# templates

name: AI-Powered Upwork Cover Letter Generator – Pinecone, Groq, Google Gemin, SerpAPI
author_name: Udit Rawat
author_username: ailistmaster
categories: []
created_at: 2025-04-20T15:24:51.535Z
updated_at: 2025-04-20T15:24:51.535Z
url: https://n8n.io/workflows/3622
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  🚀 Automated Upwork Cover Letter Generator with n8n + MacOS Shortcut + Pinecone Context Retrieval

  This n8n automation is designed to streamline the Upwork proposal process by generating highly personalized, context-aware cover letters using your own skills and project data stored in a Pinecone vector store.

  With the help of an AI Agent powered by Groq’s Qwen LLM, and triggered instantly via a MacOS Shortcut, this system takes job descriptions from your clipboard and returns a ready-to-use HTML cover letter—right on your desktop.

  ⚙️ Workflow Breakdown

  🔹 MacOS Shortcut – Trigger the Workflow Instantly
  The process begins with a MacOS Shortcut that captures job description text from your clipboard and sends it to a custom webhook in n8n.

  🔹 Webhook Node – Receive and Process Input
  The webhook node receives the clipboard data, along with the required credentials for authentication. This node serves as the entry point to the full automation.

  🔹 Field Mapping & Pre-Processing
  A series of basic logic nodes map and clean up the input fields. These are then passed to an LLM Chain to generate specific questions related to the job description.

  🔹 Question Answer Chain + Vector Search (Pinecone)
  Using your previously stored skills and project data in a Pinecone vector store, the system retrieves relevant information to answer the generated questions and builds a rich context around the job description.

  🔹 AI Agent Node – Generate Personalized Cover Letter
  With both the job post and contextual data, the AI Agent (powered by Groq’s Qwen LLM) creates a tailored cover letter.  
  The agent is equipped with:

  🔍 Google Search Tool  
  🧠 Vector Store Retriever Tool  
  🗃️ Buffer Memory  

  This ensures the generated proposal is insightful, relevant, and professional.

  🔹 Markdown to HTML – Clean Output Conversion
  The markdown output from the AI is converted into HTML using a Markdown node, making it easy to paste directly into Upwork or emails.

  🔹 Return to Shortcut – Display Final Result
  The final HTML response is sent back to the MacOS Shortcut, which displays it in a modal window for easy review and copy-paste.

  💼 Use Case

  This automation is built specifically for freelancers on Upwork (or any freelance platform) who want to:

  ✅ Save time on repetitive proposal writing  
  ✅ Create job-specific cover letters with context  
  ✅ Stand out with better personalization  
  ✅ Reduce manual effort with automation  

  Whether you’re a beginner or a seasoned pro, this tool elevates your workflow while staying simple to use.

  📦 Setup Instructions

  Import Workflow to your n8n instance  
  Create and Configure MacOS Shortcut (drag-and-drop ready)  
  Prepare and Embed Your Skills/Project Data into Pinecone  
  Add API Credentials:
     Groq (for Qwen LLM)  
     Pinecone  
     n8n Webhook (Basic Auth if needed)  
  Run the Workflow & Submit Smarter Proposals

  &gt; Note: This workflow is designed for building and returning Upwork cover letters using job descriptions copied to your clipboard. All generation is context-aware and tailored per submission.

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.vectorStorePinecone","@n8n/n8n-nodes-langchain.embeddingsGoogleGemini","n8n-nodes-base.webhook","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.lmChatGroq","n8n-nodes-base.respondToWebhook","n8n-nodes-base.markdown","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.lmChatGroq","@n8n/n8n-nodes-langchain.chainRetrievalQa","@n8n/n8n-nodes-langchain.retrieverVectorStore","@n8n/n8n-nodes-langchain.toolVectorStore","@n8n/n8n-nodes-langchain.toolSerpApi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","AI/ML","productivity"],"complexity":"complex","use_cases":["Automate and generate personalized Upwork/freelance cover letters","Retrieve contextual skills/projects from a vector store to enrich proposals","Clipboard-to-modal workflow via MacOS Shortcut for instant proposal generation","Convert AI-generated markdown to ready-to-use HTML for Upwork/email"],"estimated_setup_minutes":45,"required_services":["Pinecone (vector store)","Groq Qwen LLM (lmChatGroq)","Google Gemini embeddings (Google embeddings)","SerpAPI (or other web-search API)","n8n instance (webhooks, nodes)","MacOS Shortcuts (local trigger)"],"key_features":["Clipboard-triggered MacOS Shortcut to invoke n8n webhook","Field mapping and pre-processing of job descriptions","LLM question chain to generate clarifying/context questions","Vector search in Pinecone to pull relevant skills/projects","AI Agent with tools: web search, vector retriever, buffer memory for context-aware generation"],"target_audience":["Freelancers and Upwork users","Independent consultants and contractors","Developers/technical users who set up API integrations and automations"]}


name: AI-Powered Gmail MCP Server
author_name: Luciano Gutierrez
author_username: koresoluciones
categories: []
created_at: 2025-04-20T21:13:40.047Z
updated_at: 2025-04-20T21:13:40.047Z
url: https://n8n.io/workflows/3623
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  🤖 AI-Powered Gmail MCP Server for n8n

  Description
  This n8n workflow template leverages an external AI Model Control Plane (MCP) Server to automate various Gmail tasks, such as composing emails, replying to threads, and handling follow-ups using dynamically generated content. It uses the native n8n Gmail nodes available from v1.88.0 onwards.

  Who is this template for?
  Ideal for developers, automation engineers, and power users using self-hosted n8n (v1.88.0+) who want to integrate artificial intelligence directly into their email workflows via a dedicated MCP Server for enhanced control and customization over AI interactions.

  What problem does this workflow solve?
  ⚙️ Reduces Manual Effort: Decreases the work involved in writing, sending, and following up on emails in Gmail.
  ✅ Consistency and Quality: Ensures standardized, professional responses free from typos by leveraging controlled AI prompts.
  🔄 Complete Automation: Automates the entire email cycle: from the initial send, through waiting for a reply, to sending automated follow-ups based on AI logic.

  Workflow Overview
  This template provides a structured approach to integrating Gmail with an MCP Server:

  📡 MCP Trigger (“MCP_GMAIL”): An n8n Webhook node that receives HTTP calls from your MCP Server. It standardizes the inputs (like recipient, subject, AI prompt) for all subsequent Gmail nodes. (You will need to configure your MCP Server to call this webhook URL).
  📤 SEND_EMAIL (Gmail Node v2.1): Sends new messages. The email body (message field) is typically populated by content generated from an AI prompt processed by your MCP server and passed via the trigger.
  🔄 REPLY_EMAIL (Gmail Node v2.1): Automatically replies to existing conversations (threads). It uses AI-generated content (via MCP) to formulate the reply based on the thread context. Requires Message ID and/or Thread ID.
  📥 GET_EMAIL (Gmail Node v2.1): Fetches data for a specific message (using Message ID) for analysis, processing, or archiving. Useful for retrieving context before replying.
  ⏳ SEND_AND_WAIT (Gmail Node v2.1): Sends an email and pauses the workflow execution until a reply is received in that specific conversation (thread). This is crucial for building automated follow-up sequences. It then outputs data from the reply message.

  Note:* All Gmail nodes in this template use the native n8n *Gmail Tool*, integrated since v1.88.0. No additional installation of community nodes is required. See the official n8n documentation for more details on node configuration.

  Prerequisites
  Ensure you have the following before importing:

  🚀 A self-hosted n8n instance running version 1.88.0 or higher.
  ☁️ A Google Cloud project with the Gmail API correctly enabled.
  🔑 Gmail OAuth2 credentials configured in your n8n instance. Navigate to Settings &gt; Credentials &gt; New &gt; Google &gt; Gmail (OAuth2 API) to set this up if you haven't already.
  🧠 Access to your MCP Server and an API Key (or other authentication method) required to interact with it via HTTP requests.

  How to Import and Configure
  Follow these steps to get the template running:

  In your n8n interface, navigate to Templates → Import from URL.
  Paste the JSON link provided for this workflow template.
  Configure the necessary credentials within n8n under Credentials:
      Gmail OAuth2: Select the Google OAuth2 credential you previously configured that has access to the desired Gmail account.
      MCP API Key: You'll likely need to configure credentials for interacting with your MCP Server. This might involve setting up a Header Auth credential in n8n with your MCP API Key, or configuring the HTTP Request node within the workflow directly, depending on your MCP's authentication scheme. Link this credential where needed (e.g., in the Trigger node if MCP calls n8n with auth, or in HTTP Request nodes if n8n calls MCP).
  Activate the Workflow: Ensure the workflow toggle is set to "Active" in the top right corner.
  Webhook URL: Copy the Webhook URL from the "MCP_GMAIL" Trigger node (Test or Production URL as needed) and configure your MCP Server to send requests to this URL.
  Recommendation:
      Rename the nodes with clear, descriptive names relevant to your specific use case (e.g., ✨ Generate Sales Email Body via MCP, 📥 Fetch Customer Replies).
      Utilize the workflow notes (sticky notes on the canvas) to document specific logic, prompt details, or configuration choices for future reference.

  Customization & Technical Guidance
  Tailor the workflow to your specific needs:

  🔍 Search Filters: In nodes that fetch emails (like GET_EMAIL or if you add a Gmail - Get Many node), refine the search using the Search field (standard Gmail search operators) or filter by Label Names to process specific emails (e.g., unread from a specific sender, emails with a certain label).
  ✍️ AI Fine-Tuning (Prompt Engineering): The core of the AI integration happens in the prompts sent to your MCP Server. Modify these prompts (often constructed within function nodes or directly in the trigger input expected from MCP) in the message or body fields passed to the send/reply nodes. Adjust prompts to control:
      Tone & Style: Formal, informal, empathetic, technical, etc.
      Content & Format: Request bullet points, summaries, specific data extraction.
      Dynamic Variables: Inject data from previous n8n nodes (e.g., customer name, order details, previous email content) into the prompt for context-aware generation. Example: `"Reply to the following email thread [{{ $json.thread_content }}] addressing the customer {{ $json.customer_name }} about their query..."`
  🔗 Post-Response Actions: Extend the workflow after key actions, especially the SEND_AND_WAIT node. Add nodes to:
      Log results to a database (MySQL, PostgreSQL, Airtable).
      Update CRM records (HubSpot, Salesforce).
      Send notifications (Slack, Discord, Telegram).
      Trigger other n8n workflows.
  🛡️ Error Handling: Implement robust error handling. Connect the red output pins (error output) of critical nodes (like Gmail nodes or HTTP Requests to MCP) to an Error Trigger node. From there, you can:
      Log detailed error information to a monitoring tool or spreadsheet.
      Send failure notifications.
      Implement retry logic (using loops or specific retry settings on nodes).
      Route to alternative paths or fallback workflows.

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

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","email","AI"],"complexity":"medium","use_cases":["Automated email composition using AI","AI-driven replies to Gmail threads","Automated follow-ups and wait-for-reply flows","Context-aware personalization using dynamic prompts","Logging or syncing email activity to CRMs/databases"],"estimated_setup_minutes":60,"required_services":["Self-hosted n8n (v1.88.0+)","Google Cloud project with Gmail API enabled","Gmail OAuth2 credentials","External MCP (Model Control Plane) Server with API access"],"key_features":["Webhook trigger to receive standardized inputs from MCP","Send new emails with AI-generated content (Gmail node)","Reply to existing threads using AI-generated replies (Gmail node)","Fetch specific messages for context or archiving (GET_EMAIL)","Send-and-wait flow that pauses until a reply is received for automated follow-ups (SEND_AND_WAIT)"],"target_audience":["Developers","Automation engineers","Power users/self-hosted n8n operators (sales, support, marketing)"]}


name: Scrape Competitor Reviews & Generate Ad Creatives with Bright Data & OpenAI
author_name: Yaron Been
author_username: yaron-nofluff
categories: []
created_at: 2025-04-21T07:44:21.325Z
updated_at: 2025-04-21T07:44:21.325Z
url: https://n8n.io/workflows/3624
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  Scrape Competitor Reviews & Generate Ad Creatives with Bright data and OpenAI

  How the Flow Runs

  Fill the Form
     Enter the Amazon product URL to analyze competitor reviews.

  Trigger Bright Data Scraper
     Bright Data scrapes Amazon reviews based on the provided URL.

  Wait for Snapshot Completion
     Periodically checks Bright Data until the scraping is complete.

  Retrieve JSON Data
     Collects the scraped review data in JSON format.

  Save Reviews to Google Sheets
     Automatically appends the scraped reviews to your Google Sheets.

  Aggregate Reviews
     Consolidates all reviews into a single summary for simpler analysis.

  Analyze Reviews with OpenAI LLM
     Sends the aggregated reviews to OpenAI (GPT-4o mini) to summarize competitors’ main weaknesses clearly.

  Generate Creative Ad Image
     OpenAI generates a visually appealing 1080x1080 ad image addressing these identified pain points.

  Send Ad Creative via Gmail
     Automatically emails the creative and review summary to your media buying team for immediate use in Meta ads.

  What You Need

  Google Sheets:** Template
  Bright Data:** Dataset and API key:
  www.brightdata.com
  OpenAI API Key:** For GPT-4o mini or your preferred LLM
  Automation Tool:** Ensure it supports HTTP Requests, Wait, Conditional (If), Google Sheets integration, Form Trigger, OpenAI integration, and Gmail integration.

  Form Fields to Fill

  Amazon Product URL:** Enter the competitor’s product URL from Amazon.

  Setup Steps

  Copy the provided Google Sheet template.
  Import the JSON workflow into your automation tool.
  Update credentials for Bright Data, Google Sheets, Gmail, and OpenAI.
  Test manually by submitting the form and verifying functionality.
  Optional: Set a schedule for regular workflow execution.

  Bright Data Trigger Example

  [
    {
      "url": "https://www.amazon.com/example-product"
    }
  ]

  Tips
  Frequently update URLs to ensure fresh insights.

  Allow more wait time for extensive data scrapes.

  Focus on targeted products to optimize cost-efficiency.

  Need Help?
  Email: Yaron@nofluff.online

  Resources:

  YouTube:
  https://www.youtube.com/@YaronBeen/videos

  LinkedIn:
  https://www.linkedin.com/in/yaronbeen/

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

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

workflow_json_compressed:
  [encoded data, 5308 characters]

metadata_json:
  {"categories":["automation","data collection","marketing","web scraping","AI content generation"],"complexity":"medium","use_cases":["Competitor review aggregation and analysis","Generate ad creatives from competitor pain points","Automated reporting to media buying teams","E-commerce product & market research","Creative ideation for Meta ads"],"estimated_setup_minutes":45,"required_services":["Bright Data (dataset + API key)","OpenAI (GPT-4o mini or compatible LLM)","Google Sheets","Gmail","n8n or compatible automation tool with HTTP/Wait/If/Form/OpenAI nodes","Target website (Amazon)"],"key_features":["Scrape Amazon reviews via Bright Data and poll for completion","Retrieve and store review JSON to Google Sheets","Aggregate reviews into a single summary","Analyze aggregated reviews with OpenAI to identify competitor weaknesses","Generate a 1080x1080 ad image via OpenAI addressing pain points`,`Auto-email the ad creative and summary to media buying team via Gmail`,`Form-triggered workflow with optional scheduled runs"],"target_audience":["Growth marketers","Media buyers and ad teams","E‑commerce managers and sellers conducting competitor research`,`Marketing agencies and creative teams`,`Automation engineers or devs implementing workflows"]}


name: General 3D Presentation Workflow with Midjourney, GPT-4o-image and Kling APIs
author_name: PiAPI
author_username: piapi
categories: []
created_at: 2025-04-21T07:49:04.453Z
updated_at: 2025-04-21T07:49:04.453Z
url: https://n8n.io/workflows/3625
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  Who is this template for?
  This workflow creates 360° or 180° spinning videos of high-quality 3D models with PiAPI API. 

  Good for:

  Designers**: Generate inspiration into 3D designs and make them spin to gain concrete details in a efficient way. 
  Online shoppers**: Show protential products from all angles in videos and preview overall texture of models.
  Content Creators** (including toy bloggers): Make fun videos of collectible models. 
  3D beginners:** Get simple spinning animations easily and make fun with them in a convenient way. 

  How to customize this workflow to your need？
  To use this workflow, usually we need four steps:  
  Fill in x-api-key in Mijdourney Generator node and Generate Kling Video node, fill in Header Parameters of GPT-4o Image Generator (e.g., Bearer + your X-API-Key)
  Enter your model prompt based on your inspiration.
  Click Test Workflow.
  Get the video URL in the last node.

  Use Case 
  The prompt node concludes the main features of creations. An example for users' reference is listed as follow: 
  Input Prompt
  A blind box character design, in the chibi style, a super cute little girl wearing a white long-sleeved dress and pearl earrings with her head bowed in a prayer pose, facing upwards, wearing an oversized off-white dress with large round pearls on the shoulders, minimalist simple dress with Ruffles, against a beige background, a full-body shot in a three-quarter profile view, with a black, blue, and gray color scheme, soft lighting, 3D rendering, clay material, high detail, in the Pixar style. Clean white skin, brown renaissance braided bun. --ar 1:1 --niji 6

  Output Video
  An Example for your reference.

  More Example Results for Reference

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

workflow_json_compressed:
  [encoded data, 4480 characters]

metadata_json:
  {"categories":["automation","design","content creation","3D processing","AI-generated media"],"complexity":"medium","use_cases":["Generate 360°/180° spinning videos of 3D models","Quick prototyping of product visuals for e‑commerce","Create engaging social content and toy/collectible showcases","Convert text prompts into 3D-styled renders and animated previews","Automate multi‑API media pipelines (image generation → video creation)"],"estimated_setup_minutes":15,"required_services":["Midjourney Generator API (or equivalent image/3D generator)","GPT-4o Image (OpenAI or compatible)","Kling Video API / PiAPI (video rendering)","HTTP/Webhook accessible endpoint (for n8n)","Optional: cloud storage/CDN for hosting output videos"],"key_features":["Turn text prompts into high-quality 3D-style renders","Create 180° or 360° spinning videos from generated models","Configurable API keys/headers for Midjourney, GPT-4o-image and Kling nodes","Automated end-to-end workflow with webhook trigger and final video URL output","Suitable prompt template included for consistent results"],"target_audience":["Designers and concept artists","E‑commerce/product teams and online shoppers","Content creators and toy bloggers/social creators of visual media"]}


name: Create Animated Illustrations from Text Prompts with Midjourney and Kling API
author_name: PiAPI
author_username: piapi
categories: []
created_at: 2025-04-21T08:38:58.884Z
updated_at: 2025-04-21T08:38:58.884Z
url: https://n8n.io/workflows/3626
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  What does the workflow do?
  This workflow is primarily designed to generate animated illustrations for content creators and social media professionals with Midjourney (unoffcial) and Kling (unofficial) API served by PiAPI. 
  PiAPI is an API platform which provides professional API service. With service provided by PiAPI, users could generate a fantastic animated artwork simply using workflow on n8n without complex settings among various AI models.

