# templates

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: 2025-12-24 13:02:44
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, 4676 characters]


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: 2025-12-24 13:02:46
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"]

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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: 2025-12-24 13:02:44
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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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: 2025-12-24 13:02:44
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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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: 2025-12-24 13:02:42
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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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: 2025-12-24 13:02:45
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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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: 2025-12-24 13:02:46
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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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: 2025-12-24 13:02:43
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"]

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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: 2025-12-24 13:02:44
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"]

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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: 2025-12-24 13:02:44
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"]

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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: 2025-12-24 13:02:44
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"]

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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: 2025-12-24 13:02:41
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"]

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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: 2025-12-24 13:02:47
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:
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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: 2025-12-24 13:02:44
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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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: 2025-12-24 13:02:46
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.

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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: 2025-12-24 13:02:47
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:
  ["n8n-nodes-base.splitInBatches","n8n-nodes-base.merge","n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.html","n8n-nodes-base.html","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleTasks","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.set","n8n-nodes-base.googleSheets","n8n-nodes-base.set","n8n-nodes-base.stickyNote"]

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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: 2025-12-24 13:02:45
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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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: 2025-12-24 13:02:43
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"]

workflow_json_compressed:
  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


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: 2025-12-24 13:02:46
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"]

workflow_json_compressed:
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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: 2025-12-24 13:02:47
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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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: 2025-12-24 13:02:46
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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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: 2025-12-24 13:02:45
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, 4276 characters]


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: 2025-12-24 13:02:45
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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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: 2025-12-24 13:02:46
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:
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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: 2025-12-24 13:02:41
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:
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workflow_json_compressed:
  [encoded data, 10336 characters]


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: 2025-12-24 13:02:41
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
   

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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: 2025-12-24 13:02:41
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"]

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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: 2025-12-24 13:02:41
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:
  ["n8n-nodes-base.webhook","n8n-nodes-base.googleCalendar","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.webhook","@n8n/n8n-nodes-langchain.vectorStoreQdrant","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.toolVectorStore","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.webhook","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.respondToWebhook","n8n-nodes-base.httpRequest","n8n-nodes-base.respondToWebhook","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","@n8n/n8n-nodes-langchain.vectorStoreQdrant","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterTokenSplitter","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: 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: 2025-12-24 13:02:46
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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workflow_json_compressed:
  [encoded data, 8516 characters]


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: 2025-12-24 13:02:44
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"]

workflow_json_compressed:
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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: 2025-12-24 13:02:45
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"]

workflow_json_compressed:
  [encoded data, 5664 characters]


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: 2025-12-24 13:02:42
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:
  ["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"]

workflow_json_compressed:
  [encoded data, 5624 characters]


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: 2025-12-24 13:02:46
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:
  ["@n8n/n8n-nodes-langchain.lmChatDeepSeek","n8n-nodes-base.rssFeedRead","n8n-nodes-base.function","n8n-nodes-base.interval","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.stickyNote","n8n-nodes-base.splitInBatches","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.linkedIn","n8n-nodes-base.wait","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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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: 2025-12-24 13:02:41
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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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: 2025-12-24 13:02:42
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"]

workflow_json_compressed:
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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: 2025-12-24 13:02:42
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"]

workflow_json_compressed:
  [encoded data, 7272 characters]


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: 2025-12-24 13:02:46
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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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: 2025-12-24 13:02:41
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"]

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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: 2025-12-24 13:02:47
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"]

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name: Google Calendar MCP server for AI Agent with Dynamic Scheduling
author_name: Luciano Gutierrez
author_username: koresoluciones
categories: []
created_at: 2025-04-23T18:55:26.347Z
updated_at: 2025-04-23T18:55:26.347Z
url: https://n8n.io/workflows/3677
scraped_at: 2025-12-24 13:02:44
description:
  Google Calendar AI Agent with Dynamic Scheduling  
  Version: 1.0.0  
  n8n Version: 1.88.0+  
  Author: Koresolucoes
  License: MIT  

  Description  
  An AI-powered workflow to automate Google Calendar operations using dynamic parameters and MCP (Model Control Plane) integration. Enables event creation, availability checks, updates, and deletions with timezone-aware scheduling [[1]][[2]][[8]].  

