# templates

name: Generate and Send AI News Newsletters Automatically with GPT & Gmail
author_name: Rahul Joshi
author_username: rahul08
categories: []
created_at: 2025-06-10T08:27:24.513Z
updated_at: 2025-06-10T08:27:24.513Z
url: https://n8n.io/workflows/4847
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  Description:

  Automate your AI newsletter creation and delivery using this ready-to-deploy n8n workflow template. Powered by GPT (OpenAI/Azure) and integrated with Gmail, this workflow generates rich, structured, and engaging AI-focused newsletters and sends them out daily or weekly—completely hands-free.

  What It Does:

  📰 Fetches the latest AI trends and updates using GPT
  ✍️ Automatically formats news into structured newsletter sections: headlines, tools, stats, tips, and more
  📧 Sends HTML email newsletters via Gmail
  🕘 Runs automatically at your chosen schedule (default: 9 AM daily)

  Setup Includes:

  Connect your OpenAI or Azure GPT API
  Add Gmail SMTP or OAuth credentials
  Customize categories, schedule, and email styling

  Perfect for:

  Tech bloggers, content marketers, AI influencers, and automation enthusiasts who want to send curated AI content to their audience without manual effort.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.gmail","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatAzureOpenAi","n8n-nodes-base.scheduleTrigger"]

workflow_json_compressed:
  [encoded data, 6812 characters]

metadata_json:
  {"categories":["AI/ML"],"complexity":"simple","use_cases":["Generate AI newsletters","Automate content distribution","Create structured news summaries"],"estimated_setup_minutes":15,"required_services":["OpenAI","Gmail"],"key_features":["Automated content generation via GPT","Scheduled delivery via email","Structured newsletter formatting"],"target_audience":["writers","marketers","community managers"]}


name: Automated Music Video Creation & YouTube Publishing with AI-Generated Metadata from Google Drive
author_name: danejw
author_username: danejw
categories: []
created_at: 2025-06-10T09:31:31.721Z
updated_at: 2025-06-10T09:31:31.721Z
url: https://n8n.io/workflows/4848
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  The Problem That it Solves

  Uploading music manually to YouTube is a chore. Renaming files, filling metadata, picking the right playlist, adding keywords, and scheduling the release can eat up your creative time. This workflow removes the busy work so you can focus on making tracks instead of managing uploads.

  How it works
  Drop an MP3 into your shared Google Drive “Audio” folder and walk away. The workflow detects the new file, grabs the genre from the filename based on a set of keywords, uses the genre to map to cover art styles, and YouTube playlists, generates the description and tags for YouTube metadata, and schedules and posts to YouTube channel, while sending messages to discord for updates.

nodes_used:
  ["n8n-nodes-base.googleDriveTrigger","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.httpRequest","n8n-nodes-base.youTube","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.dateTime","n8n-nodes-base.if","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.if","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.code","n8n-nodes-base.sort","n8n-nodes-base.limit","n8n-nodes-base.discord","n8n-nodes-base.discord","n8n-nodes-base.discord","n8n-nodes-base.discord","n8n-nodes-base.discord","n8n-nodes-base.discord","n8n-nodes-base.executionData","n8n-nodes-base.executionData","n8n-nodes-base.executionData","n8n-nodes-base.executionData","n8n-nodes-base.executionData","n8n-nodes-base.executionData","n8n-nodes-base.executionData","n8n-nodes-base.executionData","n8n-nodes-base.executionData","n8n-nodes-base.executionData","n8n-nodes-base.executionData","n8n-nodes-base.dateTime","n8n-nodes-base.youTube","n8n-nodes-base.youTube","n8n-nodes-base.switch","n8n-nodes-base.code","n8n-nodes-base.dateTime","n8n-nodes-base.dateTime","n8n-nodes-base.if","n8n-nodes-base.googleDrive","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.youTube","n8n-nodes-base.youTube","n8n-nodes-base.youTube","n8n-nodes-base.code","n8n-nodes-base.discord","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.switch","n8n-nodes-base.set","n8n-nodes-base.set","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","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 5304 characters]

metadata_json:
  {"categories":["Media Production","Marketing","Automation"],"complexity":"medium","use_cases":["Automated YouTube uploads","Metadata enrichment","File monitoring"],"estimated_setup_minutes":45,"required_services":["Google Drive","YouTube Data API","Discord","AI/ML APIs"],"key_features":["AI-powered metadata extraction","File change detection","Playlist management"],"target_audience":["Music producers","Content creators","Marketing teams"]}


name: Automated Invoice Processing with Telegram, GPT-4o, OCR and SAP Integration
author_name: Raquel Giugliano
author_username: raquelgiugliano
categories: []
created_at: 2025-06-10T10:01:39.029Z
updated_at: 2025-06-10T10:01:39.029Z
url: https://n8n.io/workflows/4849
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:19
description:
  ++HOW IT WORKS:++
  This workflow automates the processing of invoices sent via Telegram. It extracts the data using LlamaIndex OCR, logs it in Google Sheets, and optionally pushes the structured data to SAP Business One

  🔹 1. Receive Invoice via Telegram:**
  A user sends a PDF of an invoice through Telegram
  A Telegram Trigger node listens for incoming messages and captures the file and metadata
  The document is downloaded and prepared for OCR

  🔹 2. OCR with LlamaIndex:
  The file is uploaded to the LlamaIndex OCR API.
  The workflow polls the API until the processing status returns SUCCESS
  Once ready, the parsed content is fetched in Markdown format

  🔹 3. Data Extraction via LLM (editable):
  The Markdown content is sent to a language model (LLM) using LangChain
  A Structured Output Parser transforms the result into a clean, structured editable JSON

  🔹 4. Save to Google Sheets:
  The structured JSON is split into:
  Header (main invoice metadata)
  Detail (individual line items)

  Each part is stored in a dedicated tab within a connected Google Sheets file

  🔹 5. Ask for SAP Confirmation:
  The bot replies to the user via Telegram:

  "Do you want to send the data to SAP?"

  If the user clicks "Yes", the next automation path is triggered.

  🔹 6. Push Data to SAP B1:
  A connection is made to SAP Business One's Service Layer API

  Header and detail data are fetched from Google Sheets

  The invoice structure is rebuilt as required by SAP (DocumentLines, CardCode, etc.)

  A POST request creates the Purchase Invoice in SAP

  A confirmation message with the created DocEntry is sent back to the user on Telegram

  ++SET UP STEPS:++
  Follow these steps to properly configure the workflow before execution:

  1️⃣ Create Required Credentials:
  Go to Credentials &gt; + New Credential and create the following:
  Telegram API (set your bot token get it from BotFather)
  Google Sheets
  OpenAI

  2️⃣ Set Up Environment Variables (Optional but Recommended):
  LLAMAINDEX_API_KEY
  SAP_USER
  SAP_PASSWORD
  SAP_COMPANY_DB
  SAP_URL

  3️⃣ Prepare Google Sheets:
  Ensure your Google Spreadsheet has the following:
  ➤ Sheet 1: Header
  ➤ Sheet 2: Details
  Contains columns for invoice lines

nodes_used:
  ["n8n-nodes-base.wait","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.noOp","n8n-nodes-base.splitOut","n8n-nodes-base.merge","n8n-nodes-base.stickyNote","n8n-nodes-base.telegramTrigger","n8n-nodes-base.switch","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.telegram","n8n-nodes-base.telegramTrigger","n8n-nodes-base.set","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.switch","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 7148 characters]

metadata_json:
  {"categories":["Data Processing","AI/ML","Automation"],"complexity":"medium","use_cases":["invoice processing","document extraction","payment automation","OCR automation"],"estimated_setup_minutes":45,"required_services":["Telegram","SAP","Google Sheets"],"key_features":["invoice data extraction","automatic OCR","payment data logging","structured data push"],"target_audience":["accountants","finance teams","admin operations"]}


name: Export Cloudflare Domains with DNS Records and Settings to Google Sheets
author_name: KPendic
author_username: kres
categories: []
created_at: 2025-06-10T10:26:19.793Z
updated_at: 2025-06-10T10:26:19.793Z
url: https://n8n.io/workflows/4850
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:20
description:
  How it works

  This workflow simply exports all your CloudFlare domains to Google Sheet to get high overview of all of your settings. This could help for easy debugging, searching or similar needs. 
  In flow simple pagging nodes are used to iterate over all your domains, because this list could be huge. 
  For each host we are merging DNS & Settings and transforming them into columns for all our domains.

  Requirements

  For storing and processing of data in this flow you will need:

  CloudFlare.com API key/token - for retrieving your data (https://dash.cloudflare.com/:account/api-tokens) (need full access)
  Google Spreadsheet auth connected in your n8n Credentials
  Google Spreadsheet template - you can copy my sheet as starting point, start by copying it to your account 
  Match Sheet ID in 'Export' node to your newly created.

  Official CloudFlare api Documentation
  For full details and specifications please use API documentation from: https://developers.cloudflare.com/api/

  Potential API timeouts
  If you encounter CF API timeouts - I would suggest to only put somewhere in the loop simple sleep/wait node - for couple of seconds - and it should resolve timeouts.

  Google Sheet
  I've used simple Google Sheet feature conditional formatting to visually distinct my on|off toggles that was of my interest to easily get high overview for debuggint some of the settings on my hosts - but please use your own logic or change it completely.

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.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.wait","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.merge","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.googleSheets"]

workflow_json_compressed:
  [encoded data, 6052 characters]

metadata_json:
  {"categories":["Data Processing","Management"],"complexity":"medium","use_cases":["monitor domain configurations","backup DNS records","search domain settings","audit DNS entries","export cloud data"],"estimated_setup_minutes":10,"required_services":["Cloudflare","Google Sheets API","n8n"],"key_features":["automatic pagination","domain data merging","DNS and settings export","cloudflare API integration","sheet export automation"],"target_audience":["developers","system administrators","infrastructure teams"]}


name: Find relevant X tweets based on your profile and suggest responses
author_name: @OnePromptMagic
author_username: one-prompt-magic
categories: []
created_at: 2025-06-10T10:37:48.703Z
updated_at: 2025-06-10T10:37:48.703Z
url: https://n8n.io/workflows/4851
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:20
description:
  What This Workflow Does

  Step 1: Analyzes your recent tweets for personality & style
  Step 2: Generates strategic keywords based on your profile
  Step 3: Searches for trending tweets in your niche
  Step 4: Creates personalized responses & original tweets
  Step 5: Displays results in beautiful HTML format

  How to Use
  Execute the workflow
  Fill out the form with:
     Your X username (from your URL)
     Your goals on X (multiple selection)
     Optional additional info
  Submit the form
  Wait for processing (2-3 minutes)
  Double-click the "Click to show Result" node

  example clip can be found here

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.formTrigger","n8n-nodes-base.splitOut","n8n-nodes-base.aggregate","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.httpRequest","n8n-nodes-base.html","n8n-nodes-base.code","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 13884 characters]

metadata_json:
  {"categories":["Social Media Management","Content Creation","Marketing Automation"],"complexity":"medium","use_cases":["discover trending tweets for niche","generate personalized tweet responses","analyze personal writing style","create draft tweets based on keywords","discover engagement opportunities on X"],"estimated_setup_minutes":15,"required_services":["n8n","X/Twitter API","AI Language Models"],"key_features":["NLP keyword extraction from social media","Personalized content generation","Automated response suggestions","Style analysis based on existing content","HTML template formatting for reviews"],"target_audience":["social media managers","content creators","community managers"]}


name: Automate GPT-4o Fine-Tuning with Google Sheets or Airtable Data
author_name: Matty Reed
author_username: mattyreed1
categories: []
created_at: 2025-06-10T12:18:16.840Z
updated_at: 2025-06-10T12:18:16.840Z
url: https://n8n.io/workflows/4853
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:20
description:
  Who is this for?  
  Anyone curating before/after text examples in a spreadsheet and wanting a push-button path to a fine-tuned GPT model—without touching curl. Works with Google Sheets or Airtable.

  What problem does it solve?  
  Manually downloading CSVs, converting to JSONL, uploading, and polling OpenAI is a slog.  
  This flow automates the whole loop: grab examples flagged Ready, build the JSONL file, start the fine-tune, then log the resulting model ID back to a registry sheet/table for reuse.

  How it works  

  | # | Node | Purpose |
  |---|------|---------|
  | 1 | Schedule Trigger | Runs weekly by default (change as needed). |
  | 2a | Get Examples from Sheet | Pulls rows where Ready = TRUE from your Google Sheet. Uses the JSONL-Template Sheet as the expected column layout. |
  | 2b | Get Examples from Airtable (disabled) | Alternate source for Airtable users. |
  | 3 | Create JSONL File (Code) | Converts each example to chat-format JSONL and splits into train.jsonl / val.jsonl (80/20). |
  | 4 | Upload JSONL | Uploads the training file to OpenAI (purpose: fine-tune). |
  | 5 | Begin Fine-Tune | Starts a fine-tune job on gpt-4o (editable). |
  | 6 | Wait → Check Job → IF | Polls every minute until status = succeeded. |
  | 7a | Write Model to Sheet | Appends the new model ID + meta to your Model Registry sheet. |
  | 7b | Write Model to Airtable (disabled) | Equivalent logging step for Airtable. |

  Setup steps  

  Import & connect credentials  
     Import the JSON flow into n8n.  
     Add your OpenAI API key.  
     Google Sheets: create an OAuth2 credential and link it to both Sheets nodes.  
     Airtable (optional): create a Personal Access Token and attach it to the Airtable nodes.

  Copy the template sheet  
     Duplicate the JSONL-Template Sheet linked above into your own Drive.  
     Required columns (exact names):  
       | systemPrompt | userPrompt | assistantResponse | Ready |  
     Tick Ready = TRUE for rows you want to include.

  Create the registry sheet/table  
     Google Sheet (or Airtable table) named Model Registry with columns:  
       Model ID, Training Examples, Epochs, Batch Size, Learning Rate, Finished At.

  Tweak model & schedule  
     Change the base model in Begin Fine-Tune if desired.  
     Adjust the Schedule Trigger for daily / on-demand runs.

  Test it  
     Mark a few examples Ready = TRUE.  
     Run the flow manually.  
     Check OpenAI for the new fine-tune job and confirm the model ID is logged in your registry.

  Resources  

  n8n Docs – &lt;https://docs.n8n.io/&gt;  
  OpenAI Fine-Tuning – &lt;https://platform.openai.com/docs/guides/fine-tuning&gt;  
  Google Sheets API – &lt;https://developers.google.com/sheets/api&gt;  
  Airtable API – &lt;https://airtable.com/api&gt;

  Extending the flow  

  Webhook trigger** – swap the schedule for a webhook to train on demand.  
  Multi-source merge* – enable both Sheets *and Airtable nodes to combine datasets.  
  Auto-deploy** – save the new model name to an env-var or Secrets Manager for downstream generation workflows.

nodes_used:
  ["n8n-nodes-base.wait","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.set","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.stopAndError","n8n-nodes-base.airtable","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","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":["AI/ML","Data Processing","Automation"],"complexity":"medium","use_cases":["Fine-tune LLM","Prepare training data","Automate model training","Track trained models"],"estimated_setup_minutes":15,"required_services":["OpenAI","Google Sheets","Airtable"],"key_features":["Automated JSONL generation","Fine-tuning job orchestration","Model registration logging","No-code training pipeline"],"target_audience":["Data Scientists","ML Engineers","Prompt Engineers"]}


name: Family Assistant: Schedule, Meal & Routine Management with Email & Telegram
author_name: Issam AGGOUR
author_username: devgor
categories: []
created_at: 2025-06-10T15:06:29.333Z
updated_at: 2025-06-10T15:06:29.333Z
url: https://n8n.io/workflows/4855
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:20
description:
  The FamilyFlow Assistant is your n8n-powered 🚀 companion designed to streamline daily parenting tasks, reduce mental load, and bring a bit more organization and fun into your family life. This versatile workflow automates reminders, generates helpful ideas, and provides gentle nudges to keep your household running smoothly.

  Why FamilyFlow Assistant? 🤔
  Parenting is a demanding (and rewarding!) job. Juggling schedules, meals, routines, and remembering everything can be overwhelming. FamilyFlow Assistant aims to:

  Reduce Mental Clutter:** Offload common reminders and to-dos to an automated system.
  Enhance Organization:** Keep track of activities, appointments, and important checks.
  Spark Joy & Creativity:** Generate meal ideas, story starters, and positive thoughts.
  Promote Routine:** Help establish and maintain consistent morning and evening routines.
  Save Time:** Automate repetitive prompts and checks, freeing you up for what matters most.

  Core Features & Automations 🧩
  This workflow bundle comes packed with several independent yet complementary automations:

  📅 Daily & Weekly Organization:

  Activity & Appointment Reminders:** Get a daily heads-up on scheduled events for the family.
  Morning Routine Checklist:** Start the day right with a friendly checklist for kids (or parents!).
  Bedtime Routine Nudge:** A gentle reminder to begin the wind-down process.
  Evening “Did You Remember?” Scan:** A quick mental checklist for common end-of-day to-dos.
  Weekly “Plan Ahead” Prompt:** A Sunday reminder to review the upcoming week.
  School/Daycare Comms Check:** Prompts to check for important updates.

  💡 Inspiration & Well-being:

  Meal Idea Generator:** Never wonder “what’s for dinner?” again (or at least, less often!).
  Positive Thought / Fun Fact Sender:** A little daily dose of brightness.
  Story Starter / Quick Activity Prompt:** Spark imagination or a quick family interaction.
  Weekly Wins & Gratitude Reminder:** Encourage reflection on positive moments.

  ⏱️ Optional Helpers:

  Screen Time Break Reminder:** (Disabled by default) A nudge for offline time.