  What is animated illustration?
  An animated illustration is a digitally enhanced artwork that combines traditional illustration styles with subtle, purposeful motion to enrich storytelling while preserving its original artistic essence.

  Who is this workflow for?

  Social Media Content Creators: Produces animated illustrations for social media posts.
  Digital Marketers: Generates marketing materials with motion graphics.
  Independent Content Producers: Creates animated content without specialized animation skills.

  Step-by-step Setting Instructions
  To simplify workflow settings, usually users just need to change basic prompt of the image and the motion of the final video following the instrution below:
  Sign in your PiAPI account and get your X-API-Key.
  Fill in your X-API-Key of PiAPI account in Midjourney and Kling nodes.
  Enter your desired image prompt in the Prompt node.
  Enter the motion prompt in Kling Video Generator node.

  For more complex or customization settings, users could also add more nodes to get more output images and generate more videos. Also, they could change the target image to gain a better result. As for recommendation,  users could change the video models for which we would recommend live-wallpaper LoRA of Wanx. Users could check API doc to see more use cases of video models and image models for best practice.

  Use Case
  Input Prompt
  A gentle girl and a fluffy rabbit explore a sunlit forest together, playing by a sparkling stream. Butterflies flutter around them as golden sunlight filters through green leaves. Warm and peaceful atmosphere, 4K nature documentary style. --s 500 --sref 4028286908  --niji 6

  Output Video
  When there is troubleshooting
  Check if the X-API-Key has been filled in nodes needed.
  Check your task status in Task History in PiAPI to get more details about task status. 

  More Generation Case for Reference

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

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metadata_json:
  {"categories":["automation","content creation","media generation","AI/ML","integration"],"complexity":"medium","use_cases":["Generate animated illustrations from text prompts for social media","Create short motion-graphic marketing assets","Rapid prototyping of animated visual content from static prompts","Produce looped or stylized videos (e.g., 4K nature / documentary style)","Batch-generate multiple images and convert to animated videos"],"estimated_setup_minutes":10,"required_services":["n8n instance","PiAPI account (X-API-Key)","Midjourney (unofficial) via PiAPI","Kling video-generation API (unofficial) via PiAPI","Optional: specific image/video model files (e.g., Wanx live-wallpaper LoRA)"],"key_features":["Text-to-image generation via Midjourney (through PiAPI)","Image-to-video animation via Kling video generator","Simple prompt and motion prompt inputs to control output","Supports multiple outputs and further node-driven customization","Task monitoring and troubleshooting via PiAPI Task History info in workflow nodes"],"target_audience":["Social media content creators","Digital marketers","Independent content producers / solo creators"]}


name: Generate Graphic Wallpaper with Midjourney, GPT-4o-mini and Canvas APIs
author_name: PiAPI
author_username: piapi
categories: []
created_at: 2025-04-21T09:05:42.375Z
updated_at: 2025-04-21T09:05:42.375Z
url: https://n8n.io/workflows/3627
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  Who is the template for?
  This workflow is specifically designed for content creators and social media professionals, enabling Instagram and X (Twitter) influencers to produce highly artistic visual posts, empowering marketing teams to quickly generate event promotional graphics, assisting blog authors in creating featured images and illustrations, and helping knowledge-based creators transform key insights into easily shareable card visuals.

  Set up Instructions
  Fill in your API  key from PiAPI.
  Fill in Basic Params Node following the sticky note guidelines.
  Set up a design template in Canvas Switchboard. 
  Make a simple template in Switchboard.

  Click Crul and get the API code to fill in JSON of Design in Canvas. 

  Click Test Workflow and get a url result.

  Use Case
  Here we will provide some setting examples to help users find a proper way to use this workflow. User could change these settings based on specific purposes.

  Basic Params Setting: 
  theme: Hope
  scenario: Don't know about the future, confused and feel lost with tech-development. 
  style: Cinematic Grandeur, Sci-Tech Aesthetic, 3D style
  example: 1. March. Because of your faith, it will happen. 2. Something in me will save me. 3. To everyone carrying a heavy heart in silence. You are going to be okay. 4. Tomorrow will be better.
  image prompt: A cinematic sci-fi metropolis where Deep Neural Nets control a hyper-connected society. Holographic interfaces glow in the air as robotic agents move among humans, symbolizing Industry 4.0. The scene contrasts organic human emotion with cold machine precision, rendered in a hyper-realistic 3D style with futuristic lighting. Epic wide shots showcase the grandeur of this civilization’s industrial evolution.

  Output Image:

  More Example Results for Reference

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

workflow_json_compressed:
  [encoded data, 4672 characters]

metadata_json:
  {"categories":["automation","content creation","design","image generation","social media"],"complexity":"medium","use_cases":["Create social media visuals for Instagram/X (Twitter) influencers","Generate event promotional graphics for marketing teams","Produce featured images and illustrations for blogs","Convert key insights into shareable card-style visuals for knowledge creators"],"estimated_setup_minutes":30,"required_services":["GPT-4o-mini (or compatible OpenAI model)","Midjourney or compatible image-generation API","Canvas Switchboard / Canvas API (design template)","PiAPI (API gateway / key as described)","HTTP/webhook endpoints for workflow triggers"],"key_features":["Automated prompt generation using GPT model","Image generation via Midjourney (or equivalent)","Inject generated images into a Canvas design template (Switchboard)","Parameterized inputs (theme, scenario, style, example text, image prompt) for reusable outputs","Webhook/HTTP endpoint to return final image URL or asset"],"target_audience":["Content creators","Social media managers/influencers","Marketing teams and designers producing promo assets  (visuals) for campaigns  )  )  )  )  )  )"]}


name: 3D Figurine Orthographic Views with Midjourney and GPT-4o-image API
author_name: PiAPI
author_username: piapi
categories: []
created_at: 2025-04-21T09:21:54.543Z
updated_at: 2025-04-21T09:21:54.543Z
url: https://n8n.io/workflows/3628
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  What this workflow does?
  This workflow primarily uses the GPT-4o API from PiAPI and automatically creates front/side/top views of 3D models from commands.

  Who is this for？
  3D Designers： Quickly generate standardized orthographic views for design review
  E-commerce Operators： Create multi-angle product display images
  3D Modeling Beginners： Instantly produce basic reference views

  Step-by-step Instruction
  Fill in X-API-Key of your PiAPI account and the image prompt based on your inspiration.
  Click Test workflow.
  Get the image url in the final node.

  OutPut

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

workflow_json_compressed:
  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

metadata_json:
  {"categories":["automation","AI/ML integration","image generation","design tools"],"complexity":"medium","use_cases":["Generate standardized orthographic views (front/side/top) from a single prompt","Produce multi-angle product images for e-commerce listings","Create quick reference images for 3D modeling and design review","Rapid prototyping of presentation assets or thumbnails for 3D assets"],"estimated_setup_minutes":10,"required_services":["PiAPI (GPT-4o / GPT-4o-image) - X-API-Key","Midjourney (optional, if using alternative image styling workflows)","n8n instance (with Webhooks/HTTP access)"],"key_features":["Automatically generates orthographic views: front, side, top","Uses GPT-4o-image via PiAPI to synthesize images from a prompt","Single configurable prompt drives multi-view output","Returns image URL in the final node for easy retrieval or embedding","Includes conditional/logic nodes and code nodes for processing and formatting"],"target_audience":["3D designers","E-commerce operators / product photographers","3D modeling beginners and students"]}


name: Ultimate AI Assistant: Automate Email, Calendar, WebSearch, Notion, RAG & X
author_name: Tianyi
author_username: muzi
categories: []
created_at: 2025-04-21T10:06:33.761Z
updated_at: 2025-04-21T10:06:33.761Z
url: https://n8n.io/workflows/3629
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  This is an ultimate AI assistant: Handle emails, schedule meetings, search the web, take notes, post to social media, and retrieve information from your knowledge base, all through simple Telegram commands (text, voice, or image). 

  What is included:

  1 Ready-to-use n8n workflow file (.json)
  4 Instructional videos demonstrating how to connect n8n with:
      Google Products (Gmail, Calendar, Sheets)
      Telegram
      Pinecone Vector Store
      Notion

  Who is this for?

  This workflow is ideal for:

  Individuals seeking to automate personal and professional tasks.
  Professionals needing efficient management of emails, calendars, and contacts.
  Content creators or researchers requiring quick information retrieval and drafting capabilities.
  Anyone wanting a centralized AI assistant.

  What problem is this workflow solving?

  Managing numerous digital tasks across different platforms can be time-consuming and fragmented. This workflow consolidates control into Telegram, leveraging AI to understand your needs and automate actions across email, calendar, contacts, web search, note-taking (Notion), social media (X), and your personal knowledge base (Pinecone RAG), saving you time and effort. It ensures tasks are handled correctly using a built-in verification step.

  What this workflow does:

  This workflow acts as a central AI Agent that:

  Receives Input via Telegram: Accepts text messages, voice notes (transcribes them using OpenAI), or images (analyzes them using OpenAI).
  Understands Requests: Uses an AI Agent (powered by models like GPT) to interpret the user's instructions.
  Orchestrates Tools: Selects and uses the appropriate tools based on the request:
      Email (Gmail): Send, read, reply, draft, and label emails.
      Calendar (Google Calendar): Create, get, update, and delete events, managing attendees if needed.
      Contacts (Google Sheets): Look up and add contact information.
      Web Search (Tavily): Search the internet for up-to-date information.
      Knowledge Retrieval (Pinecone): Query your personal vector database (RAG).
      Note Taking (Notion): Create notes in a specified Notion database.
      Social Media (X/Twitter): Post tweets directly to X.
  Maintains Context: Uses Window Buffer Memory to remember recent interactions.
  Ensures Accuracy: Employs a "Think" tool to double-check tool usage and task completion.
  Responds via Telegram: Sends the results or confirmation back to the user in the Telegram chat.

  Setup:

  Telegram: Connect your Telegram bot credentials.
  AI Model: Connect your OpenAI API key.
  Email: Connect your Gmail account.
  Calendar: Connect your Google Calendar account.
  Contacts: Connect your Google Sheets account.
  Knowledge Base (RAG): Connect Pinecone.
  Note Taking: Connect Notion and configure Notion Database.
  Web Search: Add Tavily API key.
  Social Media: Connect your X (Twitter) account.

  (Refer to the included videos for detailed setup guidance)

  How to customize this workflow:

  AI Prompts:** Modify the system messages and prompts within the "AI Agent" node.
  AI Models:** Swap out the language model.
  Tools:** Add, remove, or replace tool nodes.

  Category:

  Automation, AI Agent, Personal Assistant, Productivity, Task Management, Telegram

nodes_used:
  ["n8n-nodes-base.telegramTrigger","n8n-nodes-base.switch","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.telegram","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.vectorStorePinecone","@n8n/n8n-nodes-langchain.embeddingsOpenAi","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolThink","n8n-nodes-base.gmailTool","n8n-nodes-base.gmailTool","n8n-nodes-base.gmailTool","n8n-nodes-base.gmailTool","n8n-nodes-base.gmailTool","n8n-nodes-base.gmailTool","n8n-nodes-base.googleCalendarTool","n8n-nodes-base.googleCalendarTool","n8n-nodes-base.googleCalendarTool","n8n-nodes-base.googleCalendarTool","n8n-nodes-base.googleCalendarTool","n8n-nodes-base.googleSheetsTool","n8n-nodes-base.googleSheetsTool","@n8n/n8n-nodes-langchain.toolHttpRequest","n8n-nodes-base.twitterTool","n8n-nodes-base.notionTool","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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

metadata_json:
  {"categories":["automation","AI assistant","productivity","integration","communication"],"complexity":"complex","use_cases":["Automate email workflows (send/read/reply/draft/label)","Schedule and manage calendar events with attendees","Lookup and manage contacts","Transcribe voice and analyze images from Telegram","Perform up-to-date web searches and RAG retrieval from personal vector DB (Pinecone) for research/answers/drafting support」「Create and update notes in Notion and post to X (Twitter)"],"estimated_setup_minutes":60,"required_services":["Telegram Bot API","OpenAI (GPT + transcription/image APIs)","Gmail (Google account)","Google Calendar","Google Sheets","Pinecone Vector DB","Notion","Tavily (web search API)","X (Twitter) API"],"key_features":["Unified AI agent interface via Telegram (text, voice, image)","Tool orchestration across email, calendar, contacts, web search, RAG, notes, social posting","Gmail integration for send/read/reply/draft/label","Google Calendar event create/get/update/delete with attendee management","Contacts stored/queried via Google Sheets integration」「Pinecone-powered RAG retrieval for personalized knowledge base answers」「Notion note creation in a specified database」「Post to X (Twitter) from the agent」「Window buffer memory to maintain conversation context」「'Think' verification step to double-check actions before executing"],"target_audience":["Individuals wanting personal automation","Professionals managing email/calendar/contacts","Content creators and researchers needing fast drafting and retrieval workflows」「Users who want a centralized AI assistant accessed via Telegram"]}


name: Build your own FileSystem MCP server
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2025-04-21T10:07:32.061Z
updated_at: 2025-04-21T10:07:32.061Z
url: https://n8n.io/workflows/3630
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  This template is for self-hosted n8n instances only.
  This n8n demonstrates how to build a simple FileSystem MCP server. Connecting to this server allows MCP clients and agents to list, read and create directories and files on the local machine or remote server.

  This MCP example is based off an official MCP reference implementation which can be found here -https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem

  How it works
  A MCP server trigger is used and connected to 5 tools: 3 Execute Command tools and 2 custom workflow tools.
  The 3 Execute Command tools allow for listing, searching and creating directories. 
  The 2 custom workflow tools are for reading and writing files to disk.
  Special care has been to not allow the MCP agent to execute arbitrary linux commands on the target server. This is achieved by only allowing the agent to provide parameters such as filenames and paths rather than raw commands. 

  How to use
  This Filesystem MCP server will write to the server which hosts the n8n instance - this can be your local machine or a remove server. If your target filesystem is on neither, then modify the commands to connect to the desired server.
  Connect your MCP client by following the n8n guidelines here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop
  Try the following queries in your MCP client:
    "Please help me list all folders under the project directory."
    "Help me create a bash script to send a notification to Slack."
    "Search for the log file on the 22nd April and read its contents. What was the cause of the outage?"

  Requirements
  Linux file system for this example template. Feel free to modify if working on Windows.
  MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download

  Customising this workflow
  Implement the moving and renaming of files by adding more custom workflow tools to the MCP server.
  Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.executeCommandTool","n8n-nodes-base.executeCommandTool","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.switch","n8n-nodes-base.executeCommand","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.executeCommand","n8n-nodes-base.executeCommandTool","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","file management","self‑hosting"],"complexity":"medium","use_cases":["Expose local/remote filesystem operations to MCP clients (list, read, create files/folders)","Search and read log files for troubleshooting","Allow agents to create scripts or files on the host machine","Build custom file-management actions (move/rename/modify) by extending the workflow"],"estimated_setup_minutes":30,"required_services":["n8n self‑hosted instance (required)","MCP client/agent such as Claude Desktop","Target Linux filesystem where n8n runs (or adjusted commands/SSH access for a remote host)"],"key_features":["MCP server trigger to accept client requests","Execute Command tools for listing/searching/creating directories","Custom workflow tools for safe read/write file operations","Parameter whitelisting to prevent arbitrary command execution","Extendable to add move/rename operations"],"target_audience":["developers","DevOps / system administrators","power users self‑hosting automation tools"]}


name: Build your own PostgreSQL MCP server
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2025-04-21T10:09:09.689Z
updated_at: 2025-04-21T10:09:09.689Z
url: https://n8n.io/workflows/3631
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:31
description:
  This n8n demonstrates how to build a simple PostgreSQL MCP server to manage your PostgreSQL database such as HR, Payroll, Sale, Inventory and More!

  This MCP example is based off an official MCP reference implementation which can be found here -https://github.com/modelcontextprotocol/servers/tree/main/src/postgres

  How it works
  A MCP server trigger is used and connected to 5 tools: 2 postgreSQL and 3 custom workflow.
  The 2 postgreSQL tools are simple read-only queries and as such, the postgreSQL tool can be simply used.
  The 3 custom workflow tools are used for select, insert and update queries as these are operations which require a bit more discretion.
  Whilst it may be easier to allow the agent to use raw SQL queries, we may find it a little safer to just allow for the parameters instead. The custom workflow tool allows us to define this restricted schema for tool input which we'll use to construct the SQL statement ourselves.
  All 3 custom workflow tools trigger the same "Execute workflow" trigger in this very template which has a switch to route the operation to the correct handler.
  Finally, we use our standard PostgreSQL node to handle select, insert and update operations. The responses are then sent back to the the MCP client.

  How to use
  This PostgreSQL MCP server allows any compatible MCP client to manage a PostgreSQL database by supporting select, create and update operations. You will need to have a database available before you can use this server.
  Connect your MCP client by following the n8n guidelines here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop
  Try the following queries in your MCP client:
    "Please help me check if Alex has an entry in the users table. If not, please help me create a record for her."
    "What was the top selling product in the last week?"
    "How many high priority support tickets are still open this morning?"

  Requirements
  PostgreSQL for database. This can be an external database such as Supabase or one you can host internally.
  MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download

  Customising this workflow
  If the scope of schemas or tables is too open, try restrict it so the MCP serves a specific purpose for business operations. eg. Confine the querying and editing to HR only tables before providing access to people in that department.
  Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

nodes_used:
  ["n8n-nodes-base.postgresTool","n8n-nodes-base.postgresTool","n8n-nodes-base.executeWorkflowTrigger","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.postgres","n8n-nodes-base.switch","n8n-nodes-base.postgres","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.postgres","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["integration","automation","database-management"],"complexity":"medium","use_cases":["Expose PostgreSQL read/write operations to an MCP client/agent","Provide controlled select/insert/update access for HR, Payroll, Sales, Inventory","Build an agent-accessible DB management endpoint for QA or business users","Create parameter-restricted tools to avoid raw SQL exposure"],"estimated_setup_minutes":30,"required_services":["PostgreSQL database (self-hosted or hosted e.g., Supabase)","n8n instance","MCP-compatible client/agent (e.g., Claude Desktop)"],"key_features":["MCP server trigger to accept agent queries","Two read-only PostgreSQL query nodes for safe reads","Three custom tool workflows for parameterized select/insert/update","Central executeWorkflow trigger with switch routing","Constructed SQL from validated parameters to limit raw SQL use"],"target_audience":["developers building agent-driven workflows","data engineers and DBAs","automation/platform engineers"]}


name: Build your own SQLite MCP server
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2025-04-21T10:10:32.218Z
updated_at: 2025-04-21T10:10:32.218Z
url: https://n8n.io/workflows/3632
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  This template is for Self-Hosted N8N Instances only.

  This n8n demonstrates how to build a simple SQLite MCP server to perform local database operations as well as use it for Business Intelligence.

