# templates

name: Qualify Real Estate Leads Automatically with OpenAI, Gmail & Airtable CRM
author_name: Marth
author_username: marth
categories: []
created_at: 2025-06-28T06:14:18.530Z
updated_at: 2025-06-28T06:14:18.530Z
url: https://n8n.io/workflows/5428
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:32
description:
  🧠 How It Works
  This AI Agent automatically qualifies property buyer leads from form submissions.
  Form Submission Trigger
  When a user submits their details via a property inquiry form, the workflow is triggered.
  AI Lead Classification
  The buyer's input (budget, location, timeline, etc.) is analyzed by OpenAI to extract structured data and generate a lead score (0–100).
  Lead Qualification Logic
  Leads with a score of 70 or above are marked as qualified, the rest are ignored or stored separately.
  Follow-Up Action
  Qualified leads trigger:
  Email notification to the agent
  Record creation in Airtable as CRM

  ⚙️ How to Set Up
  Form Setup
  Replace the form trigger with your preferred source (Typeform, Google Form, etc.)
  Make sure the form includes: Name, Email, Budget, Location, Timeline, Property Type
  Connect Your Credentials
  Add your OpenAI API key for the LLM node
  Connect your Gmail account for notifications
  Link your Airtable base and table to store qualified leads
  Customize Scoring Logic (Optional)
  You can tweak the prompt in the Information Extractor node to change how scoring works
  Test the Workflow
  Submit a test entry via the form
  Check if you receive an email and see the lead in Airtable
  Activate & Go Live
  Turn on the workflow and start qualifying real buyer leads in real time

  Connect with my linkedin: https://www.linkedin.com/in/bheta-dwiki-maranatha-15654b227/

nodes_used:
  ["n8n-nodes-base.formTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.gmail","n8n-nodes-base.airtable","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","lead management","CRM","AI/ML"],"complexity":"medium","use_cases":["Automated lead qualification for property buyer inquiries","Prioritizing and scoring incoming leads","Automatically creating CRM records for qualified leads","Sending agent notifications for high-quality leads","Extracting structured lead data (budget, location, timeline, etc.) from free-text form input"],"estimated_setup_minutes":20,"required_services":["OpenAI API (LLM)","Gmail (or other email provider)","Airtable","Form provider (Typeform, Google Forms, custom form)","n8n (workflow automation platform)"],"key_features":["AI-powered information extraction and lead scoring (0–100)","Configurable qualification threshold (default: 70)","Conditional logic to filter qualified vs unqualified leads","Automatic Airtable CRM record creation for qualified leads","Email notifications to agents for qualified leads (via Gmail node)"],"target_audience":["Real estate agents and brokers","Sales and lead qualification teams","Small agencies and CRM administrators using Airtable and n8n no-code automation"]}


name: Market Research & Business Case Generator with GPT-4o, Perplexity & Claude
author_name: Automate With Marc
author_username: marconi
categories: []
created_at: 2025-06-28T10:25:17.885Z
updated_at: 2025-06-28T10:25:17.885Z
url: https://n8n.io/workflows/5430
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 

  🧠 Market Research & Business Case Study Generator
  Category: AI + Research | GPT + Perplexity | Business Strategy
  Skill Level: Intermediate
  Use Case: Market Research, Business Planning, Strategic Analysis

  📌 Description:
  This template automates the creation of comprehensive, data-backed business case studies—perfect for entrepreneurs, analysts, consultants, and market researchers.

  For more of such build + step-by-step video tutorials, check out:
  https://www.youtube.com/@Automatewithmarc

  Just send a simple message like:

  “Give me a market opportunity analysis of a bicycle rental business in North Africa.”

  And the workflow does the rest. It scopes your research topic, performs live web research, and crafts a well-structured 1500-word business case study—all automatically saved to Google Docs.

  🔧 How It Works:
  🟢 Chat Trigger:
  Start the workflow by sending a prompt via the built-in Chat interface (Langchain Chat Trigger).

  🧭 Research Scope Definer (GPT-4o):
  Breaks down the user input into structured components like industry, geography, trends, and challenges.

  🌐 Deep Research (Perplexity Sonar):
  Performs live research to retrieve relevant industry data, consumer trends, competitive insights, and more.

  📘 Business Case Writer (Claude Sonnet):
  Synthesizes the findings into a detailed case study with sections including:
  Executive Summary
  Market Overview
  Opportunity Analysis
  Competitive Landscape
  Risks & Challenges
  Strategic Recommendations
  Conclusion
  📄 Google Docs Integration:
  The final output is appended to a connected Google Doc, so all your insights are neatly stored and ready to share.
  🧰 Tools Used:
  OpenAI GPT-4o
  Perplexity Sonar Deep Research
  Anthropic Claude Sonnet
  Google Docs
  Chat Trigger
  ✅ Ideal For:
  Business consultants & strategy teams
  Market researchers & analysts
  Startup founders & product managers
  Educators & MBA students

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.lmChatAnthropic","n8n-nodes-base.googleDocs","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.perplexity","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 4292 characters]

metadata_json:
  {"categories":["automation","AI research","business strategy","document generation","web research"],"complexity":"medium","use_cases":["Market research","Business planning","Strategic analysis","Competitive analysis","Automated report generation"],"estimated_setup_minutes":45,"required_services":["OpenAI (GPT-4o)","Perplexity Sonar (live web research)","Anthropic Claude Sonnet","Google Docs API / Google account","n8n self-hosted with community nodes","LangChain Chat Trigger"],"key_features":["Breaks user prompt into research scope (GPT-4o)","Performs live web research (Perplexity Sonar)","Synthesizes findings into a structured ~1500-word business case (Claude)","Appends final report to Google Docs automatically","Multi-model orchestration (GPT, Perplexity, Anthropic) integrated in one workflow"],"target_audience":["Business consultants","Market researchers & analysts","Startup founders & product managers」「Educators & MBA students"]}


name: Scrape Reuters News & Send AI Summaries with Brightdata, Claude 4 & Telegram
author_name: ankitkansaldev
author_username: ankitkansaldev
categories: []
created_at: 2025-06-28T10:54:11.258Z
updated_at: 2025-06-28T10:54:11.258Z
url: https://n8n.io/workflows/5431
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  📰 Comprehensive Reuters News Intelligence System With Brightdata & Telegram Alerts

  A powerful n8n automation workflow that scrapes the latest Reuters news articles using Bright Data's web scraping capabilities and delivers intelligent news summaries directly to your Telegram chat.

  📋 Overview

  This workflow provides an automated news intelligence solution that monitors Reuters for breaking news, analyzes content using Claude AI, and delivers personalized news alerts. Perfect for journalists, researchers, traders, and anyone who needs real-time access to Reuters content with AI-powered insights.

  ✨ Key Features

  🎯 Form-Based Input: Easy web form to specify keywords and news type preferences  
  🤖 AI-Powered Processing: Uses Claude 4 Sonnet for intelligent content analysis  
  🌐 Professional Scraping: Leverages Bright Data's Reuters dataset for reliable data extraction  
  📱 Telegram Integration: Instant notifications delivered to your preferred chat  
  ⏰ Smart Waiting: Built-in delays to ensure data processing completion  
  🔄 Status Monitoring: Automatic scraping status checks with retry logic  
  📊 Data Formatting: Clean, structured output with essential article fields  
  🚀 Scalable Design: Handles multiple articles with batch processing  

  🎯 What This Workflow Does

  Input
  Keywords**: Search terms for Reuters articles (e.g., "Election", "Gas shocks", "Technology")
  News Type**: Sorting preference (newest, oldest, relevance)
  Form Submission**: Web-based interface for easy interaction

  Processing
  Form Trigger: Captures user input via web form interface
  AI Agent Orchestration: Claude processes requirements and coordinates actions
  Bright Data Request: Initiates Reuters scraping with specified keywords
  Status Monitoring: Checks scraping progress with smart retry logic
  Data Retrieval: Fetches completed article data when ready
  Content Processing: Extracts and formats essential article information
  Telegram Delivery: Sends structured news updates to specified chat

  Output Data Points

  | Field | Description | Example |
  |-------|-------------|---------|
  | article_title | The main headline of the article | "Global Energy Markets Face Uncertainty" |
  | headline | Reuters display headline | "Oil Prices Surge Amid Supply Concerns" |
  | description | Article summary/meta description | "Energy markets react to geopolitical tensions..." |
  | content | Full article body text | "LONDON (Reuters) - Oil prices jumped 3%..." |
  | article_url | Direct link to Reuters article | "https://reuters.com/business/energy/..." |

  🚀 Setup Instructions

  Prerequisites
  n8n instance (self-hosted or cloud)
  Bright Data account with Reuters dataset access
  Telegram bot and channel setup
  Claude API access (Anthropic)
  15-20 minutes for complete setup

  Step 1: Import the Workflow
  Copy the JSON workflow code from the provided file
  In n8n: Workflows → + Add workflow → Import from JSON
  Paste JSON content and click Import
  Save the workflow with a descriptive name

  Step 2: Configure Bright Data Integration
  Set up Bright Data credentials:
     In n8n: Credentials → + Add credential → HTTP Header Auth
     Name: "Bright Data API"
     Add header: Authorization: Bearer YOUR_BRIGHT_DATA_API_KEY
     Test the connection

  Configure Reuters dataset:
     Ensure access to dataset ID: gd_lyptx9h74wtlvpnfu
     Verify Reuters scraping permissions in Bright Data dashboard
     Check monthly quota and usage limits

  Step 3: Configure Anthropic Claude Integration
  Set up Anthropic credentials:
     In n8n: Credentials → + Add credential → Anthropic API
     Enter your Anthropic API key
     Test the connection

  Update model settings:
     Open "Anthropic Chat Model" node
     Verify model is set to: claude-sonnet-4-20250514
     Adjust temperature and other parameters if needed

  Step 4: Configure Telegram Notifications
  Create Telegram Bot:
     Message @BotFather on Telegram
     Use /newbot command and follow instructions
     Save the bot token provided

  Get Chat ID:
     Add your bot to desired channel/group
     Send a test message
     Visit: https://api.telegram.org/bot{BOT_TOKEN}/getUpdates
     Find your chat ID in the response

  Set up Telegram credentials:
     In n8n: Credentials → + Add credential → Telegram API
     Enter bot token from BotFather
     Test the connection

  Update Telegram node:
     Open "Telegram" node
     Replace DEMO_CHAT_ID with your actual chat ID
     Customize message format if needed

  Step 5: Configure Web Form
  Set up form trigger:
     Open "On form submission" node
     Note the webhook URL provided
     Customize form title and fields if needed

  Test form functionality:
     Access the webhook URL in your browser
     Fill out test form with sample keywords
     Verify form submission triggers workflow

  Step 6: Update Node Configurations
  Update HTTP Request nodes:
     Replace BRIGHT_DATA_API_KEY with actual credentials reference
     Verify dataset ID matches your Bright Data setup
     Check request parameters and headers

  Configure Data Formatting:
     Open "Data Formatting" node
     Review JavaScript code for field extraction
     Modify output fields if additional data needed

  Step 7: Test & Activate
  Run initial test:
     Submit form with test keywords (e.g., "Technology")
     Monitor workflow execution in n8n
     Check for Telegram message delivery

  Verify data flow:
     Confirm Bright Data snapshot creation
     Check status monitoring functionality
     Validate final data formatting

  Activate workflow:
     Toggle workflow to "Active" status
     Monitor for any execution errors
     Set up error notifications if needed

  📖 Usage Guide

  Submitting News Requests
  Access the form:
     Navigate to your webhook URL
     Form title: "Reuters News Intelligence"

  Fill required fields:
     Keywords: Enter search terms (e.g., "Climate Change", "Tech Earnings")
     News Type: Select sorting preference:
       newest: Most recent articles first
       oldest: Historical articles first  
       relevance: Best matching articles

  Submit and wait:
     Click submit to trigger workflow
     Expect 1-3 minutes for processing
     Check Telegram for article delivery

  Understanding the Process
  The workflow follows this sequence:
  Form submission triggers Claude AI agent
  Claude coordinates all scraping and processing steps
  Bright Data scrapes Reuters with your keywords
  System waits for scraping completion (60 seconds)
  Status check confirms data readiness
  Article data is retrieved and formatted
  Telegram message delivers final results

  Reading Telegram Results
  Each article includes:
  Clickable URL** to full Reuters article
  Headline** for quick scanning
  Description** with article summary
  Content preview** with key details

  🔧 Customization Options

  Modifying Search Parameters
  Edit the "HTTP Request" node to adjust:
  {
    "keyword": "Your search terms",
    "sort": "newest|oldest|relevance",
    "limit_per_input": "2-10 articles"
  }

  Customizing Telegram Messages
  Update the "Telegram" node message format:
  🗞️ {{ $json.heading }}

  📖 {{ $json.description }}

  🔗 Read Full Article

  📅 Retrieved: {{ $now.format('YYYY-MM-DD HH:mm') }}

  Adding Email Notifications
  Add "Email" node after "Data Formatting"
  Configure SMTP credentials
  Create HTML email template with article data
  Connect to same input as Telegram node

  Enhancing AI Processing
  Modify the MCP Agent prompt to:
  Request specific article sections
  Add sentiment analysis
  Include market impact assessment
  Generate executive summaries
  Extract key quotes and statistics

  Adding Data Storage
  Include database storage by:
  Adding "Postgres" or "MySQL" node
  Creating articles table with schema
  Storing full article data for analysis
  Building historical news database

  🚨 Troubleshooting

  Common Issues & Solutions

  1. "Bright Data snapshot failed"
  Cause**: Invalid API key or dataset access
  Solution**: Verify credentials and dataset permissions in Bright Data dashboard

  2. "No articles found"
  Cause**: Keywords too specific or no matching content
  Solution**: Try broader search terms, check Reuters availability

  3. "Telegram message not sent"
  Cause**: Invalid bot token or chat ID
  Solution**: Re-verify bot setup with @BotFather, confirm chat ID

  4. "Workflow timeout"
  Cause**: Bright Data scraping taking too long
  Solution**: Increase timeout in "sleep tool" or add retry logic

  5. "Data formatting errors"
  Cause**: Unexpected response structure from Bright Data
  Solution**: Check "Data Formatting" node logs, adjust parsing logic

  6. "Claude API errors"
  Cause**: API key issues or rate limiting
  Solution**: Verify Anthropic credentials, check usage limits

  Advanced Troubleshooting
  Monitor execution logs** in n8n for detailed error messages
  Test individual nodes** by running them separately
  Verify JSON structures** ensure data flows correctly between nodes
  Check rate limits** for both Bright Data and Claude API
  Add error handling** implement try-catch logic for robust operation

  📊 Use Cases & Examples

  1. Financial News Monitoring
  Goal: Track market-moving Reuters financial news
  Keywords: "earnings", "fed rates", "market outlook"
  Instant alerts for breaking financial news
  Support trading and investment decisions

  2. Competitive Intelligence
  Goal: Monitor industry-specific news for business insights
  Keywords: Company names, industry terms
  Track competitor mentions and market developments
  Generate competitive analysis reports

  3. Crisis Communications
  Goal: Stay informed during breaking news events
  Keywords: "breaking", location names, event types
  Rapid response to developing situations
  Crisis management team notifications

  4. Research & Academia
  Goal: Gather news data for academic research
  Keywords: Research topics, geographic regions
  Build datasets for media analysis
  Track news coverage patterns over time

  ⚙ Advanced Configuration

  Scaling for High Volume
  To handle larger news monitoring needs:

  Increase batch processing:
     Modify limit_per_input parameter
     Add parallel processing branches
     Implement queue management

  Add rate limiting:
     Insert delays between requests
     Monitor API usage quotas
     Implement exponential backoff

  Database integration:
     Store articles in PostgreSQL/MySQL
     Add deduplication logic
     Create search and filter capabilities

  Multi-Channel Distribution
  Expand beyond Telegram:

  Slack integration:
     Add Slack webhook node
     Format messages for team channels
     Include interactive buttons

  Email newsletters:
     Compile daily/weekly summaries
     HTML formatting with images
     Subscriber management

  API endpoints:
     Create webhook responses
     Build news API for other systems
     Real-time data streaming

  AI Enhancement Options
  Leverage Claude's capabilities further:

  Sentiment analysis:
     Add sentiment scoring to articles
     Track market sentiment trends
     Generate mood indicators

  Summarization:
     Create executive summaries
     Extract key points
     Generate abstracts

  Classification:
     Categorize articles by topic
     Tag with relevant industries
     Priority scoring system

  📈 Performance & Limits

  Expected Performance
  Single request**: 60-120 seconds average processing time
  Articles per request**: 2-10 (configurable)
  Data accuracy**: 95%+ for standard Reuters articles
  Success rate**: 90%+ for accessible content
  Daily capacity**: Limited by Bright Data quotas

  Resource Usage
  Memory**: ~200MB per execution
  API calls**: 1 Bright Data + 1 Claude + 1 Telegram per execution
  Bandwidth**: ~5-10MB per article scraped
  Execution time**: 1-3 minutes per request

  Scaling Considerations
  Rate limiting**: Respect API quotas and limits
  Error handling**: Implement comprehensive retry logic
  Data validation**: Verify article quality and completeness
  Cost monitoring**: Track API usage across services
  Performance optimization**: Cache common requests when possible

  🤝 Support & Community

  Getting Help
  n8n Community**: community.n8n.io
  Bright Data Support**: Contact through dashboard
  Anthropic Documentation**: docs.anthropic.com
  Telegram Bot API**: core.telegram.org/bots

  Contributing
  Share workflow improvements with the community
  Report issues and suggest enhancements  
  Create variations for specific news sources
  Document best practices and optimizations

  📋 Quick Setup Checklist

  Before You Start
  ☐ n8n instance running (self-hosted or cloud)  
  ☐ Bright Data account with Reuters dataset access  
  ☐ Anthropic API key for Claude access  
  ☐ Telegram bot created via @BotFather  
  ☐ 20 minutes for complete setup  

  Setup Steps
  ☐ Import Workflow - Copy JSON and import to n8n  
  ☐ Configure Bright Data - Set up API credentials and test  
  ☐ Configure Claude - Add Anthropic API credentials  
  ☐ Setup Telegram - Create bot and get chat ID  
  ☐ Update Credentials - Replace all demo values with real ones  
  ☐ Test Form - Submit test request and verify flow  
  ☐ Check Telegram - Confirm message delivery  
  ☐ Activate Workflow - Turn on for production use  

  Ready to Use! 🎉
  Your workflow form URL: https://your-n8n-instance.com/webhook/your-webhook-id

  🎯 Happy News Monitoring! 

  This workflow provides a solid foundation for automated Reuters news intelligence. Customize it to fit your specific monitoring needs and use cases. The combination of Bright Data's reliable scraping, Claude's AI analysis, and Telegram's instant delivery creates a powerful news monitoring solution.

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatAnthropic","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.httpRequestTool","n8n-nodes-base.formTrigger","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.toolCode","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.code"]

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metadata_json:
  {"categories":["automation","integration","web scraping","data processing","AI processing"],"complexity":"complex","use_cases":["Real-time news monitoring and alerts","Financial/market news monitoring","Competitive intelligence","Crisis communications and rapid response","Research and academic news collection"],"estimated_setup_minutes":20,"required_services":["n8n instance (self-hosted or cloud)","Bright Data (Reuters dataset)","Anthropic Claude API (claude-sonnet-4)","Telegram Bot API"],"key_features":["Form-based input for search keywords and sort preferences","Bright Data Reuters scraping with status monitoring and retry logic","Claude 4 (Anthropic) AI-powered summarization and analysis","Telegram notification delivery of structured article summaries","Batch processing and scalable design (limit_per_input configurable)」「Data formatting and extraction of article fields (title, headline, description, content, url)"],"target_audience":["Journalists and newsrooms","Researchers and academics","Traders and financial analysts」「Competitive intelligence and corporate strategy teams"]}


name: Centralize your forms and reply automatically with Tally + Airtable + Gmail
author_name: Baptiste Fort
author_username: baptistefort
categories: []
created_at: 2025-06-28T12:58:39.946Z
updated_at: 2025-06-28T12:58:39.946Z
url: https://n8n.io/workflows/5433
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Still manually copy-pasting your Tally form responses?

