# templates

name: Add new clients from Notion to Clockify
author_name: Manuel
author_username: automate-everything
categories: []
created_at: 2024-11-16T14:08:29.825Z
updated_at: 2024-11-16T14:08:29.825Z
url: https://n8n.io/workflows/2551
scraped_at: 2025-09-14 11:38:24
description:
  Who is this template for?

  This workflow template is ideal for anyone using Notion for project management and Clockify for time tracking. The workflow automatically adds all new clients from Notion to Clockify.

  How it works
  Scans your Notion client table every minute for new clients
  Adds all new clients to your Clockify workspace

  Set up Steps
  Set up the Notion trigger node by adding your Notion API credentials as described in the n8n Notion docs.
  Go to your Notion clients page/table and give your integration permission to acces the data on this page.  
  Go back to n8n and select your Notion client page in the Notion trigger node.
  Set up the Clockify node by adding your Clockify API credentials as described in the n8n Clockify docs, select your Clockify workspace and map your client name column from Notion to the Clockify "Client Name" field.

nodes_used:
  ["n8n-nodes-base.clockify","n8n-nodes-base.notionTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: AI Powered Web Scraping with Jina, Google Sheets and OpenAI : the EASY way
author_name: Derek Cheung
author_username: derekcheungsa
categories: []
created_at: 2024-11-17T03:48:20.277Z
updated_at: 2024-11-17T03:48:20.277Z
url: https://n8n.io/workflows/2552
scraped_at: 2025-09-14 11:38:21
description:
  Purpose of workflow:
  The purpose of this workflow is to automate  scraping of a website, transforming it into a structured format, and loading it directly into a Google Sheets spreadsheet. 

  How it works:

  Web Scraping: Uses the Jina AI service to scrape website data and convert it into LLM-friendly text.
  Information Extraction: Employs an AI node to extract specific book details (title, price, availability, image URL, product URL) from the scraped data.
  Data Splitting: Splits the extracted information into individual book entries.
  Google Sheets Integration: Automatically populates a Google Sheets spreadsheet with the structured book data.

  Step by step setup:

  Set up Jina AI service:
     Sign up for a Jina AI account and obtain an API key.


  Configure the HTTP Request node:
     Enter the Jina AI URL with the target website.
     Add the API key to the request headers for authentication.

  Set up the Information Extractor node:
     Use Claude AI to generate a JSON schema for data extraction.
     Upload a screenshot of the target website to Claude AI.
     Ask Claude AI to suggest a JSON schema for extracting required information.
     Copy the generated schema into the Information Extractor node.

  Configure the Split node:
     Set it up to separate the extracted data into individual book entries.

  Set up the Google Sheets node:
     Create a Google Sheets spreadsheet with columns for title, price, availability, image URL, and product URL.
     Configure the node to map the extracted data to the appropriate columns.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.splitOut","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: MongoDB AI Agent - Intelligent Movie Recommendations
author_name: Pavel Duchovny
author_username: pash
categories: []
created_at: 2024-11-17T12:54:00.225Z
updated_at: 2024-11-17T12:54:00.225Z
url: https://n8n.io/workflows/2554
scraped_at: 2025-09-14 11:38:24
description:
  Who is this for?
  This workflow is designed for:
  Database administrators and developers working with MongoDB
  Content managers handling movie databases
  Organizations looking to implement AI-powered search and recommendation systems
  Developers interested in combining LangChain, OpenAI, and MongoDB capabilities

  What problem does this workflow solve?
  Traditional database queries can be complex and require specific MongoDB syntax knowledge. This workflow addresses:
  The complexity of writing MongoDB aggregation pipelines
  The need for natural language interaction with movie databases
  The challenge of maintaining user preferences and favorites
  The gap between AI language models and database operations

  What this workflow does
  This workflow creates an intelligent agent that:
  Accepts natural language queries about movies
  Translates user requests into MongoDB aggregation pipelines
  Queries a movie database containing detailed information including:
     Plot summaries
     Genre classifications
     Cast and director information
     Runtime and release dates
     Ratings and awards
  Provides contextual responses using OpenAI's language model
  Allows users to save favorite movies to the database
  Maintains conversation context using a window buffer memory

  Setup
  Required Credentials:
     OpenAI API credentials
     MongoDB connection details

  Node Configuration:
     Configure the MongoDB connection in the MongoDBAggregate node
     Set up the OpenAI Chat Model with your API key
     Ensure the webhook trigger is properly configured for receiving chat messages

  Database Requirements:
     A MongoDB collection named "movies" with the specified document structure
     Proper indexes for efficient querying
     Appropriate user permissions for read/write operations

  How to customize this workflow
  Modify the Document Structure:
     Update the tool description in the MongoDBAggregate node to match your collection schema
     Adjust the aggregation pipeline templates for your specific use case

  Enhance the AI Agent:
     Customize the prompt in the "AI Agent - Movie Recommendation" node
     Modify the window buffer memory size based on your context needs
     Add additional tools for more functionality

  Extend Functionality:
     Add more MongoDB operations beyond aggregation
     Implement additional workflows for different types of queries
     Create custom error handling and validation
     Add user authentication and rate limiting

  Integration Options:
     Connect to external APIs for additional movie data
     Add webhook endpoints for different platforms
     Implement caching mechanisms for frequent queries
     Add data transformation nodes for specific output formats

  This workflow serves as a foundation that can be adapted to various use cases beyond movie recommendations, such as e-commerce product search, content management systems, or any scenario requiring intelligent database interaction.

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.mongoDbTool","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: Send Webflow form data to Google Sheets
author_name: Giovanni Ruggieri
author_username: gioru
categories: []
created_at: 2024-11-17T13:03:12.374Z
updated_at: 2024-11-17T13:03:12.374Z
url: https://n8n.io/workflows/2555
scraped_at: 2025-09-14 11:38:24
description:
  Send Webflow form data to Google Sheets 🚀  
  How It Works  
  This workflow connects your Webflow form submissions to Google Sheets with style and efficiency. Here's the magic in action:  
  Form Submitted 🎉 – A Webflow form submission triggers the flow.  
  Fields Prepared 🛠️ – Data gets organized, and a submission date is added.  
  Data Logged 📋 – Your data finds a cozy new home in Google Sheets.  

  Set-Up Steps  
  ⏱️ Estimated Time: 5-10 minutes  
  Connect Webflow 🌐 – Hook up your Webflow account and disable legacy APIs (instructions inside the workflow).  
  Set Up Google Sheets 🧾 – Choose your spreadsheet and authenticate.  
  You're Ready! 💫 – Let the workflow take it from here.  

  Why You'll Love It ❤️  
  No More Manual Copy-Paste:** Save time and keep data clean.  
  Smart Field Mapping:** Automatically creates columns and fits your data.  
  Helpful Notes Along the Way:** Friendly tips guide you through setup like a pro.  

  Enjoy the smooth ride from Webflow to Google Sheets! 🚀  

nodes_used:
  ["n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.webflowTrigger","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: Exponential Backoff for Google APIs
author_name: Alex Kim
author_username: alexk1919
categories: []
created_at: 2024-11-18T21:52:56.975Z
updated_at: 2024-11-18T21:52:56.975Z
url: https://n8n.io/workflows/2556
scraped_at: 2025-09-14 11:38:24
description:
  n8n Workflow: Exponential Backoff for Google APIs

  Overview
  This n8n workflow implements an Exponential Backoff mechanism to handle retries when interacting with Google APIs. It ensures that failed API requests are retried with increasing delays, up to a specified maximum retry count. This approach helps mitigate transient errors (e.g., rate limits or temporary network issues) while maintaining workflow efficiency.

  Key Features:
  Exponential Backoff Logic**: Dynamically increases wait time between retries based on the retry count.
  Error Handling**: Stops the workflow and raises an error after a specified number of retries.
  Dynamic Waiting**: Waits for a calculated duration before each retry.
  Scalable Design**: Modular nodes for easy debugging and customization.

  Workflow Details

  Nodes in the Workflow:

  Trigger (When clicking "Test Workflow"):
     Manually starts the workflow for testing.

  Loop Over Items:
     Iterates over multiple input items to process Google API requests row by row.

  Google API Node (Example: Update Sheet):
     Sends a request to a Google API endpoint (e.g., updating a row in Google Sheets).
     On success: Moves to the next item in the loop.
     On error: Passes the error to the Exponential Backoff node.

  Exponential Backoff:
     Calculates the delay for the next retry based on the retry count.
     Logic:
            const retryCount = $json["retryCount"] || 0;
       const maxRetries = 5;
       const initialDelay = 1; // in seconds

       if (retryCount &lt; maxRetries) {
           const currentDelayInSeconds = initialDelay * Math.pow(2, retryCount);
           return {
               json: {
                   retryCount: retryCount + 1,
                   waitTimeInSeconds: currentDelayInSeconds,
                   status: 'retrying',
               }
           };
       } else {
           return {
               json: {
                   error: 'Max retries exceeded',
                   retryCount: retryCount,
                   status: 'failed'
               }
           };
       }
       
  Wait:
     Dynamically waits for the waitTimeInSeconds value calculated in the Exponential Backoff node.
     Configuration:
       Resume: After Time Interval
       Wait Amount: {{ $json["waitTimeInSeconds"] }}
       Unit: Seconds

  Check Max Retries:
     Evaluates whether the retry count has exceeded the maximum limit.
     Routes the workflow:
       True: Passes to the Stop and Error node.
       False: Loops back to the Google API node for retry.

  Stop and Error:
     Stops the workflow and logs the error when the maximum retry count is reached.

  Parameters
  Configurable Settings:
  Max Retries:
     Defined in the Exponential Backoff node (const maxRetries = 5).
     Adjust this value based on your requirements.

  Initial Delay:
     The starting wait time for retries, defined as 1 second.

  Google API Configuration:
     Ensure your Google API node is properly authenticated and configured with the desired endpoint and parameters.

  How to Use
  Import the Workflow:
     Copy the workflow JSON and import it into your n8n instance.

  Configure Google API Node:
     Set up the Google API node with your credentials and target API endpoint (e.g., Google Sheets, Gmail, etc.).

  Test the Workflow:
     Manually trigger the workflow and observe the retry behavior in case of errors.

  Monitor Logs:
     Use the console logs in the Exponential Backoff node to debug retry timings and status.

  Example Scenarios
  Scenario 1: Successful Execution
  The Google API processes all requests without errors.
  Workflow completes without triggering the retry logic.

  Scenario 2: Transient API Errors
  The Google API returns an error (e.g., 429 Too Many Requests).
  The workflow retries the request with increasing wait times.

  Scenario 3: Maximum Retries Exceeded
  The workflow reaches the maximum retry count (e.g., 5 retries).
  An error is raised, and the workflow stops.

  Considerations
  Jitter:
     This workflow does not implement jitter (randomized delay) since it's not required for low-volume use cases.
     If needed, jitter can be added to the exponential backoff calculation.

  Retry Storms:
     If multiple workflows run simultaneously, ensure your API quotas can handle potential retries.

  Error Handling Beyond Max Retries:
     Customize the Stop and Error node to notify stakeholders or log errors in a centralized system.

  Customization Options
  Adjust the maximum retry limit and delay calculation to suit your use case.
  Add additional logic to handle specific error codes differently.
  Extend the workflow to notify stakeholders when an error occurs (e.g., via Slack or email).

  Troubleshooting
  Retry Not Triggering**:
    Ensure the retryCount variable is passed correctly between nodes.
    Confirm that the error output from the Google API node flows to the Exponential Backoff node.

  Incorrect Wait Time**:
    Verify the Wait node is referencing the correct field for waitTimeInSeconds.

  Request for Feedback
  We are always looking to improve this workflow. If you have suggestions, improvements, or ideas for additional features, please feel free to share them. Your feedback helps us refine and enhance this solution!

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

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name: Hacker News to Video Content
author_name: Alex Kim
author_username: alexk1919
categories: []
created_at: 2024-11-19T16:37:48.304Z
updated_at: 2024-11-19T16:37:48.304Z
url: https://n8n.io/workflows/2557
scraped_at: 2025-09-14 11:38:21
description:
  Hacker News to Video Content

  Overview
  This workflow converts trending articles from Hacker News into engaging video content. It integrates AI-based tools to analyze, summarize, and generate multimedia content, making it ideal for content creators, educators, and marketers.

  Features
  Article Retrieval:
     Pulls trending articles from Hacker News.
     Limits the number of articles to process (configurable).

  Content Analysis:
     Uses OpenAI's GPT model to:
       Summarize articles.
       Assess their relevance to specific topics like automation or AI.
       Extract key image URLs.