  Key Features:  
  📅 Full Calendar CRUD: Create, read, update, and delete events in Google Calendar.  
  ⏰ Availability Checks: Verify time slots using AVALIABILITY_CALENDAR node with timezone support (e.g., America/Sao_Paulo).  
  🤖 AI-Driven Parameters: Use $fromAI() to inject dynamic values like Start_Time, End_Time, and Description [[3]][[4]].  
  🔗 MCP Integration: Connects to an MCP server for centralized AI agent control [[5]][[6]].  

  Use Cases  
  Automated Scheduling: Book appointments based on AI-recommended time slots.  
  Meeting Coordination: Sync calendar events with CRM/task management systems.  
  Resource Management: Check room/equipment availability before event creation.  

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

  2. Configure Credentials  
  Add Google Calendar OAuth2 credentials under Settings &gt; Credentials.  
  Ensure the calendar ID matches your target (e.g., ODONTOLOGIA group calendar).  

  3. Set Up Dynamic Parameters  
  Use $fromAI('Parameter_Name') in nodes like CREATE_CALENDAR to inject AI-generated values (e.g., event descriptions).  

  4. Activate & Test  
  Enable the workflow and send test requests to the webhook path /mcp/:tool/calendar.  

  Tags  
  Google Calendar Automation MCP AI Agent Scheduling CRUD  

  Screenshots  

  License  
  This template is licensed under the MIT License.  

  Notes:  
  Extend multi-tenancy by adding :userId to the webhook path (e.g., /mcp/:userId/calendar) [[7]].  
  For timezone accuracy, always specify options.timezone in availability checks [[8]].  
  Refer to n8n’s Google Calendar docs for advanced field mappings.  

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

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name: Query PostgreSQL Database with Natural Language Using Groq AI Chatbot
author_name: Gegenfeld
author_username: gegenfeld
categories: []
created_at: 2025-04-23T23:07:27.296Z
updated_at: 2025-04-23T23:07:27.296Z
url: https://n8n.io/workflows/3680
scraped_at: 2025-12-24 13:02:46
description:
  This guide shows you how to deploy a chatbot that lets you query your PostgreSQL database using natural language. You will build a system that accepts chat messages, retains conversation history, constructs dynamic SQL queries, and returns responses generated by an AI model. By following these instructions, you will have a working solution that integrates n8n’s AI Agent capabilities with PostgreSQL.

  Prerequisites

  Before you begin, ensure that you have the following:

  An active n8n instance (self-hosted or cloud) running version 1.50.0 or later.
  Valid PostgreSQL credentials configured in n8n.
  API credentials for the Groq Chat Model (or your preferred AI language model).
  Basic familiarity with SQL (specifically PostgreSQL syntax) and n8n node concepts such as chat triggers and memory buffers.
  Access to the n8n Docs on AI Agents for further reference.

  Workflow Setup

  Chat Interface & Trigger
      When Chat Message Received: This node listens for incoming chat messages via a webhook. When a message arrives, it triggers the workflow immediately.

  Conversation Memory
      Chat History: This memory buffer node stores the last 10 interactions. It supplies conversation context to the AI Agent, ensuring that responses consider previous messages.

  AI Agent Core
      AI Agent (Tools Agent): The AI Agent node orchestrates the conversation by receiving the chat input and conversation history. It dynamically generates PostgreSQL-compatible SQL queries based on your requests and coordinates calls to external tools (such as PostgreSQL nodes).

  Database Interactions
      PostgreSQL Node (Query Execution): This node executes the SQL query generated by the AI Agent against your PostgreSQL database. You reference the query using an expression (e.g., {{$node["AI Agent"].json.sql_query}}), allowing the agent’s output to control data retrieval.
      PostgreSQL Node (Schema Retrieval): This node (or a dedicated step using the PostgreSQL node) retrieves a list of relevant tables from your PostgreSQL database (e.g., from the public schema, excluding system schemas like pg_catalog or information_schema). The agent uses this information to understand the available tables. This typically involves executing a query like SELECT table_name FROM information_schema.tables WHERE table_schema = 'public';.
      PostgreSQL Node (Table Definition Retrieval): This node (or another dedicated step using the PostgreSQL node) fetches detailed metadata (such as column names, data types, and potentially relationships using foreign keys) for a specific table. The table name (and schema if necessary) is supplied dynamically by the AI Agent. This often involves querying information_schema.columns, e.g., SELECT column_name, data_type FROM information_schema.columns WHERE table_name = '{{dynamic_table_name}}' AND table_schema = 'public';.