  Getting Started: Setup & Customization 🛠️
  Make FamilyFlow Assistant truly yours with these simple steps:

  Import the Workflow: Add the provided .json file to your n8n instance.
  Configure Credentials:
      Email: Set up your preferred email sending credential (e.g., SMTP, Gmail) in all “Email Send” nodes. You’ll likely use the same one for all.
  Personalize Content (Crucial!):
      Email Addresses: Update parents@example.com to your actual email(s) in all Email nodes.
      Schedules & Activities: Edit the “Define Daily Schedules” (Set node) with your family’s actual events.
      Meal Ideas: Customize the mealIdeas array in the “Generate Meal Idea” (Function node).
      Checklists & Prompts: Tailor the text/HTML in Email nodes for morning/bedtime routines, check-ins, story starters, etc., to fit your family’s style.
      Positive Thoughts/Facts: Update the lists in the “Get Positive Thought/Fact” (Function node).
  Adjust Triggers (Cron Nodes):
      Modify the “Rule” in each Cron node to set the exact time and frequency for each automation.
  Activate Automations:
      The main workflow and most automations are inactive by default. Toggle the “Active” switch ON for the entire workflow or for individual automations/nodes you wish to use.
      Remember, the “Screen Time Break Reminder” is disabled by default; enable its Email node if desired.

  How Each Automation Works (Briefly) ⚙️
  Each automation generally follows this pattern:

  TRIGGER: An event starts the flow (e.g., a scheduled time via Cron).
  PROCESS: Data is defined (e.g., list of activities, meal ideas) or generated (e.g., a random selection, a formatted message).
  RESPOND: An action is taken, typically sending an email with the relevant information or reminder.

  For instance, the “Daily Activity Reminder” uses a Cron trigger, a Set node to hold your schedule, a Function node to filter for today’s events, an IF node to check if there are any, and finally an Email node to send the reminder.

  Expanding Your Assistant 🚀
  FamilyFlow Assistant is a great starting point. Consider these ideas to extend its capabilities:

  Integrate Calendars:** Connect to Google Calendar or Outlook Calendar to fetch events dynamically.
  Use Different Notifiers:** Send messages via Telegram, WhatsApp, Slack, or push notifications.
  Smart Home Integration:** If you have smart home devices, trigger lights or announcements for routines.
  Shared To-Do Lists:** Update a shared Todoist or Microsoft To Do list.
  Interactive Input:** Use n8n forms or Telegram commands to add events or trigger specific actions.

  Requirements 📋
  An active n8n instance (self-hosted or cloud).
  Email sending credentials configured in n8n.
  A little time to customize the content to perfectly match your family’s needs!

  Embrace the flow and let your FamilyFlow Assistant help make parenting a little smoother!

nodes_used:
  ["n8n-nodes-base.start","n8n-nodes-base.cron","n8n-nodes-base.if","n8n-nodes-base.emailSend","n8n-nodes-base.emailSend","n8n-nodes-base.emailSend","n8n-nodes-base.cron","n8n-nodes-base.emailSend","n8n-nodes-base.cron","n8n-nodes-base.emailSend","n8n-nodes-base.cron","n8n-nodes-base.emailSend","n8n-nodes-base.emailSend","n8n-nodes-base.telegramTrigger","n8n-nodes-base.cron","n8n-nodes-base.emailSend","n8n-nodes-base.cron","n8n-nodes-base.emailSend","n8n-nodes-base.cron","n8n-nodes-base.emailSend","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.cron","n8n-nodes-base.cron","n8n-nodes-base.cron","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.telegram","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["Communication","Personal Productivity"],"complexity":"medium","use_cases":["sending family reminders","generating meal ideas","managing daily routines","tracking medical appointments","sharing daily notifications"],"estimated_setup_minutes":30,"required_services":["Telegram","Gmail","Google Calendar"],"key_features":["automated daily routines","meal planning integration","reminder notifications","appointment tracking","family schedule synchronization"],"target_audience":["parents","household managers","caregivers"]}


name: Configure Telegram Bot Webhooks with Form Automation
author_name: Un tal Camilo Medina
author_username: untalcamilomedina
categories: []
created_at: 2025-06-10T16:26:48.501Z
updated_at: 2025-06-10T16:26:48.501Z
url: https://n8n.io/workflows/4856
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:20
description:
  🤖 Telegram Bot Webhook Configuration Tool

  This workflow creates a simple web form that helps you configure Telegram bot webhooks quickly. Instead of manually constructing the Telegram API URL, this tool does it for you automatically.

  How It Works

  The workflow consists of three main steps:

  Form Input: A web form collects your bot token and webhook URL
  URL Construction: Automatically builds the correct Telegram API URL
  Redirect: Takes you directly to the Telegram API to complete the configuration

  What You Need

  Bot Token**: Get this from @BotFather on Telegram (format: 123456789:ABCdefGHIjklMNOpqrsTUVwxyz)
  Webhook URL**: Your n8n webhook endpoint (must be HTTPS)

  Setup Instructions

  Import this workflow into your n8n instance
  Activate the workflow
  Access the generated form URL
  Fill in your bot details and submit

  Form Fields

  | Field | Description | Example |
  |-------|-------------|---------|
  | Bot API Token | Token from BotFather | 123456789:ABCdefGHIjklMNOpqrsTUVwxyz |
  | Webhook URL | Your n8n webhook endpoint | https://your-instance.app.n8n.cloud/webhook/telegram |

  What Happens

  You enter your bot token and webhook URL in the form
  The workflow constructs this URL: https://api.telegram.org/bot{TOKEN}/setWebhook?url={WEBHOOK_URL}
  You're redirected to that URL where Telegram configures your webhook
  Telegram shows you a success or error message

  Benefits

  No Manual URL Building**: Eliminates copy-paste errors
  Quick Setup**: Configure webhooks in seconds
  Privacy Focused**: No data is stored anywhere
  Team Friendly**: Share the form URL with team members

  Common Webhook URLs

  n8n Cloud: https://your-instance.app.n8n.cloud/webhook/telegram-bot
  Self-hosted: https://your-domain.com/webhook/telegram-bot

  Requirements

  n8n with form trigger support
  Valid Telegram bot token
  Publicly accessible webhook URL (HTTPS required)

  Troubleshooting

  Invalid Token Error: Make sure you copied the complete token from BotFather

  Webhook Error: Ensure your URL is publicly accessible and uses HTTPS

  SSL Error: Verify your webhook URL has a valid SSL certificate

  This tool simply automates the manual process of visiting the Telegram API URL to configure your bot's webhook. Perfect for developers who frequently set up or change Telegram bot configurations.

nodes_used:
  ["n8n-nodes-base.formTrigger","n8n-nodes-base.set","n8n-nodes-base.form","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":["Communication"],"complexity":"simple","use_cases":["configure telegram bot webhooks","validate bot tokens","build api urls","simplify webhook setup"],"estimated_setup_minutes":5,"required_services":["Telegram Bot API"],"key_features":["automatic webhook url construction","web form for token input","redirect to api provider"],"target_audience":["developers","system administrators","telecommunications staff"]}


name: Create a Notion AI Assistant with Google Gemini for Managing Tasks & Content
author_name: Laura Piraux
author_username: lauraslap
categories: []
created_at: 2025-06-10T17:23:09.147Z
updated_at: 2025-06-10T17:23:09.147Z
url: https://n8n.io/workflows/4857
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:20
description:
  This n8n workflow template uses community nodes and is only compatible with the self-hosted version of n8n. 

  Build an AI agent for Notion (with Notion official MCP server)

  Use case

  This template empowers Notion power-users to build their own AI assistant, deeply integrated with their workspace. It solves the constant problem of copy-pasting and context-switching between a separate AI chat and Notion by creating a direct, conversational bridge. Now you can interact with an intelligent agent that can create, retrieve, and update your Notion databases and pages on your behalf, turning your workspace into a truly dynamic productivity hub.

  How it works

  When you send a message via the chat interface, the workflow passes it to your chosen AI model. The model, connected to the official Notion tool server, analyzes your request to see if it can be fulfilled by one of its available Notion actions. If it matches a tool, the workflow executes the command using the Notion API—like creating a new page or searching a database—and the AI then confirms the action is complete back in the chat.

  Setup

  Prerequisite: This template is for self-hosted n8n instances only, as it requires a community node. 

  Copy this workflow into your self-hosted n8n instance 
  Install the required community node (n8n-nodes-mcp).
  Add your credentials for your chosen AI Model and the Notion MCP Server.
  Test the workflow by starting chatting with your new Notion assistant.

  How to adjust it to your needs

  You can use the AI model you want and even easily compare different AI models.
  You can start from this template and then provide other tools to your AI agent to build more powerful workflows.

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-mcp.mcpClientTool","n8n-nodes-mcp.mcpClientTool","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":["Productivity","AI/ML","Development"],"complexity":"medium","use_cases":["Create Notion AI agents","Integrate LLM with Notion","Automate task management"],"estimated_setup_minutes":20,"required_services":["Notion","Google Gemini"],"key_features":["Embed Google Gemini in Notion","Use MCP server pattern","Context-aware task queries","Conversational Notion interface"],"target_audience":["developers","power users","productivity enthusiasts"]}


name: WhatsApp Dietitian AI Chatbot Workflow
author_name: Tamer
author_username: tamerelzein
categories: []
created_at: 2025-06-10T17:54:54.633Z
updated_at: 2025-06-10T17:54:54.633Z
url: https://n8n.io/workflows/4858
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  WhatsApp Dietitian AI Chatbot Workflow

  Transform your WhatsApp into a professional nutrition consultant with this comprehensive AI-powered dietitian assistant. Built using n8n automation and Google Gemini AI, this workflow provides instant, intelligent nutrition advice to your clients 24/7.

  🍽️ What It Does

  Meal Analysis: Users can send photos of their meals with captions like "analyze this meal" and receive detailed nutritional breakdowns including calories, macronutrients, and personalized recommendations.

  Nutrition Guidance: Answers questions about general nutrition, dietary restrictions, meal planning, and healthy eating habits with evidence-based information.

  Meal Recommendations: Suggests customized meals based on specific criteria like calorie targets, dietary preferences (vegan, keto, etc.), or available ingredients.

  🚀 Key Features

  Dual Input Processing**: Handles both text questions and image analysis seamlessly
  Professional AI Responses**: Uses Google Gemini 2.0 Flash for accurate, contextual nutrition advice
  WhatsApp Optimized**: Responses formatted specifically for mobile messaging with emojis and clear structure
  Smart Routing**: Automatically detects message type (text vs image) and processes accordingly
  Structured Output**: Consistent, professional formatting for all responses
  Scope Management**: Stays within nutrition expertise and includes appropriate medical disclaimers

  🛠️ Technical Stack

  Platform**: n8n workflow automation
  AI Model**: Google Gemini 2.0 Flash
  Integration**: WhatsApp Business API
  Message Types**: Text messages, images with captions
  Output**: Structured, conversational responses

  📱 Perfect For

  Nutritionists and dietitians offering remote consultations
  Health coaches providing 24/7 client support
  Fitness professionals adding nutrition guidance
  Wellness businesses expanding their service offerings
  Anyone wanting to monetize nutrition expertise

  🎯 Ready to Deploy

  This workflow comes with comprehensive documentation, clear setup instructions, and professional prompt engineering. Simply configure your WhatsApp Business API and Google Gemini credentials to start serving clients immediately.

  Transform your nutrition expertise into an automated, scalable WhatsApp service that works around the clock!

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.whatsAppTrigger","n8n-nodes-base.whatsApp","n8n-nodes-base.httpRequest","n8n-nodes-base.whatsApp","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.switch","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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metadata_json:
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name: Invoice Verification and Validation with Gmail, Drive, Sheets and OCR AI
author_name: Dhrumil Patel
author_username: itechdp
categories: []
created_at: 2025-06-10T19:56:07.238Z
updated_at: 2025-06-10T19:56:07.238Z
url: https://n8n.io/workflows/4860
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:20
description:
  📝 Description (Full, User-Centric & Sales-Driven):

  Tired of manually verifying purchase order invoices every single day? This plug-and-play n8n automation template saves your accounts team hours of work by automatically downloading, storing, extracting, and validating invoice data against your master item sheet — all without human intervention.

  Perfect for SMEs, startups, procurement teams, and accounts departments, this automation handles PO invoice verification from email to final validation with 99% accuracy.

  🚀 What This Automation Does End-to-End:

  📥 Invoice Retrieval & Organization:

  Runs Monday to Saturday, 8 hours/day
  Reads invoices from specific vendor email(s) (e.g., abc@company.com)
  Extracts the invoice date from the email subject
  Automatically creates month & day-wise folders in Google Drive
  Uploads each invoice PDF into its respective folder

  🔍 AI + OCR Data Extraction & Validation:

  Uses OCR to extract item data (name, quantity, rate, HSN, etc.) from each invoice PDF
  Converts messy PDF data into clean structured format using AI Agent Model
  Stores the data into a dedicated Google Sheet: "Store Invoice Data"

  ✅ Invoice vs. Master Data Validation:

  Automatically fetches a second Google Sheet: "Store Master Data"
  Compares each invoice item line-by-line with the master sheet:

    Checks for Item Code, Name, Price mismatches
    Calculates price differences and total loss/gain per invoice
    Marks each line as Matched / Mismatched with notes

  📬 Final Reporting:

  Sends an automated summary email once all invoices are processed
  Alerts if any mismatches found, allowing for early intervention

  📂 Example Output:

  Check how data is validated in real time:

  🔗 Store Invoice Data Sheet
  🔗 Store Master Data Sheet

  👥 Who Is This For?

  Accounts & Finance Teams
  Procurement Departments
  Growing Startups with Vendors
  ERP/Automation Enthusiasts
  Anyone tired of manually checking vendor bills

  📦 What You’ll Get:

  Ready-to-import n8n workflow (JSON)
  Setup Guide (PDF or Notion link)
  Sample Google Sheet template
  Email + Google Drive + OCR setup guide
  AI Agent configuration tips
  BONUS: Customization support (optional)

nodes_used:
  ["n8n-nodes-base.extractFromFile","@n8n/n8n-nodes-langchain.lmChatOpenRouter","n8n-nodes-base.splitOut","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.splitInBatches","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.wait","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.googleDrive","n8n-nodes-base.code","n8n-nodes-base.merge","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.if","n8n-nodes-base.googleDrive","n8n-nodes-base.if","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.code","n8n-nodes-base.dateTime","n8n-nodes-base.gmail","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.gmail","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.gmail"]

workflow_json_compressed:
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metadata_json:
  {"categories":["Financial Operations","Data Processing","Automation"],"complexity":"medium","use_cases":["Automated invoice verification","PO invoice validation","Data extraction from documents"],"estimated_setup_minutes":15,"required_services":["Gmail","Google Drive","Google Sheets","OpenRouter"],"key_features":["Document OCR text extraction","Automated data validation","AI-powered data matching","End-to-end document workflow"],"target_audience":["Accounting professionals","Procurement managers","Finance teams"]}


name: Google SERP + Trends and Recommendations with Bright Data & Google Gemini
author_name: Ranjan Dailata
author_username: ranjancse
categories: []
created_at: 2025-06-10T23:11:07.974Z
updated_at: 2025-06-10T23:11:07.974Z
url: https://n8n.io/workflows/4861
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  Who this is for?

  Google SERP Tracker + Trends and Recommendations is an AI-powered n8n workflow that extracts Google search results via Bright Data, parses them into structured JSON using Google Gemini, and generates actionable recommendations and search trends. It outputs CSV reports and sends real-time Webhook notifications.

  This workflow is ideal for:

  SEO Agencies needing automated rank & trend tracking

  Growth Marketers seeking daily/weekly search-based insights

  Product Teams monitoring brand or competitor visibility

  Market Researchers performing search behavior analysis

  No-code Builders automating search intelligence workflows

  What problem is this workflow solving?

  Traditional tracking of search engine rankings and search trends is often fragmented and manual. Analyzing SERP changes and trends requires:

  Manual extraction or using unstable scrapers

  Unstructured or cluttered HTML data

  Lack of actionable insights or recommendations

  This workflow solves the problem by:

  Automating real-time Google SERP data extraction using Bright Data

  Structuring unstructured search data using Google Gemini LLM

  Generating actionable recommendations and trends

  Exporting both CSV reports automatically to disk for downstream use

  Notifying external systems via Webhook

  What this workflow does

  Accepts search input, zone name, and webhook notification URL

  Uses Bright Data to extract Google Search Results

  Uses Google Gemini LLM to parse the SERP data into structured JSON

  Loops over structured results to:

  Extract recommendations

  Extract trends

  Saves both as .csv files (example below):

  	Google_SERP_Recommendations_Response_2025-06-10T23-01-50-650Z.csv

  	Google_SERP_Trends_Response_2025-06-10T23-01-38-915Z.csv

  Sends a Webhook with the summary or file reference

  LLM Usage

  Google Gemini LLM handles:

  Parsing Google Search HTML into structured JSON

  Summarizing recommendation data

  Deriving trends from the extracted SERP metadata

  Setup

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



  The Value field should be set with the 
  Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token.
  A Google Gemini API key (or access through Vertex AI or proxy).
  Update the Set input fields with the search criteria, Bright Data Zone name, Webhook notification URL.

  How to customize this workflow to your needs

  Input Customization

  Set your target keyword/phrase in the search field

  Add your webhook_notification_url for external triggers or notifications

  SERP Source

  You can extend the Bright Data search logic to include other engines like Bing or DuckDuckGo.

  Output Format

  Edit the .csv structure in the Convert to File nodes if you want to include/exclude specific columns.

  LLM Prompt Tuning

  The Gemini LLM prompt inside the Recommendation or Trends extractor nodes can be fine-tuned for domain-specific insight (e.g., SEO vs eCommerce focus).

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

workflow_json_compressed:
  [encoded data, 4724 characters]

metadata_json:
  {"categories":["SEO & Marketing","Data Processing","AI/ML"],"complexity":"medium","use_cases":["automated rank tracking","search trend analysis","keyword monitoring","competitor search analysis","automated reports"],"estimated_setup_minutes":45,"required_services":["Google SERP Tracker","Bright Data","Google Gemini","Webhook"],"key_features":["extracts structured Google search results","parses plain text into JSON using AI","generates actionable recommendations","tracks search trends automatically","exports to CSV format"],"target_audience":["SEO specialists","marketing managers","product analysts"]}


name: Log Daily Mood & Weather to Notion with Telegram and OpenWeatherMap
author_name: Issam AGGOUR
author_username: devgor
categories: []
created_at: 2025-06-10T23:19:17.739Z
updated_at: 2025-06-10T23:19:17.739Z
url: https://n8n.io/workflows/4862
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  The Daily Mood & Weather Logger 🚀 is more than just a utility; it's a hands-on n8n workflow designed to teach you core automation concepts. By setting up and exploring this template, you'll gain practical experience with triggers, API calls, data manipulation using the Code node, and integrating with external services like Notion. It automates tracking your daily mood alongside local weather, logging it all into a Notion database.

