_username: amjid
categories: []
created_at: 2025-04-19T20:25:03.594Z
updated_at: 2025-04-19T20:25:03.594Z
url: https://n8n.io/workflows/3617
scraped_at: 2025-12-24 13:02:41
description:
  🚀 AI-Powered Business Performance Reporting Automation

  Unlock executive-level insights with ZERO manual work!  
  This n8n template empowers you to automate your entire monthly business performance reporting using dynamic SQL queries, AI-driven analysis, and beautiful HTML dashboards — all delivered directly to your inbox.

  🎯 What This Automation Does

  📆 Triggers automatically every month (5th of each month)
  🧮 Fetches financial data from SQL (ERPNext or any database)
  🔁 Loops over cost centers to analyze each business unit individually
  📊 Generates Profit & Loss reports, WIP, Employee stats, and vertical breakdowns
  🤖 Uses Google Gemini 2.5 AI to perform advanced financial analysis
  💌 Delivers a polished HTML report to your email inbox
  🔧 Fully modular – replace data source with Excel, Google Sheets, or APIs

  🧑‍🏫 Step-by-Step Video Tutorial

  🎥 Watch the full tutorial on YouTube:  
    
  📌 Learn how each node works and see the AI-generated report in action.

  🌐 Useful Links

  🔗 Sign up for n8n Cloud (recommended for non-tech users):  
    👉 https://n8n.syncbricks.com

  📘 Download the step-by-step Guidebook (Free):  
    👉 https://lms.syncbricks.com/books/n8n

  📚 Explore the full course on n8n (includes templates, workflows, and AI integrations):  
    👉 https://lms.syncbricks.com/courses/n8n

  🛠 Requirements

  ✅ n8n (Self-hosted or Cloud)
  ✅ SQL Database (MySQL / PostgreSQL / ERPNext)
  ✅ Microsoft Outlook or Gmail (to send the report)
  ✅ Gemini API Key (for AI analysis)
  ✅ Basic understanding of your data schema

  💡 Why Use This Template?

  ⏱ Saves 2-3 days of manual work every month
  📈 Improves financial visibility across business units
  🤝 Great for CFOs, COOs, Finance Analysts, and BI teams
  🔄 Scales across multiple divisions and companies
  🧠 Leverages AI for actionable insights and recommendations

  🧩 Customize It Your Way

  Replace the SQL nodes with:
    Excel / Google Sheets
    Airtable / APIs
    Custom Applications

  Swap the AI model:
    OpenAI GPT
    Claude
    DeepSeek

  Adjust the report structure or HTML style

  🙌 Get Started Now

  🎯 Import the JSON template → Connect your data → Receive business insights via email.  
  Don’t let manual reporting slow down your decision-making.

  👉 Sign up for n8n Cloud  
  👉 Learn n8n with Amjid  
  👉 Download Guide

  Created by Amjid Ali | SyncBricks™ – Automation for Everyone

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