  Language Model Processing
      Groq Chat Model: This node connects to the Groq Chat API to generate text completions. It processes the combined input (chat message, context, and data fetched from PostgreSQL) and produces the final response.

  Guidance & Customization
      Sticky Notes: These nodes provide guidance on:
          Switching the chat model if you wish to use another provider (e.g., OpenAI or Anthropic).
          Adjusting the maximum token count per interaction.
          Customizing the SQL queries (ensuring PostgreSQL compatibility) and the context window size.
      They help you modify the workflow to suit your environment and requirements.

  Workflow Connections

  The Chat Trigger passes the incoming message to the AI Agent.
  The Chat History node supplies conversation context to the AI Agent.
  The AI Agent calls the PostgreSQL nodes as external tools, generating and sending dynamic SQL queries.
  The Groq Chat Model processes the consolidated input from the agent and outputs the natural language response delivered to the user.

  Testing the Workflow

  Send a chat message using the chat interface.
  Observe how the AI Agent processes the input and generates a corresponding PostgreSQL SQL query.
  Verify that the PostgreSQL nodes execute the query correctly against your database and return data.
  Confirm that the Groq Chat Model produces a coherent natural language response based on the query results.
  Refer to the sticky notes for guidance if you need to fine-tune any node settings or SQL queries.

  Next Steps and References

  Customize Your AI Model:** Replace the Groq Chat Model with another language model (such as the OpenAI Chat Model) by updating the node credentials and configuration.
  Enhance Memory Settings:** Adjust the Chat History node’s context window to retain more or fewer messages based on your needs.
  Modify SQL Queries:* Update the SQL queries within the *PostgreSQL* nodes or refine the prompts for the AI Agent to ensure they match your specific database schema and desired data, adhering to *PostgreSQL** syntax.
  Further Reading:** Consult the n8n Docs on AI Agents for additional details and examples to expand your workflow’s capabilities.
  Set Up a Website Chatbot:** Copy & Paste and replace the placeholders in the following code to embed the chatbot into your personal or company’s website: View in CodePen 🡥

  By following these steps, you will deploy a robust AI chatbot workflow that integrates with your PostgreSQL database, allowing you to query data using natural language.

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

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name: Extract Amazon Best Seller Electronic Info with Bright Data and Google Gemini
author_name: Ranjan Dailata
author_username: ranjancse
categories: []
created_at: 2025-04-23T23:12:11.729Z
updated_at: 2025-04-23T23:12:11.729Z
url: https://n8n.io/workflows/3681
scraped_at: 2025-12-24 13:02:44
description:
  Who this is for?

  Extract Amazon Best Seller Electronic Info is an automated workflow that extracts best seller data from Amazon's Electronics section using Bright Data Web Unlocker, transform it into structured JSON using Google Gemini's LLM, and forwards a fully structured JSON response to a specified webhook for downstream use.

  This workflow is tailored for:

  eCommerce Analysts**
  Who need to monitor Amazon best-seller trends in the Electronics category and track changes in real-time or on a schedule.

  Product Intelligence Teams**
  Who want structured insights on competitor offerings, including rankings, prices, ratings, and promotions.

  AI-powered Chatbot Developers**
  Who are building assistants capable of answering product-related queries with fresh, structured data from Amazon.

  Growth Hackers & Marketers**
  Looking to automate competitive research and surface trending product data to inform pricing strategies.

  Data Aggregators and Price Trackers**
  Who need reliable and smart scraping of Amazon data enriched with AI-driven parsing.

  What problem is this workflow solving?
  Keeping up with Amazon's best sellers in Electronics is a time-consuming, error-prone task when done manually.This workflow automates the process, ensuring:​

  Automating Data Extraction from Amazon Best Sellers using Bright Data, ensuring reliable access to real-time, structured data.