  🎯 Learning Objectives: What You'll Master

  This workflow template is specifically structured to help you understand and practice:

  Workflow Triggers:**

    Scheduled Triggers: Learn how the Cron node initiates actions at specific times (e.g., daily).
    Webhook/Event-Based Triggers: Understand how the Telegram Trigger node listens for and reacts to external events (like a new message).

  Making API Calls:**

    See the HTTP Request node in action, querying the external OpenWeatherMap API to fetch real-time data.
    Understand how to configure API endpoints, query parameters, and handle API keys (using environment variables).

  Data Manipulation with Code:**

    Explore the Code node to write simple JavaScript for combining data from different sources (Telegram input and API response).
    Learn basic data structuring and transformation within n8n.

  Database Integration (Notion):**

    Discover how to use the Notion node to interact with a Notion database.
    Practice retrieving database information and, more importantly, creating new pages (entries/rows) with dynamic data.

  Environment Variables:** Learn the importance of using environment variables for managing sensitive information (like API keys) and configurable parameters (like city/location).

  Workflow Logic & Data Flow:** Observe how data passes between nodes and how different components work together to achieve a complete automation.

  Why This Workflow? (Beyond Learning) 🤔

  While the primary goal is educational, the workflow itself is useful for:

  Automated Data Collection:** It prompts for mood via Telegram and fetches weather automatically.
  Consistent Logging:** Ensures daily entries into your Notion journal.
  Centralized Information:** Stores mood and weather data in a structured Notion database.

  Core Features & How They Teach You 🧩

  Let’s break down how each part of the workflow demonstrates key n8n concepts:

  📅 Daily Automation Cycle & Triggers:

  Daily Trigger (Cron node):* ⏰ Initiates the workflow daily. *Learning: This is your introduction to time-based automation. Observe its simple configuration for scheduling.
  Telegram Mood Prompt (Telegram node):* 📩 Sends a message. *Learning: Shows a basic "action" node interacting with an external service.
  Telegram Mood Capture (Telegram Trigger node):* 🎣 Listens for your reply. *Learning: A prime example of an event-driven trigger. See how it waits for user input to continue the workflow.

  🌍 Fetching External Data (APIs):

  Weather Data Fetch (HTTP Request node for OpenWeatherMap):* 🌦️ Retrieves current weather. *Learning: The core of API interaction. Examine how the URL, query parameters (appid, q or lat/lon), and environment variables are used. Try changing the parameters!

  ⚙️ Processing Data (Code Node):

  Data Consolidation (Code node):* 🛠️ Combines mood and weather data. *Learning: A practical use of the Code node. Look at the JavaScript to understand how it accesses data from previous nodes ($input, $('Node Name')) and structures the output.

  📝 Storing Data (Notion Integration):

  Notion Database Interaction (Notion nodes):** ✍️

    "Retrieve Database": Gets database info. Learning: Demonstrates reading from Notion.
    "Add row into Notion": Logs the data. Learning: Shows how to write data to Notion, mapping your workflow data to specific Notion properties. This is a common pattern for many database integrations.

  Note: The template includes two paths for weather fetching (city name vs. lat/lon). This allows you to explore different ways to configure the HTTP Request node and manage input data.

  Getting Started: Your Learning Lab Setup 🛠️

  Set up your learning environment:

  Import the Workflow: Add the .json to n8n. This is your starting blueprint!
  Set Up Environment Variables in n8n:

     OPEN_WEATHER_API_KEY: Your OpenWeatherMap API key.
     City: (e.g., "Paris") For city-based weather.
     latitude: (e.g., "18.834366") For coordinate-based weather.
     longitude: (e.g., "35.352342") For coordinate-based weather.
  Configure Node Credentials:

     Telegram: Link your Telegram API credentials in the "Send Mood Prompt" and "Wait for Mood Response" nodes.
     Notion: Connect your Notion API credentials in the "Retrieve Database" and "Add row into Notion" nodes.
  Personalize Node Settings (Experiment Here!):

     Daily Trigger (Cron Node): Change the "Trigger Times". How does this affect when the workflow runs?
     Send Mood Prompt (Telegram Node):

       Update the Chat ID (e.g., from =83495863) to your own.
       Modify the Text of the prompt.
     Notion Nodes:

       Set your target Database ID (e.g., replace 20e9c592...).
       Ensure your Notion database has properties like city, description, temperature, mood, etc., as defined in the "Add row into Notion" node. Try adding or removing properties to see what happens!
     Weather Fetching Path:

       Experiment with enabling one path (city name or lat/lon) and disabling the other. Observe how the HTTP Request node parameters differ. Ensure relevant environment variables are set.
  Activate and Test: Turn the workflow ON. Trigger it manually or wait for the Cron. Check each node’s output to understand the data flow.

  How The Automation Works (A Learning Recap) ⚙️

  Follow the data and actions:

  TRIGGER: Cron node (scheduled) or Telegram Trigger (event-based after prompt).
  ACTION (Prompt): Telegram node sends a message.
  INPUT (User): You reply to the Telegram message.
  API CALL: HTTP Request node fetches weather using your API key and location.
  CODE PROCESSING: Code node merges your mood and the weather data.
  DATABASE WRITE: Notion node creates a new entry with the combined data.

  Use the n8n execution log to inspect the input and output of each node. This is invaluable for learning and debugging!

  Next Steps in Your Learning Journey 🚀

  Once you're comfortable, try these to deepen your understanding:

  Modify the Code Node:** Add new data transformations. Can you format the date or extract more specific weather details?
  Change API Parameters:** Explore the OpenWeatherMap API docs. Can you fetch forecast data instead of current weather?
  Add Error Handling:** What happens if the API call fails? Explore the "Error Trigger" or "Continue on Fail" settings.
  Integrate a New Service:** Try sending a summary to email or another messaging app.
  Explore Different Trigger Types:** What other ways could this workflow be initiated?

  Requirements (Your Learning Kit) 📋

  An active n8n instance.
  Telegram Bot API credentials.
  An OpenWeatherMap API Key.
  Notion API credentials & a configured Notion Database.
  Curiosity and a willingness to experiment!

  May your mood tracking be seamless and your weather always logged!

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

workflow_json_compressed:
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metadata_json:
  {"categories":["Health & Wellness","Personal Productivity","Automation"],"complexity":"simple","use_cases":["track daily mood and weather","automate logging personal data","learn n8n workflow basics"],"estimated_setup_minutes":15,"required_services":["n8n","notion","telegram","openweathermap"],"key_features":["cron trigger for daily execution","code node for data manipulation","external API integration","data syncing to notion database","simple automation setup"],"target_audience":["beginners","automation enthusiasts","health trackers"]}


name: YouTube Comment Scraper & Analyzer with GPT-4o + Email Summary Report
author_name: Amit Mehta
author_username: amitswba
categories: []
created_at: 2025-06-11T00:37:10.771Z
updated_at: 2025-06-11T00:37:10.771Z
url: https://n8n.io/workflows/4863
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  How it Works

  This workflow automates the collection and analysis of YouTube comments from a video and sends a summary report via email, using Google Sheets, the YouTube API, OpenAI (GPT-4o), and Gmail.

  Whether you're a content creator, brand manager, or social media analyst, this workflow helps you automate sentiment analysis and receive insights directly in your inbox — all triggered from a simple spreadsheet.

  🎯 Use Case

  Ideal for:
  YouTubers** monitoring audience sentiment
  Marketing teams** analyzing campaign feedback
  Community managers** summarizing engagement

  Setup Instructions

  1. Upload the Spreadsheet

  File name: Youtube_Video
  Sheet structure:
  | ID | Video Title | YouTube Video ID | Status |
  Add video IDs and set their Status as Pending

  2. Configure Google Sheets Nodes

  Connect your Google account to:
  Pick Video IDs from Google Sheet
  Update Status on Google Sheet

  3. Add API Credentials

  YouTube API Key** → for comment + video scraping nodes
  OpenAI API Key** → for analyzing comments
  Gmail Account** → for sending the summary email

  4. Activate the Workflow

  Once live, the workflow will:
  Watch for new or updated rows in the spreadsheet
  Scrape comments using the YouTube API
  Analyze sentiment and key themes via GPT-4o
  Send a formatted HTML email with the summary
  Update the spreadsheet status to Mail sent

  🔁 Workflow Logic

  Trigger: New/updated row in Google Sheet
  Retrieve: YouTube video metadata + comments
  Analyze: Comments using GPT-4o
  Email: Summary report via Gmail
  Update: Spreadsheet status to Mail sent

  🧩 Node Descriptions

  | Node Name | Description |
  |-----------|-------------|
  | Pick Video IDs from Google Sheet | Watches the spreadsheet and retrieves pending video IDs |
  | If | Checks whether status is 'Pending' |
  | Limit | Restricts the number of processed rows |
  | Set Video Details | Prepares video info (e.g., title, channel) |
  | Get YouTube Video Details | Fetches metadata (title, channel, etc.) |
  | Get YouTube Video Comments | Pulls top-level comments using YouTube API |
  | Prepare Comments Data | Formats comment text for OpenAI |
  | AI Agent | Summarizes comments using OpenAI's GPT-4o |
  | Prepare HTML for Email | Converts summary into HTML for email body |
  | Gmail Account Configuration | Sends the email report via Gmail |
  | Update Status on Google Sheet | Marks the row as 'Mail sent' |

  🛠️ Customization Tips

  Change the AI prompt for tone, length, or custom metrics
  Send results to Slack or Telegram instead of Gmail
  Export summaries to Notion, Airtable, or PDF
  Schedule it daily/weekly for recurring analysis

  📒 Suggested Sticky Notes for Workflow

  | Node/Section | Sticky Note Content |
  |--------------|---------------------|
  | Pick Video IDs from Google Sheet | "Triggers on new YouTube videos in your spreadsheet" |
  | AI Agent | "Uses OpenAI to generate an analysis summary – customize prompt as needed" |
  | Gmail | "Sends summary report – you can update subject, recipients, or style" |
  | Update Status | "Marks video as processed to avoid duplicate runs" |

  📎 Required Files

  | File Name | Purpose |
  |-----------|---------|
  | Youtube_Video | Google Sheet to hold YouTube video IDs and status |
  | Youtube_Comment_Scraper.json | Main n8n workflow export for this automation |

  🧪 Testing Tips

  Add one test video with a valid YouTube video ID and status = Pending
  Monitor the workflow logs to confirm API responses
  Confirm summary delivery in your inbox
  Verify that status updates in the sheet

  🏷 Suggested Tags & Categories

  #YouTube
  #OpenAI
  #Automation
  #Marketing
  #Email
  #Analytics

nodes_used:
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metadata_json:
  {"categories":["AI/ML","Communication"],"complexity":"medium","use_cases":["monitor audience sentiment","extract comments from YouTube","generate AI summaries","receive email reports"],"estimated_setup_minutes":15,"required_services":["YouTube API","OpenAI GPT-4o","Google Sheets","Gmail"],"key_features":["scheduled scraping","sentiment analysis","summarization with LLM","automated email reporting"],"target_audience":["content creators","marketing managers","social media analysts"]}


name: Automated News Summarizer with GPT-4o + Email Delivery
author_name: Amit Mehta
author_username: amitswba
categories: []
created_at: 2025-06-11T01:06:35.204Z
updated_at: 2025-06-11T01:06:35.204Z
url: https://n8n.io/workflows/4864
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  How it Works

  This workflow fetches top news headlines every 10 minutes from NewsAPI, summarizes them using OpenAI's GPT-4o model, and sends a concise email digest to a list of recipients defined in a Google Spreadsheet. It's ideal for anyone who wants to stay updated with the latest news in a short, digestible format.

  🎯 Use Case

  Professionals who want summarized daily news
  Newsletters or internal communication updates
  Teams that require contextual summaries of the latest events

  Setup Instructions

  1. Upload the Spreadsheet
  File name: Emails
  Column: Email with recipient addresses
  2. Configure Google Sheets Nodes
  Connect your Google account to:
    Email List
    Send Email
  3. Add API Credentials
  NewsAPI Key** → for fetching top headlines
  OpenAI API Key** → for summarizing headlines
  Gmail Account** → for sending the email digest
  4. Activate the Workflow
  Once active, the workflow runs every 10 minutes via a cron trigger
  Summarized news is sent to the list of emails in the spreadsheet

  🔁 Workflow Logic

  Trigger: Every 10 minutes via Cron
  Fetch News: HTTP request to NewsAPI for top headlines
  Summarize: Headlines are passed to OpenAI's GPT-4o for 5-bullet summary
  Read Recipients: Google Sheet is used to collect email recipients
  Send Email: Summary is formatted and sent via Gmail

  🧩 Node Descriptions

  | Node Name | Description |
  |-----------|-------------|
  | Cron | Triggers the workflow every 10 minutes. |
  | HTTP Request - NewsAPI | Fetches top news headlines using NewsAPI. |
  | Set | Formats or structures raw news data before processing. |
  | AI Agent | Summarizes the news content using OpenAI into 5 bullet points. |
  | Email List | Reads recipient email addresses from the 'Emails' Google Spreadsheet. |
  | Send Email | Sends the email digest to all recipients using Gmail. |

  🛠️ Customization Tips

  Modify the AI prompt for tone, length, or content type
  Send summaries to Slack, Telegram, or Notion instead of Gmail
  Adjust cron interval for more/less frequent updates
  Change email formatting (HTML vs plain text)

  📎 Required Files

  | File Name | Purpose |
  |-----------|---------|
  | Emails spreadsheet | Google Sheet containing the list of email recipients |
  | daily_news.json | Main n8n workflow file to automate daily news digest |

  🧪 Testing Tips

  Add 1–2 test email addresses in your spreadsheet
  Temporarily change the Cron node to run every minute for testing
  Check email inbox for delivery and formatting
  Inspect the execution logs for API errors or formatting issues

  🏷 Suggested Tags & Categories

  #News #OpenAI #Automation #Email #Digest #Marketing

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.httpRequest","n8n-nodes-base.gmail","n8n-nodes-base.googleSheets"]

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metadata_json:
  {"categories":["AI/ML","Communication","Data Processing"],"complexity":"medium","use_cases":["generate news summaries","automate email newsletters","maintain team briefings"],"estimated_setup_minutes":15,"required_services":["NewsAPI","OpenAI","Google Sheets"],"key_features":["scheduled execution","AI summarization","external API integration","list-based delivery","spreadsheet management"],"target_audience":["professionals","communications managers","newsletter editors"]}


name: Google Sheets Duplication & Enrichment Automation
author_name: Amit Mehta
author_username: amitswba
categories: []
created_at: 2025-06-11T01:38:07.829Z
updated_at: 2025-06-11T01:38:07.829Z
url: https://n8n.io/workflows/4865
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:20
description:
  How it Works

  This workflow reads sheet details from a source Google Spreadsheet, creates a new spreadsheet, replicates the sheet structure, enriches the content by reading data, and writes it into the corresponding sheets in the new spreadsheet. The process is looped for every sheet, providing an automated way to duplicate and transform structured data.

  🎯 Use Case

  Automate duplication and data enrichment for multi-sheet Google Spreadsheets
  Replicate templates across new documents with consistent formatting
  Data team workflows requiring repetitive structured Google Sheets setup

  Setup Instructions

  1. Required Google Sheets
  You must have a source spreadsheet with multiple sheets.
  The destination spreadsheet will be created automatically.
  2. API Credentials
  Google Sheets OAuth2** – connect to both read and write spreadsheets.
  HTTP Request Auth** – if external API headers are needed.
  3. Configure Fields in Write Sheet
  Ensure you define appropriate columns and mapping for the destination sheet.

  🔁 Workflow Logic

  Manual Trigger: Starts the flow on user demand.
  Create New Spreadsheet: Generates a blank spreadsheet.
  HTTP Request: Retrieves all sheet names from the source spreadsheet.
  JavaScript Code: Extracts titles and metadata from the HTTP response.
  Loop Over Sheets: Iterates through each sheet retrieved.
  Delete Default Sheet: Removes the placeholder 'Sheet1'.
  Create Sheets: Replicates each original sheet in the new document.
  Read Spreadsheet1: Pulls data from the matching original sheet.
  Write Sheet: Appends the data to the newly created sheets.

  🧩 Node Descriptions

  | Node Name | Description |
  |-----------|-------------|
  | Manual Trigger | Starts the workflow manually by user test. |
  | Create New Spreadsheet | Creates a new Google Spreadsheet for output. |
  | HTTP Request | Fetches metadata from the source spreadsheet including sheet names. |
  | Code | Processes sheet metadata into a list for iteration. |
  | Loop Over Items | Loops over each sheet to replicate and populate. |
  | Google Sheets2 | Deletes the default 'Sheet1' from the new spreadsheet. |
  | Create Sheets | Creates a new sheet matching each source sheet. |
  | Read Spreadsheet1 | Reads data from the source sheet. |
  | Write sheet | Writes the data into the corresponding new sheet. |

  🛠️ Customization Tips

  Adjust the Google Sheet title to be dynamic or user-input driven
  Add filtering logic before writing data
  Append custom audit columns like 'Timestamp' or 'Processed By'
  Enable logging or Slack alerts after each sheet is created

  📎 Required Files

  | File Name | Purpose |
  |-----------|---------|
  | My_workflow_4.json | Main workflow JSON file for sheet duplication and enrichment |

  🧪 Testing Tips

  Test with a spreadsheet containing 2–3 simple sheets
  Validate whether all sheets are duplicated
  Check if columns and data structure remain intact
  Watch for authentication issues in Google Sheets nodes

  🏷 Suggested Tags & Categories

  #GoogleSheets #Automation #DataEnrichment #Workflow #Spreadsheet

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

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metadata_json:
  {"categories":["Data Processing"],"complexity":"medium","use_cases":["Duplicate spreadsheets","Enrich data","Replicate templates","Automate multi-sheet cloning","Transform structured data"],"estimated_setup_minutes":15,"required_services":["Google Sheets"],"key_features":["Multi-sheet loop","Automated duplication","Data enrichment","Structural replication","Batch processing"],"target_audience":["administrators","data analysts"]}


name: Create Shopify customers from a Google Sheet
author_name: Richard Uren
author_username: elricho
categories: []
created_at: 2025-06-11T02:51:27.534Z
updated_at: 2025-06-11T02:51:27.534Z
url: https://n8n.io/workflows/4866
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Task

  Read a list of customers from a GoogleSheet and create them in Shopify using Shopify's Admin API (GraphQL).