  Image and Video Generation:
     Leonardo.ai: Creates stunning AI-generated images based on extracted prompts.
     RunwayML: Converts images into high-quality videos.

  Structured Content Creation:
     Parses content into structured formats for easy reuse.
     Generates newsletter-friendly blurbs and social media-ready captions.

  Cloud Integration:
     Uploads generated assets to:
       Dropbox
       Google Drive
       Microsoft OneDrive
       MinIO

  Social Media Posting (Optional):
     Supports posting to YouTube, X (Twitter), LinkedIn, and Instagram.

  Workflow Steps

  1. Trigger
  Initiated manually via the "Test Workflow" button.

  2. Fetch Articles
  Retrieves articles from Hacker News.
  Limits the results to avoid processing overload.

  3. Content Filtering
  Evaluates if articles are related to AI/Automation using OpenAI's language model.

  4. Image and Video Generation
  Generates:
    AI-driven image prompts via Leonardo.ai.
    Videos using RunwayML.

  5. Asset Management
  Saves the output to cloud storage services or uploads directly to social media platforms.

  Prerequisites

  API Keys**:
    Hacker News
    OpenAI
    Leonardo.ai
    RunwayML
    Creatomate

  n8n Installation**:
    Ensure n8n is installed and configured locally or on a server.

  Credentials**:
    Set up credentials in n8n for all external services used in the workflow.

  Customization
  Replace Hacker News with any other data source node if needed.
  Configure the "Article Analysis" node for different topics.
  Adjust the cloud storage services or add custom storage options.

  Usage
  Import this workflow into your n8n instance.
  Configure your API credentials.
  Trigger the workflow manually or schedule it as needed.
  Check the outputs in your preferred cloud storage or social media platform.

  Notes
  Extend this workflow further by automating social media posting or newsletter integration.
  For any questions, refer to the official documentation or reach out to the creator.

  About the Creator
  This workflow was built by AlexK1919, an AI-native workflow automation architect. Check out the overview video for a quick demo.

  Tools Used
  Leonardo.ai**
  RunwayML**
  Creatomate**
  Hacker News API**
  OpenAI GPT**

  Feel free to adapt and extend this workflow to meet your specific needs! 🎉

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.hackerNews","n8n-nodes-base.splitInBatches","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.s3","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.limit","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.wait","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.dropbox","n8n-nodes-base.googleDrive","n8n-nodes-base.microsoftOneDrive","n8n-nodes-base.youTube","n8n-nodes-base.twitter","n8n-nodes-base.httpRequest","n8n-nodes-base.linkedIn","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: Visualize your SQL Agent queries with OpenAI and Quickchart.io
author_name: Agent Studio
author_username: agentstudio
categories: []
created_at: 2024-11-20T10:54:23.294Z
updated_at: 2024-11-20T10:54:23.294Z
url: https://n8n.io/workflows/2559
scraped_at: 2025-09-14 11:38:22
description:
  Overview  
  This workflow aims to provide data visualization capabilities to a native SQL Agent.  
  Together, they can help foster data analysis and data visualization within a team.  
  It uses the native SQL Agent that works well and adds visualization capabilities thanks to OpenAI’s Structured Output and Quickchart.io.  

  How it works  
  Information Extraction:  
     The Information Extractor identifies and extracts the user's question.  
     If the question includes a visualization aspect, the SQL Agent alone may not respond accurately.  

  SQL Querying:  
     It leverages a regular SQL Agent: it connects to a database, queries it, and translates the response into a human-readable format.  

  Chart Decision:  
     The Text Classifier determines whether the user would benefit from a chart to support the SQL Agent's response.  

  Chart Generation:  
     If a chart is needed, the sub-workflow dynamically generates a chart and appends it to the SQL Agent’s response.  
     If not, the SQL Agent’s response is output as is.  

  Calling OpenAI for Chart Definition:  
     The sub-workflow calls OpenAI via the HTTP Request node to retrieve a chart definition.  

  Building and Returning the Chart:  
     In the "Set Response" node, the chart definition is appended to a Quickchart.io URL, generating the final chart image.  
     The AI Agent returns the response along with the chart.  

  How to use it  
  Use an existing database or create a new one.  
  For example, I've used this Kaggle dataset and uploaded it to a Supabase DB.  
  Add the PostgreSQL or MySQL credentials.  
  Alternatively, you can use SQLite binary files (check this template).  
  Activate the workflow.  
  Start chatting with the AI SQL Agent.  
  If the Text Classifier determines a chart would be useful, it will generate one in addition to the SQL Agent's response.  

  Notes  
  The full Quickchart.io specifications have not been fully integrated, so there may be some glitches (e.g., radar graphs may not display properly due to size limitations).  

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.executeWorkflow","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.set","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.textClassifier","n8n-nodes-base.stickyNote","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.informationExtractor"]

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name: Send daily translated Calvin and Hobbes Comics to Discord
author_name: Nathan Lee
author_username: datapopcorn
categories: []
created_at: 2024-11-20T13:01:10.683Z
updated_at: 2024-11-20T13:01:10.683Z
url: https://n8n.io/workflows/2560
scraped_at: 2025-09-14 11:38:24
description:
  How it works
  Automates the retrieval of Calvin and Hobbes daily comics.
  Extracts the comic image URL from the website.
  Translates comic dialogues to English and Korean.
  Posts the comic and translations to Discord daily.

  Set up steps
  Estimated setup time: ~10-15 minutes.
  Use a Schedule Trigger to automate the workflow at 9 AM daily.
  Add nodes for parameter setup, HTTP request, data extraction, and integration with Discord.
  Add detailed notes to each node in the workflow for easy understanding.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.scheduleTrigger","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.set","n8n-nodes-base.discord","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.httpRequest"]

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name: Send Matomo analytics data to A.I. to analyze then save results in Baserow
author_name: Keith Rumjahn
author_username: rumjahn
categories: []
created_at: 2024-11-21T10:17:45.990Z
updated_at: 2024-11-21T10:17:45.990Z
url: https://n8n.io/workflows/2561
scraped_at: 2025-09-14 11:38:24
description:
  Who's this for?

  If you own a website and need to analyze your Matomo analytics data so you can increse the number of frequent visitors
  If you need to create an SEO report on what are the common trends amongst your most frequent visitors
  If you want to grow your site based on suggestions from data

  Matomo is an analytics tool that can give you details of each individual visitor. Much more powerful than Google analytics.

  Watch youtube tutorial here

  Get my SEO A.I. agent system here




  Here's the A.I. output:



  Keywords showing the most improvement:
  Openrouter N8N.

  Keywords needing attention:
  Ai Generated Reference Letter

  Obsidian Second Brain

  Suggested actions for improvement: 
  Optimize for "best Docker Synology" despite stable ranking, an improvement to top 10 is an achievable goal.

  Since "2nd brain app for developer"  is of interest to a developer. Consider writing a blog post on how the app addresses the specific pain points of developers.

  Use case
  Instead of hiring an SEO expert, I run this report weekly. It looks at the data for the past week and looks for visitors with more than 3 visits and recommends ideas to convert more visitors into frequent visitors.

  How it works
  The workflow gathers matomo analytics for the past 7 days.
  We then parse the data 
  The data is sent to Openrouter and using a FREE LLM, it analyses the data.
  It stores the results in baserow

  How to use this
  Input your Matomo analytics credentials
  Input your Matomo site ID
  Input your Openrouter.ai credentials
  Input your baserow credentials
  You will need to create a baserow database with columns: Dates, Notes, Blog.

  Created by Rumjahn

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

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name: Simple Bluesky multi-image post using native Bluesky API
author_name: Tom Sekula
author_username: tsekula
categories: []
created_at: 2024-11-21T13:17:26.961Z
updated_at: 2024-11-21T13:17:26.961Z
url: https://n8n.io/workflows/2562
scraped_at: 2025-09-14 11:38:24
description:
  Who is this workflow for?
  This workflow is the baseline workflow for anyone who needs to automate the process of posting 1-4 images to Bluesky using the Bluesky API. It is ideal for anyone looking to streamline their social media posting process, saving time and ensuring consistent content delivery.

  Use Case / Problem Solved
  Manually posting images and captions on Instagram can be time-consuming, especially for businesses and content creators managing multiple accounts. This workflow automates the process from image preparation to publishing, reducing manual effort and increasing efficiency.

  What this workflow does
  Trigger Initialization: The workflow starts with a manual trigger that can be adapted to other triggers (e.g., HTTP webhook or schedule).
  Set Parameters: The workflow includes a node that sets essential parameters, such as the Bluesky account ID, image URLs, and caption.
  Prepare Bluesky Session: A node creates the authenticated session data used by the upload and post operations later in the workflow.
  Publish Media: Nodes retrieve image files from the specified URLS and uploads them as blobs to Bluesky, a necessary pre-requisite for a Bluesky post to have images attached.
  Post text caption + images: A node does the final call to the Bluesky API, including the text caption and relevant image references.

  Setup
  Sign-in to Bluesky and create an App Password
  Add your username and App Password to the Define Credentials node.
  Set the caption (text content) of your post in the Set Caption node.
  Set 1-4 image URLs in the Set Images node
  Adapt the initial trigger as needed to fit your workflow's requirements (e.g., schedule, webhook). 
  Adapt the caption and images nodes to accept dynamic parameters.

  Limitations
  This workflow assumes a minimum of 1 image URL to function. If you want a text-only post, remove the whole embed section from the JSON in last Post to Bluesky node, as well as relevant image attachment nodes.

  The 300-character limit in Bluesky includes the caption + hashtags + image alt text. Going over 300 will return a Record/description must not be longer than 300 graphemes error.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.splitOut","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.aggregate","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.set"]

workflow_json_compressed:
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name: ✨ Vision-Based AI Agent Scraper - with Google Sheets, ScrapingBee, and Gemini
author_name: Dataki
author_username: dataki
categories: []
created_at: 2024-11-21T16:28:54.000Z
updated_at: 2024-11-21T16:28:54.000Z
url: https://n8n.io/workflows/2563
scraped_at: 2025-09-14 11:38:21
description:
  Important Notes:
  Check Legal Regulations:  
  This workflow involves scraping, so ensure you comply with the legal regulations in your country before getting started. Better safe than sorry!

  Workflow Description:  
  😮‍💨 Tired of struggling with XPath, CSS selectors, or DOM specificity when scraping ?

  This AI-powered solution is here to simplify your workflow! With a vision-based AI Agent, you can extract data effortlessly without worrying about how the DOM is structured.  

  This workflow leverages a vision-based AI Agent, integrated with Google Sheets, ScrapingBee, and the Gemini-1.5-Pro model, to extract structured data from webpages. The AI Agent primarily uses screenshots for data extraction but switches to HTML scraping when necessary, ensuring high accuracy.  

  Key Features:
  Google Sheets Integration**: Manage URLs to scrape and store structured results.  
  ScrapingBee**: Capture full-page screenshots and retrieve HTML data for fallback extraction.  
  AI-Powered Data Parsing**: Use Gemini-1.5-Pro for vision-based scraping and a Structured Output Parser to format extracted data into JSON.  
  Token Efficiency**: HTML is converted to Markdown to optimize processing costs.  

  This template is designed for e-commerce scraping but can be customized for various use cases.

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workflow_json_compressed:
  [encoded data, 8428 characters]


name: Automatically promote your YouTube video on X
author_name: Alex Kim
author_username: alexk1919
categories: []
created_at: 2024-11-21T21:26:34.300Z
updated_at: 2024-11-21T21:26:34.300Z
url: https://n8n.io/workflows/2564
scraped_at: 2025-09-14 11:38:23
description:
  YouTube to X Post

  Overview

  This n8n workflow automates the process of promoting your latest YouTube videos on X (formerly Twitter). It ensures that posts are engaging, concise, and follow platform-specific requirements. Additionally, the workflow logs social posts into a Google Sheet for record-keeping.

  Features

  Fetch Latest YouTube Videos:** Pulls recently uploaded videos from a specified channel.
  Generate Engaging X Posts:** Uses OpenAI's GPT-4 to create tailored, witty posts about your videos.
  Character Limit Validation:** Ensures posts comply with X's character limit by rewriting if necessary.
  Post Scheduling:** Automates post publishing to X.
  Google Sheets Integration:** Logs posts and their status for tracking and management.
  Optional Notifications:** Sends updates via Discord, Slack, or Gmail (disabled by default).

  How It Works

  Trigger:
     Manually trigger the workflow or set a scheduled check for new videos (disabled by default).
     
  Fetch YouTube Videos:
     Retrieves the latest videos from your YouTube channel, filtering duplicates.

  Content Creation:
     OpenAI generates a witty and humanized post under 220 characters, using video title and description.
     If the post exceeds the character limit, it is rewritten automatically.