  Enhancing Raw Data with Google Gemini, turning product lists into structured JSON using the Google Gemini LLM.

  Sending Results to a Webhook, enabling seamless integration into dashboards, databases, or chatbots.

  What this workflow does

  The workflow performs the following steps:​

  Extracts Amazon Best Seller Electronics page info using Bright Data's Web Unlocker API.

  Processes the unstructured content using Google Gemini's Flash Exp model to extract structured product data.

  Sends the structured information to a webhook endpoint.

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

  The Value field should be set with the
  Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token.
  In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy).
  Update the Amazon URL with the Bright Data zone by navigating to the Amazon URL with the Bright Data Zone node.
  Update the Webhook HTTP Request node with the Webhook endpoint of your choice.

  How to customize this workflow to your needs

  This workflow is built to be flexible - whether you're a market researcher, e-commerce entrepreneur, or data analyst. Here's how you can adapt it to fit your specific use case:

  Change the Amazon Category**
  Update the Amazon URL with the topic of your interest such as Computers & Accessories, Home Audio, etc.

  Customize the Gemini Prompt**
  Update the Gemini prompt to get different styles of output — comparison tables, summaries, feature highlights, etc.

  Send Output to Other Destinations**
  Replace the Webhook URL to forward output to:

  Google Sheets
  Airtable
  Slack or Discord
  Custom API endpoints

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

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name: Extract & Summarize Yelp Business Review with Bright Data and Google Gemini
author_name: Ranjan Dailata
author_username: ranjancse
categories: []
created_at: 2025-04-24T00:20:49.982Z
updated_at: 2025-04-24T00:20:49.982Z
url: https://n8n.io/workflows/3682
scraped_at: 2025-12-24 13:02:46
description:
  Who this is for?
  Extract & Summarize Yelp Business Review is an automated workflow that extracts the Yelp business reviews using Bright Data Web Unlocker, process and formats the raw data, summarizes using the Google Gemini's LLM, and forward the concise summary with the review respose to a specified webhook endpoint.

  This workflow is tailored for:

  Local SEO Specialists who need structured insights from Yelp reviews to optimize listings.

  Business Owners wanting quick summaries of what customers love or complain about.

  Reputation Managers who monitor brand sentiment and identify customer pain points.

  Data Analysts & Researchers extracting Yelp review patterns at scale.

  AI Product Builders needing clean Yelp review data as input for their LLMs or recommender systems.

  What problem is this workflow solving?

  Yelp reviews are rich in customer sentiment but messy to work with manually. This workflow solves:

  The pain of scraping Yelp review content manually.

  The challenge of building the structured data with the summary.

  The need for structured outputs suitable for analysis, reports, or AI input.

  What this workflow does
  This automated pipeline does the following:

  Bright Data Integration**: Queries Yelp and scrapes business listing data using Bright Data's Web Unlocker.

  Structured Data Formatting**: Formats the Yelp review data to a structured response in JSON format.

  Google Gemini Summarization**: Sends the cleaned reviews to Google Gemini to:

  Output Delivery**: Returns the structured response with the concise summary over the webhook endpoint.

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

  The Value field should be set with the
  Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token.
  In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy).
  Update the Yelp Business Review URL with the Bright Data zone by navigating to the Set Yelp URL with the Bright Data Zone node.
  Update the Webhook Notifier for the merged response node with the Webhook endpoint of your choice.

  How to customize this workflow to your needs

  This workflow is built to be flexible - whether you’re a market researcher, entrepreneur, or data analyst. Here's how you can adapt it to fit your specific use case:

  Target Specific Business Categories**
  Update the Yelp Business Review input to scrape different businesses like gyms, salons etc.

  Limit Reviews**
  Add filters by description, location, page range to get the top reviews.

  Tweak the Data Extraction Node**
  Update the Structured Data Extractor node Output Parser for building the JSON response with the appropriate fields or attributes.

  Tweak the Summarization Prompt**
  Modify the Gemini prompt to generate a comprehensive summary.