  What if every submission went straight into Airtable — and the user got an automatic email right after?

  That’s exactly what this workflow does.
  No code, no headache — just a simple and fast automation:

  Tally → Airtable → Gmail.

  STEP 1 — Capture Tally Form Responses

  Goal
  Trigger the workflow automatically every time someone submits your Tally form.

  What we're setting up
  A webhook that catches form responses and kicks off the rest of the flow.

  Steps to follow
  Add a Webhook node

  Parameter :	Value
  Method :	POST
  Path :	formulaire-tally
  Authentication : None
  Respond : Immediately


  Save the workflow
  → This will generate a URL like:

  https://your-workspace.n8n.cloud/webhook-test/formulaire-tally
  💡 Use the Test URL first (found under Parameters &gt; Test URL)

  Head over to Tally
  Go to your form → Form Settings &gt; Integrations &gt; Webhooks
  Paste the Test URL into the Webhook field
  Enable the webhook ✅

  Submit a test entry
  → Tally won’t send anything until a real submission is made.
  This step is required for n8n to capture the structure.

  Expected output



  n8n receives a JSON object containing:

  General info (IDs, timestamps, etc.)

  A fields[] array with all the form inputs (name, email, etc.)

  Each field is nicely structured with a label, key, type, and most importantly, a value.

  Perfect foundation for the next step: data cleanup.

  STEP 2 — Clean and Structure the Form Data (Set node)

  Goal

  Take the raw data sent by Tally and turn it into clean, readable JSON that's easy to use in the rest of the workflow.

  Tally sends the responses inside a big array called field.

  Can you grab a field directly with something like {{$json"fields"["value"]}}? Yes.

  But a good workflow is like a sock drawer — when everything’s folded and labeled, life’s just easier.

  So we’re going to clean it up using a Set node.

  Steps to follow

  Add a Set node right after the Webhook.

  Enable the “Keep only set” option.

  Define the following fields in the Set node:

  Field name:	Expression
  full_name:	{{$json"fields"["value"]}}
  company_name:	{{$json"fields"["value"]}}
  job_title:	{{$json"fields"["value"]}}
  email:	{{$json"fields"["value"]}}
  phone_number:	{{$json"fields"["value"] ?? ""}}
  submission_date:	{{$now.toISOString()}}

  ⚠️ The order of fields[] depends on your Tally form. If you change the question order, make sure to update these indexes accordingly.

  Expected output

  You’ll get a clean, structured JSON like this:



  Now your data is clear, labeled, and ready for the rest of your workflow.

  STEP 3 — Save Data in Airtable

  Goal

  Every time someone submits your Tally form, their info is automatically added to an Airtable base. No more copy-pasting — everything lands right where it should.

  Steps to follow
  Create your Airtable base
  Start by creating a base named Leads (or whatever you prefer), with a table called Form Submissions.

  Add the following columns in this exact order so everything maps correctly later:

  Generate an Airtable token
  So n8n can send data into your base:

  Go to 👉 [ https://airtable.com/create/tokens](https://airtable.com/create/tokens
  )

  Click Create token

  Give it a name (e.g. Tally Automation)

  Check the following permissions:

  data.records:read

  data.records:write

  schema.bases:read

  Under Base access, either choose your base manually or select “All current and future bases”

  Click Create token and copy the generated key

  Add configure the Airtable node in n8n

  Node: Airtable

  Operation: Create

  Authentication: Personal Access Token

  Paste your token

  n8n will suggest your base and table (or you can manually grab the IDs from the URL:
  https://airtable.com/appXXXXXXXX/tblYYYYYYYY/...)

  Map your fields
  Inside the Airtable node, add the following field mappings:




  Every new Tally form submission automatically creates a new row in your Airtable base.

  STEP 4 — Send an Automatic Confirmation Email

  Goal

  Send a professional email as soon as a form is completed

  Steps to follow

  Add a Wait node

  You don’t want the email to go out instantly — it feels cold and robotic.
  → Add a Wait node right after Airtable.

  Mode: Wait for a period of time

  Delay: 5 to 10 minutes

  Unit: Minutes

  Add a Gmail &gt; Send Email node
  Authentication: OAuth2

  Connect a Gmail account (business or test)
  ⚠️ No API keys here — Gmail requires OAuth.

  Configure the Send Email node



  Field	Value

  Credential to connect with Gmail account via OAuth2

  Resource :	Message
  Operation :	Send
  To :	{{ $json.fields["Email"] }}
  Subject : Thanks for reaching out!
  Email Type : HTML
  Message: (but do the mapping correctly using the Input so that lead receives its name correctly )

  End of the Workflow

  And that’s it — your automation is live!

  Your lead fills out the Tally form → the info goes to Airtable → they get a clean, professional email without you doing a thing.

nodes_used:
  ["n8n-nodes-base.set","n8n-nodes-base.wait","n8n-nodes-base.webhook","n8n-nodes-base.airtable","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 5916 characters]

metadata_json:
  {"categories":["automation","integration","data processing","lead management","CRM"],"complexity":"medium","use_cases":["Automatically capture form responses into a database","Send confirmation emails to form submitters","Centralize lead data for follow-up and analytics","Replace manual copy-paste form handling"],"estimated_setup_minutes":20,"required_services":["n8n (cloud or self-hosted)","Tally (form with webhook support)","Airtable (base + Personal Access Token)","Gmail (OAuth2 account for sending email)"],"key_features":["Webhook trigger to capture Tally submissions","Set node to clean and structure form data","Create records automatically in an Airtable base","Wait node to delay email for a human touch","Gmail Send Email via OAuth2 for confirmations (HTML support)"],"target_audience":["marketers","sales teams / SDRs","operations / growth teams at SMBs and startups ￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼￼"]}


name: Scrape TikTok Influencer Profiles with Bright Data API to Google Sheets
author_name: ankitkansaldev
author_username: ankitkansaldev
categories: []
created_at: 2025-06-28T13:13:34.718Z
updated_at: 2025-06-28T13:13:34.718Z
url: https://n8n.io/workflows/5434
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:32
description:
  🎬 TikTok Influencer Scraper (URL Input) via Bright Data + n8n & Sheets

  A comprehensive n8n automation that scrapes TikTok influencer profiles using Bright Data's TikTok dataset and automatically saves detailed profile information to Google Sheets.

  📋 Overview

  This workflow provides an automated TikTok influencer data collection solution that scrapes comprehensive profile information and saves it to Google Sheets. Perfect for influencer marketing research, competitor analysis, social media monitoring, and marketing campaign planning.

  ✨ Key Features

  📝 Form-Based Input: Simple web form to submit TikTok profile URLs
  🤖 Bright Data Integration: Uses Bright Data's TikTok dataset for reliable scraping
  ⏳ Status Monitoring: Intelligent polling system to check scraping progress
  🔄 Retry Logic: Automatic retry mechanism with 30-second intervals
  📊 Data Extraction: Comprehensive profile data including engagement metrics
  📈 Google Sheets Storage: Automatic data storage and organization
  ⚡ Error Handling: Built-in error handling and status reporting
  🎯 Custom Fields: Configurable output fields for specific data needs

  🎯 What This Workflow Does

  Input
  Profile URLs**: TikTok profile URLs submitted through web form
  Custom Fields**: Configurable data fields for extraction
  Country Settings**: Geo-targeting for accurate data collection

  Processing
  Form Submission: User submits TikTok profile URL through web form
  API Trigger: Sends profile data to Bright Data for scraping
  Status Polling: Continuously checks scraping progress
  Wait & Retry: Implements 30-second delays between status checks
  Data Retrieval: Fetches complete profile data when ready
  Sheet Update: Saves extracted data to Google Sheets
  Status Reporting: Provides completion status and messages

  Output Data Points

  | Field | Description | Example |
  |-------|-------------|---------|
  | Account ID | Unique TikTok account identifier | @username123 |
  | Nickname | Display name on profile | "John Doe" |
  | Biography | Profile bio/description | "Content creator & influencer" |
  | Followers | Number of followers | 1,250,000 |
  | Following | Number of accounts following | 500 |
  | Likes | Total likes across all videos | 50,000,000 |
  | Videos Count | Total number of videos posted | 1,200 |
  | Profile URL | Direct link to TikTok profile | https://www.tiktok.com/@username |
  | Profile Picture | Profile image URL | https://p16-sign-sg.tiktokcdn.com/... |
  | Profile Picture HD | High-definition profile image | https://p16-sign-sg.tiktokcdn.com/... |
  | Is Verified | Verification status | true/false |
  | Bio Link | External link in bio | https://linktr.ee/username |
  | Like Engagement Rate | Engagement rate based on likes | 5.2% |
  | Comment Engagement Rate | Engagement rate based on comments | 2.1% |
  | Top Videos | List of top performing videos | [video_objects] |
  | Region | Geographic region | "US" |
  | Is Under Age 18 | Age status indicator | true/false |

  🚀 Setup Instructions

  Prerequisites
  n8n instance (self-hosted or cloud)
  Google account with Sheets access
  Bright Data account with TikTok dataset access
  Valid TikTok profile URLs for testing
  10-15 minutes for setup

  Step 1: Import the Workflow
  Copy the JSON workflow code from the provided file
  In n8n: Workflows → + Add workflow → Import from JSON
  Paste JSON and click Import

  Step 2: Configure Bright Data
  Set up Bright Data credentials:
     In n8n: Credentials → + Add credential → HTTP Request Generic Credential
     Name: "Bright Data API"
     Authentication: Bearer Token
     Token: Your Bright Data API key
     Test the connection

  Configure dataset:
     Ensure you have access to TikTok dataset (gd_l1villgoiiidt09ci)
     Verify dataset permissions in Bright Data dashboard
     Check dataset limits and pricing

  Step 3: Configure Google Sheets Integration
  Create a Google Sheet:
     Go to Google Sheets
     Create a new spreadsheet named "TikTok Influencer Data"
     Create a sheet tab named "TikTok profile by url"
     Copy the Sheet ID from URL: https://docs.google.com/spreadsheets/d/SHEET_ID_HERE/edit

  Set up Google Sheets credentials:
     In n8n: Credentials → + Add credential → Google Sheets OAuth2 API
     Complete OAuth setup and test connection

  Prepare your data sheet with columns:
     Column A: Account ID
     Column B: Nickname
     Column C: Biography
     Column D: Followers
     Column E: Following
     Column F: Likes
     Column G: Videos Count
     Column H: Profile URL
     Column I: Is Verified
     Column J: Bio Link
     Column K: Like Engagement Rate
     Column L: Comment Engagement Rate
     Column M: Region
     Column N: Status
     Column O: Message

  Step 4: Update Workflow Settings
  Update API credentials:
     Open "Sends profile URLs to Bright Data to trigger scraping" node
     Replace BRIGHT_DATA_API_KEY with your actual API key
     Update dataset ID if different

  Update Google Sheets nodes:
     Open "Google Sheets" node
     Replace document ID: 1OeqtCFm4Wek9DI5YFOWQXTpQJS-SJxC10iAPKEKkmiY
     Select your Google Sheets credential
     Choose the correct sheet/tab name

  Configure form settings:
     Open "Search by Profile URL" node
     Customize form title and field labels as needed
     Note the webhook URL for form access

  Step 5: Test & Activate
  Add test profiles:
     Access the form using the webhook URL
     Submit 1-2 TikTok profile URLs for testing
     Use full URLs (e.g., https://www.tiktok.com/@username)

  Test the workflow:
     Submit a test profile through the form
     Monitor execution in n8n
     Verify data appears in Google Sheet
     Check for any error messages

  📖 Usage Guide

  Submitting TikTok Profiles
  Navigate to your form URL (found in Form Trigger node)
  Enter TikTok profile URL in the format: https://www.tiktok.com/@username
  Click Submit to start the scraping process
  Wait for processing (typically 1-3 minutes)

  Understanding the Process
  The workflow follows this sequence:
  Form Submission → Profile URL captured
  API Trigger → Scraping job submitted to Bright Data
  Status Polling → Checks every 30 seconds if data is ready
  Data Retrieval → Fetches complete profile information
  Sheet Update → Saves data to Google Sheets

  Monitoring Progress
  Check n8n execution logs for real-time status
  Bright Data dashboard shows scraping progress
  Google Sheets will populate when data is ready
  Status column shows "ready" when complete

  Reading the Results
  Your Google Sheet will show:
  Complete TikTok profile information
  Engagement metrics and statistics
  Profile verification status
  Bio links and external connections
  Timestamp of data collection

  🔧 Customization Options

  Adding More Data Points
  Edit the JSON body in "Sends profile URLs to Bright Data" node to include additional fields:

  "custom_output_fields": [
    "account_id",
    "nickname",
    "biography",
    "followers",
    "following",
    "likes",
    "videos_count",
    "language",
    "creation_time",
    "last_post_time",
    "avg_video_duration",
    "hashtags_used",
    "music_used"
  ]

  Modifying Input Parameters
  Customize the scraping parameters:
  Country targeting**: Change "country" field in input
  Search limits**: Adjust "limit_per_input" value
  Discovery method**: Modify "discover_by" parameter
  Error handling**: Toggle "include_errors" setting

  Batch Processing Multiple Profiles
  To process multiple profiles simultaneously:
  Modify the input array in the API call
  Add multiple profile URLs in single request
  Implement loop logic for processing results
  Add rate limiting between requests

  Custom Form Fields
  Enhance the form with additional inputs:
  Open "Search by Profile URL" node
  Add form fields for:
     Country selection
     Number of videos to analyze
     Specific date ranges
     Custom tags or categories

  🚨 Troubleshooting

  Common Issues & Solutions

  "Bright Data connection failed"
     Cause: Invalid API credentials or dataset access
     Solution: Verify API key in Bright Data dashboard, check dataset permissions

  "Profile not found or private"
     Cause: Invalid TikTok URL or private profile
     Solution: Verify profile URL format, ensure profile is public

  "Google Sheets permission denied"
     Cause: Incorrect credentials or sheet permissions
     Solution: Re-authenticate Google Sheets, check sheet sharing settings

  "Scraping timeout"
     Cause: Profile data taking too long to process
     Solution: Increase wait time or implement longer polling intervals

  "Invalid dataset ID"
     Cause: Incorrect or expired dataset configuration
     Solution: Check Bright Data dashboard for correct dataset ID

  "Form submission failed"
     Cause: Webhook configuration issues
     Solution: Verify webhook URL and form trigger settings

  Advanced Troubleshooting
  Check execution logs** in n8n for detailed error messages
  Test individual nodes** by running them separately
  Verify data formats** ensure URLs are properly formatted
  Monitor API limits** check Bright Data usage quotas
  Add error handling** implement try-catch logic for robust operation

  📊 Use Cases & Examples

  1. Influencer Marketing Research
  Goal: Identify and analyze potential influencers for campaigns
  Research influencers in specific niches
  Analyze engagement rates and audience size
  Compare multiple influencers for campaign selection
  Track influencer growth over time

  2. Competitive Analysis
  Goal: Monitor competitors' TikTok presence and performance
  Track competitor follower growth
  Analyze content strategies and engagement
  Monitor posting frequency and timing
  Identify trending content themes

  3. Social Media Monitoring
  Goal: Track brand mentions and user-generated content
  Monitor branded hashtag usage
  Track brand advocates and micro-influencers
  Analyze sentiment and engagement patterns
  Identify trending topics in your industry

  4. Market Research Pipeline
  Goal: Gather social media intelligence for business decisions
  Analyze target audience behavior
  Study content preferences and trends
  Generate reports for stakeholders
  Support marketing strategy development

  ⚙ Advanced Configuration

  Rate Limiting and Performance
  To optimize for large-scale scraping:
  Adjust wait times between status checks
  Implement exponential backoff for retries
  Add batch processing for multiple profiles
  Monitor API usage to avoid limits

  Data Validation and Cleaning
  Enhance data quality with validation:
  Add data type validation for numeric fields
  Implement URL format checking
  Clean and standardize text fields
  Add data completeness checks

  Integration with Business Tools
  Connect the workflow to your existing systems:
  CRM Integration**: Update customer records with influencer data
  Slack Notifications**: Send alerts when new data is available
  Database Storage**: Store data in PostgreSQL/MySQL for analysis
  BI Tools**: Connect to Tableau/Power BI for visualization

  Webhook Integration
  For real-time updates:
  Add webhook triggers for immediate profile checks
  Integrate with external systems via webhooks
  Create API endpoints for programmatic access
  Implement authentication for secure access

  📈 Performance & Limits

  Expected Performance
  Single Profile**: 30-60 seconds average processing time
  Concurrent Requests**: 5-10 simultaneous (depends on Bright Data plan)
  Data Accuracy**: 95%+ for public TikTok profiles
  Success Rate**: 90%+ for accessible profiles
  Daily Capacity**: 100-1000 profiles (depends on rate limits)

  Resource Usage
  Memory**: ~50MB per execution
  Storage**: Minimal (data stored in Google Sheets)
  API Calls**: 3-5 Bright Data calls per profile (including status checks)
  Bandwidth**: ~1-2MB per profile scraped
  Execution Time**: 1-2 minutes per profile

  Scaling Considerations
  Rate Limiting**: Add delays for high-volume scraping
  Error Handling**: Implement retry logic for failed requests
  Data Validation**: Add checks for malformed profile data
  Monitoring**: Track success/failure rates over time
  Cost Optimization**: Monitor API usage to control costs

  🤝 Support & Community

  Getting Help
  n8n Community Forum**: community.n8n.io
  Documentation**: docs.n8n.io
  Bright Data Support**: Contact through your dashboard
  GitHub Issues**: Report bugs and feature requests

  Contributing
  Share improvements with the community
  Report issues and suggest enhancements
  Create variations for specific use cases
  Document best practices and lessons learned

  📋 Quick Setup Checklist

  Before You Start
  ☐ n8n instance running (self-hosted or cloud)  
  ☐ Google account with Sheets access  
  ☐ Bright Data account with TikTok dataset access  
  ☐ Valid TikTok profile URLs for testing  
  ☐ 15 minutes for setup  

  Setup Steps
  ☐ Import Workflow - Copy JSON and import to n8n  
  ☐ Configure Bright Data - Set up API credentials and test  
  ☐ Create Google Sheet - New sheet with proper column structure  
  ☐ Set up Google Sheets credentials - OAuth setup and test  
  ☐ Update workflow settings - Replace sheet ID and API keys  
  ☐ Test with sample profiles - Submit 1-2 URLs and verify results  
  ☐ Activate workflow - Enable form trigger for production use  

  Ready to Use! 🎉
  Your form URL: https://your-n8n-instance.com/form/[webhook-id]

  🎯 Happy TikTok Scraping! 

  This workflow provides a solid foundation for automated TikTok influencer data collection. Customize it to fit your specific needs and use cases for influencer marketing, competitive analysis, and social media research.

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

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metadata_json:
  {"categories":["automation","integration","data processing","social media scraping","marketing"],"complexity":"medium","use_cases":["Influencer marketing research","Competitive analysis","Social media monitoring","Market research and reporting","CRM enrichment / lead generation"],"estimated_setup_minutes":15,"required_services":["Bright Data API (TikTok dataset)","Google Sheets (Google OAuth2 API)","n8n instance (self-hosted or cloud)"],"key_features":["Form-based URL input (webhook)","Bright Data TikTok dataset integration","Status polling with retry logic (30s intervals)","Comprehensive profile data extraction (followers, likes, videos, engagement rates, etc.)","Automatic Google Sheets storage and mapping to columns (append/update) and status reporting columns (Status, Message) ]"],"target_audience":["influencer marketers","social media analysts","growth teams and marketers at agencies or startups ]"]}


name: AI Agent to chat with Snowflake database
author_name: Mark Shcherbakov
author_username: lowcodingdev
categories: []
created_at: 2025-06-28T13:33:34.514Z
updated_at: 2025-06-28T13:33:34.514Z
url: https://n8n.io/workflows/5435
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  Video Guide

  I prepared a detailed guide showcasing the process of building an AI agent that interacts with a Snowflake database using n8n. This setup enables conversational querying, secure execution of SQL queries, and dynamic report generation with rich visualization capabilities.