  Google Sheets Logging:
     Logs the generated post to a Google Sheet with details like time, date, and post status.

  Post to X:
     Publishes the generated post on X. Updates the Google Sheet with the post URL.

  Optional Notifications:
     Sends notifications about the published post via Discord, Slack, or Gmail.

  Setup Instructions

  Credentials:
     Add credentials for:
       YouTube API
       OpenAI API
       Google Sheets API
       X (formerly Twitter) API
     Use the accounts associated with "AlexK1919."

  YouTube Node:
     Specify your YouTube Channel ID in the Fetch Latest Videos node.
     Find your Channel ID here.

  Google Sheets Node:
     Configure the node with your Google Sheet for logging posts. Use the linked sheet: AlexK1919 Social Posts.

  OpenAI Node:
     Set up your OpenAI API key for generating posts.

  X Post Node:
     Connect your X account to the Post to X node.

  Optional Notifications:
     Configure Discord, Slack, or Gmail nodes for custom notifications.

  Customization

  Schedule:**
    Enable and modify the Check Every 2 Hours node to automatically check for new videos.

  Post Format:**
    Edit the OpenAI prompt in the Generate X Post node to tweak the tone or style of the generated posts.

  Additional Platforms:**
    Expand this workflow to post on other social platforms by adding respective nodes (e.g., Facebook, LinkedIn).

  Notes

  Posts are validated for character limits but are restricted to 220 characters to ensure compliance with X's guidelines and leave space for links.
  Posts are logged in the Google Sheet with details, including post status and timestamps.
  Current integrations are focused on promoting YouTube content; additional types can be supported with minor adjustments.

  Contact

  For support or inquiries:
  Alex Kim**
  About Me

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name: Summarize SERPBear data with AI (via Openrouter) and save it to Baserow
author_name: Keith Rumjahn
author_username: rumjahn
categories: []
created_at: 2024-11-22T12:55:27.465Z
updated_at: 2024-11-22T12:55:27.465Z
url: https://n8n.io/workflows/2565
scraped_at: 2025-09-14 11:38:24
description:
  Who's this for?
  If you own a website and need to analyze your keyword rankings
  If you need to create a keyword report on your rankings
  If you want to grow your keyword positions

  SerpBear is an opensourced SEO tool specifically for keyword analytics.

  Click here to watch youtube tutorial





  Example output of A.I.
  Key Observations about Ranking Performance:

  The top-performing keyword is “Openrouter N8N” with a current position of 7 and an improving trend.
  Two keywords, “Best Docker Synology” and “Bitwarden Synology”, are not ranking in the top 100 and have a stable trend.
  Three keywords, “Obsidian Second Brain”, “AI Generated Reference Letter”, and “Actual Budget Synology”, and “N8N Workflow Generator” are not ranking well and have a declining trend.

  Keywords showing the most improvement:

  “Openrouter N8N” has an improving trend and a relatively high ranking of 7.

  Keywords needing attention:

  “Obsidian Second Brain” has a declining trend and a low ranking of 69.
  “AI Generated Reference Letter” has a declining trend and a low ranking of 84.
  “Actual Budget Synology”, “N8N Workflow Generator”, “Best Docker Synology”, and “Bitwarden Synology” are not ranking in the top 100.

  Use case
  Instead of hiring an SEO expert, I run this report weekly. It checks the keyword rankings of the past week and gives me recommendations on what to improve.

  How it works
  The workflow gathers SerpBear analytics for the past 7 days.
  It passes the data to openrouter.ai for A.I. analysis.
  Finally it saves to baserow.

  How to use this
  Input your SerpBearcredentials
  Enter your domain name
  Input your Openrouter.ai credentials
  Input your baserow credentials
  You will need to create a baserow database with columns: Date, Note, Blog

  Created by Rumjahn

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  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.baserow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: Conversational Interviews with AI Agents and n8n Forms
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2024-11-24T09:23:26.791Z
updated_at: 2024-11-24T09:23:26.791Z
url: https://n8n.io/workflows/2566
scraped_at: 2025-09-14 11:38:21
description:
  This n8n template combines an AI agent with n8n's multi-page forms to create a novel interaction which allows automated question-and-answer sessions. One of the more obvious use-cases of this interaction is what I'm calling the AI interviewer.

  You can read the full post here: https://community.n8n.io/t/build-your-own-ai-interview-agents-with-n8n-forms/62312

  Live demo here: https://jimleuk.app.n8n.cloud/form/driving-lessons-survey

  How it works

  A form trigger is used to start the interview and a new session is created in redis to capture the transcript.
  An AI agent is then tasked to ask questions to the user regarding the topic of the interview. This is setup as a loop so the questions never stop unless the user wishes to end the interview.
  Each answer is recorded in our session set up earlier between questions.
  When the user requests to end the interview we break the loop and show the interview completion screen.
  Finally, the session is then saved in a Google Sheet which can then be shared with team members and for the purpose of data analysis.

  How to use

  You'll need to be on a n8n instance that is accessible to your target audience. Not technical enough to setup your own server? Try out n8n cloud and instantly deploy template!
  Remember to activate the workflow so the form trigger is published and available for users to use.

  Requirements
  Groq LLM for AI agent. Feel free to swap this out for any other LLM.
  Redis(-compatible) storage for capturing sessions

  Customising this workflow

  The next step would be adding tools! AI interviews with knowledge retrieval could definitely open up other possibilities. Eg. An onboarding wizard generating questions by pulling facts from internal knowledgebase.

nodes_used:
  ["n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.memoryManager","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.formTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.form","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.crypto","n8n-nodes-base.set","n8n-nodes-base.redis","n8n-nodes-base.redis","n8n-nodes-base.redis","n8n-nodes-base.redis","n8n-nodes-base.if","n8n-nodes-base.respondToWebhook","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.stickyNote","n8n-nodes-base.form","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.webhook","n8n-nodes-base.html","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.lmChatGroq","n8n-nodes-base.html","n8n-nodes-base.googleSheets","n8n-nodes-base.splitOut","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.redis","n8n-nodes-base.redis"]

workflow_json_compressed:
  [encoded data, 13700 characters]


name: Scrape business emails from Google Maps without the use of any third party APIs
author_name: Akram Kadri
author_username: akramkadri
categories: []
created_at: 2024-11-24T10:56:50.080Z
updated_at: 2024-11-24T10:56:50.080Z
url: https://n8n.io/workflows/2567
scraped_at: 2025-09-14 11:38:20
description:
  Who is this template for?
  This workflow template is designed for sales, marketing, and business development professionals who want a cost-effective and efficient way to generate leads. By leveraging n8n core nodes, it scrapes business emails from Google Maps without relying on third-party APIs or paid services, ensuring there are no additional costs involved.

  Ideal for small business owners, freelancers, and agencies, this template automates the process of collecting contact information for targeted outreach, making it a powerful tool for anyone looking to scale their lead generation efforts without incurring extra expenses.

  You can watch the video tutorial here: https://youtu.be/HaiO-UeiKBA

  How it works
  This template streamlines email scraping from Google Maps using only n8n core nodes, ensuring a completely free and self-contained solution. Here’s how it operates:

  Input Queries
  You provide a list of queries, each consisting of keywords related to the type of business you want to target and the specific region or subregion you’re interested in.

  Iterates through Queries
  The workflow processes each query one at a time. For each query, it triggers a sub-workflow dedicated to handling the scraping tasks.

  Scrapes Google Maps for URLs
  Using these queries, the workflow scrapes Google Maps to collect URLs of business listings matching the provided criteria.

  Fetches HTML Content
  The workflow then fetches the HTML pages of the collected URLs for further processing.

  Extracts Emails

  Using a Code Node with custom JavaScript, the workflow runs regular expressions on the HTML content to extract business email addresses.

  Setup
  Add Queries:
  Open the first node, "Run Workflow" and input a list of queries, each containing the business keywords and the target region.

  Configure the Google Sheets Node:
  Open the Google Sheets node and select a document and specific sheet where the scraped results will be saved.

  Run the workflow:
  Click on "Test workflow" and watch your Google Sheets document gradually receive business email addresses. 

  Customize as Needed:

  You can adjust the regular expressions in the Code Node to refine the email extraction logic or add logic to extract other kinds of information.

nodes_used:
  ["n8n-nodes-base.removeDuplicates","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.filter","n8n-nodes-base.httpRequest","n8n-nodes-base.splitInBatches","n8n-nodes-base.splitInBatches","n8n-nodes-base.code","n8n-nodes-base.aggregate","n8n-nodes-base.splitOut","n8n-nodes-base.removeDuplicates","n8n-nodes-base.filter","n8n-nodes-base.googleSheets","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.manualTrigger","n8n-nodes-base.wait","n8n-nodes-base.executeWorkflow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 4364 characters]


name: Upsert huge documents in a vector store with Supabase and Notion
author_name: Mario
author_username: octionic
categories: []
created_at: 2024-11-24T18:37:24.868Z
updated_at: 2024-11-24T18:37:24.868Z
url: https://n8n.io/workflows/2568
scraped_at: 2025-09-14 11:38:22
description:
  Purpose

  This workflow adds the capability to build a RAG on living data. In this case Notion is used as a Knowledge Base. Whenever a page is updated, the embeddings get upserted in a Supabase Vector Store.

  It can also be fairly easily adapted to PGVector, Pinecone, or Qdrant by using a custom HTTP request for the latter two.

  Demo

  How it works

  A trigger checks every minute for changes in the Notion Database. The manual polling approach improves accuracy and prevents changes from being lost between cached polling intervals.
  Afterwards every updated page is processed sequentially
  The Vector Database is searched using the Notion Page ID stored in the metadata of each embedding. If old entries exist, they are deleted.
  All blocks of the Notion Database Page are retrieved and combined into a single string
  The content is embedded and split into chunks if necessary. Metadata, including the Notion Page ID, is added during storage for future reference.
  A simple Question and Answer Chain enables users to ask questions about the embedded content through the integrated chat function

  Prerequisites

  To setup a new Vector Store in Supabase, follow this guide
  Prepare a simple Database in Notion with each Database Page containing at least a title and some content in the blocks section. You can of course also connect this to an existing Database of your choice.

  Setup

  Select your credentials in the nodes which require those
  If you are on an n8n cloud plan, switch to the native Notion Trigger by activating it and deactivating the Schedule Trigger along with its subsequent Notion Node
  Choose your Notion Database in the first Node related to Notion
  Adjust the chunk size and overlap in the Token Splitter to your preference
  Activate the workflow

  How to use

  Populate your Notion Database with useful information and use the chat mode of this workflow to ask questions about it. Updates to a Notion Page should quickly reflect in future conversations.

nodes_used:
  ["@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.textSplitterTokenSplitter","n8n-nodes-base.splitInBatches","@n8n/n8n-nodes-langchain.chainRetrievalQa","@n8n/n8n-nodes-langchain.retrieverVectorStore","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.limit","n8n-nodes-base.limit","n8n-nodes-base.supabase","n8n-nodes-base.notion","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","n8n-nodes-base.stickyNote","n8n-nodes-base.noOp","n8n-nodes-base.notionTrigger","n8n-nodes-base.notion","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.vectorStoreSupabase","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.vectorStoreSupabase","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.summarize"]

workflow_json_compressed:
  [encoded data, 5140 characters]


name: Send a welcome private message to your new BlueSky followers
author_name: Nukeador
author_username: nukeador
categories: []
created_at: 2024-11-25T10:13:44.297Z
updated_at: 2024-11-25T10:13:44.297Z
url: https://n8n.io/workflows/2570
scraped_at: 2025-09-14 11:38:24
description:
  Who is this for?

  BlueSky users who are looking to send a "welcome message" to their new followers as a private message.

  What this workflow does

  This worflow will check for new followers on BlueSky every 60 minutes and send a private message to the new ones. 

  Setup

  You need to create a BlueSky app password with private messages access.
  Fill your credentials and the message text on the corresponding nodes (see sticky notes).
  Manually run once the `Save followers to file` node to generate your initial followers list.
  Enable the workflow

  How to customize this workflow to your needs

  You can adjust the check frecuency, but be careful to avoid hitting the 100 createSession per day rate limit

  Feedback or comments

  You can leave comments, feedback or improvements about this workflow on the n8n forums

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.convertToFile","n8n-nodes-base.extractFromFile","n8n-nodes-base.splitOut","n8n-nodes-base.splitInBatches","n8n-nodes-base.wait","n8n-nodes-base.code","n8n-nodes-base.readWriteFile","n8n-nodes-base.readWriteFile","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.noOp"]

workflow_json_compressed:
  [encoded data, 4200 characters]


name: Post new RSS feed items as BlueSky posts
author_name: Nukeador
author_username: nukeador
categories: []
created_at: 2024-11-25T10:23:12.892Z
updated_at: 2024-11-25T10:23:12.892Z
url: https://n8n.io/workflows/2571
scraped_at: 2025-09-14 11:38:24
description:
  Who is this for?