  This MCP example is based off an official MCP reference implementation which can be found here -https://github.com/modelcontextprotocol/servers/tree/main/src/sqlite

  How it works
  A MCP server trigger is used and connected to 5 tools: 2 Code Node and 3 Custom Workflow.
  The 2 Code Node tools use the SQLLite3 library and are simple read-only queries and as such, the Code Node tool can be simply used.
  The 3 custom workflow tools are used for select, insert and update queries as these are operations which require a bit more discretion.
  Whilst it may be easier to allow the agent to use raw SQL queries, we may find it a little safer to just allow for the parameters instead. The custom workflow tool allows us to define this restricted schema for tool input which we'll use to construct the SQL statement ourselves.
  All 3 custom workflow tools trigger the same "Execute workflow" trigger in this very template which has a switch to route the operation to the correct handler.
  Finally, we use our Code nodes to handle select, insert and update operations. The responses are then sent back to the the MCP client.

  How to use
  This SQLite MCP server allows any compatible MCP client to manage a SQLite database by supporting select, create and update operations. You will need to have a SQLite database available before you can use this server.
  Connect your MCP client by following the n8n guidelines here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop
  Try the following queries in your MCP client:
    "Please create a table to store business insights and add the following..."
    "what business insights do we have on current retail trends?"
    "Who has contributed the most business insights in the past week?"

  Requirements
  SQLite for database.
  MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download

  Customising this workflow
  If the scope of schemas or tables is too open, try restrict it so the MCP serves a specific purpose for business operations. eg. Confine the querying and editing to HR only tables before providing access to people in that department.
  Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

nodes_used:
  ["n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.switch","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.toolCode","@n8n/n8n-nodes-langchain.toolCode","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["integration","automation","data processing","business intelligence","self-hosted"],"complexity":"medium","use_cases":["Expose a local SQLite database to MCP agents/clients","Run read (select) and write (insert/update) DB operations via an MCP server","Provide a restricted schema toolset for safer agent-driven SQL operations","Enable BI-style queries against local data from MCP clients"],"estimated_setup_minutes":30,"required_services":["Self-hosted n8n instance","SQLite database file/engine","MCP client/agent (e.g., Claude Desktop)"],"key_features":["MCP server trigger to accept agent requests","Custom workflow tools for controlled select/insert/update operations","Code nodes using sqlite3 for read-only queries and DB handling","Routing via switch to appropriate handler workflow","Sends query results back to MCP client"],"target_audience":["n8n administrators / self-hosting engineers","Developers building agent integrations","Data analysts / BI engineers wanting agent-accessible local data"]}


name: Automated AI Product Photography and Instagram Post Generator (Deepseek/Segmind)
author_name: Tianyi
author_username: muzi
categories: []
created_at: 2025-04-21T10:10:43.292Z
updated_at: 2025-04-21T10:10:43.292Z
url: https://n8n.io/workflows/3633
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  Automatically generate professional-grade product photography and ready-to-use Instagram posts using the power of AI, delivered straight to your Telegram for approval.

  Setup is incredibly simple: All you need is your product image URL and a API key from Segmind.

  Who is this for?

  This template is ideal for:

  E-commerce store owners** looking to create high-quality product visuals affordably (The estimated cost is approximately $0.10 per product photography and accompanying Instagram post).
  Dropshippers** needing unique product images to stand out.
  Social Media Managers & Marketers** seeking to automate content creation for platforms like Instagram.
  Small Businesses** wanting professional marketing assets without the high cost or effort.
  Anyone needing consistent, eye-catching product photos and social media captions on a regular schedule.

  What problem is this workflow solving?

  Creating professional product images is often expensive and time-consuming, requiring photographers, studios, and editing time. Consistently generating fresh and engaging social media content, especially for visual platforms like Instagram, adds another layer of complexity and effort. This workflow eliminates these bottlenecks by automating both high-quality image generation and relevant caption creation, freeing up your time and budget.

  What this workflow does:

  This n8n workflow automates the entire process on a schedule you define (e.g., every hour, every day):

  Scheduled Start: Triggers automatically based on your chosen time interval (e.g., every hour).
  Product Analysis: Takes your input product image URL and uses AI (GPT) to understand the product details.
  AI Prompt Generation: Based on the product analysis and your preference (whether to include a human model or not), it uses AI (Deepseek) to craft a sophisticated prompt specifically for generating professional product photography via Segmind.
  Instagram Post Creation: Simultaneously, the AI (Deepseek) generates an engaging Instagram post caption, complete with relevant hashtags, tailored to your product.
  AI Image Generation: Sends the product image, the generated prompt, and product details to the Segmind API to create a brand new, studio-quality product photograph.
  Telegram Validation: Downloads the newly generated product photo and sends both the photo and the generated Instagram post text directly to your specified Telegram chat. This allows you to quickly review and approve the content before using it.

  Setup: Get Running in Minutes!

  This workflow is designed for maximum ease of setup:

  Get Segmind API Key: Sign up or log in to Segmind and grab your API key here: https://cloud.segmind.com/console/api-keys
  Enter API Key: In the n8n workflow, click on the SegmindAPIKey node and paste your copied API key into the Value field.
  Enter Product Image URL: Click the InputYourImageURL node and paste the web address (URL) of your product image into the Value field.
  (Optional) Human Model Preference: Click the ImageInstruction node. Set the HumanModel value to true if you want a human model potentially included in the photo, or false if you want the product showcased alone or in a setting.
  Set Your Schedule: Click the Schedule Trigger node. Define how often you want the workflow to run (e.g., under Interval, set 1 and select Hours from the dropdown for hourly runs).
  Configure Telegram: Make sure you have a Telegram Bot credential configured in n8n. Then, in the SendProductPhotography and SendInstagramPost nodes, enter the correct Chat ID for where you want to receive the validation messages. [A video guidance is made to help you with telegram setup]
  Activate Workflow: Toggle the workflow to "Active" in the top right corner of n8n.

  That's it! The workflow will now automatically generate and send product photos and Instagram posts to your Telegram at your defined interval.

  How to customize this workflow:

  While the default setup works great, you can easily tweak it:

  Photography Style:** Modify the main prompt within the AI Agent1 node to guide the AI towards a specific aesthetic (e.g., "minimalist background," "bright natural lighting," "dark moody atmosphere").
  Instagram Post Tone:** Adjust the instructions within the AI Agent1 node to change the style or focus of the generated Instagram captions.
  Schedule:** Change the trigger interval in the Schedule Trigger node anytime.
  AI Models:** Experiment by changing the selected models in the OpenAI, OpenAI Chat Model1 (Deepseek).

  Category:

  Marketing, Social Media, E-commerce, Automation, AI, Content Creation, Product Photography

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.telegram","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.set"]

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metadata_json:
  {"categories":["Automation","Marketing","Social Media","E-commerce","AI / Generative Media / Image Generation"],"complexity":"medium","use_cases":["Automated product photography generation","Automated Instagram post & caption + hashtag creation","Scheduled delivery of review-ready assets to Telegram for approval","Generate consistent creative assets for dropshipping/SMB storefronts","Rapidly iterate product visuals and social captions"],"estimated_setup_minutes":10,"required_services":["n8n (automation platform)","Segmind (image-generation API / API key)","Deepseek (AI prompt/caption agent) or equivalent GPT model (OpenAI)","Telegram Bot (bot token + chat ID for delivery)"],"key_features":["Scheduled end-to-end automation from product image → new studio-quality product photo","AI product analysis to derive generation prompts","AI prompt generation tailored for professional product photography","Segmind-backed image generation of final photos","AI-generated Instagram captions with hashtags optimized for the product tone/style (ready-to-post) and delivered alongside images for quick validation in Telegram chat (approval workflow) ] Note: customizable photography style and caption tone"],"target_audience":["E‑commerce store owners","Dropshippers","Social media managers and marketers (Instagram-focused) as well as small businesses wanting low-cost professional visuals"]}


name: Build your own Google Drive MCP server
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2025-04-21T10:11:41.815Z
updated_at: 2025-04-21T10:11:41.815Z
url: https://n8n.io/workflows/3634
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:31
description:
  This n8n demonstrates how to build a simple Google Drive MCP server to search and get contents of files from Google Drive.

  This MCP example is based off an official MCP reference implementation which can be found here -https://github.com/modelcontextprotocol/servers/tree/main/src/gdrive

  How it works
  A MCP server trigger is used and connected to 1x Google Drive tool and 1x Custom Workflow tool.
  The Google Drive tool is set to perform a search on files within our Google Drive folder.
  The Custom Workflow tool downloads target files found in our drive and converts the binaries to their text representation. Eg. PDFs have only their text contents extracted and returned to the MCP client.

  How to use
  This Google Drive MCP server allows any compatible MCP client to manage a person or shared Google Drive. Simple select a drive or for better control, specify a folder within the drive to scope the operations to.
  Connect your MCP client by following the n8n guidelines here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop
  Try the following queries in your MCP client:
    "Please help me search for last month's expense reports."
   "What does the company policy document say about cancellations and refunds?"

  Requirements
  Google Drive for documents.
  OpenAI for image and audio understanding.
  MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download

  Customising this workflow
  Add additional capabilities such as renaming, moving and/or deleting files.
  Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.executeWorkflowTrigger","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.googleDrive","n8n-nodes-base.switch","n8n-nodes-base.switch","n8n-nodes-base.extractFromFile","n8n-nodes-base.extractFromFile","n8n-nodes-base.set","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.googleDriveTool","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 5060 characters]

metadata_json:
  {"categories":["integration","automation","document processing","search","AI/LLM integration"],"complexity":"medium","use_cases":["Search and retrieve files from Google Drive via MCP","Extract and return text contents from binary files (PDFs, images, audio)","Expose a Google Drive-backed MCP server for LLM/agent access","Scope document searches to a drive or specific folder","Enable LLM-based understanding of images/audio using OpenAI"],"estimated_setup_minutes":30,"required_services":["n8n instance","Google Drive (OAuth access)","OpenAI (for image/audio understanding)","MCP client/agent (e.g., Claude Desktop)"],"key_features":["MCP server trigger to accept client queries","Google Drive file search and retrieval","Custom workflow node to download and convert binaries to text","PDF text extraction and binary-to-text conversion","Ability to scope searches to a drive or folder level','Supports adding actions like rename/move/delete with customization"],"target_audience":["Developers building MCP integrations","IT admins or platform engineers managing shared drives","Data engineers and knowledge managers enabling LLM access to documents"]}


name: Build your own Github MCP server
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2025-04-21T10:13:11.626Z
updated_at: 2025-04-21T10:13:11.626Z
url: https://n8n.io/workflows/3635
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  This n8n demonstrates how to build your own Github MCP server to personalise it to your organisation's repositories, issues and pull requests.

  This n8n implementation, though not as fully featured as the official MCP server offered by Github, allows you to control precisely what access and/or functionality is granted to users which can make MCP use simpler and in some cases, more secure. The use-case in this template is to simply view and comment on issues within a specific repository but can be extended to meet the needs of your team.

  This MCP example is based off an official MCP reference implementation which can be found here https://github.com/modelcontextprotocol/servers/tree/main/src/github

  How it works
  A MCP server trigger is used and connected to 3 custom workflow tools. We're using custom workflow tools as there is quite a few nodes required for each task.
  Behind these tools are regular Github nodes although preconfigured with credentials and targeted repository.
  The "Get Issue Comments" and "Create Issue Comment" tools depend on obtaining an Issue Number first. The agent should call the "Get Latest Issues" tool for this.

  How to use
  This Github MCP server allows any compatible MCP client to view and comment on Github Issues. You will need to have a Github account and repository access available before you can use this server.
  Connect your MCP client by following the n8n guidelines here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop
  Try the following queries in your MCP client:
    "Can you get me the latest issues about MCP?"
    "What is the current progress on Issue 12345?"
    "Please can you add a comment to Issue 12345 that they should try installing the latest version and see if that works?"

  Requirements
  Github for account and repository access. The repository need not be your own but you'll still need to ensure you have the correct permissions.
  MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download

  Customising this workflow
  Extend this template to interactive with pull requests or workflows within your own company's Github repositories. Alternatively,  pull in metrics and generate reports for programme managers.
  Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

nodes_used:
  ["n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.switch","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.set","n8n-nodes-base.aggregate","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.aggregate","n8n-nodes-base.github","n8n-nodes-base.github","n8n-nodes-base.github","n8n-nodes-base.set","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 6012 characters]

metadata_json:
  {"categories":["integration","developer tools","collaboration","automation"],"complexity":"medium","use_cases":["Expose repository issues to MCP clients","Allow MCP clients to view and comment on GitHub issues","Create a scoped/custom MCP server for controlled access to repo data","Integrate MCP clients with GitHub workflows and PRs (extensible)","Generate issue/metrics reports for programme managers"],"estimated_setup_minutes":30,"required_services":["GitHub (account + repository access)","n8n instance","MCP-compatible client (e.g., Claude Desktop)","GitHub API credentials"],"key_features":["MCP Trigger to receive client requests","Custom workflow tools: Get Latest Issues, Get Issue Comments, Create Issue Comment","Preconfigured GitHub nodes scoped to a target repository","Controlled permission scoping for safer MCP usage","Extensible to pull requests, workflows, and reporting/metrics"],"target_audience":["Developers","Repository maintainers","Platform/DevOps engineers`,`Program/product managers"]}


name: Build your own Qdrant Vector Store MCP server
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2025-04-21T10:14:56.423Z
updated_at: 2025-04-21T10:14:56.423Z
url: https://n8n.io/workflows/3636
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  This n8n demonstrates how to build your own Qdrant MCP server to extend its functionality beyond that of the official implementation.

  This n8n implementation exposes other cool API features from Qdrant such as facet search, grouped search and recommendations APIs. With this, we can build an easily customisable and maintainable Qdrant MCP server for business intelligence.

  This MCP example is based off an official MCP reference implementation which can be found here - https://github.com/qdrant/mcp-server-qdrant

  How it works
  A MCP server trigger is used and connected to 5 custom workflow tools. We're using custom workflow tools as there is quite a few nodes required for each task.
  We use a mix of n8n supported Qdrant nodes for simple operations such as insert documents and similarity search, and HTTP node to hit the Qdrant API directly for Facet search, group search and recommendations.
  We use "Edit Field" and "Aggregate" nodes to return suitable responses to the MCP client.

  How to use
  This Qdrant MCP server allows any compatible MCP client to manage a Qdrant Collection by supporting select and create operations. You will need to have a collection available before you can use this server. Use the Prerequisite manual steps to get started!
  Connect your MCP client by following the n8n guidelines here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop
  Try the following queries in your MCP client:
    "Can you help me list the available companies in the collection?"
    "What do customers say about product deliveries from company X?"
    "What do customers of company X and company Y say about product ease of use?"

  Requirements
  Qdrant for vector store. This can be an a cloud-hosted instance or one you can self-host internally.
  MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download

  Customising this workflow
  Depending on what queries you'll receive, adjust the tool inputs to make it easier for the agent to set the right parameters.
  Not interested in Reviews? The techniques shared in this template can be used for other types of collections.
  Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

nodes_used:
  ["n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.switch","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.aggregate","n8n-nodes-base.httpRequest","n8n-nodes-base.aggregate","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.aggregate","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.aggregate","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.vectorStoreQdrant","@n8n/n8n-nodes-langchain.vectorStoreQdrant","n8n-nodes-base.splitOut","n8n-nodes-base.filter","n8n-nodes-base.aggregate","n8n-nodes-base.if","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 9100 characters]

metadata_json:
  {"categories":["integration","automation","data processing","vector databases","AI/ML"],"complexity":"complex","use_cases":["Expose advanced Qdrant APIs (facet search, grouped search, recommendations)","Build a customizable MCP server for business intelligence","Manage and query Qdrant collections via an MCP client","Semantic similarity search and document insertion","Customer feedback analysis and cross-company comparisons"],"estimated_setup_minutes":60,"required_services":["Qdrant (cloud or self‑hosted)","n8n instance (with web access for MCP trigger/webhooks)","MCP client/agent (e.g., Claude Desktop)","OpenAI API (for embeddings)"],"key_features":["Custom MCP server exposing Qdrant facet/grouped search and recommendations","Uses n8n Qdrant nodes plus HTTP calls to Qdrant API","Supports select and create operations from MCP clients","Prebuilt tools/workflows for insert, similarity search, facet/grouped search, recommendations","Response formatting using Edit Field and Aggregate nodes for MCP responses"],"target_audience":["developers","data engineers","ML/AI engineers working with vector stores and agents (MCP)」「n8n users and automation engineers」「BI analysts who need custom search/recommendation endpoints"]}


name: Build your own Youtube MCP server
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2025-04-21T10:19:42.144Z
updated_at: 2025-04-21T10:19:42.144Z
url: https://n8n.io/workflows/3637
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  This n8n demonstrates how to build a simple Youtube MCP server to look up videos on Youtube and download their transcripts for research purposes.

  Youtube videos are a great source of new and updated information on a variety of cutting edge developments but they''re are not always simple to understand and lengthy videos may take too much time. Using this MCP server, you extract and feed their transcripts for your AI agents which then allows it to breakdown the content into manageble learnings and insights.

  How it works
  A MCP server trigger is used and connected to 3 custom workflow tools: Youtube Search, Youtube Transcripts and Usage Reports.
  Both Youtube tools use an external scraping service called APIFY.com. This is my preference as it's a much simpler interface and there are no rate limits.  
  The Youtube Search fetches 10 results based on the user's query.
  The Youtube Transcripts downloads the subtitles from one or more given URL.
  The usage reports pulls in your monthly APIFY.com monthly spending and limits as a way to check your account.

  How to use
  This Apify Youtube MCP server allows any compatible MCP client to research YouTube videos for any desired topic. An Apify account is required however to connect and use the service.
  Connect your MCP client by following the n8n guidelines here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop
  Alternatively, connect any n8n AI agent with the MCP client tool.
  Try the following queries in your MCP client:
    "what is MCP?"
    "How can I use MCP in n8n?"
    "How can I use Apify's official MCP server?"

  Requirements
  APIFY.com for Youtube Scraping. This is a paid service but there is a $5 free tier which is ample for this template.
  MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download

  Customising this workflow
  Add as many APIFY.com actors as required for your use-case or users. Consider using Apify's official MCP server for 4000+ available tools.
  Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

nodes_used:
  ["n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.switch","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.aggregate","n8n-nodes-base.aggregate","n8n-nodes-base.set","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.mcpTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 6828 characters]

metadata_json:
  {"categories":["integration","data_processing","automation","research"],"complexity":"medium","use_cases":["Researching YouTube content","Extracting and ingesting transcripts into AI agents","Content summarization and analysis","Monitoring APIFY usage/costs","Building searchable video knowledge bases"],"estimated_setup_minutes":30,"required_services":["Apify.com (YouTube scraping actors)","n8n (hosted or self-hosted)","MCP client or agent (e.g., Claude Desktop)","YouTube (video sources)"],"key_features":["MCP server trigger to accept client queries","YouTube Search tool (fetches top 10 results via Apify)","YouTube Transcripts tool (download subtitles for one or more URLs)","Usage Reports tool (APIFY monthly spending/limits)","Integrates transcripts into AI agents/workflows for analysis and summarization','Supports adding multiple Apify actors and credential protection before production"],"target_audience":["developers","AI practitioners","researchers and academics doing video research or literature review on YouTube content`,`content creators and marketers seeking transcripts and summaries`,`data analysts/automation engineers"]}


name: Build your own CUSTOM API MCP server
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2025-04-21T10:21:15.404Z
updated_at: 2025-04-21T10:21:15.404Z
url: https://n8n.io/workflows/3638
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
description:
  This n8n demonstrates how any organisation can quickly and easily build and offer MCP servers to their customers or internal staff to improve productivity.