  Send Output to Other Destinations**
  Replace the Webhook URL to forward output to:

  Google Sheets
  Airtable
  Slack or Discord
  Custom API endpoints

nodes_used:
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name: 🐶 AI Petshop Assistant with GPT-4o, Google Calendar & WhatsApp/Instagram/FB
author_name: Amanda Benks
author_username: amanda
categories: []
created_at: 2025-04-24T00:22:01.705Z
updated_at: 2025-04-24T00:22:01.705Z
url: https://n8n.io/workflows/3683
scraped_at: 2025-12-24 13:02:44
description:
  Hi! I’m Amanda 🐶💕
  This is honestly one of the cutest, smartest workflows I’ve ever built. It was designed with a lot of love to help petshops give their customers fast, modern, and sweet service — without losing the personal touch.

  Imagine a client sending a message on WhatsApp, Instagram or Messenger, and in seconds, they’re greeted with a kind message, a helpful menu, and everything they need. No delays. No stress. Just smooth, intelligent automation.

  This AI agent chats, books, checks, confirms, charges, and saves — all powered by GPT-4o, LangChain, Google Calendar, Asaas, Supabase, and more. It’s like having a super capable receptionist who works 24/7 and never forgets a detail 🐾

  🌟 What this workflow does (and it does it with love)

  💬 Multichannel AI Support  
  Serve customers across WhatsApp (official or Evolution API), Instagram Direct, Facebook Messenger, and more — all in one single flow.

  🧠 Smart, friendly conversations  
  Powered by OpenAI GPT-4o, this assistant understands natural language, voice messages, even PDFs and images. It keeps context, remembers the client, and replies with clarity and warmth.

  🗓️ Real-time booking with Google Calendar  
  Book baths, check-ups or grooming sessions instantly. It checks availability, suggests times, and even handles rescheduling when needed.

  🧾 Automatic billing with Asaas  
  It can generate payment links or invoices through the Asaas API and send them straight to the customer — no manual work involved.

  📦 Check product availability  
  Clients can ask things like “Do you have Royal Canin puppy food?” and get a real-time answer based on your inventory stored in Supabase.

  📂 Understands voice, images & files  
  Whether it’s a voice note describing symptoms or a picture of a vaccine card, the assistant processes and responds intelligently.

  💌 Fully customizable tone and flow  
  You can adjust the AI’s prompt, tweak the tone of voice, include emojis, and make the whole flow match your brand’s style perfectly.

  💾 Save customer history  
  All conversations, bookings, names, preferences and contact info are stored in Supabase — safe and organized.

  🕒 Handles time rules beautifully  
  The flow respects your opening hours and avoids scheduling on holidays or weekends (unless you say otherwise). It can even respond politely if a message arrives after hours.

  🛠️ How to set it up

  Connect your APIs: OpenAI, Supabase, Google Calendar, Asaas, Evolution or WhatsApp API  
  Customize your business hours, greeting text and booking options  
  Add your services menu or calendar rules  
  Test and go live — your smart, kind AI receptionist is ready to help 💛

  ✅ Works on n8n Cloud and Self-hosted  
  🔒 Secure credential management built into n8n  
  🐾 Recommended for users with basic n8n experience

  Want to adapt this flow to your business?  
  ❤️ Buy Workflows: https://iloveflows.gumroad.com  

  Oi! Eu sou a Amanda 🐶💕
  E olha… esse aqui é um dos meus fluxos favoritos da vida! Ele foi feito com muito amor pra ajudar petshops a oferecerem um atendimento moderno, gentil e automático — sem perder o toque humano, sabe?

  Imagina só: seu cliente envia uma mensagem por WhatsApp, Instagram ou Facebook, e em segundos, ele já recebe um “oi” carinhoso, com menu de opções, horários disponíveis e tudo o que precisa. ✨

  Esse fluxo conversa, entende, agenda, confirma, cobra, atualiza e registra — tudo com a inteligência do GPT-4o e várias integrações superpoderosas. É tipo ter uma atendente virtual carismática, atenta e que nunca tira férias 🐾

  🌟 O que esse fluxo faz (e faz com amor)

  💬 Atende por multicanais  
  Isso mesmo! Seu cliente pode falar com você pelo WhatsApp API oficial, Evolution API, Instagram Direct, Messenger do Facebook e muito mais. Um só fluxo pra todos os lugares onde seu cliente está.  