  BlueSky users looking to automate the publication of new posts based on new items from a RSS feed.

  What this workflow does

  This will create a BlueSky post with each new RSS feed item, including the feed title, post image, link and content (up to 200 characters)

  Setup

  You'll need to generate a BlueSky app password
  Configure your feed URL in the first node
  Configure your credentials in the second node

  How to customize this workflow to your needs

  You can modify the message posted in the `Create post node, changing the JSON text` value, in case you want to include only the feed item title instead of the content.

  If you RSS feed doesn't provide an image, you can define a static one on the `Download image` node.

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.rssFeedReadTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.dateTime","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: CV Screening with OpenAI
author_name: Mark Shcherbakov
author_username: lowcodingdev
categories: []
created_at: 2024-11-25T10:38:26.100Z
updated_at: 2024-11-25T10:38:26.100Z
url: https://n8n.io/workflows/2572
scraped_at: 2025-09-14 11:38:22
description:
  Video Guide

  I prepared a detailed guide that showed the whole process of building a resume analyzer.

  Who is this for?
  This workflow is ideal for recruitment agencies, HR professionals, and hiring managers looking to automate the initial screening of CVs. It is especially useful for organizations handling large volumes of applications and seeking to streamline their recruitment process.

  What problem does this workflow solve?
  Manually screening resumes is time-consuming and prone to human error. This workflow automates the process, providing consistent and objective analysis of CVs against job descriptions. It helps filter out unsuitable candidates early, reducing workload and improving the overall efficiency of the recruitment process.

  What this workflow does
  This workflow automates the resume screening process using OpenAI for analysis. It provides a matching score, a summary of candidate suitability, and key insights into why the candidate fits (or doesn’t fit) the job. 

  Retrieve Resume: The workflow downloads CVs from a direct link (e.g., Supabase storage or Dropbox).
  Extract Data: Extracts text data from PDF or DOC files for analysis.
  Analyze with OpenAI: Sends the extracted data and job description to OpenAI to:
     Generate a matching score.
     Summarize candidate strengths and weaknesses.
     Provide actionable insights into their suitability for the job.

  Setup

  Preparation
  Create Accounts:
     N8N: For workflow automation.
     OpenAI: For AI-powered CV analysis.
  Get CV Link:
     Upload CV files to Supabase storage or Dropbox to generate a direct link for processing.
  Prepare Artifacts for OpenAI:
     Define Metrics: Identify the metrics you want from the analysis (e.g., matching percentage, strengths, weaknesses).
     Generate JSON Schema: Use OpenAI to structure responses, ensuring compatibility with your database.
     Write a Prompt: Provide OpenAI with a clear and detailed prompt to ensure accurate analysis.

  N8N Scenario
  Download File: Fetch the CV using its direct URL.
  Extract Data: Use N8N’s PDF or text extraction nodes to retrieve text from the CV.
  Send to OpenAI:
     URL: POST to OpenAI’s API for analysis.
     Parameters:
       Include the extracted CV data and job description.
       Use JSON Schema to structure the response.

  Summary
  This workflow provides a seamless, automated solution for CV screening, helping recruitment agencies and HR teams save time while maintaining consistency in candidate evaluation. It enables organizations to focus on the most suitable candidates, improving the overall hiring process.

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

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name: Notify on Telegram When a New Order is Created in WooCommerce 📦
author_name: AmirHossein MnasouriZade
author_username: amir676080
categories: []
created_at: 2024-11-25T15:44:47.163Z
updated_at: 2024-11-25T15:44:47.163Z
url: https://n8n.io/workflows/2575
scraped_at: 2025-09-14 11:38:23
description:
  📦 Send Telegram Notifications for New WooCommerce Orders

  This workflow automatically sends a Telegram notification when an order status in WooCommerce changes to "Processing." Perfect for online store owners who want instant updates on order fulfillment.

  ⚙️ Set Up Telegram Alerts for WooCommerce Orders
  Configure WooCommerce Webhook to trigger on order updates.



  Create a Telegram Bot and obtain the API token.
  Set Up Telegram Credentials in n8n.
  Configure the Telegram Node with your chat ID.
  Activate and Test the workflow by placing a new order.

  ##💡 Notes
  You can customize the message format in the 🖋️ Design Message Template node to include additional order details.

  Contact me on [Telegram]: https://t.me/amir676080

  Message structure includes the following details

  🆔 Order Number: 11234
  👦🏻 Customer Name: John Doe
  💵 Amount: 299.99 USD
  📅 Order Date:
  ➖ 25th November 2024 at 14:42
  🏙 City: New York
  📞 Phone: +1 555-1234
  ✍🏻 Order Note: Fast delivery requested
  📦 Ordered Products:
  🔹 Wireless Earbuds (2 items)
  📝 Type: Premium Sound Edition



  Contact me on [Telegram]: https://t.me/amir676080

nodes_used:
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name: Import Productboard Notes, Companies and Features into Snowflake
author_name: Romain Jouhannet
author_username: rjouhann
categories: []
created_at: 2024-11-25T17:27:56.565Z
updated_at: 2024-11-25T17:27:56.565Z
url: https://n8n.io/workflows/2576
scraped_at: 2025-09-14 11:38:24
description:
  This workflow imports Productboard data into Snowflake, automating data extraction, mapping, and updates for features, companies, and notes. It supports scheduled weekly updates, data cleansing, and Slack notifications summarizing the latest insights.

  Features
  Fetches data from Productboard (features, companies, notes).
  Maps and processes data for Snowflake tables.
  Automates table creation, truncation, and updates.
  Summarizes new and unprocessed notes.
  Sends weekly Slack notifications with key insights.

  Setup
  Configure Productboard and Snowflake credentials in n8n.
  Update Snowflake table schemas to match your setup.
  Replace Slack channel ID and dashboard URL in the notification node.
  Activate the workflow and set the desired schedule.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.splitOut","n8n-nodes-base.splitOut","n8n-nodes-base.splitOut","n8n-nodes-base.splitOut","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.snowflake","n8n-nodes-base.snowflake","n8n-nodes-base.snowflake","n8n-nodes-base.snowflake","n8n-nodes-base.snowflake","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.snowflake","n8n-nodes-base.snowflake","n8n-nodes-base.set","n8n-nodes-base.snowflake","n8n-nodes-base.snowflake","n8n-nodes-base.splitInBatches","n8n-nodes-base.splitInBatches","n8n-nodes-base.snowflake","n8n-nodes-base.snowflake","n8n-nodes-base.splitInBatches","n8n-nodes-base.set","n8n-nodes-base.snowflake","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.slack","n8n-nodes-base.snowflake","n8n-nodes-base.stickyNote"]

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name: New TheHive Case Slack Notification Bot
author_name: Angel Menendez
author_username: djangelic
categories: []
created_at: 2024-11-26T05:32:11.636Z
updated_at: 2024-11-26T05:32:11.636Z
url: https://n8n.io/workflows/2577
scraped_at: 2025-09-14 11:38:24
description:
  Streamline Case Management in TheHive via Slack!

  Our TheHive Slack Integration empowers SOC analysts by allowing them to efficiently manage and update case attributes directly within Slack, reducing the need to switch contexts and enhancing response time.


  Key Features:
  Direct Case Management**: Modify case details such as assignee, severity, status, and more through intuitive form inputs embedded within Slack messages.
  Seamless Integration**: Assumes matching email addresses between TheHive and Slack users for straightforward assignee updates. Note: Ensure email consistency to avoid assignment errors.
  Instant Case Actions**: Quickly close cases as false positives or adjust threat levels with minimal clicks, directly impacting case status in TheHive and reflecting updates immediately in Slack.
  Task Management**: Add tasks to cases through a user-friendly modal popup, fostering better task tracking and delegation within your team.


  Operational Benefits:
  Efficiency**: Enables analysts to perform multiple case actions without leaving Slack, streamlining workflows and saving valuable time.
  Accuracy**: Reduces the chances of human error by providing a controlled interface for case updates.
  Agility**: Enhances the SOC team's agility by providing tools for rapid response and case management, crucial for effective security operations.


  Setup Tips:
  Verify that all SOC team members have matching email IDs in TheHive and Slack.
  Familiarize your team with the Slack form inputs and ensure they understand the importance of accurate data entry.
  Regularly review and update the integration settings to accommodate any changes in your security operations protocols.


  Need Help?
  For detailed setup instructions or troubleshooting, refer to our Integration Guide or reach out on our Support Forum.


  Leverage this integration to maximize your SOC team's efficiency and responsiveness, ensuring that case management is as streamlined and effective as possible.

nodes_used:
  ["n8n-nodes-base.theHiveProjectTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.theHiveProject","n8n-nodes-base.theHiveProject","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.slack","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.theHiveProject","n8n-nodes-base.theHiveProject","n8n-nodes-base.theHiveProject","n8n-nodes-base.respondToWebhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.stickyNote","n8n-nodes-base.respondToWebhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.slack","n8n-nodes-base.theHiveProject","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.slack","n8n-nodes-base.theHiveProject","n8n-nodes-base.webhook"]

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name: Linear to Productboard feature Sync
author_name: Romain Jouhannet
author_username: rjouhann
categories: []
created_at: 2024-11-26T08:14:27.367Z
updated_at: 2024-11-26T08:14:27.367Z
url: https://n8n.io/workflows/2578
scraped_at: 2025-09-14 11:38:24
description:
  Linear Project/Issue Status and End Date to Productboard feature Sync

  Sync project and issue data between Linear and Productboard to keep teams aligned. This workflow updates Productboard features with the status and end date from Linear projects or due date from Linear issues. It ensures consistent data and sends a Slack notification whenever changes are made.

  Features
  Listens for updates in Linear projects/issues.
  Maps Linear statuses to Productboard feature statuses.
  Updates Productboard feature details including timeframe.
  Sends a Slack notification summarizing the updates.

  Setup
  Linear Credentials: Add your Linear API credentials in n8n.  
  Productboard Credentials: Configure the Productboard API credentials in n8n.  
  Linear Projects or Issues: Select the Linear project(s) or Issue(s) you want to monitor for updates.  
  Productboard Custom Field: Create a custom field in Productboard named "Linear". This field should store the URL of the Linear project or issue you want to sync. Retrieve the UUID of the custom field in Productboard and set it up in the "Get Productboard Feature ID" node.
  Slack Notification: Update the Slack node with the desired Slack channel ID.  
  Activate the Workflow: Enable the workflow to automatically sync data when triggered by updates in Linear.

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.slack","n8n-nodes-base.httpRequest","n8n-nodes-base.linearTrigger","n8n-nodes-base.linearTrigger","n8n-nodes-base.linearTrigger","n8n-nodes-base.linearTrigger","n8n-nodes-base.linearTrigger","n8n-nodes-base.linearTrigger","n8n-nodes-base.if","n8n-nodes-base.code","n8n-nodes-base.linearTrigger","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.slack","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.if"]

workflow_json_compressed:
  [encoded data, 6096 characters]


name: Handling Job Application Submissions with AI and n8n Forms
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2024-11-26T17:34:46.794Z
updated_at: 2024-11-26T17:34:46.794Z
url: https://n8n.io/workflows/2579
scraped_at: 2025-09-14 11:38:22
description:
  This n8n template leverages n8n's multi-form feature to build a 2 part job application submission journey which aims to eliminate the need for applicants to re-enter data found on their CVs/Resumes.

  How it works
  The application submission process starts with an n8n form trigger to accept CV files in the form of PDFs.
  The PDF is validated using the text classifier node to determine if it is a valid CV else the applicant is asked to reupload.
  A basic LLM node is used to extract relevant information from the CV as data capture. A copy of the original job post is included to ensure relevancy.
  Applicant's data is then sent to an ATS for processing. For our demo, we used airtable because we could attach PDFs to rows.
  Finally, a second form trigger is used for the actual application form. However, it is prefilled to save the applicant's time and allow them to amend any of the generated application fields.

  How to use
  Ensure to change the redirect URL in the form ending node to use the host domain of your n8n instance.