  Why ?

  Generate test users for development stores.
  Migrate customers from other platforms.
  Easy intro to Shopify's GraphQL API.

  Setup 

  Setting up Google Sheets access

  Follow the instructions in the N8N Docs for granting Oauth2 access to Google services. You'll need to grant API access to Google Sheets and Google Drive (to list available sheets). 

  Setting up Shopify access

  Shopify's Admin API uses 'Header Auth' with a key of X-Shopify-Access-Token and a value of your shopify access token which starts with shpat_ .

  How to generate a Shopify Access Token 
  To generate a Shopify Access Token create an app, grant the app the necessary scopes, then generate a token. 

  From inside a store do the following :
  click Settings (nav link) 
  click Apps and sales channels (nav link) 
  click Develop Apps (button)
  click Create App (button)
  give the app a name
  click configure Admin API Scopes (button)
  at a minimum grant read_customers and write_customers scope. Grant additional scopes if you plan on accessing other parts of the API.
  click save

  To generate the token 
  click install app (button)
  click install on the dialog that pops up (button)
  click 'reveal token once' (button)
  copy the token into a password vault or somewhere secure.

  Template Updates

  To test this out you'll need to make the following changes :
  1) Create a header credential where the key is X-Shopify-Access-Token and the value is your Shopify Access Token (it starts with shpat_ 
  2) In the GraphQL node change the endpoint URL to your store. Something like  https://{your store goes here}.myshopify.com/admin/api/2025-04/graphql.json 

  Google Sheet Structure

  Columns can be in any order, because the rows will be mapped to fields in a json object. N8N will treat the first row in the sheet as a column name, so at a minimum use the column names below in row 1 of your sheet. 
  first_name : Any string
  last_name : Any string
  email : Valid email 
  mobile_phone : International mobile phone format with no spaces eg. +61414708406 (Shopify will reject anything else).

  Example CSV

  "first_name","last_name","email","mobile_phone"
  "Bob","Smith","bob@example.com","+61414999999"

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

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metadata_json:
  {"categories":["eCommerce","Data Processing"],"complexity":"medium","use_cases":["import customers from spreadsheets","generate test users","migrate platform data","integrate google sheets and shopify"],"estimated_setup_minutes":15,"required_services":["Shopify","Google Sheets"],"key_features":["read spreadsheets","create customer accounts via GraphQL API","request user input without automation","connect external SaaS platforms"],"target_audience":["eCommerce developers","operations managers"]}


name: AI-Powered Stock Market Summary Bot
author_name: kenandrewmiranda
author_username: kenandrewmiranda
categories: []
created_at: 2025-06-11T04:29:53.154Z
updated_at: 2025-06-11T04:29:53.154Z
url: https://n8n.io/workflows/4867
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:19
description:
  An automated n8n workflow that analyzes stocks using RSI and MACD, summarizes insights with OpenAI, and sends a Slack-ready market update every hour.

  This workflow:
  Runs hourly from 6:30 AM to 2:30 PM PT, Mon–Fri
  Checks if the U.S. stock market is open using Alpaca’s /clock API
  Pulls daily stock bars for a list of tickers via Alpaca’s /v2/stocks/bars
  Calculates RSI and MACD using a Python code node
  Categorizes each stock as Buy / Hold / Sell
  Uses OpenAI Assistant to summarize the results in Slack markdown
  Sends the message to a specific Slack user or channel

nodes_used:
  ["n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.slack","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.if","n8n-nodes-base.noOp","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 6668 characters]

metadata_json:
  {"categories":["Finance","AI/ML","Automation"],"complexity":"medium","use_cases":["Automated stock analysis","Market summary generation","Slack alerts for investments","RSI/MACD calculation","Trading signal summarization"],"estimated_setup_minutes":30,"required_services":["Alpaca","OpenAI","Slack","Python"],"key_features":["Hourly automated execution","Real-time technical indicator calculation","Text analysis and summarization","Customizable trading rules","Event-driven notifications"],"target_audience":["quantitative analysts","portfolio managers","trading bots developer"]}


name: Extract Invoice Data from Google Drive to Sheets with Mistral OCR and Gemini
author_name: Ysqander
author_username: ysqander
categories: []
created_at: 2025-06-11T06:15:58.579Z
updated_at: 2025-06-11T06:15:58.579Z
url: https://n8n.io/workflows/4868
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:19
description:
  Extract data from any PDF or image invoice dropped in Google Drive directly into Google Sheets – powered by AI OCR. Free, fully modifiable n8n workflow. Optional add-ons for pro features.

  🚀 What this template does

  Stop typing invoice data by hand. Drop a PDF or phone-snapshot into your Invoices Inbox folder in Google Drive and this n8n workflow will:





  Auto-OCR the document with the Mistral OCR API



  Match any fields you list in Row 1 of your Google Sheet (totally schema-agnostic)



  Append a clean, structured row – every time

  Works with both PDFs and images, in any language supported by Mistral.

  Template JSON included, ready to import into self-hosted or n8n Cloud

  👀 Who’s this for?





  Freelancers & agencies processing client invoices



  Small finance teams on Google Workspace



  Anyone self-hosting n8n who wants an AI OCR flow without glue-code

  No coding skills required – but flow tweaking is possible for power users.

  🛠 Upcoming PRO Add-Ons
  I am also working on PRO add-ons for this template: 

  Add-On #1 – Error Handling & Alerts (ships Jul 2025)• Flags missing fields, branches to Email/Slack notification; prevents silent failures



  Add-On #2 – Auto-Currency Converter (ships Jul 2025)• Detects invoice currency symbol/code → converts Total into your base currency via a free FX API



  Add-On #3 – VAT / GST Breakdown (ships Jul 2025)• Extracts VAT number, net, tax rate, tax amount, gross – ready for EU/UK/AU filings

  To pre-order these please see: https://ysqander.gumroad.com/l/N8N-AI-Workflow-Invoice-Data-Extraction-LITE

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.googleDrive","n8n-nodes-base.merge","n8n-nodes-base.googleDrive","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["Finance","Data Processing","Automation"],"complexity":"medium","use_cases":["Extract invoice data","Digitize receipts","Automate accounting entry","Process scanned documents","Sync document to spreadsheet"],"estimated_setup_minutes":30,"required_services":["Google Drive","Google Sheets","Mistral AI","Google Generative AI"],"key_features":["OCR from PDF and images","Schema-agnostic field matching","AI-powered text extraction","Automatic document processing","Dynamic column mapping"],"target_audience":["accountants","finance managers","bookkeepers"]}


name: Process Legal Documents with Ollama AI & Generate HTML Reports (100% Local)
author_name: Rish
author_username: b2brish
categories: []
created_at: 2025-06-11T08:09:42.500Z
updated_at: 2025-06-11T08:09:42.500Z
url: https://n8n.io/workflows/4869
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  TUTORIAL VIDEO

  Key Features
  🧠 AI-Powered Analysis - Use any Ollama-compatible model (Llama, Mistral, CodeLlama, etc.)
  🔧 Flexible Input - Supports PDFs, Word docs, text files, and more
  📊 Structured Output - Generates clean HTML reports and summaries
  🔄 Automated Pipeline - Set-and-forget document processing

  Perfect For:
  Enterprise Environments - Where data privacy is paramount
  Research Organizations - Processing sensitive or proprietary documents
  Small Teams - Cost-effective alternative to cloud AI services
  Compliance-Heavy Industries - Healthcare, finance, legal where data must stay local
  Developers - Experimenting with different open-source models
  Content Teams - Batch processing large document libraries

  Prerequisites

  n8n instance (self-hosted recommended)
  Ollama server running locally
  Compatible AI models downloaded (Llama 3.1, Mistral, etc.)
  Local file system access
  Documents to process

  Customization Ideas
  🎯 Multi-Model Pipeline - Route different document types to specialized models
  🌐 Language Detection - Automatically select appropriate language models
  ⚡ Batch Processing - Handle multiple files simultaneously
  🎖️ Quality Scoring - Use multiple models to cross-validate outputs
  ✏️ Custom Prompts - Tailor analysis for specific document types or industries
  🔗 Integration Hooks - Connect to databases, CMS, or notification systems

  Why Choose Local Processing?
  🏆 Data Sovereignty - Your sensitive documents never leave your control
  💰 Predictable Costs - No surprise API bills or usage limits
  🎨 Customization Freedom - Fine-tune models for your specific use cases
  📶 Offline Capability - Works without internet connectivity
  ✅ Compliance Ready - Meets strict data residency requirements

  Conclusion
  This workflow represents the future of document processing - combining the convenience of automation with the security and control of local AI models. It's particularly valuable for organizations that need to balance AI capabilities with strict data governance requirements.
  Ready to build your own private AI document processing pipeline? This workflow shows you exactly how to get started with local Ollama integration! 🏠🤖

nodes_used:
  ["n8n-nodes-base.extractFromFile","n8n-nodes-base.localFileTrigger","n8n-nodes-base.readWriteFile","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.convertToFile","n8n-nodes-base.readWriteFile"]

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metadata_json:
  {"categories":["AI/ML","Data Processing","Documents"],"complexity":"medium","use_cases":["Analyze legal documents","Extract insights from text","Generate HTML reports","Process PDFs locally"],"estimated_setup_minutes":15,"required_services":["Ollama"],"key_features":["100% local execution","Multi-format document support","AI-powered analysis","Automated pipeline"],"target_audience":["legal professionals","data analysts","privacy engineers"]}


name: Automated Multilingual Gmail Draft Reply with OpenAI GPT-4o
author_name: Henry
author_username: henry
categories: []
created_at: 2025-06-11T08:11:17.450Z
updated_at: 2025-06-11T08:11:17.450Z
url: https://n8n.io/workflows/4870
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  Automated Multilingual Gmail Draft Reply with OpenAI GPT-4o in n8n

  Who is this for?
  This workflow is ideal for anyone who receives a high volume of Gmail inquiries, especially those providing multilingual customer support or handling diverse client 
  communications.

  What problem is this workflow solving? 
  Managing frequent emails in multiple languages can be overwhelming. This workflow reduces manual drafting by automatically generating context-aware replies using OpenAI GPT-4o, letting users focus on personalization and quality assurance.

  What this workflow does
  Monitors your Gmail inbox for new emails with a specific label (e.g., "Inquiry").
  Uses OpenAI GPT-4o for message assessment and language detection.
  Parses information using a JSON parser.
  Generates an AI-powered draft reply in the detected language via OpenAI GPT-4o.
  Converts the reply to HTML and saves it as a draft in the original Gmail thread for your review.

  Setup
  Connect your Gmail account and set up relevant labels in both Gmail and the workflow.
  Integrate your OpenAI credentials in n8n.
  Configure the workflow trigger for your desired labels.

  How to customize this workflow to your needs
  Adjust label names in both Gmail and the workflow for different email categories.
  Define custom starting and ending phrases for draft replies per supported language.
  Expand supported languages or modify AI prompt instructions to suit your brand’s tone.

nodes_used:
  ["n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.stickyNote","n8n-nodes-base.gmailTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi"]

workflow_json_compressed:
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metadata_json:
  {"categories":["Communication","AI/ML","Customer Support"],"complexity":"medium","use_cases":["Automate multilingual email replies","Generate context-aware responses","Draft emails with AI assistance"],"estimated_setup_minutes":30,"required_services":["Gmail","OpenAI GPT-4o"],"key_features":["Multilingual support","Context-aware AI responses","Automated draft creation"],"target_audience":["customer support agents","content managers","team leads"]}


name: 🏠 Find your Home with Real Estate Agent and Bright Data
author_name: Miquel Colomer
author_username: mcolomer
categories: []
created_at: 2025-06-11T09:15:41.161Z
updated_at: 2025-06-11T09:15:41.161Z
url: https://n8n.io/workflows/4872
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  📝 Overview
  This workflow transforms n8n into a smart real-estate concierge by combining an AI chat interface with Bright Data’s marketplace datasets. Users interact via chat to specify city, price, bedrooms, and bathrooms—and receive a curated list of three homes for sale, complete with images and briefings.

  🎥 Workflow in Action
  Want to see this workflow in action? Play the video


  🔑 Key Features

  AI-Powered Chat Trigger:** Instantly start conversations using LangChain’s Chat Trigger node.
  Contextual Memory:** Retain up to 30 recent messages for coherent back-and-forth.
  Bright Data Integration:** Dynamically filter “FOR\_SALE” properties by city, price, bedrooms, and bathrooms (limit = 3).
  Automated Snapshot Retrieval:** Poll for dataset readiness and fetch full snapshot content.
  HTML-Formatted Output:** Present results as a ` of ` items, embedding property images.

  🚀 How It Works (Step-by-Step)

  Prerequisites:

     n8n ≥ v1.0
     Community nodes: install n8n-nodes-brightdata (the unverified community node)
     API credentials: OpenAI, Bright Data
     Webhook endpoint to receive chat messages

  Node Configuration:

     Chat Trigger: Listens for incoming chat messages; shows a welcome screen.
     Memory Buffer: Stores the last 30 messages for context.
     OpenAI Chat Model: Uses GPT-4o-mini to interpret user intent.
     Real Estate AI Agent: Orchestrates filtering logic, calls tools, and formats responses.
     Bright Data “Filter Dataset” Tool: Applies user-defined filters plus homeStatus = FOR_SALE.
     Wait & Recover Snapshot: Polls until snapshot is ready, then fetches content.
     Get Snapshot Content: Converts raw JSON into a structured list.

  Workflow Logic:

     User sends search criteria → Agent validates inputs.
     Agent invokes “Filter Dataset” once all filters are present.
     Upon dataset readiness, the snapshot is retrieved and parsed.
     Final output rendered as a bullet list with property images.

  Testing & Optimization:

     Use the built-in Execute Workflow trigger for rapid dry runs.
     Inspect node outputs in n8n’s UI; adjust filter defaults or snapshot limits.
     Tune OpenAI model parameters (e.g., maxIterations) for faster responses.

  Deployment & Monitoring:

     Activate the main workflow and expose its webhook URL.
     Monitor executions in the “Executions” panel; set up alerts for errors.
     Archive or duplicate workflows as needed; update credentials via credential manager.

  ✅ Pre-requisites

  Bright Data Account:** API key for marketplaceDataset.
  OpenAI Account:** Access to GPT-4o-mini model.
  n8n Version:** v1.0 or later with community node support.
  Permissions:** Webhook access, credential vault read/write.

  👤 Who Is This For?

  Real-estate agencies and brokers seeking to automate client queries.
  PropTech startups building conversational search tools.
  Data analysts who want on-demand property snapshots without manual scraping.

  📈 Benefits & Use Cases

  Time Savings:** Replace manual MLS searches with an AI-driven chat.
  Scalability:** Serve multiple clients simultaneously via webchat or embedded widget.
  Consistency:** Always report exactly three properties, ensuring concise results.
  Engagement:** Visual listings with images boost user satisfaction and conversion.


  Workflow created and verified by Miquel Colomer https://www.linkedin.com/in/miquelcolomersalas/ and N8nHackers https://n8nhackers.com

nodes_used:
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metadata_json:
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name: 🎯 Precision Prospecting: Automate LinkedIn Lead Gen with Bright Data
author_name: Miquel Colomer
author_username: mcolomer
categories: []
created_at: 2025-06-11T09:26:01.909Z
updated_at: 2025-06-11T09:26:01.909Z
url: https://n8n.io/workflows/4873
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  🎯 Precision Prospecting: Automate LinkedIn Lead Gen with n8n & Bright Data

  📝 Overview
  This workflow turns n8n into an AI-powered prospector, automatically searching Google for LinkedIn profiles, scraping profile data via Bright Data, and summarizing key details. Ideal for sales and recruitment teams seeking targeted lead lists without manual research.

  🎥 Workflow in Action
  Want to see this workflow in action? You have a chat window output below:


  🔑 Key Features

  AI Chat Trigger**: Start prospecting via conversational prompts.
  Contextual Memory**: Retains the last 20 messages for coherent dialogue.
  Automated Google Search**: Generates site-restricted queries and fetches the top result.
  Bright Data Scraping**: Synchronously scrapes LinkedIn profile details by URL.
  Intelligent Filtering**: Extracts only valid LinkedIn profile links.
  Limit Control**: Returns a single, most relevant profile per request.
  LLM Summary**: Uses GPT-4o-mini to interpret and present scraped data.

  🚀 How It Works (Step-by-Step)

  Prerequisites:

     n8n ≥ v1.0 with community nodes: install n8n-nodes-brightdata (not verified community node).
     API credentials: OpenAI, Bright Data (web unlocker zone “web\_unlocker1”).
     Webhook endpoint for chat trigger.

  Node Configuration:

     When chat message received (chatTrigger): Fires on user prompt.
     Simple Memory1 (memoryBufferWindow): Stores the last 20 chat messages.
     AI Prospector Agent (agent): Orchestrates search logic.
     Get 1 Google Result (brightData): Performs a Google search with site:linkedin.com/in.
     Get Links from Body (html): Extracts all `` hrefs from the search result page.
     Extract Links (splitOut): Splits out individual link entries.
     Filter only LinkedIn Profiles (filter): Ensures the URL contains “linkedin.com/” and starts with “https\://”.
     Limit (limit): Restricts output to the first valid profile URL.
     Search LinkedIn URI (toolWorkflow): Passes the URL to a secondary workflow to fetch the first link.
     Get LinkedIn Profile Data (brightDataTool): Scrapes the profile JSON.
     OpenAI Chat Model (lmChatOpenAi): Summarizes and formats the scraped data.

  Workflow Logic:

     User asks for a person by company & name, company & position, or LinkedIn URL.
     Agent builds a Google query (e.g., site:linkedin.com/in bright data cmo) and calls “Get 1 Google Result.”
     Extracted links are filtered and limited to the top valid profile.
     If user provided a direct LinkedIn URL, Agent skips search and scrapes immediately.
     Scraped profile JSON is passed to GPT-4o-mini to generate a concise summary.