  This MCP example uses PayCaptain.com as an example and shows how to create an MCP server which can search for and update employee data.

  How it works
  A MCP server trigger is used and connected to 3 custom workflow tools: Search Employee, Get Employee by ID and Update Employee.
  Each tool makes calls to the PayCaptain API to perform their respective tasks. Extra care  is performed to strip out sensitive data and ensure we're not sharing too much.
  The Update Employee too also guards against updating fields which would preferably remain readonly. When you control the MCP server, you can determine behaviour of the tool.
  Finally, a Google Sheet node is used to log all operations for later audit. This will add a tiny bit of latency but recommended if sensitive data is being accessed.

  How to use
  This MCP server allows any compatible MCP client to manage their PayCaptain employee database. You will need to have a PayCaptain account and developer key to use it.
  Connect your MCP client by following the n8n guidelines here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop
  Try the following queries in your MCP client:
    "When did Sarah start here employment at the company?"
    "Does Jack work Wednesdays or Fridays?"
    "Please update Tracy's NI number to ABCD123456"

  Requirements
  PayCaptain Account and Developer Key.
  Google Sheets to log actions for later audit.
  MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download

  Customising this workflow
  Add or remove employee attributes as required for your user case.
  If Google Sheets is too slow, consider an API call to a faster service to log calls to the MCP server.
  Remember to set the MCP server to require credentials before going to production and sharing this MCP server with others!

nodes_used:
  ["n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.switch","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.mcpTrigger","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.googleSheets","n8n-nodes-base.filter","n8n-nodes-base.aggregate","n8n-nodes-base.httpRequest","n8n-nodes-base.filter","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.aggregate","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","internal tools","data processing","security"],"complexity":"medium","use_cases":["Expose internal employee database actions via an MCP API","Search and retrieve employee records for HR or support agents","Controlled updates to employee fields with readonly protection","Audit logging of access and changes for compliance","Rapidly build custom API endpoints for third‑party clients"],"estimated_setup_minutes":60,"required_services":["n8n instance (hosted or self‑hosted)","PayCaptain API account + developer key","Google Sheets (for audit logging) with API access","MCP client (e.g., Claude Desktop) to connect and test"],"key_features":["MCP server trigger exposing custom tools (Search, Get by ID, Update)","Calls to PayCaptain API to search, fetch, and update employee data","Sensitive data stripping before returning results","Protection against updating readonly or disallowed fields","Audit trail of operations stored in Google Sheets (configurable logging target)"],"target_audience":["developers building internal/external APIs","IT/DevOps teams deploying automation","HR/admin teams wanting controlled programmatic access to employee data"]}


name: 🧑‍🦯Improve your Website Accessibility with GPT-4o and Google Sheet
author_name: Samir Saci
author_username: samirsaci
categories: []
created_at: 2025-04-21T14:01:46.813Z
updated_at: 2025-04-21T14:01:46.813Z
url: https://n8n.io/workflows/3640
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  Tags: Accessibility, SEO, Blogging, Marketing, Automation, AI, Web Auditing

  Context

  Hey! I’m Samir, a Supply Chain Engineer and Data Scientist from Paris, and the founder of LogiGreen Consulting.

  In my personal blog, I share insights on how to use AI, automation, and data analytics to improve logistics, operations, and digital sustainability practices.



  &gt; Have you heard about accessibility?

  In this workflow, I use n8n to improve the quality of alternative texts for images on my personal website.

  📬 For business inquiries, you can connect with me on LinkedIn

  Who is this template for?

  This workflow is for:
  Bloggers* and *website owners* who want to *improve accessibility**
  SEO professionals** looking to boost page performance
  Web developers* and *product teams** automating web audits

  What does it do?

  This n8n workflow:
  🔍 Downloads the HTML of a blog or web page  
  🖼️ Extracts all ` tags and their alt` attributes  
  📉 Detects missing or too-short alt texts  
  🤖 Sends those images to GPT-4o (with vision) to generate new alt descriptions  
  📄 Saves the results into a Google Sheet, updating the alt text when needed  

  How it works



  Set a page URL using the Set node  
  Download HTML content  
  Extract image src and alt using a Code node  
  Store results in a Google Sheet  
  Filter images with altLength &lt; 50  
  Send image URL to GPT-4o 
  Update the Google Sheet with the newly generated newAlt text

  The AI alt texts are concise, descriptive, and accessibility-compliant.

  What do I need to get started?

  You’ll need:
  A Google Sheet to store the audit results
  An OpenAI account with GPT-4o access

  Follow the Guide!
  Follow the sticky notes in the workflow or check my tutorial to configure each node and start using AI to improve the accessibility of your website.



  🎥 Watch My Tutorial

  Notes

  GPT-generated alt texts are limited to ~125–150 characters for best results
  Use this to comply with WCAG and improve Google indexing
  Easily adapt it to audit multiple domains or e-commerce catalogues

  This workflow was built using n8n version 1.85.4  
  Submitted: April 21, 2025

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.splitInBatches","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.googleSheets","n8n-nodes-base.if","n8n-nodes-base.limit","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.httpRequest","n8n-nodes-base.code"]

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metadata_json:
  {"categories":["automation","accessibility","AI-driven web auditing","SEO"],"complexity":"medium","use_cases":["Generate descriptive alt text for images","Automated website accessibility audits","Improve SEO/image indexing","Bulk updating and tracking alt text in Google Sheets","E-commerce/catalogue image accessibility enhancements"],"estimated_setup_minutes":20,"required_services":["n8n instance","Google Sheets (Google Workspace)","OpenAI (GPT-4o with vision) account","Publicly accessible website/pages to audit (HTTP/HTTPS)"],"key_features":["Download page HTML and extract image src/alt","Detect missing or too-short alt attributes","Generate concise, WCAG-friendly alt text via GPT-4o","Write and update results to a Google Sheet","Filter and batch-process images for scalable audits"],"target_audience":["Bloggers and website owners","SEO professionals","Web developers and product teams','Accessibility auditors and consultants"]}


name: MCP Supabase Agent – Manage Your Database with AI
author_name: Amanda Benks
author_username: amanda
categories: []
created_at: 2025-04-21T14:38:16.426Z
updated_at: 2025-04-21T14:38:16.426Z
url: https://n8n.io/workflows/3641
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  Hi, I’m Amanda 🌷
  This workflow was built with love to help you manage your Supabase database using natural language, powered by the MCP (Multi-Channel Protocol) AI Agent on n8n.

  With just a message like “update the status to active where city is New York”, your agent will know exactly what to do — safely, step by step, and always asking for confirmation before deleting anything.

  It’s ideal for developers who want a smart assistant to create, update, delete or search database records without writing SQL or opening Supabase.

  What this workflow does

  Receives messages through a chat interface or webhook  
  Translates them into actions using GPT-4o (via LangChain Agent)  
  Interacts with Supabase using a custom MCP tool  
  Supports create, update, delete, search (single or all rows)  
  Handles confirmations, validations, and missing data checks  

  Setup (quick and easy 💫)

  Connect your Supabase credentials  
  Adjust your table and field names  
  Link the MCP webhook to your AI frontend (Typebot, WhatsApp, etc.)  
  Customize the system message to reflect your tone of voice  
  Start chatting — your agent is ready to assist!

  ✅ Works on n8n Cloud and Self-Hosted  
  🧠 Built with GPT-4o + LangChain + Supabase + MCP Trigger  
  💡 No code or SQL required

  Want it tailored to your project?
  ❤️ Buy Workflows: https://iloveflows.gumroad.com 
  💬 Hire My Services: +5517991557874 (WhatsApp)

nodes_used:
  ["n8n-nodes-base.supabaseTool","n8n-nodes-base.supabaseTool","n8n-nodes-base.supabaseTool","n8n-nodes-base.supabaseTool","n8n-nodes-base.supabaseTool","@n8n/n8n-nodes-langchain.mcpTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.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":["automation","database management","AI assistant","integration"],"complexity":"medium","use_cases":["Natural-language CRUD on Supabase","Search and retrieve records via chat/webhook","Safe deletes with confirmation and validation","Embed AI-driven DB agent into chat frontends (Typebot, WhatsApp, etc.)","Avoid writing SQL for routine data tasks"],"estimated_setup_minutes":15,"required_services":["Supabase","MCP (Multi-Channel Protocol) webhook/client","OpenAI GPT-4o (via LangChain)","n8n (Cloud or Self‑Hosted)","Optional: Typebot / WhatsApp or other chat frontend"],"key_features":["Translate natural-language requests into DB actions","Supports create, update, delete, and search (single/all rows)","LangChain agent powered by GPT-4o for stepwise reasoning","Confirmation prompts and validations before destructive actions","Custom MCP tool to interact with Supabase securely from n8n nodes','Memory/context window to handle multi-turn conversations"],"target_audience":["Developers who manage Supabase databases","No-code/low-code builders wanting AI assistants","Product managers or PMs needing quick data edits without SQL access」「Ops/DBAs who want safe, auditable DB actions through chat"]}


name: Customer Feedback Analysis with AI, QuickChart & HTML Report Generator
author_name: hippolyte-hu
author_username: hippolyte-hu
categories: []
created_at: 2025-04-21T15:24:43.394Z
updated_at: 2025-04-21T15:24:43.394Z
url: https://n8n.io/workflows/3642
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  Generative Customer Insights from Feedback Data using AI Agents & Charts

  This workflow automates the analysis of customer feedback or social media data from Google Sheets using DeepSeek LLM, transforming raw text into structured semantic insights. The workflow also generates data visualizations and produces a final HTML report, ready for email delivery.

  Table of Contents
  What This Workflow Does
  Pre-conditions and Requirements
  Step-by-Step Workflow Explanation
  Example Results
  Customization Guide

  What This Workflow Does

  This workflow performs automated semantic analysis on unstructured feedback data (from Google Sheets), using LLM-based agents and a sequence of transformations. It achieves:

  Prompt proposal generation**: AI generates generalizable prompts for various analysis dimensions.
  Row-level analysis**: Each row of data is evaluated against all prompts.
  Output merging and refinement**: Raw analysis outputs are merged, deduplicated, and semantically clustered.
  Visualization and report generation**: QuickChart is used to create graphs, and an HTML report is produced.
  Email delivery**: The full report is sent automatically via Gmail.

  Pre-conditions and Requirements

  API Keys**
    DeepSeek API Key
    Gmail OAuth2 (for sending results)

  Google Sheet Access**
    A preformatted Google Sheet containing social listening feedback
    The sheet must include at least 20 rows for sample prompt generation

  n8n Configuration**
    Nodes used: Google Sheets, LangChain (LLM/Agent/Parser), Function, Merge, QuickChart (via URL), Gmail
    Ensure all credentials are configured properly in n8n’s credential manager

  Step-by-Step Workflow Explanation

  Google Sheet Import
     Retrieves feedback rows from a specific Google Sheet tab
     Filters to the first 20 rows for prompt generation

  Prompt Proposal Agent
     AI generates 3–6 row-level analysis prompts in a structured JSON format
     Prompts must be agnostic to product names and column headers

  Prompt Injection and Pairing
     Each row is paired with all prompts
     Combined into a single dataset for row-by-row LLM evaluation

  First Iteration of Analysis
     An LLM answers all injected prompts row-by-row
     Output is parsed and transformed into structured fields

  Semantic Merging and Refinement
     Merged lists of values from all rows
     AI clusters synonyms and regenerates improved prompt definitions

  Second Iteration of Analysis
     The refined prompts are used to re-analyze each row
     A new structured output per row is generated and merged into one object

  Summarization and Visualization
     AI generates summaries per dimension (e.g., sentiment)
     QuickChart visualizations are created and URL-encoded
     Cross-dimensional insights and a global narrative are generated

  Final Report Generation and Emailing
     A final HTML report is generated
     Sent to the specified email using Gmail node

  Example Results

  Customization Guide

  1. Modify Data Source
  Change the Google Sheet ID or sheet tab
  Add filters for specific time periods or product names

  2. Adjust Prompt Definitions
  Refine the initial prompt agent instruction to tailor the type of analysis

  3. Swap LLM Models
  Replace DeepSeek with OpenAI or another LLM via LangChain nodes

  4. Visual Styling
  Customize QuickChart configurations to adjust chart types, colors, legends

  5. Report Format
  Update the final HTML prompt to reflect brand design or restructure sections

  6. Add Report Destinations
  Replace Gmail with Google Drive upload, Notion page creation, or Slack post

  This end-to-end AI-powered social listening workflow enables scalable, repeatable, and customizable insights generation from unstructured customer feedback.

nodes_used:
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metadata_json:
  {"categories":["automation","integration","data processing","analytics","reporting"],"complexity":"complex","use_cases":["Customer feedback analysis","Social listening and monitoring","Sentiment and thematic analysis","Automated report generation and delivery","Data visualization and dashboarding"],"estimated_setup_minutes":60,"required_services":["DeepSeek LLM API (or swapable LLM via LangChain)","Google Sheets (data source)","Gmail (OAuth2) for email delivery","QuickChart (chart generation via URL)","n8n with LangChain/LLM nodes and configured credentials"],"key_features":["AI-generated prompt proposals for multi-dimensional analysis","Row-level LLM evaluation with prompt injection","Semantic merging, deduplication and clustering of concepts","Iterative refinement using agent-driven prompts","QuickChart visualizations URL-encoded into report assets」「Final HTML report assembly and automated emailing via Gmail"],"target_audience":["Data analysts","Product managers / Customer success teams","Marketing and social listening teams」、「Automation/DevOps engineers implementing n8n workflows"]}


name: 🌳 EU Green Legislation Tracker with GPT-4o, Google Sheets and Tasks
author_name: Samir Saci
author_username: samirsaci
categories: []
created_at: 2025-04-21T15:29:48.646Z
updated_at: 2025-04-21T15:29:48.646Z
url: https://n8n.io/workflows/3644
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  Tags: EU Legislation, Sustainability, Automation, Web Scraping, OpenAI, Google Sheets, Policy Monitoring, Climate

  Context

  Hey! I’m Samir, a Supply Chain Engineer and Data Scientist from Paris, and the founder of LogiGreen Consulting.

  We use AI, automation, and data to support sustainable business practices for small, medium and large companies.

  This workflow is part of our broader initiative to monitor and act on sustainability legislation in Europe.



  &gt; How do you know if new EU laws will impact your business's sustainability goals?

  This n8n workflow automatically scrapes the EU Parliament’s legislative portal to find and flag procedures related to environmental sustainability.

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

  Who is this template for?

  This workflow is useful for:
  Sustainability consultants** monitoring legal frameworks
  NGOs and researchers** tracking environmental regulations
  Companies* aligning with *CSRD* or *EU Green Deal** objectives
  Policy analysts** looking for automation tools

  What does it do?

  This n8n workflow:
  🌐 Scrapes the EU Parliament legislative portal for yesterday’s entries  
  🧠 Uses OpenAI to classify if each procedure is related to sustainability  
  🗂️ Filters out irrelevant items  
  📊 Saves the results in a Google Sheet  
  ✅ Creates a Google Task for each relevant file to review the legislation  

  How it works



  Trigger manually or on schedule
  Scrape HTML blocks for scheduled debates
  Parse each procedure to extract Title, Committee, Rapporteur, PDF link
  Call GPT-4-turbo to check if the topic matches sustainability criteria
  Filter responses based on “yes” or “no”
  Store valid items into Google Sheets
  Generate tasks in Google Tasks

  The AI only flags procedures that directly impact the environment, circular economy, or pollution control.

  What do I need to get started?

  You’ll need:
  A Google Sheet connected to your n8n instance  
  An OpenAI account with GPT-4 access  
  A Google Task List

  Follow the Guide!
  Follow the sticky notes in the workflow or check my tutorial to configure each node and start using AI to monitor sustainability regulations in Europe.



  🎥 Watch My Tutorial

  Notes

  AI filters are strict — you can customise the system prompt to match your needs  
  This is ideal for tracking legislative risk for climate regulations

  This workflow was built using n8n version 1.85.4  
  Submitted: April 21, 2025

nodes_used:
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metadata_json:
  {"categories":["automation","integration","data processing","monitoring","web scraping"],"complexity":"medium","use_cases":["Monitor new EU legislation related to sustainability","Automated compliance and regulatory risk tracking (CSRD, Green Deal)","Research/NGO monitoring of environmental policy changes","Generate review tasks for legal/policy teams","Aggregate and store legislative metadata for analysis"],"estimated_setup_minutes":30,"required_services":["n8n instance","OpenAI (GPT-4/GPT-4o) API access","Google Sheets","Google Tasks","EU Parliament legislative portal (public website) / HTTP access"],"key_features":["Scrapes EU Parliament legislative portal for recent entries","Parses procedure metadata (Title, Committee, Rapporteur, PDF link)","Uses GPT-4 to classify sustainability relevance","Filters irrelevant items and stores results in Google Sheets","Creates Google Tasks for each relevant procedure to assign review workstreamable tasks) (sic?)"],"target_audience":["Sustainability consultants","Policy analysts and researchers","Corporate compliance and sustainability teams (CSRD/ESG leads)"]}


name: 💬 Daily WhatsApp Group Summarizer – GPT-4o, Google Sheets & Evolution API
author_name: Amanda Benks
author_username: amanda
categories: []
created_at: 2025-04-21T15:35:02.399Z
updated_at: 2025-04-21T15:35:02.399Z
url: https://n8n.io/workflows/3646
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  Hey! I’m Amanda ❤️
  I made this little workflow with care for people like you who are part of busy WhatsApp groups and want a simple way to keep track of everything.

  It connects to Evolution API, collects all the group messages throughout the day, stores them in Google Sheets, and uses GPT-4o to generate a daily summary. The summary is saved as a document in Google Drive — ready to read, share, or archive.

  It’s perfect for teams, communities, classes, or any group that talks a lot but doesn’t want to miss important info.

  What it does

  Collects WhatsApp group messages using Evolution API  
  Saves the messages in Google Sheets (organized by date)  
  Creates a clean, structured summary using GPT-4o  
  Saves the summary in Google Drive as a doc  
  Can run daily at a set time (fully automated)

  How to set it up

  Connect your Evolution API and provide the group ID  
  Use this Google Sheets template  
  Connect Google Sheets and Google Drive in n8n  
  Add your OpenAI API key  
  (Optional) Adjust the AI prompt for a custom tone or structure

  ✅ Works on both n8n Cloud and Self-hosted  
  🔐 All credentials stay safe in n8n

  Want to customize this for your group or business?  
  ❤️ Buy Workflows: https://iloveflows.gumroad.com  
  💬 Hire My Services: +5517991557874 (WhatsApp)

  Tradução em Português (pt-br):
  Oi! Eu sou a Amanda 💬
  Se você participa de grupos movimentados no WhatsApp e quer transformar tudo isso em resumos diários organizadinhos, esse fluxo foi feito com todo carinho pra você!