  Requirements
  OpenAI for LLM
  Airtable to capture applicant data

  Customising the workflow
  Application form is pretty basic for this demonstration but could be extended to ask more in-depth questions.
  If it fits the job, why not ask applicants to upload portfolio works and have AI describe/caption them.

nodes_used:
  ["n8n-nodes-base.extractFromFile","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.formTrigger","n8n-nodes-base.airtable","@n8n/n8n-nodes-langchain.textClassifier","n8n-nodes-base.httpRequest","n8n-nodes-base.form","n8n-nodes-base.airtable","n8n-nodes-base.formTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.form","n8n-nodes-base.form","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.form"]

workflow_json_compressed:
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name: Qualifying Appointment Requests with AI & n8n Forms
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2024-11-26T17:49:51.712Z
updated_at: 2024-11-26T17:49:51.712Z
url: https://n8n.io/workflows/2580
scraped_at: 2025-09-14 11:38:22
description:
  This n8n template builds upon a simple appointment request form design which uses AI to qualify if the incoming enquiry is suitable and/or time-worthy of an appointment.

  This demonstrates a lighter approach to using AI in your templates but handles a technically difficult problem - contextual understanding! This example can be used in a variety of contexts where figuring out what is and isn't relevant can save a lot of time for your organisation.

  How it works
  We start with a form trigger which asks for the purpose of the appointment.
  Instantly, we can qualify this by using a text classifier node which uses AI's contextual understanding to ensure the appointment is worthwhile. If not, an alternative is suggested instead.
  Multi-page forms are then used to set the terms of the appointment and ask the user for a desired date and time.
  An acknowledgement is sent to the user while an approval by email process is triggered in the background.
  In a subworkflow, we use Gmail with the wait for approval operation to send an approval form to the admin user who can either confirm or decline the appointment request.
  When approved, a Google Calendar event is created. When declined, the user is notified  via email that the appointment request was declined.

  How to use
  Modify the enquiry classifier to determine which contexts are relevant to you.
  Configure the wait for approval node to send to an email address which is accessible to all appropriate team members.

  Requirements
  OpenAI for LLM
  Gmail for Email
  Google Calendar for Appointments

  Customising this workflow
  Not using Google Mail or Calendar? Feel free to swap this with other services.
  The wait for approval step is optional. Remove if you wish to handle appointment request resolution in another way.

nodes_used:
  ["n8n-nodes-base.formTrigger","n8n-nodes-base.form","n8n-nodes-base.form","n8n-nodes-base.set","n8n-nodes-base.form","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.if","n8n-nodes-base.gmail","n8n-nodes-base.gmail","n8n-nodes-base.if","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.googleCalendar","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.form","n8n-nodes-base.form","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.executeWorkflow","n8n-nodes-base.executeWorkflowTrigger","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.textClassifier"]

workflow_json_compressed:
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name: Simple Lead Capture and Follow-on with n8n Multi-Forms
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2024-11-26T18:04:48.815Z
updated_at: 2024-11-26T18:04:48.815Z
url: https://n8n.io/workflows/2581
scraped_at: 2025-09-14 11:38:23
description:
  This n8n template demonstrates the easiest way to build a lead capture flow for your side project, startup or small business where simple works best!

  If Typeform's costs are getting you down or you feel Google form URLs are off-putting, then definitely give this a try.

  How it works
  Our flow begins with a form trigger to capture a newsletter signup and the user's email is captured into a google sheet. Google Sheet is used for demonstration purposes but this could be any database.
  Multi-page forms allow you to continue the onboarding experience with a short survey. 3 form nodes are chained to capture more details from the user which update the same row in the google sheet.
  Finally, a form ending node shows a customised completion screen for our user.

  Check out the example sheet here: https://docs.google.com/spreadsheets/d/15W1PiFjCoiEBHHKKCRVMLmpKg4AWIy9w1dQ2Dq8qxPs/edit?usp=sharing

  How to use
  Keeping forms simple may serve to increase form completion rates. If you feel the need to add additional fields, consider breaking them up into more forms and group them contextually.

  Requirements
  Google Sheets for data capture
  Slack for notifications

  Feel free to swap these out for services that you use!

  Customising this workflow
  Play with multi-form design to maximise the opportunity of getting to know the user better. That said, lengthy flows are likely to put people off.
  Instead of showing a static completion screen, perhaps redirecting to your blog or other more interesting page.

nodes_used:
  ["n8n-nodes-base.form","n8n-nodes-base.form","n8n-nodes-base.form","n8n-nodes-base.formTrigger","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.form","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.slack"]

workflow_json_compressed:
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name: Automate Sales Meeting Prep with AI & APIFY Sent To WhatsApp
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2024-11-26T23:00:48.040Z
updated_at: 2024-11-26T23:00:48.040Z
url: https://n8n.io/workflows/2582
scraped_at: 2025-09-14 11:38:22
description:
  This n8n template builds a meeting assistant that compiles timely reminders of upcoming meetings filled with email history and recent LinkedIn activity of other people on the invite. This is then discreetly sent via WhatsApp ensuring the user is always prepared, informed and ready to impress!

  How it works
  A scheduled trigger fires hourly to check for upcoming personal meetings.
  When found, the invite is analysed by an AI agent to pull email and LinkedIn details of the other invitees.
  2 subworkflows are then triggered for each invitee to (1) search for last email correspondence with them and (2) scrape their LinkedIn profile + recent activity for social updates.
  Using both available sources, another AI agent is used to summarise this information and generate a short meeting prep message for the user.
  The notification is finally sent to the user's WhatsApp, allowing them ample time to review.

  How to use
  There are a lot of moving parts in this template so in it's current form, it's best to use this for personal rather than team calendars.
  The LinkedIn scraping method used in this workflow requires you to paste in your LinkedIn cookies from your browser which essentially let's n8n impersonate you. You can retrieve this from dev console or ask someone technical for help!

  Note: It may be wise to switch to other LinkedIn scraping approaches which do not impersonate your own account for production.

  Requirements
  OpenAI for LLM
  Gmail for Email
  Google Calendar for upcoming events
  WhatsApp Business account for notifications

  Customising this workflow
  Try adding information sources which are relevant to you and your invitees. Such as company search, other social media sites etc.
  Create an on-demand version which doesn't rely on the scheduled trigger. Sometimes you want to know prepare for meetings hours or days in advance where this could help immensely.

nodes_used:
  ["n8n-nodes-base.gmail","n8n-nodes-base.set","n8n-nodes-base.googleCalendar","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.executeWorkflowTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.gmail","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.whatsApp","n8n-nodes-base.stickyNote","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.switch","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.splitOut","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.splitOut","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.executeWorkflow","n8n-nodes-base.merge","n8n-nodes-base.aggregate","n8n-nodes-base.aggregate","n8n-nodes-base.html","n8n-nodes-base.splitOut","n8n-nodes-base.httpRequest","n8n-nodes-base.html","n8n-nodes-base.html","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.html","n8n-nodes-base.merge","n8n-nodes-base.if","n8n-nodes-base.html","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.executeWorkflow","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.chainLlm"]

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name: Optimize & Update Printify Title and Description Workflow
author_name: Alex Kim
author_username: alexk1919
categories: []
created_at: 2024-11-28T00:40:31.475Z
updated_at: 2024-11-28T00:40:31.475Z
url: https://n8n.io/workflows/2583
scraped_at: 2025-09-14 11:38:24
description:
  Printify Automation - Update Title and Description Workflow

  This n8n workflow automates the process of retrieving products from Printify, generating optimized product titles and descriptions, and updating them back to the platform. It leverages OpenAI for content generation and integrates with Google Sheets for tracking and managing updates.

  Features

  Integration with Printify**: Fetch shops and products through Printify's API.
  AI-Powered Optimization**: Generate engaging product titles and descriptions using OpenAI's GPT model.
  Google Sheets Tracking**: Log and manage updates in Google Sheets.
  Custom Brand Guidelines**: Ensure consistent tone by incorporating brand-specific instructions.
  Loop Processing**: Iteratively process each product in batches.

  Workflow Structure

  Nodes Overview

  Manual Trigger: Manually start the workflow for testing purposes.
  Printify - Get Shops: Retrieves the list of shops from Printify.
  Printify - Get Products: Fetches product details for each shop.
  Split Out: Breaks down the product list into individual items for processing.
  Loop Over Items: Iteratively processes products in manageable batches.
  Generate Title and Desc: Uses OpenAI GPT to create optimized product titles and descriptions.
  Google Sheets Integration:
     Trigger: Monitors Google Sheets for changes.
     Log Updates: Records product updates, including old and new titles/descriptions.
  Conditional Logic:
     If Nodes: Ensure products are ready for updates and stop processing once completed.
  Printify - Update Product: Sends updated titles and descriptions back to Printify.
  Brand Guidelines + Custom Instructions: Sets brand tone and seasonal instructions.

  Setup Instructions

  Prerequisites

  n8n Instance: Ensure n8n is installed and configured.
  Printify API Key:
     Obtain an API key from your Printify account.
     Add it to n8n under HTTP Header Auth.
  OpenAI API Key:
     Obtain an API key from OpenAI.
     Add it to n8n under OpenAI API.
  Google Sheets Integration:
     Share your Google Sheets with the Google API service account.
     Configure Google Sheets credentials in n8n.

  Workflow Configuration

  Set Brand Guidelines:
     Update the Brand Guidelines + Custom Instructions node with your brand name, tone, and seasonal instructions.
  Batch Size:
     Configure the Loop Over Items node for optimal batch sizes.
  Google Sheets Configuration:
     Set the correct Google Sheets document and sheet names in the integration nodes.
  Run the Workflow:
     Start manually or configure the workflow to trigger automatically.

  Key Notes

  Customization**:
    Modify API calls to support other platforms like Printful or Vistaprint.
  Scalability**:
    Use batch processing for efficient handling of large product catalogs.
  Error Handling**:
    Configure retries or logging for any failed nodes.

  Output Examples

  Optimized Content Example

  Input Title**: "Classic White T-Shirt"
  Generated Title**: "Stylish Classic White Tee for Everyday Wear"
  Input Description**: "Plain white T-shirt made of cotton."
  Generated Description**: "Discover comfort and style with our classic white tee, crafted from premium cotton for all-day wear. Perfect for casual outings or layering."

  Next Steps

  Monitor Updates:
     Use Google Sheets to review logs of updated products.
  Expand Integration:
     Add support for more Printify shops or integrate with other platforms.
  Enhance AI Prompts:
     Customize prompts for different product categories or seasonal needs.

  Feel free to reach out for additional guidance or troubleshooting!

nodes_used:
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name: Telegram AI Bot: NeurochainAI Text & Image - NeurochainAI Basic API Integration
author_name: NeurochainAI
author_username: neurochainai
categories: []
created_at: 2024-11-28T14:46:40.622Z
updated_at: 2024-11-28T14:46:40.622Z
url: https://n8n.io/workflows/2584
scraped_at: 2025-09-14 11:38:24
description:
  This template provides a workflow to integrate a Telegram bot with NeurochainAI's inference capabilities, supporting both text processing and image generation. Follow these steps to get started:

  &gt; Purpose: Enables seamless integration between your Telegram bot and NeurochainAI for advanced AI-driven text and image tasks.

  Requirements
  Telegram Bot Token.
  NeurochainAI API Key.
  Sufficient credits to utilize NeurochainAI services.

  Features
  Text processing through NeurochainAI's inference engine.
  AI-powered image generation (Flux).
  Easy customization and scalability for your use case.

  Setup
  Import the template into N8N.
  Add your Telegram Bot Token and NeurochainAI API Key where prompted.
  Follow the step-by-step instructions embedded in the template for configuration.

  [NeurochainAI Website](https://www.neurochain.ai/
  )
  NeurochainAI Guides

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

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  [encoded data, 6120 characters]


name: Upload images to an S3 Bucket via a Slack Bot
author_name: Angel Menendez
author_username: djangelic
categories: []
created_at: 2024-11-29T06:14:01.540Z
updated_at: 2024-11-29T06:14:01.540Z
url: https://n8n.io/workflows/2585
scraped_at: 2025-09-14 11:38:24
description:
  Upload Public-Facing Images to an S3 Cloudflare Bucket via Slack Modal

  🛠 Who is this for?

  This workflow is for teams that use Slack for internal communication and need a streamlined way to upload public-facing images to an S3 Cloudflare bucket. It's especially beneficial for DevOps, marketing, or content management teams who frequently share assets and require efficient cloud storage integration.

  💡 What problem does this workflow solve?

  Manually uploading images to cloud storage can be time-consuming and disruptive, especially if you're already working in Slack. This workflow automates the process, allowing you to upload images directly from Slack via a modal popup. It reduces friction and keeps your workflow within a single platform.

  🔍 What does this workflow do?