  Testing & Optimization:

     Trigger via Execute Workflow for dry runs.
     Inspect intermediate node outputs in n8n’s Execution panel.
     Adjust maxIterations or memory window length for performance.
     Tune Bright Data zone or country settings to optimize scraping speed.

  Deployment & Monitoring:

     Activate the workflow and expose its webhook URL.
     Use n8n’s built-in Alerts or external monitoring (e.g., Slack notifications) on failures.
     Rotate credentials via n8n’s Credential Vault when needed.
     Version-control workflow via duplicates or Git-backed n8n instances.

  ✅ Pre-requisites

  OpenAI Account**: API key for GPT-4o-mini.
  Bright Data Account**: Zone “web\_unlocker1” and dataset gd_l1viktl72bvl7bjuj0.
  n8n Version**: v1.0+ with community nodes installed.
  Permissions**: Webhook access, Credential Vault read/write.

  👤 Who Is This For?

  Sales teams automating outbound LinkedIn prospecting.
  Recruiters sourcing candidates without manual scraping.
  Marketing ops looking to enrich CRM with accurate profile data.

  📈 Benefits & Use Cases

  Efficiency**: Reduces hours of manual search and data entry to seconds.
  Accuracy**: Filters out non-LinkedIn links and ensures high-quality results.
  Scalability**: Handle multiple prospect requests concurrently via chat or API.
  Integration**: Easily hook into CRMs or email sequencers downstream.

  Workflow created and verified by Miquel Colomer https://www.linkedin.com/in/miquelcolomersalas/ and N8nHackers https://n8nhackers.com

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.executeWorkflowTrigger","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-brightdata.brightDataTool","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.splitOut","n8n-nodes-base.html","n8n-nodes-base.filter","n8n-nodes-base.limit","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","n8n-nodes-brightdata.brightData"]

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metadata_json:
  {"categories":["AI/ML","Marketing"],"complexity":"medium","use_cases":["generate lead lists","research prospects","automate data gathering"],"estimated_setup_minutes":45,"required_services":["Google Custom Search API","Bright Data","LinkedIn"],"key_features":["AI-powered search","data extraction","profile summarization","chat interaction"],"target_audience":["sales professionals","recruiters"]}


name: Transform Voice Transcripts to LinkedIn Posts with Claude AI and Email Automation
author_name: Kyle Morse
author_username: s52
categories: []
created_at: 2025-06-11T13:05:06.724Z
updated_at: 2025-06-11T13:05:06.724Z
url: https://n8n.io/workflows/4874
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Takes your raw, unpolished voice transcripts and transforms them into well-structured LinkedIn posts using AI. Perfect for when you have good ideas but they come out as rambling thoughts.

  The Problem: You record voice memos with great ideas, but when you read the transcript, it's full of "ums," incomplete sentences, and scattered thoughts. Turning that into a professional LinkedIn post takes forever.

  The Solution: Email your raw transcript to this workflow. It combines your unpolished content with examples from your inspiration document (posts you've saved that match your desired style), then uses AI to create a clean, engaging LinkedIn post.

  What actually happens:
  You email a raw voice transcript to your workflow email
  -The workflow pulls style examples from your Google Doc
  AI reformats your scattered thoughts into a coherent 150-300 word LinkedIn post
  You get an email back with the polished content + suggested image description
  Copy, paste, and post to LinkedIn

  You provide: The raw transcript (from your phone's voice recorder or any transcription tool), a Google Doc with LinkedIn posts you admire for style reference.

  You get: Professional LinkedIn content that sounds like you, but organized and polished.

  Technical requirements: Anthropic API, email account, Google Doc with example posts.

  This is basically having an AI writing assistant that knows your voice and preferred style, turning your brain dumps into professional content.

nodes_used:
  ["n8n-nodes-base.set","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.lmChatAnthropic","n8n-nodes-base.httpRequest","n8n-nodes-base.merge","n8n-nodes-base.aggregate","n8n-nodes-base.code","n8n-nodes-base.emailReadImap","n8n-nodes-base.filter","n8n-nodes-base.filter","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.emailSend"]

workflow_json_compressed:
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metadata_json:
  {"categories":["AI/ML","Communication"],"complexity":"medium","use_cases":["Transform voice transcripts into social media posts","Convert unpolished voice memos into professional content","Automate LinkedIn content generation from AI"],"estimated_setup_minutes":5,"required_services":["Anthropic Claude","Permanent Email Provider"],"key_features":["Integrate AI writing assistants","Automated transcript processing","Content refinement workflows"],"target_audience":["content creators","social media managers","knowledge workers"]}


name: Create a Telegram Customer Support Bot with GPT4-mini and Google Docs Knowledge
author_name: Automate With Marc
author_username: marconi
categories: []
created_at: 2025-06-11T14:02:23.307Z
updated_at: 2025-06-11T14:02:23.307Z
url: https://n8n.io/workflows/4875
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:19
description:
  🤖 AI Customer Support Agent with Google Docs Knowledge (Telegram + OpenAI)

  This no-code workflow turns your Telegram bot into an intelligent, always-on AI support agent that references your business documentation in Google Docs to respond to customer queries—instantly and accurately.

  Watch full step-by-step video tutorial of the build here:
  https://youtu.be/Mlv7CjGO7wI

  🔧 How it works:
  Telegram Trigger – Captures incoming messages from users on your Telegram bot

  Langchain AI Agent (OpenAI GPT) – Interprets the message and uses RAG (retrieval-augmented generation) techniques to craft an answer

  Google Docs Tool – Connects to and retrieves context from your specified Google Doc (e.g. FAQ, SOPs, policies)

  Memory Buffer – Keeps track of recent chat history for more human-like conversations

  Telegram Reply Node – Sends the AI-generated response back to the user

  💡 Use Cases:
  E-commerce customer service

  SaaS product onboarding

  Internal helpdesk bot for teams

  WhatsApp-style support for digital businesses

  🧠 What makes this powerful:
  Supports complex questions by referencing a live Google Doc knowledge base

  Works in plain conversational language (no buttons or forms needed)

  Runs 24/7 with zero code

  Easily extendable to Slack, WhatsApp, or email support

  🛠️ Tools used:
  Telegram Node (trigger + send)

  Langchain Agent with OpenAI GPT

  Google Docs Tool

  Memory Buffer

  Sticky Notes for easy understanding

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

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metadata_json:
  {"categories":["Communication"],"complexity":"medium","use_cases":["Resolve customer inquiries","Leverage business documentation","Automate support responses"],"estimated_setup_minutes":15,"required_services":["Telegram","OpenAI","Google Docs"],"key_features":["Natural language understanding","Document retrieval","Contextual responses"],"target_audience":["support staff","business owners"]}


name: Auto-classify Gmail emails with AI and apply labels for inbox organization
author_name: Alex Dunlop
author_username: alexdunlop
categories: []
created_at: 2025-06-11T14:08:25.008Z
updated_at: 2025-06-11T14:08:25.008Z
url: https://n8n.io/workflows/4876
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  Who is this for?
  Professionals and individuals who receive high volumes of emails, those who want to automatically organize their Gmail inbox using AI classification.

  What problem is this workflow solving?

  Manual email sorting is time-consuming and inconsistent. This workflow automatically categorizes incoming emails into 8 predefined labels (To respond, FYI, Comment, Notification, Meeting update, Awaiting reply, Actioned, Marketing) to help maintain inbox zero and prioritize responses.

  What this workflow does

  Monitors Gmail for new incoming emails
  Uses AI to analyze email content and classify into appropriate categories
  Automatically applies the corresponding Gmail label
  Runs on a schedule to process emails consistently

  Setup

  Prerequisites

  n8n instance (cloud or self-hosted)
  Gmail account with API access enabled
  Access to an LLM provider (OpenAI, Anthropic Claude, or similar)

  Step-by-Step

  Configure Gmail Credentials
  Create Gmail Labels
  Configure LLM Chain
  Set Email Polling Schedule
  Test the Workflow

  Create Gmail Labels
  Before running the workflow, create these 8 labels in your Gmail account:

  To respond
  FYI
  Comment
  Notification
  Meeting update
  Awaiting reply
  Actioned
  Marketing

  How to customize this workflow to your needs

  Modify Classification Categories


  To change the email categories, update two places:

  In the AI prompt (Basic LLM Chain node):

  Your new category - Description of what emails fit here
  Another category - Description
  [... continue with your categories]

  In Gmail labels:
  Create corresponding labels in your Gmail account with the exact same names and numbering.

  Adjust Classification Rules

  The AI prompt contains specific rules for each category. To modify:

  Edit the "Key classification rules" section in the LLM prompt
  Add examples of emails that should go into each category
  Specify edge cases and how they should be handled

  Change Email Sources

  Currently monitors all incoming emails. To filter specific emails:

  In the Gmail Trigger node, add filters such as:

  from:specific-sender@domain.com
  subject:contains-keyword
  -label:already-processed

  You can also change this use Outlook

  Modify Polling Frequency

  More frequent**: Add multiple poll times (e.g., 9 AM, 12 PM, 6 PM)
  Less frequent**: Change to once daily or weekly
  Real-time**: Switch to webhook-based triggering (requires Gmail API setup)

  I choose daily for cost.

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenRouter","n8n-nodes-base.aggregate","n8n-nodes-base.merge","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.gmailTrigger","n8n-nodes-base.gmail","n8n-nodes-base.code","n8n-nodes-base.gmail"]

workflow_json_compressed:
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metadata_json:
  {"categories":["AI/ML","Communication"],"complexity":"simple","use_cases":["sort emails by priority","automate inbox organization","categorize correspondence"],"estimated_setup_minutes":20,"required_services":["Gmail","OpenRouter"],"key_features":["AI-powered email classification","customizable label categories","automated inbox management"],"target_audience":["professionals","administrators","managers"]}


name: Automated video creation using Google Veo3 and n8n workflow
author_name: Lakshit Ukani
author_username: lakshit1996
categories: []
created_at: 2025-06-11T14:24:53.629Z
updated_at: 2025-06-11T14:24:53.629Z
url: https://n8n.io/workflows/4877
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:19
description:
  Who is this for?
  Content creators, social media managers, digital marketers, and businesses looking to automate video production without expensive equipment or technical expertise.

  What problem is this workflow solving?
  Traditional video creation requires cameras, editing software, voice recording equipment, and hours of post-production work. This workflow eliminates all these barriers by automatically generating professional videos with audio using just text prompts.

  What this workflow does
  This automated workflow takes video ideas from Google Sheets, generates optimized prompts using AI, creates videos through Google's V3 model via Fal AI, monitors the generation progress, and saves the final video URLs back to your spreadsheet for easy access and management.

  Setup
  Sign up for Fal AI account and obtain API key
  Create Google Sheet with video ideas and status columns
  Configure n8n with required credentials (Google Sheets, Fal AI API)
  Import the workflow template
  Set up authentication for all connected services
  Test with sample video idea

  How to customize this workflow to your needs
  Modify the AI prompts to match your brand voice, adjust video styles and camera movements, change polling intervals for video generation status, customize Google Sheet column mappings, and add additional processing steps like thumbnail generation or social media posting.

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

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metadata_json:
  {"categories":["Marketing","Content Creation"],"complexity":"medium","use_cases":["Automate video production","Generate social media content","Create video ads","Produce marketing videos"],"estimated_setup_minutes":20,"required_services":["Google Veo3","Google Sheets","Webhooks"],"key_features":["Text-to-video generation","Automatic audio insertion","Scheduled content creation","No technical expertise required"],"target_audience":["Content creators","Marketing managers","Social media managers"]}


name: GitHub Fork Status Monitor
author_name: Manish
author_username: manish
categories: []
created_at: 2025-06-11T15:39:39.572Z
updated_at: 2025-06-11T15:39:39.572Z
url: https://n8n.io/workflows/4878
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  This workflow helps you keep an eye on your GitHub forks, notifying you when they fall behind or pull ahead of their upstream repositories.

  How It Works
  Fetches All Your Repos: The workflow starts by grabbing a list of all repositories owned by your GitHub account.
  Filters for Forks: It then intelligently filters this list to identify only your forked repositories.
  Compares Branches: For each identified fork, it compares its default branch against the upstream repository's default branch to find out how many commits it's ahead or behind.
  Filters for Changes: Only forks that are either ahead or behind their upstream (i.e., not perfectly in sync) are processed further.
  Generates Report: A concise, well-formatted report is compiled, highlighting the status and commit differences for each relevant fork.
  Sends Telegram Notification: Finally, this report is sent directly to your Telegram chat, keeping you informed in real-time.

  Setup Steps
  Copy the template 
  Update triggers ( optional ) 
  Update the credentials 

  Prerequisites
  GitHub Credentials**: You'll need to provide your GitHub personal access token for the "Get All Repositories" and "Compare Branches API Call" nodes.
  Telegram Bot Setup**: Configure a Telegram Bot and obtain its API token and your chat ID for the "Send Report" node.
  Github Owner Username**: Update the "Get All Repositories" node with the GitHub username of the repository owner whose forks you want to monitor.


  Explore & Fine-Tune: All detailed instructions and explanations, including how to adjust the filtering logic or output formatting, are provided in sticky notes directly within the workflow canvas.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.splitInBatches","n8n-nodes-base.github","n8n-nodes-base.telegram","n8n-nodes-base.telegramTrigger","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.merge","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.github","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.filter"]

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metadata_json:
  {"categories":["DevOps"],"complexity":"medium","use_cases":["monitor fork sync","implement upstream tracking","receive fork status alerts","manage repository sync","detect repository divergence"],"estimated_setup_minutes":45,"required_services":["GitHub","Telegram"],"key_features":["automated fetch upstream fork list","compare default branches","push notifications on sync events","batch processing for repositories","conditional notifications based on sync status"],"target_audience":["developers","repository managers","devops engineers"]}


name: Personalized Outreach for Lawyers with LinkedIn Scraping, GPT-4o, Google Sheets
author_name: Ibrahim Malick
author_username: imalick
categories: []
created_at: 2025-06-11T16:50:07.774Z
updated_at: 2025-06-11T16:50:07.774Z
url: https://n8n.io/workflows/4879
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
   ⚠️ This template uses only official n8n nodes. No community nodes required.

  🧑‍💼 Who is this for?

  This workflow is designed for:
  Legal tech founders
  Marketing freelancers or consultants
  Agencies supporting lawyers and small law firms
  Anyone doing outbound outreach in the legal niche

  ❓ What problem is this solving?

  LinkedIn is a goldmine for targeting legal professionals — but scraping and personalizing outreach is tedious and expensive. Most tools either:
  Require paid LinkedIn Sales Navigator
  Can’t personalize at scale
  Violate LinkedIn’s TOS

  This workflow solves that by using free Google Search, OpenRouter AI, and GPT-4o to find, enrich, and message up to 1,000 solo lawyers per day — without using browser automation or scrapers.

  ⚙️ What this workflow does

  Uses Google Programmable Search to find solo lawyers and small firm founders on LinkedIn
  Parses each profile’s name, title, profile URL, and snippet
  Saves raw lead data to Google Sheets
  Uses OpenRouter Sonar Pro to enrich each profile with external content
  Generates a personalized, 1-line message using GPT-4o
  Appends the final message into Google Sheets for outreach

  🛠️ Setup

  Estimated time: 15–20 minutes

  ✅ Google Programmable Search
  Enable the Custom Search API on Google Cloud
  Create a programmable search engine set to search the full web
  Copy your API key and CX ID

  ✅ Google Sheets
  Create a sheet with columns: Name, Title, Profile URL, Outreach Message
  Share the sheet with your OAuth-connected Google account

  ✅ OpenRouter
  Sign up at openrouter.ai
  Fund with at least $5 and generate your API key
  Use the model perplexity/sonar-pro for real-time research

  ✅ GPT-4o (optional)
  You can use your OpenAI key or route GPT-4o via OpenRouter

  All setup-specific values are marked clearly in sticky notes and placeholders.

  🛠️ How to customize this workflow to your needs

  Change the Google search query to match your industry (e.g., "founder" AND "therapist" site:linkedin.com/in)
  Modify the AI prompt to match your tone (formal, casual, humorous)
  Connect the final output to your CRM (like HubSpot, Airtable, etc.)
  Add a second outreach message variant to A/B test performance

  📌 Sticky Notes & Annotations

  All nodes are clearly renamed for understandability (e.g., Find Lawyer Profiles, Parse LinkedIn Search Results)
  Color-coded sticky notes explain:
    Setup instructions
    Required credentials
    Use case

  🗂 Category

  AI
  Sales
  Marketing

nodes_used:
  ["n8n-nodes-base.set","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.lmChatOpenRouter","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.scheduleTrigger"]

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metadata_json:
  {"categories":["Marketing"],"complexity":"medium","use_cases":["LinkedIn lead generation","legal professional outreach","personalized email campaigns"],"estimated_setup_minutes":30,"required_services":["LinkedIn","OpenRouter (GPT-4o)","Google Sheets"],"key_features":["LinkedIn scraping integration","AI-driven message personalization","automated workflow scheduling"],"target_audience":["legal professionals","marketing freelancers","law firm agencies"]}


name: Segment WooCommerce Customers for Targeted Marketing with RFM Analysis
author_name: Mohammadreza azari
author_username: mrazari
categories: []
created_at: 2025-06-11T17:58:53.119Z
updated_at: 2025-06-11T17:58:53.119Z
url: https://n8n.io/workflows/4880
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:20
description:
  Overview

  This workflow is designed for eCommerce store owners and marketing teams who use WooCommerce. It helps segment customers based on their purchasing behavior using the RFM (Recency, Frequency, Monetary) model. By identifying high-value customers, new buyers, and at-risk segments, you can tailor your marketing strategies and improve customer retention.

  How It Works

  Trigger: The workflow can be started manually or on a scheduled basis (e.g., weekly).
  Retrieve Orders: It fetches completed orders from your WooCommerce store from the past year.
  RFM Analysis: It groups orders by customer and calculates their RFM scores.
  Customer Segmentation: Based on RFM scores, customers are categorized into marketing segments (e.g., Champions, At Risk, Lost).
  Summary Report: Generates a styled HTML report with a table summarizing customer segments and suggested marketing actions.