  Youtube Link

  Who is this for?

  This workflow is designed for developers, data analysts, and business professionals who want to interact with their Snowflake data conversationally. It suits users looking to automate SQL query generation with AI, manage large datasets efficiently, and produce interactive reports without deep technical knowledge.

  What problem does this workflow solve?

  Querying Snowflake databases typically requires SQL proficiency and can lead to heavy token usage if large datasets are sent to AI models directly. This workflow addresses these challenges by:

  Guiding AI to generate accurate SQL queries based on user chat input while referencing live database schema to avoid errors.
  Executing queries safely on Snowflake with proper credential management.
  Aggregating large result sets to reduce token consumption.
  Offering a user-friendly report link with pagination, filtering, charts, and CSV export instead of returning overwhelming raw data.
  Providing an error-resilient environment that prompts regenerations for SQL errors or connectivity issues.

  What this workflow does

  The scenario consists of multiple focused n8n workflows orchestrated for smooth, secure, and scalable interactions:

  Agent Workflow  
     Starts with a chat node and sets the system role as "Snowflake SQL assistant."  
     AI generates SQL after verifying database schema and table definitions to avoid hallucinations.  
     Reinforcement rules ensure schema validation before query creation.

  Data Retrieval Workflow  
     Receives SQL queries from the agent workflow.  
     Executes them against the Snowflake database using user-provided credentials (hostname, account, warehouse, database, schema, username, password).  
     Optionally applies safety checks on SQL to prevent injection attacks.

  Aggregation and Reporting Decision  
     Aggregates returned data into arrays for efficient processing.  
     Applies a threshold (default 100 records) to decide whether to return raw data or generate a dynamic report link.  
     Prepares report links embedding URL-encoded SQL queries to securely invoke a separate report workflow.

  Report Viewing Workflow  
     Triggered via webhook from the report link.  
     Re-executes SQL queries to fetch fresh data.  
     Displays data with pagination, column filtering, and selectable chart visualizations.  
     Supports CSV export and custom HTML layouts for tailored user experience.  
     Provides proper error pages in case of SQL or data issues.

  Schema and Table Definition Retrieval Tools  
     Two helper workflows that fetch the list of tables and column metadata from Snowflake.  
     Require the user to replace placeholders with actual database and data source names.  
     Crucial for AI to maintain accurate understanding of the database structure.

  N8N Workflow

  Preparation  
  Create your Snowflake credentials in n8n with required host and account details, warehouse (e.g., "computer_warehouse"), database, schema, username, and password.  
  Replace placeholder variables in schema retrieval workflows with your actual database and data source names.  
  Verify the credentials by testing the connection; reset passwords if needed.

  Workflow Logic  
  The Agent Workflow listens to user chats, employs system role "Snowflake SQL assistant," and ensures schema validation before generating SQL queries.  
  Generated SQL queries pass to the Data Retrieval Workflow, which executes them against Snowflake securely.  
  Retrieved data is aggregated and evaluated against a configurable threshold to decide between returning raw data or creating a report link.  
  When a report link is generated, the Report Viewing Workflow renders a dynamic interactive HTML-based report webpage, including pagination, filters, charts, and CSV export options.  
  Helper workflows periodically fetch or update the current database schema and table definitions to maintain AI accuracy and prevent hallucinations in SQL generation.  
  Error handling mechanisms provide user-friendly messages both in the agent chat and report pages when issues arise with SQL or connectivity.

  This modular, secure, and extensible setup empowers you to build intelligent AI-driven data interactions with Snowflake through n8n automations and custom reporting.

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.snowflakeTool","n8n-nodes-base.snowflakeTool","n8n-nodes-base.webhook","n8n-nodes-base.set","n8n-nodes-base.respondToWebhook","n8n-nodes-base.snowflake","n8n-nodes-base.aggregate","n8n-nodes-base.executeWorkflowTrigger","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.snowflake","n8n-nodes-base.aggregate","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.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","data processing","analytics","reporting','ai'.replace("],"complexity":"complex","use_cases":["Conversational querying of Snowflake using natural language","AI-assisted SQL generation with schema validation","Secure execution of SQL against Snowflake","Aggregating large result sets to reduce LLM token usage","Generating interactive, paginated reports with charts and CSV export"],"estimated_setup_minutes":60,"required_services":["Snowflake (account, warehouse, database, schema)","n8n (self-hosted or cloud instance with public webhook URL)","OpenAI or compatible LLM connector (lmChatOpenAi or similar)"],"key_features":["System role set to 'Snowflake SQL assistant' to guide SQL generation","Schema/table metadata lookup to prevent hallucinated SQL","Safe SQL execution against Snowflake with credential management","Aggregation and thresholding to avoid sending large raw datasets to LLMs","Dynamic report links that re-run queries via webhook for fresh data retrieval"],"target_audience":["Developers","Data analysts","Business professionals and product owners"]}


name: Automate a Tally Form: Store with Airtable, Notify via Slack
author_name: Baptiste Fort
author_username: baptistefort
categories: []
created_at: 2025-06-28T16:58:08.775Z
updated_at: 2025-06-28T16:58:08.775Z
url: https://n8n.io/workflows/5437
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  🎯 Workflow Goal

  Still manually checking form responses in your inbox?

  What if every submission landed neatly in Airtable — and you got a clean Slack message instantly?

  That’s exactly what this workflow does.

  No code, no delay — just a smooth automation to keep your team in the loop:

  Tally → Airtable → Slack

  Build an automated flow that:
  receives Tally form submissions,
  cleans up the data into usable fields,
  stores the results in Airtable,
  and automatically notifies a Slack channel.

  Step 1 – Connect Tally to n8n

  What we’re setting up

  A Webhook node in POST mode.

  Technical

  Add a Webhook node.  
  Set it to POST.  
  Copy the generated URL.  
  In Tally → Integrations → Webhooks → paste this URL.  
  Submit a test response on your form to capture a sample structure.

  Step 2 – Clean the data



  After connecting Tally, you now receive raw data inside a fields[] array.

  Let’s convert that into something clean and structured.

  Goal

  Extract key info like Full Name, Email, Phone, etc. into simple keys.

  What we’re doing

  Add a Set node to remap and clean the fields.

  Technical

  Add a Set node right after the Webhook.  
  Add new values (String type) manually:
     Name: Full Name → Value: {{$json"fields"["value"]}}  
     Name: Email → Value: {{$json"fields"["value"]}}  
     Name: Phone → Value: {{$json"fields"["value"]}}  
     (Adapt the indexes based on your form structure.)

  Use the data preview in the Webhook node to check the correct order.

  Output

  You now get clean data like:

  { "Full Name": "Jane Doe",
    "Email": "jane@example.com",
    "Phone": "+123456789" }

  Step 3 – Send to Airtable



  ✅ Once the data is cleaned, let’s store it in Airtable automatically.

  Goal

  Create one new Airtable row for each form submission.

  What we’re setting up

  An Airtable – Create Record node.

  Technical

  Add an Airtable node.  
  Authenticate or connect your API token.  
  Choose the base and table.  
  Map the fields:
     Name: {{$json["Full Name"]}}  
     Email: {{$json["Email"]}}  
     Phone: {{$json["Phone"]}}

  Output

  Each submission creates a clean new row in your Airtable table.

  Step 4 – Add a delay



  ⌛ After saving to Airtable, it’s a good idea to insert a short pause — this prevents actions like Slack messages from stacking too fast.

  Goal

  Wait a few seconds before sending a Slack notification.

  What we’re setting up

  A Wait node for X seconds.

  ✅ Technical
  Add a Wait node.  
  Choose Wait for X minutes.  

  Step 5 – Send a message to Slack



  💬 Now that the record is stored, let’s send a Slack message to notify your team.

  Goal
  Automatically alert your team in Slack when someone fills the form.

  What we’re setting up

  A Slack – Send Message node.

  Technical

  Add a Slack node.  
  Connect your account.  
  Choose the target channel, like #leads.  
  Use this message format:

  New lead received!

  Name: {{$json["Full Name"]}}  

  Email: {{$json["Email"]}}  

  Phone: {{$json["Phone"]}}  


  Output

  Your Slack team is notified instantly, with all lead info in one clean message.

  Workflow Complete

  Your automation now looks like this:  

  Tally → Clean → Airtable → Wait → Slack

  Every submission turns into clean data, gets saved in Airtable, and alerts your team on Slack — fully automated, no extra work.

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.airtable","n8n-nodes-base.slack","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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  [encoded data, 5024 characters]

metadata_json:
  {"categories":["automation","integration","data processing","notifications"],"complexity":"simple","use_cases":["Lead capture and CRM entry","Contact form handling","Customer inquiry routing","Event or registration sign-ups","Team notification for new submissions"],"estimated_setup_minutes":20,"required_services":["Tally (form)","Airtable","Slack","n8n (webhook endpoint)"],"key_features":["Receive Tally form submissions via webhook","Parse and remap fields[] into clean keys","Create a new Airtable record per submission","Configurable short delay before downstream actions","Send formatted Slack notification to a channel"],"target_audience":["Marketers","Sales teams","Operations/ops teams / business admins who manage forms and leads/no-code builders"]}


name: Get IPO Calendar Alerts via Telegram with Finnhub API and Gemini AI
author_name: Malik Hashir
author_username: malikx
categories: []
created_at: 2025-06-28T18:00:48.859Z
updated_at: 2025-06-28T18:00:48.859Z
url: https://n8n.io/workflows/5439
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  Get instant notifications about new US companies going public (IPOs) sent straight to your Telegram—easy setup, no programming skills needed. 

  Perfect for investors, traders, and anyone interested in the latest stock market opportunities.

  Purpose & Audience
  This n8n workflow is designed for investors, financial analysts, and community managers who want real-time, structured US companies recent and upcoming IPO (Initial Public Offerings) updates directly to their Telegram channels or chats. It’s perfect for anyone running a financial community, managing a trading group, or investing in new companies for personal investment decisions.

  What It Does
  Fetches recent and upcoming IPO calendar updates for US companies expected to be listed on US exchanges using the Finnhub API (free-tier), with a default window of the next 7 days (customizable to any date range).
  Uses Google Gemini AI to automatically format and structure the calendar data into a clean, grouped, and enhanced message, including company names, tickers, total number of shares, total value of shares, and expected stock price (with intelligent scaling for billions/millions).
  Sends the formatted IPO updates directly to your chosen Telegram chat or channel via a Telegram bot. (can be customised for email updates or any other preferred channel for updates)

  Who Is It For?
  Retail and professional investors
  Financial influencers and Telegram group admins
  Trading desk or community managers
  Anyone who wants hands-off, AI-powered IPO calendar alerts for US companies

  How to Set Up
  Get Finnhub API Key:
  Sign up at https://www.finnhub.io and copy your API key.

  Connect Google Gemini (or OpenAI) Model:
  Add your Gemini API credentials in n8n for advanced AI formatting and structuring of earnings updates.

  Set Up Your Telegram Bot:
  Create a bot with @BotFather, get your API token, and add it to n8n’s Telegram credentials.
  Obtain your Telegram chat or group ID (use @RawDataBot for this).

  Customize Date Range (Optional):
  By default, the workflow fetches upcoming IPOs for the next 7 days, but you can adjust it to any custom window (e.g., today to 3 days in the future).

  Activate the Workflow:
  Schedule how often you want updates (default: every 7 days; can be as frequent as daily).
  The workflow will automatically fetch, format, and send the latest US IPO calendar updates to your Telegram.

  Key Features
  No coding required—fully automated and customizable.
  Handles Telegram’s message length limit by default.
  AI-enhanced formatting for maximum readability and engagement.
  Free to use with Finnhub’s free API tier.

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.set","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.telegram"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","finance","notifications","data-enrichment"],"complexity":"medium","use_cases":["Real-time IPO alerts to Telegram channels/chats","Investor research and deal discovery","Community or trading-group notifications","Automated content generation for newsletters/social posts","Monitoring upcoming US IPOs within a custom date window"],"estimated_setup_minutes":20,"required_services":["Finnhub API (IPO calendar)","Google Gemini (or OpenAI) API for formatting","Telegram Bot API (BotFather + chat/group ID)","n8n instance"],"key_features":["Fetches recent and upcoming US IPOs with configurable date range","AI-powered formatting and grouping of IPO data (company, ticker, shares, value, expected price)","Scheduled automation to send periodic alerts","Respects Telegram message length limits and batches messages","No-code setup in n8n with configurable nodes and schedule"],"target_audience":["Retail investors","Professional investors and financial analysts","Trading desk/community managers and Telegram admins/ moderators of finance groups / influencers"]}


name: Generate Professional Document Drafts from PDFs with Google Drive, GPT-4 & Email Notifications
author_name: Michael Gullo
author_username: mgullo
categories: []
created_at: 2025-06-28T20:41:22.710Z
updated_at: 2025-06-28T20:41:22.710Z
url: https://n8n.io/workflows/5441
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  Automate Drafts From Google Drive 

  This workflow automates the end-to-end process of extracting and summarizing information from PDFs stored in a specific Google Drive folder. When a new PDF or any binary data is added, the workflow is triggered and begins by downloading and processing the PDF to extract all available text. If multiple PDFs are detected, their content is aggregated into a single, combined dataset. This automation eliminates the time consuming task of manually reading, taking notes, and drafting documents. By removing this burden, users can focus on more meaningful tasks while the workflow handles the repetitive, tedious work.

  The extracted content is then passed through an AI-powered information extractor that identifies key details such as names, dates, addresses, and any other structured data points the user wants to extract from the PDF. This step is highly customizable, allowing the user to define exactly what type of information should be extracted. While the workflow is designed to extract all available content from the PDF, specifying additional structured data points ensures that critical details are accurately captured. A second OpenAI Node uses the extracted information to draft a professional, formal summary suitable for documentation. This is the most important part of the workflow and can be fully customized to meet the user's specific needs. By editing the prompts, users can tailor the workflow to generate a wide variety of draft formats based on the extracted content. 

  The workflow then generates a new Google Document containing the full draft and composes an email summarizing the key points in 3 to 5 bullet points. This email is automatically sent to the designated recipient along with a direct link to the Google Doc. This solution is ideal for insurance, legal, or administrative use cases where timely, accurate extraction and reporting from incoming PDFs is essential.

  How To Use The Workflow
  Step 1 - Place any binary data (e.g., PDF files) into the designated Google Drive folder.

  Step 2 - The workflow will automatically download each PDF, extract the text, and if multiple PDFs are present combine them into a single dataset for analysis.

  Step 3 - The OpenAI Draft Agent will analyze the extracted information, generate a formal draft, and create a Google Document. This document will be updated with the draft content and saved back into the same Google Drive folder.

  Step 4 - An email will be sent to the designated recipient(s), including a summary of the draft and key extracted information, along with a link to view the Google Document.

  Need Help? Have Questions?
  For consulting and support, or if you have questions, please feel free to connect with me on LinkedIn  or email michael.gullo@outlook.com.

nodes_used:
  ["n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.extractFromFile","@n8n/n8n-nodes-langchain.informationExtractor","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.googleDocs","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDocs","n8n-nodes-base.set","n8n-nodes-base.gmail"]

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metadata_json:
  {"categories":["automation","document processing","AI summarization","integration","notifications"],"complexity":"medium","use_cases":["Automated PDF-to-draft conversion","Contract or legal document summarization","Insurance claim intake and reporting","Administrative report preparation","Automated notification with document links"],"estimated_setup_minutes":30,"required_services":["Google Drive API","Google Docs API","OpenAI (GPT-4) API","Email service (Gmail/SMTP)","n8n"],"key_features":["Automatically detects and downloads new PDFs from a Drive folder","Extracts and aggregates text from single or multiple PDFs","Customizable structured data extraction (names, dates, addresses, etc.)","Uses GPT-4 to generate professional/formal draft documents","Creates and saves a Google Doc with the draft in Drive','Sends email notifications with 3–5 bullet summary and link to the Google Doc"],"target_audience":["Legal teams","Insurance adjusters and processors","Administrative staff and office managers」「Operations teams and knowledge workers"]}


name: Google Maps Business Scraper & Lead Enricher with Bright Data & Google Gemini
author_name: Ranjan Dailata
author_username: ranjancse
categories: []
created_at: 2025-06-29T03:29:25.021Z
updated_at: 2025-06-29T03:29:25.021Z
url: https://n8n.io/workflows/5443
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:32
description:
  Notice
  Community nodes can only be installed on self-hosted instances of n8n.

  Description

  This workflow automates the process of scraping local business data from Google Maps and enriching it using AI to generate lead profiles. It's designed to help sales, marketing, and outreach teams collect high-quality B2B leads from Google Maps and enrich them with contextual insights without manual data entry.

  Overview

  This workflow scrapes business listings from Google Maps, extracts critical information like name, category, phone, address, and website using Bright Data, and passes the results to Google Gemini to generate enriched summaries and lead insights such as company description, potential services offered, and engagement score. The data is then structured and stored in spreadsheets for outreach.

  Tools Used

  n8n: The core automation engine to manage flow and trigger actions.

  Bright Data: Scrapes business information from Google Maps at scale with proxy rotation and CAPTCHA-solving.

  Google Gemini: Enriches the raw scraped data with smart business summaries, categorization, and lead scoring.

  Google Sheets : For storing and acting upon the enriched leads.

  How to Install

  Import the Workflow: Download the .json file and import it into your n8n instance.

  Set Up Bright Data: Insert your Bright Data credentials and configure the Google Maps scraping proxy endpoint.

  Configure Gemini API: Add your Google Gemini API key (or use via Make.com plugin).

  Customize the Inputs: Choose your target location, business category, and number of results per query.

  Choose Storage: Connect to your preferred storage like Google Sheets.

  Test and Deploy: Run a test scrape and enrichment before deploying for bulk runs.

  Use Cases

  Sales Teams: Auto-generate warm B2B lead lists with company summaries and relevance scores.

  Marketing Agencies: Identify local business prospects for SEO, web development, or ads services.

  Freelancers: Find high-potential clients in specific niches or cities.

  Business Consultants: Collect and categorize local businesses for competitive analysis or partnerships.

  Recruitment Firms: Identify and score potential company clients for talent acquisition.

  Connect with Me

  Email: ranjancse@gmail.com

  LinkedIn: https://www.linkedin.com/in/ranjan-dailata/

  Get Bright Data: Bright Data (Supports free workflows with a small commission)

  #n8n #automation #leadscraping #googlemaps #brightdata #leadgen #b2bleads #salesautomation #nocode #leadprospecting #marketingautomation #googlemapsdata #geminiapi #googlegemini #aiworkflow #scrapingworkflow #businessleads #datadrivenoutreach #crm #workflowautomation #salesintelligence #b2bmarketing

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.set","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.function","n8n-nodes-base.readWriteFile","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.splitInBatches","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.wait","n8n-nodes-base.stickyNote","n8n-nodes-mcp.mcpClient","n8n-nodes-mcp.mcpClient","n8n-nodes-base.function","n8n-nodes-base.readWriteFile","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","data processing","web scraping","lead enrichment"],"complexity":"complex","use_cases":["Automated B2B lead discovery from Google Maps","Enriching scraped business listings with AI-generated summaries and scores","Populating CRM or outreach spreadsheets with structured leads","Local market research and competitive analysis","High-volume prospecting with proxy-backed scraping"],"estimated_setup_minutes":60,"required_services":["n8n (self-hosted with community nodes enabled)","Bright Data (Google Maps scraping + proxy/CAPTCHA handling)","Google Gemini API (or Make.com Gemini integration)","Google Sheets (or other spreadsheet/storage)"],"key_features":["Scales Google Maps scraping via Bright Data with proxy rotation and CAPTCHA handling","AI enrichment using Google Gemini to generate company descriptions, categorizations and engagement scores","Configurable inputs: location, business category, result limit","Structured output saved to Google Sheets for outreach/CRM use","End-to-end automation in n8n with scheduling and bulk runs"],"target_audience":["Sales teams and SDRs","Marketing agencies and consultants","Freelancers doing client prospecting (SEO, dev, ads)  "]}


name: AI Email Assistant: Prioritize Gmail with ChatGPT Summaries and Slack Digests
author_name: Darsheel
author_username: n8ndarsheel
categories: []
created_at: 2025-06-29T06:47:29.729Z
updated_at: 2025-06-29T06:47:29.729Z
url: https://n8n.io/workflows/5446
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  This n8n workflow acts as an AI-powered Inbox Assistant that automatically summarizes and classifies Gmail emails, prioritizes important messages, and sends a daily digest to Slack. It’s ideal for startup founders and small teams juggling investor intros, customer leads, and support queries — all from a busy Gmail inbox.