  This workflow connects Slack with an S3 Cloudflare bucket to simplify the image-uploading process:
  Slack Modal Interaction**: Users trigger a Slack modal to select images for upload.
  Dynamic Folder Management**: Choose to create a new folder or use an existing one for uploads.
  S3 Integration**: Automatically uploads the images to a specified S3 Cloudflare bucket.
  Slack Confirmation**: After upload, Slack sends a confirmation with the uploaded file URLs.

  🚀 Setup Instructions

  Prerequisites
  Slack Bot with the following permissions:
     commands
     files:write
     files:read
     chat:write
  Cloudflare S3 Credentials: Create an API token with write access to your S3 bucket.
  n8n Instance: Ensure n8n is properly set up with webhook capabilities.

  Steps
  Configure Slack Bot:
     Set up a Slack app and enable the Events API.
     Add your n8n webhook URL to the Events Subscription section.
  Add Credentials:
     Add your Slack API and S3 Cloudflare credentials to n8n.
  Customize the Workflow:
     Open the Idea Selector Modal node and update folder options to suit your needs.
     Update the Post Image to Channel node with your Slack channel ID.
  Deploy the Workflow:
     Activate the workflow and test by triggering the Slack modal.

  🛠 How to Customize This Workflow

  Adjust the Slack Modal
  You can modify the modal layout in the Idea Selector Modal node to add additional fields or adjust the styling.

  Change the Bucket Structure
  Update the Upload to S3 Bucket node to customize the folder paths or change naming conventions.

  🔗 References and Helpful Links
  Slack API Documentation
  Cloudflare S3 Setup
  n8n Documentation

  📓 Workflow Notes
  Key Features:
  Slack Integration**: Uses Slack modal interactions to streamline the upload process.
  Cloud Storage**: Automatically uploads to a Cloudflare S3 bucket.
  User Feedback**: Sends a Slack message with file URLs upon successful upload.

  Setup Dependencies:
  Slack API token
  Cloudflare S3 credentials
  n8n webhook configuration

  Sticky Notes Included
  Sticky notes are embedded within the workflow to guide you through configuration and explain node functionality.

  🌟 Why Use This Workflow?

  This workflow keeps your image-uploading process intuitive, efficient, and fully integrated with tools you already use. By leveraging n8n's flexibility, you can ensure smooth collaboration and quick sharing of public-facing assets without switching contexts.

nodes_used:
  ["n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.switch","n8n-nodes-base.switch","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.splitInBatches","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.noOp","n8n-nodes-base.noOp","n8n-nodes-base.aggregate","n8n-nodes-base.switch","n8n-nodes-base.webhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.respondToWebhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.httpRequest","n8n-nodes-base.s3","n8n-nodes-base.slack","n8n-nodes-base.splitOut"]

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name: Weekly dinner meal plan using recipes from Mealie
author_name: Damian Karzon
author_username: dkarzon
categories: []
created_at: 2024-11-29T06:35:38.804Z
updated_at: 2024-11-29T06:35:38.804Z
url: https://n8n.io/workflows/2586
scraped_at: 2025-09-14 11:38:24
description:
  This workflow randomly select recipes from a Mealie instance (can use a specific category) and then creates a meal plan in Mealie with those recipes.

  How it works:
  Workflow has a scheduled trigger (set to run weekly on a Friday)
  Config node sets a few properties to configure the workflow
  A call to the Mealie API to get the list of recipes
  The code node holds most of the logic, this will loop through the number of recipes defined in the config node and randomly select a recipe from the list (making sure not to double up any recipes)
  Once all the recipes are selected it will call the Mealie API to set up the meal plan on the days

  Setup
  Add your Mealie API token as a credential and set it on the Http Request nodes
  Set the relevant schedule trigger to run when you like
  Update the Config node with the config you want
  	numberOfRecipes - Number of recipes to populate for the meal plan
  	offsetPlanDays - Number of days in the future to start the plan (0 will start it today, 1 tomorrow, etc.)
  	mealieCategoryId - A category id of the category you want to pull in recipes from (default to select from all recipes)
  	mealieBaseUrl - The base url of your Mealie instance

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

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name: Sync Entra User to Zammad User
author_name: Sirhexalot
author_username: sirhexalot
categories: []
created_at: 2024-11-29T09:59:23.069Z
updated_at: 2024-11-29T09:59:23.069Z
url: https://n8n.io/workflows/2587
scraped_at: 2025-09-14 11:38:24
description:
  This workflow facilitates seamless synchronization between Entra (Microsoft Azure AD) and Zammad. It automates the following processes:

  Fetch Entra Group Members: Retrieves users from a designated Entra group. These users are candidates for synchronization.
  Create Universal User Object: Extracts key user information, such as email, phone, and name, and formats it for Zammad compatibility.
  Synchronize with Zammad:
     Identifies users in Zammad who need updates based on Entra data.
     Adds new users from Entra to Zammad.
     Deactivates users in Zammad if they are no longer in the Entra group.

  Key Features

  Dynamic Matching**: Compares users from Entra with existing Zammad users based on email and updates records accordingly.
  Efficient Management**: Automatically creates, updates, or deactivates Zammad users based on their status in Entra.
  Custom Fields**: Supports custom field mapping, ensuring enriched user profiles in Zammad.

  Setup Instructions

  Microsoft Entra Integration:
     Ensure proper API permissions for accessing Entra groups and members.
     Configure Microsoft OAuth2 credentials in n8n.

  Zammad Integration:
     Set up Zammad API credentials with appropriate access rights.
     Customize the workflow to include additional fields or map existing fields as needed.

  Run Workflow:
     Trigger the workflow manually or set up an automation schedule (e.g., daily sync).
     Review created/updated/deactivated users in Zammad.

  Use Cases

  IT Administration**: Keep your support system in sync with the organization’s Entra data.
  User Onboarding**: Automatically onboard new hires into Zammad based on Entra groups.
  Access Management**: Ensure accurate and up-to-date user records in Zammad.

  Prerequisites

  Access to an Entra (Azure AD) environment with group data.
  A Zammad instance with API credentials for user management.
  A custom field in Zammad User Object (entra_key) of type String.



  A custom field in Zammad User Object (entra_object_type) of type `Single selection field with two key value pairs
    user = User
    contact = Contact`




  This workflow is fully customizable and can be adapted to your organization’s specific needs. Save time and reduce manual errors by automating your user sync process with this template!

  If you have found an error or have any suggestions, please report them here on Github.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.if","n8n-nodes-base.splitOut","n8n-nodes-base.set","n8n-nodes-base.zammad","n8n-nodes-base.merge","n8n-nodes-base.httpRequest","n8n-nodes-base.compareDatasets","n8n-nodes-base.zammad","n8n-nodes-base.zammad","n8n-nodes-base.zammad","n8n-nodes-base.compareDatasets","n8n-nodes-base.if","n8n-nodes-base.if"]

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name: Assign Zammad Users to Organizations Based on Email Domain
author_name: Sirhexalot
author_username: sirhexalot
categories: []
created_at: 2024-11-29T10:07:15.757Z
updated_at: 2024-11-29T10:07:15.757Z
url: https://n8n.io/workflows/2588
scraped_at: 2025-09-14 11:38:24
description:
  This n8n workflow automates the assignment of existing users to organizations in Zammad based on their email domains, utilizing Zammad’s "domain-based assignment" feature.

  Use Case
  Automate the post-hoc assignment of existing users to organizations in Zammad by leveraging their email domains. This ensures efficient management of user-organization relationships using Zammad’s "domain-based assignment" feature.

  Requirements
  An active Zammad account with API access.
  Existing users and organizations in Zammad with proper email and domain configurations.
  Zammad organizations must have domain-based assignment enabled.

  Credentials
  Set up your Zammad API credentials in n8n to allow the workflow to interact with your Zammad instance securely.

  If you have found an error or have any suggestions, please report them here on Github.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.zammad","n8n-nodes-base.zammad","n8n-nodes-base.merge","n8n-nodes-base.zammad"]

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name: Daily GitHub Release notification by Email
author_name: Dionysus
author_username: dionysus
categories: []
created_at: 2024-11-30T13:14:35.977Z
updated_at: 2024-11-30T13:14:35.977Z
url: https://n8n.io/workflows/2590
scraped_at: 2025-09-14 11:38:24
description:
  Automating daily notifications of the latest releases from a GitHub repository. This template is ideal for developers and project managers looking to stay up-to-date with software updates.

  How it Works:

  Daily Trigger: The workflow initiates daily using the Schedule Trigger node.
  Fetch Repository Data: The HTTP Request node retrieves the latest release details from the specified GitHub repository.
  Check if new: The IF node check if the release was done in the last 24 hours.
  Split Content: The Split Out node processes the JSON response to extract and structure relevant data.
  Convert Markdown: The Markdown node converts release notes from Markdown format to HTML, making them ready to use in emails.
  Send a notification by email

  Key Features:

  Simple to customize by modifying the GitHub URL.
  Automatically processes and formats release notes for better readability.
  Modular design, allowing integration with other workflows like Gmail or Slack notifications.

  Setup Steps:

  Modify Repository URL: Update the Sticky Note node with the URL of the repository you want to monitor.
  Modify SMTP details: Update the Send Email node with your SMTP details.

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.markdown","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.emailSend","n8n-nodes-base.if"]

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name: Send Emails via Gmail from Obsidian
author_name: Obsidi8n
author_username: derruttie
categories: []
created_at: 2024-11-30T13:25:25.711Z
updated_at: 2024-11-30T13:25:25.711Z
url: https://n8n.io/workflows/2591
scraped_at: 2025-09-14 11:38:23
description:
  How it Works
  This n8n template makes it possible to send emails directly from your Obsidian notes. 
  It leverages the power of the Obsidian Post Webhook plugin, allowing seamless integration between your notes and the email workflow. 

  What it does:
  Receives note content and metadata from Obsidian via a Webhook.
  Parses YAML frontmatter to define email recipients, subject, and more.
  Automatically processes attachments, encoding them into an email-friendly format.
  Sends emails via Gmail and confirms the status back to Obsidian.
  Includes a testing feature to verify everything works before going live.

  Set-up Steps
  Webhook Configuration: Set your n8n POST Webhook URL in the Obsidian Obsidian Post Webhook plugin settings.
  Email Integration: Submit the Gmail credentials in n8n email nodes.
  Test the Workflow: Run a test from Obsidian to ensure the template functions correctly.
  Activate and Enjoy: Start sending customized emails with attachments from your notes in no time!

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.splitOut","n8n-nodes-base.stickyNote","n8n-nodes-base.splitInBatches","n8n-nodes-base.convertToFile","n8n-nodes-base.aggregate","n8n-nodes-base.gmail","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.respondToWebhook","n8n-nodes-base.set","n8n-nodes-base.respondToWebhook","n8n-nodes-base.dateTime","n8n-nodes-base.webhook"]

workflow_json_compressed:
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name: Agentic Telegram AI bot with with LangChain nodes and new tools
author_name: Yulia
author_username: yulia
categories: []
created_at: 2024-11-30T17:40:46.911Z
updated_at: 2024-11-30T17:40:46.911Z
url: https://n8n.io/workflows/2592
scraped_at: 2025-09-14 11:38:21
description:
  Create a Telegram bot that combines advanced AI functionalities with LangChain nodes and new tools.

  Nodes as tools and the HTTP request tool are a new n8n feature that extend custom workflow tool and simplify your setup. We used the workflow tool in the previous Telegram template to call the Dalle-3 model.

  In the new version, we've achieved similar results using the HTTP Request tool and the Telegram node tool instead. The main difference is that Telegram bot becomes more flexible. The LangChain Agent node can decide which tool to use and when. In the previous version, all steps inside the custom workflow tool were executed sequentially.

  ⚠️ Note that you'd need to select the Tools Agent to work with new tools. 

  Before launching the template, make sure to set up your OpenAI and Telegram credentials.

  Here’s how the new Telegram bot works:

  Telegram Trigger listens for new messages in a specified Telegram chat. This node activates the rest of the workflow after receiving a message.

  AI Tool Agent receives input text, processes it using the OpenAI model and replies to a user. It addresses users by name and sends image links when an image is requested.

  The OpenAI GPT-4o model generates context-aware responses. You can configure the model parameters or swap this node entirely. 

  Window buffer memory helps maintain context across conversations. It stores the last 10 interactions and ensures that the agent can access previous messages within a session. Conversations from different users are stored in different buffers.

  The HTTP request tool connects with OpenAI's DALL-E-3 API to generate images based on user prompts. The tool is called when the user asks for an image.

  Telegram node tool sends generated images back to the user in a Telegram chat. It retrieves the image from the URL returned by the DALL-E-3 model. This does not happen directly, however. The response from the HTTP request tool is first stored in the Agent’s scratchpad (think of it as a short-term memory). In the next iteration, the Agent sends the updated response to the GPT model once again. The GPT model will then create a new tool request to send the image back to the user. To pass the image URL, the tool uses the new $fromAI() expression. 