  Setup Instructions

  Connect WooCommerce:

     Go to the WooCommerce node.
     Add or select your WooCommerce API credentials. You need the Base URL, Consumer Key, and Consumer Secret.
     Ensure API access is enabled in your WooCommerce settings.

  Customize Segmentation (Optional):

     In the "Calculate RFM Scores" code node, you can adjust the logic that assigns segment labels based on score combinations.
     You can also update the marketing suggestions in the second "Code" node.

  Run the Workflow:

     Use the "Manual Start" node for testing.
     Enable the "Weekly Trigger" node to automate execution.

  View Report:

     The final HTML node outputs a complete styled report.
     You can send this via email or integrate it with other services.

  Requirements

  WooCommerce store with API access enabled.
  Valid API credentials (Base URL, Consumer Key, Consumer Secret).
  n8n instance with access to the internet.

nodes_used:
  ["n8n-nodes-base.start","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.wooCommerce","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.html","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["Marketing","ECommerce"],"complexity":"medium","use_cases":["Segment customers by RFM model","Identify high-value buyers","Tailor marketing campaigns"],"estimated_setup_minutes":30,"required_services":["WooCommerce"],"key_features":["Cron-based automation","Customer segmentation","Database query execution","HTML report generation"],"target_audience":["eCommerce owners","Marketing managers","Store administrators"]}


name: Turn Google Sheets Ideas into AI Videos with GPT-4o and Fal.AI Veo 3
author_name: Maximiliano Rojas-Delgado
author_username: maxr
categories: []
created_at: 2025-06-11T19:54:02.502Z
updated_at: 2025-06-11T19:54:02.502Z
url: https://n8n.io/workflows/4881
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:19
description:
  Turn Your Ideas into Videos—Right from Google Sheets!

  This workflow helps you make cool 8-second videos using Fal.AI and Veo 3, just by typing your idea into a Google Sheet. You can even choose if you want your video to have sound or not. It’s super easy—no tech skills needed!

  Why use this?

  Just type your idea in a sheet—no fancy tools or uploads.

  Get a video link back in the same sheet.

  Works with or without sound—your choice!

  How does it work?

  You write your idea, pick the video shape, and say if you want sound (true or false) in the Google Sheet.

  n8n reads your idea and asks Fal.AI to make your video.

  When your video is ready, the link shows up in your sheet.

  What do you need?

  A Google account and Google Sheets connected with service account (check this link for reference)
  A copy of the following Google Spreadsheet:
  Spreadsheet to copy

  An OpenAI API key

  A Fal.AI account with some money in it

  That’s it! Just add your ideas and let the workflow make the videos for you. Have fun creating!

  If you have any questions, just contact me in X @maxrojasdelgado.

nodes_used:
  ["n8n-nodes-base.wait","n8n-nodes-base.if","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheetsTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","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":["AI/ML","Marketing"],"complexity":"simple","use_cases":["generate AI videos from spreadsheet text","create short video clips with video generation APIs","schedule video content creation directly from Google Sheets"],"estimated_setup_minutes":10,"required_services":["Google Sheets","Fal.ai","Webhooks"],"key_features":["video generation from plain text prompts","audio inclusion toggle","direct integration with Google Sheets","prompt engineering for Veo 3"],"target_audience":["marketers","content creators","menters"]}


name: AI Prospect Researcher +ISCP only need Company Name and Domain
author_name: LukaszB
author_username: lukaszb
categories: []
created_at: 2025-06-11T20:46:18.131Z
updated_at: 2025-06-11T20:46:18.131Z
url: https://n8n.io/workflows/4882
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:20
description:
  AI Prospect Researcher +ISCP only from Domain and Company Name 

  This workflow finds available email addresses, researches the company using public data (LinkedIn, website scraping, etc.), calculates the ISCP score, and delivers a complete, ready-to-use report.

  This workflow is designed for professionals and teams seeking to scale their B2B research with comprehensive company intelligence. It automates the full prospect analysis process — from extracting contact data and scraping website content to gathering market insights and generating structured reports with GPT-4.

  Using a combination of Hunter.io, Perplexity AI, Airtop, and OpenAI, this system creates a detailed Google Doc profile for each company, complete with decision-maker identification and ISCP scoring. Whether you're conducting market research, qualifying leads, or preparing investor briefings — this tool delivers actionable business intelligence in minutes, without manual data collection.

  The process is battle-tested, with built-in error handling, and suitable for sales teams, market researchers, or founders conducting competitive analysis.

  How it works

  Once triggered (via form or spreadsheet), the workflow:
  Uses Hunter.io to discover professional emails associated with the domain
  Leverages Perplexity AI to gather web-wide company intelligence and industry context
  Scrapes the company website using Airtop to extract services, products, and key pages
  Optionally enriches with LinkedIn data via Apify (company size, specialties)
  Processes all inputs through GPT-4 to:
     Generate an executive summary
     Identify potential decision-makers
     Calculate an Ideal Service Customer Profile (ISCP) match score
  Produces a finalized report in Google Docs with:
     Company overview
     Key contacts
     Market positioning
     Recommended outreach approach

  This is a turnkey solution for teams who need deep company profiles without the hours of manual research and data stitching.

nodes_used:
  ["n8n-nodes-base.hunter","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.airtop","n8n-nodes-base.airtop","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.merge","n8n-nodes-base.googleDocs","n8n-nodes-base.googleDocs","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.aggregate","n8n-nodes-base.set","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.googleDocs","@n8n/n8n-nodes-langchain.lmChatOpenAi","@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.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.formTrigger","n8n-nodes-base.googleSheets","n8n-nodes-base.splitInBatches","n8n-nodes-base.set","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["AI/ML","Data Processing"],"complexity":"medium","use_cases":["B2B prospect research","company intelligence gathering","email address discovery","automated lead scoring","competitor analysis"],"estimated_setup_minutes":15,"required_services":["googleDocs","Hunter"],"key_features":["AI-powered company insights","web scraping automation","ISCP score calculation","multichannel data aggregation"],"target_audience":["sales professionals","marketing researchers","business analysts"]}


name: Convert PDF Documents to AI Podcasts with Google Gemini and Text-to-Speech
author_name: Mathis
author_username: iamthis
categories: []
created_at: 2025-06-11T20:48:43.573Z
updated_at: 2025-06-11T20:48:43.573Z
url: https://n8n.io/workflows/4883
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  Convert PDF documents to AI-generated podcasts with Google Gemini and Text-to-Speech

  Transform any PDF document into an engaging, natural-sounding podcast using Google's Gemini AI and advanced Text-to-Speech technology. This automated workflow extracts text content, generates conversational scripts, and produces high-quality audio files.

  Who is this for?

  This workflow template is perfect for content creators, educators, researchers, and marketing professionals who want to repurpose written content into audio format. Ideal for creating podcast episodes, educational content, or making documents more accessible.

  What problem does this solve?

  Converting written documents to engaging audio content manually is time-consuming and requires scriptwriting skills. This workflow automates the entire process, turning static PDFs into dynamic, conversational podcasts that sound natural and engaging.

  What this workflow does

  Extracts text from uploaded PDF documents
  Generates podcast script using Google Gemini AI with conversational tone
  Converts script to speech using Google's advanced TTS with customizable voices
  Processes audio into properly formatted WAV files
  Saves final podcast ready for distribution

  Setup

  Obtain API credentials:
     Get Google Gemini API key from AI Studio
     Configure credentials in n8n as "Google Gemini(PaLM) Api account"

  Configure voice settings:
     Choose from available voices: Kore (professional), Aoede (conversational), Laomedeia (energetic)
     Customize script generation prompts if needed

  Test the workflow:
     Upload a sample PDF file
     Verify audio output quality
     Adjust voice settings as preferred

  How to customize this workflow

  Modify script style:** Edit the prompt in the "Generate Podcast Script" node to change tone, length, or format
  Change voice:** Update the voice name in "Prepare TTS Request" node
  Add preprocessing:** Insert text cleaning nodes before script generation
  Integrate with storage:** Connect to Google Drive, Dropbox, or other storage services
  Add notifications:** Include Slack or email notifications when podcasts are ready

  Note: This template requires Google Gemini API access and works best with text-based PDF files under 10MB.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.extractFromFile","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.writeBinaryFile","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 4364 characters]

metadata_json:
  {"categories":["AI/ML","Content Repurposing"],"complexity":"medium","use_cases":["convert PDFs to audio","transform documents into podcasts","generate speech from text"],"estimated_setup_minutes":15,"required_services":["Google Gemini API","Google Cloud Text-to-Speech"],"key_features":["automatic PDF text extraction","AI-driven script generation","natural speech synthesis"],"target_audience":["content creators","educators","marketing professionals"]}


name: Auto-Generate AI News Commentary with Dumpling AI and GPT-4o
author_name: Yang
author_username: yang
categories: []
created_at: 2025-06-11T21:42:44.885Z
updated_at: 2025-06-11T21:42:44.885Z
url: https://n8n.io/workflows/4884
scraped_at: 2026-05-02 08:04:50
metadata_generated_at: 2026-05-02 08:36:20
description:
  This workflow turns trending news into thoughtful first-person commentary for platforms like LinkedIn. It uses Dumpling AI’s News Search and Scraping APIs to find and extract article content, then feeds the cleaned text to GPT-4o to write personalized insights. The final output is saved back to Google Sheets as a draft for easy review or posting.

  ✅ What this workflow does

  Triggers daily using a Schedule node.
  Fetches a list of content topics from a Google Sheet.
  Uses Dumpling AI to search for relevant news articles based on each topic.
  Scrapes the article content with Dumpling AI’s /scrape endpoint.
  Cleans and aggregates the article content using a Code node.
  Generates first-person commentary with GPT-4o tailored for LinkedIn.
  Appends the generated post back to the Google Sheet next to its topic.

  🧩 Nodes in this workflow

  Schedule Trigger**: Starts the workflow daily.
  Google Sheets (Read Topics)**: Pulls topic rows that don’t have a generated commentary yet.
  Split In Batches**: Processes each topic one at a time.
  Wait**: Adds a delay to manage API limits.
  HTTP Request (Search News)**: Uses Dumpling AI's /search-news to find relevant articles for the topic.
  Split Out**: Iterates over the list of article results.
  HTTP Request (Scrape Article)**: Extracts the full article text using Dumpling AI’s /scrape.
  Aggregate**: Collects and merges article content fields.
  Code (Clean Article)**: Strips links, formatting, and irrelevant text.
  OpenAI (GPT-4o)**: Generates a short, first-person LinkedIn post-style commentary using a custom prompt.
  Google Sheets (Write Back)**: Appends the final output next to the original topic in the sheet.

  🧑‍💼 Who is this for?

  Founders, content creators, marketers, or **agency teams looking to maintain an active presence on LinkedIn or newsletters by sharing smart takes on industry trends.

  💡 What problem does this solve?

  Most people want to comment on current events but don't have the time to summarize articles or write well-structured posts. This automation saves hours of manual work by:
  Finding the right article.
  Extracting and cleaning the content.
  Writing it in a natural, first-person voice using AI.

  ⚙️ What you need to use this:

  A Google Sheet with at least two columns: topic and generated commentary.
  A Dumpling AI API Key with access to the /search-news and /scrape endpoints.
  An OpenAI GPT-4o connection.

nodes_used:
  ["n8n-nodes-base.noOp","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.googleSheets","n8n-nodes-base.splitInBatches","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.httpRequest","n8n-nodes-base.aggregate","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["AI/ML","Marketing","Content Creation"],"complexity":"medium","use_cases":["generate daily news commentary","create LinkedIn posts","extract and summarize articles","draft blog insights"],"estimated_setup_minutes":30,"required_services":["Dumpling AI","OpenAI","Google Sheets"],"key_features":["automated content discovery","AI-powered summarization","first-person perspective generation","scheduled execution"],"target_audience":["content creators","marketing managers","social media managers"]}


name: AI Icebreaker Builder: Scrape Sites with Dumpling AI and Save to Airtable
author_name: Yang
author_username: yang
categories: []
created_at: 2025-06-11T21:52:55.601Z
updated_at: 2025-06-11T21:52:55.601Z
url: https://n8n.io/workflows/4885
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  Who is this for?

  This workflow is ideal for sales teams, marketers, and virtual assistants who manage outbound campaigns and want to improve their cold outreach personalization. It helps automate the research and writing process for each lead, saving time while improving quality.

  What problem is this workflow solving?

  Cold outreach often lacks personalization because manually reviewing each lead's website takes time. This workflow eliminates that bottleneck by using AI to auto-generate personalized icebreakers, summaries, and outreach emails based on a lead’s website—without human research.

  What this workflow does

  This n8n workflow runs on a schedule and pulls leads from Airtable who don't yet have an "Ice breaker" field filled out. For each lead, it does the following:

  Trigger: Scheduled daily via the Run Daily to Process New Leads node.
  Search Airtable: Finds leads in Airtable where the Ice breaker field is empty using the Search Cold Leads Without Icebreaker node.
  Split in Batches: Iterates through each lead one by one using Loop Through Each Lead.
  Rate Limiting: Waits briefly before each request using Wait Before Making Request to avoid rate limits.
  Scrape Website: Sends each lead’s website to Dumpling AI's /scrape endpoint via the Scrape Lead Website with Dumpling AI HTTP request.
  Generate AI Copy: Sends the scraped content to GPT-4o using the Generate Icebreaker, Summary & Email (GPT-4o) node. It asks the LLM to create:
     A short personalized icebreaker
     A 2–3 line website summary
     A short email body for cold outreach
  Save Results: Updates the original Airtable record with the generated content using the Save AI Output Back to Airtable node.
  Sticky Note: Provides an overview of the workflow and usage instructions for future editors or collaborators.

  This loop continues for all leads found, updating Airtable with fresh AI-generated outreach content.

  Integration Requirements

  Airtable (Personal Access Token)
  Dumpling AI API Key (Header Auth)
  OpenAI (GPT-4o)

nodes_used:
  ["n8n-nodes-base.noOp","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.airtable","n8n-nodes-base.splitInBatches","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.airtable","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["Artificial Intelligence"],"complexity":"medium","use_cases":["automated lead research","personalized outreach generation","cold outreach personalization"],"estimated_setup_minutes":30,"required_services":["Dumpling AI","Airtable","n8n"],"key_features":["AI-generated website scraping","automated icebreaker drafting","batch-sized lead processing","scheduling automation"],"target_audience":["sales teams","marketers"]}


name: Auto-Create Podcast from YouTube Transcript using Dumpling AI and GPT-4o
author_name: Yang
author_username: yang
categories: []
created_at: 2025-06-11T22:06:28.989Z
updated_at: 2025-06-11T22:06:28.989Z
url: https://n8n.io/workflows/4887
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:19
description:
  🔎 Who is this for?
  This workflow is designed for podcast creators, content marketers, and video producers who want to convert YouTube videos into podcast-ready scripts. It's perfect for anyone repurposing long-form content to reach audio-first audiences without manual effort.

  🧠 What problem is this workflow solving?
  Creating podcast scripts from YouTube videos manually is time-consuming. This workflow automates the process by pulling transcripts, cleaning the text, organizing the dialogue, summarizing the key points, and saving everything in one place. It removes the need for manual transcription, formatting, and structuring.

  ⚙️ What this workflow does

  This workflow uses Dumpling AI and GPT-4o to automate the transformation of YouTube video transcripts into polished podcast scripts. Here's how it works:

  RSS Feed Trigger
     Monitors a YouTube RSS feed for new video uploads. When a new video is detected, the workflow begins automatically.

  Get YouTube Transcript (Dumpling AI)
     Uses Dumpling AI's get-youtube-transcript endpoint to extract the full transcript from the video URL.

  Generate Podcast Script with GPT-4o
     GPT-4o receives the transcript and generates a structured JSON output including:
       Cleaned transcript with filler words removed
       Speaker labels for clarity
       A short, engaging podcast title
       A concise summary of the episode

  Save to Airtable
     The structured data (title, summary, cleaned transcript) is saved to Airtable for easy review, editing, or publishing.

  This automation is an ideal workflow for repurposing video content into audio-friendly formats, cutting down production time while increasing content output across platforms.

nodes_used:
  ["n8n-nodes-base.rssFeedReadTrigger","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.airtable","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["AI/ML","Content Creation","Media Processing"],"complexity":"simple","use_cases":["convert YouTube videos to podcast scripts","automate podcast content repurposing","extract transcripts from videos"],"estimated_setup_minutes":5,"required_services":["Dumpling AI","GPT-4o","YouTube API"],"key_features":["YouTube transcript extraction","AI-powered script organization","automated content repurposing"],"target_audience":["podcast creators","content marketers","video producers"]}


name: Voice-Powered Marketing Assistant with ElevenLabs, OpenAI & Content Generation
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-06-12T00:20:50.025Z
updated_at: 2025-06-12T00:20:50.025Z
url: https://n8n.io/workflows/4888
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:19
description:
  🧠 Gwen – The AI Voice Marketing Agent
  Gwen is your intelligent voice-powered marketing assistant built in n8n. She combines the power of OpenAI, ElevenLabs, and automation workflows to handle content creation, image generation, and voice delivery — all from a single agent interface.

  This template shows a graphical illustration of how Gwen will work with subworkflows. These subworkflows are modular placeholders and need to be linked into Gwen for full deployment.