  Ele conecta com a API da Evolution, coleta as mensagens trocadas em grupos, armazena tudo no Google Sheets, e no fim do dia gera um resumo completo usando GPT-4o. Esse resumo é salvo como documento no seu Google Drive — pronto pra ser lido, compartilhado ou arquivado.

  Ideal pra equipes, comunidades, projetos colaborativos ou até grupos de estudos 📚

  O que o fluxo faz

  Monitora e salva conversas do grupo no Google Sheets  
  Gera resumos diários com IA (formato estruturado e pronto pra leitura)  
  Salva o resumo como documento no seu Google Drive  
  Funciona com qualquer grupo conectado à sua conta Evolution  
  Pode ser agendado pra rodar automaticamente todo fim de dia

  Como configurar

  Conecte sua API Evolution e informe o ID do grupo  
  Use essa planilha modelo para armazenar as mensagens  
  Conecte sua conta do Google (Sheets + Drive)  
  Adicione sua chave da OpenAI  
  Personalize o prompt do resumo (opcional)

  ✅ Compatível com n8n Cloud e Auto-hospedado  
  🔐 Tudo seguro, simples e sem complicações

  Quer adaptar esse fluxo pro seu sistema?  
  ❤️ Buy Workflows: https://iloveflows.gumroad.com  
  💬 Hire My Services: +5517991557874 (WhatsApp)

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metadata_json:
  {"categories":["automation","integration","data processing","summarization","communication"],"complexity":"medium","use_cases":["Daily summaries of WhatsApp group conversations","Meeting or class highlights and action items","Community or project update digests","Archiving group discussions for search/compliance","Distributing concise reports to stakeholders"],"estimated_setup_minutes":20,"required_services":["Evolution API (WhatsApp)","Google Sheets","Google Drive","OpenAI (GPT-4o)","n8n (Cloud or self-hosted)"],"key_features":["Collects WhatsApp group messages via Evolution API","Stores messages in Google Sheets organized by date","Generates structured daily summary using GPT-4o","Saves summary as a Google Doc in Drive","Runs on a scheduled daily trigger (fully automated)"],"target_audience":["community managers","team leads and project managers","educators and course organizers`,`small business owners and admins"]}


name: 📥 Transform Google Drive Documents into Vector Embeddings
author_name: Alex Kim
author_username: alexk1919
categories: []
created_at: 2025-04-21T18:34:36.809Z
updated_at: 2025-04-21T18:34:36.809Z
url: https://n8n.io/workflows/3647
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:32
description:
  Automatically convert documents from Google Drive into vector embeddings using OpenAI, LangChain, and PGVector — fully automated through n8n.

  ⚙️ What It Does

  This workflow monitors a Google Drive folder for new files, supports multiple file types (PDF, TXT, JSON), and processes them into vector embeddings using OpenAI’s text-embedding-3-small model. These embeddings are stored in a Postgres database using the PGVector extension, making them query-ready for semantic search or RAG-based AI agents.

  After successful processing, files are moved to a separate “vectorized” folder to avoid duplication.

  💡 Use Cases

  Powering Retrieval-Augmented Generation (RAG) AI agents  
  Semantic search across private documents  
  AI assistant knowledge ingestion  
  Automated document pipelines for indexing or classification  

  🧠 Workflow Highlights

  Trigger Options:** Manual or Scheduled (3 AM daily by default)  
  Supported File Types:** PDF, TXT, JSON  
  Embedding Stack:** LangChain Text Splitter, OpenAI Embeddings, PGVector  
  Deduplication:** Files are moved after processing  
  License:** CC BY-SA 4.0  
  Author:** AlexK1919

  🛠 What You’ll Need

  Google Drive OAuth2** credentials (connected to Search Folder, Download File, and Move File nodes)  
  OpenAI API Key** (used in the Embeddings OpenAI node)  
  Postgres + PGVector** database (connected in the Postgres PGVector Store node)

  🔧 Step-by-Step Setup Instructions

  Create Google OAuth2 credentials in n8n and connect them to all Google Drive nodes.
  Set your source folder ID in the Search Folder node — this is where incoming files are placed.
  Set your processed folder ID in the Move File node — files will be moved here after vectorization.
  Ensure you have a PGVector-enabled Postgres instance and input the table name and collection in the Postgres PGVector Store node.
  Add your OpenAI credentials to the Embeddings OpenAI node and select text-embedding-3-small.
  Optional: Activate the Schedule Trigger node to run daily or configure your own schedule.
  Run manually by triggering When clicking ‘Test workflow’ for on-demand ingestion.

  🧩 Customization Tips

  Want to support more file types or enhance the pipeline?

  Add new extractors**: Use Extract from File with other formats like DOCX, Markdown, or HTML.
  Refine logic by file type**: The Switch node routes files to the correct extraction method based on MIME type (application/pdf, text/plain, application/json).
  Pre-process with OCR**: Add an OCR step before extraction to handle scanned PDFs or images.
  Add filters**: Enhance the Search Folder or Switch node logic to skip specific files or folders.

  📄 License

  This workflow is available under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. You are free to use, adapt, and share this workflow for non-commercial purposes under the terms of this license.

  Full license details: https://creativecommons.org/licenses/by-nc-sa/4.0/

nodes_used:
  ["@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","@n8n/n8n-nodes-langchain.vectorStorePGVector","n8n-nodes-base.manualTrigger","n8n-nodes-base.splitInBatches","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.extractFromFile","n8n-nodes-base.extractFromFile","n8n-nodes-base.extractFromFile","@n8n/n8n-nodes-langchain.embeddingsOpenAi"]

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metadata_json:
  {"categories":["automation","integration","data processing","ai"],"complexity":"medium","use_cases":["Build Retrieval-Augmented Generation (RAG) agents","Semantic search over private documents","Knowledge ingestion for AI assistants","Automated document indexing and classification pipelines"],"estimated_setup_minutes":45,"required_services":["Google Drive (OAuth2)","OpenAI API (text-embedding-3-small)","Postgres database with PGVector extension","n8n instance"],"key_features":["Monitors Google Drive folder for new files (manual or scheduled)","Supports PDF, TXT, JSON extraction (extensible to DOCX/MD/HTML)","LangChain text splitting for chunking","Generates embeddings via OpenAI text-embedding-3-small","Stores vectors in Postgres using PGVector for semantic search/RAG usage','Moves processed files to a 'vectorized' folder to avoid duplication"],"target_audience":["Developers","ML engineers","Data engineers/architects','AI product teams','Knowledge managers/Analysts"]}


name: Telegram bot for item multi select and saving to Postgres (Module "Checkbox")
author_name: Andrew
author_username: boanse
categories: []
created_at: 2025-04-21T19:58:39.148Z
updated_at: 2025-04-21T19:58:39.148Z
url: https://n8n.io/workflows/3648
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Who is this for?

  This template is for developers and automation specialists looking to create a Telegram bot that enables users to select items from a predefined list and save their choices to a Postgres database.

  What problem is this workflow solving?

  It provides a simple and efficient way to collect structured user input via Telegram and store it in a Postgres database, useful for inventory selection, order systems, or preference tracking.

  What this workflow does

  Displays a list of selectable options from a Postgres shop_list table in Telegram  
  Saves the user’s selection back to the database  
  Automatically deletes messages at each step to keep the chat clean  

  Setup

  1. Create the required tables in Postgres

  Replace "n8n" in the provided SQL script with the appropriate schema name for your database  
  Run the script to create the shop_list table  

  2. Add necessary credentials in n8n

  Telegram**: Connect your Telegram bot using the Bot Token  
  Postgres**: Add your Postgres DB credentials to allow the workflow to read/write data  

  How to customize this workflow to your needs

  Modify the shop_list table to include your specific options.
  Adjust the Telegram messages and logic to match your use case.

nodes_used:
  ["n8n-nodes-base.telegram","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.telegram","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.telegramTrigger","n8n-nodes-base.telegram","n8n-nodes-base.if","n8n-nodes-base.switch","n8n-nodes-base.switch","n8n-nodes-base.telegram","n8n-nodes-base.postgres","n8n-nodes-base.if","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.set","n8n-nodes-base.summarize","n8n-nodes-base.if","n8n-nodes-base.telegram","n8n-nodes-base.switch","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.telegram","n8n-nodes-base.code","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.telegram","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.telegram","n8n-nodes-base.postgres","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","chatbot","data collection","database"],"complexity":"medium","use_cases":["Collecting structured choices via Telegram (inventory selection)","Order or reservation selection systems","User preference tracking or settings","Quick multi-select surveys or RSVP flows","Syncing chat selections into a Postgres-backed system"],"estimated_setup_minutes":20,"required_services":["Telegram (Bot API)","Postgres database","n8n instance"],"key_features":["Displays selectable options from a Postgres shop_list table","Supports multi-select item selection via Telegram (Checkbox module)","Saves user selections back to Postgres","Automatically deletes intermediate messages to keep chat clean","Includes SQL table creation guidance and is customizable to your option list"],"target_audience":["Developers","Automation specialists","Integration engineers"]}


name: Automate WhatsApp tests and rank results with PostgreSQL (Module "Quiz")
author_name: Andrew
author_username: boanse
categories: []
created_at: 2025-04-21T20:00:01.680Z
updated_at: 2025-04-21T20:00:01.680Z
url: https://n8n.io/workflows/3649
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  Who is this for?

  This workflow is designed for educators, HR teams, or anyone who needs to create and distribute quizzes or tests via WhatsApp and automatically store and analyze responses in a PostgreSQL database.

  What problem is this workflow solving?

  Managing tests or quizzes manually across platforms can be time-consuming and error-prone. This workflow automates the entire process — from test creation to distribution and result collection — providing an efficient way to manage assessments at scale.

  What this workflow does

  Adds test content (including questions and answers) to a PostgreSQL database.
  Allows users to take the test directly via WhatsApp.
  Displays a final ranking to test takers upon completion.

  Setup

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

  2. Add required credentials
  WhatsApp**: Set up OAuth and connect your WhatsApp account.
  PostgreSQL**: Add your Postgres DB credentials to n8n.

  How to customize this workflow to your needs

  Update the test questions and answers directly in the database.
  Modify the bot flow to suit your test structure or messaging preferences.
  Extend the ranking logic based on specific grading criteria or scoring rules.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.whatsApp","n8n-nodes-base.postgres","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.whatsApp","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.stickyNote","n8n-nodes-base.postgres","n8n-nodes-base.set","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.sort","n8n-nodes-base.if","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.set","n8n-nodes-base.summarize","n8n-nodes-base.postgres","n8n-nodes-base.whatsAppTrigger","n8n-nodes-base.whatsApp","n8n-nodes-base.if","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.whatsApp","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.whatsApp","n8n-nodes-base.postgres","n8n-nodes-base.whatsApp","n8n-nodes-base.postgres","n8n-nodes-base.postgres","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":["education","communication","automation","integration","data storage"],"complexity":"complex","use_cases":["Distribute quizzes/tests via WhatsApp","Collect and store responses in PostgreSQL","Automatically rank and display results to participants","HR assessments and candidate screening","Remote learning and training evaluations"],"estimated_setup_minutes":45,"required_services":["WhatsApp (WhatsApp Business API or provider)","PostgreSQL","n8n platform"],"key_features":["Add and manage test content in PostgreSQL","Deliver quizzes and capture answers over WhatsApp","Automated response storage and processing","Score calculation and final ranking display","Customizable bot flow and question set via DB updates"],"target_audience":["Educators and teachers","HR teams and recruiters","Training and L&D teams','Community managers and event organizers','Developers/consultants building assessment flows"]}


name: Auto-reply to FAQs on WhatsApp using Postgres (Module "FAQ")
author_name: Andrew
author_username: boanse
categories: []
created_at: 2025-04-21T20:01:28.735Z
updated_at: 2025-04-21T20:01:28.735Z
url: https://n8n.io/workflows/3650
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  Who is this for?

  This workflow is designed for businesses or individuals who want to automate responses to frequently asked questions via WhatsApp, while managing their question-and-answer database using Postgres.

  What problem is this workflow solving?

  This workflow addresses the challenge of efficiently managing and automating responses to customer inquiries. It helps reduce manual effort and ensures quick access to information, while providing an option for customers to request live assistance when needed.

  What this workflow does

  It allows you to store questions and answers in a Postgres database.
  A link to a bot is shared with customers, enabling them to read the available questions and answers.
  If a customer does not find an answer to their query, they can request a consultation with a manager.

  Setup

  Create tables in Postgres DB  
     Replace "n8n" in the provided SQL script with the name of your schema in the database.
     Run the SQL script in your database to set up the necessary tables. (The script is available in the workflow.)

  Add Credentials  
     Set up the WhatsApp integration (OAuth and Account credentials).
     Connect the Postgres database by adding the necessary credentials.

  How to customize this workflow to your needs

  Modify the Postgres schema name in the SQL script to match your database configuration.
  Update the questions and answers in the database to suit the information you want to provide via the bot.
  Customize the WhatsApp integration settings to match your account credentials and API details.

nodes_used:
  ["n8n-nodes-base.whatsAppTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.whatsApp","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.switch","n8n-nodes-base.whatsApp","n8n-nodes-base.set","n8n-nodes-base.summarize","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.whatsApp","n8n-nodes-base.whatsApp","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.whatsApp","n8n-nodes-base.postgres","n8n-nodes-base.whatsApp","n8n-nodes-base.postgres","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.whatsApp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","customer_support","messaging","database_integration"],"complexity":"medium","use_cases":["Automate FAQ responses over WhatsApp","Manage Q&A knowledge base in Postgres","Offer option to escalate to a live agent","Reduce response time and manual workload for support teams"],"estimated_setup_minutes":45,"required_services":["WhatsApp Business API / Meta WhatsApp (OAuth)","Postgres database","n8n instance"],"key_features":["Store and retrieve questions & answers from Postgres","Auto-respond to user FAQs via WhatsApp","Link/share bot with customers to browse FAQs","Fallback path to request live consultation with a manager","Customizable Postgres schema via provided SQL script"],"target_audience":["Small-to-medium businesses","Customer support teams","Developers/automation engineers implementing chatbots on WhatsApp\",\"Operations or community managers responsible for user inquiries"]}


name: Automated Customer Reservations via Telegram and PostgreSQL (Module "Booking")
author_name: Andrew
author_username: boanse
categories: []
created_at: 2025-04-21T20:02:55.145Z
updated_at: 2025-04-21T20:02:55.145Z
url: https://n8n.io/workflows/3651
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  Who is this for?  
  This workflow is for businesses or services that require managing customer reservations or appointments through a Telegram bot and storing reservation details in a PostgreSQL database.

  What problem is this workflow solving?  
  This workflow automates the process of capturing reservation data via a Telegram bot and storing it in a PostgreSQL database. It eliminates the need for manual entry and reduces errors, improving efficiency in managing bookings.

  What this workflow does  
  The workflow allows customers to select a day and time for their reservation through a Telegram bot.
  Customers provide their phone number and name, which are then added to the reservation table in the PostgreSQL database.
  Successfully added reservations are confirmed via the bot, ensuring both the business and customer are updated.

  Setup  
  Create tables in Postgres DB  
     Replace "n8n" in the provided SQL script with the name of your schema in the database.
     Execute the SQL script to set up the required tables.

  Add Credentials  
     Add your Telegram bot credentials.
     Add your PostgreSQL database credentials to the workflow for seamless integration.

  How to customize this workflow to your needs  
  Modify the available days and times in the Telegram bot according to your business hours or schedule.
  Adjust the database schema or add additional fields for customer preferences, special requests, or payment information as needed.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.if","n8n-nodes-base.switch","n8n-nodes-base.stickyNote","n8n-nodes-base.whatsApp","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.whatsAppTrigger","n8n-nodes-base.switch","n8n-nodes-base.set","n8n-nodes-base.postgres","n8n-nodes-base.whatsApp","n8n-nodes-base.whatsApp","n8n-nodes-base.set","n8n-nodes-base.summarize","n8n-nodes-base.postgres","n8n-nodes-base.if","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.merge","n8n-nodes-base.set","n8n-nodes-base.summarize","n8n-nodes-base.merge","n8n-nodes-base.set","n8n-nodes-base.switch","n8n-nodes-base.postgres","n8n-nodes-base.if","n8n-nodes-base.whatsApp","n8n-nodes-base.whatsApp","n8n-nodes-base.whatsApp","n8n-nodes-base.whatsApp","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.whatsApp","n8n-nodes-base.postgres","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.whatsApp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 4184 characters]

metadata_json:
  {"categories":["automation","integration","customer service","scheduling"],"complexity":"medium","use_cases":["Chat-based appointment and reservation booking","Automated confirmation messaging to customers","Centralized storage of bookings in a PostgreSQL database","Reducing manual entry and booking errors"],"estimated_setup_minutes":45,"required_services":["Telegram Bot API","PostgreSQL database","n8n (workflow automation platform)"],"key_features":["Interactive Telegram bot for selecting day and time","Collects customer name and phone number","Persists reservations into PostgreSQL","Sends confirmation messages on successful booking","Configurable available days/times and database schema (SQL script included)"],"target_audience":["Small businesses (restaurants, salons, clinics)","Operations and customer service teams","Developers implementing chat-based bookings and automations\",\"No-code/low-code automation users"]}


name: WhatsApp Product Catalog Bot with PostgreSQL Database
author_name: Andrew
author_username: boanse
categories: []
created_at: 2025-04-21T20:04:07.361Z
updated_at: 2025-04-21T20:04:07.361Z
url: https://n8n.io/workflows/3652
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  Who is this for?
  This workflow is designed for businesses or developers who want to integrate product information into a WhatsApp bot and allow users to retrieve details about products from a database.

  What problem is this workflow solving?
  This workflow automates the process of managing and retrieving product information via WhatsApp, allowing businesses to easily share product details with customers without manual interaction.

  What this workflow does
  Basis version:
  It adds product data to a Postgres database.
  It enables a WhatsApp bot to retrieve a list of products.
  Users can select a product to receive detailed information about it.

  Additional version:
  All features from Basis Version.
  Get a list of product categories.
  Get a list of products in a category.
  Add product to cart.
  Go to the cart or select more products.
  Remove unnecessary items in the cart or clear the entire cart.
  When all the desired items are in the cart, click Buy.
  The bot will send you a payment link.

  Setup
  Create Tables in Postgres DB
     Modify the SQL script to replace "n8n" with your schema name.
     Run the provided SQL script in your database (available in the workflow).

  Add Credentials
     Add WhatsApp credentials (OAuth, Account).
     Add Postgres credentials to connect the bot to your database.