  Send final reply node sends the final response message created by the agent back to the user on Telegram. Even though the image was already passed to the user, the Agent always stops with the final response that comes from dedicated output.

  ⚠️ Note, that the Agent may not adhere to the same sequence of actions in 100% of situations. For example, sometimes it could skip sending the file via the Telegram node tool and instead just send an URL in the final reply. If you have a longer series of predefined steps, it may be better to use the “old” custom workflow tool.

  This template is perfect as a starting point for building AI agentic workflow. Take a look at another agentic Telegram AI template that can handle both text and voice messages.

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

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name: Export Zammad Objects (Users, Roles, Groups, Organizations) to Excel
author_name: Sirhexalot
author_username: sirhexalot
categories: []
created_at: 2024-12-01T11:12:12.671Z
updated_at: 2024-12-01T11:12:12.671Z
url: https://n8n.io/workflows/2596
scraped_at: 2025-09-14 11:38:24
description:
  This n8n workflow enables you to export data from Zammad, including Users, Roles, Groups, and Organizations, into individual Excel files. It simplifies data handling and reporting by creating structured outputs for further processing or sharing.

  Features
  Export Users with associated details such as email, firstname, lastname, role_ids, and group_ids.
  Export Roles and Organizations with their respective identifiers and names.
  Convert all data into separate Excel files for easy access and use.

  Usage
  Import this workflow into your n8n instance.
  Configure the required Zammad API credentials (zammad_base_url and zammad_api_key) in the Basic Variables node.
  Run the workflow to generate Excel files containing Zammad data.

  Issues and Suggestions
  If you encounter any issues or have suggestions for improvement, please report them on the GitHub repository.

  We appreciate your feedback to help enhance this workflow!

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.zammad","n8n-nodes-base.httpRequest","n8n-nodes-base.convertToFile","n8n-nodes-base.convertToFile","n8n-nodes-base.convertToFile","n8n-nodes-base.zammad","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.convertToFile","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.if"]

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name: Update all Zammad Roles to default values
author_name: Sirhexalot
author_username: sirhexalot
categories: []
created_at: 2024-12-01T12:02:43.884Z
updated_at: 2024-12-01T12:02:43.884Z
url: https://n8n.io/workflows/2597
scraped_at: 2025-09-14 11:38:24
description:
  This n8n workflow allows you to reset all user roles in Zammad to specified default roles. It ensures consistency in role management across your Zammad instance.

  Features
  Retrieve all active users from Zammad.
  Update each user's roles to predefined default role IDs.
  Exclude specific users by their IDs from the update process.
  Simple configuration for default roles and excluded users.

  Usage
  Import the Workflow: Upload the provided .json file into your n8n instance.
  Configure Variables:
     zammad_base_url: Your Zammad instance URL.
     zammad_api_key: Your Zammad API key.
     default_roles: List of default role IDs to apply to all users.
     exclude_zammad_users_by_id: List of user IDs to exclude from the update.
  Run the Workflow: Execute the workflow to update roles automatically.

  Issues and Suggestions
  For issues or suggestions, visit the GitHub Repository.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.set","n8n-nodes-base.zammad","n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.convertToFile","n8n-nodes-base.if","n8n-nodes-base.httpRequest"]

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name: Update Zammad Roles by Excel
author_name: Sirhexalot
author_username: sirhexalot
categories: []
created_at: 2024-12-01T12:18:42.597Z
updated_at: 2024-12-01T12:18:42.597Z
url: https://n8n.io/workflows/2598
scraped_at: 2025-09-14 11:38:24
description:
  This n8n workflow allows you to update user roles in Zammad based on data from an Excel file. The workflow automates role assignments, ensuring efficient and consistent updates.

  Features
  Excel Integration**: Import user data from an Excel file containing emails and role assignments.
  Dynamic Updates**: Match Zammad users by email and update their roles.
  Error Handling**: Continue workflow execution even if some updates fail.
  Customizable Variables**: Configure Zammad API URL, API key, and Excel file URL.

  Usage
  Import the Workflow: Upload the provided .json file into your n8n instance.
  Set Variables:
     zammad_base_url: Your Zammad instance URL.
     excel_source_url: URL of the Excel file containing user data.
  Authentication for Zammad
  Create in the Node "Find Zammad User by email" and "Update User Roles" a Header Auth Authentication
  Name**: Authorization
  Value**: Bearer &lt;put here your zammad api token&gt;
  Run the Workflow: Execute the workflow to update user roles based on the Excel data.

  Issues and Suggestions
  For issues or suggestions, visit the GitHub Repository.

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

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name: Entra Contacts to Zammad User Sync
author_name: Sirhexalot
author_username: sirhexalot
categories: []
created_at: 2024-12-01T13:44:32.857Z
updated_at: 2024-12-01T13:44:32.857Z
url: https://n8n.io/workflows/2599
scraped_at: 2025-09-14 11:38:24
description:
  This workflow facilitates seamless synchronization between Entra (Microsoft Azure AD) and Zammad. It automates the following processes:

  Fetch Entra Contacts: 
  Create Universal User Object: Extracts key user information, such as email, phone, and name, and formats it for Zammad compatibility.
  Synchronize with Zammad:
     Identifies users in Zammad who need updates based on Entra data.
     Adds new users from Entra to Zammad.
     Deactivates users in Zammad if they are no longer in Entra.

  Key Features

  Dynamic Matching**: Compares contacts from Entra with existing Zammad users based on email and updates records accordingly.
  Efficient Management**: Automatically creates, updates, or deactivates Zammad users based on their status in Entra.
  Custom Fields**: Supports custom field mapping, ensuring enriched user profiles in Zammad.

  Setup Instructions

  Microsoft Entra Integration:
     Ensure proper API permissions for accessing Entra contacts.
     Configure Microsoft OAuth2 credentials in n8n.

  Zammad Integration:
     Set up Zammad API credentials with appropriate access rights.
     Customize the workflow to include additional fields or map existing fields as needed.

  Run Workflow:
     Trigger the workflow manually or set up an automation schedule (e.g., daily sync).
     Review created/updated/deactivated users in Zammad.

  Use Cases

  IT Administration**: Keep your support system in sync with the organization’s Entra data.
  Customer Management**: Ensure accurate and up-to-date user records in Zammad.

  Prerequisites

  Access to an Entra (Azure AD) environment with contacts data.
  A Zammad instance with API credentials for user management.
  A custom field in Zammad User Object (entra_key) of type String.



  A custom field in Zammad User Object (entra_object_type) of type `Single selection field with two key value pairs
    user = User
    contact = Contact`



  This workflow is fully customizable and can be adapted to your organization’s specific needs. Save time and reduce manual errors by automating your user sync process with this template!

  If you have found an error or have any suggestions, please report them here on Github.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.zammad","n8n-nodes-base.merge","n8n-nodes-base.compareDatasets","n8n-nodes-base.zammad","n8n-nodes-base.zammad","n8n-nodes-base.zammad","n8n-nodes-base.compareDatasets","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.if","n8n-nodes-base.if"]

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name: Generate n8n Forms from Airtable and BaseRow Tables
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2024-12-03T14:34:43.509Z
updated_at: 2024-12-03T14:34:43.509Z
url: https://n8n.io/workflows/2603
scraped_at: 2025-09-14 11:38:24
description:
  This n8n template showcases a cool feature of n8n Forms where the form itself can be defined dynamically using the form fields schema.

  It may be debateable how useful this template actually is since both Airtable and Baserow provide form interfaces already but still a great exercise and demonstration if ever the use-case comes around.

  How it works
  A form trigger is used to dynamically select a database/table from which to build the n8n form from.
  the table's schema is imported into the workflow and using the code node, is converted into the n8n form fields schema.
  This let's us dynamically build the fields in our n8n form when we choose to define the form using the JSON option.
  Once the n8n form submits, we convert the values back into our table's API schema so that we can create a new row.
  Note any files/attachments fields are removed as they need to be handled separately.
  Files are processed separately as they may first need to be stored. Once complete, the reference is saved into the newly created row.

  Check out the example Airtable here - https://airtable.com/appfP15Xd0aVZR9xV/shrGFgXLyQ4Jg58SU

  How to use
  The n8n form is autogenerated which means you only need provide access to the table. Using this approach, this template can be reused for any number of Airtable and/or Baserow tables.

  Requirements
  You'll need either an Airtable account or a Baserow account to use this template.
  Accessible n8n instance to your users

  Customising this workflow
  Not using either Airtable or Baserow? Theoretically any datastore which provides a fields schema can be used with this template.
  If you're feeling creative, split the table into multiple forms for a better user experience.

nodes_used:
  ["n8n-nodes-base.airtable","n8n-nodes-base.formTrigger","n8n-nodes-base.filter","n8n-nodes-base.aggregate","n8n-nodes-base.form","n8n-nodes-base.code","n8n-nodes-base.splitOut","n8n-nodes-base.code","n8n-nodes-base.filter","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.aggregate","n8n-nodes-base.filter","n8n-nodes-base.form","n8n-nodes-base.httpRequest","n8n-nodes-base.formTrigger","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.airtable","n8n-nodes-base.form","n8n-nodes-base.form"]

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name: Time logging on Clockify using Slack
author_name: Blockia Labs
author_username: blockia
categories: []
created_at: 2024-12-03T18:12:28.591Z
updated_at: 2024-12-03T18:12:28.591Z
url: https://n8n.io/workflows/2604
scraped_at: 2025-09-14 11:38:23
description:
  Time Logging on Clockify Using Slack

  How it works
  This workflow simplifies time tracking for teams and agencies by integrating Slack with Clockify. It enables users to log, update, or delete time entries directly within Slack, leveraging an AI-powered assistant for seamless and conversational interactions. Key features include:

  Effortless Time Logging**: Create and manage time entries in Clockify without leaving Slack.
  AI-Powered Assistant**: Get step-by-step guidance to ensure accurate and efficient time logging.
  Project and Client Management**: Retrieve project and client information from Clockify effortlessly.
  Overlap Prevention**: Avoid overlapping entries with built-in time validation.
  Automated Descriptions**: Generate ethical, grammatically correct descriptions for time logs.

  Set up steps
  1. Prepare your integrations
  Ensure you have active accounts for both Slack and Clockify.
  Generate your Clockify API credentials for integration.

  2. Import the workflow
  Download and import the workflow template into your n8n instance.
  Configure the workflow to connect with your Slack and Clockify accounts.

  3. Configure the workflow
  Add your Clockify API credentials in the workflow settings.
  Set up the Slack Trigger to listen for app mentions or specific commands.

  4. Test the workflow
  Use Slack to create a time entry and verify it in Clockify.
  Test updating and deleting existing entries to ensure smooth functionality.
  Check for any overlapping time logs or incorrect data entries.

  Why use this workflow?
  Efficiency**: Eliminate the need to switch between tools for time tracking.
  Accuracy**: AI-driven validation ensures error-free entries.
  Automation**: Simplify repetitive tasks like updating or deleting time logs.
  Proactive Guidance**: Conversational assistant ensures smooth operations.

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.toolCalculator","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.toolCode","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.slackTrigger","n8n-nodes-base.executionData","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.slack","n8n-nodes-base.slack"]

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name: Generate Leads with Google Maps
author_name: Alex Kim
author_username: alexk1919
categories: []
created_at: 2024-12-04T22:16:13.814Z
updated_at: 2024-12-04T22:16:13.814Z
url: https://n8n.io/workflows/2605
scraped_at: 2025-09-14 11:38:21
description:
  This workflow leverages n8n to perform automated Google Maps API queries and manage data efficiently in Google Sheets. It's designed to extract specific location data based on a given list of ZIP codes and categories.

  Features
  Queries the Google Maps API for location data using predefined ZIP codes and subcategories.
  Filters, de-duplicates, and organizes data into structured rows in Google Sheets.
  Implements exponential backoff retries to handle API rate limits.
  Logs and updates statuses directly in Google Sheets for easy tracking.

  Prerequisites
  Google OAuth Credentials: A configured Google Cloud project for Google Maps API and Sheets API access.
  Google Sheets: A sheet with ZIP codes and categories defined (e.g., "AZ Zips").
  n8n Setup: A running instance of n8n with credentials configured for Google OAuth.

  Setup Instructions

  1. Prepare Google Sheets
  Add the ZIP codes to the "AZ Zips" sheet.
  Define subcategories in another sheet (e.g., "Google Maps Categories").
  Provide the sheet's URL in the Settings node of the workflow.

  2. Configure API Access
  Set up Google OAuth credentials for Maps and Sheets APIs in n8n.
  Ensure your API key has access to the places.searchText endpoint.