  ✨ What Gwen Can Do
  📝 Generate Voice-Optimized Blog Posts
  Tailored for your target audience with engaging intros, real-time research, and polished structure.
  🖼️ Create AI-Generated Visuals
  From simple concepts to detailed image prompts and Google Drive uploads.
  🧑‍🎨 Edit Images On Demand
  Modify past images with a few words — powered by OpenAI's image editing API.
  🔍 Search Image Database
  Quickly find past content using title or intent.
  🧠 Think Tool
  Gwen uses this to clarify uncertain tasks or analyze complex requests.
  🔊 Deliver Results in Natural Voice
  With ElevenLabs, Gwen transforms all responses into human-like audio, perfect for marketing, social content, or voice interfaces.
  🛠️ Setup Instructions
  Estimated Time: 15–30 mins

  ✅ Step 1: Subworkflows

  Import These Workflows
  Blog Post, Create Image, Edit Image, Search Images
  Connect Them to Gwen
  Assign as tools inside the Gwen agent node (Langchain AI Agent in n8n).
  🎙️ Step 2: Enable ElevenLabs Voice Agent

  Sign up or log in: https://try.elevenlabs.io
  Copy your API key
  In the ElevenLabs interface, create a new tool:
  Method: POST
  URL: https://your-n8n-domain/webhook/042cc868-28b7-42a2-ab65-bc2944fc5a54
  Under Body Parameters, add:
  prompt → value type: LLM Prompt
  sessionId → value type: Dynamic variable, name: system__conversation_id
  Save and connect this tool to your ElevenLabs agent
  Run a test and check n8n execution logs to confirm Gwen’s voice integration is active
  🔐 Step 3: Credentials to Set

  OpenAI – For text and image generation
  ElevenLabs – For voice output
  Tavily – For real-time research in blog generation
  Telegram – For sending content to users
  Google Sheets – To log all outputs like blogs and images

nodes_used:
  ["n8n-nodes-base.webhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.toolThink","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.respondToWebhook","n8n-nodes-base.convertToFile","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.googleDrive","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.googleDrive","n8n-nodes-base.stickyNote","n8n-nodes-base.googleDrive","n8n-nodes-base.httpRequest","n8n-nodes-base.convertToFile","n8n-nodes-base.googleSheets","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.convertToFile","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.stickyNote","n8n-nodes-base.telegram","n8n-nodes-base.googleDrive","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.googleSheetsTool","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.googleDrive","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["AI/ML","Marketing"],"complexity":"medium","use_cases":["generate marketing content","create voiceovers","automate image generation","integrate social media platforms"],"estimated_setup_minutes":45,"required_services":["OpenAI","ElevenLabs","Google Drive","Telegram"],"key_features":["voice synthesis integration","multi-modal content generation","subworkflow modularity","automated image creation","structured output parsing"],"target_audience":["marketers","content creators","marketing managers"]}


name: LinkedIn Auto Message Router and Responder with Request Detection
author_name: Angel Menendez
author_username: djangelic
categories: []
created_at: 2025-06-12T00:34:35.995Z
updated_at: 2025-06-12T00:34:35.995Z
url: https://n8n.io/workflows/4889
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  Who is this for?

  Public-facing professionals (developer advocates, founders, marketers, content creators) who get bombarded with LinkedIn messages that aren't actually for them - support requests when you're in marketing, sales inquiries when you're a devrel, partnership pitches when you handle content, etc.

  What problem is this workflow solving?

  When you're visible online, people assume you handle everything at your company. You end up spending hours daily playing human router, forwarding messages like "How do I reset my password?" or "What's your enterprise pricing?" to the right teams. This LinkedIn automation workflow stops you from being your company's unofficial customer service representative.

  What this workflow does

  This AI-powered LinkedIn DM management workflow automatically assesses incoming LinkedIn messages and routes them intelligently:

  Automated Message Assessment: Receives inbound LinkedIn messages via UniPile and looks up sender details from both personal and company LinkedIn profiles.

  Smart Route Matching: Compares the message content against your message routing workflow table in Notion, which contains:
    Question: "How can I become an n8n ambassador?"
    Description: "Route here when a user is requesting to become an n8n ambassador. Also when they're asking how they could do more to evangelize n8n in their city, or to start organizing n8n meetups and events in their city."
    Action: "Tell the user to open the following notion page which has details on ambassador program including how to apply, as well as perks of the program: https://www.notion.so/n8n-Ambassador-Program-d883b2a130e5448faedbebe5139187ea?pvs=21"

  AI Response Generation: When a message matches an existing route, this AI assistant generates a personalized response draft based on the "Action" instructions from your routing table.

  Human-in-the-Loop Approval: Sends the draft response to Slack with approve/reject buttons, so you maintain control while saving time. Draft can be edited from within Slack on desktop and mobile.

  Automated LinkedIn Responses: Once approved, sends the reply back via LinkedIn and marks the original message as handled.

  The result: You stop being a human switchboard and can focus on your actual job while people still get helpful, timely responses through automated customer service. You can also add routes for things you do handle but get asked about daily (like 'How do I join your beta?' or 'What's your content strategy?') to standardize your responses.

  Setup

  Sign up for a UniPile account and create a webhook under the Messaging section
  Set the callback URL to this workflow's production URL
  Generate a UniPile API key with all required scopes and store it in your n8n credentials
  Create a Slack app and enable interactive message buttons and webhooks

  Here is a slack App manifest template for easy deployment in slack:

  {
      "display_information": {
          "name": "Request Router",
          "description": "A bot that alerts when a new linkedin question comes in.",
          "background_color": "#12575e"
      },
      "features": {
          "bot_user": {
              "display_name": "Request Router",
              "always_online": false
          }
      },
      "oauth_config": {
          "scopes": {
              "bot": [
                  "chat:write",
                  "chat:write.customize",
                  "chat:write.public",
                  "links:write",
                  "im:history",
                  "im:read",
                  "im:write"
              ]
          }
      },
      "settings": {
          "interactivity": {
              "is_enabled": true,
              "request_url": "Your webhook url here"
          },
          "org_deploy_enabled": false,
          "socket_mode_enabled": false,
          "token_rotation_enabled": false
      }
  }


  Set up your Notion database with the three-column structure (Question, Description, Action)
  Configure the AI node with your preferred provider (OpenAI, Gemini, Ollama etc)
  Replace placeholder LinkedIn user and organization IDs with your own

  How to customize this workflow to your needs

  Database Options**: Swap Notion with Google Sheets, Airtable, or another database
  Filtering Logic**: Add custom filters based on keywords, message length, follower count, or business logic
  AI Customization**: Adjust the system prompt to match your brand tone and response goals
  Approval Platform**: Replace Slack with email, Discord, or another review platform
  Team Routing**: Use Slack metadata to route approvals to specific team members based on message category
  Enrichment**: Add secondary data enrichment using tools like Clearbit or FullContact
  Response Rules**: Create conditional logic for different response types based on sender profile or message content

  Perfect for anyone who's tired of being their company's accidental customer service department while trying to do their real job.

  This LinkedIn automation template was inspired by a live build done by Max Tkacz and Angel Menendez for The Studio.

nodes_used:
  ["n8n-nodes-base.webhook","n8n-nodes-base.if","n8n-nodes-base.noOp","n8n-nodes-base.noOp","n8n-nodes-base.set","n8n-nodes-base.webhook","n8n-nodes-base.set","n8n-nodes-base.respondToWebhook","n8n-nodes-base.if","n8n-nodes-base.slack","n8n-nodes-base.slack","n8n-nodes-base.slack","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.switch","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.slack","n8n-nodes-base.slack","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.notion","n8n-nodes-base.set","n8n-nodes-base.aggregate","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.aggregate","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.aggregate","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 12692 characters]

metadata_json:
  {"categories":["Communication","Data Processing","AI/ML","Marketing","DevOps"],"complexity":"medium","use_cases":["auto-route messages","detect message intent","conditional responses","proxy support requests","filter partnership pitches"],"estimated_setup_minutes":60,"required_services":["LinkedIn","Slack","Email","OpenAI","Custom Webhook"],"key_features":["AI-powered intent classification","Dynamic routing logic","Integrated notification system","Conditional response generation","Message persistence"],"target_audience":["developer advocates","content creators","marketing specialists"]}


name: UniPile LinkedIn Profile Lookup Subworkflow
author_name: Angel Menendez
author_username: djangelic
categories: []
created_at: 2025-06-12T00:39:27.995Z
updated_at: 2025-06-12T00:39:27.995Z
url: https://n8n.io/workflows/4890
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  Who is this for?

  This subworkflow is ideal for developers and automation builders working with UniPile and n8n to automate message enrichment and LinkedIn lead routing.

  What problem is this workflow solving?

  UniPile separates personal and organization accounts into two different API endpoints. This flow handles both intelligently so you're not missing sender context due to API quirks or bad assumptions.

  What this workflow does

  This subworkflow is used by:

  LinkedIn Auto Message Router with Request Detection**  
  LinkedIn AI Response Generator with Slack Approval**

  It receives a message sender ID and tries to enrich it using UniPile's /people and /organizations endpoints. It returns a clean, consistent profile object regardless of which source was used.

  Setup

  Generate a UniPile API token and save it in your n8n credentials
  Make sure this subworkflow is triggered correctly by your parent flows
  Test both people and organization lookups to verify responses are normalized

  How to customize this workflow to your needs

  Add a secondary enrichment layer using tools like Clearbit or FullContact
  Customize the fallback logic or error handling
  Expand the returned data for more AI context or user routing (e.g., job title, region)

nodes_used:
  ["n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.aggregate","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.aggregate","n8n-nodes-base.set"]

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metadata_json:
  {"categories":["Communication","Automation","Developer Tools"],"complexity":"medium","use_cases":["enrich message data","manage API endpoints","route leads based on context","handle personal organization APIs"],"estimated_setup_minutes":30,"required_services":["UniPile","LinkedIn API","n8n"],"key_features":["intelligent API endpoint separation","sender context preservation","workflow integration","stateless stitching"],"target_audience":["developers","automation engineers"]}


name: Personalized LinkedIn Responses with OpenAI GPT & Notion-based Routing
author_name: Angel Menendez
author_username: djangelic
categories: []
created_at: 2025-06-12T00:44:08.589Z
updated_at: 2025-06-12T00:44:08.589Z
url: https://n8n.io/workflows/4891
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  Who is this for?

  This workflow is for professionals and teams who want to automate LinkedIn message replies with intelligent, human-like responses — without losing control over tone or accuracy. Ideal for founders, sales teams, DevRel, or community managers handling high-volume inbound messages.

  What problem is this workflow solving?

  Responding to every LinkedIn message manually is slow and inconsistent. Basic AI bots generate replies without context or nuance. This subworkflow solves both problems by using structured message routing from Notion and profile insights from UniPile to craft smart, context-aware responses.

  What this workflow does

  This workflow takes the sender’s message and profile (from LinkedIn Auto Message Router with Request Detection) and references your centralized Notion database of message types. It uses that to either match the message to a known response or generate a new one using OpenAI's GPT model — all while following professional tone guidelines.

  This is the third workflow in a 3-part automation system:

  Receives data from LinkedIn Auto Message Router with Request Detection
  Uses UniPile LinkedIn Profile Lookup Subworkflow to enrich responses based on follower count or org data

  Example Use Case

  If a message comes from someone with low reach (e.g., under 1,000 followers), the AI politely deflects a meeting request. If an influencer reaches out, the AI immediately offers a booking link. Your team controls this logic by updating the Notion database — no edits to the workflow required.

  Setup

  Connect this workflow as a subworkflow in your router or Slack approval flow
  Store your Notion API key and database ID in n8n
  Provide the following parent inputs:
     message – The LinkedIn message text
     sender – Name of the sender
     chatid – Session ID (optional for memory)
     linkedinprofile – Enriched array with LinkedIn context (follower count, connection info, etc.)

  Add your preferred AI model credentials (supports OpenAI, Gemini, or Ollama)
  Optional: Customize system prompt to better match your brand voice

  How to customize this workflow to your needs

  Update the Notion schema to include industry-specific categories or actions
  Change the AI tone (e.g., humorous, more corporate, etc.)
  Add conditional logic for auto-sending messages without Slack approval
  Extend to support multiple platforms (e.g., email, X/Twitter, Instagram DMs)

nodes_used:
  ["n8n-nodes-base.executeWorkflowTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.notion","n8n-nodes-base.set","n8n-nodes-base.aggregate","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["Communication","AI/ML","Marketing"],"complexity":"medium","use_cases":["Automate LinkedIn message replies","Generate human-like responses","Route messages to Notion via AI classification","Maintain brand tone consistency"],"estimated_setup_minutes":15,"required_services":["OpenAI","Notion"],"key_features":["AI-driven content generation","Context-aware routing","Structured output parsing","Custom prompt management","Stateful conversation memory"],"target_audience":["Sales professionals","Community managers","Founders"]}


name: Analyze Stock Charts with GPT-4 Vision and Send Results via Telegram
author_name: Fenngbrotalk
author_username: yihaohuang
categories: []
created_at: 2025-06-12T01:53:49.406Z
updated_at: 2025-06-12T01:53:49.406Z
url: https://n8n.io/workflows/4892
scraped_at: 2026-05-02 08:04:48
metadata_generated_at: 2026-05-02 08:36:19
description:
  n8n Workflow: AI-Powered Stock Chart Analysis Bot for Telegram

  This is a powerful n8n automation workflow that integrates a Telegram bot with OpenAI's multimodal large language model (GPT-4 Vision) to provide users with real-time stock chart analysis.

  Workflow Breakdown

  Receive Image:** The workflow is initiated by a Telegram Trigger. It activates whenever a user sends an image (e.g., a stock's candlestick chart) to a designated Telegram chat, automatically downloading the file.
  Image Pre-processing:** To optimize the AI's performance and efficiency, the Edit Image node resizes the incoming image to a standard 512x512 pixel format.
  AI Vision Analysis:** The processed image is then passed to a LangChain Chain, which utilizes the OpenAI GPT-4 Vision model. A sophisticated system prompt instructs the AI to act as a professional stock analyst.
  Intelligent Interpretation:** The AI analyzes the image to identify the stock's name, price trend (uptrend, downtrend, or sideways), key support/resistance levels, and volume changes. It then generates a comprehensive analysis report combining technical indicators and market sentiment.
  Structured Output:** To ensure reliability and consistency, the AI's output is parsed into a specific JSON format. This structure includes a search_word (for the industry/sector) and the main content (the analysis text).
  Send Response:** Finally, the workflow extracts the content field from the JSON output and uses the Telegram node to send this professional analysis back to the user as a text message in the same chat.

  Key Features

  User-Friendly:** Users simply send an image to get an analysis, requiring no complex commands.
  Instant & Efficient:** The entire analysis and response process is fully automated and completed within moments.
  Professional-Grade Analysis:** Leverages the advanced image recognition and reasoning capabilities of GPT-4 Vision to deliver insights comparable to those of a human analyst.
  Reliable & Consistent:** The use of structured output ensures that the format of the response is always consistent and easy to read or process further.

nodes_used:
  ["n8n-nodes-base.telegramTrigger","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.editImage","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured"]

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metadata_json:
  {"categories":["Trading","AI/ML"],"complexity":"medium","use_cases":["analyze stock charts","get AI insights","receive alerts","automate financial research"],"estimated_setup_minutes":30,"required_services":["telegram","openai_gpt4"],"key_features":["multimodal image processing","real-time analysis","financial insights","bot communication"],"target_audience":["traders","analysts"]}


name: Automated Daily Email Analysis & Summary with GPT-4o and Gmail
author_name: Zach @BrightWayAI
author_username: ajenta
categories: []
created_at: 2025-06-12T04:19:38.502Z
updated_at: 2025-06-12T04:19:38.502Z
url: https://n8n.io/workflows/4893
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:19
description:
  Daily Email Pulse Summary:

  This agent summarizes a user's daily emails into a clean, actionable summary. It uses OpenAI to analyze content and sends a formatted "Daily Pulse" email at the end of each day.

  Main use cases:
  Keep track of open loops and next steps across all email conversations

  Identify high-potential leads and flag conversations going nowhere

  Eliminate the need to manually review your inbox at day’s end

  Build a smart summary layer using AI without hallucination or noise

  How it works
  This workflow can be divided into eight core nodes, each serving a distinct purpose in helping a user stay on top of their day. The result is a curated, AI-generated summary delivered to your inbox — crafted from real message content, not guesswork.

  Schedule Trigger (Trigger Node – Runs Daily at Set Time)

  Kicks off the workflow at a specific time each day (e.g. 6:00 PM).
  Ensures you receive your Daily Pulse consistently, without needing to run it manually.

  Date Transformer (Function Node – Define Today & Tomorrow Range)
  Uses JavaScript to calculate the current day’s date range:
  	 today: Start of day (00:00:00)
  	 tomorrow: Start of next day (used as a cutoff)
  This ensures only emails from today are analyzed, keeping the summary focused and current.

  Get All Messages (Gmail Node – Fetch Filtered Emails)
  Pulls in all Gmail messages with internalDate between today and tomorrow.
  Outputs structured data: from, subject, and body text of each email.
  This forms the raw data for the daily business pulse.

  Aggregator (Function or Item Lists Node – Combine Message Fields)
  Aggregates each message into a readable format:

  From: John@example.com  
  Subject: Demo Follow-up  
  Body: Let’s schedule a time this week...

  All messages are stitched together into a single combinedText string for analysis.
  This gives the AI model full context for the day in one unified document.

  Email Cleanup (Function Node – Remove Noise & Normalize Text)
  Cleans the combinedText blob to remove:
  	HTML tags
  	Marketing footers (e.g., unsubscribe links)
  	Redundant whitespace or formatting artifacts
  Ensures GPT gets clean, relevant message content with no distractions.

  Agent (OpenAI Node – Generate Structured Summary)
  Uses a System Prompt to define its role as an AI Chief of Staff.
  Uses a User Prompt that instructs it to categorize messages into sections:

  	📝 Open Loops / Pending Follow-Up
  	🚀 Next Steps You’ve Committed To
  	🧲 Leads Worth Following Up On
  	🛑 Conversations That Aren’t Leading Anywhere
  	🧠 Strategy Notes
  	✅ Top 3 Tasks for Tomorrow

  Built-in guardrails ensure the model only uses real content (no hallucination).
  Sections with no relevant data are omitted to keep it concise.

  HTML Formatter (Function Node – Wrap Markdown in Email-Ready HTML)
  Wraps the GPT-generated markdown summary in a simple &lt;html&gt;&lt;body&gt; structure.
  Applies white-space: pre-wrap to preserve formatting and spacing.
  The result is a clean, readable email that renders well across all inboxes (especially Gmail).

  Email Send (Email Node – Deliver the Final Pulse)
  Sends the formatted summary to your email inbox.
  Subject: Your Daily Business Pulse – {{today}}
  HTML body: Uses the formatted output from the previous step.
  Final output: a well-organized, scannable summary of the day’s communication — focused on what matters.