  How to customize this workflow to your needs
  Update the database schema or table structure if you need additional product information.
  Modify the bot interaction to suit your specific product listing and display preferences.

nodes_used:
  ["n8n-nodes-base.whatsAppTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.whatsApp","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.if","n8n-nodes-base.switch","n8n-nodes-base.whatsApp","n8n-nodes-base.set","n8n-nodes-base.summarize","n8n-nodes-base.set","n8n-nodes-base.postgres","n8n-nodes-base.whatsApp","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.if","n8n-nodes-base.whatsApp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","e-commerce","messaging","data processing"],"complexity":"medium","use_cases":["Expose product catalog over WhatsApp","Allow customers to browse products and categories","Manage shopping cart via chat","Send payment links for purchases","Sync product data with PostgreSQL"],"estimated_setup_minutes":45,"required_services":["WhatsApp Business API (or WhatsApp credentials)","PostgreSQL database","Payment link provider or endpoint (e.g., Stripe/PayPal)","n8n instance"],"key_features":["Add and store products in Postgres","Retrieve product lists and details via WhatsApp bot","Browse by category and list products in a category","Cart management: add, remove, clear, view cart","Checkout flow sending payment link to user via WhatsApp"],"target_audience":["Developers","E‑commerce businesses and merchants","Customer support teams automating sales via chatbots','Small to medium businesses looking to sell via WhatsApp"]}


name: Collect WhatsApp questionnaire responses in Postgres (Module "Anketa")
author_name: Andrew
author_username: boanse
categories: []
created_at: 2025-04-21T20:05:33.864Z
updated_at: 2025-04-21T20:05:33.864Z
url: https://n8n.io/workflows/3653
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Who is this for?
  This workflow is designed for businesses or organizations looking to collect and store user responses from questionnaires via a WhatsApp bot, with data saved in a Postgres database.

  What problem is this workflow solving?
  This workflow streamlines the process of collecting and managing user responses to questionnaires. It helps automate data collection from multiple users and stores the responses in a centralized database for easy access and analysis.

  What this workflow does
  Basis version:
  Add questions to the anketa_question table.
  Save the responses of users who complete the questionnaire in whatsapp into the database.

  Additional version:
  All features from basis version.
  The bot sends a lead magnet from Google Drive after filling out the form.
  If the user has not answered all the questions, the bot sends a reminder after a certain time (configurable).

  Setup
  Create Tables in Postgres DB  
     Replace "n8n" in the provided SQL script with your database schema name.
     Run the SQL script in your Postgres database (this is available within the workflow).

  Add Credentials  
     Set up WhatsApp OAuth credentials and account details.
     Set up Postgres credentials to connect the workflow to your database.

  How to customize this workflow to your needs
  Modify the questions in the anketa_question table to suit your specific survey or data collection requirements.
  Adjust the WhatsApp link and communication flow to align with your branding or messaging needs.
  Customize the database table schema if you need to capture additional fields or change the format of the data stored.

nodes_used:
  ["n8n-nodes-base.whatsAppTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.whatsApp","n8n-nodes-base.postgres","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.whatsApp","n8n-nodes-base.set","n8n-nodes-base.whatsApp","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.whatsApp","n8n-nodes-base.whatsApp","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.whatsApp","n8n-nodes-base.postgres","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","data collection","messaging"],"complexity":"medium","use_cases":["Collect questionnaire/survey responses via WhatsApp","Store and centralize responses in Postgres for analysis","Send lead magnets (files/links) after completion","Send configurable reminders for incomplete questionnaires","Automate follow-ups and data capture for marketing or support"],"estimated_setup_minutes":30,"required_services":["WhatsApp Business API / OAuth credentials","Postgres database","Google Drive (optional, for lead magnet delivery)"],"key_features":["Questions stored in anketa_question table (SQL provided)","Saves WhatsApp user responses to Postgres","Optional delivery of lead magnet from Google Drive after completion","Configurable reminder messages for incomplete surveys","Customizable database schema and WhatsApp flow via n8n nodes"],"target_audience":["Marketing teams","Customer success/support teams","Small to medium businesses collecting user feedback or leads (via WhatsApp)`,`Developers and automation engineers implementing messaging workflows"]}


name: Auto-create and publish AI social videos with Telegram, GPT-4 and Blotato
author_name: Dr. Firas
author_username: drfiras
categories: []
created_at: 2025-04-21T23:28:43.913Z
updated_at: 2025-04-21T23:28:43.913Z
url: https://n8n.io/workflows/3654
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-30 08:33:37
description:
  Auto-create and publish AI social videos with Telegram, GPT-4 and Blotato

  &gt; ⚠️ Disclaimer: This workflow uses Community Nodes and must be run on a self-hosted instance of n8n.

  Who is this for?

  This template is perfect for social media managers, content creators, AI enthusiasts, and automation pros who want to generate short-form videos (Reels) from a simple Telegram message, then publish them across multiple platforms—all without video editing or manual uploads.

  What problem is this workflow solving?

  Creating content is only half the job. The real bottleneck comes in:
  Rendering the video,
  Adding voice or music,
  Writing captions and titles,
  Publishing to multiple platforms.

  This workflow automates all of that using AI. It saves hours every week and guarantees consistent output.

  What this workflow does

  This end-to-end automation handles everything from AI video generation to social publishing:

  Starts with a Telegram message (text or image prompt)
  Generates video using Kling or Blotato, based on the input
  Creates music with Piapi and merges it with the video
  Generates text overlays and captions with GPT-4
  Builds a stylized video using JSON2Video
  Logs results to Google Sheets
  Sends final output back to Telegram
  Auto-posts the video to 9 platforms via Blotato (Instagram, TikTok, YouTube, Facebook, LinkedIn, Threads, Twitter/X, Pinterest, Bluesky)

  Setup

  Connect your Telegram bot to the trigger node.
  Add your OpenAI API key for all GPT nodes.
  Set up Kling and Piapi API access (for video and music generation).
  Connect your Cloudinary account to upload images.
  Link a Google Sheet with columns: Title, Caption, URL.
  Set your Blotato API key and fill in the platform-specific account IDs.

  How to customize this workflow to your needs

  Change prompt formatting** to control GPT responses and video tone.
  Edit text styling** in JSON2Video to match your brand.
  Add a Telegram approval step** before publishing, if needed.
  Disable platforms** you don’t use by deleting their HTTP Request nodes.
  Use a Google Sheet filter** to only process new rows or drafts.


  📄 Documentation: Notion Guide

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.telegramTrigger","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.telegram","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.googleSheets","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.set"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","social media","content creation","AI-generated video","integration"],"complexity":"complex","use_cases":["Create short-form social videos (Reels/TikToks) from Telegram prompts","Automate end-to-end content generation (script, media, audio, captions)","Publish AI-generated videos across multiple platforms","Log and track content metadata in spreadsheets","Quick prototyping of AI-driven social campaigns"],"estimated_setup_minutes":90,"required_services":["n8n self-hosted instance (community nodes enabled)","Telegram Bot (for trigger messages)","OpenAI GPT-4 (or compatible LLM API)","Blotato account/API (for video publishing/distribution)","Publicly reachable webhook endpoint (or tunneling like ngrok)","Spreadsheet service (e.g., Google Sheets) or other logging storage"],"key_features":["Trigger workflow from a Telegram message","Use GPT-4 to generate video scripts/captions and metadata","AI/ML nodes to create or assemble video assets (audio, visuals, captions)","Automated publishing to platforms via Blotato","Tracking/logging of outputs in spreadsheets and workflow notes (stickyNote nodes)"],"target_audience":["social media managers","content creators","AI enthusiasts experimenting with video automation"]}


name: Create Animated Stories using GPT-4o-mini, Midjourney, Kling and Creatomate API
author_name: PiAPI
author_username: piapi
categories: []
created_at: 2025-04-22T01:47:37.016Z
updated_at: 2025-04-22T01:47:37.016Z
url: https://n8n.io/workflows/3655
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
description:
  What does the workflow do? 
  This workflow is designed to generate high-quality short videos, primarily uses GPT-4o-mini (unofficial), Midjourney (unofficial) and Kling (unofficial) APIs from PiAPI and Creatomate API mainly for content creator, social media bloggers and short-form video creators. Through this short video workflow, users can quickly validate their creative ideas and focus more on enhancing the quality of their video concepts.

  Who is the workflow for?
  Social Media Influencers: produce content videos based on inspiration efficiently.

  Vloggers: generate vlogs based on inspiration.

  Educational Creators: explain specific topics via animated short videos or demonstrate a specific imagined scenario to students for enhanced educational impact.

  Advertising Agencies: generate short videos based on specific products.

  AI Tool Developers: automatically generate product demo videos.

  Step-by-step Instructions
  Fill in X-API-key of PiAPI account in Basic Params node.
  Fill in the scenario of the image and video prompt.
  Set a video template on Creatomate and make an API call in the final node with core and processing modules provided in Creatomate. Before full video generation, you can first use basic assets in Creatomate for a prototype demo, then integrate with n8n after verifying the expected results.

  Fill in your Creatomate account settings following the image guildline.
  Click Test Workflow and wait for a generation (within 10~20min).

  In this workflow, we've established a basic structure for image-to-video generation with subtitle integration. You can further enhance it by adding music nodes using either PiAPI's audio models or your preferred music solution. All video elements will ultimately be composited through Creatomate. For best practice, please refer to PiAPI's official API documentation or Creatomate's API documentation to comprehend more use cases.

  Use Case
  Params Settings
   style: a children’s book cover, ages 6-10. --s 500 --sref 4028286908  --niji 6

  character: A gentle girl and a fluffy rabbit explore a sunlit forest together, playing by a sparkling stream
  situational_keywords: Butterflies flutter around them as golden sunlight filters through green leaves. Warm and peaceful atmosphere

  Output Video
   

nodes_used:
  ["n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.switch","n8n-nodes-base.switch","n8n-nodes-base.switch","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.wait","n8n-nodes-base.wait","n8n-nodes-base.wait","n8n-nodes-base.wait","n8n-nodes-base.switch","n8n-nodes-base.switch","n8n-nodes-base.switch","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","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, 7200 characters]

metadata_json:
  {"categories":["automation","content creation","video production","AI-generated media"],"complexity":"complex","use_cases":["Short-form social video generation","Image-to-video conversion with subtitles","Rapid prototyping of video concepts","Educational explainer videos","Product demo and ad video creation"],"estimated_setup_minutes":20,"required_services":["PiAPI (GPT-4o-mini, Midjourney, Kling via PiAPI) - X-API-key required","Creatomate API / Creatomate account"],"key_features":["Automated image-to-video pipeline","Prompt generation using GPT-4o-mini","Image generation via Midjourney/Kling (via PiAPI)","Video compositing and rendering with Creatomate","Subtitle integration into videos','Prototype mode with basic assets for validation','Supports adding audio/music (PiAPI audio models or external)"],"target_audience":["Social media influencers","Vloggers","Educational content creators/teachers on video-based lessons and scenarios simulations for students (animated shorts) — use for classroom engagement or e-learning modules — simplified workflow to generate illustrative videos from prompts — low-code video prototyping tech demoers — marketing and advertising teams seeking quick product video variants — AI tool developers building automated demo generators — short-form video creators and bloggers"]}


name: AI-Powered Telegram Task Manager with MCP Server
author_name: Francis Njenga
author_username: gatura
categories: []
created_at: 2025-04-22T04:27:33.277Z
updated_at: 2025-04-22T04:27:33.277Z
url: https://n8n.io/workflows/3656
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
description:
  Detailed Description
  The ToDo App workflow is designed to streamline task management through Telegram and Google Tasks integration. This workflow allows users to create, update, and manage tasks via Telegram messages, leveraging AI capabilities to enhance user interaction. The expected outcome is a seamless experience where users can manage their tasks efficiently without needing to switch between applications.

  Who is this for?
  This workflow is intended for:
  Individuals** looking for an efficient way to manage their tasks directly from Telegram.
  Teams** that require a collaborative task management solution integrated with Google Tasks.
  Developers** interested in automating task management processes using n8n and Telegram.

  What problem does this workflow solve?
  Managing tasks can often be cumbersome, especially when switching between different applications. This workflow addresses the following problems:
  Fragmented Task Management**: Users can manage tasks directly from Telegram, reducing the need to switch to Google Tasks.
  Inefficient Communication**: By integrating AI, users can interact with the task management system in a conversational manner, making it more intuitive.
  Task Updates**: Users can easily update task statuses and details through simple messages, enhancing productivity.

  What this workflow does
  The ToDo App workflow performs the following functions:
  Incoming Message Handling: Listens for messages sent to a Telegram bot.
  Task Creation: Allows users to create new tasks based on their messages.
  Task Updates: Users can update existing tasks by sending specific commands.
  Task Retrieval: Retrieves today's and upcoming tasks from Google Tasks.
  Voice Note Transcription: Supports voice messages, converting them into text for task management.
  AI Assistance: Utilizes an AI agent to assist users in managing their tasks effectively.

  Setup
  Prerequisites
  Before setting up the workflow, ensure you have the following:
  n8n Account**: Sign up for an n8n account if you don't have one.
  Telegram Bot**: Create a Telegram bot and obtain the API token.
  Google Tasks API**: Set up Google Tasks API and obtain OAuth2 credentials.
  OpenAI API Key**: Sign up for OpenAI and obtain an API key for AI functionalities.

  Setup Process
  Upload the JSON for this workflow and setup the authentication for the different tools.

  How to customize this workflow
  To adapt the ToDo App workflow to different needs, consider the following customizations:
  Change Task Management Platform**: If you prefer a different task management tool, replace the Google Tasks nodes with your preferred service's API.
  Modify AI Responses**: Adjust the AI agent's system message to change how it interacts with users.
  Add Additional Commands**: Expand the workflow by adding more commands for different task management functionalities (e.g., deleting tasks).
  Integrate Other Messaging Platforms**: If you want to use a different messaging service, replace the Telegram nodes with the appropriate nodes for that service.

  Conclusion
  The ToDo App workflow provides a powerful solution for managing tasks through Telegram, enhancing productivity and user experience. By following the setup instructions and customization options, users can tailor the workflow to meet their specific needs, making task management more efficient and accessible.

nodes_used:
  ["n8n-nodes-base.telegramTrigger","@n8n/n8n-nodes-langchain.mcpTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.mcpClientTool","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.set","n8n-nodes-base.googleTasksTool","n8n-nodes-base.set","n8n-nodes-base.telegram","n8n-nodes-base.googleTasksTool","n8n-nodes-base.googleTasksTool","n8n-nodes-base.googleTasksTool","n8n-nodes-base.googleTasksTool","n8n-nodes-base.switch","n8n-nodes-base.set","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","productivity","communication"],"complexity":"complex","use_cases":["Manage personal tasks via Telegram messages","Team task collaboration with Google Tasks sync","Create and update tasks using conversational AI","Transcribe voice notes into tasks","Automate task retrieval (today/upcoming) and notifications"],"estimated_setup_minutes":45,"required_services":["n8n","Telegram Bot API (Bot token)","Google Tasks API (OAuth2 credentials)","OpenAI API (or other AI provider for agent & transcription)","MCP server / mcpClientTool (as used by the workflow)"],"key_features":["Create, update, and retrieve Google Tasks from Telegram","AI-powered conversational agent for task handling","Voice message transcription to text tasks","Support for multiple users/teams via Telegram","Customizable AI prompts and extendable command set"],"target_audience":["individuals","teams","developers interested in automation"]}


name: Build a Chatbot, Voice and Phone Agent with Voiceflow, Google Calendar and RAG
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-04-22T06:42:30.604Z
updated_at: 2025-04-22T06:42:30.604Z
url: https://n8n.io/workflows/3657
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-30 08:33:37
description:
  Voiceflow is a no-code platform that allows you to design, prototype, and deploy conversational assistants across multiple channels—such as chat, voice, and phone—with advanced logic and natural language understanding. It supports integration with APIs, webhooks, and even tools like Twilio for phone agents. It's perfect for building customer support agents, voice bots, or intelligent assistants.

  This workflow connects n8n and Voiceflow with tools like Google Calendar, Qdrant (vector database), OpenAI, and an order tracking API to power a smart, multi-channel conversational agent.



  There are 3 main webhook endpoints in n8n that Voiceflow interacts with:

  n8n_order – receives user input related to order tracking, queries an API, and responds with tracking status.
  n8n_appointment – processes appointment booking, reformats date input using OpenAI, and creates a Google Calendar event.
  n8n_rag – handles general product/service questions using a RAG (Retrieval-Augmented Generation) system backed by:
     Google Drive document ingestion,
     Qdrant vector store for search,
     and OpenAI models for context-based answers.

  Each webhook is connected to a corresponding "Capture" block inside Voiceflow, which sends data to n8n and waits for the response.

  How It Works  
  This n8n workflow integrates Voiceflow for chatbot/voice interactions, Google Calendar for appointment scheduling, and RAG (Retrieval-Augmented Generation) for knowledge-based responses. Here’s the flow:  

  Trigger**:  
    Three webhooks (n8n_order, n8n_appointment, n8n_rag) receive inputs from Voiceflow (chat, voice, or phone calls).  
    Each webhook routes requests to specific functions:  
      Order Tracking: Fetches order status via an external API.  
      Appointment Scheduling: Uses OpenAI to parse dates, creates Google Calendar events, and confirms via WhatsApp.  
      RAG System: Queries a Qdrant vector store (populated with Google Drive documents) to answer customer questions using GPT-4.  

  AI Processing**:  
    OpenAI Chains: Convert natural language dates to Google Calendar formats and generate responses.  
    RAG Pipeline: Embeds documents (via OpenAI), stores them in Qdrant, and retrieves context-aware answers.  
    Voiceflow Integration: Routes responses back to Voiceflow for multi-channel delivery (chat, voice, or phone).  

  Outputs**:  
    Confirmation messages (e.g., "Event created successfully").  
    Dynamic responses for orders, appointments, or product support. 

  Setup Steps  

  Prerequisites:  
  APIs**:  
    Google Calendar & Drive OAuth credentials.  
    Qdrant vector database (hosted or cloud).  
    OpenAI API key (for GPT-4 and embeddings).  

  Configuration:  
  Qdrant Setup:  
     Run the "Create collection" and "Refresh collection" nodes to initialize the vector store.  
     Populate it with documents using the Google Drive → Qdrant pipeline (embeddings generated via OpenAI).  

  Voiceflow Webhooks:  
     Link Voiceflow’s "Captures" to n8n’s webhook URLs (n8n_order, n8n_appointment, n8n_rag).  

  Google Calendar:  
     Authenticate the Google Calendar node and set event templates (e.g., summary, description).  

  RAG System:  
     Configure the Qdrant vector store and OpenAI embeddings nodes.  
     Adjust the Retrieve Agent’s system prompt for domain-specific queries (e.g., electronics store support).   

  Optional:  
  Add Twilio for phone-agent capabilities.  
  Customize OpenAI prompts for tone/accuracy.  