  🧠 Conversa com inteligência (e simpatia)  
  O assistente usa OpenAI GPT-4o com LangChain pra entender texto, áudio e até imagens. Ele mantém o contexto da conversa, lembra do cliente, responde com naturalidade e se adapta à personalidade do seu petshop.  

  🗓️ Faz agendamentos em tempo real  
  Seja banho, tosa, consulta ou vacinação, o fluxo se conecta com o Google Calendar e encontra o horário perfeito. Se o cliente quiser reagendar, ele oferece novas datas com todo o cuidado.  

  🧾 Gera cobranças automáticas  
  Integrado com a API do Asaas, o assistente pode gerar boletos ou links de pagamento e enviar para o cliente direto no chat. Tudo isso com confirmação automática.  

  📦 Consulta produtos no estoque  
  O cliente pode perguntar: “tem ração Golden para gatos de 10kg?” — e pronto, o assistente verifica no Supabase e responde rapidinho se tem ou não, com base no que você cadastrou.

  📂 Entende arquivos, áudios e imagens  
  Sim! Se o cliente mandar um áudio explicando o problema do pet, ou até uma foto da carteirinha de vacinação, o assistente analisa com a IA e responde com base nessas informações.  

  💌 Personaliza o atendimento  
  Você pode editar o prompt da IA, mudar o tom de voz, escolher emojis, ajustar frases… assim, o atendimento fica com a carinha e a linguagem da sua marca.  

  💾 Salva tudo com segurança  
  O fluxo guarda todo o histórico no Supabase: nome do cliente, horário do atendimento, preferência, pedidos anteriores, endereço e muito mais.  

  🕓 Respeita horários e feriados  
  Não tem risco de marcar agendamentos fora do horário. Você define os dias e períodos de atendimento, e ele segue direitinho — com lembretes e mensagens fofas se o cliente escrever fora do horário.

  🛠️ Como configurar?

  Conecte suas APIs: OpenAI, Supabase, Google Calendar, Asaas, Evolution (ou WhatsApp API oficial)  
  Preencha seus horários de atendimento e personalize seu menu  
  Adicione links do seu cardápio de serviços, regras ou política de agendamento  
  Teste tudo com carinho e pronto! Você tem um atendente virtual incrível 💕

  ✅ Compatível com n8n Cloud e n8n Auto-hospedado  
  🔒 Segurança total usando credenciais protegidas no n8n  
  🐾 Recomendado para quem já conhece um pouquinho de n8n

  Quer adaptar esse fluxo pro seu sistema?  
  ❤️ Buy Workflows: https://iloveflows.gumroad.com  

nodes_used:
  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workflow_json_compressed:
  [encoded data, 17328 characters]


name: 🗞️ AI-Powered Sustainability Newsletter for Marketing with Gmail, GPT-4o
author_name: Samir Saci
author_username: samirsaci
categories: []
created_at: 2025-04-24T05:06:47.509Z
updated_at: 2025-04-24T05:06:47.509Z
url: https://n8n.io/workflows/3684
scraped_at: 2025-12-24 13:02:46
description:
  Tags: Sustainability, Web Scraping, OpenAI, Google Sheets, Newsletter, Marketing

  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.

  I use this workflow to bring awareness about sustainability and promote my business by delivering automated daily news digests.




  &gt; Promote your business with a fully automated newsletter powered by AI!

  This n8n workflow scrapes articles from the official EU news website and sends a daily curated digest, highlighting only the most relevant sustainability news.

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

  Who is this template for?

  This workflow is useful for:
  Business owners** who want to promote their service or products with a fully automated newsletter
  Sustainability professionals** staying informed on EU climate news  
  Consultants and analysts** working on CSRD, Green Deal, or ESG initiatives  
  Corporate communications teams** tracking relevant EU activity  
  Media curators** building newsletters  

  What does it do?