  3. Workflow Customization
  Modify textQuery parameters in the GMaps API node to match your query needs.
  Adjust trigger intervals as required (e.g., manual or scheduled execution).

  4. Run the Workflow
  Execute the workflow manually or schedule periodic runs to keep your data updated.

  Notes
  This workflow includes robust error handling to retry failed API calls with exponential backoff.
  All data is organized and logged directly in Google Sheets for easy reference and updates.

  For more information or issues, feel free to reach out!

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.if","n8n-nodes-base.stopAndError","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.removeDuplicates","n8n-nodes-base.wait","n8n-nodes-base.if","n8n-nodes-base.stopAndError","n8n-nodes-base.code","n8n-nodes-base.wait","n8n-nodes-base.set","n8n-nodes-base.googleSheets","n8n-nodes-base.set","n8n-nodes-base.googleSheets","n8n-nodes-base.code","n8n-nodes-base.splitInBatches","n8n-nodes-base.splitInBatches","n8n-nodes-base.googleSheets","n8n-nodes-base.if","n8n-nodes-base.stopAndError","n8n-nodes-base.code","n8n-nodes-base.wait","n8n-nodes-base.limit","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.filter","n8n-nodes-base.filter","n8n-nodes-base.if","n8n-nodes-base.splitOut","n8n-nodes-base.stickyNote"]

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name: AI Youtube Trend Finder Based On Niche
author_name: Leonardo Grigorio
author_username: leonardogrig
categories: []
created_at: 2024-12-05T03:44:49.638Z
updated_at: 2024-12-05T03:44:49.638Z
url: https://n8n.io/workflows/2606
scraped_at: 2025-09-14 11:38:21
description:
  Youtube Video

  This n8n workflow is designed to assist YouTube content creators in identifying trending topics within a specific niche. By leveraging YouTube's search and data APIs, it gathers and analyzes video performance metrics from the past two days to provide insights into what content is gaining traction. Here's how the workflow operates:

  Trigger Setup: The workflow begins when a user sends a query through the chat_message_received node. If no niche is provided, the AI prompts the user to select or input one.

  AI Agent (Language Model): The central node utilizes a GPT-based AI agent to:
     Understand the user's niche or content preferences.
     Generate tailored search terms related to the niche.
     Process YouTube API responses and summarize trends using insights such as common themes, tags, and audience engagement metrics (views, likes, and comments).

  YouTube Search: The youtube_search node runs a secondary workflow to query YouTube for relevant videos published within the last two days. It retrieves basic video data such as video IDs, relevance scores, and publication dates.

  Video Details Retrieval: The workflow fetches additional details for each video:
     Video Snippet: Metadata like title, description, and tags.
     Video Statistics: Metrics such as views, likes, and comments.
     Content Details: Video duration, ensuring only content longer than 3 minutes and 30 seconds is analyzed.

  Data Processing:
     Video metadata is cleaned, sanitized, and stored in memory.
     Tags, titles, and descriptions are analyzed to identify patterns and trends across multiple videos.

  Output: The workflow compiles insights and presents them to the user, highlighting:
     The most common themes or patterns within the niche.
     URLs to trending videos and their respective channels.
     Engagement statistics, helping the user understand the popularity of the content.

  Key Notes for Setup:
  API Keys**: Ensure valid YouTube API credentials are configured in the get_videos, find_video_snippet, find_video_statistics, and find_video_data nodes.
  Memory Buffer**: The window_buffer_memory node ensures the AI agent retains context during analysis, enhancing the quality of the generated insights.
  Search Term Customization**: The AI agent dynamically creates search terms based on the user’s niche to improve search precision.

  Use Case:
  This workflow is ideal for YouTubers or marketers seeking data-driven inspiration for creating content that aligns with current trends, maximizing the potential to engage their audience.

  Example Output:
  For the niche "digital marketing":
  Trending Topic: Videos about "mental triggers" and "psychological marketing."
  Tags: "SEO," "Conversion Rates," "Social Proof."
  Engagement: Videos with over 200K views and high likes/comment ratios are leading trends.
  Video links:
    https://www.youtube.com/watch?v=video_id1
    https://www.youtube.com/watch?v=video_id2

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.youTube","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.splitInBatches","n8n-nodes-base.if","n8n-nodes-base.stickyNote"]

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name: Research AI Agent Team with auto citations using OpenRouter and Perplexity
author_name: Derek Cheung
author_username: derekcheungsa
categories: []
created_at: 2024-12-05T04:31:18.702Z
updated_at: 2024-12-05T04:31:18.702Z
url: https://n8n.io/workflows/2607
scraped_at: 2025-09-14 11:38:23
description:
  Purpose of workflow:
  This AI-powered workflow is designed to automatically generate comprehensive, well-researched articles on any given topic. It utilizes a team of AI agents to streamline the research and writing process, producing high-quality content with proper citations and credible sources.

  How it works

  Multi-agent team:

  Research Leader: Plans and conducts initial research, creating a table of contents.
  Project Planner: Breaks down the table of contents into manageable sections.
  Research Assistants: Multiple agents that conduct in-depth research on assigned sections.
  Editor: Compiles and refines the final article, ensuring coherence and proper citations.

  Key features:
  Utilizes Perplexity AI for internet search and citation capabilities
  Produces well-structured articles with proper citations
  Customizable parameters (topic, tone, word count, number of sections)


  Step by step setup:

  Get account from OpenRouter.ai to access Perplexity API
  Set API key in the Perplexity API node
  Credential key name : Authorization and key value Bearer &lt;api-key value&gt;

nodes_used:
  ["n8n-nodes-base.merge","n8n-nodes-base.code","n8n-nodes-base.formTrigger","n8n-nodes-base.ghost","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.set","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.splitOut","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.stickyNote"]

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name: Automatically Correct Wrong Shipping Addresses in Billbee Orders
author_name: Simon
author_username: simonfes
categories: []
created_at: 2024-12-05T10:23:11.248Z
updated_at: 2024-12-05T10:23:11.248Z
url: https://n8n.io/workflows/2609
scraped_at: 2025-09-14 11:38:24
description:
  Address Validation Workflow

  About
  This workflow automates the process of validating and correcting client shipping addresses in Billbee, ensuring accurate delivery information. It's ideal for e-commerce businesses looking to save time and reduce errors in their order fulfillment process. The workflow uses Billbee, an order management platform for small to medium-sized online retailers, and the Endereco API for address validation.

  Who Is This For?
  E-Commerce Businesses**: Streamline order fulfillment by automatically correcting common shipping address errors.
  Warehouse Teams**: Reduce manual work and ensure packages are shipped to the correct address.
  Small to Medium-Sized Retailers**: Businesses using Billbee to manage orders and requiring efficient, automated solutions for address validation.

  How it Works
  Trigger: Workflow starts via a Billbee Webhook when an order is imported.  
  Fetch Data: Retrieve the client's shipping address using the Order ID.  
  Validate Address: Send the address to the Endereco API for validation and correction (e.g., house number errors).  
  Conditional Actions:  
     Valid Address: Update the address in Billbee.  
     Invalid Address: Tag the order with "Validation Error."  
  Track Status: Add tags in Billbee for processed orders.

  Setup Steps
  API Keys: Obtain Billbee Developer/User API Keys and Endereco API Key.  
  Billbee Rule: Create an automation rule:  
     Trigger: Order imported.  
     Action: Call External URL with OrderId to trigger n8n workflow.  
  Optional: Use a secondary trigger (e.g., order state changes to "gepackt") for manual corrections.

  Customization Options
  Filter Delivery Addresses:  
     Customize filters to exclude specific delivery types, such as pickup shops ("Postfiliale," "Paketshop," or "Packstation").  
     Filters can be adjusted within Billbee or in the workflow.

  Error Handling:  
     Configure additional actions for orders that fail validation, such as notifying your team or flagging orders for manual review.

  Order Tags:  
     Define custom tags in Billbee to better track order statuses (e.g., "Address Corrected," "Validation Error").

  Trigger Types:  
     Use additional triggers such as changes to order states (e.g., "gepackt" or "In Fulfillment") for manual corrections or validations.

  Address Fields:  
     Modify the workflow to focus on specific address components, such as postal codes, city names, or country codes.

  Validation Rules:  
     Adjust Endereco API settings or add custom logic to refine validation criteria based on your business needs.

  API Documentation
  Endereco**: Endereco API Docs  
  Billbee**: Billbee API Docs

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name: Smartlead to HubSpot Performance Analytics
author_name: victor de coster
author_username: vliegendepater
categories: []
created_at: 2024-12-05T13:51:28.938Z
updated_at: 2024-12-05T13:51:28.938Z
url: https://n8n.io/workflows/2610
scraped_at: 2025-09-14 11:38:24
description:
  *Smartlead to HubSpot Performance Analytics
  A streamlined workflow to analyze your Smartlead performance metrics by tracking lifecycle stages in HubSpot and generating automated reports.*

  Who is this for?

  (Outbound) Automation Agencies, Sales and marketing teams using Smartlead for outreach campaigns who want to track their performance metrics and lead progression in HubSpot.

  What problem does this workflow solve?

  Manual tracking of lead performance across Smartlead and HubSpot is time-consuming and error-prone. This workflow automates performance reporting by connecting your Smartlead data with HubSpot lifecycle stages, providing clear insights into your outreach campaign effectiveness.

  What this workflow does

  Automatically pulls performance data from your Smartlead campaigns
  Cross-references contact status with HubSpot lifecycle stages
  Generates comprehensive performance reports in Google Sheets
  Provides customizable reporting schedules to match your team's needs

  Setup Requirements

  PostgreSQL Database

  Set up your PostgreSQL instance (includes $300 free GCP credits)
  Follow our step-by-step setup guide: Find a step-by-step guide here

  Google Account Integration

  Connect your Google Account to n8n
  Find the guide here

  Smartlead Configuration

  Connect your Smartlead instance:
  Detailed connection guide included in workflow

  How to customize this workflow

  Configure the Trigger node to adjust report frequency
  Modify the Google Sheets template to match your specific KPIs
  Customize HubSpot lifecycle stage mapping in the Function node
  Adjust PostgreSQL queries to track additional metrics

  Need assistance or have suggestions? 
  lmk here

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.splitInBatches","n8n-nodes-base.postgres","n8n-nodes-base.merge","n8n-nodes-base.code","n8n-nodes-base.splitInBatches","n8n-nodes-base.hubspot","n8n-nodes-base.if","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.postgres","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.postgres","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets"]

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name: AI Agent to chat with Supabase/PostgreSQL DB
author_name: Mark Shcherbakov
author_username: lowcodingdev
categories: []
created_at: 2024-12-06T14:08:05.408Z
updated_at: 2024-12-06T14:08:05.408Z
url: https://n8n.io/workflows/2612
scraped_at: 2025-09-14 11:38:22
description:
  Video Guide

  I prepared a detailed guide that showed the whole process of building a resume analyzer.

  Who is this for?
  This workflow is ideal for developers, data analysts, and business owners who want to enable conversational interactions with their database. It’s particularly useful for cases where users need to extract, analyze, or aggregate data without writing SQL queries manually.

  What problem does this workflow solve?
  Accessing and analyzing database data often requires SQL expertise or dedicated reports, which can be time-consuming. This workflow empowers users to interact with a database conversationally through an AI-powered agent. It dynamically generates SQL queries based on user requests, streamlining data retrieval and analysis.

  What this workflow does
  This workflow integrates OpenAI with a Supabase database, enabling users to interact with their data via an AI agent. The agent can:
  Retrieve records from the database.
  Extract and analyze JSON data stored in tables.
  Provide summaries, aggregations, or specific data points based on user queries.

  Dynamic SQL Querying: The agent uses user prompts to create and execute SQL queries on the database.
  Understand JSON Structure: The workflow identifies JSON schema from sample records, enabling the agent to parse and analyze JSON fields effectively.
  Database Schema Exploration: It provides the agent with tools to retrieve table structures, column details, and relationships for precise query generation.

  Setup

  Preparation
  Create Accounts:
     N8N: For workflow automation.
     Supabase: For database hosting and management.
     OpenAI: For building the conversational AI agent.
  Configure Database Connection:
     Set up a PostgreSQL database in Supabase.
     Use appropriate credentials (username, password, host, and database name) in your workflow.

  N8N Workflow

  AI agent with tools:

  Code Tool:
     Execute SQL queries based on user input.
  Database Schema Tool:
     Retrieve a list of all tables in the database.
     Use a predefined SQL query to fetch table definitions, including column names, types, and references.
  Table Definition:
     Retrieve a list of columns with types for one table.

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

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