  Each email is processed using ChatGPT to generate a concise summary, classify the message (e.g., Support, Investor, Spam), and determine its urgency. High and medium priority messages are forwarded to Slack instantly. Lower priority emails are logged to Google Sheets for review. A daily 7 PM digest summarizes the day’s most important messages.

  💡 Use Cases
  Preventing missed investor or lead emails

  Lightweight CRM alternative using Google Sheets

  Slack summaries of critical Gmail activity

  🔧 How It Works
  Gmail node fetches new messages

  ChatGPT summarizes and extracts urgency + type

  High/medium urgency → sent to Slack + labeled in Gmail

  Low urgency → logged in Google Sheets

  Cron node triggers a daily 7 PM Slack summary

  ✅ Requirements
  OpenAI API Key (GPT-4 or GPT-4o recommended)

  Gmail access with read and label permission

  Slack Bot Token or Webhook URL

  Google Sheets integration (optional)

  🛠 Customization Ideas
  Replace Slack with Telegram or WhatsApp

  Route investor leads to Airtable or Notion

  Add multi-language support in ChatGPT prompt

  Create weekly summaries via email

nodes_used:
  ["n8n-nodes-base.gmailTrigger","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.if","n8n-nodes-base.code","n8n-nodes-base.slack","n8n-nodes-base.gmail","n8n-nodes-base.gmail","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.slack"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","email management","AI","productivity","integration"],"complexity":"medium","use_cases":["Prevent missing investor or lead emails","Lightweight CRM / lead logging into Google Sheets","Triage and prioritize support requests","Send Slack summaries of critical Gmail activity","Daily digest of important messages"],"estimated_setup_minutes":45,"required_services":["OpenAI API (GPT-4 / GPT-4o recommended)","Gmail (read & label access)","Slack (Bot token or Webhook URL)","Google Sheets API (optional for logging)","n8n account (to run workflow and schedule)"],"key_features":["ChatGPT-generated concise summaries for each email","Automatic classification (Support, Investor, Spam, etc.)","Urgency detection and priority-based routing","Immediate Slack notifications for high/medium priority","Labeling emails in Gmail according to classification/priority and forwarding where needed (optional forwarding behavior implied by Slack alerts or email forwarding node usage). Note: explicit forwarding to Slack via messages.)"],"target_audience":["Startup founders","Small teams","Customer support reps / managers handling inboxes at scale-ish volumes (SMB level rather than enterprise volumes without adjustments.)"]}


name: Advanced Retry and Delay Logic
author_name: Viktor Klepikovskyi
author_username: vklepikovskyi
categories: []
created_at: 2025-06-29T08:01:28.282Z
updated_at: 2025-06-29T08:01:28.282Z
url: https://n8n.io/workflows/5447
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  Advanced Retry and Delay Logic

  This template provides a robust solution for handling API rate limits and temporary service outages in n8n workflows. It overcomes the limitations of the default node retry settings, which cap retries at 5 and delays at 5 seconds. By using a custom loop with a Set, If, and Wait node, this workflow gives you complete control over the number of retries and the delay between them.

  Instructions:

  Replace the placeholder HTTP Request node with your target node (the one that might fail).
  In the initial Set Fields node, modify the max_tries value to set the total number of attempts for your workflow.
  Adjust the delay_seconds value to define the initial delay between retries.
  Optionally, configure the Edit Fields node to implement exponential backoff by adjusting the delay_seconds expression (e.g., {{$json.delay_seconds * 2}}).

  For a more detailed breakdown and tutorial of this template, you can find additional information here.

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

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metadata_json:
  {"categories":["automation","integration","error-handling","workflow-orchestration"],"complexity":"medium","use_cases":["Handling API rate limits","Recovering from temporary service outages","Implementing exponential backoff for retries","Reliable automated HTTP/third‑party API calls","Longer/more flexible retry strategies than built‑in node settings"],"estimated_setup_minutes":10,"required_services":["n8n (self-hosted or cloud)","Target external API or service you are calling (e.g., REST API)"],"key_features":["Custom loop-based retry control beyond built‑in limits","Configurable max attempts and delay between retries","Support for exponential backoff via editable expressions","Use of Set, If and Wait nodes to manage retry logic","Optional stop/error node to exit after final failure or bubble errors up"],"target_audience":["Developers","DevOps / SREs","Integration engineers / iPaaS users"]}


name: Clone Voices from Text to Speech with Zyphra Zonos API
author_name: Tiartyos
author_username: tiartyos
categories: []
created_at: 2025-06-29T08:44:47.300Z
updated_at: 2025-06-29T08:44:47.300Z
url: https://n8n.io/workflows/5448
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  Voice Cloning Workflow - Zyphra Zonos API

  Who is this for?
  This workflow is designed for developers, content creators, and businesses looking to automate high-quality voice synthesis using AI voice cloning technology.

  What problem does this solve?
  It automates the process of generating natural-sounding speech from text using a sample voice file, eliminating the need for manual voice recording and providing consistent voice output for applications like audiobooks, virtual assistants, or content localization.

  What this workflow does
  The workflow receives text and voice cloning parameters via webhook, reads a sample voice file from your storage, sends the data to Zyphra's Zonos API for voice synthesis, and saves the generated audio file to your specified output location.

  Prerequisites
  You'll need:
  API key from Zyphra (obtain from https://playground.zyphra.com/settings/api-keys)
  Account registration at https://playground.zyphra.com  
  Sample voice file stored on accessible disk/cloud storage
  n8n instance running with webhook capabilities

  Setup
  Configure your Zyphra API key in the "Call Zyphra Clone API" node under Header Parameters (Name: X-API-Key, Value: your-api-key)
  Ensure your sample voice files are accessible at the paths you'll specify
  Test the webhook endpoint is accessible

  Supported Audio Formats
  The API supports multiple output formats through the mime_type parameter:

  WebM** (default) - audio/webm
  Ogg** - audio/ogg
  WAV** - audio/wav
  MP3** - audio/mp3 or audio/mpeg
  MP4/AAC** - audio/mp4 or audio/aac

  Usage Example

  Endpoint: POST http://localhost:5678/webhook-test/voice-clone

  Headers: Content-Type: application/json

  Request Body:
  {
  "text": "Hello there! This voice sounds just like the sample!",
  "speaking_rate": 18,
  "sample_voice_path": "/data/output/sampleVoice.wav",
  "output_path": "/data/output/",
  "language_iso_code": "en-us",
  "mime_type": "audio/wav",
  "model": "zonos-v0.1-transformer",
  "emotion": {
  "happiness": 0.8,
  "neutral": 0.3,
  "sadness": 0.05,
  "disgust": 0.05,
  "fear": 0.05,
  "surprise": 0.05,
  "anger": 0.05,
  "other": 0.5
  }
  }

  Parameters

  Required Parameters
  text**: Text to synthesize into speech
  sample_voice_path**: Path to your voice sample file
  output_path**: Directory where generated audio will be saved

  Optional Parameters (with defaults)
  speaking_rate**: 15 - Speech speed
  language_iso_code**: "en-us" - Language code
  mime_type**: "audio/wav" - Output audio format
  model**: "zonos-v0.1-transformer" - AI model to use
  emotion**: Object with emotion levels (0-1 scale)

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

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metadata_json:
  {"categories":["integration","automation","speech synthesis","content creation","media processing"],"complexity":"medium","use_cases":["Generate audiobooks or narrated content from text","Create consistent voice for virtual assistants and IVR","Automated voiceovers for videos and marketing content","Content localization with cloned voice","Personalized messages and notifications"],"estimated_setup_minutes":20,"required_services":["Zyphra Zonos API (API key and account)","n8n instance with webhook exposure (or tunneling service like ngrok)","Accessible storage for sample voice and output files (local disk, S3, GCS, etc.)"],"key_features":["Webhook-triggered workflow to synthesize speech from text","Voice cloning using a user-provided sample voice file","Configurable parameters: model, speaking_rate, language, mime_type, emotion","Supports multiple output audio formats (WebM, Ogg, WAV, MP3, MP4/AAC)","Reads sample voice file from storage and saves generated audio to specified output path"],"target_audience":["Developers","Content creators","Businesses and product teams implementing voice features"]}


name: Automate Sales Cold Calling Pipeline with Apify, GPT-4o, and WhatsApp
author_name: Khairul Muhtadin
author_username: khmuhtadin
categories: []
created_at: 2025-06-29T08:45:59.943Z
updated_at: 2025-06-29T08:45:59.943Z
url: https://n8n.io/workflows/5449
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
description:
  This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 

  Cold Calling Automation - End-to-End Automated Cold Calling with Apify, RAG, and WhatsApp



  The "Cold Calling Automation" workflow is designed to fully automate the end-to-end cold calling process by intelligently combining web scraping, AI-powered research, and WhatsApp messaging. Leveraging key technologies such as Apify for data scraping, RAG (Retrieval-Augmented Generation) for intelligent content creation, and WhatsApp integration for automated outreach, this workflow transforms raw prospect data into personalized, high-converting cold calling campaigns with minimal manual intervention.

  💡 Why Use Cold Calling Automation?
  Scale Your Outreach:** Automate hundreds of personalized cold calls without manual effort or hiring additional staff.  
  Intelligent Personalization:** RAG technology creates highly relevant, personalized messages based on prospect research.  
  Multi-Channel Approach:** Seamlessly integrate WhatsApp messaging with traditional cold calling methods.  
  Real-Time Optimization:** Continuously improve message performance and conversion rates through AI analysis.  
  Cost-Effective:** Reduce cold calling costs while dramatically increasing reach and response rates.

  ⚡ Who Is This For?
  Sales Teams:** Looking to scale their cold calling efforts with intelligent automation and personalization.  
  Lead Generation Agencies:** Needing to deliver high-volume, high-quality cold calling services to clients.  
  Business Development Professionals:** Seeking to maximize outreach efficiency while maintaining personal touch.  
  Small Business Owners:** Who want professional-grade cold calling capabilities without hiring expensive sales teams.  
  Marketing Agencies:** Offering comprehensive lead generation and conversion services to clients.

  ❓ What Problem Does It Solve?
  Traditional cold calling is time-consuming, expensive, and often ineffective due to lack of personalization and poor timing. Manual prospect research, script writing, and call execution create bottlenecks that limit outreach scale. Generic messages result in low response rates and damaged brand reputation. This workflow solves these problems by automating the entire cold calling pipeline - from prospect identification and research to personalized message creation and delivery - while maintaining high quality and relevance that converts prospects into qualified leads.

  🔧 What This Workflow Does
  ⏱ Prospect Scraping: Uses Apify to automatically scrape and identify high-quality prospects based on your target criteria.  
  🔍 Intelligent Research: Employs RAG technology to research each prospect and gather relevant business intelligence.  
  ✍️ Personalized Content: Automatically generates custom messages, scripts, and talking points for each prospect.  
  📱 WhatsApp Integration: Delivers personalized messages through WhatsApp automation for maximum engagement.  
  📊 Performance Tracking: Monitors response rates, engagement metrics, and conversion data for continuous optimization.  
  🤖 AI-Powered Follow-up: Automatically handles initial responses and schedules appropriate follow-up actions.  
  📈 Campaign Analytics: Provides detailed insights on campaign performance and ROI metrics.  
  🔄 Continuous Learning: Improves message effectiveness and targeting based on campaign results.

  This workflow also using community node: `@devlikeapro/n8n-nodes-waha`

  🔐 Setup Instructions
  Import the provided workflow JSON into your n8n instance (Cloud or self-hosted).  
  Set up credentials:  
      Apify API credentials for prospect scraping  
      OpenAI API key for RAG and content generation  
      WhatsApp Business API credentials or WAHA integration  
      Database credentials for prospect and campaign tracking  
      Email credentials for notifications and reporting  
  Customize parameters:  
      Target prospect criteria and scraping parameters  
      Message templates and personalization rules  
      Campaign timing and frequency settings  
      Response handling and follow-up logic  
      Performance tracking and reporting preferences  
  Test the complete workflow with a small prospect list to verify scraping, personalization, and delivery.

  🧩 Pre-Requirements
  Active n8n instance (Cloud or Self-hosted)  
  Apify account with appropriate scraping credits  
  OpenAI API key with sufficient usage limits  
  WhatsApp Business account or WAHA setup  
  Database system for prospect and campaign management  
  Basic understanding of your target audience and value proposition  

  🛠️ Customize It Further
  Integrate with CRM systems to sync prospects and track conversion through sales pipeline.  
  Add voice calling capabilities using VoIP services for complete omnichannel outreach.  
  Implement A/B testing for message templates and timing optimization.  
  Connect with social media platforms for multi-channel prospecting and engagement.  
  Add sentiment analysis to optimize message tone and approach for different prospect types.  
  Integrate with calendar systems for automatic meeting scheduling from qualified responses.

  🧠 Nodes Used
  Apify nodes for prospect scraping and data collection  
  OpenAI Chat Model and Embeddings for RAG implementation  
  WhatsApp/WAHA nodes for message delivery and response handling  
  Database nodes for prospect storage and campaign tracking  
  HTTP Request nodes for API integrations and webhooks  
  Code nodes for data processing and personalization logic  
  Schedule Trigger for automated campaign execution  
  Conditional nodes for response handling and follow-up logic  
  Set nodes for parameter configuration and data transformation  
  Split In Batches for efficient bulk processing

  📊 Expected Results
  50-80% increase** in cold calling efficiency and prospect reach  
  25-40% higher** response rates compared to generic cold calling  
  60-75% reduction** in manual research and message preparation time  
  Real-time insights** into campaign performance and prospect engagement  
  Scalable system** that grows with your business needs

  📞 Support
  Made by: khaisa Studio  
  Tag: automation, cold calling, lead generation, apify, RAG, whatsapp, AI, sales automation, outreach  
  Category: Sales Automation & Lead Generation  
  Need a custom? contact me for more tailored templates

nodes_used:
  ["n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","@n8n/n8n-nodes-langchain.vectorStoreSupabase","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","n8n-nodes-base.splitInBatches","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryPostgresChat","@n8n/n8n-nodes-langchain.embeddingsOpenAi","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.googleSheets","n8n-nodes-base.webhook","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.vectorStoreSupabase","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","n8n-nodes-base.googleSheetsTrigger","n8n-nodes-base.code","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.vectorStoreSupabase","@n8n/n8n-nodes-langchain.vectorStoreSupabase","@n8n/n8n-nodes-langchain.embeddingsOpenAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@devlikeapro/n8n-nodes-waha.wahaTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.vectorStoreSupabase","@n8n/n8n-nodes-langchain.vectorStoreSupabase","@n8n/n8n-nodes-langchain.embeddingsOpenAi","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.memoryPostgresChat","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.rerankerCohere","@n8n/n8n-nodes-langchain.rerankerCohere","n8n-nodes-base.set"]

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metadata_json:
  {"categories":["automation","sales","lead generation","integration","data processing"],"complexity":"complex","use_cases":["Automated cold outreach via WhatsApp","Prospect scraping and enrichment","RAG-powered personalized message and script generation","Automated follow-ups and response handling","Campaign performance tracking and optimization"],"estimated_setup_minutes":120,"required_services":["n8n (self-hosted for community nodes)","Apify (scraping)","OpenAI / GPT-4o (RAG & embeddings)","WhatsApp Business API or WAHA (@devlikeapro/n8n-nodes-waha)","Database / Vector store (e.g., Supabase)","Google Sheets / Google Drive (optional)","Email service for notifications (optional)"],"key_features":["End-to-end prospect scraping with Apify","Retrieval-Augmented Generation (RAG) for contextual personalization","Embeddings & vector store for similarity search (Supabase)","Automated WhatsApp outreach and response handling (WAHA)","Batch processing and scheduling for high-volume campaigns"],"target_audience":["Sales teams","Lead generation agencies","Business development professionals / SDRs」、「Marketing agencies"]}


name: Send AI-Enhanced Economic Calendar Alerts to Telegram with Gemini-2.0-Flash
author_name: Malik Hashir
author_username: malikx
categories: []
created_at: 2025-06-29T10:23:41.436Z
updated_at: 2025-06-29T10:23:41.436Z
url: https://n8n.io/workflows/5450
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  Stay ahead of the markets with this fully automated n8n workflow that delivers real-time, AI-formatted economic calendar updates directly to your Telegram channel or group. 

  Powered by the Economic Events Calendar API via RapidAPI, this workflow is perfect for traders, investors, and financial community managers who want timely notifications about high- and medium-impact global economic events—no coding required.

  Key Features
  Automated Alerts:** Receive upcoming economic calendar updates for the next 3 days (customizable window) straight to Telegram.
  AI-Enhanced Formatting:** Economic news are grouped by date and importance, with clear, emoji-rich summaries for maximum readability and engagement.
  Flexible Scheduling:** Default update interval is every 3 days, adjustable to daily or any custom frequency.
  Customizable Date Range:** Easily set the window for economic event coverage (e.g., today to 7 days ahead).
  No Coding Needed:** Simple setup—just plug in your API keys and Telegram credentials.

  How to Set Up
  ++Step 1:++ Get Your RapidAPI Key & Subscribe to the Economic Calendar Endpoint
  Visit RapidAPI (https://rapidapi.com/) and sign up for a free account (free plan includes limited requests per month).
  After registering, click your profile icon in the top-right corner and select "User Settings".
  From the left menu, choose "Applications".
  Click on "default app" and copy your API Key.
  Paste this key inside the workflow node labeled "Set API Key for RapidAPI & Dates" under the "x-rapidapi-key" field.

  ++Step 2:++ Connect Google Gemini (or OpenAI) Model
  Add your Gemini API credentials in n8n for advanced AI formatting and structuring of economic updates.

  ++Step 3:++ Set Up Your Telegram Bot
  Create a bot with @BotFather, get your API token, and add it to n8n’s Telegram credentials.
  Obtain your Telegram chat or group ID (use @RawDataBot for this).

  ++Step 4:++ Customize Date Range (Optional)
  By default, the workflow fetches economic calendar updates for the next 3 days, but you can adjust it to any custom window (e.g., today to 7 days ahead).

  ++Step 5:++ Activate the Workflow
  Schedule how often you want news updates (default: every 3 days; can be as frequent as daily).
  The workflow will automatically fetch, format, and send the latest economic calendar updates to your Telegram.

  Who Is This For?
  Retail and professional traders
  Financial influencers and Telegram group admins
  Community managers seeking hands-off, real-time macroeconomic alerts

  Why Choose This Workflow?
  Save Time: No more manual economic calendar checks.
  Stay Informed: Instantly know about key market-moving events.
  Easy Integration: Works out-of-the-box with n8n and Telegram.

  Get started today and never miss a critical economic event again!