  PS. You can import a Twilio number to assign it to your agent for becoming a Phone Agent

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

nodes_used:
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workflow_json_compressed:
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metadata_json:
  {"categories":["automation","conversational AI","integration","voice/telephony","knowledge management"],"complexity":"complex","use_cases":["Multi‑channel chatbot (chat, voice, phone)","Customer support and phone agent","Appointment scheduling and calendar-aware assistants","Retrieval‑Augmented Generation (RAG) for knowledge lookup","Automated call routing and voice interactions"],"estimated_setup_minutes":120,"required_services":["n8n","Voiceflow","Google Calendar API","Qdrant (vector database)","LLM provider (e.g., OpenAI, Anthropic)","Google Drive API","Twilio (or other telephony provider for phone channels)","Public webhook endpoint/hosting"],"key_features":["Multi‑channel assistant deployment (chat, voice, phone)","RAG using Qdrant vector store for knowledge retrieval","Google Calendar integration for availability and scheduling","Webhook driven flows and structured output parsing","AI/ML nodes for prompt/response processing and intent handling (RAG + LLM)」「Google Drive integration for document ingestion/attachments"],"target_audience":["Developers and integrators","Conversation designers and no‑code builders","Customer support and operations teams implementing bots/phone agents 」「Product teams building voice-enabled assistants"]}


name: Business Model Canvas AI-Powered Generator (LLM Flexible)
author_name: Sina
author_username: sina2266
categories: []
created_at: 2025-04-22T20:10:18.366Z
updated_at: 2025-04-22T20:10:18.366Z
url: https://n8n.io/workflows/3663
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:32
description:
  👥 Who is this for?

  Startup founders validating or pitching new ideas  
  Business consultants running strategy sessions  
  Product teams defining business logic visually  
  Agencies offering planning frameworks to clients  

  ❓ What problem does this workflow solve?

  Creating a Business Model Canvas manually is time-consuming and often scattered across tools. This workflow solves that by allowing users to generate a fully populated, formatted, and printable Business Model Canvas in seconds using the power of AI, all structured in a professional A4 landscape layout.

  ⚙️ What this workflow does

  Starts with a chat input asking for your business idea  
  Sends it to 9 separate AI agents, each focused on one section:
    Key Partners  
    Key Activities  
    Value Proposition  
    Customer Relationships  
    Customer Segments  
    Key Resources  
    Channels  
    Cost Structure  
    Revenue Streams  
  Uses your preferred LLM (see below) to generate meaningful bullet points  
  Converts output into a specific format  
  Merges all sections into a clean, A4-styled HTML canvas  
  Exports the result as a downloadable .html file

  🛠️ Setup

  Import the workflow into your n8n instance  
  Start the flow from the “When chat message received” node  
  Describe your business idea when prompted (e.g., “Online bookshop with rare Persian literature”)  
  Wait for AI processing to complete  
  Visit the last node “HTML code to HTML file”  
  Click Download to get your final canvas in .html format  

  🤖 LLM Flexibility (Choose Your Model)

  This template supports any AI model with a chat interface:

  Ollama (self-hosted models like LLaMA, etc.)  
  OpenAI (GPT-4, GPT-3.5)  
  Anything with a compatible node

  You can easily change the LLM by updating the Language Model Node.  
  No need to modify any other logic or formatting.

  🧪 How to customize this workflow

  Change the LLM** model from the Ollama node to OpenAI, etc.  
  Modify the final HTML layout in the “Turn to HTML” node  
  Add a PDF export, email delivery, or Google Drive sync  
  Replace the chat trigger with a webform, CRM hook, etc.

  ✅ Requirements

  A working LLM integration (Ollama or OpenAI recommended)
  n8n (self-hosted or cloud)

  📌 Notes

  Sticky notes included for setup and instructions  
  Each node clearly named by function (e.g. "Customer Segments Generator")  
  Designed for speed, structure, and professional presentation  

  📩 Need help?

  For setup questions, custom features, or LLM integration support, contact:  
  sinamirshafiee@gmail.com

nodes_used:
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metadata_json:
  {"categories":["automation","AI-powered content generation","document generation","business planning"],"complexity":"medium","use_cases":["Rapidly generate a complete Business Model Canvas from a short idea prompt","Prepare investor or internal pitch materials","Run client strategy or consulting sessions with structured outputs","Embed canvas generation into product workflows or CRMs"],"estimated_setup_minutes":15,"required_services":["n8n (self-hosted or cloud)","LLM provider with chat API (Ollama, OpenAI, or any compatible chat model)"],"key_features":["Nine specialized AI agents each generate one BMC section","Flexible LLM node—swap Ollama/OpenAI without changing logic","Merges section outputs into a printable A4 landscape HTML canvas","Exports final result as a downloadable .html file","Simple chat-triggered workflow start and clear, named nodes for maintainability"],"target_audience":["Startup founders","Business consultants and strategists","Product managers and teams defining business logic visually」「Agencies offering planning frameworks to clients"]}


name: Automated Real Estate Property Lead Scoring with BatchData
author_name: Preston Zeller
author_username: zellerhaus
categories: []
created_at: 2025-04-22T20:19:04.372Z
updated_at: 2025-04-22T20:19:04.372Z
url: https://n8n.io/workflows/3664
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  How It Works
  This workflow automates the real estate lead qualification process by leveraging property data from BatchData. The automation follows these steps:

  When a new lead is received through your CRM webhook, the workflow captures their address information
  It then makes an API call to BatchData to retrieve comprehensive property details
  A sophisticated scoring algorithm evaluates the lead based on property characteristics like:

  Property value (higher values earn more points)
  Square footage (larger properties score higher)
  Property age (newer constructions score higher)
  Investment status (non-owner occupied properties earn bonus points)
  Lot size (larger lots receive additional score)

  Leads are automatically classified into categories (high-value, qualified, potential, or unqualified)
  The workflow updates your CRM with enriched property data and qualification scores
  High-value leads trigger immediate follow-up tasks for your team
  Notifications are sent to your preferred channel (Slack in this example)

  The entire process happens within seconds of receiving a new lead, ensuring your sales team can prioritize the most valuable opportunities immediately..

  Who It's For
  This workflow is perfect for:

  Real estate agents and brokers looking to prioritize high-value property leads
  Mortgage lenders who need to qualify borrowers based on property assets
  Home service providers (renovators, contractors, solar installers) targeting specific property types
  Property investors seeking specific investment opportunities
  Real estate marketers who want to segment audiences by property value
  Home insurance agents qualifying leads based on property characteristics

  Any business that bases lead qualification on property details will benefit from this automated qualification system.

  About BatchData
  BatchData is a comprehensive property data provider that offers detailed information about residential and commercial properties across the United States. Their API provides:

  Property valuation and estimates
  Ownership information
  Property characteristics (size, age, bedrooms, bathrooms)
  Tax assessment data
  Transaction history
  Occupancy status (owner-occupied vs. investment)
  Lot details and dimensions

  By integrating BatchData with your lead management process, you can automatically verify and enrich leads with accurate property information, enabling more intelligent lead scoring and routing based on actual property characteristics rather than just contact information.

  This workflow demonstrates how to leverage BatchData's property API to transform your lead qualification process from manual research into an automated, data-driven system that ensures high-value leads receive immediate attention.

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

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metadata_json:
  {"categories":["automation","integration","data-processing","real-estate"],"complexity":"medium","use_cases":["Automatically enrich incoming real estate leads with property data","Score and prioritize leads based on property characteristics","Route high-value leads to sales for immediate follow-up","Segment marketing audiences by property value and characteristics","Qualify loan or insurance applicants using property data"],"estimated_setup_minutes":45,"required_services":["BatchData API","CRM with webhook support (e.g., Salesforce, HubSpot)","Notification channel (e.g., Slack)","Optional: task management or CRM task API"],"key_features":["Real-time webhook-triggered processing","BatchData property enrichment (value, size, age, occupancy, lot)","Custom scoring algorithm using property metrics","Automatic lead classification (high-value, qualified, potential, unqualified)","CRM record updates with enriched data and scores"],"target_audience":["Real estate agents and brokers","Mortgage lenders","Home service providers (contractors, renovators, installers) 
  (solar, HVAC, etc.) 
  ]﻿?﻿"]}


name: Automated Property Lead Generation with BatchData and CRM Integration
author_name: Preston Zeller
author_username: zellerhaus
categories: []
created_at: 2025-04-22T21:17:22.569Z
updated_at: 2025-04-22T21:17:22.569Z
url: https://n8n.io/workflows/3665
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  How It Works
  This N8N workflow creates an automated system for discovering high-potential real estate investment opportunities. The workflow runs on a customizable schedule to scan the market for properties that match your specific criteria, then alerts your team about the most promising leads.

  The process follows these steps:

  Connects to BatchData API on a regular schedule to search for properties matching your parameters
  Compares new results with previous scans to identify new listings and property changes
  Applies intelligent filtering to focus on high-potential opportunities (high equity, absentee owners, etc.)
  Retrieves comprehensive property details and owner information for qualified leads
  Delivers formatted alerts through multiple channels (email and Slack/Teams)

  Each email alert includes detailed property information, owner details, equity percentage, and a direct Google Maps link to view the property location. The workflow also posts concise notifications to your team's communication channels for quick updates.

  Who It's For
  This workflow is designed for:

  Real Estate Investors: Find off-market properties with high equity and motivated sellers
  Real Estate Agents: Identify potential listing opportunities before they hit the market
  Property Acquisition Teams: Streamline the lead generation process with automated scanning
  Real Estate Wholesalers: Discover properties with significant equity spreads for potential deals
  REITs and Property Management Companies: Monitor market changes and expansion opportunities

  The workflow is especially valuable for professionals who want to:

  Save hours of manual market research time
  Get early notifications about high-potential properties
  Access comprehensive property and owner information in one place
  Focus their efforts on the most promising opportunities

  About BatchData
  BatchData is a powerful property data platform for real estate professionals. Their API provides access to comprehensive property and owner information across the United States, including:

  Property details (bedrooms, bathrooms, square footage, year built, etc.)
  Valuation and equity estimates
  Owner information (name, mailing address, contact info)
  Transaction history and sales data
  Foreclosure and distressed property status
  Demographic and neighborhood data

  The platform specializes in providing accurate, actionable property data that helps real estate professionals make informed decisions and identify opportunities efficiently. BatchData's extensive database covers millions of properties nationwide and is regularly updated to ensure data accuracy.
  The API's flexible search capabilities allow you to filter properties based on numerous criteria, making it an ideal data source for automated lead generation workflows like this one.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.spreadsheetFile","n8n-nodes-base.hubspot","n8n-nodes-base.emailSend","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.filter","n8n-nodes-base.splitOut"]

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metadata_json:
  {"categories":["automation","integration","lead generation","real estate data"],"complexity":"medium","use_cases":["Automated discovery of off‑market/targeted property leads","Enriching leads with owner, valuation and transaction data","Filtering and prioritizing properties by equity/distress signals","Delivering alerts to email and team chat (Slack/Teams)","Syncing qualified leads into CRM and spreadsheets for follow‑up"],"estimated_setup_minutes":45,"required_services":["BatchData API","HubSpot (CRM)","Email provider / SMTP","Slack or Microsoft Teams","Google Sheets or other spreadsheet service","n8n (self‑hosted or cloud)"],"key_features":["Scheduled market scans via BatchData API","Delta detection comparing new scans to previous results","Intelligent filtering for high‑potential properties (equity, absentee owners, distress)","Property and owner enrichment (valuation, transaction history, contact info)","Multi‑channel alerts: formatted emails and concise team messages (Slack/Teams)"],"target_audience":["Real estate investors","Real estate agents and brokers","Property acquisition teams and wholesalers (lead generation)  REITs and property management companies"]}


name: Real Estate Lead Generation with BatchData Skip Tracing & CRM Integration
author_name: Preston Zeller
author_username: zellerhaus
categories: []
created_at: 2025-04-22T21:27:31.650Z
updated_at: 2025-04-22T21:27:31.650Z
url: https://n8n.io/workflows/3666
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:31
description:
  How It Works
  This workflow automates the entire property lead generation process in a few simple steps:

  Property Search: Connects to BatchData's Property Search API with customizable parameters (location, property type, value range, equity percentage, etc.)
  Lead Filtering & Scoring: Processes results to identify the most promising leads based on criteria like absentee ownership, years owned, equity percentage, and tax status. Each property receives a lead score to prioritize follow-up.
  Skip Tracing: Automatically retrieves owner contact information (phone, email, mailing address) for each qualified property.
  Data Formatting: Structures all property and owner data into a clean, organized format ready for your systems.
  Multi-Channel Output:
  Generates an Excel spreadsheet with all lead details
  Pushes leads directly to your CRM (configurable for HubSpot, Salesforce, etc.)
  Sends a summary email with the spreadsheet attached



  The workflow can run on a daily schedule or be triggered manually as needed. All parameters are easily configurable through dedicated nodes, requiring no coding knowledge.

  Who's It For
  This workflow is perfect for:

  Real Estate Investors looking to find off-market properties with motivated sellers
  Real Estate Agents who want to generate listing leads from distressed or high-equity properties
  Investment Companies that need regular lead flow for acquisitions
  Real Estate Marketers who run targeted campaigns to property owners
  Wholesalers seeking to build a pipeline of potential deals
  Property Service Providers (roof repair, renovation contractors, etc.) who target specific property types

  Anyone who needs reliable, consistent lead generation for real estate without the manual work of searching, filtering, and organizing property data will benefit from this automation.

  About BatchData
  BatchData is a comprehensive property data provider that offers access to nationwide property information, owner details, and skip tracing services. Key features include:

  Extensive Database: Covers 150+ million properties across all 50 states
  Rich Property Data: Includes ownership information, tax records, sales history, valuation estimates, equity positions, and more
  Skip Tracing Services: Provides owner contact information including phone numbers, email addresses, and mailing addresses
  Distressed Property Indicators: Flags for pre-foreclosure, tax delinquency, vacancy, and other motivation factors
  RESTful API: Professional API for programmatic access to all property data services
  Regular Updates: Continuously refreshed data for accurate information

  BatchData's services are designed for real estate professionals who need reliable property and owner information to power their marketing and acquisition strategies. Their API-first approach makes it ideal for workflow automation tools like N8N.

nodes_used:
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metadata_json:
  {"categories":["automation","integration","data processing","real estate","lead generation"],"complexity":"medium","use_cases":["Off-market property lead generation","Owner skip-tracing and contact enrichment","Automated lead scoring and filtering","Push leads to CRM and maintain prospect lists","Batch export of leads to Excel and email summaries"],"estimated_setup_minutes":45,"required_services":["BatchData API (property search & skip tracing)","CRM (e.g., HubSpot, Salesforce, or other supported CRM)","Email/SMTP provider for summary emails","Spreadsheet/storage service (e.g., Google Drive, OneDrive) or local Excel export","n8n instance/runtime"],"key_features":["Customizable property search parameters (location, type, value, equity, etc.)","Lead filtering and automated scoring based on multiple criteria","Skip tracing to retrieve owner phone, email, and mailing address","Data formatting/structuring for downstream systems","Export leads to Excel spreadsheet automatically attached to emails or stored to drive locations when configured (Spreadsheets node support)"],"target_audience":["Real estate investors","Real estate agents and brokers","Wholesalers and acquisition specialists"]}


name: Altcoin News to LinkedIn Posts with DeepSeek AI & Google Sheets
author_name: Lawal Ridwan
author_username: letsautomate
categories: []
created_at: 2025-04-23T08:18:57.744Z
updated_at: 2025-04-23T08:18:57.744Z
url: https://n8n.io/workflows/3668
scraped_at: 2025-09-14 19:11:02
metadata_generated_at: 2025-09-14 21:18:33
description:
  Altcoin News to LinkedIn Posts with DeepSeek AI & Google Sheets

  Automate your daily crypto content creation and publishing using this n8n workflow. This automation fetches the latest altcoin news from an RSS feed, rewrites it into LinkedIn-style posts using DeepSeek AI, stores it in Google Sheets, and schedules it for publishing—completely hands-free.

  🔧 Prerequisites
  n8n instance (cloud or self-hosted)

  DeepSeek AI or OpenAI API key

  Google account with Sheets API enabled

  RSS feed URL of your preferred altcoin news source

  (Optional) LinkedIn API or scheduling tool

  ⚙️ Workflow Breakdown
  Fetch RSS Feed – Retrieve altcoin news articles.

  Filter Articles – Only select posts from today and containing keywords like "altcoin", "crypto", "defi", etc.

  Rewrite with DeepSeek – Convert article into a LinkedIn-style post using a prompt template (includes Hook, Body, CTA).

  Store in Google Sheets – Save the generated content into your sheet.

  Post to LinkedIn (optional) – Schedule or publish via LinkedIn API or integration.

  Setup
  Connect RSS Feed: Set up the CoinDesk RSS feed to be fetched automatically.

  API Keys: Add your DeepSeek AI API key to enable content generation.

  Google Sheets: Link your Google Sheet to store both raw RSS data and AI-generated LinkedIn posts.

  Scheduling: Set the time intervals for when the posts will be scheduled (e.g., every 6 hours).

  Customization Instructions
  **Change RSS Feed URL:
  **
  Open the Fetch RSS Feed node.

  Replace the current URL with your new RSS feed URL in the "URL" field.

  **Change GPT Model:
  **
  Open the DeepSeek Chat Model node.

  Find the "Model" field and select your preferred model (e.g., gpt-4).

  **Change Prompt:
  **
  Open the DeepSeek Chat Model node.

  Edit the prompt text in the "Prompt" field to customize the tone, content, or keywords.

  **Change Wait Time:
  **
  Open the Schedule Tweet node.

  Adjust the "Wait Time" (in seconds) to your desired interval:

  For 6 hours: 21600

  For 1 hour: 3600

  For 30 minutes: 1800

  For 15 minutes: 900

nodes_used:
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metadata_json:
  {"categories":["automation","social media","content creation","AI integration","integration"],"complexity":"medium","use_cases":["Automate daily altcoin/crypto content creation","Generate LinkedIn-style posts from news articles","Schedule or publish social posts automatically","Archive AI-generated content in Google Sheets","Curate filtered news based on keywords and recency"],"estimated_setup_minutes":30,"required_services":["n8n instance (cloud or self-hosted)","DeepSeek AI API key or OpenAI API key","Google account with Google Sheets API enabled","RSS feed source for altcoin news (e.g., CoinDesk RSS)","LinkedIn API or external scheduling/publishing tool (optional)"],"key_features":["Fetches and parses RSS feed articles","Filters articles by date and crypto-related keywords","Uses DeepSeek AI (or OpenAI) to rewrite articles into LinkedIn-style posts with Hook/Body/CTA","Stores raw and generated content in Google Sheets","Optional scheduling/publishing to LinkedIn or a posting tool via API or wait nodes
  /intervals/batching support for controlled posting cadence"],"target_audience":["Social media managers","Crypto content creators and community managers","Marketing teams focused on crypto/altcoins, DeFi and NFTs marketing
  campaigns or daily outreach workflows for content teams"]}


name: Publish image & video to multiple social media (X, Instagram, Facebook and more)
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-04-23T10:11:00.160Z
updated_at: 2025-04-23T10:11:00.160Z
url: https://n8n.io/workflows/3669
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
description:
  This Workflow streamlines the process of publishing posts (image or video) to multiple social media platforms using a unified form and a third-party API service called Upload-Post.

  The automation starts with a form trigger, allowing users to submit content (text and media) through a simple frontend interface. Users select the platform (Instagram, LinkedIn, Facebook, X, TikTok, Threads), choose the profile name, write a caption, and upload a photo or video.