  This n8n workflow:
  ⏰ Triggers automatically every morning  
  🌍 Scrapes articles from the EU Commission News Portal  
  🧠 Uses OpenAI GPT-4o to classify each article for sustainability relevance  
  📄 Stores the results in a Google Sheet for tracking  
  🧾 Generates a beautiful HTML digest email, including titles, summaries, and images  
  📬 Sends the digest via Gmail to your mailing list  

  How it works




  Trigger at 08:30 every morning
  Scrape and extract article blocks from the EU news site  
  Use OpenAI to decide if articles are sustainability-related  
  Store relevant entries in Google Sheets  
  Generate HTML email with a professional layout and logo  
  Send the digest via Gmail to a configured recipient list

  What do I need to get started?

  You’ll need:
  A Google Sheet connected to your n8n instance  
  An OpenAI account with GPT-4 or GPT-4o access  
  A Gmail OAuth credential setup  

  Follow the Guide!

  Follow the sticky notes inside the workflow or check out my step-by-step tutorial on how to configure and deploy it.



  🎥 Watch My Tutorial

  Notes

  You can customise the system prompt to adjust how AI classifies “sustainability”  
  Works well for tracking updates relevant to climate action, green transition, and circular economy

  This workflow was built using n8n version 1.85.4  
  Submitted: April 24, 2025

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  ["n8n-nodes-base.if","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.googleSheets","n8n-nodes-base.gmail","n8n-nodes-base.code","n8n-nodes-base.html","n8n-nodes-base.html","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.splitInBatches","n8n-nodes-base.set","n8n-nodes-base.merge","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 6356 characters]


name: Backlink Monitoring Automation with Google Sheets + DataForSEO
author_name: Mykolas Bartkus
author_username: mykolas-saaslinkbuilder
categories: []
created_at: 2025-04-24T05:34:57.912Z
updated_at: 2025-04-24T05:34:57.912Z
url: https://n8n.io/workflows/3685
scraped_at: 2025-12-24 13:02:45
description:
  What This Workflow Does

  This n8n workflow reads backlinks from a Google Sheet, sends each one to the DataForSEO On-Page API, and checks:

  Whether the backlink is still live on the target page
  Whether it's dofollow or nofollow
  Whether it's missing (i.e., lost)

  The result is then written back to the same Google Sheet under a Status column.

  Your result will look like this:

  Step-by-Step Setup Instructions

  Add your DataForSEO and Google Sheets credentials in n8n
  Make sure your Google Sheet has these columns: Backlink URL, Landing page, and Status
  Click the Test Workflow button to check a batch of backlinks

  Workflow Breakdown
  Trigger: Manual test start
  Read Data: Pulls backlink URLs and target pages from Google Sheets
  Format URLs: Extracts domain from URL
  Send POST Request to DataForSEO: Triggers a crawl on the backlink URL
  Wait 20 seconds: Allows crawl to finish
  Fetch Link Results: Retrieves backlink data from DataForSEO
  Validate Backlink: Checks if the backlink is live, and whether it’s dofollow
  Update Google Sheets: Logs the status as Live, Lost, or Lost (Nofollow)

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workflow_json_compressed:
  [encoded data, 4340 characters]


name: AI-Powered Gmail Email Organization with Auto-Archiving and Priority Labels
author_name: Junichiro Tobe
author_username: junichiro
categories: []
created_at: 2025-04-24T06:03:12.578Z
updated_at: 2025-04-24T06:03:12.578Z
url: https://n8n.io/workflows/3686
scraped_at: 2025-12-24 13:02:42
description:
  Who is this for?  
  This workflow is perfect for busy professionals, students, or anyone who struggles to keep their Gmail inbox organized and clutter-free.

  What problem is this workflow solving?  
  It helps you avoid email overload by automating the process of organizing your Gmail inbox. Unnecessary emails are archived, while important emails are categorized into "MustRead" or "NotNeed" for better prioritization.

  What this workflow does  
  Connects to your Gmail inbox.  
  Automatically archives emails that are unnecessary or irrelevant.  
  Sorts remaining emails into two categories:  
    MustRead: Emails that require immediate attention.  
    NotNeed: Less critical emails for review later.  

  Setup  
  Connect your Gmail account to the workflow.  
  Define the criteria for "MustRead" and "NotNeed" emails by updating the filter rules in the nodes.  
  Activate the workflow to start organizing your inbox.