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.code","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.code","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.telegram","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.code"]

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metadata_json:
  {"categories":["automation","integration","finance","notifications","AI"],"complexity":"medium","use_cases":["Real-time economic calendar alerts","Market monitoring for traders and investors","Automated, AI-formatted news delivery to Telegram communities","Community/Telegram group engagement with concise summaries","Scheduled macroeconomic event notifications"],"estimated_setup_minutes":20,"required_services":["Economic Events Calendar API via RapidAPI","Google Gemini (or OpenAI) API for AI formatting","Telegram Bot API (bot token and chat/group ID)","n8n instance/workflow hosting"],"key_features":["Automated fetch of upcoming economic events (default 3-day window, customizable)","AI-enhanced grouping and emoji-rich summaries by date and importance","Flexible scheduling (default every 3 days, adjustable)","No-code setup — configure API keys and Telegram credentials","Sends formatted messages directly to Telegram channels/groups"],"target_audience":["Retail traders","Professional traders and investors","Financial influencers and Telegram group admins/community managers"]}


name: AI Auto-Save Gmail Receipts to Google Sheets + Google Drive
author_name: Matt Chong | n8n Creator
author_username: mattxchong
categories: []
created_at: 2025-06-29T12:07:36.666Z
updated_at: 2025-06-29T12:07:36.666Z
url: https://n8n.io/workflows/5451
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:33
description:
  Who is this for?

  This workflow is ideal for:

  For freelancers, business owners, and finance teams who receive receipts via Gmail  
  Automatically logs expenses for tax, 
  bookkeeping and year-end audits

  What problem is this workflow solving?

  When tax season hits, missing receipts create panic.

  This workflow keeps everything in one place. It uses AI to extract details from Gmail attachments, logs them in a Google Sheet, and stores the PDFs in Google Drive.

  No digging. No copying. Just everything where it should be.

  How it works?

  Apply the label receipt to any incoming Gmail email. Do not mark it as read.

  On a schedule (e.g. daily at 8:00 AM), the workflow triggers.

  It searches for unread emails with the label receipt.

  For each matching email, it downloads the attached receipt file.

  It extracts text content from the receipt file.

  It uploads the original receipt file to a specified folder in Google Drive.

  It merges the extracted text with email metadata.

  It sends this combined data to OpenAI.  
     OpenAI extracts structured fields:
     date
     merchant
     category
     description
     subtotal
     tax
     total

  The extracted data is appended as a new row in the specific Google Sheet.

  Finally, the email is marked as read to prevent it from being processed again.

  How to set up?

  Connect these services in your n8n credentials:
     Gmail (OAuth2)
     Google Drive
     Google Sheets
     OpenAI
  Configure the Google Drive upload:
     In the “Upload File” node, select the target folder where you want receipt PDFs stored.
  Set your execution schedule:
     Open the “Schedule Trigger” node and choose when it should run (default is once daily at 8:00 AM).
  Choose your Google Sheet and tab:
     In the “Append to Google Sheet” node, select your document and tab
     Ensure the sheet contains these columns:  
       Date, Merchant, Category, Description, Subtotal, Tax, Total.

  How to customize this workflow to your needs? 
  Change the Gmail label or search filter** to match your needs.
  Modify the OpenAI schema** to extract additional fields like currency, project, or notes.

nodes_used:
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metadata_json:
  {"categories":["automation","integration","data processing","finance"],"complexity":"medium","use_cases":["Automatically log receipts and expenses","Centralize receipt PDFs in Google Drive","Extract structured receipt data for bookkeeping or tax","Reduce manual data entry for finance teams"],"estimated_setup_minutes":20,"required_services":["Gmail (OAuth2)","Google Drive","Google Sheets","OpenAI"],"key_features":["Scheduled search for labeled unread Gmail receipts","Download and extract text from attached receipt files","Upload original PDFs to a specified Google Drive folder","Use OpenAI to parse and return structured fields (date, merchant, category, description, subtotal, tax, total)","Append extracted data as rows in a Google Sheet tab with required columns set up (Date, Merchant, Category, Description, Subtotal, Tax, Total)"],"target_audience":["freelancers","small business owners","finance teams and accountants/bookkeepers"]}


name: Resume Screening & Evaluation System with Gemini AI, Google Sheets & Drive for HR
author_name: Tharwat Mohamed
author_username: tharwatelsayed
categories: []
created_at: 2025-06-29T13:23:27.792Z
updated_at: 2025-06-29T13:23:27.792Z
url: https://n8n.io/workflows/5453
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:32
description:
  🚀 AI Resume Screener (n8n Workflow Template)

  An AI-powered resume screening system that automatically evaluates applicants from a simple web form and gives you clear, job-specific scoring — no manual filtering needed.



  ⚡ What the workflow does





  📄 Accepts CV uploads via a web form (PDF)



  🧠 Extracts key info using AI (education, skills, job history, city, birthdate, phone)



  🎯 Dynamically matches the candidate to job role criteria stored in Google Sheets



  📝 Generates an HR-style evaluation and a numeric score (1–10)



  📥 Saves the result in a Google Sheet and uploads the original CV to Google Drive



  💡 Why you’ll love it

  FeatureBenefitAI scoringInstantly ranks candidate fit without reading every CVGoogle Sheet-drivenEasily update job profiles — no code changesFast setupConnect your accounts and you're live in ~15 minsScalableWorks for any department, team, or organizationDeveloper-friendlyExtend with Slack alerts, translations, or automations



  🧰 Requirements





  🔑 OpenAI or Google Gemini API Key



  📄 Google Sheet with 2 columns: Role, Profile Wanted



  ☁️ Google Drive account



  🌐 n8n account (self-hosted or cloud)



  🛠 Setup in 5 Steps





  Import the workflow into n8n



  Connect Google Sheets, Drive, and OpenAI or Gemini



  Add your job roles and descriptions in Google Sheets



  Publish the form and test with a sample CV



  Watch candidate profiles and scores populate automatically



  🤝 Want help setting it up?

  Includes free setup guidance by the creator — available by email or WhatsApp after purchase.
  I’m happy to assist you in customizing or deploying this workflow for your team.

  📧 Email: tharwat.elsayed2000@gmail.com
  💬 WhatsApp: +20106 180 3236

nodes_used:
  ["n8n-nodes-base.formTrigger","n8n-nodes-base.extractFromFile","@n8n/n8n-nodes-langchain.informationExtractor","@n8n/n8n-nodes-langchain.chainSummarization","n8n-nodes-base.merge","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.googleDrive","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.googleSheets","n8n-nodes-base.merge"]

workflow_json_compressed:
  [encoded data, 6932 characters]

metadata_json:
  {"categories":["automation","HR / recruitment","integration","AI / ML","data processing"],"complexity":"medium","use_cases":["Automated resume screening and ranking","Applicant intake and basic ATS functionality","Prioritizing candidates for recruiters/hiring managers","Storing candidate data and CVs for later review","Customizable job-profile driven matching"],"estimated_setup_minutes":15,"required_services":["n8n (cloud or self-hosted)","Google Sheets","Google Drive","OpenAI API or Google Gemini API (LLM access)"],"key_features":["Web form intake for PDF CV uploads","AI extraction of education, skills, job history, location, contact details","Dynamic matching to role criteria stored in Google Sheets","Generates HR-style evaluation and numeric score (1–10)","Saves results to Google Sheets and uploads original CV to Google Drive"],"target_audience":["HR teams and recruiters","Hiring managers and talent acquisition","Small/medium businesses and startups without an ATS or wanting lightweight automation"]}


name: Automated Reservation System with Telegram, Google Gemini AI, and Google Sheets
author_name: Tharwat Mohamed
author_username: tharwatelsayed
categories: []
created_at: 2025-06-29T13:27:37.239Z
updated_at: 2025-06-29T13:27:37.239Z
url: https://n8n.io/workflows/5454
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  💡 What It Is

  SmartReserve is a flexible, automated Telegram chatbot built in n8n that allows users to request and confirm reservations for any kind of resource—training sessions, equipment, appointments, event slots, or more. It connects with Google Sheets for live availability tracking and automatically sends confirmation emails to your users.



  ⚙️ How It Works





  Telegram Chatbot Interface
  Users interact with a friendly bot to submit their reservation request. The bot collects:





  Date



  Name



  Email



  Resource / Service



  Start Time & End Time



  Final confirmation
  All in one seamless message.



  Conflict-Free Booking System
  The bot checks your existing reservation sheet to avoid time overlaps before confirming.



  Google Sheets Integration
  Two spreadsheets are used:





  Resource Info: Define available services, resources, or assets.



  Reservation Log: Store confirmed reservations in structured rows.



  Confirmation via Email
  Once a reservation is accepted, the bot sends a detailed confirmation email to the user.



  🚀 Setup Steps





  Import the n8n Workflow





  Use the provided .json template inside your n8n workspace.



  Create Your Google Sheets





  Sheet 1: Resource Info (e.g., rooms, courts, sessions, etc.)



  Sheet 2: Reservation Log with these headers:



  CopyEditDate | Name | Email | Resource | Start Time | End Time | Status





  Set Telegram Bot Token





  Create a Telegram bot and paste the token into n8n credentials.



  Connect Google Sheets





  Add your Google account to n8n and allow spreadsheet access.



  Customize for Your Use Case





  Rename “Resource” to anything (e.g., Room, Coach, Equipment).



  Edit confirmation text and branding inside the “Set” and “Email” nodes.



  Go Live!





  Enable the workflow, and you’re ready to accept real-time reservations.

  📦 What You Get





  ✅ One-click Telegram reservation system



  ✅ Conflict checker with Google Sheets



  ✅ Auto email confirmation



  ✅ User-friendly one-shot data collection



  ✅ Fully editable & extendable workflow



  ✅ Future updates and support options

  🙋 Need Help Setting It Up?

  If you'd like help customizing or deploying this workflow, I offer quick setup assistance and extended support.📧 Contact: tharwat.elsayed.hamad@gmail.com
  💬 Whatsapp: +201061803236
  Whether you're setting it up for your team, your club, or your business—I’m here to help!

nodes_used:
  ["n8n-nodes-base.telegramTrigger","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.gmailTool","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.aiTransform","@n8n/n8n-nodes-langchain.memoryPostgresChat","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 5788 characters]

metadata_json:
  {"categories":["automation","integration","scheduling","customer-communication"],"complexity":"medium","use_cases":["Room/space booking","Equipment rental scheduling","Appointment booking for services","Event slot reservations","Team or club session sign-ups"],"estimated_setup_minutes":30,"required_services":["Telegram Bot API","Google Sheets (Google account)","Google Gemini (lmChat Google Gemini) / Google Cloud AI","Email provider or SMTP credentials for confirmations","Postgres (optional, for memory/chat state)","n8n"],"key_features":["Telegram one-shot chatbot for collecting reservation details","Conflict checker against Google Sheets to prevent overlapping bookings","Two-sheet structure: Resource Info and Reservation Log","Automated confirmation emails to users","Customizable fields and confirmation text/branding in nodes','Uses Google Gemini for conversational handling / AI-driven dialog','Optional Postgres memory node for chat state persistence"],"target_audience":["small businesses and startups","Community clubs and sports teams","Event organizers and facility managers with booking needs','n8n users and workflow builders/developers"]}


name: Automate Instagram & Facebook Posting with Meta Graph API & System User Tokens
author_name: Brian
author_username: typebconsulting
categories: []
created_at: 2025-06-29T19:31:37.528Z
updated_at: 2025-06-29T19:31:37.528Z
url: https://n8n.io/workflows/5457
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  This template automates posting to Instagram Business and Facebook Pages using the Meta Graph API. It supports both short-lived and long-lived tokens, with a secure approach using System User tokens for reliable, ongoing automation. Includes detailed guidance for authentication, token refresh logic, and API use.

  Features:

  📸 Publish to Instagram via /media + /media_publish

  📘 Post to Facebook Pages via /photos

  🔐 Long-lived token support via Meta Business System User

  ♻️ Token refresh support using staticData in n8n

  🧠 In-line sticky note instructions

  Use Cases:

  Schedule and publish branded social media content

  Automate marketing flows with CRM + social sync

  Empower internal teams or clients to post without manual steps

  Tags: Instagram, Facebook, Meta Graph API, Social Media, Token Refresh, Long-Lived Token, Marketing Automation, System User

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

workflow_json_compressed:
  [encoded data, 5544 characters]

metadata_json:
  {"categories":["automation","integration","social media","marketing automation","scheduling"],"complexity":"medium","use_cases":["Schedule and publish branded Instagram and Facebook content","Automate marketing workflows by syncing CRM or content sources to social channels","Enable teams or clients to post reliably without manual token handling","Implement reliable long-running social posting using System User tokens"],"estimated_setup_minutes":45,"required_services":["Meta Graph API (Facebook & Instagram Business)","Meta Business Manager (to create System User and assign permissions)","Instagram Business Account","Facebook Page","n8n (self-hosted or cloud) with HTTP/Webhook access"],"key_features":["Publish to Instagram using /media and /media_publish endpoints","Post to Facebook Pages via /photos endpoint","Support for short-lived and long-lived tokens","System User token support for reliable long-running automation","Token refresh logic using n8n staticData storage (sticky refresh flow included in workflow code nodes and notes)"],"target_audience":["Marketing teams","Social media managers","Agencies handling client social accounts (especially multiple accounts/long-running automation)"]}


name: Automate Quote Request Processing with Tally, Airtable, Slack, and Gmail
author_name: Baptiste Fort
author_username: baptistefort
categories: []
created_at: 2025-06-29T19:34:09.144Z
updated_at: 2025-06-29T19:34:09.144Z
url: https://n8n.io/workflows/5458
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  What if your quote requests managed themselves?

  Every quote request is a potential deal — but only if it's handled quickly, properly, and without things falling through the cracks. What if instead of copy-pasting emails and pinging teammates manually, your entire process just... ran itself?

  This automation makes it happen: it captures form submissions, notifies your sales team on Slack, stores leads in Airtable, and sends an email confirmation to the client — all in one seamless n8n flow.

  ⚙️ Tools used

  Tally** – to collect client quote requests
  n8n** – to automate everything, no code needed
  Airtable** – to store leads and track status
  Slack** – to instantly notify your sales team
  Gmail** – to confirm the request with the client

  🧩 Flow structure overview

  Trigger from a Tally form using a webhook
  Extract and format the data
  Create a new record in Airtable
  Send a message to Slack
  Wait 5 minutes
  Send an email confirmation via Gmail

  📥 Step 1 – Webhook (Tally)

  This node listens for incoming quote requests from the Tally form.

  HTTP Method:** POST
  Path:** /Request a Quote
  Authentication:** None
  Respond:** Immediately

  The data arrives as an array inside body.data.fields. Each field has a label and a value that we’ll need to map manually.

  🧹 Step 2 – Edit Fields (Set)

  This step extracts usable values from the raw form data.

  Example mapping:

  Name = {{ $json.body.data.fields[0].label }}
  Email Address = {{ $json.body.data.fields[1].value }}
  Type of Service Needed = {{ $json.body.data.fields[2].value }}
  Estimated Budget = {{ $json.body.data.fields[3].value }}
  Preferred Timeline = {{ $json.body.data.fields[4].value }}
  Additional Details or Questions = {{ $json.body.data.fields[5].value }}

  📊 Step 3 – Create record in Airtable

  We send the cleaned fields into a database (CRM) in Airtable.

  Operation:** Create
  Base & Table:** Request a Quote - Airtable Base
  Mapping:** Manual field-to-column matching

  Each quote submission becomes a new record with all project details.

  📣 Step 4 – Send a message to Slack

  This node notifies your sales team immediately in a Slack channel.

  Message format:

  :new: New quote request received!

  👤 Name: {{ $json.fields.Name }}
  📧 Email: {{ $json.fields.Email }}
  💼 Service: {{ $json.fields["Type of Service"] }}
  💰 Budget: {{ $json.fields["Estimated Budget (€)"] }}
  ⏱️ Timeline: {{ $json.fields["Preferred Timeline"] }}
  📝 Notes: {{ $json.fields["Additional Details"] }}

  ⏳ Step 5 – Wait 5 minutes

  This node simply delays the email by 5 minutes.
  Why? To give a human salesperson time to reach out manually before the automated confirmation goes out. It adds a personal buffer.

  📧 Step 6 – Send confirmation via Gmail

  To:** {{ \$('Edit Fields').item.json\["Email Address"] }}
  Subject:** Thanks for your quote request 🙌
  Email Type:** HTML

  Message body:

  Hi {{ $('Edit Fields').item.json.Name }},

  Thanks a lot for your quote request — we’ve received your information!

  Our team will get back to you within the next 24 hours to discuss your project.

  Talk soon,  
  — The WebExperts Team

  ✅ Final result

  With this automation in place:

  The client feels acknowledged and taken seriously
  Your team gets notified in real time
  You store everything in a clean, structured database

  All this without writing a single line of backend code. It’s fast, scalable, and business-ready.

nodes_used:
  ["n8n-nodes-base.wait","n8n-nodes-base.webhook","n8n-nodes-base.gmail","n8n-nodes-base.set","n8n-nodes-base.airtable","n8n-nodes-base.slack","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 4704 characters]

metadata_json:
  {"categories":["automation","sales & CRM","integration","lead management"],"complexity":"medium","use_cases":["Automated quote/request intake","Lead capture and CRM population","Real-time sales notifications","Automated client confirmation emails","Preventing missed leads and follow-ups"],"estimated_setup_minutes":20,"required_services":["Tally","n8n","Airtable","Slack","Gmail"],"key_features":["Webhook trigger from Tally form","Map and transform form fields","Create records in Airtable (CRM)","Immediate Slack notification to sales channel","Configurable delay before auto-response (5 minutes) to allow manual follow-up
  send)"],"target_audience":["Sales teams","Small business owners","Marketing/ops teams implementing no-code workflows\",\"Customer success/operations"]}


name: AI-Powered Telegram Trivia Bot with Auto Question Generation & User Management
author_name: Luka Zivkovic
author_username: zivkovic58
categories: []
created_at: 2025-06-29T20:19:24.617Z
updated_at: 2025-06-29T20:19:24.617Z
url: https://n8n.io/workflows/5459
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  Complete Telegram Trivia Bot with AI Question Generation

  Build a fully-featured Telegram trivia bot that automatically generates fresh questions daily using OpenAI and tracks user progress with NocoDB. Perfect for communities, education, or entertainment!

  ✨ Key Features

  🤖 AI Question Generation: Automatically creates 40+ new trivia questions daily across 8 categories
  📊 Smart User Management: Tracks scores, prevents question repeats, maintains leaderboards
  🎮 Game Mechanics: Star-based difficulty scoring, answer history, progress tracking
  🏆 Competitive Elements: Real-time leaderboards with emoji rankings and user positioning
  🛡️ Robust Architecture: Error handling, state management, and data validation

  🚀 Perfect For

  Community Engagement**: Keep Telegram groups active with daily trivia challenges
  Educational Content**: Create learning experiences with categorized questions
  Business Applications**: Employee training, customer engagement, lead generation
  Personal Projects**: Learn n8n automation while building something fun

  📱 Supported Commands

  /start - Welcome new users with setup instructions
  /question - Get personalized trivia questions (never repeats correctly answered ones)
  /score - View current points and statistics
  /leaderboard - See top 10 players with rankings
  /stats - Detailed accuracy and performance metrics
  /help - Complete command reference

  🔧 How It Works

  User Journey:
  User sends /question command to bot
  System checks their answer history to avoid repeats
  Displays fresh question with multiple choice options
  Processes answer, updates score based on difficulty stars
  Saves complete answer history for future filtering

  AI Content Pipeline:
  Daily scheduler triggers question generation
  OpenAI creates 5 questions per category (8 categories total)
  Questions automatically saved to NocoDB with difficulty ratings
  Content includes explanations and proper formatting

  🛠️ Set Up Steps

  Prerequisites:
  n8n instance (cloud or self-hosted)
  NocoDB database (free tier works)
  OpenAI API key (Not required if you want to add questions yourself)
  Telegram bot token

  Database Setup:
  Create 3 NocoDB tables with the exact field specifications provided in the sticky notes. The workflow includes complete schema documentation.

  Configuration Time: ~15 minutes for database setup + API keys

  Detailed Setup Instructions:
  All setup steps, database schemas, and configuration details are documented in the workflow's sticky notes for easy implementation.