  How It Works  
  Automates cross-platform social media posting via Upload-Post, handling both images (JPEG) and videos (MP4). Here’s the process:  

  Trigger**:  
    A form submission captures user inputs:  
      Platform (Instagram, LinkedIn, Facebook, X, TikTok, Threads).  
      Account (pre-configured profile name).  
      Caption and file (image/video).  
      Optional Facebook Page ID for targeted posting.  

  Routing**:  
    The "Video or Photo?" Switch node checks the file’s MIME type:  
      Image: Routes to the "Post photo" HTTP node (uploads via upload_photos API).  
      Video: Routes to the "Post video" HTTP node (uploads via upload API).  

  API Integration**:  
    Both nodes send data to Upload-Post.com’s API, including:  
      Caption, account name, platform, and file binary.  
      Facebook ID (if provided).  

  Success/Failure Handling**:  
    The "Result Photo/Video" nodes parse the API response.  

  Setup Steps  

  Prerequisites:  
  Upload-Post.com API Key**:  
    Get it from the API Keys dashboard.  
    Free tier allows 10 uploads/month.  

  Configuration:  
  API Authentication:  
     In the HTTP Request nodes (Post photo/Post video), set the Authorization header:  
       Name: Authorization  
       Value: Apikey YOUR_API_KEY_HERE.  

  Form Customization:  
     Adjust the "On form submission" node to:  
       Add/remove platforms (e.g., YouTube when approved).  
       Modify file type restrictions (default: .jpg, .mp4).  

  Account Mapping:  
     Ensure the "Account" field matches profiles configured in Upload-Post.com (e.g., test1, test2).  

  Facebook Page Integration:  
     Optional: Add a Facebook Page ID field for page-specific posts.  

  Testing:  
     Submit test forms with images/videos.  
     Verify API responses and success/failure messages.  

  Optional Enhancements:  
  Add error logging (e.g., save failed attempts to a database).  
  Extend to YouTube once API support is confirmed.  

  Key Features:  
  Multi-Platform**: Post to 6+ social networks simultaneously.  
  User-Friendly**: Simple form interface for non-technical users.  
  Error Handling**: Clear feedback for success/failure cases.  

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

nodes_used:
  ["n8n-nodes-base.formTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.form","n8n-nodes-base.form","n8n-nodes-base.form","n8n-nodes-base.form","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.switch","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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metadata_json:
  {"categories":["automation","integration","social media management","content publishing"],"complexity":"medium","use_cases":["Cross-posting image and video content to multiple social platforms","Simplified content publishing for marketing teams or individuals","Front-end form for non-technical users to publish posts","Targeted Facebook Page posting via Page ID"],"estimated_setup_minutes":20,"required_services":["Upload-Post.com API (API key)","Configured social media accounts/profiles on Upload-Post (Instagram, LinkedIn, Facebook, X, TikTok, Threads)","n8n instance (or hosted n8n) to run the workflow","Optional frontend/form hosting if not using n8n's built-in form trigger"],"key_features":["Multi-platform publishing (6+ networks)","Supports image (JPEG) and video (MP4) uploads","Single unified form for caption, account selection and media upload","Automatic routing based on MIME type (photo vs video)","Account/profile mapping to Upload-Post configured names, optional Facebook Page ID support for page posts






























































  "],"target_audience":["Social media managers","Marketing teams and small business owners","Content creators and agencies wanting streamlined cross-posting





  "]}


name: Medical Q&A Chatbot for Urology using RAG with Pinecone and GPT-4o
author_name: HoangSP
author_username: hoangsp
categories: []
created_at: 2025-04-23T10:20:39.925Z
updated_at: 2025-04-23T10:20:39.925Z
url: https://n8n.io/workflows/3670
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-30 08:33:37
description:
  Medical Q&A Chatbot for Urology using RAG with Pinecone and GPT-4o

  This template provides an AI-powered Q&A assistant for the Urology domain using Retrieval-Augmented Generation (RAG). It uses Pinecone for vector search and GPT-4o for conversational responses.

  🧠 Use Case

  This chatbot is designed for clinics or medical pages that want to automate question answering for Urology-related conditions. It uses a vector store of domain knowledge to return verified responses.

  🔧 Requirements

  ✅ OpenAI API key (GPT-4o or GPT-4o-mini)
  ✅ Pinecone account with an active index
  ✅ Verified Urology documents embedded into Pinecone

  ⚙️ Setup Instructions

  Create a Pinecone vector index and connect it using the Pinecone credentials node.
  Upload Urology-related documents to embed using the Create Embeddings for Urology Docs node.
  Customize the chatbot system message to reflect your medical specialty.
  Deploy this chatbot on your website or link it with Telegram via the chat trigger node.

  🛠️ Components

  chatTrigger: Listens for user messages and starts the workflow.
  Medical AI Agent: GPT-based agent guided by domain-specific instructions.
  RAG Tool Vector Store: Fetches relevant documents from Pinecone using vector search.
  Memory Buffer: Maintains conversation context.
  Create Embeddings for Urology Docs: Encodes documents into vector format.

  📝 Customization

  You can replace the knowledge base with any other medical domain by:
  Updating the documents stored in Pinecone.
  Modifying the system prompt in the AI Agent node.

  📣 CTA

  This chatbot is ideal for clinics, medical consultants, or educational websites wanting a reliable AI assistant in Urology.

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.toolVectorStore","@n8n/n8n-nodes-langchain.vectorStorePinecone","@n8n/n8n-nodes-langchain.embeddingsOpenAi","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:
  {"categories":["healthcare","chatbot","AI/ML","knowledge management","integration"],"complexity":"complex","use_cases":["Automated Urology Q&A on clinic websites","Patient pre-screening and education","Internal knowledge base for clinical staff","Support triage and frequently asked questions","Enhancing telemedicine workflows with domain answers"],"estimated_setup_minutes":60,"required_services":["Pinecone (vector database)","OpenAI (GPT-4o) or equivalent LLM API","Storage for source documents (S3, Google Drive, or HTTP access)","n8n hosting/instance"],"key_features":["Retrieval-Augmented Generation (RAG) workflow","Pinecone vector search for domain knowledge","GPT-4o-powered conversational responses","Memory buffer for conversational context","Domain-specific (Urology) knowledge base ingestion and search"],"target_audience":["Clinics and medical websites","Healthcare administrators and IT teams","Developers building medical chatbots or RAG systems"]}


name: SEO Blog Generator with GPT-4o, Perplexity, and Telegram Integration
author_name: HoangSP
author_username: hoangsp
categories: []
created_at: 2025-04-23T13:11:34.466Z
updated_at: 2025-04-23T13:11:34.466Z
url: https://n8n.io/workflows/3672
scraped_at: 2025-09-14 19:11:00
metadata_generated_at: 2025-09-14 21:18:32
description:
  SEO Blog Generator with GPT-4o, Perplexity, and Telegram Integration

  This workflow helps you automatically generate SEO-optimized blog posts using Perplexity.ai, OpenAI GPT-4o, and optionally Telegram for interaction.

  🚀 Features

  🧠 Topic research via Perplexity sub-workflow
  ✍️ AI-written blog post generated with GPT-4o
  📊 Structured output with metadata: title, slug, meta description
  📩 Integration with Telegram to trigger workflows or receive outputs (optional)

  ⚙️ Requirements

  ✅ OpenAI API Key (GPT-4o or GPT-3.5)
  ✅ Perplexity API Key (with access to /chat/completions)
  ✅ (Optional) Telegram Bot Token and webhook setup

  🛠 Setup Instructions

  Credentials:
     Add your OpenAI credentials (openAiApi)
     Add your Perplexity credentials under httpHeaderAuth
     Optional: Setup Telegram credentials under telegramApi

  Inputs:
     Use the Form Trigger or Telegram input node to send a Research Query

  Subworkflow:
     Make sure to import and activate the subworkflow Perplexity_Searcher to fetch recent search results

  Customization:
     Edit prompt texts inside the Blog Content Generator and Metadata Generator to change writing style or target industry
     Add or remove output nodes like Google Sheets, Notion, etc.

  📦 Output Format

  The final blog post includes:

  ✅ Blog content (1500-2000 words)
  ✅ Metadata: title, slug, and meta description
  ✅ Extracted summary in JSON
  ✅ Delivered to Telegram (if connected)

  Need help? Reach out on the n8n community forum

nodes_used:
  ["n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.merge","n8n-nodes-base.stickyNote","n8n-nodes-base.formTrigger","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.aggregate","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.telegramTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.memoryBufferWindow"]

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metadata_json:
  {"categories":["content_generation","automation","integration","seo"],"complexity":"medium","use_cases":["Automated SEO blog post generation","Topic research and idea discovery","Generating structured metadata (title, slug, meta description)","Delivering posts or notifications via Telegram","Feeding outputs to CMS or spreadsheets (Notion, Google Sheets)"],"estimated_setup_minutes":30,"required_services":["OpenAI (GPT-4o or GPT-3.5)","Perplexity.ai (API access to /chat/completions)","n8n platform"],"key_features":["Perplexity sub-workflow for topic research","GPT-4o-powered blog content generator (1500–2000 words)","Structured output with title, slug, meta description and JSON summary","Optional Telegram trigger and delivery","Customizable prompts and easy integration with other output nodes"],"target_audience":["Content marketers","SEO specialists","Bloggers and writers looking to scale content production`,`Growth marketers and product teams seeking automated content workflows`,`Developers and automation engineers implementing content integrations"]}


name: AI-Powered Research Assistant with Perplexity Sonar API
author_name: HoangSP
author_username: hoangsp
categories: []
created_at: 2025-04-23T13:15:00.750Z
updated_at: 2025-04-23T13:15:00.750Z
url: https://n8n.io/workflows/3673
scraped_at: 2025-09-14 19:11:01
metadata_generated_at: 2025-09-14 21:18:32
description:
  Name:
  AI-Powered Research Agent using Perplexity Sonar

  Description:
  This workflow acts as an AI-powered research assistant using the Perplexity Sonar model. When triggered by another workflow, it sends a user-defined prompt to the Perplexity API to retrieve up-to-date search results. The response is then parsed into a clean format for downstream processing.

  How it Works:
  Trigger: Activated from another workflow via Execute Workflow Trigger.

  Prompt Setup: Sets a system role message and user query dynamically.

  API Call: Sends a POST request to Perplexity's /chat/completions endpoint with your credentials.

  Response Handling: Extracts the message content from the API response.

  Output: Returns the result, ready for display or further processing.

  Requirements:
  A Perplexity AI API Key

  Set up authentication via Header Auth with Bearer token

  Ensure your account allows outbound HTTP requests in n8n

  Customization Tips:
  Modify the system prompt to suit your research domain

  Chain this workflow with other automation like blog creation, summaries, etc.

  Replace the output handling logic to fit into Google Sheets, Notion, or Telegram

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

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metadata_json:
  {"categories":["automation","AI","research","integration"],"complexity":"medium","use_cases":["On-demand web research and fact-finding","Gathering up-to-date information for content creation","Supplementing summaries or reports with live search results","Automating research steps in larger workflows (e.g., blog generation, brief creation)","Enriching datasets with current answers or citations"],"estimated_setup_minutes":10,"required_services":["Perplexity AI API (Perplexity Sonar)","n8n instance with outbound HTTP requests enabled"],"key_features":["Triggerable Execute Workflow for reuse in other automations","Dynamic system role and user prompt setup","POST request to Perplexity /chat/completions endpoint","Parses and extracts content from API response for downstream use","Supports Header Auth (Bearer token) for Perplexity credentials"],"target_audience":["developers building AI automations","content creators and marketers","researchers and analysts needing quick lookups or summaries"]}


name: MCP Supabase Server for AI Agent with RAG & Multi-Tenant CRUD
author_name: Luciano Gutierrez
author_username: koresoluciones
categories: []
created_at: 2025-04-23T15:41:14.267Z
updated_at: 2025-04-23T15:41:14.267Z
url: https://n8n.io/workflows/3675
scraped_at: 2025-09-14 19:10:59
metadata_generated_at: 2025-09-14 21:18:31
description:
  Supabase AI Agent with RAG & Multi-Tenant CRUD  
  Version: 1.0.0  
  n8n Version: 1.88.0+  
  Author: Koresolucoes 
  License: MIT  

  Description  
  A stateful AI agent workflow powered by Supabase and Retrieval-Augmented Generation (RAG). Enables persistent memory, dynamic CRUD operations, and multi-tenant data isolation for AI-driven applications like customer support, task orchestration, and knowledge management.  

  Key Features:  
  🧠 RAG Integration: Leverages OpenAI embeddings and Supabase vector search for context-aware responses.  
  🗃️ Full CRUD: Manage agent_messages, agent_tasks, agent_status, and agent_knowledge in real time.  
  📤 Multi-Tenant Ready: Supports per-user/organization data isolation via dynamic table names and webhooks.  
  🔒 Secure: Role-based access control via Supabase Row Level Security (RLS).  

  Use Cases  
  Customer Support Chatbots: Persist conversation history and resolve queries using institutional knowledge.  
  Automated Task Management: Track and update task statuses dynamically.  
  Knowledge Repositories: Store and retrieve domain-specific information for AI agents.  

  Instructions  
  1. Import Template  
  Go to n8n &gt; Templates &gt; Import from File and upload this workflow.  

  2. Configure Credentials  
  Add your Supabase and OpenAI API keys under Settings &gt; Credentials.  

  3. Set Up Multi-Tenancy (Optional)  
  Dynamic Webhook Path**:  
    Replace the default webhook path with /mcp/tool/supabase/:userId to enable per-user routing.  
  Table Names**:  
    Use a Set Node to dynamically generate table names (e.g., agent_messages_{{userId}}).  

  4. Activate & Test  
  Enable the workflow and send test requests to the webhook URL.  

  Tags  
  AI Agent RAG Supabase CRUD Multi-Tenant OpenAI Automation  

  Screenshots  

  License  
  This template is licensed under the MIT License.  

nodes_used:
  ["@n8n/n8n-nodes-langchain.mcpTrigger","@n8n/n8n-nodes-langchain.vectorStoreSupabase","@n8n/n8n-nodes-langchain.embeddingsOpenAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.supabaseTool","n8n-nodes-base.supabaseTool","n8n-nodes-base.supabaseTool","n8n-nodes-base.supabaseTool","n8n-nodes-base.supabaseTool","n8n-nodes-base.supabaseTool","n8n-nodes-base.supabaseTool","n8n-nodes-base.supabaseTool","n8n-nodes-base.supabaseTool","n8n-nodes-base.supabaseTool","n8n-nodes-base.supabaseTool","n8n-nodes-base.supabaseTool","n8n-nodes-base.supabaseTool","n8n-nodes-base.supabaseTool","n8n-nodes-base.supabaseTool","n8n-nodes-base.supabaseTool","n8n-nodes-base.supabaseTool","n8n-nodes-base.supabaseTool","n8n-nodes-base.supabaseTool","n8n-nodes-base.supabaseTool"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","integration","data processing","AI/ML","multi-tenancy"],"complexity":"complex","use_cases":["Customer support chatbots with persistent context","Automated task management and orchestration","Domain-specific knowledge repositories for agents","Stateful AI agents using RAG for context-aware responses","Multi-tenant AI applications with per-tenant data isolation"],"estimated_setup_minutes":45,"required_services":["Supabase (Postgres + vector search)","OpenAI (embeddings and LLM API)","n8n (self-hosted or cloud)","A publicly reachable webhook endpoint / hosting for webhooks"],"key_features":["Retrieval-Augmented Generation (OpenAI embeddings + Supabase vector search)","Persistent agent memory and state (agent_messages, agent_tasks, agent_status, agent_knowledge)","Full CRUD operations against agent tables","Multi-tenant support via dynamic table names and webhook routing","Security via Supabase Row Level Security (RLS) and role-based access controls"],"target_audience":["Developers","AI/ML engineers","Backend/DevOps engineers building integrations and automation workflows (n8n)\",\"Customer support/product teams implementing AI assistants"]}


name: Capture Gumroad sales, add buyer to MailerLite group, log to Google Sheets CRM
author_name: Aitor | 1Node
author_username: aitoralonso
categories: []
created_at: 2025-04-23T18:03:02.278Z
updated_at: 2025-04-23T18:03:02.278Z
url: https://n8n.io/workflows/3676
scraped_at: 2025-09-14 19:11:03
metadata_generated_at: 2025-09-14 21:18:33
description:
  Turn Gumroad buyers into loyal email subscribers and keep your CRM up‑to‑date.

  When someone makes a purchase on your Gumroad store, this n8n workflow instantly adds that customer to the right MailerLite group (so your nurture sequence starts on time) and writes the sale details into your Google Sheets CRM. You’ll never copy‑and‑paste orders again, and every buyer begins receiving your follow‑up emails the moment they purchase.

  Requirements
  A Gumroad account with a product listed
  A MailerLite account. 
  A MailerLite group of subscribers created
  Enabled APIs and credentials for Google Sheets, MailerLite and Gumroad

  How it works
  Listen for a new sale on Gumroad**
  The Gumroad trigger watches your account 24/7 and fires as soon as a sale is completed.

  Create (or update) the subscriber in MailerLite**
  Their name and email are added to MailerLite. If they already exist, the workflow simply updates their profile.

  Assign the subscriber to your Gumroad group**
  Grouping lets your MailerLite automation send the right onboarding or upsell sequence without manual tagging.

  Log the purchase in Google Sheets**
  The buyer’s contact details, product, price, and date are appended as a new row in your CRM sheet.

  Set‑up steps
  Create an application in Gumroad. Copy the access token, you’ll paste it into the Gumroad trigger node.

  Grab your MailerLite API key
  MailerLite dashboard → Integrations → API. Paste it into the two MailerLite nodes.

  Prepare a Google Sheets spreadsheet
  Add column headers like Name, Email, Product, Price, Date.

  Open the template in n8n Cloud or Desktop

  In the Gumroad node, paste your token.

  In the MailerLite nodes, paste your API keys and replace the group id.

  In the Google Sheets node, replace the credentials, pick your spreadsheet and worksheet.

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

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.gumroadTrigger","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.mailerLite","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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metadata_json:
  {"categories":["automation","e-commerce integration","CRM sync","marketing automation"],"complexity":"medium","use_cases":["Automatically add Gumroad buyers to email lists","Trigger onboarding or upsell sequences via MailerLite groups","Log purchases into a Google Sheets CRM for reporting/audit","Eliminate manual order copying and subscriber management"],"estimated_setup_minutes":15,"required_services":["Gumroad (with product and API token)","MailerLite (with API key and group created)","Google Sheets (Google account with Sheets API/credentials)","n8n (Cloud or Desktop instance)"],"key_features":["Real-time Gumroad sale trigger","Create or update subscriber in MailerLite","Assign subscriber to a MailerLite group","Append sale details (Name, Email, Product, Price, Date) to Google Sheets CRM","Single-point setup of API credentials and group ID"],"target_audience":["Digital product creators","E‑commerce store owners using Gumroad","Marketers running email nurture sequences/automations','Small businesses and solopreneurs who maintain a Google Sheets CRM"]}