  Why It Works
  Automates the end-of-day review ritual without effort
  Prioritizes follow-ups, action items, and time-sensitive leads
  Filters out noise and low-value conversations
  Leverages GPT without risk of hallucination or irrelevant output
  Delivers clarity, helping you focus on tomorrow’s most important tasks

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

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metadata_json:
  {"categories":["Communication","AI/ML","Productivity"],"complexity":"medium","use_cases":["Daily email summarization","Identify action items","Automated inbox management"],"estimated_setup_minutes":15,"required_services":["OpenAI","Gmail"],"key_features":["Daily email aggregation","AI content summarization","Automated email delivery"],"target_audience":["executives","owners"]}


name: YouTube Comment Analysis with GPT-4o & Automated Email Reports via Gmail
author_name: Rahul Joshi
author_username: rahul08
categories: []
created_at: 2025-06-12T08:20:52.596Z
updated_at: 2025-06-12T08:20:52.596Z
url: https://n8n.io/workflows/4895
scraped_at: 2026-05-02 08:04:51
metadata_generated_at: 2026-05-02 08:36:21
description:
  Description:

  Leverage the power of ChatGPT and YouTube API to monitor, analyze, and report on video feedback automatically. This n8n automation template fetches comments from a YouTube video, performs advanced AI sentiment analysis, identifies key themes, and emails a professional report—all triggered from a Google Sheet.

  What This Template Does:

  🔁 Auto-Triggers from Google Sheets every minute
  🎥 Fetches YouTube video details and top comments
  🧠 Uses GPT-4o for comment sentiment analysis, theme detection, viewer questions & improvement insights
  📧 Generates and sends an HTML email report with all key insights
  📝 Marks videos as “Mail Sent” in the sheet to prevent duplicate reports

  Setup Requirements:

  YouTube API Credentials
  OpenAI API Credentials 
  Gmail credentials for email delivery
  Google Sheet with video tracking

  Ideal for:

  YouTube creators, marketers, social media analysts, or agencies seeking automated YouTube comment sentiment analysis and actionable content insights—without writing a line of code.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.limit","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.youTube","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.gmail","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheetsTrigger","@n8n/n8n-nodes-langchain.lmChatAzureOpenAi"]

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metadata_json:
  {"categories":["AI/ML","Communication"],"complexity":"medium","use_cases":["Analyze video comments","Email sentiment reports","Monitor social feedback"],"estimated_setup_minutes":45,"required_services":["You Tube API","G oogle Sheets","Gmail API"],"key_features":["Real-time comment monitoring","AI sentiment analysis","Automated email reporting"],"target_audience":["Content creators","Community managers","Marketers"]}


name: Automated Cold Email Outreach with Gmail & Google Sheets
author_name: Rahul Joshi
author_username: rahul08
categories: []
created_at: 2025-06-12T08:24:58.336Z
updated_at: 2025-06-12T08:24:58.336Z
url: https://n8n.io/workflows/4896
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  Description:

  Boost your sales pipeline with this fully automated Cold Email Outreach workflow in n8n, integrating Gmail and Google Sheets. This template sends  cold emails daily at 2 PM to leads fetched from a Google Sheet. Once an email is sent, the workflow updates the lead's status to prevent duplicates.

  ✅ Features:

  Daily scheduled trigger at 2 PM
  Fetches leads with status "not emailed" from Google Sheets
  Sends emails via Gmail
  Updates lead status in Google Sheets post-email
  Batch processing for efficient outreach

  Perfect for freelancers, agencies, and startups looking to automate lead engagement and track responses in real-time — all without writing a single line of code.

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.splitInBatches","n8n-nodes-base.gmail","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets"]

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metadata_json:
  {"categories":["Sales","Communication"],"complexity":"simple","use_cases":["Automate daily cold email campaigns","Sync lead status from CRM to email","Prevent duplicate email attempts"],"estimated_setup_minutes":15,"required_services":["Gmail","Google Sheets"],"key_features":["Scheduled daily triggers","Lead status filtering","Duplicate prevention logic","Batch email processing"],"target_audience":["sales professionals","marketing managers"]}


name: Scheduled YouTube Video Uploads with Google Sheets & Drive Integration
author_name: Rahul Joshi
author_username: rahul08
categories: []
created_at: 2025-06-12T08:34:05.302Z
updated_at: 2025-06-12T08:34:05.302Z
url: https://n8n.io/workflows/4900
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  Description:

  Effortlessly automate YouTube video uploads with this ready-to-use n8n workflow. Integrated with Google Sheets, Google Drive, and YouTube API, this template streamlines your video publishing process based on scheduled metadata entries.

  Key Features:

  🗓️ Scheduled Uploads (Mon–Fri at 9 AM, 12 PM, 3 PM)
  📋 Reads video metadata (title, description, filename) from Google Sheets
  🔍 Locates video files in Google Drive by filename
  ⬇️ Downloads and uploads videos to YouTube with the correct metadata
  ✅ Updates the status in the sheet to avoid duplicates
  📂 Moves uploaded files to a specified archive folder for organization

  Requirements:

  Google Sheets with metadata (Title, Description, File Name, Status)
  Google Drive with video files
  YouTube API access and OAuth credentials
  A pre-set folder structure for organization

  Perfect For:

  Content creators, YouTube channel managers, marketers, and editors who want to automate repetitive upload tasks, maintain consistent publishing schedules, and organize video assets—without manual effort.

nodes_used:
  ["n8n-nodes-base.googleSheets","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.youTube","n8n-nodes-base.googleSheets","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote"]

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metadata_json:
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name: Daily Business Idea Insights Aggregator from IdeaBrowser to Google Docs
author_name: mahavishnu
author_username: mahavishnu
categories: []
created_at: 2025-06-12T08:54:41.448Z
updated_at: 2025-06-12T08:54:41.448Z
url: https://n8n.io/workflows/4901
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:55
description:
  This automation runs daily at 8:00 AM to automatically collect and organize business idea insights from IdeaBrowser.com into a structured Google Docs document.
  The workflow performs the following actions:
  Data Collection: Fetches the "idea of the day" content from ideabrowser.com/idea-of-the-day using authenticated HTTP requests.
  Content Processing: Extracts the base idea path and generates links to all related insight pages including value ladder, market analysis, proof signals, execution plans, and community insights. The workflow also cleans the HTML content to extract readable text.
  Document Creation: Creates a new Google Docs document in a specified folder with a timestamp and idea name in the title format.
  Content Aggregation: Systematically visits each insight page (main idea page, value ladder, why now, proof signals, market gap, execution plan, value equation, value matrix, ACP, community signals, and keywords) and collects their content.
  Document Population: Processes the collected content through markdown formatting and appends it to the Google Docs document, creating a comprehensive report of the daily business idea with all its associated insights.
  Automated Scheduling: Runs automatically every day at 8 AM, ensuring you have fresh business idea analysis delivered to your Google Drive without manual intervention.
  This automation is perfect for entrepreneurs, business analysts, or anyone who wants to stay updated with curated business ideas and their detailed market analysis in an organized, searchable format.

nodes_used:
  ["n8n-nodes-base.markdown","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.googleDocs","n8n-nodes-base.merge","n8n-nodes-base.splitOut","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.googleDocs","n8n-nodes-base.scheduleTrigger"]

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metadata_json:
  {"categories":["Data Processing","Productivity"],"complexity":"simple","use_cases":["automate content aggregation","collect business ideas","organize research findings"],"estimated_setup_minutes":15,"required_services":["IdeaBrowser","Google Docs"],"key_features":["scheduled data fetching","automated content extraction","structured document generation"],"target_audience":["entrepreneurs","researchers","product managers"]}


name: Telegram AI Assistant with Gmail, Google Calendar, Google Sheets & MCP Tools
author_name: David Olusola
author_username: dae221
categories: []
created_at: 2025-06-12T14:17:02.535Z
updated_at: 2025-06-12T14:17:02.535Z
url: https://n8n.io/workflows/4902
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  ✨ What It Is
  Automate your professional communication directly from Telegram using this powerful AI assistant built with n8n.

  This no-code agent understands natural language and integrates seamlessly with:

  Gmail (send, receive, draft emails)
  Google Calendar (create, delete, get events)
  Google Sheets (contact database)
  MCP Tools (agent framework for dynamic tool routing)
  Perfect for:

  Virtual Assistants
  Freelancers & Teams
  Sales & Outreach Professionals
  Calendar Managers
  Customer Support Teams
  🔧 What It Can Do
  Just send a message like:

  “Send email to Jane at Acme about pricing”
  “Create a Zoom meeting with Victor next Friday at 3pm”
  “Cancel meeting with Alex”
  “Follow up with Sarah tomorrow”
  ✅ Features:

  🔄 Understands natural language Telegram messages
  📧 Sends or drafts emails using Gmail
  📅 Creates/deletes/syncs calendar events with Google Calendar
  📊 Fetches contact info from Google Sheets
  🤖 Powered by a lightweight LLM (Gemini 2.0)
  💼 Smart memory & session context via MCP tools
  📥 Telegram replies with formatted confirmation (e.g., ✅, 📧, 📅)
  🛠️ Setup Guide (Step-by-Step)

  Prerequisites
  An n8n.cloud or self-hosted n8n instance
  Telegram Bot (create one via @BotFather)
  Gmail, Google Calendar, and Google Sheets accounts
  Access to MCP tools (optional: Daex AI Cloud or equivalent)
  Authorize Accounts
  Connect Gmail, Google Calendar, and Google Sheets credentials in n8n under "Credentials".
  Authorize your Telegram bot via n8n's Telegram Trigger node.
  Configure Your Google Sheet
  Create a Google Sheet titled Contact Data with these columns:

  Name
  Email
  Company
  Role
  Make sure the Google Sheets node points to the correct documentId and gid.

  Connect MCP Tools (Optional)
  If you're using Daex MCP, input the SSE path and trigger ID in the MCP Client Tool and MCP Server Trigger.

  Enable Telegram Bot
  Deploy your workflow in n8n
  Send any command to your Telegram bot like:
  “Send email to Lisa about invoice update”
  The AI agent will understand the command, fetch the contact, and either send/draft an email or update the calendar accordingly.

  Customize Prompts or Actions (Optional)
  Edit the system prompt in the AI Agent node to:

  Add company branding
  Adjust supported commands
  Modify language or tone
  💬 Example Prompts

  “Send email to Sam about project delay”
  “Schedule call with Mabel next Monday at 10am”
  “Delete event with Kevin”
  “Follow up with Clara at noon tomorrow”
  📦 What’s Included

  🧠 Preconfigured n8n Workflow
  🔗 Connected nodes: Telegram, Gmail, Google Calendar, Google Sheets, MCP Tools
  🗂️ AI Memory Context (via MCP memory buffer)
  ✍️ Ready-to-use prompt template
  🎯 Tested commands and use cases
  🛍️ License

  ✅ Commercial use included
  🚫 Do not resell the template without modifications

nodes_used:
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metadata_json:
  {"categories":["AI/ML","Communication","Productivity"],"complexity":"medium","use_cases":["automate email and calendar tasks from chat","manage contacts via spreadsheets","integrate with MCP tools","handle natural language queries"],"estimated_setup_minutes":60,"required_services":["Telegram","Google Gmail","Google Calendar","Google Sheets","Google Gemini"],"key_features":["natural language processing","tool routing through MCP","conversation memory","multi-service automation","no-code configuration"],"target_audience":["virtual assistants","sales teams","freelancers"]}


name: Pyragogy AI-Driven Handbook Generator with Multi-Agent Orchestration
author_name: Fabrizio Terzi
author_username: org
categories: []
created_at: 2025-06-12T15:46:54.341Z
updated_at: 2025-06-12T15:46:54.341Z
url: https://n8n.io/workflows/4904
scraped_at: 2026-05-02 08:04:49
metadata_generated_at: 2026-05-02 08:36:20
description:
  AI-Driven Handbook Generator with Multi-Agent Orchestration (Pyragogy AI Village)

  This n8n workflow is a modular, multi-agent AI orchestration system designed for the collaborative generation of Markdown-based handbooks. Inspired by peer learning and open publishing workflows, it simulates a content pipeline where specialized AI agents act in defined roles, enabling true AI–human co-creation and iterative refinement.

  This project is a core component of Pyragogy, an open framework dedicated to ethical cognitive co-creation, peer AI–human learning, and human-in-the-loop automation for open knowledge systems. It implements the master orchestration architecture for the Pyragogy AI Village, managing a complex sequence of AI agents to process input, perform review, synthesis, and archiving, with a crucial human oversight step for final approval.

  How It Works: A Deep Dive into the Workflow's Architecture

  The workflow orchestrates a sophisticated content generation and review process, ideal for creating AI-driven knowledge bases or handbooks with human oversight.

  Webhook Trigger & Input:* The process begins when the workflow receives a JSON input via a *Webhook** (specifically at /webhook/pyragogy/process). This input typically includes details like the handbook's title, initial text, and relevant tags.

  Database Verification:* It first verifies the connection to a *PostgreSQL database** to ensure data persistence.

  Meta-Orchestrator:* A powerful *Meta-Orchestrator** (powered by gpt-4o from OpenAI) analyzes the initial request. Its role is to dynamically determine and activate the optimal sequence of specialized AI agents required to fulfill the input, ensuring tasks are dynamically routed and assigned based on each agent’s responsibility.

  Agent Execution & Iteration:** Each activated agent executes its step using OpenAI or custom endpoints. This involves:

      Content Generation: Agents like the Summarizer and the Synthesizer generate new content or refine existing text.

      Peer Review Board: A crucial aspect is the Peer Review Board, comprised of AI agents like the Peer Reviewer, the Sensemaking Agent, and the Prompt Engineer. This board evaluates the output for quality, coherence, and accuracy.

      Reprocessing & Redrafting: If the review agents flag a major_issue, they trigger redrafting loops by generating specific feedback for the Synthesizer. This mechanism ensures iterative refinement until the content meets the required standards.

  Human-in-the-Loop (HITL) Review:* For final approval, particularly for the Archivist agent's output, a *human review process* is initiated. An email is sent to a human reviewer, prompting them to approve, reject, or comment via a "Wait for Webhook" node. This ensures *human oversight** and quality control.

  Content Persistence & Versioning:** If the content is approved by the human reviewer:

      It's saved to a PostgreSQL database (specifically to the handbook_entries and agent_contributions tables).

      Optionally, the content can be committed to a GitHub repository for version control, provided the necessary environment variables are configured.

  Notifications:* The final output and the sequence of executed agents can be sent as a notification to *Slack**, if configured.

  Observe the dynamic loop: orchestrate → assign → generate → review (AI/human) → store

  Included AI Agents

  This workflow leverages a suite of specialized AI agents, each with a distinct role in the content pipeline:

  Meta-Orchestrator:** Determines the optimal sequence of agents to execute based on the input.

  Summarizer Agent:** Summarizes text into key points (e.g., 3 key points).

  Synthesizer Agent:** Synthesizes new text and effectively incorporates reprocessing feedback from review agents.

  Peer Reviewer Agent:** Reviews generated text, highlighting strengths, weaknesses, and suggestions, and indicates major_issue flags.

  Sensemaking Agent:** Analyzes input within existing context, identifying patterns, gaps, and areas for improvement.

  Prompt Engineer Agent:** Refines or generates prompts for subsequent agents, optimizing their output.

  Onboarding/Explainer Agent:** Provides explanations of the process or offers guidance to users.

  Archivist Agent:** Prepares content for the handbook, manages the human review process, and handles archiving to the database and GitHub.

  Setup Steps & Prerequisites

  To get this powerful workflow up and running, follow these steps:

  Import the Workflow: Import the pyragogy_master_workflow.json (or generate-collaborative-handbooks-with-gpt4o-multi-agent-orchestration-human-review.json) into your n8n instance.

  Connect Credentials:

      Postgres: Set up a Postgres Pyragogy DB credential (ID: pyragogy-postgres).

      OpenAI: Configure an OpenAI Pyragogy credential (ID: pyragogy-openai) for all OpenAI agents. GPT-4o is highly suggested for optimal performance.

      Email Send: Set up a configured email credential (e.g., for sending human review requests).

  Define Environment Variables: Define essential environment variables (an .env.template is included in the repository). These include:

      API base for OpenAI.

      Database connection details.

      (Optional) GitHub: For content persistence and versioning, configure GITHUB_ACCESS_TOKEN, GITHUB_REPOSITORY_OWNER, and GITHUB_REPOSITORY_NAME.

      (Optional) Slack: For notifications, configure SLACK_WEBHOOK_URL.

  Send a sample payload to your webhook URL (/webhook/pyragogy/process):

  {
  "title": "History of Peer Learning",
  "text": "Peer learning is an educational approach where students learn from and with each other...",
  "tags": ["education", "pedagogy"],
  "requireHitl": true
  }

  Ideal For

  This workflow is perfectly suited for:

  Educators and researchers exploring AI-assisted publishing and co-authoring with AI.

  Knowledge teams looking to automate content pipelines for internal or external documentation.

  Anyone building collaborative Markdown-driven tools or AI-powered knowledge bases.

  Documentation & Contributions: An Open Source and Collaborative Project

  This workflow is an open-source project and community-driven. Its development is transparent and open to everyone.

  We warmly invite you to:

  Review it:** Contribute your analysis, identify potential improvements, or report issues.

  Remix it:** Adapt it to your specific needs, integrate new features, or modify it for a different use case.

  Improve it:** Propose and implement changes that enhance its efficiency, robustness, or capabilities.

  Share it back:** Return your contributions to the community, either through pull requests or by sharing your implementations.

  Every contribution is welcome and valued! All relevant information for verification, improvement, and collaboration can be found in the official repository:

  🔗 GitHub – pyragogy-handbook-n8n-workflow

nodes_used:
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workflow_json_compressed:
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metadata_json:
  {"categories":["AI/ML","Development"],"complexity":"complex","use_cases":["generate technical handbooks","collaborative content creation","AI agent orchestration","markdown document generation"],"estimated_setup_minutes":90,"required_services":["GitHub","AI Model Provider (Anthropic)","Vector Database"],"key_features":["multi-agent collaboration","iterative refinement","role-based generation","open publishing workflows"],"target_audience":["technical writers","developers","content creators"]}