  📈 Advanced Features

  Question History Tracking**: Users never see correctly answered questions again
  Difficulty-Based Scoring**: 1-5 star rating system with corresponding points
  Category Management**: 8 different trivia categories for variety
  State Management**: Proper game flow with idle/waiting states
  Error Handling**: Graceful fallbacks for all edge cases
  Scalable Architecture**: Supports unlimited concurrent users

  🎯 Business Applications

  Lead Generation**: Capture user data through engaging trivia
  Employee Training**: Create custom questions for onboarding
  Customer Engagement**: Keep users active in your Telegram community
  Educational Tools**: Subject-specific learning with progress tracking
  Event Activation**: Conferences, workshops, or team building

  💡 Customization Options

  Modify question categories for your niche
  Adjust scoring systems and difficulty levels
  Add custom commands and features
  Integrate with other platforms or APIs
  Create specialized question sets

  🔗 Get Started

  Ready to build your own AI-powered trivia bot? Start with n8n and follow the comprehensive setup guide included in this workflow template.

  Next Steps:
  Import this workflow template
  Follow the database setup instructions in sticky notes
  Configure your API credentials
  Test with sample questions
  Launch your trivia bot!

  Turn your friend group into trivia champions with AI-generated questions that spark friendly competition!

nodes_used:
  ["n8n-nodes-base.telegramTrigger","n8n-nodes-base.code","n8n-nodes-base.nocoDb","n8n-nodes-base.if","n8n-nodes-base.nocoDb","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.switch","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.nocoDb","n8n-nodes-base.nocoDb","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.nocoDb","n8n-nodes-base.code","n8n-nodes-base.nocoDb","n8n-nodes-base.code","n8n-nodes-base.telegram","n8n-nodes-base.merge","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.nocoDbTool","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.code","n8n-nodes-base.telegram","n8n-nodes-base.merge","n8n-nodes-base.aggregate","n8n-nodes-base.nocoDb","n8n-nodes-base.nocoDb","n8n-nodes-base.aggregate","n8n-nodes-base.nocoDb","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 12132 characters]

metadata_json:
  {"categories":["automation","integration","chatbot","education","gamification"],"complexity":"complex","use_cases":["Community engagement via daily trivia","Educational learning & assessments","Employee training & onboarding","Customer engagement & lead generation","Personal projects / learning n8n"],"estimated_setup_minutes":15,"required_services":["OpenAI API","Telegram Bot API (bot token)","NocoDB (database)","n8n instance (cloud or self-hosted)"],"key_features":["AI-driven daily question generation (40+ questions across 8 categories)","User management with score tracking and answer history","Difficulty-based star scoring and points system","Real-time leaderboards with rankings and emoji badges","Question repeat prevention per user and progress tracking"],"target_audience":["Community managers","Educators and instructional designers","Developers building chatbots or automations」「Product owners / marketers looking for engagement tools"]}


name: Parse Natural Language Dates with OpenAI GPT-4o for Smart Scheduling
author_name: JaredCo
author_username: jaredco
categories: []
created_at: 2025-06-29T23:06:55.530Z
updated_at: 2025-06-29T23:06:55.530Z
url: https://n8n.io/workflows/5460
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:33
description:
  This n8n workflow demonstrates how to transform natural language date and time expressions into structured data with 96%+ accuracy. Parse complex expressions like "early next July", "2 weeks after project launch", or "end of Q3" into precise datetime objects with confidence scoring, timezone intelligence, and business rules validation for any automation workflow.

  Good to know
  Achieves 96%+ accuracy on complex natural language date expressions
  At time of writing, this is the most advanced open-source date parser available
  Includes AI learning that improves over time with user corrections
  Supports 6 languages with auto-detection (English, Spanish, French, German, Italian, Portuguese)
  Sub-millisecond response times with intelligent caching
  Enterprise-grade with business intelligence and timezone handling

  How it works
  Natural Language Input**: Receives date expressions via webhook, form, email, or chat
  AI-Powered Parsing**: Your world-class date parser processes the text through:
   50+ custom rule patterns for complex expressions
   Multi-language auto-detection and smart translation
   Confidence scoring (0.0-1.0) for AI decision-making
   Ambiguity detection with helpful suggestions
  Business Intelligence**: Applies enterprise rules automatically:
   Holiday calendar awareness (US + International)
   Working hours validation and warnings
   Business day auto-adjustment
   Timezone normalization (IANA format)
  Smart Scheduling**: Creates calendar events with:
   Structured datetime objects (start/end times)
   Confidence metadata for workflow decisions
   Alternative interpretations for ambiguous inputs
   Rich context for follow-up actions
  Integration Ready**: Outputs connect seamlessly to:
   Google Calendar, Outlook, Apple Calendar
   CRM systems (HubSpot, Salesforce)
   Project management tools (Notion, Asana)
   Communication platforms (Slack, Teams)

  How to use
  The webhook trigger receives natural language date requests from any source
  Replace the MCP server URL with your deployed date parser endpoint
  Configure timezone preferences for your organization
  Customize business rules (working hours, holidays) in the parser settings
  Connect calendar integration nodes for automatic event creation
  Add notification workflows for scheduling confirmations

  Use Cases
  Meeting Scheduling**: "Schedule our quarterly review for early Q3"
  Project Management**: "Set deadline 2 weeks after product launch"
  Event Planning**: "Book venue for the weekend before Labor Day"
  Personal Assistant**: "Remind me about dentist appointment next Tuesday morning"
  International Teams**: "Team standup tomorrow morning" (auto-timezone conversion)
  Seasonal Planning**: "Launch campaign in late spring 2025"

  Requirements
  Natural Language Date Parser MCP server (provided code)
  Webhook endpoint or form trigger
  Calendar integration (Google Calendar, Outlook, etc.)
  Optional: Slack/Teams for notifications
  Optional: Database for learning pattern storage

  Customizing this workflow
  Multi-language Support**: Enable auto-detection for global teams
  Business Rules**: Configure company holidays and working hours
  Learning System**: Enable AI learning from user corrections
  Integration Depth**: Connect to your existing calendar and CRM systems
  Confidence Thresholds**: Set minimum confidence levels for auto-scheduling
  Ambiguity Handling**: Route unclear dates to human review or clarification requests

  Sample Input/Output

  Input Examples:
  "early next July"
  "2 weeks after Thanksgiving"
  "next Wednesday evening"
  "Q3 2025"
  "mañana por la mañana" (Spanish)
  "first thing Monday"

  Rich Output:
  {
    "parsed": [{
      "start": "2025-07-01T00:00:00Z",
      "end": "2025-07-10T23:59:59Z",
      "timezone": "America/New_York"
    }],
    "confidence": 0.95,
    "method": "custom_rules",
    "business_insights": [{
      "type": "business_warning",
      "message": "Selected date range includes July 4th holiday"
    }],
    "predictions": [{
      "type": "time_preference",
      "suggestion": "You usually schedule meetings at 10 AM"
    }],
    "ambiguities": [],
    "alternatives": [{
      "interpretation": "Early July 2026",
      "confidence": 0.15
    }],
    "performance": {
      "cache_hit": true,
      "response_time": "0.8ms"
    }
  }

  Why This Workflow is Unique

  World-Class Accuracy**: 96%+ success rate on complex expressions
  AI Learning**: Improves over time with user feedback  
  Global Ready**: Multi-language and timezone intelligence
  Business Smart**: Enterprise rules and holiday awareness
  Performance Optimized**: Sub-millisecond cached responses
  Context Aware**: Provides confidence scores and alternatives for AI decision-making

  Transform your scheduling workflows from rigid form inputs to natural, conversational date requests that your users will love!

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.httpRequestTool","n8n-nodes-base.set","n8n-nodes-base.manualTrigger"]

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metadata_json:
  {"categories":["automation","natural language processing","scheduling","integration"],"complexity":"complex","use_cases":["Meeting scheduling from natural language requests","Project deadline and milestone generation","Event planning and date-range resolution","Personal assistant reminders","Cross-timezone team scheduling and normalization"],"estimated_setup_minutes":60,"required_services":["Natural Language Date Parser (MCP) server (self-hosted or deployed endpoint)","OpenAI (GPT-4o) or compatible LLM API","Calendar API (Google Calendar, Microsoft Outlook/Office 365, or Apple Calendar)","Webhook endpoint or form trigger","Optional: Slack/Teams for notifications, CRM (HubSpot/Salesforce), database for learning storage"],"key_features":["AI-powered parsing with 96%+ accuracy on complex expressions","Multi-language auto-detection (6 languages) and translation","Confidence scoring, ambiguity detection and alternative interpretations","Timezone normalization (IANA) and business-hours/holiday awareness","Business rule validation (working days, company holidays) and configurable ruleset"],"target_audience":["Developers implementing scheduling automations","Product managers and automation leads","IT/DevOps teams deploying enterprise integrations"]}


name: iMessage Food Photo Nutritional Analysis with GPT-4 Vision & Memory Storage
author_name: David Harvey
author_username: xtgy
categories: []
created_at: 2025-06-30T00:28:05.321Z
updated_at: 2025-06-30T00:28:05.321Z
url: https://n8n.io/workflows/5461
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  iMessage AI-Powered Smart Calorie Tracker


  &gt; 📌 What it looks like in use:  
     
  &gt; This image shows a visual of the workflow in action. Use it for reference when replicating or customizing the template.

  This n8n template transforms a user-submitted food photo into a detailed, friendly, AI-generated nutritional report — sent back seamlessly as a chat message. It combines OpenAI's visual reasoning, Postgres-based memory, and real-time messaging with Blooio to create a hands-free calorie and nutrition tracker.

  🧠 Use Cases

  Auto-analyze meals based on user-uploaded images.
  Daily/weekly/monthly diet summaries with no manual input.
  Virtual food journaling integrated into messaging apps.
  Nutrition companion for healthcare, fitness, and wellness apps.

  📌 Good to Know

  ⚠️ This uses GPT-4 with image capabilities, which may incur higher usage costs depending on your OpenAI pricing tier. Review OpenAI’s pricing.
  The model uses visual reasoning and estimation to determine nutritional info — results are estimates and should not replace medical advice.
  Blooio is used for sending/receiving messages. You will need a valid API key and project set up with webhook delivery.
  A Postgres database is required for long-term memory (optional but recommended). You can use any memory node with it.

  ⚙️ How It Works

  Webhook Trigger  
     The workflow begins when a message is received via Blooio. This webhook listens for user-submitted content, including any image attachments.

  Image Validation and Extraction  
     A conditional check verifies the presence of attachments. If images are found, their URLs are extracted using a Code node and prepared for processing.

  Image Analysis via AI Agent  
     Images are passed to an OpenAI-based agent using a custom system prompt that:
     Identifies the meal,
     Estimates portion sizes,
     Calculates calories, macros, fiber, sugar, and sodium,
     Scores the meal with a health and confidence rating,
     Responds in a chatty, human-like summary format.

  Memory Integration  
     A Postgres memory node stores user interactions for recall and contextual continuity, allowing day/week/month reports to be generated based on cumulative messages.

  Response Aggregation & Summary  
     Messages are aggregated and summarized by a second AI agent into a single concise message to be sent back to the user via Blooio.

  Message Dispatch  
     The final message is posted back to the originating conversation using the Blooio Send Message API.

  🚀 How to Use

  The included webhook can be triggered manually or programmatically by linking Blooio to a frontend chat UI.
  You can test the flow using a manual POST request containing mock Blooio payloads.
  Want to use a different messages app? Replace the Blooio nodes with your preferred messaging API (e.g., Twilio, Slack, Telegram).

  ✅ Requirements

  OpenAI API access with GPT-4 Vision or equivalent multimodal support.
  Blooio account with access to incoming and outgoing message APIs.
  Optional: Postgres DB (e.g., via Neon) for tracking message context over time.

  🛠️ Customising This Workflow

  Prompt Tuning**  
    Tailor the system prompt in the AI Agent node to fit specific diets (e.g., keto, diabetic), age groups, or regionally-specific foods.

  Analytics Dashboards**  
    Hook up your Postgres memory to a data visualization tool for nutritional trends over time.

  Multilingual Support**  
    Adjust the response prompt to translate messages into other languages or regional dialects.

  Image Preprocessing**  
    Insert a preprocessing node before sending images to the model to resize, crop, or enhance clarity for better results.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryPostgresChat","n8n-nodes-base.webhook","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.splitInBatches","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.aggregate","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","data-processing","AI/ML","messaging"],"complexity":"complex","use_cases":["Auto-analyze meals from user-uploaded images","Hands-free food journaling via messaging apps","Daily/weekly/monthly dietary summaries and trend reports","Nutrition companion for fitness, healthcare, and wellness apps"],"estimated_setup_minutes":45,"required_services":["OpenAI (GPT-4 with vision/multimodal support)","Blooio (incoming/outgoing messaging and webhooks)","Postgres-compatible DB (e.g., Neon) for memory storage","n8n instance"],"key_features":["Image-based nutritional analysis using GPT-4 Vision","Agent-driven prompt that identifies meal, estimates portions, calories, macros","Health and confidence scoring of analyses","Postgres memory for long-term user context and aggregated summaries","Webhook trigger and real-time message send/receive via Blooio API"],"target_audience":["Developers building chat-based nutrition/health apps","Product teams for fitness and wellness services","Nutritionists and healthcare app integrators (for non-clinical guidance workflows)"]}


name: Generate Images with OpenAI DALL-E via Telegram & Log to Google Sheets
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-06-30T03:09:14.415Z
updated_at: 2025-06-30T03:09:14.415Z
url: https://n8n.io/workflows/5462
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  📌 Workflow Description

  This workflow allows users to generate AI images directly from Telegram while automatically logging each request to Google Sheets for tracking. When a user sends a prompt to your Telegram bot, the workflow receives the prompt, generates an image using OpenAI, saves the prompt and generated image link to Google Sheets, and then sends the image back to the user in Telegram.

  ⚙️ How to Set It Up


  Connect your Telegram credentials by adding your bot token in both the Telegram Trigger and Telegram Send Message nodes. Ensure your bot is added to the intended chat.

  Connect your OpenAI credentials by inserting your API key in the OpenAI node and configuring it for image generation.

  Connect your Google Sheets account by authorising the Google Sheets node and selecting the target spreadsheet and worksheet where you want to save prompts and image links.

  Finally, activate the workflow and test by sending a prompt to your Telegram bot to confirm that the image is generated, saved to Google Sheets, and sent back to Telegram seamlessly.

  That’s it – your AI Image Generator with Google Sheets logging is ready to use.
  		
  ⸻

  ✅ That’s it – your AI Image Generator with Google Sheets logging is ready for use.

nodes_used:
  ["@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.telegramTrigger","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets"]

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metadata_json:
  {"categories":["automation","integration","content creation","AI image generation"],"complexity":"simple","use_cases":["Generate AI images from chat prompts","Log prompts and image URLs for tracking","Build a Telegram-based image-generation bot","Prototype creative/content workflows"],"estimated_setup_minutes":10,"required_services":["Telegram Bot (bot token)","OpenAI API (Images/DALL·E)","Google Sheets (Google account & spreadsheet)"],"key_features":["Receive prompts via Telegram trigger","Generate images using OpenAI Images (DALL·E)","Save prompt and image link to Google Sheets","Send generated image back to the Telegram user","Lightweight 5-node workflow with simple configuration"],"target_audience":["Developers building chatbots","Marketers and content creators","Community managers and moderators」「No-code/low-code users experimenting with AI"]}


name: Create Comprehensive Research Reports with Jina AI & Gemini 2.5 Flash
author_name: AppStoneLab Technologies LLP
author_username: appstonelab
categories: []
created_at: 2025-06-30T06:11:27.934Z
updated_at: 2025-06-30T06:11:27.934Z
url: https://n8n.io/workflows/5463
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  Automated AI Research Assistant: From Query to Polished Report with Jina & Gemini
  Turn a single research question into a comprehensive, multi-source report with proper citations. This workflow automates the entire research process by leveraging the web-crawling power of Jina AI and the advanced reasoning capabilities of Google's Gemini models.
  Simply input your query, and this AI-powered assembly line will search the web, scrape relevant sources, summarize the content, draft a structured research paper, and finally, evaluate and polish the report for accuracy and formatting.
  ✨ Key Features
  🔎 Dynamic Web Search**: Kicks off by searching the web with Jina AI based on your initial query.
  📚 Multi-Source Content Scraping**: Automatically reads and extracts content from the top 10 search results.
  🧠 AI-Powered Summarization**: Uses a Gemini agent to intelligently summarize each webpage, retaining the core information.
  ✍️ Automated Report Generation**: A specialized "Generator Agent" synthesizes the summarized data into a structured research paper, complete with an executive summary, introduction, discussion, and conclusion.
  ✅ Citation & Quality Verification**: A final "Evaluator Agent" meticulously checks the generated report for citation accuracy, logical flow, and markdown formatting, delivering a polished final document.
  📈Rate-Limit Ready**: Includes a configurable Wait node to ensure stable execution when dealing with multiple API calls.
  📝 What This Workflow Does
  This workflow is designed to be your personal research assistant. It addresses the time-consuming process of gathering, reading, and synthesizing information from multiple online sources. Instead of spending hours manually searching, reading, and citing, you can delegate the entire task to this workflow and receive a well-structured and cited report as the final output.
  It's perfect for students, researchers, content creators, and analysts who need to quickly compile information on any given topic.
  ⚙️ How It Works (Step-by-Step)
  Initiate with a Query: The workflow starts when you send your research question or topic to the Chat Trigger node.
  Search the Web: The user's query is passed to the Jina AI node, which performs a web search and returns the top 10 most relevant URLs.
  Scrape, Summarize, Repeat: The workflow then loops through each URL:
      Read Content: The Jina AI node scrapes the full text content from the URL.
      Summarize: A Summarizer Agent powered by Google Gemini reads the scraped content and the original user query, then generates a concise summary.
      Wait: A one-second pause helps to avoid hitting API rate limits before processing the next URL.
  Aggregate the Knowledge: Once the loop is complete, a Code node gathers all 10 individual summaries into a single, neatly structured list.
  Draft the Research Report: This aggregated data is fed to the Generator Agent. Following a detailed prompt, this Gemini-powered agent writes a full research report, structuring it with headings and adding inline citations for every piece of information it uses.
  Evaluate and Finalize: The generated draft is passed to the final Evaluator Chain. This agent acts as a quality control supervisor. It verifies that all claims are correctly cited, refines the content for clarity and academic tone, and polishes the markdown formatting to produce the final, ready-to-use report.
  🚀 How to Use This Workflow
  Credentials: Click on Use template, then configure your credentials for the following nodes:
      Jina AI: You will need a Jina AI API key for the Search web and Read URL content nodes. Get your key from here: JinaAI API Key
      Google Gemini: You will need a Google Gemini API key for the Summarizer Model, Generator Model, and Evaluator Model nodes. Get your key from here: Gemini API Key
  Activate Workflow: Make sure the workflow is active in your n8n instance.
  Start Research: Send a chat message with your research topic to the webhook URL provided in the When chat message received node.
  Get Your Report: Check the output of the final node, Evaluator Chain, to find your completed and polished research report.
  Nodes Used
  Chat Trigger
  Jina AI
  Code (Python)
  Split in Batches (Looping)
  Wait
  AI Agent
  Basic LLM Chain
  Google Gemini Chat Model

nodes_used:
  ["n8n-nodes-base.jinaAi","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.splitInBatches","n8n-nodes-base.code","n8n-nodes-base.jinaAi","n8n-nodes-base.wait","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["research automation","AI integration","web scraping","content generation","data processing"],"complexity":"complex","use_cases":["Automated literature reviews and research reports","Academic paper drafting with citations","Market/competitive research summaries","Content creation for blogs or whitepapers","Analyst briefs and executive summaries"],"estimated_setup_minutes":20,"required_services":["n8n instance","Jina AI (web search & scraping)","Google Gemini (Gemini 2.5 Flash) / Google Cloud API credentials","Internet access to target websites","(Optional) proxy or scraping service for restricted sites"],"key_features":["Dynamic web search of top results via Jina AI","Multi-source content scraping and extraction","Per-page AI summarization using Gemini","Aggregation of summaries into a structured dataset","Generator agent creates a full research paper with headings and inline citations using Gemini models','Evaluator agent verifies citations, logic, and formats final markdown"],"target_audience":["Researchers and academics","Students","Content creators and writers producing long-form content or reports"]}


name: Screen Resumes & Send Follow-ups with OpenAI GPT-4o, Google Sheets & Gmail
author_name: Abdulaziz
author_username: abdulaziz747
categories: []
created_at: 2025-06-30T06:30:44.467Z
updated_at: 2025-06-30T06:30:44.467Z
url: https://n8n.io/workflows/5464
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:33
description:
  This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 

  🧠 How It Works

  This workflow automates the process of screening resumes using AI, logging results to Google Sheets, and sending follow-up emails via Gmail.