  How to customize this workflow to your needs  
  Adjust the filters for archiving emails based on your specific preferences.  
  Modify the sorting rules for "MustRead" and "NotNeed" categories to match your workflow.  
  Add additional actions, such as sending notifications for "MustRead" emails.

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name: Generate Images with OpenAI new GPT-Image-1 Model via User-Friendly Form
author_name: Friedemann Schuetz
author_username: freddy-schuetz
categories: []
created_at: 2025-04-24T12:18:27.259Z
updated_at: 2025-04-24T12:18:27.259Z
url: https://n8n.io/workflows/3688
scraped_at: 2025-12-24 13:02:46
description:
  Welcome to my Simple OpenAI Image Generator Workflow!

  This workflow creates an image with the new OpenAI image model "GPT-Image-1" based on a form input.

  This workflow has the following sequence:

  Form trigger (image prompt and image size input)
  Generate the Image via OpenAI API.
  Return the image to the input form for download.

  The following accesses are required for the workflow:
  OpenAI API access: Documentation

  Instructions
  Link your OpenAI Platform account in the “OpenAI Image Generation” node ("Credential Type")

  You can contact me via LinkedIn, if you have any questions: https://www.linkedin.com/in/friedemann-schuetz

nodes_used:
  ["n8n-nodes-base.convertToFile","n8n-nodes-base.formTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.form","n8n-nodes-base.stickyNote"]

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name: Analyze & Tag User Feedback in Notion with GPT-4 Sentiment Analysis
author_name: Agent Studio
author_username: agentstudio
categories: []
created_at: 2025-04-24T12:31:36.032Z
updated_at: 2025-04-24T12:31:36.032Z
url: https://n8n.io/workflows/3689
scraped_at: 2025-12-24 13:02:46
description:
  Overview
  This n8n workflow processes user feedback automatically, tags it with sentiment, and links it to relevant insights in Notion.
  It uses GPT-4 to analyze each feedback entry, determine whether it corresponds to an existing insight or a new one, and update the Notion databases accordingly.
  It helps teams centralize and structure qualitative user feedback at scale.

  Who It’s For
  Product teams looking to organize and prioritize user feedback.
  Founders or solo builders seeking actionable insights from qualitative data.
  Anyone managing a Notion workspace where feedback is collected and needs to be tagged or linked to features and improvements.

  Prerequisites
  A Notion account with:
    A Feedback database (must include fields for feedback content and status).
    An Insights database with multi-select fields for Solution, User Persona, and a relation to Feedback.
    The Notion template (linked below) helps you get started quickly — just remove the mock data.
  A configured Notion API integration in n8n.  
    👉 Don’t forget to connect the n8n integration to the correct Notion page.
  An OpenAI API key

  Notion Template
  This workflow is designed to work seamlessly with a pre-configured Notion template that includes the required feedback and insights structure.  
  👉 User Feedback Analysis – Notion Template

  How It Works
  The workflow is triggered when a feedback item is updated in Notion (e.g. new feedback is submitted).
  Sentiment analysis (Positive, Neutral, or Negative) is run using OpenAI and stored in a select field in Notion.
  The AI agent analyzes the feedback to:
    Identify whether it matches an existing insight.
    Or create a new insight in Notion with a concise name, solution, and user persona.
  The feedback is then linked to the appropriate insight and marked as "Processed."

  How to Use It
  Connect your Notion databases in all Notion nodes (including those used by the AI agent) for both Feedback and Insights — follow the node names provided.
  Ensure your OpenAI and Notion credentials are correctly set.
  Set up your product context:
     Define a “Product Overview” and list your “Core Features”.
     This helps the AI agent categorize insights more accurately.
     (The Basecamp product is used as an example in the template.)
  (Optional) Modify the prompt to better fit your specific product context.
  Once feedback is added or updated in Notion, the workflow triggers automatically.

  Notes
  Only feedback with the status Received is processed.
  New insights are only created if no relevant match is found.
  Feedback is linked to insights via Notion’s relation property.
  A fallback parser is included to fix potential formatting issues in the AI output.
  You can swap the default n8n memory for a more robust backend like Supabase.

  🙏 Please share your feedback with us. It helps us tremendously!

nodes_used:
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