  User uploads their resume via a form (PDF only).
  Resume content is extracted and sent to OpenAI for evaluation.
  AI scores the resume based on:
     Role-specific must-have qualifications
     Soft skills / strategic fit questions
  Based on the score threshold, the candidate is classified as:
     ✅ Accepted → Saved to Accepted sheet + Gmail invite
     ❌ Rejected → Logged to Rejected sheet + Gmail rejection message
  Output is stored in Google Sheets with detailed justification.

  ⚙️ Set Up Steps

  🔑 Connect your OpenAI and Google Sheets credentials.
  Replace the placeholder tags in the AI prompt:
     COMPANY_NAME
     ROLE_NAME
     ROLE_DESCRIPTION
     CRITERIA_1 to CRITERIA_5
     Q1 to Q5
     THRESHOLD (score to pass)
  Customize Gmail messages (optional).
  ✅ Make sure your sheet has two tabs: Accepted and Rejected.

  📌 Notes

  Sticky Notes included in the flow explain:
  What each node does
  Where to replace variables
  Tips for improving match scoring

nodes_used:
  ["n8n-nodes-base.formTrigger","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.textClassifier","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.extractFromFile","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.gmail","n8n-nodes-base.gmail"]

workflow_json_compressed:
  [encoded data, 6772 characters]

metadata_json:
  {"categories":["automation","integration","recruitment","ai"],"complexity":"medium","use_cases":["Automated resume screening and scoring","Candidate triage (accept/reject)","Logging candidate decisions and rationale to Google Sheets","Sending personalized follow-up or rejection emails via Gmail","Building an initial hiring pipeline and audit trail"],"estimated_setup_minutes":30,"required_services":["OpenAI (GPT-4o) API","Google Sheets (Google account / Sheets API)","Gmail (Google account)","n8n self-hosted instance (community nodes support)"],"key_features":["PDF resume content extraction","AI-powered scoring and justification using customizable prompts","Threshold-based accept/reject classification","Write accepted/rejected candidates to separate Google Sheets tabs","Automated personalized Gmail follow-ups/invites or rejection messages','Sticky notes and inline tips for where to replace variables and improve scoring"],"target_audience":["Recruiters and HR teams","Hiring managers","Startup founders building lightweight hiring automation workflows with n8n self-hosted instance of n8n developers/ops who manage integrations"]}


name: Automated Restaurant Call Handling & Table Booking System with VAPI and PostgreSQL
author_name: Oneclick AI Squad
author_username: oneclick-ai
categories: []
created_at: 2025-06-30T08:25:18.865Z
updated_at: 2025-06-30T08:25:18.865Z
url: https://n8n.io/workflows/5466
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  Acts as a virtual receptionist for the restaurant, handling incoming calls via VAPI without human intervention. It collects user details (name, booking time, number of people) for table bookings, checks availability in a PostgreSQL database using n8n, books the table if available, and sends confirmation. It also provides restaurant details to users, mimicking a human receptionist.

  Key Insights

  VAPI must be configured to accurately capture user input for bookings and inquiries.
  PostgreSQL database requires a table to manage restaurant bookings and availability.

  Workflow Process

  Initiate the workflow with a VAPI call to collect user details (name, time, number of people).  
  Use n8n to query the PostgreSQL database for table availability.  
  If a table is available, book it using n8n and update the PostgreSQL database.  
  Send a booking confirmation and hotel service details back to VAPI via n8n.  
  Store and update restaurant table data in the PostgreSQL database using n8n.

  Usage Guide

  Import the workflow into n8n and configure VAPI and PostgreSQL credentials.  
  Test with a sample VAPI call to ensure proper data collection and booking confirmation.

  Prerequisites

  VAPI API credentials for call handling  
  PostgreSQL database with booking and availability tables  

  Customization Options

  Modify the VAPI input fields to capture additional user details or adjust the PostgreSQL query for specific availability criteria.

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

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metadata_json:
  {"categories":["automation","telephony","integration","reservation management"],"complexity":"medium","use_cases":["Automated virtual receptionist for incoming calls","Table booking and availability checks","Booking confirmations via voice/SMS","Reducing front-desk workload and missed calls","Collecting customer details for reservations"],"estimated_setup_minutes":60,"required_services":["VAPI (voice/telephony API) with inbound call handling","PostgreSQL database for bookings/availability","n8n instance with webhook exposure (public endpoint)"],"key_features":["Collects caller details (name, time, party size) via VAPI","Queries PostgreSQL for table availability","Creates/updates bookings in PostgreSQL","Sends booking confirmation and restaurant info back through VAPI","Configurable input fields and availability criteria"],"target_audience":["Restaurant owners and managers","Small business operators handling reservations","Developers/automation engineers implementing telephony workflows"]}


name: Multi-User Telegram Bot to Summarize & Repurpose YouTube Videos with GPT-4o
author_name: Aashiq
author_username: aashiqq
categories: []
created_at: 2025-06-30T08:46:22.578Z
updated_at: 2025-06-30T08:46:22.578Z
url: https://n8n.io/workflows/5468
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  👤 Who’s it for
  This workflow is for content creators, marketers, educators, or anyone who wants to instantly summarize YouTube videos and repurpose them into different formats (LinkedIn post, tweet, etc.) via a simple Telegram chatbot.

  ⚙️ How it works
  This n8n automation listens for messages in Telegram. If the message contains a YouTube link, it:
  Extracts the video ID
  Fetches the video transcript using RapidAPI
  Cleans the transcript of any special characters
  Sends it to OpenAI to generate a summary

  If the message is not a link, it simply acts as an AI chatbot using OpenAI with memory support.

  ✅ Supports follow-up prompts like:
  “Make it shorter”
  “Turn this into a LinkedIn post”
  “Create a tweet thread”

  🧑‍🤝‍🧑 Multi-User Support

  This Telegram bot supports multiple users simultaneously. It tracks memory and context separately for each user using Telegram's unique chat_id.

  ✅ Each user gets personalized AI replies  
  ✅ Follow-up commands work per user  
  ✅ No interference between users

  🛠️ Requirements
  A Telegram bot token (get via @BotFather)
  An OpenAI API Key (from https://platform.openai.com/account/api-keys)
  A RapidAPI Key and Host (typically youtube-transcript3.p.rapidapi.com)

  &gt; 🚨 API keys must be added manually — they are not included in the template.

  🧩 How to Set It Up
  Configure the Telegram Trigger node with your bot token.
  In the HTTP Request node, set:
     X-RapidAPI-Key: your RapidAPI key
     X-RapidAPI-Host: your RapidAPI host URL
  Add your OpenAI API credentials to the AI Agent node.

  Use the provided sticky notes for guidance inside the workflow itself.

  🎛️ How to Customize
  Modify AI prompt behavior in the AI Agent node
  Change the text formatting in the Code node
  Use a different transcript API if preferred
  Add commands like make it into a blog post, summarize in bullet points, etc.

  📌 Notes
  All nodes are renamed to reflect their function
  API credentials are removed for security
  Includes colored boxes and sticky notes to guide the user
  Compatible with n8n cloud and self-hosted setups

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

workflow_json_compressed:
  [encoded data, 5188 characters]

metadata_json:
  {"categories":["integration","automation","chatbot","AI content repurposing"],"complexity":"medium","use_cases":["Summarize YouTube videos","Repurpose video transcripts into LinkedIn posts, tweets, threads, blog snippets","Interactive AI chatbot for non-link queries","Multi-user conversational memory and follow-up commands"],"estimated_setup_minutes":20,"required_services":["Telegram Bot (BotFather token)","OpenAI (GPT-4o API key)","RapidAPI (youtube-transcript3 or equivalent transcript API key & host)","n8n (cloud or self-hosted)"],"key_features":["Telegram trigger for messages and links","YouTube video ID extraction and transcript fetching","Transcript cleaning/preprocessing","GPT-4o summarization and repurposing prompts","Per-user memory/context tracking using Telegram chat_id (memoryBufferWindow/agent) for multi-user support and follow-ups','Follow-up command handling (e.g., shorten, make LinkedIn post, create tweet thread)','Customizable AI prompts and formatting (via AI Agent and Code nodes)','Guidance via sticky notes and renamed nodes for easier setup"],"target_audience":["Content creators","Marketers and social media managers","Educators and trainers repurposing video content','Developers/automation builders using n8n"]}


name: Daily AI News Digest with Perplexity Pro, GPT Format & Gmail Delivery
author_name: Automate With Marc
author_username: marconi
categories: []
created_at: 2025-06-30T08:57:55.721Z
updated_at: 2025-06-30T08:57:55.721Z
url: https://n8n.io/workflows/5469
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 

  🧠 AI News Update Every 24 Hours (with Perplexity + GPT Formatter)
  Description:

  This workflow automatically delivers a clean, AI-curated summary of the latest AI news headlines from the past 24 hours directly to your inbox — every morning at 9 AM.

  For step-by-step video tutorial for this build, watch here:
  https://youtu.be/O-DLvaMVLso

  🧰 How It Works:
  🕘 Schedule Trigger
   Runs daily at 9 AM to start the workflow.

  🔎 Perplexity AI Agent
   Searches for AI-related headlines published in the last 24 hours, including:

  Headline

  1-sentence summary

  Source

  Full URL

  🧠 GPT Formatter AI Agent
   Uses an OpenAI language model (GPT-4.1-mini) to reformat raw news data into a clean, readable email update.

  🧷 Memory Buffer (Optional)
   Gives the formatter context and continuity if you want to personalize formatting further over time.

  📧 Gmail Node
   Sends the formatted AI news digest to your inbox (or your team’s) daily.

  📦 Tools & APIs Required:
  ✅ Perplexity AI API

  ✅ OpenAI API

  ✅ Gmail Account (OAuth2 credentials)

  🔄 Use Cases:
  Daily AI trend monitoring for individuals or teams

  Automating internal market intelligence

  Research triggers for blog or content creation

  Email digests for newsletters or Slack updates

  🛠️ Customizable Ideas:
  Swap Gmail for Slack, Telegram, Discord, etc.

  Modify the topic (e.g., Climate Tech, Crypto News)

  Add sentiment analysis or AI-generated commentary

  Send summary to Google Docs or Notion instead

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.gmail","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.perplexity","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","news aggregation","AI integration","email delivery"],"complexity":"medium","use_cases":["Daily AI trend monitoring for individuals or teams","Automating internal market intelligence reports","Triggering research or content creation workflows","Sending daily newsletter-style email digests"],"estimated_setup_minutes":30,"required_services":["Perplexity AI API","OpenAI API (GPT-4.1-mini)","Gmail account with OAuth2 credentials","Self-hosted n8n (community nodes required)"],"key_features":["Scheduled daily trigger (9 AM)","Perplexity agent to fetch AI headlines from last 24 hours","GPT formatter to create clean, readable email summaries","Optional memory buffer for personalization/continuity","Gmail node to send the digest; delivery channel can be swapped (Slack/Discord/Telegram)"],"target_audience":["Product managers and execs monitoring AI trends","Researchers and analysts","Content creators and newsletter authors/marketers\",\"Developers/ops running self-hosted n8n"]}


name: Discord Daily Digest for Multiple Google Analytics Accounts
author_name: Jay Emp0
author_username: jay-emp0
categories: []
created_at: 2025-06-30T09:34:33.394Z
updated_at: 2025-06-30T09:34:33.394Z
url: https://n8n.io/workflows/5470
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  Overview
  Fetch Multiple Google Analytics GA4 metrics daily, post to Discord, update previous day’s entry as GA data finalizes over seven days.

  Benefits
  Automates daily traffic reporting
  Maintains single message per day, avoids channel clutter
  Provides near–real-time updates by editing prior messages

  Use Case
  Teams tracking website performance via Discord (or any chat tool) without manual copy–paste. Marketing managers, community moderators, growth hackers.
  If your manager asks you for daily marketing report every morning, you can now automate it

  Notes
  google analytics node in n8n does not provide real time data. The node updates previous values for the next 7 days
  discord node on n8n does not have features to update an exisiting message by message id. So we have used the discord api for this
  most businesses use multiple google analytics properties across their digital platforms

  Core Logic

  Schedule trigger fires once a day.
  Google Analytics node retrieves metrics for date ranges (past 7 days)
  Aggregate node collates all records.
  Discord node fetches the last 10 messages in the broadcast channel
  Code node maps existing Discord messages by to the google analytics data using the date fields
  For each GA record:
  	If no message exists → send new POST to the discord channel
  	If message exists and metrics changed, send an update patch to the existing discord message
  Batch loops + wait nodes prevent rate-limit.

  Setup Instructions

  Import workflow JSON into n8n.
  Follow the n8n guide to Create Google Analytics OAuth2 credential with access to all required GA accounts. 
  Follow the n8n guide to Create Discord OAuth2 credential for “Get Messages” operations.
  Follow the Discord guide to Create HTTP Header Auth credential named “Discord-Bot” with header

  Key: Authorization

  Value: Bot &lt;your-bot-token&gt;


  In the two Set nodes in the beginning of the flow, assign discord_channel_id and google_analytics_id.

  	Get your discord channel id by sending a text on your discord channel and then copy message link
  	Paste the text below and you will see your message link in the form of https://discord.com/channels/server_id/channel_id/message_id , you will want to get the channel_id which is the number in the middle

  	Find your google analytics id by going to google analytics dashboard, seeing the properties in the top right and copy paste that number to the flow



  Adjust schedule trigger times to your preferred report hour.

  Activate workflow.

  Customization

  Replace Discord HTTP Request nodes with Slack, ClickUp, WhatsApp, Telegram integrations by swapping POST/PATCH endpoints and authentication.

nodes_used:
  ["n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.googleAnalytics","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.sort","n8n-nodes-base.discord","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.switch","n8n-nodes-base.splitInBatches","n8n-nodes-base.wait","n8n-nodes-base.splitInBatches","n8n-nodes-base.wait","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.sort","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 6940 characters]

metadata_json:
  {"categories":["automation","integration","reporting","marketing analytics"],"complexity":"medium","use_cases":["Daily consolidated website traffic reports in chat","Automated morning marketing reports","Monitoring multiple GA4 properties in one channel","Keeping a single editable daily message to avoid channel clutter","Near-real-time updates as GA4 data finalizes over 7 days"],"estimated_setup_minutes":45,"required_services":["n8n instance","Google Analytics (GA4) with OAuth2 access to required properties","Discord bot token and access to channel (Discord API)"],"key_features":["Fetches GA4 metrics for past 7 days across multiple properties","Aggregates and collates records into a single daily report","Posts one message per day and edits that message as metrics change","Uses Discord HTTP API to PATCH messages (message edit)","Maps existing Discord messages to GA date records to decide create vs update actions」「Rate-limit safe: batch loops and wait nodes to avoid API limits"],"target_audience":["Marketing managers","Community moderators","Growth/product teams tracking traffic metrics daily by chat notifications','Data analysts who want automated daily summaries"]}


name: Multi-Platform UAE Real Estate Lead Generation with GPT-4 Analysis
author_name: papcy
author_username: papcy
categories: []
created_at: 2025-06-30T10:01:55.624Z
updated_at: 2025-06-30T10:01:55.624Z
url: https://n8n.io/workflows/5472
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:33
description:
  Who's it for
  Real estate agents, property investment companies, and relocation services operating in the UAE market who want to automate their lead generation and find qualified prospects 24/7.

  How it works
  This workflow automatically searches 6 major platforms (Reddit, Facebook, LinkedIn, Twitter, Bayut, PropertyFinder, and news sites) every 6 hours for people looking to buy, sell, or invest in UAE properties. An AI agent then analyzes all results to extract qualified leads with contact information, scores them based on intent and availability, and saves everything to Google Sheets for follow-up.

  What it does
  Multi-platform search**: Finds prospects across social media, property portals, and news sites
  AI-powered qualification**: Distinguishes real customers from competitors and low-quality leads  
  Contact extraction**: Automatically pulls emails, phone numbers, and WhatsApp contacts
  Lead scoring**: Rates prospects 1-100 based on buying intent and contact quality
  Automated organization**: Saves all data to Google Sheets with follow-up priorities

  Setup requirements
  Serper.dev API key (2,500 free searches/month)
  OpenAI API key for AI analysis
  Google Sheets account for lead storage
  10 minutes initial configuration

  How to customize
  Target markets**: Adjust search terms for luxury, commercial, or specific emirates
  Search frequency**: Change from 6 hours to your preferred schedule  
  Additional platforms**: Add more sources by duplicating HTTP request nodes
  Lead criteria**: Modify AI scoring based on your qualification requirements
  CRM integration**: Connect output to Salesforce, HubSpot, or other systems

  Expected results
  Generate 10-50 qualified leads daily with contact information, saving 5+ hours of manual research while improving lead quality through AI-powered analysis and scoring.

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

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","lead generation","integration","data processing","AI analysis"],"complexity":"medium","use_cases":["Automated real estate lead generation in the UAE","Prospect discovery across social and property portals","Contact extraction and enrichment (email/phone/WhatsApp)","Lead scoring and prioritization for sales follow-up","Market monitoring for buying/selling intent and news-driven opportunities"],"estimated_setup_minutes":10,"required_services":["Serper.dev API (search)","OpenAI API (GPT-4/LangChain/analysis)","Google Sheets account","Access to platform APIs or scraping for Reddit, Facebook, LinkedIn, Twitter, Bayut, PropertyFinder and news sites"],"key_features":["Multi-platform scheduled search (social, portals, news)","AI-powered lead qualification and intent extraction","Automatic contact extraction (email, phone, WhatsApp)","Lead scoring (1–100) based on intent and contact quality","Automated saving to Google Sheets with follow-up priorities and metadata (timestamps, source)"],"target_audience":["Real estate agents (UAE)","Property investment companies","Relocation and relocation-assist services in the UAE market\",\"Sales and marketing teams targeting UAE property buyers/investors"]}


name: AI-Powered Email to Jira Ticket Creation with Llama 3.2
author_name: Oneclick AI Squad
author_username: oneclick-ai
categories: []
created_at: 2025-06-30T10:09:56.953Z
updated_at: 2025-06-30T10:09:56.953Z
url: https://n8n.io/workflows/5473
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  This AI-powered workflow reads emails, understands the request using an LLM, and creates structured Jira issues.

  Key Insights

  Poll for new emails every 5 minutes; ensure Gmail/IMAP is properly configured.  
  AI analysis requires a reliable LLM model (e.g., Chat Model or AI Tool).

  Workflow Process

  Trigger the workflow with the Check for New Emails Gmail Trigger node.  
  Fetch full email content using the Fetch Full Email Content get message node.  
  Analyze email content with the Analyze Email & Extract Tasks node using AI.  
  Parse the AI-generated JSON output into tasks with the Parse JSON Output from AI node.  
  Create the main Jira issue with the Jira - Create Main Issue create: issue node.  
  Split subtasks from JSON and create them with the Split Subtasks JSON Items and Create Subtasks create: issue nodes.

  Usage Guide

  Import the workflow into n8n and configure Gmail and Jira credentials.  
  Test with a sample email to ensure ticket creation and subtask assignment.

  Prerequisites

  Gmail/IMAP credentials for email polling  
  Jira API credentials with issue creation permissions  

  Customization Options

  Adjust the Analyze Email & Extract Tasks node to refine AI task extraction or modify the polling frequency in the trigger node.

nodes_used:
  ["n8n-nodes-base.gmail","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.toolThink","@n8n/n8n-nodes-langchain.lmChatOllama","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.gmailTrigger","n8n-nodes-base.jira","n8n-nodes-base.jira","n8n-nodes-base.code","n8n-nodes-base.wait"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","AI","IT service management"],"complexity":"medium","use_cases":["Automatic ticket creation from incoming email","Email triage and task extraction using LLM","Customer support / incident intake automation","Generating Jira issues and subtasks from unstructured requests"],"estimated_setup_minutes":30,"required_services":["Gmail or IMAP account","Jira (Cloud or Server) with API credentials","LLM provider (Llama 3.2 via Ollama or compatible Chat Model)","n8n instance"],"key_features":["Polls email inbox and fetches full message content","LLM-powered analysis to extract tasks and produce structured JSON","Parses AI JSON output into main issue and subtasks","Creates Jira main issue and splits/creates subtasks automatically","Configurable polling frequency and AI prompt tuning"],"target_audience":["IT/support teams","Customer success and support managers","Automation engineers implementing ticketing workflows','n8n users and integrators"]}


name: Airline Web Check-in Data Extraction with Ollama AI, Google Sheets & Postgres Vector DB
author_name: Oneclick AI Squad
author_username: oneclick-ai
categories: []
created_at: 2025-06-30T10:40:22.588Z
updated_at: 2025-06-30T10:40:22.588Z
url: https://n8n.io/workflows/5474
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  Overview

  This workflow retrieves airline web check-in URLs from Google Sheets, scrapes their content, employs an LLM to generate structured JSON data, refreshes the sheet, creates embeddings, and saves them in a Postgres vector DB for future semantic searches or question-answering.

  Quick Notes

  Verify that Google Sheets has accurate URLs for scraping.  
  Ensure the Postgres vector DB is set up correctly for embedding storage.

  Process Flow

  Start the workflow with the Chat Trigger - Start node.  
  Retrieve airline check-in URLs using the Fetch Airline URLs node.  
  Scrape webpage data with the Scrape Airline Webpage node.  
  Extract JSON data using the Extract info with LLM node with a Chat Model.  
  Pause for a response with the Wait for Response node.  
  Update Google Sheets with the Store Extracted Data node.  
  Create embeddings with the Generate Embeddings node and store in Postgres vector DB with the Save to Vector DB node.  
  Break down long text with the Split Long Text node and delay the next batch with the Wait Before Next Batch node.

  Getting Started

  Import the workflow into n8n and set up Google Sheets and Postgres vector DB credentials.  
  Run a test with a sample URL to confirm scraping and embedding storage.

  Tailored Adjustments

  Tweak the Extract info with LLM node to adjust JSON output or modify the Fetch Airline URLs node to pull from different sheet fields.

nodes_used:
  ["n8n-nodes-base.splitInBatches","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.lmChatOllama","n8n-nodes-base.wait","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.embeddingsOllama","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterTokenSplitter","@n8n/n8n-nodes-langchain.vectorStorePGVector","n8n-nodes-base.wait","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","data processing","AI/ML"],"complexity":"medium","use_cases":["Automated extraction of airline web check-in details into structured JSON","Populate a vector database for semantic search/question-answering over check-in content","Keep Google Sheets synchronized with scraped check-in data","Batch processing and rate-limited scraping of web pages","Preprocessing long text and creating embeddings for similarity search"],"estimated_setup_minutes":45,"required_services":["Google Sheets API / Google account","Postgres with vector support (e.g., pgvector)","Ollama AI (LLM + embeddings) reachable from n8n","Internet access to scrape airline check-in webpages (HTTP)","n8n instance configured with relevant credentials"],"key_features":["Fetch check-in URLs from Google Sheets","Scrape webpage content","Use an LLM (Ollama) to extract structured JSON","Pause/wait for human response or review","Update Google Sheets with extracted data automatically"],"target_audience":["Developers / Automation engineers","Data engineers","ML engineers and AI/LLM practitioners building semantic search systems`,`Travel/product operations teams managing check-in metadata"]}


name: Automate Email Management with OpenAI Classification, Gmail Drafts & Slack Alerts
author_name: Fabian ZNTL
author_username: zntl
categories: []
created_at: 2025-06-30T10:47:56.703Z
updated_at: 2025-06-30T10:47:56.703Z
url: https://n8n.io/workflows/5475
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  What it does
  This workflow automatically processes incoming emails with intelligent AI classification, creating draft responses and sending Slack notifications based on email content.

  How it works
  Monitors emails with the 'AI-Agent' label
  AI classification into categories: Inquiry, Support, Newsletter, Action Item
  Adds appropriate labels to emails automatically
  Creates draft replies for Support and Inquiry emails
  Sends Slack notifications for Action Items and Newsletter summaries

  Setup Requirements
  Gmail OAuth2 credentials configured
  OpenAI API credentials (or other AI provider)
  Slack OAuth2 credentials (if notifications desired)
  Gmail labels created (see setup instructions below)

  How to customize
  Modify classification categories in the AI Agent
  Adjust label mappings in the Parse Classification node
  Customize draft response templates
  Configure different Slack channels for different email types

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.gmailTrigger","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.gmail","n8n-nodes-base.switch","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.gmail","n8n-nodes-base.slack","n8n-nodes-base.slack","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","email management","AI-assisted workflows"],"complexity":"medium","use_cases":["Automated email triage and labeling","Generate draft replies for support/inquiry emails","Send Slack alerts for action items and newsletter summaries","Summarize and route incoming messages to teams"],"estimated_setup_minutes":20,"required_services":["Gmail (OAuth2)","OpenAI or compatible AI provider (API key)","Slack (OAuth2) — optional for notifications"],"key_features":["Monitors emails with a specific label","AI classification into categories (Inquiry, Support, Newsletter, Action Item)","Automatically adds labels based on classification","Creates Gmail draft replies for Support and Inquiry","Sends Slack notifications for Action Items and Newsletter summaries (configurable channels)`,`Customizable classification categories, label mappings, and reply templates"],"target_audience":["Customer support teams","Small business owners / operations","Marketing teams (newsletter handling)`,`Developers or automation engineers setting up workflows"]}


name: Capture and Structure Web Form Leads for Odoo CRM (v15-v18 Compatible)
author_name: Evozard
author_username: evozardconsulting
categories: []
created_at: 2025-06-30T11:48:15.303Z
updated_at: 2025-06-30T11:48:15.303Z
url: https://n8n.io/workflows/5477
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:33
description:
  This workflow captures raw lead data from a Webhook and formats it into a clean, structured object — perfectly tailored for Odoo CRM and create lead. It supports Odoo versions 15, 16, 17, and 18, both Community and Enterprise editions.

  🔧 How It Works:
  Captures POST data from any web form or lead tool using Webhook.

  Extracts fields like:
  email
  phone
  name
  company
  company_name
  contact_name
  description

  🔐 Credentials Required:
  Odoo credentials
  https://docs.n8n.io/integrations/builtin/credentials/odoo/

  Optionally, connect to Odoo's /crm.lead endpoint via an HTTP Request node.

  🎯 Use Cases:
  Standardize messy input from lead forms
  Prepare API-ready data for direct CRM insert
  Connect web campaigns to Odoo CRM
  Automate follow-up, lead scoring, or notifications

  🧰 Compatibility:
  ✅ Odoo v15, v16, v17, v18
  ✅ Odoo Community & Enterprise
  ✅ API or custom model compatible
  ✅ All n8n versions 1.0+

  🚀 Setup Instructions:
  Open n8n and import the workflow file.
  Configure the Webhook URL in your form or tool.
  Test the Webhook with sample data.
  Review the structured JSON output.
  (Optional) Add an HTTP node to push to Odoo CRM API.

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

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metadata_json:
  {"categories":["integration","automation","data processing","crm"],"complexity":"simple","use_cases":["Standardize messy web form lead input","Prepare API-ready JSON for Odoo CRM","Connect web campaigns/lead tools to Odoo","Automate follow-up, lead scoring, or notifications"],"estimated_setup_minutes":10,"required_services":["Odoo (v15-v18) — Community or Enterprise (credentials required)","Web form or lead tool capable of sending POST to a webhook (e.g., custom form, Typeform, Gravity Forms)","Optional: Odoo /crm.lead HTTP API endpoint (if not using n8n Odoo node)"],"key_features":["Captures POST data via Webhook","Extracts and normalizes fields (email, phone, name, company, company_name, contact_name, description)","Produces clean, structured JSON tailored for Odoo CRM","Compatible with Odoo v15–v18 (Community & Enterprise)","Optional HTTP Request node to push leads to Odoo CRM API"],"target_audience":["CRM administrators","Sales/marketing teams","Integration specialists/engineers using n8n"]}


name: AI-Powered Scrum Master Assistant with OpenAI, Slack and Asana Integration
author_name: Łukasz
author_username: lukaszpp
categories: []
created_at: 2025-06-30T12:07:43.607Z
updated_at: 2025-06-30T12:07:43.607Z
url: https://n8n.io/workflows/5478
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:33
description:
  What is This?

  This automation simulates Scrum Master role on daily meetings. Essentially it is an AI Scrum Master using different sources of data.

  As intelligent support system for Scrum Masters that leverages data from Asana, Slack, and direct developer responses for comprehensive sprint status analysis and identification of areas requiring intervention.

  As such it is usable for Scrum Masters (of course) but Scrum Team aswell, Product Owner and possibly Business Owner.

  Who is it For?

  This automation is designed for Agile teams to  support the Scrum Master role by collecting and analyzing data from various sources to identify potential impediments and support the team in sprint delivery.

  How Does It Work?

  The workflow has four main data entry points, that are launched either on-click or on workdays.

  First is collecting project section information from Asana. The automation retrieves project structure, available sections, and their organization, allowing the AI to understand the team's work context.

  Second is getting recently modified tasks in the Asana project. The system tracks changes in tasks, their status, assignments, and updates to detect potential delays or issues.

  Third is obtaining communication in the team's Slack channel. The flow collects data about recent conversations, discussion threads, and team communication to identify warning signals or areas requiring attention.

  Fourth is directly collecting responses from developers about the current sprint - their progress, impediments, concerns, and support needs.

  All collected data is passed to an AI model that analyzes it within the Scrum methodology context and identifies:
  Potential impediments in sprint delivery
  Areas requiring Scrum Master intervention
  Recommendations for team support
  Warning signals regarding Sprint Goal achievement

  Output is being pushed to Slack channel so it can be potentially used by another iteration of same flow itself via Slack channel history.

  Requirements

  You need Asana oAuth credentials
  You need OpenAI / alternative AI for processing data
  You need to have Slack app with proper permissions
  channels:history
  chat:write
  groups:history
  im:history
  mpim:history
  users.profile:write
  users:write

  Configuration 

  Set up node "Asana Project and Slack Channel". Provide Asana project ID and Slack Channel ID
  (optional) Set up node "Get Scrum Master Answers". There are daily questions/answers that are being sent to channel.

  Alternative use

  You can get rid of the whole "Ask Users Daily ScrumMaster Questions" part if you don't want to do it simirarly as "daily Scrum standups". In such case whole flow is essentially changed to static analyzer of project status based on Slack and Asana.

  Extensions and Customizations

  There are many possibilities to extend this automation depending on team needs. For example, you can add integration with additional project management tools, implement different notification schemes based on detected issue criticality, or adjust data collection frequency to match the team's work rhythm.

  Disclaimers and Notes

  Whole automation has one important assumption: project is run on single Slack channel and on single Asana board. Of cource this can be extended, but is beyond currently designed scope.

  Adding new sources for AI to analyze should be fairly easy - just add another branch of data and push it to AI prompt.

  This automation represents a proof-of-concept and should not replace an actual Scrum Master. The Scrum Master role extends far beyond data collection and analysis - it requires deep understanding of team dynamics, business context, and interpersonal skills.

  As Scrum.org emphasizes, the Scrum Master doesn't need to be present during Daily Scrum, and their role is to ensure the meeting happens, but developers are responsible for conducting the meeting. Mindlessly executing daily questions without proper context analysis can lead to situations where the Scrum Master becomes a team manager instead of a self-organization facilitator.

  A real Scrum Master analyzes much more data than what's collected by automation - they observe team dynamics, understand business context, identify deeper root causes of problems, and support the team in developing self-organization skills. AI can be a valuable support tool, but it cannot replace the human intuition, empathy, and experience essential in this role.

  The automation should be treated as a tool supporting the Teams's work, providing additional insights and helping identify areas requiring attention, but always under the supervision and interpretation of an experienced Scrum practitioner.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.slack","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.slack","n8n-nodes-base.asana","n8n-nodes-base.asana","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.slack","n8n-nodes-base.splitInBatches","n8n-nodes-base.slack","n8n-nodes-base.merge","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.html","n8n-nodes-base.html","n8n-nodes-base.html","n8n-nodes-base.html","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.slack","n8n-nodes-base.merge","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 6360 characters]

metadata_json:
  {"categories":["automation","integration","project management","team collaboration","AI analytics"],"complexity":"complex","use_cases":["Automated daily standup summaries","Sprint health and impediment detection","Aggregating Asana task changes for status reporting","Monitoring Slack discussions for warning signals","Providing AI-driven recommendations for Scrum Master interventions"],"estimated_setup_minutes":90,"required_services":["Asana (OAuth)","Slack (app with channels:history, chat:write, groups:history, im:history, mpim:history, users.profile:write, users:write)","OpenAI or alternative LLM API","n8n instance (workflow runner)","(Optional) Webhook endpoint for manual triggers"],"key_features":["Collects Asana project structure and recent task changes","Gathers team communication from Slack channels and threads","Collects direct developer responses (daily questions)","Passes aggregated data to an AI model for analysis","Identifies potential impediments and sprint risks and issues recommendations to the team/SMs via Slack posts and channel history reuse support (feedback loop)  "],"target_audience":["Scrum Masters","Scrum Teams / Developers","Product Owners / Business Owners for visibility and prioritization decisions"]}


name: AI-Powered Automated Outreach with Scheduling, Gemini, Gmail & Google Sheets
author_name: Sarfaraz Muhammad Sajib
author_username: sarfarazmuhammad
categories: []
created_at: 2025-06-30T12:54:11.853Z
updated_at: 2025-06-30T12:54:11.853Z
url: https://n8n.io/workflows/5479
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  AI-Powered Automated Outreach Scheduling with Gemini, Gmail & Google Sheets

  Automate your lead generation and outreach process seamlessly using AI, Gmail, and Google Sheets—all within n8n. No complicated setup—just import, activate, and start reaching prospects with personalized messages generated by Google Gemini’s AI model.

  Quick Setup

  Import the Workflow
     Download and import the provided workflow into your n8n instance.

  Connect Your Accounts

     Authenticate your Google Sheets account.
     Connect your Gmail account for sending emails.

  Prepare the Spreadsheet
     Use this template to set up your leads and tracking sheet.

  Configure the Gemini API

     Obtain your Gemini API key. Here
     Add it to the Gemini API credentials within n8n.

  Set Scheduling Preferences
     Customize the Schedule Trigger node to control when the workflow runs.

  Edit Email Prompts
     Update the initial and follow-up email prompts to match your outreach tone and goals.

  Set Rate Limits
     Configure the rate limiting settings to comply with Gmail sending limits and avoid spam filters.

  Activate the Workflow
     Enable the workflow to begin automated outreach to your leads.

  Track and Manage Leads
     Monitor responses and update lead statuses directly in your Google Sheet.
  How It Works

  Schedule Trigger:** Automatically starts outreach based on your defined schedule  
  Google Sheets Integration:** Fetches leads and updates their status after outreach  
  Email Validation:** Checks if lead emails are valid before sending  
  Website Scraper:** Gathers info from lead websites to personalize messages  
  Google Gemini AI:** Generates tailored cold outreach messages optimized for high response  
  Gmail Node:** Sends personalized emails directly from your Gmail account  

  Core Features

  Pull leads automatically from Google Sheets  
  Validate emails to avoid bounces  
  Scrape lead websites for custom messaging context  
  Generate AI-crafted outreach emails with dynamic personalization  
  Send emails on schedule without manual intervention  
  Update lead status to track outreach progress  

  AI Integration

  Uses Google Gemini AI to create professional, friendly, and engaging outreach emails  
  Dynamic prompt templates tailored to each lead’s company and website content  
  Structured JSON output to easily map subject, greeting, and body content  

  💡 Usage Examples

  B2B cold outreach campaigns with personalized emails  
  Automated follow-ups based on lead engagement  
  Lead nurturing with context-aware messaging  
  Sales prospecting workflows integrated into your CRM  

  ✨ Benefits

  Save hours by automating personalized outreach  
  Increase response rates with AI-optimized messaging  
  Keep lead data organized and updated in Google Sheets  
  Fully scalable and customizable n8n workflow  
  Minimal setup, ready to run out-of-the-box  

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.gmail","n8n-nodes-base.wait","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.code","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 4952 characters]

metadata_json:
  {"categories":["automation","sales & marketing","integration","AI"],"complexity":"medium","use_cases":["B2B cold outreach with personalized emails","Automated scheduled outreach campaigns","Automated follow-ups and lead nurturing","Sales prospecting integrated with Google Sheets/CRM"],"estimated_setup_minutes":20,"required_services":["n8n instance","Google Sheets (authenticated account)","Gmail (authenticated account)","Google Gemini API (API key)","(Optional) Email validation API/service","Target lead websites for scraping (HTTP access)"],"key_features":["Scheduled trigger for automated runs","Pull and update leads in Google Sheets","Email validation to reduce bounces","Website scraping to gather personalization context","Google Gemini AI to generate tailored outreach messages (structured JSON)"],"target_audience":["sales development reps (SDRs)","growth/marketing teams","small business owners/founders doing outreach at scale but without engineering resources\",\"automation engineers/integrators"]}


name: Automated Resume Scoring with Gemini LLM, Gmail and Notion Job Profiles
author_name: Agentick AI
author_username: arpanjain29
categories: []
created_at: 2025-06-30T13:03:09.720Z
updated_at: 2025-06-30T13:03:09.720Z
url: https://n8n.io/workflows/5480
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:33
description:
  This n8n template demonstrates how to use AI to score the all Resumes by matching it with Job profile

  Problem Statement: A Hr person is flooded with resume and spends hours manually checking each to find most suitable ones.

  How it works
  It is linked to Gmail Trigger which upon receving any mail with specific subject will check for the attachment.
  Attachment will be parsed to understand the resume
  Candidate informtion will be broken into Personal, Eductional and Professional type
  Job profile will be pulled from Notion Board
  A HR expert powered by Gemini LLM will score each profile on basis on its relevancy 
  Information will be updated back to Gsheet 
  Message lable will be updated back for clarity

  How to use
  The gmail trigger node is used as an example but feel free to replace this with other triggers such as webhook or even a form.


  Requirements
  Gemini account for LLM
  Google sheet for upload
  Gmail as trigger
  Llama parse credentials

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.switch","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.gmailTrigger","@n8n/n8n-nodes-langchain.informationExtractor","@n8n/n8n-nodes-langchain.informationExtractor","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.merge","n8n-nodes-base.merge","n8n-nodes-base.httpRequest","n8n-nodes-base.notion","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.googleSheets","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 6400 characters]

metadata_json:
  {"categories":["automation","integration","HR/recruiting","AI-assisted screening"],"complexity":"medium","use_cases":["Automated resume screening and scoring","Candidate shortlisting for recruiters","Parsing CVs into structured fields (personal, education, professional)","Matching candidates to job profiles stored in Notion","Populating Google Sheets with scored candidate data"],"estimated_setup_minutes":45,"required_services":["Google Gmail (trigger)","Google Sheets","Notion","Gemini LLM account","Llama Parse (informationExtractor)"],"key_features":["Email trigger to detect resumes and parse attachments","Resume parsing into Personal, Educational, Professional sections","Pull job profiles from Notion for matching","Gemini LLM scores candidate relevancy against job profile","Write results back to Google Sheets and label emails for clarity (message labels)"],"target_audience":["HR recruiters","Talent acquisition teams","Hiring managers/HR ops at SMBs and startups"]}


